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1. A review on the biology and management of potato tuber moth

Author: Adhikari Aakash, Oli Dipiza, Pokhrel Ashim, Dhungana Babita, Paudel Bipul, Pandit Shishir, Bigyan G C, and Dhakal Apurba
Subjects: tuber, foliage, larvae, storage, insect, potato, Agriculture
Abstract: The potato tuber moth (PTM; caused by Phthorimaea operculella) is one of the most serious insect pests that causes damage to crop production and storage of potatoes. It causes damage to the potato, while larvae of P. operculella feed on and grow in the leaves and tubers of potatoes, resulting in direct product losses. The larvae construct tunnels through tubers and mine leaves, stems, and petioles, resulting in uneven galleries. Foliar damage to the potato crop does not normally result in major output losses, although contaminated tubers may have lower marketability and storage losses of up to 100%. Light watering every 4 days and mulching with neem leaves during the last 4 weeks before the harvest was found to be a successful treatment. Spraying a thin layer of testing natural oils also acts as a defensive layer, which causes confusion or disturbance to the searching neonate larvae. The pest is tough to eradicate, so producers rely heavily on insecticides and biological approaches. Potato tuberworm damage was reduced by rotating esfenvalerate and indoxacarb treatments before and after vine kill. At 5 g/kg potato tuber in farmer rustic storage, Acorus calamus dust exhibited great efficiency in preventing PTM tuber damage. In different regions of the world, parasitoids such as Copidosoma spp. and Apanteles spp. are significant in PTM management. Pheromone traps are used for pest population monitoring as well as pest management in the field and during storage.
Published: 2022
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2. Small-strain shear stiffness of sand-gravel mixtures

Author: Pokhrel Abilash and Chiaro Gabriele
Subjects: small-strain shear stiffness, sand-gravel mixtures, equivalent void ratio, Environmental sciences, GE1-350
Abstract: Small-strain shear stiffness (G0) is a key parameter for seismic ground response analysis and performance evaluation of various earth structures and foundations. To account for its pressure and density dependency, G0 has been conveniently correlated to mean effective stress (σm′) and void ratio (e). Such an approach is suitable for conventional uniform materials. However as found in this study, it is not always applicable to estimate the G0 of sand-gravel mixtures (SGMs), because it essentially fails to account for the combined effects of density and gravel content (GC). In this study, aimed at addressing this issue and developing a theoretical framework and empirical correlations suitable for estimating the G0 of SGMs, a series of bender element laboratory tests were carried out on selected SGMs. Specifically, SGMs were obtained by mixing two clean sands – namely New Brighton Sand (mean diameter, D50 = 0.2 mm) and Dalton River Washed Sand (D50 = 0.75 mm) – and rounded pea gravel (D50 = 5.5 mm). Shear wave velocity of specimens having GC = 0, 10, 25, 40 and 60% and prepared at a relative density (Dr) of 20, 30, 45 and 60% was measured at σm′ = 50, 100, 150 and 200 kPa. The laboratory results indicated that G0 of SGMs increases with increasing both the Dr and σm′, whereas the effect of GC would be marginal to significant depending on the limiting and threshold sand contents. To correlate G0 simultaneously with both GC and Dr, the equivalent void ratio (ef(eq)) was adopted. It is shown that the use of ef(eq) makes it possible to uniquely describe the G0 of SGMs for any combination of GC and Dr over the full range of σm′ level applied in this study.
Published: 2024
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3. Hydrodynamical properties of baryon rich thermal plasma with flavour quarks

Author: Pokhrel, Rishi and Dey, Tanay K.
Subjects: High Energy Physics - Theory
Abstract: In this work, we holographically study the hydrodynamical properties of strongly coupled $\mathcal{N} = 4$ SYM baryon rich thermal plasma with large number of flavour quarks. Specifically, we study the drag force acting on the moving heavy probe quark and corresponding energy loss. We also study the jet quenching parameter, screening length and binding energy of the quark-antiquark pair. Due to the presence of finite baryon density and flavour quarks the drag force, energy loss, jet quenching parameter and binding energy of the quark-antiquark pair are enhanced for the increase in temperature. However, the screening length of the quark-antiquark pair is reduced, leading to the thermal plasma phase being achieved at a lower temperature, which is consistent with the thermal phase diagram of the quark-gluon plasma. We observed that the perpendicular orientation of quark-antiquark pair with respect to the direction of motion deconfined early compare to the parallel orientation once temperature raises., Comment: 28 pages, 56 figures
Published: 2024

4. M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions

Author: Datar, Aniket, Pokhrel, Anuj, Nazeri, Mohammad, Rao, Madhan B., Pan, Chenhui, Zhang, Yufan, Harrison, Andre, Wigness, Maggie, Osteen, Philip R., Ye, Jinwei, and Xiao, Xuesu
Subjects: Computer Science - Robotics
Abstract: Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy systems heavily rely on active sensing, e.g., LiDAR, RADAR, and Time-of-Flight sensors, or use (stereo) visible light imaging sensors, e.g., color cameras, to perceive environment geometry and semantics. In scenarios where fully passive perception is required and lighting conditions are degraded to an extent that visible light cameras fail to perceive, most downstream mobility tasks such as obstacle avoidance become impossible. To address such a challenge, this paper presents a Multi-Modal Passive Perception dataset, M2P2, to enable off-road mobility in low-light to no-light conditions. We design a multi-modal sensor suite including thermal, event, and stereo RGB cameras, GPS, two Inertia Measurement Units (IMUs), as well as a high-resolution LiDAR for ground truth, with a novel multi-sensor calibration procedure that can efficiently transform multi-modal perceptual streams into a common coordinate system. Our 10-hour, 32 km dataset also includes mobility data such as robot odometry and actions and covers well-lit, low-light, and no-light conditions, along with paved, on-trail, and off-trail terrain. Our results demonstrate that off-road mobility is possible through only passive perception in extreme low-light conditions using end-to-end learning and classical planning. The project website can be found at https://cs.gmu.edu/~xiao/Research/M2P2/
Published: 2024

5. Traverse the Non-Traversable: Estimating Traversability for Wheeled Mobility on Vertically Challenging Terrain

Author: Pan, Chenhui, Datar, Aniket, Pokhrel, Anuj, Choulas, Matthew, Nazeri, Mohammad, and Xiao, Xuesu
Subjects: Computer Science - Robotics
Abstract: Most traversability estimation techniques divide off-road terrain into traversable (e.g., pavement, gravel, and grass) and non-traversable (e.g., boulders, vegetation, and ditches) regions and then inform subsequent planners to produce trajectories on the traversable part. However, recent research demonstrated that wheeled robots can traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves), which unfortunately would be deemed as non-traversable by existing techniques. Motivated by such limitations, this work aims at identifying the traversable from the seemingly non-traversable, vertically challenging terrain based on past kinodynamic vehicle-terrain interactions in a data-driven manner. Our new Traverse the Non-Traversable(TNT) traversability estimator can efficiently guide a down-stream sampling-based planner containing a high-precision 6-DoF kinodynamic model, which becomes deployable onboard a small-scale vehicle. Additionally, the estimated traversability can also be used as a costmap to plan global and local paths without sampling. Our experiment results show that TNT can improve planning performance, efficiency, and stability by 50%, 26.7%, and 9.2% respectively on a physical robot platform., Comment: for associated video file, see https://www.youtube.com/watch?v=Shcalb8sGcA
Published: 2024

6. VertiEncoder: Self-Supervised Kinodynamic Representation Learning on Vertically Challenging Terrain

Author: Nazeri, Mohammad, Datar, Aniket, Pokhrel, Anuj, Pan, Chenhui, Warnell, Garrett, and Xiao, Xuesu
Subjects: Computer Science - Robotics
Abstract: We present VertiEncoder, a self-supervised representation learning approach for robot mobility on vertically challenging terrain. Using the same pre-training process, VertiEncoder can handle four different downstream tasks, including forward kinodynamics learning, inverse kinodynamics learning, behavior cloning, and patch reconstruction with a single representation. VertiEncoder uses a TransformerEncoder to learn the local context of its surroundings by random masking and next patch reconstruction. We show that VertiEncoder achieves better performance across all four different tasks compared to specialized End-to-End models with 77% fewer parameters. We also show VertiEncoder's comparable performance against state-of-the-art kinodynamic modeling and planning approaches in real-world robot deployment. These results underscore the efficacy of VertiEncoder in mitigating overfitting and fostering more robust generalization across diverse environmental contexts and downstream vehicle kinodynamic tasks., Comment: 8 pages. Code: https://github.com/mhnazeri/VertiEncoder
Published: 2024

7. Measurement of the nucleon spin structure functions for $0.01<Q^2<1$~GeV$^2$ using CLAS

Author: Deur, A., Kuhn, S. E., Ripani, M., Zheng, X., Acar, A. G., Achenbach, P., Adhikari, K. P., Alvarado, J. S., Amaryan, M. J., Armstrong, W. R., Atac, H., Avakian, H., Baashen, L., Baltzell, N. A., Barion, L., Bashkanov, M., Battaglieri, M., Benkel, B., Benmokhtar, F., Bianconi, A., Biselli, A. S., Booth, W. A., ossu, F. B, Bosted, P., Boiarinov, S., Brinkmann, K. Th., Briscoe, W. J., Bueltmann, S., Burkert, V. D., Carman, D. S., Chatagnon, P., Chen, J. P., Ciullo, G., Cole, P. L., Contalbrigo, M., Crede, V., D'Angelo, A., Dashyan, N., De Vita, R., Defurne, M., Diehl, S., Djalali, C., Drozdov, V. A., Dupre, R., Egiyan, H., Alaoui, A. El, Fassi, L. El, Elouadrhiri, L., Eugenio, P., Faggert, J. C., Fegan, S., Fersch, R., Filippi, A., Gates, K., Gavalian, G., Gilfoyle, G. P., Gothe, R. W., Guo, L., Hakobyan, H., Hattawy, M., Hauenstein, F., Heddle, D., Hobart, A., Holtrop, M., Ireland, D. G., Isupov, E. L., Jiang, H., Jo, H. S., Joosten, S., Kang, H., Keith, C., Khandaker, M., Kim, W., Klein, F. J., Klimenko, V., Konczykowski, P., Kovacs, K., Kripko, A., Kubarovsky, V., Lanza, L., Lee, S., Lenisa, P., Li, X., Long, E., MacGregor, I. J. D., Marchand, D., Mascagna, V., Matamoros, D., McKinnon, B., Meekins, D., Migliorati, S., Mineeva, T., Mirazita, M., Mokeev, V., Munoz-Camacho, C., Nadel-Turonski, P., Nagorna, T., Neupane, K., Niccolai, S., Osipenko, M., Ostrovidov, A. I., Pandey, P., Paolone, M., Pappalardo, L. L., Paremuzyan, R., Pasyuk, E., Paul, S. J., Phelps, W., Phillips, S. K., Pierce, J., Pilleux, N., Pokhrel, M., Price, J. W., Prok, Y., Radic, A., Reed, T., Richards, J., Rosner, G., Rossi, P., Rusova, A. A., Salgado, C., Schmidt, A., Schumacher, R. A., Sharabian, Y. G., Shirokov, E. V., Shrestha, U., Sirca, S., Sparveris, N., Spreafico, M., Stepanyan, S., Strakovsky, I. I., Strauch, S., Sulkosky, V., Tan, J. A., Tenorio, M., Trotta, N., Tyson, R., Ungaro, M., Upton, D. W., Vallarino, S., Venturelli, L., Voskanyan, H., Voutier, E., Watts, D. P., Wei, X., Wood, M. H., Zachariou, N., Zhang, J., and Zurek, M.
Subjects: Nuclear Experiment
Abstract: The spin structure functions of the proton and the deuteron were measured during the EG4 experiment at Jefferson Lab in 2006. Data were collected for longitudinally polarized electron scattering off longitudinally polarized NH$_3$ and ND$_3$ targets, for $Q^2$ values as small as 0.012 and 0.02 GeV$^2$, respectively, using the CEBAF Large Acceptance Spectrometer (CLAS). This is the archival paper of the EG4 experiment that summaries the previously reported results of the polarized structure functions $g_1$, $A_1F_1$, and their moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$, for both the proton and the deuteron. In addition, we report on new results on the neutron $g_1$ extracted by combining proton and deuteron data and correcting for Fermi smearing, and on the neutron moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$ formed directly from those of the proton and the deuteron. Our data are in good agreement with the Gerasimov-Drell-Hearn sum rule for the proton, deuteron, and neutron. Furthermore, the isovector combination was formed for $g_1$ and the Bjorken integral $\overline \Gamma_1^{p-n}$, and compared to available theoretical predictions. All of our results provide for the first time extensive tests of spin observable predictions from chiral effective field theory ($\chi$EFT) in a $Q^2$ range commensurate with the pion mass. They motivate further improvement in $\chi$EFT calculations from other approaches such as the lattice gauge method., Comment: 33 pages. 26 figures. Data table provided in supplementary material (30 pages)
Published: 2024

8. Digital Technologies in Physics Education: Exploring Practices and Challenges

Author: Sunil Pokhrel
Abstract: Recent research has indicated that the utilization of digital technologies among Nepal's secondary school teachers is minimal. A multiple case study research design was employed to explore how Physics teachers at the secondary level utilized digital technologies for teaching Physics and the challenges they encountered in adopting new instructional tools. The study revealed that Physics teachers occasionally incorporated YouTube videos to aid in explaining complex concepts. However, the abundance of digital resources available for Physics classes remains significantly underutilized. Furthermore, students were not encouraged to engage in collaborative learning and communication platforms. Nepal's Physics teachers faced various challenges, such as time constraints, in utilizing digital technologies for teaching purposes. These findings highlight the need for greater support and resources to assist teachers in overcoming these challenges and effectively integrating digital technologies into the classroom. This research provides valuable insights into the level of integration of digital technology in the classrooms of teachers who have access to digital resources and possess the necessary skills to use them effectively.
Published: 2024

9. The Effects of Reality Pedagogy on Motivation and Academic Performance to Learn Biology in Class IX

Author: Karma Wangda, Prem Kumar Ghalley, Sangay Chhophel, Bal Krishna Pokhrel, and Pema Wangdi
Abstract: Teaching Biology in an innovative way has always posed challenges due to its complex terminology and abstract concepts. This study explored the effects of reality pedagogy as an alternative instructional strategy on learner motivation and academic performance in learning biology at the ninth-grade level. To achieve this, a quasi-experimental research design was employed, including a pre-test/post-test assessment, biology learning motivation questionnaires, semistructured interviews, and classroom observations. Descriptive and inferential statistics were used to analyse the data. The findings of the study revealed that the implementation of reality pedagogy had a positive influence on learner motivation in learning biology. Additionally, a significant correlation was identified and observed between motivation levels and academic achievement in the subject. The implications of these findings, as well as recommendations, are also discussed in the study.
Published: 2024

10. The Evolution of Protostellar Outflow Opening Angles and the Implications for the Growth of Protostars

Author: Dunham, Michael M., Stephens, Ian W., Myers, Philip C., Bourke, Tyler L., Arce, Héctor G., Pokhrel, Riwaj, Pineda, Jaime E., and Vargas, Joseph
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: We use 1-4" (300-1200 au) resolution 12CO(2-1) data from the MASSES (Mass Assembly of Stellar Systems and their Evolution with the SMA) project to measure the projected opening angles of 46 protostellar outflows in the Perseus Molecular Cloud, 37 of which are measured with sufficiently high confidence to use in further analysis. We find that there is a statistically significant difference in the distributions of outflow opening angles for Class 0 and Class I outflows, with a distinct lack of both wide-angle Class 0 outflows and highly collimated Class I outflows. Synthesizing our results with several previous studies, we find that outflows widen with age through the Class 0 stage but do not continue to widen in the Class I stage. The maximum projected opening angle reached is approximately 90 degrees +/- 20 degrees, with the transition between widening and remaining constant occurring near the boundary between the Class 0 and Class I phases of evolution. While the volume fractions occupied by these outflows are no more than a few tens of percent of the total core volume, at most, recent theoretical work suggests outflows may still be capable of playing a central role in setting the low star formation efficiencies of 25%-50% observed on core scales., Comment: 35 pages, accepted by MNRAS
Published: 2024

11. TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision

Author: Pokhrel, Sandesh, Bhandari, Sanjay, Vazquez, Eduard, Lambrou, Tryphon, Gyawali, Prashnna, and Bhattarai, Binod
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Deep learning has significantly advanced the field of gastrointestinal vision, enhancing disease diagnosis capabilities. One major challenge in automating diagnosis within gastrointestinal settings is the detection of abnormal cases in endoscopic images. Due to the sparsity of data, this process of distinguishing normal from abnormal cases has faced significant challenges, particularly with rare and unseen conditions. To address this issue, we frame abnormality detection as an out-of-distribution (OOD) detection problem. In this setup, a model trained on In-Distribution (ID) data, which represents a healthy GI tract, can accurately identify healthy cases, while abnormalities are detected as OOD, regardless of their class. We introduce a test-time augmentation segment into the OOD detection pipeline, which enhances the distinction between ID and OOD examples, thereby improving the effectiveness of existing OOD methods with the same model. This augmentation shifts the pixel space, which translates into a more distinct semantic representation for OOD examples compared to ID examples. We evaluated our method against existing state-of-the-art OOD scores, showing improvements with test-time augmentation over the baseline approach.
Published: 2024

12. Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework

Author: Pokhrel, Shiva Raj, Yang, Luxing, Rajasegarar, Sutharshan, and Li, Gang
Subjects: Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract: This paper introduces a robust zero-trust architecture (ZTA) tailored for the decentralized system that empowers efficient remote work and collaboration within IoT networks. Using blockchain-based federated learning principles, our proposed framework includes a robust aggregation mechanism designed to counteract malicious updates from compromised clients, enhancing the security of the global learning process. Moreover, secure and reliable trust computation is essential for remote work and collaboration. The robust ZTA framework integrates anomaly detection and trust computation, ensuring secure and reliable device collaboration in a decentralized fashion. We introduce an adaptive algorithm that dynamically adjusts to varying user contexts, using unsupervised clustering to detect novel anomalies, like zero-day attacks. To ensure a reliable and scalable trust computation, we develop an algorithm that dynamically adapts to varying user contexts by employing incremental anomaly detection and clustering techniques to identify and share local and global anomalies between nodes. Future directions include scalability improvements, Dirichlet process for advanced anomaly detection, privacy-preserving techniques, and the integration of post-quantum cryptographic methods to safeguard against emerging quantum threats.
Published: 2024

13. First Measurement of Deeply Virtual Compton Scattering on the Neutron with Detection of the Active Neutron

Author: CLAS Collaboration, Hobart, A., Niccolai, S., Čuić, M., Kumerički, K., Achenbach, P., Alvarado, J. S., Armstrong, W. R., Atac, H., Avakian, H., Baashen, L., Baltzell, N. A., Barion, L., Bashkanov, M., Battaglieri, M., Benkel, B., Benmokhtar, F., Bianconi, A., Biselli, A. S., Boiarinov, S., Bondi, M., Booth, W. A., Bossù, F., Brinkmann, K. -Th., Briscoe, W. J., Brooks, W. K., Bueltmann, S., Burkert, V. D., Cao, T., Capobianco, R., Carman, D. S., Chatagnon, P., Ciullo, G., Cole, P. L., Contalbrigo, M., D'Angelo, A., Dashyan, N., De Vita, R., Defurne, M., Deur, A., Diehl, S., Dilks, C., Djalali, C., Dupre, R., Egiyan, H., Alaoui, A. El, Fassi, L. El, Elouadrhiri, L., Fegan, S., Filippi, A., Fogler, C., Gates, K., Gavalian, G., Gilfoyle, G. P., Glazier, D., Gothe, R. W., Gotra, Y., Guidal, M., Hafidi, K., Hakobyan, H., Hattawy, M., Hauenstein, F., Heddle, D., Holtrop, M., Ilieva, Y., Ireland, D. G., Isupov, E. L., Jiang, H., Jo, H. S., Joo, K., Kageya, T., Kim, A., Kim, W., Klimenko, V., Kripko, A., Kubarovsky, V., Kuhn, S. E., Lanza, L., Leali, M., Lee, S., Lenisa, P., Li, X., MacGregor, I. J. D., Marchand, D., Mascagna, V., Maynes, M., McKinnon, B., Meziani, Z. E., Migliorati, S., Milner, R. G., Mineeva, T., Mirazita, M., Mokeev, V., Camacho, C. Muñoz, Nadel-Turonski, P., Naidoo, P., Neupane, K., Niculescu, G., Osipenko, M., Pandey, P., Paolone, M., Pappalardo, L. L., Paremuzyan, R., Pasyuk, E., Paul, S. J., Phelps, W., Pilleux, N., Pokhrel, M., Rafael, S. Polcher, Poudel, J., Price, J. W., Prok, Y., Reed, T., Richards, J., Ripani, M., Ritman, J., Rossi, P., Golubenko, A. A., Salgado, C., Schadmand, S., Schmidt, A., Scott, Marshall B. C., Seroka, E. M., Sharabian, Y. G., Shirokov, E. V., Shrestha, U., Sparveris, N., Spreafico, M., Stepanyan, S., Strakovsky, I. I., Strauch, S., Tan, J. A., Trotta, N., Tyson, R., Ungaro, M., Vallarino, S., Venturelli, L., Tommaso, V., Voskanyan, H., Voutier, E., Watts, D. P, Wei, X., Williams, R., Wood, M. H., Xu, L., Zachariou, N., Zhang, J., Zhao, Z. W., and Zurek, M.
Subjects: High Energy Physics - Experiment, Nuclear Experiment
Abstract: Measuring Deeply Virtual Compton Scattering on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). Neutron targets play a complementary role to transversely polarized proton targets in the determination of the GPD $E$. This poorly known and poorly constrained GPD is essential to obtain the contribution of the quarks' angular momentum to the spin of the nucleon. DVCS on the neutron was measured for the first time selecting the exclusive final state by detecting the neutron, using the Jefferson Lab longitudinally polarized electron beam, with energies up to 10.6 GeV, and the CLAS12 detector. The extracted beam-spin asymmetries, combined with DVCS observables measured on the proton, allow a clean quark-flavor separation of the imaginary parts of the GPDs $H$ and $E$., Comment: 7 pages, 6 figures
Published: 2024

14. Analysis, Modeling and Design of Personalized Digital Learning Environment

Author: Khanal, Sanjaya and Pokhrel, Shiva Raj
Subjects: Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract: This research analyzes, models and develops a novel Digital Learning Environment (DLE) fortified by the innovative Private Learning Intelligence (PLI) framework. The proposed PLI framework leverages federated machine learning (FL) techniques to autonomously construct and continuously refine personalized learning models for individual learners, ensuring robust privacy protection. Our approach is pivotal in advancing DLE capabilities, empowering learners to actively participate in personalized real-time learning experiences. The integration of PLI within a DLE also streamlines instructional design and development demands for personalized teaching/learning. We seek ways to establish a foundation for the seamless integration of FL into learning systems, offering a transformative approach to personalized learning in digital environments. Our implementation details and code are made public., Comment: IEEE Trans on Education, 2024
Published: 2024

15. An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data

Author: Singh, Navneet and Pokhrel, Shiva Raj
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: In this paper, we explore the power of Quantum Machine Learning as we extend, implement and evaluate algorithms like Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), and Quantum Neural Networks (QNN) in Qiskit with diverse feature mapping techniques for genomic sequence classification., Comment: 2 pager extended abstract
Published: 2024

16. Quantum Federated Learning Experiments in the Cloud with Data Encoding

Author: Pokhrel, Shiva Raj, Yash, Naman, Kua, Jonathan, Li, Gang, and Pan, Lei
Subjects: Computer Science - Machine Learning, Computer Science - Emerging Technologies, Quantum Physics
Abstract: Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results., Comment: SIGCOMM 2024, Quantum Computing, Federated Learning, Qiskit
Published: 2024

17. Correlations of event activity with hard and soft processes in $p$ + Au collisions at $\sqrt{s_\mathrm{NN}}$ = 200 GeV at STAR

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T. L., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment
Abstract: With the STAR experiment at the BNL Relativisic Heavy Ion Collider, we characterize $\sqrt{s_\mathrm{NN}}$ = 200 GeV p+Au collisions by event activity (EA) measured within the pseudorapidity range $eta$ $in$ [-5, -3.4] in the Au-going direction and report correlations between this EA and hard- and soft- scale particle production at midrapidity ($\eta$ $\in$ [-1, 1]). At the soft scale, charged particle production in low-EA p+Au collisions is comparable to that in p+p collisions and increases monotonically with increasing EA. At the hard scale, we report measurements of high transverse momentum (pT) jets in events of different EAs. In contrast with the soft particle production, high-pT particle production and EA are found to be inversely related. To investigate whether this is a signal of jet quenching in high-EA events, we also report ratios of pT imbalance and azimuthal separation of dijets in high- and low-EA events. Within our measurement precision, no significant differences are observed, disfavoring the presence of jet quenching in the highest 30% EA p+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 200 GeV., Comment: 12 page, 9 figures
Published: 2024
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18. Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terrain

Author: Datar, Aniket, Pan, Chenhui, Nazeri, Mohammad, Pokhrel, Anuj, and Xiao, Xuesu
Subjects: Computer Science - Robotics
Abstract: Wheeled robots have recently demonstrated superior mechanical capability to traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves). Negotiating such terrain introduces significant variations of vehicle pose in all six Degrees-of-Freedom (DoFs), leading to imbalanced contact forces, varying momentum, and chassis deformation due to non-rigid tires and suspensions. To autonomously navigate on vertically challenging terrain, all these factors need to be efficiently reasoned within limited onboard computation and strict real-time constraints. In this paper, we propose a 6-DoF kinodynamics learning approach that is attentive only to the specific underlying terrain critical to the current vehicle-terrain interaction, so that it can be efficiently queried in real-time motion planners onboard small robots. Physical experiment results show our Terrain-Attentive Learning demonstrates on average 51.1% reduction in model prediction error among all 6 DoFs compared to a state-of-the-art model for vertically challenging terrain.
Published: 2024

19. Predictors of HIV Care Outcomes among Recently Out-of-Care Individuals in The Massachusetts Cooperative Re-engagement Controlled Trial Cohort

Author: Pokhrel, Dinesh, Lang, Simona G., Elder, Heather, John, Betsey, Roosevelt, Kathleen A., Klevens, Monina, Randall, Liisa M., and DeMaria, Jr, Alfred
Published: 2024
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20. Estimation of Domain Mean Using General Class of Imputation Methods

Author: Bhushan, Shashi, Kumar, Anoop, and Pokhrel, Rohini
Published: 2024
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21. Lambda-Cyhalothrin induced behavioural, neurotoxic and oxidative stress on vertebrate model Danio rerio (Hamilton-Buchanan 1822)

Author: Sharma, Darshana, Sarmah, Raktim, Sarmah, Rimon, Pokhrel, Hemanta, Bhagabati, Sarada Kanta, Sarma, Dipak Kumar, Patowary, Arnab Narayan, and Mili, Karishma
Published: 2024
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22. CAHSOR: Competence-Aware High-Speed Off-Road Ground Navigation in SE(3)

Author: Pokhrel, Anuj, Datar, Aniket, Nazeri, Mohammad, and Xiao, Xuesu
Subjects: Computer Science - Robotics
Abstract: While the workspace of traditional ground vehicles is usually assumed to be in a 2D plane, i.e., SE(2), such an assumption may not hold when they drive at high speeds on unstructured off-road terrain: High-speed sharp turns on high-friction surfaces may lead to vehicle rollover; Turning aggressively on loose gravel or grass may violate the non-holonomic constraint and cause significant lateral sliding; Driving quickly on rugged terrain will produce extensive vibration along the vertical axis. Therefore, most offroad vehicles are currently limited to drive only at low speeds to assure vehicle stability and safety. In this work, we aim at empowering high-speed off-road vehicles with competence awareness in SE(3) so that they can reason about the consequences of taking aggressive maneuvers on different terrain with a 6-DoF forward kinodynamic model. The model is learned from visual and inertial Terrain Representation for Off-road Navigation (TRON) using multimodal, self-supervised vehicle-terrain interactions. We demonstrate the efficacy of our Competence-Aware High-Speed Off-Road (CAHSOR) navigation approach on a physical ground robot in both an autonomous navigation and a human shared-control setup and show that CAHSOR can efficiently reduce vehicle instability by 62% while only compromising 8.6% average speed with the help of TRON.
Published: 2024

23. JWST observations of $^{13}$CO$_{2}$ ice: Tracing the chemical environment and thermal history of ices in protostellar envelopes

Author: Brunken, Nashanty G. C., Rocha, Will R. M., van Dishoeck, Ewine F., Gutermuth, Robert, Tyagi, Himanshu, Slavicinska, Katerina, Nazari, Pooneh, Megeath, S. Thomas, Evans II, Neal J., Narang, Mayank, Manoj, P., Rubinstein, Adam E., Watson, Dan M., Looney, Leslie W., Linnartz, Harold, Garatti, Alessio Caratti o, Beuther, Henrik, Linz, Hendrik, Klaassen, Pamela, Poteet, Charles A., Federman, Samuel, Anglada, Guillem, Atnagulov, Prabhani, Bourke, Tyler L., Fischer, William J., Furlan, Elise, Green, Joel, Habel, Nolan, Hartmann, Lee, Karnath, Nicole, Osorio, Mayra, Page, James Muzerolle, Pokhrel, Riwaj, Rahatgaonkar, Rohan, Sheehan, Patrick, Stanke, Thomas, Stutz, Amelia M., Tobin, John J., Tychoniec, Lukasz, Wolk, Scott, and Yang, Yao-Lun
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract: The structure and composition of simple ices can be modified during stellar evolution by protostellar heating. Key to understanding the involved processes are thermal and chemical tracers that can diagnose the history and environment of the ice. The 15.2 $\mu$m bending mode of $^{12}$CO$_2$ has proven to be a valuable tracer of ice heating events but suffers from grain shape and size effects. A viable alternative tracer is the weaker $^{13}$CO$_2$ isotopologue band at 4.39 $\mu$m which has now become accessible at high S/N with the $\textit{James Webb}$ Space Telescope (JWST). We present JWST NIRSpec observations of $^{13}$CO$_2$ ice in five deeply embedded Class 0 sources spanning a wide range in luminosities (0.2 - 10$^4$ L$_{\odot}$ ) taken as part of the Investigating Protostellar Accretion Across the Mass Spectrum (IPA) program. The band profiles vary significantly, with the most luminous sources showing a distinct narrow peak at 4.38 $\mu$m. We first apply a phenomenological approach and show that a minimum of 3-4 Gaussian profiles are needed to fit the $^{13}$CO$_2$ absorption feature. We then combine these findings with laboratory data and show that a 15.2 $\mu$m $^{12}$CO$_2$ band inspired five-component decomposition can be applied for the isotopologue band where each component is representative of CO$_2$ ice in a specific molecular environment. The final solution consists of cold mixtures of CO$_2$ with CH$_3$OH, H$_2$O and CO as well as segregated heated pure CO$_2$ ice. Our results are in agreement with previous studies of the $^{12}$CO$_2$ ice band, further confirming that $^{13}$CO$_{2}$ is a useful alternative tracer of protostellar heating events. We also propose an alternative solution consisting only of heated CO$_2$:CH$_3$OH and CO$_2$:H$_2$O ices and warm pure CO$_2$ ice for decomposing the ice profiles of the two most luminous sources in our sample.
Published: 2024
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24. Design, Construction, and Performance of the GEM based Radial Time Projection Chamber for the BONuS12 Experiment with CLAS12

Author: Albayrak, I., Aune, S., Gayoso, C. Ayerbe, Baron, P., Bültmann, S., Charles, G., Christy, M. E., Dodge, G., Dzbenski, N., Dupré, R., Griffioen, K., Hattawy, M., Hung, Y. C., Kalantarians, N., Kuhn, S., Mandjavidze, I., Nadeeshani, A., Ouillon, M., Pandey, P., Payette, D., Pokhrel, M., Poudel, J., Tadepalli, A. S., and Vandenbroucke, M.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Nuclear Experiment
Abstract: A new radial time projection chamber based on Gas Electron Multiplier amplification layers was developed for the BONuS12 experiment in Hall B at Jefferson Lab. This device represents a significant evolutionary development over similar devices constructed for previous experiments, including cylindrical amplification layers constructed from single continuous GEM foils with less than 1\% dead area. Particular attention had been paid to producing excellent geometric uniformity of all electrodes, including the very thin metalized polyester film of the cylindrical cathode. This manuscript describes the design, construction, and performance of this new detector.
Published: 2024
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25. TM7SF3 controls TEAD1 splicing to prevent MASH-induced liver fibrosis

Author: Isaac, Roi, Bandyopadhyay, Gautam, Rohm, Theresa V, Kang, Sion, Wang, Jinyue, Pokhrel, Narayan, Sakane, Sadatsugu, Zapata, Rizaldy, Libster, Avraham M, Vinik, Yaron, Berhan, Asres, Kisseleva, Tatiana, Borok, Zea, Zick, Yehiel, Telese, Francesca, Webster, Nicholas JG, and Olefsky, Jerrold M
Subjects: Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Liver Disease, Genetics, 2.1 Biological and endogenous factors, TEA Domain Transcription Factors, Animals, Liver Cirrhosis, Transcription Factors, Humans, Mice, DNA-Binding Proteins, Alternative Splicing, Mice, Inbred C57BL, Nuclear Proteins, Hepatic Stellate Cells, Male, Fatty Liver, Mice, Knockout, ASO, Hippo pathway, MASH, NASH, TEAD1, TM7SF3, alternative splicing, fibrosis, hepatic stellate cells, Medical Biochemistry and Metabolomics, Endocrinology & Metabolism, Biochemistry and cell biology, Medical biochemistry and metabolomics
Abstract: The mechanisms of hepatic stellate cell (HSC) activation and the development of liver fibrosis are not fully understood. Here, we show that deletion of a nuclear seven transmembrane protein, TM7SF3, accelerates HSC activation in liver organoids, primary human HSCs, and in vivo in metabolic-dysfunction-associated steatohepatitis (MASH) mice, leading to activation of the fibrogenic program and HSC proliferation. Thus, TM7SF3 knockdown promotes alternative splicing of the Hippo pathway transcription factor, TEAD1, by inhibiting the splicing factor heterogeneous nuclear ribonucleoprotein U (hnRNPU). This results in the exclusion of the inhibitory exon 5, generating a more active form of TEAD1 and triggering HSC activation. Furthermore, inhibiting TEAD1 alternative splicing with a specific antisense oligomer (ASO) deactivates HSCs in vitro and reduces MASH diet-induced liver fibrosis. In conclusion, by inhibiting TEAD1 alternative splicing, TM7SF3 plays a pivotal role in mitigating HSC activation and the progression of MASH-related fibrosis.
Published: 2024

26. TTA-OOD: Test-Time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision

Author: Pokhrel, Sandesh, Bhandari, Sanjay, Vazquez, Eduard, Lambrou, Tryphon, Gyawali, Prashnna, Bhattarai, Binod, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhattarai, Binod, editor, Ali, Sharib, editor, Rau, Anita, editor, Caramalau, Razvan, editor, Nguyen, Anh, editor, Gyawali, Prashnna, editor, Namburete, Ana, editor, and Stoyanov, Danail, editor
Published: 2025
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27. Cross-Task Data Augmentation by Pseudo-Label Generation for Region Based Coronary Artery Instance Segmentation

Author: Pokhrel, Sandesh, Bhandari, Sanjay, Vazquez, Eduard, Shrestha, Yash Raj, Bhattarai, Binod, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhattarai, Binod, editor, Ali, Sharib, editor, Rau, Anita, editor, Caramalau, Razvan, editor, Nguyen, Anh, editor, Gyawali, Prashnna, editor, Namburete, Ana, editor, and Stoyanov, Danail, editor
Published: 2025
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28. A Case Study on Effectiveness of Online Teaching and Learning Mathematics: Teacher's Perspective

Author: Kunwar, Rajendra, Pokhrel, Jagat Krishna, Khanal, Baikuntha, and Sapkota, Hari Prasad
Abstract: The study on the effectiveness of teaching and learning mathematics online focused on teachers' perspectives on the experiences they face in real-life situations in the Nepalese context. This study examines the effectiveness of online mathematics teaching and learning in higher education during the COVID-19 pandemic. It is based on a qualitative exploratory case study design. The results of the study are based on the holistic views of eight persistently selected university mathematics teachers. It examines the effectiveness of teaching mathematics online covering four areas; teacher preparation and technological competence, the issue concerning students, access to resources, and the nature of mathematics. Teachers' technical competence and preparation for online lessons were rated as satisfactory. Similarly, student-related issues were found to be uncooperative and dependent on teacher guidance during online learning. Likewise, students were found to benefit according to their access to resources. Urban students have an advantage in online learning over remote students. Similarly, the results suggest that online math classes were perceived as difficult due to the nature of mathematics. Thus, the study concludes that higher-level online mathematics education has several determinants that make online mathematics education effective compared to face-to-face classroom instruction.
Published: 2023

29. Cardiac troponin I directly binds and inhibits mitochondrial ATP synthase with a noncanonical role in the post-ischemic heart

Author: Elezaby, Aly, Lin, Amanda J., Vijayan, Vijith, Pokhrel, Suman, Kraemer, Benjamin R., Bechara, Luiz R. G., Larus, Isabel, Sun, Junhui, Baena, Valentina, Syed, Zulfeqhar A., Murphy, Elizabeth, Glancy, Brian, Ostberg, Nicolai P., Queliconi, Bruno B., Campos, Juliane C., Ferreira, Julio C. B., Haileselassie, Bereketeab, and Mochly-Rosen, Daria
Published: 2024
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30. Physio-metabolic alterations in Labeo rohita (Hamilton, 1822) and native predator Chitala chitala (Hamilton, 1822) in presence of an invasive species Piractus brachypomus (G. Cuvier, 1818)

Author: Borah, Kankana, Phukan, Bipul, Talukdar, Avinash, Deka, Pankaj, Pokhrel, Hemanta, Kalita, Manoj, Kumar, Annam Pavan, Ali, Ayub, Bhuyan, Pradip Chandra, Patowary, Arnab Narayan, Kumar Sarma, Dipak, Ahmed, Mustafa, Kalita, Rinku, and Xavier, Martin
Published: 2024
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31. Development of a framework for the prediction of slope stability using machine learning paradigms

Author: Rajan, K. C., Aryal, Milan, Sharma, Keshab, Bhandary, Netra Prakash, Pokhrel, Richa, and Acharya, Indra Prasad
Published: 2024
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32. Impact of Left Renal Vein Ligation on Renal Function Following En Bloc Resection of Segmental Inferior Vena Cava and Right Kidney

Author: Wang, Shengzheng, Gaurab, Pokhrel, Cui, Jinshan, Yu, Shuanbao, Fan, Yafeng, Zhu, Zhaowei, Tao, Jin, and Zhang, Xuepei
Published: 2024
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33. Real Estate Market Prediction Using Deep Learning Models

Author: Rimal, Ramchandra, Rimal, Binod, Bhandari, Hum Nath, Pokhrel, Nawa Raj, and Dahal, Keshab R.
Published: 2024
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34. Production of Protons and Light Nuclei in Au+Au Collisions at $\sqrt{s_{\mathrm{NN}}}$ = 3 GeV with the STAR Detector

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Q., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Deshpande, A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Naim, C. J., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T. L., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, N., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment, Nuclear Theory
Abstract: We report the systematic measurement of protons and light nuclei production in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 3 GeV by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The transverse momentum ($p_{T}$) spectra of protons ($p$), deuterons ($d$), tritons ($t$), $^{3}\mathrm{He}$, and $^{4}\mathrm{He}$ are measured from mid-rapidity to target rapidity for different collision centralities. We present the rapidity and centrality dependence of particle yields ($dN/dy$), average transverse momentum ($\langle p_{T}\rangle$), yield ratios ($d/p$, $t/p$,$^{3}\mathrm{He}/p$, $^{4}\mathrm{He}/p$), as well as the coalescence parameters ($B_2$, $B_3$). The 4$\pi$ yields for various particles are determined by utilizing the measured rapidity distributions, $dN/dy$. Furthermore, we present the energy, centrality, and rapidity dependence of the compound yield ratios ($N_{p} \times N_{t} / N_{d}^{2}$) and compare them with various model calculations. The physics implications of those results on the production mechanism of light nuclei and on QCD phase structure are discussed., Comment: 17 pages, 17 figures
Published: 2023

35. Measurements of charged-particle multiplicity dependence of higher-order net-proton cumulants in $p$+$p$ collisions at $\sqrt{s} =$ 200 GeV from STAR at RHIC

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nishitani, R., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Nuclear Theory
Abstract: We report on the charged-particle multiplicity dependence of net-proton cumulant ratios up to sixth order from $\sqrt{s}=200$ GeV $p$+$p$ collisions at the Relativistic Heavy Ion Collider (RHIC). The measured ratios $C_{4}/C_{2}$, $C_{5}/C_{1}$, and $C_{6}/C_{2}$ decrease with increased charged-particle multiplicity and rapidity acceptance. Neither the Skellam baselines nor PYTHIA8 calculations account for the observed multiplicity dependence. In addition, the ratios $C_{5}/C_{1}$ and $C_{6}/C_{2}$ approach negative values in the highest-multiplicity events, which implies that thermalized QCD matter may be formed in $p$+$p$ collisions., Comment: 12 pages, 6 figures, accepted version by PLB
Published: 2023
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36. Discovery of a collimated jet from the low luminosity protostar IRAS 16253$-$2429 in a quiescent accretion phase with the JWST

Author: Narang, Mayank, P., Manoj, Tyagi, Himanshu, Watson, Dan M., Megeath, S. Thomas, Federman, Samuel, Rubinstein, Adam E., Gutermuth, Robert, Garatti, Alessio Caratti o, Beuther, Henrik, Bourke, Tyler L., Van Dishoeck, Ewine F., Evans II, Neal J., Anglada, Guillem, Osorio, Mayra, Stanke, Thomas, Muzerolle, James, Looney, Leslie W., Yang, Yao-Lun, Tobin, John J., Klaassen, Pamela, Karnath, Nicole, Atnagulov, Prabhani, Brunken, Nashanty, Fischer, William J., Furlan, Elise, Green, Joel, Habel, Nolan, Hartmann, Lee, Linz, Hendrik, Nazari, Pooneh, Pokhrel, Riwaj, Rahatgaonkar, Rohan, Rocha, Will R. M., Sheehan, Patrick, Slavicinska, Katerina, Stutz, Amelia, Tychoniec, Lukasz, and Wolk, Scott
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Investigating Protostellar Accretion (IPA) is a JWST Cycle~1 GO program that uses NIRSpec IFU and MIRI MRS to obtain 2.9--28~$\mu$m spectral cubes of young, deeply embedded protostars with luminosities of 0.2 to 10,000~L$_{\odot}$ and central masses of 0.15 to 12~M$_{\odot}$. In this Letter, we report the discovery of a highly collimated atomic jet from the Class~0 protostar IRAS~16253$-$2429, the lowest luminosity source ($L_\mathrm{bol}$ = 0.2 $L_\odot$) in the IPA program. The collimated jet is detected in multiple [Fe~II] lines, [Ne~II], [Ni~II], and H~I lines, but not in molecular emission. The atomic jet has a velocity of about 169~$\pm$~15~km\,s$^{-1}$, after correcting for inclination. The width of the jet increases with distance from the central protostar from 23 to~60 au, corresponding to an opening angle of 2.6~$\pm$~0.5\arcdeg. By comparing the measured flux ratios of various fine structure lines to those predicted by simple shock models, we derive a shock {speed} of 54~km\,s$^{-1}$ and a preshock density of 2.0$\times10^{3}$~cm$^{-3}$ at the base of the jet. {From these quantities and using a suite of jet models and extinction laws we compute a mass loss rate between $0.4 -1.1\times10^{-10}~M_{\odot}$~yr~$^{-1}$.} The low mass loss rate is consistent with simultaneous measurements of low mass accretion rate ($2.4~\pm~0.8~\times~10^{-9}~M_{\odot}$~yr$^{-1}$) for IRAS~16253$-$2429 from JWST observations (Watson et al. in prep), indicating that the protostar is in a quiescent accretion phase. Our results demonstrate that very low-mass protostars can drive highly collimated, atomic jets, even during the quiescent phase., Comment: Accepted to ApJL. Comments and feedback welcome
Published: 2023

37. Observation of the Antimatter Hypernucleus $^4_{\bar{\Lambda}}\overline{\hbox{H}}$

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment, High Energy Physics - Experiment
Abstract: At the origin of the Universe, asymmetry between the amount of created matter and antimatter led to the matter-dominated Universe as we know today. The origins of this asymmetry remain not completely understood yet. High-energy nuclear collisions create conditions similar to the Universe microseconds after the Big Bang, with comparable amounts of matter and antimatter. Much of the created antimatter escapes the rapidly expanding fireball without annihilating, making such collisions an effective experimental tool to create heavy antimatter nuclear objects and study their properties, hoping to shed some light on existing questions on the asymmetry between matter and antimatter. Here we report the first observation of the antimatter hypernucleus \hbox{$^4_{\bar{\Lambda}}\overline{\hbox{H}}$}, composed of a $\bar{\Lambda}$ , an antiproton and two antineutrons. The discovery was made through its two-body decay after production in ultrarelativistic heavy-ion collisions by the STAR experiment at the Relativistic Heavy Ion Collider. In total, 15.6 candidate \hbox{$^4_{\bar{\Lambda}}\overline{\hbox{H}}$} antimatter hypernuclei are obtained with an estimated background count of 6.4. The lifetimes of the antihypernuclei \hbox{$^3_{\bar{\Lambda}}\overline{\hbox{H}}$} and \hbox{$^4_{\bar{\Lambda}}\overline{\hbox{H}}$} are measured and compared with the lifetimes of their corresponding hypernuclei, testing the symmetry between matter and antimatter. Various production yield ratios among (anti)hypernuclei and (anti)nuclei are also measured and compared with theoretical model predictions, shedding light on their production mechanisms., Comment: 28 pages, 5 figures in the main paper; 16 pages, 5 figures in the methods part
Published: 2023

38. Estimate of Background Baseline and Upper Limit on the Chiral Magnetic Effect in Isobar Collisions at $\sqrt{s_{\text{NN}}}=200$ GeV at the Relativistic Heavy-Ion Collider

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adams, J. R., Agakishiev, G., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aitbaev, A., Alekseev, I., Alpatov, E., Aparin, A., Aslam, S., Atchison, J., Averichev, G. S., Bairathi, V., Cap, J. G. Ball, Barish, K., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bordyuzhin, I. G., Brandenburg, J. D., Brandin, A. V., Broodo, C., Cai, X. Z., Caines, H., Calderón~de~la~Barca~Sánchez, M., Cebra, D., Ceska, J., Chakaberia, I., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Dale-Gau, G., Das, A., Dedovich, T. G., Deppner, I. M., Derevschikov, A. A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Hamed, A., Han, Y., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kechechyan, A., Khanal, A., Kiselev, A., Knospe, A. G., Ko, H. S., Kochenda, L., Korobitsin, A. A., Kraeva, A. Yu., Kravtsov, P., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lebedev, A., Lednicky, R., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Lin, T., Lin, Y., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Luong, V. B., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Manikandhan, R., Margetis, S., Matis, H. S., McNamara, G., Mezhanska, O., Mi, K., Minaev, N. G., Mohanty, B., Mondal, M. M., Mooney, I., Morozov, D. A., Mudrokh, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Nedorezov, E., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nogach, L. V., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okorokov, V. A., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pandey, A. K., Panebratsev, Y., Pani, T., Parfenov, P., Paul, A., Perkins, C., Pokhrel, B. R., Posik, M., Povarov, A., Protzman, T., Pruthi, N. K., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Ritter, H. G., Robertson, C. W., Rogachevsky, O. V., Aguilar, M. A. Rosales, Roy, D., Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Samigullin, E., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seger, J., Seto, R., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stewart, D. J., Strikhanov, M., Stringfellow, B., Su, Y., Sun, C., Sun, X., Sun, Y., Surrow, B., Svirida, D. N., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Taranenko, A., Tarnowsky, T., Thomas, J. H., Tlusty, D., Todoroki, T., Tokarev, M. V., Trentalange, S., Tribedy, P., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vasiliev, A. N., Verkest, V., Videbæk, F., Vokal, S., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wieman, H., Wilks, G., Wissink, S. W., Wu, J., Wu, X., Wu, Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment, Nuclear Theory
Abstract: For the search of the chiral magnetic effect (CME), STAR previously presented the results from isobar collisions (${^{96}_{44}\text{Ru}}+{^{96}_{44}\text{Ru}}$, ${^{96}_{40}\text{Zr}}+{^{96}_{40}\text{Zr}}$) obtained through a blind analysis. The ratio of results in Ru+Ru to Zr+Zr collisions for the CME-sensitive charge-dependent azimuthal correlator ($\Delta\gamma$), normalized by elliptic anisotropy ($v_{2}$), was observed to be close to but systematically larger than the inverse multiplicity ratio. The background baseline for the isobar ratio, $Y = \frac{(\Delta\gamma/v_{2})^{\text{Ru}}}{(\Delta\gamma/v_{2})^{\text{Zr}}}$, is naively expected to be $\frac{(1/N)^{\text{Ru}}}{(1/N)^{\text{Zr}}}$; however, genuine two- and three-particle correlations are expected to alter it. We estimate the contributions to $Y$ from those correlations, utilizing both the isobar data and HIJING simulations. After including those contributions, we arrive at a final background baseline for $Y$, which is consistent with the isobar data. We extract an upper limit for the CME fraction in the $\Delta\gamma$ measurement of approximately $10\%$ at a $95\%$ confidence level on in isobar collisions at $\sqrt{s_{\text{NN}}} = 200$ GeV, with an expected $15\%$ difference in their squared magnetic fields., Comment: 19 pages, 14 figures
Published: 2023
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39. Co-Evolution of Stars and Gas: Using Analysis of Synthetic Observations to Investigate the Star-Gas Correlation in STARFORGE

Author: Millstone, Samuel, Gutermuth, Robert, Offner, Stella S. R., Pokhrel, Riwaj, and Grudić, Michael Y.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract: We explore the relationship between stellar surface density and gas surface density (the star-gas or S-G correlation) in a 20,000 M$_{\odot}$ simulation from the STAR FORmation in Gaseous Environments (STARFORGE) Project. We create synthetic observations based on the Spitzer and Herschel telescopes by modeling active galactic nuclei contamination, smoothing based on angular resolution, cropping the field-of-view, and removing close neighbors and low-mass sources. We extract star-gas properties such as the dense gas mass fraction, the Class II:I ratio, and the S-G correlation ($\Sigma_{\rm YSO}/\Sigma_{\rm gas}$) from the simulation and compare them to observations of giant molecular clouds, young clusters, and star-forming regions, as well as to analytical models. We find that the simulation reproduces trends in the counts of young stellar objects and the median slope of the S-G correlation. This implies that the S-G correlation is not simply the result of observational biases but is in fact a real effect. However, other statistics, such as the Class II:I ratio and dense gas mass fraction, do not always match observed equivalents in nearby clouds. This motivates further observations covering the full simulation age range and more realistic modeling of cloud formation., Comment: 25 pages, 16 figures. To be published in The Astrophysical Journal
Published: 2023

40. Higher-order Lie bracket approximation and averaging of control-affine systems with application to extremum seeking

Author: Pokhrel, Sameer and Eisa, Sameh A.
Subjects: Mathematics - Optimization and Control
Abstract: This paper provides a rigorous derivation for what is known in the literature as the Lie bracket approximation of control-affine systems in a more general and sequential framework for higher-orders. In fact, by using chronological calculus, we show that said Lie bracket approximations can be derived, and considered, as higher-order averaging terms. Hence, the theory provided in this paper unifies both averaging and approximation theories of control-affine systems. In particular, the Lie bracket approximation of order ($n$) turns out to be a higher-order averaging of order ($n+1$). The derivation and formulation provided in this paper can be directly reduced to the first and second-order Lie bracket approximations available in the literature. However, we do not need to make many of the unproven assumptions provided in the literature and show that they are in fact natural corollaries from our work. Moreover, we use our results to show that important and useful information about control-affine extremum seeking systems can be obtained and used for significant performance improvement, including a faster convergence rate influenced by higher-order derivatives. We provide multiple numerical simulations to demonstrate both the conceptual elements of this work as well as the significance of our results on extremum seeking with comparison against the literature.
Published: 2023

41. Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation

Author: Pokhrel, Sandesh, Bhandari, Sanjay, Vazquez, Eduard, Shrestha, Yash Raj, and Bhattarai, Binod
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Coronary Artery Diseases (CADs) although preventable, are one of the leading causes of death and disability. Diagnosis of these diseases is often difficult and resource intensive. Angiographic imaging segmentation of the arteries has evolved as a tool of assistance that helps clinicians make an accurate diagnosis. However, due to the limited amount of data and the difficulty in curating a dataset, the task of segmentation has proven challenging. In this study, we introduce the use of pseudo-labels to address the issue of limited data in the angiographic dataset to enhance the performance of the baseline YOLO model. Unlike existing data augmentation techniques that improve the model constrained to a fixed dataset, we introduce the use of pseudo-labels generated on a dataset of separate related task to diversify and improve model performance. This method increases the baseline F1 score by 9% in the validation data set and by 3% in the test data set., Comment: arXiv admin note: text overlap with arXiv:2310.04749
Published: 2023

42. ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis

Author: Pokhrel, Sandesh, Bhandari, Sanjay, Vazquez, Eduard, Shrestha, Yash Raj, and Bhattarai, Binod
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has proved challenging. This study addresses the automation of resource-intensive and time-consuming process of manually detecting stenotic lesions in coronary arteries in X-ray coronary angiography images. To overcome this challenge, we employ a specialized Convnext-V2 backbone based Mask RCNN model pre-trained for instance segmentation tasks. Our empirical findings affirm that the proposed model exhibits commendable performance in identifying stenotic lesions. Notably, our approach achieves a substantial F1 score of 0.5353 in this demanding task, underscoring its effectiveness in streamlining this intensive process.
Published: 2023

43. Investigating Protostellar Accretion-Driven Outflows Across the Mass Spectrum: JWST NIRSpec IFU 3-5~$\mu$m Spectral Mapping of Five Young Protostars

Author: Federman, Samuel, Megeath, S. Thomas, Rubinstein, Adam E., Gutermuth, Robert, Narang, Mayank, Tyagi, Himanshu, Manoj, P., Anglada, Guillem, Atnagulov, Prabhani, Beuther, Henrik, Bourke, Tyler L., Brunken, Nashanty, Garatti, Alessio Caratti o, Evans II, Neal J., Fischer, William J., Furlan, Elise, Green, Joel, Habel, Nolan, Hartmann, Lee, Karnath, Nicole, Klaassen, Pamela, Linz, Hendrik, Looney, Leslie W., Osorio, Mayra, Page, James Muzerolle, Pokhrel, Riwaj, Rahatgaonkar, Rohan, Rocha, Will R. M., Sheehan, Patrick, Slavicinska, Katerina, Stanke, Thomas, Stutz, Amelia M., Tobin, John J., Tychoniec, Lukasz, Van Dishoeck, Ewine F., Watson, Dan M., Wolk, Scott, and Yang, Yao-Lun
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Investigating Protostellar Accretion is a Cycle 1 JWST program using the NIRSpec+MIRI integral field units to obtain 2.9--28 $\mu$m spectral cubes of five young protostars with luminosities of 0.2-10,000 L$_{\odot}$ in their primary accretion phase. This paper introduces the NIRSpec 2.9--5.3 $\mu$m data of the inner 840-9000 au with spatial resolutions from 28-300 au. The spectra show rising continuum emission; deep ice absorption; emission from H$_{2}$, H~I, and [Fe~II]; and the CO fundamental series in emission and absorption. Maps of the continuum emission show scattered light cavities for all five protostars. In the cavities, collimated jets are detected in [Fe~II] for the four $< 320$~L$_{\odot}$ protostars, two of which are additionally traced in Br-$\alpha$. Knots of [Fe~II] emission are detected toward the most luminous protostar, and knots of [FeII] emission with dynamical times of $< 30$~yrs are found in the jets of the others. While only one jet is traced in H$_2$, knots of H$_2$ and CO are detected in the jets of four protostars. H$_2$ is seen extending through the cavities, showing that they are filled by warm molecular gas. Bright H$_2$ emission is seen along the walls of a single cavity, while in three cavities narrow shells of H$_2$ emission are found, one of which has an [Fe~II] knot at its apex. These data show cavities containing collimated jets traced in atomic/ionic gas surrounded by warm molecular gas in a wide-angle wind and/or gas accelerated by bow shocks in the jets., Comment: 26 pages, 12 figures
Published: 2023
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44. Results on Elastic Cross Sections in Proton-Proton Collisions at $\sqrt{s} = 510$ GeV with the STAR Detector at RHIC

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matis, H. S., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: High Energy Physics - Experiment, Nuclear Experiment
Abstract: We report results on an elastic cross section measurement in proton-proton collisions at a center-of-mass energy $\sqrt{s}=510$ GeV, obtained with the Roman Pot setup of the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The elastic differential cross section is measured in the four-momentum transfer squared range $0.23 \leq -t \leq 0.67$ GeV$^2$. We find that a constant slope $B$ does not fit the data in the aforementioned $t$ range, and we obtain a much better fit using a second-order polynomial for $B(t)$. The $t$ dependence of $B$ is determined using six subintervals of $t$ in the STAR measured $t$ range, and is in good agreement with the phenomenological models. The measured elastic differential cross section $\mathrm{d}\sigma/\mathrm{dt}$ agrees well with the results obtained at $\sqrt{s} = 546$ GeV for proton--antiproton collisions by the UA4 experiment. We also determine that the integrated elastic cross section within the STAR $t$-range is $\sigma^\mathrm{fid}_\mathrm{el} = 462.1 \pm 0.9 (\mathrm{stat.}) \pm 1.1 (\mathrm {syst.}) \pm 11.6 (\mathrm {scale})$~$\mu\mathrm{b}$., Comment: 9 pages, 9 figures Version as published in Physics Letters B. HEPDATA: https://www.hepdata.net/record/144920
Published: 2023
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45. Longitudinal and transverse spin transfer to $\Lambda$ and $\overline{\Lambda}$ hyperons in polarized $p$+$p$ collisions at $\sqrt{s} = 200$ GeV

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Anderson, D. M., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Baker, W., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Daugherity, M., Deppner, I. M., Dhamija, A., Di Carlo, L., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Ewigleben, A., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kabir, M. L., Kagamaster, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kelsey, M., Khyzhniak, Y. V., Kikoła, D. P., Kimelman, B., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kramarik, L., Kumar, L., Kumar, S., Elayavalli, R. Kunnawalkam, Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lisa, M. A., Liu, C., Liu, F., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Llope, W. J., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Markert, C., Matis, H. S., Mazer, J. A., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Mukherjee, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nishitani, R., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pan, J., Pandav, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Raha, N., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Szymanski, P. R., Tamis, A., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Tomkiel, C. A., Trentalange, S., Tribble, R. E., Tribedy, P., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Wu, Y., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: High Energy Physics - Experiment
Abstract: The longitudinal and transverse spin transfers to $\Lambda$ ($\overline{\Lambda}$) hyperons in polarized proton-proton collisions are expected to be sensitive to the helicity and transversity distributions, respectively, of (anti-)strange quarks in the proton, and to the corresponding polarized fragmentation functions. We report improved measurements of the longitudinal spin transfer coefficient, $D_{LL}$, and the transverse spin transfer coefficient, $D_{TT}$, to $\Lambda$ and $\overline{\Lambda}$ in polarized proton-proton collisions at $\sqrt{s}$ = 200 GeV by the STAR experiment at RHIC. The data set includes longitudinally polarized proton-proton collisions with an integrated luminosity of 52 pb$^{-1}$, and transversely polarized proton-proton collisions with a similar integrated luminosity. Both data sets have about twice the statistics of previous results and cover a kinematic range of $|\eta_{\Lambda(\overline{\Lambda})}|$ $<$ 1.2 and transverse momentum $p_{T,{\Lambda(\overline{\Lambda})}}$ up to 8 GeV/$c$. We also report the first measurements of the hyperon spin transfer coefficients $D_{LL}$ and $D_{TT}$ as a function of the fractional jet momentum $z$ carried by the hyperon, which can provide more direct constraints on the polarized fragmentation functions.
Published: 2023
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46. Reaction plane correlated triangular flow in Au+Au collisions at $\sqrt{s_{NN}}=3$ GeV

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matis, H. S., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pandav, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment
Abstract: We measure triangular flow relative to the reaction plane at 3 GeV center-of-mass energy in Au+Au collisions at the BNL Relativistic Heavy Ion Collider. A significant $v_3$ signal for protons is observed, which increases for higher rapidity, higher transverse momentum, and more peripheral collisions. The triangular flow is essentially rapidity-odd with a slope at mid-rapidity, $dv_3/dy|_{(y=0)}$, opposite in sign compared to the slope for directed flow. No significant $v_3$ signal is observed for charged pions and kaons. Comparisons with models suggest that a mean field potential is required to describe these results, and that the triangular shape of the participant nucleons is the result of stopping and nuclear geometry., Comment: 12 pages, 14 figures
Published: 2023
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47. Strangeness production in sNN = 3 GeV Au+Au collisions at RHIC

Author: Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Ball Cap, J. G., Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Calderón de la Barca Sánchez, M., Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T. L., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Rosales Aguilar, M. A., Roy, D., Roy Chowdhury, P., Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Y., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, J., Wu, X., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Y., Xu, Z., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Ye, Z., Yi, L., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Y., Zhang, Y., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Published: 2024
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48. Community Gender Norms and Gender Gaps in Adolescent Agency in Nepal

Author: Yount, Kathryn M., Durr, Robert L., Bergenfeld, Irina, Clark, Cari Jo, Khan, Zara, Laterra, Anne, Pokhrel, Pankaj, and Sharma, Sudhindra
Abstract: Sustainable Development Goal 5 challenges governments to address child marriage, which may inhibit girls from developing an agentic self. This paper assesses the direct influence of community gender norms on adolescent agency, and the normative contexts in which gender gaps in adolescent agency are larger or smaller in Nepal. Using baseline data for adolescent girls, adolescent boys, and adults in 54 clusters participating in the CARE Tipping Point Trial, multilevel analysis tested whether: adolescent boys had higher agency than girls; and community gender norms among adults "partly accounted for or modified" gender gaps in adolescent agency. Gender gaps in agency disfavoring girls were common. Community gender norms were more positively associated with intrinsic agency among girls than boys, and more negatively associated with instrumental and collective agency among girls than boys. Enhancing girls' agency while promoting gender-equitable community norms may empower girls' transition to adulthood.
Published: 2024
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49. Upper Limit on the Chiral Magnetic Effect in Isobar Collisions at the Relativistic Heavy-Ion Collider

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adams, J. R., Agakishiev, G., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aitbaev, A., Alekseev, I., Alpatov, E., Aparin, A., Aslam, S., Atchison, J., Averichev, G. S., Bairathi, V., Cap, J. G. Ball, Barish, K., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bordyuzhin, I. G., Brandenburg, J. D., Brandin, A. V., Broodo, C., Cai, X. Z., Caines, H., Calderón~de~la~Barca~Sánchez, M., Cebra, D., Ceska, J., Chakaberia, I., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Dale-Gau, G., Das, A., Dedovich, T. G., Deppner, I. M., Derevschikov, A. A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Hamed, A., Han, Y., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kechechyan, A., Khanal, A., Kiselev, A., Knospe, A. G., Ko, H. S., Kochenda, L., Korobitsin, A. A., Kraeva, A. Yu., Kravtsov, P., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lebedev, A., Lednicky, R., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Lin, T., Lin, Y., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Luong, V. B., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Manikandhan, R., Margetis, S., Matis, H. S., McNamara, G., Mezhanska, O., Mi, K., Minaev, N. G., Mohanty, B., Mondal, M. M., Mooney, I., Morozov, D. A., Mudrokh, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Nedorezov, E., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nogach, L. V., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okorokov, V. A., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pandey, A. K., Panebratsev, Y., Pani, T., Parfenov, P., Paul, A., Perkins, C., Pokhrel, B. R., Posik, M., Povarov, A., Protzman, T., Pruthi, N. K., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Ritter, H. G., Robertson, C. W., Rogachevsky, O. V., Aguilar, M. A. Rosales, Roy, D., Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Samigullin, E., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seger, J., Seto, R., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stewart, D. J., Strikhanov, M., Stringfellow, B., Su, Y., Sun, C., Sun, X., Sun, Y., Surrow, B., Svirida, D. N., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Taranenko, A., Tarnowsky, T., Thomas, J. H., Tlusty, D., Todoroki, T., Tokarev, M. V., Trentalange, S., Tribedy, P., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vasiliev, A. N., Verkest, V., Videbæk, F., Vokal, S., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wieman, H., Wilks, G., Wissink, S. W., Wu, J., Wu, X., Wu, Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment, Nuclear Theory
Abstract: The chiral magnetic effect (CME) is a phenomenon that arises from the QCD anomaly in the presence of an external magnetic field. The experimental search for its evidence has been one of the key goals of the physics program of the Relativistic Heavy-Ion Collider. The STAR collaboration has previously presented the results of a blind analysis of isobar collisions (${^{96}_{44}\text{Ru}}+{^{96}_{44}\text{Ru}}$, ${^{96}_{40}\text{Zr}}+{^{96}_{40}\text{Zr}}$) in the search for the CME. The isobar ratio ($Y$) of CME-sensitive observable, charge separation scaled by elliptic anisotropy, is close to but systematically larger than the inverse multiplicity ratio, the naive background baseline. This indicates the potential existence of a CME signal and the presence of remaining nonflow background due to two- and three-particle correlations, which are different between the isobars. In this post-blind analysis, we estimate the contributions from those nonflow correlations as a background baseline to $Y$, utilizing the isobar data as well as Heavy Ion Jet Interaction Generator simulations. This baseline is found consistent with the isobar ratio measurement, and an upper limit of 10% at 95% confidence level is extracted for the CME fraction in the charge separation measurement in isobar collisions at $\sqrt{s_{\rm NN}}=200$ GeV., Comment: 8 pages, 4 figures
Published: 2023
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50. Jet-hadron correlations with respect to the event plane in $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV Au+Au collisions in STAR

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Daugherity, M., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, H-S., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Markert, C., Matis, H. S., Mazer, J., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pandav, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment, High Energy Physics - Experiment
Abstract: Angular distributions of charged particles relative to jet axes are studied in $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV Au+Au collisions as a function of the jet orientation with respect to the event plane. This differential study tests the expected path-length dependence of energy loss experienced by a hard-scattered parton as it traverses the hot and dense medium formed in heavy-ion collisions. A second-order event plane is used in the analysis as an experimental estimate of the reaction plane formed by the collision impact parameter and the beam direction. Charged-particle jets with $15 < p_{\rm T, jet} <$ 20 and $20 < p_{\rm T, jet} <$ 40 GeV/$c$ were reconstructed with the anti-$k_{\rm T}$ algorithm with radius parameter setting of (R=0.4) in the 20-50\% centrality bin to maximize the initial-state eccentricity of the interaction region. The reaction plane fit method is implemented to remove the flow-modulated background with better precision than prior methods. Yields and widths of jet-associated charged-hadron distributions are extracted in three angular bins between the jet axis and the event plane. The event-plane (EP) dependence is further quantified by ratios of the associated yields in different EP bins. No dependence on orientation of the jet axis with respect to the event plane is seen within the uncertainties in the kinematic regime studied. This finding is consistent with a similar experimental observation by ALICE in $\sqrt{s_{\mathrm{NN}}}$ = 2.76 TeV Pb+Pb collision data.
Published: 2023

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