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Using first principles calculations, we have demonstrated the creation of multiple quantum states, in the experimentally accessible metal organic framework BHT-Ni. Specifically, quantum spin Hall and quantum anomalous Hall states are induced by two and four electron doping, respectively. The geometrical symmetry breaking, is also investigated. For a low electron doping concentration of two electrons per unit cell, the Fermi energy shifts to a nontrivial band gap, between Dirac bands and a quantized spin Hall conductivity is predicted. Subsequently in a high electron doping concentration, Anomalous Hall conductivity with a quantized value was observed. In addition, for centrosymmetric (trans-like) and non-centrosymmetric (cis-like) structures, we found that the trans-like structure preserves quantum spin Hall and quantized spin Hall conductivity. In contrast, in the cis-like structure, space inversion symmetry breaking leads to the appearance of valley Hall effect and the disappearance of spin Hall conductivity.

The total deuterium abundance [D/H] in the universe is set by just two processes: the creation of deuterium in Big Bang Nucleosynthesis at an abundance of [D/H]$=2.58\pm0.13\times10^{-5}$, and its destruction within stellar interiors. Measurements of the total [D/H] abundance can potentially provide a probe of Galactic chemical evolution, however, most measurements of [D/H] are only sensitive to the gas-phase deuterium, and the amount of deuterium sequestered in carbonaceous dust grains is debated. With the launch of JWST, it is now possible to measure the gas-phase [D/H] at unprecedented sensitivity and distances through observation of mid-IR lines of H$_2$ and HD. We employ data from the JWST Observations of Young protoStars (JOYS) program to measure the gas-phase [D/H] abundance with a rotation diagram analysis towards 5 nearby low-mass and 5 distant high-mass protostellar outflows. The gas-phase [D/H] varies between low-mass sources by up to a factor of $\sim4$, despite these sources likely having formed in a region of the Galactic disk that would be expected to have nearly constant total [D/H]. Most measurements of gas-phase [D/H] from our work or previous studies produce [D/H] $\lesssim 1.0\times10^{-5}$, a factor of $2-4$ lower than found from local UV absorption lines and as expected from Galactic chemical evolution models. The variations in [D/H] between our low-mass sources and the low [D/H] with respect to Galactic chemical evolution models suggest that our observations are not sensitive to the total [D/H]. Significant depletion of deuterium onto carbonaceous dust grains is a possible explanation, and tentative evidence of enhanced [D/H] towards shock positions with higher gas-phase Fe abundance is seen in the HH 211 outflow. Deeper observations of HD and H$_2$ in shocked environments and modelling of dust-grain destruction are warranted to test for the effects of depletion., Comment: 30 Pages, 20 Figures. Accepted for publication in Astronomy and Astrophysics

Social media has revolutionized communication, allowing people worldwide to connect and interact instantly. However, it has also led to increases in cyberbullying, which poses a significant threat to children and adolescents globally, affecting their mental health and well-being. It is critical to accurately detect the roles of individuals involved in cyberbullying incidents to effectively address the issue on a large scale. This study explores the use of machine learning models to detect the roles involved in cyberbullying interactions. After examining the AMiCA dataset and addressing class imbalance issues, we evaluate the performance of various models built with four underlying LLMs (i.e., BERT, RoBERTa, T5, and GPT-2) for role detection. Our analysis shows that oversampling techniques help improve model performance. The best model, a fine-tuned RoBERTa using oversampled data, achieved an overall F1 score of 83.5%, increasing to 89.3% after applying a prediction threshold. The top-2 F1 score without thresholding was 95.7%. Our method outperforms previously proposed models. After investigating the per-class model performance and confidence scores, we show that the models perform well in classes with more samples and less contextual confusion (e.g., Bystander Other), but struggle with classes with fewer samples (e.g., Bystander Assistant) and more contextual ambiguity (e.g., Harasser and Victim). This work highlights current strengths and limitations in the development of accurate models with limited data and complex scenarios.

In this study, we focus on the radiative capture process of the deuteron on alpha particle leading to the formation of $^6{\textrm{Li}}$ in the two-body formalism through the cluster effective field theory~(CEFT). It was the primitive nuclear reaction to produce ${^6 \textrm{Li}}$ in a few minutes after the Big Bang. In detail, we outline the calculation of the dominant $E1$ and $E2$ electromagnetic transition amplitudes of $d(\alpha,\gamma ){^6\textrm{Li}}$. Then, we obtain the astrophysical S-factor by fitting it to the experimental data. Finally, we compare the obtained CEFT results for the astrophysical S-factor with the other theoretical results.

Intracranial EEG (iEEG) recording, characterized by high spatial and temporal resolution and superior signal-to-noise ratio (SNR), enables the development of precise brain-computer interface (BCI) systems for neural decoding. However, the invasive nature of the procedure significantly limits the availability of iEEG datasets in terms of both the number of participants and the duration of recorded sessions. To address this limitation, we propose a single-participant machine learning model optimized for decoding iEEG signals. The model employs 18 key features and operates in two modes: best channel and combined channel. The combined channel mode integrates spatial information from multiple brain regions, leading to superior classification performance. Evaluations across three datasets -- Music Reconstruction, Audio Visual, and AJILE12 -- demonstrate that the combined channel mode consistently outperforms the best channel mode across all classifiers. In the best-performing cases, Random Forest achieved an F1 score of 0.81 +/- 0.05 in the Music Reconstruction dataset and 0.82 +/- 0.10 in the Audio Visual dataset, while XGBoost achieved an F1 score of 0.84 +/- 0.08 in the AJILE12 dataset. Furthermore, the analysis of brain region contributions in the combined channel mode revealed that the model identifies relevant brain regions aligned with physiological expectations for each task and effectively combines data from electrodes in these regions to achieve high performance. These findings highlight the potential of integrating spatial information across brain regions to improve task decoding, offering new avenues for advancing BCI systems and neurotechnological applications.

Reliable large-scale data on the state of forests is crucial for monitoring ecosystem health, carbon stock, and the impact of climate change. Current knowledge of tree species distribution relies heavily on manual data collection in the field, which often takes years to complete, resulting in limited datasets that cover only a small subset of the world's forests. Recent works show that state-of-the-art deep learning models using Light Detection and Ranging (LiDAR) images enable accurate and scalable classification of tree species in various ecosystems. While LiDAR images contain rich 3D information, most previous works flatten the 3D images into 2D projections to use Convolutional Neural Networks (CNNs). This paper offers three significant contributions: (1) we apply the deep learning framework for tree classification in tropical savannas; (2) we use Airborne LiDAR images, which have a lower resolution but greater scalability than Terrestrial LiDAR images used in most previous works; (3) we introduce the approach of directly feeding 3D point cloud images into a vision transformer model (PCTreeS). Our results show that the PCTreeS approach outperforms current CNN baselines with 2D projections in AUC (0.81), overall accuracy (0.72), and training time (~45 mins). This paper also motivates further LiDAR image collection and validation for accurate large-scale automatic classification of tree species.

The documentation of the Neo-Aramaic dialects before their extinction has been described as the most urgent task in all of Semitology today. The death of this language will be an unfathomable loss to the descendents of the indigenous speakers of Aramaic, now predominantly diasporic after forced displacement due to violence. This paper develops an ASR model to expedite the documentation of this endangered language and generalizes the strategy in a new framework we call NoLoR.

Unmanned aerial vehicles (UAVs) have the potential for time-sensitive applications. Due to wireless channel variation, received data may have an expiration time, particularly in critical situations such as rescue operations, natural disasters, or the military. Age of Information (AoI) is a metric that measures the freshness of received packets to specify the validity period of information. In addition, it is necessary to guarantee the privacy of confidential information transmission through air-to-ground links against eavesdroppers. This paper investigates UAV-assisted covert communication to minimize AoI in the presence of an aerial eavesdropper for the first time. However, to ensure the eavesdropper's error detection rate, UAV-enabled beamforming employs the power-domain non-orthogonal multiple access (PD-NOMA) technique to cover the covert user by a public user. PD-NOMA technique significantly improves the user's AoI, too. The joint optimization problem contains non-convex constraints and coupled optimization variables, including UAV trajectory, beamforming design, and the user's AoI which is challenging to derive a direct solution. We have developed an efficient alternating optimization technique to address the formulated optimization problem. Numerical results demonstrate the impact of the main parameters on the performance of the proposed communication system.

Introduction Speech is an integral component of human communication, requiring the coordinated efforts of various organs to produce sound (Titze & Alipour, 2006). The glottis region, a key player in voice production, assumes a crucial role in this intricate process. As air, emanating from the lungs in a confined space, interacts with the vocal folds (VFs) within the human body, it gives rise to the creation of voice (Alipour & Vigmostad, 2012). Understanding the mechanical intricacies of this process is very important. Studying VFs in vivo situations is hard work. However, the orientation, shape and size of VFs fibers have been extracted with synchrotron X-ray microtomography. (Bailly et al., 2018) The investigation of mechanical properties of both human and animal VFs has been carried out through various methodologies in the literature. The mechanical properties of VFs have been studied using the uniaxial extension test (Alipour & Vigmostad, 2012) assuming a linear behavior, while the nonlinearity and anisotropy of VFs has been determined using a multiscale method as in Miri et al. (2013). Pipette aspiration has also been used to extract in vivo elastic properties of VFs (Scheible et al., 2023). Mechanical behavior of VFs layers in tension, compression and shear has been studied. (Cochereau et al., 2020). Fluid-structure interaction (FSI) simulations provide a valuable tool to gain a deeper understanding of voice production (Ghorbani et al. 2022). These simulations allow us to model the dynamic interplay between the VFs and air. Our research focuses on investigating the mechanical properties of canine vocal folds and utilizing these findings in an FSI simulation. Through this simulation, we aim to unravel how these mechanical properties affect voice production.Methods To investigate the mechanical properties of canine VFs, an in vitro study was conducted involving 6 mixedbreed dogs. The samples were harvested from canine cadavers euthanized for reasons unrelated to this study. In the following, the VFs were harvested and tested upon 3-4 hours post-animal sacrifice. Experimental trials were carried out using the STM-1 device (SANTAM Co.), equipped with a 100 kg load cell. Seven uniaxial tensile tests were done on each sample, with displacement rates of 1, 5, 10, 20, 40, 60, and 120 mm/min. The very slow rate of 1 mm/min was chosen to assess only elastic properties eliminating viscosity effects. Various hyperelastic models were used to fit the experimental data. Subsequently, for each model, both the mean and standard deviation (SD) were determined for the hyperelastic model parameters and their residuals. For FSI analysis we used a simplified laryngeal model as a hollow cylinder with a diameter of 50 mm and a thickness of 3 mm. The overall length of the larynx was set at 100 mm. The VFs were modeled as a circular disc with a small elliptical fissure in the midst of the cylinder section. Boundary conditions were established based on pressure differentials, with the inlet gauge pressure set at 1200 Pa and the relative pressure at the outlet set to 0. To account for the turbulent nature of airflow within the larynx, we employed the K-epsilon method to solve the motion differential equations in a two-way fluid-structure interaction simulation using ANSYS FLUENT 2021. This approach enabled us to investigate how the acquired mechanical properties of canine vocal folds affect the FSI simulations during phonation, resulting in a more comprehensive understanding of their impact. To determine the vibrational frequency of VFs, we calculated the time it took to reach maximum displacement and then quadrupled this value to obtain the period of vibration.

Exchange couplings are fundamental to our understanding of many physical phenomena in condensed matter physics and material science. Model systems provide a controlled environment to investigate such phenomena, effectively. In this study, we employ first-principle calculations based on density functional theory and Green's function (GF) method to explore the impact of chemical structure on the sign and magnitude of exchange coupling, systematically. By designing model systems with bcc-Fe bulk doped with nonmagnetic X= (H, B, C, N, O, and F) atoms, we examine the effects of different ligands on the behavior of Fe-Fe exchange coupling, and demonstrate that the chemical environment surrounding the metal atom significantly influences the Fe-Fe exchange coupling. Our results highlight the tunability of exchange coupling based on Fe-dopant bond length(s), where the nature of ligand atoms and their electron correlation play a crucial role. This work illuminates the complex relationship between structure, and magnetism in magnetic materials, providing insights into the development of high-performance magnetic materials.

The onset of planet formation is actively under debate. Recent mass measurements of disks around protostars suggest an early start of planet formation in the Class 0/I disks. However, dust substructures, one possible signature of forming planets, are rarely observed in the young Class 0/I disks, while they are ubiquitous in the mature Class II disks. It is not clear whether the lack of dust substructures in the Class 0/I disks indicates absence of planets or whether it is due to other physical effects such as temperature and dust opacity. Here we consider the effect of temperature on the ability of planets to produce dust substructures. We prescribe the evolution of the disk and the protostar from Class 0 to Class II phase and calculate the disk temperature using radiative transfer models at various stages of the evolution. We use the mid-plane temperature to calculate the disk scale height and the minimum planet mass needed to open observable dust gaps using the thermal criterion. We find that this minimum planet mass decreases as a function of time. Particularly, we find that if a planet up to ${\sim}5$ M$_{\oplus}$ in the inner ${\sim}5$ au or up to ${\sim}10-50$ M$_{\oplus}$ at radii ${\gtrsim}5$ au was already formed in the early protostellar phase ($t< 2\times 10^5$ yr) it would barely produce any dust substructures. We conclude that a major contribution to the observed lack of substructures (if produced by planets) in the early protostellar phase - lowering their frequency by ${\sim}50\%$ - could be elevated temperatures rather than the absence of planets., Comment: Re-submitted to A&A, 14 pages, first and second author had similar contributions

The composition of protoplanetary disks, and hence the initial conditions of planet formation, may be strongly influenced by the infall and thermal processing of material during the protostellar phase. Composition of dust and ice in protostellar envelopes, shaped by energetic processes driven by the protostar, serves as the fundamental building material for planets and complex organic molecules. As part of the JWST GO program, "Investigating Protostellar Accretion" (IPA), we observed an intermediate-mass protostar HOPS 370 (OMC2-FIR3) using NIRSpec/IFU and MIRI/MRS. This study presents the gas and ice phase chemical inventory revealed with the JWST in the spectral range of $\sim$2.9 to 28 $\mu$m and explores the spatial variation of volatile ice species in the protostellar envelope. We find evidence for thermal processing of ice species throughout the inner envelope. We present the first high-spatial resolution ($\sim 80$ au) maps of key volatile ice species H$_{2}$O, CO$_{2}$, $^{13}$CO$_2$, CO, and OCN$^-$, which reveal a highly structured and inhomogeneous density distribution of the protostellar envelope, with a deficiency of ice column density that coincides with the jet/outflow shocked knots. Further, we observe high relative crystallinity of H$_{2}$O ice around the shocked knot seen in the H$_2$ and OH wind/outflow, which can be explained by a lack of outer colder material in the envelope along the line of sight due to the irregular structure of the envelope. These observations show clear evidence of thermal processing of the ices in the inner envelope, close to the outflow cavity walls, heated by the luminous protostar., Comment: Submitted to ApJ. Main text:16 pages with 11 figures

The MIRI-MRS instrument onboard JWST allows for probing the molecular gas composition at mid-IR wavelengths at unprecedented resolution and sensitivity. It is important to study these features in low-mass embedded protostellar systems since the formation of planets is thought to start in this phase. We present JWST/MIRI-MRS data of 18 low-mass protostellar systems in the JOYS program, focusing on gas-phase molecular lines in spectra extracted from the central protostellar positions. Besides H2, the most commonly detected molecules are H2O, CO2, CO, and OH. Other molecules such as 13CO2, C2H2, 13CCH, HCN, C4H2, CH4, and SO2 are detected only toward at most three of the sources. The JOYS data also yield the surprising detection of SiO gas toward two sources (BHR71-IRS1, L1448-mm) and for the first time CS and NH3 at mid-IR wavelengths toward a low-mass protostar (B1-c). The temperatures derived for the majority of the molecules are 100-300 K, much lower than what is typically derived toward more evolved Class II sources (>500 K). Toward three sources (e.g., TMC1-W), hot (~1000 K) H2O is detected, indicative of the presence of hot molecular gas in the embedded disks, but such warm emission from other molecules is absent. The agreement in abundance ratios with respect to H2O between ice and gas point toward ice sublimation in a hot core for a few sources (e.g., B1-c) whereas their disagreement and velocity offsets hint at high-temperature (shocked) conditions toward other sources (e.g., L1448-mm, BHR71-IRS1). The typical temperatures of the gas-phase molecules of 100-300 K are consistent with both ice sublimation in hot cores as well as high-temperature gas phase chemistry. Molecular features originating from the inner embedded disks are not commonly detected, likely because they are too extincted even at mid-IR wavelengths by small not-settled dust grains in upper layers of the disk., Comment: 25 pages, 11 figures, accepted for publication in A&A

The carbon isotope ratio is a powerful tool for studying the evolution of stellar systems. Recent detections of CO isotopologues in disks and exoplanet atmospheres pointed towards significant fractionation in these systems. In order to understand the evolution of this quantity, it is crucial to trace the isotope abundance from stellar nurseries to planetary systems. During the protostellar stage the multiple vibrational modes of CO$_2$ and CO ice provide a unique opportunity to examine the carbon isotope ratio in the solid state. Now with the sensitivity of the \textit{James Webb Space Telescope}, these absorption features have become accessible at high S/N in Solar-mass systems. We quantify the $^{12}$CO$_2$/$^{13}$CO$_2$ and the $^{12}$CO/$^{13}$CO isotope ratios in 17 class 0/I low mass protostars from the $^{12}$CO$_2$ combination modes (2.70 ${\mu}$m and 2.77 ${\mu}$m), the $^{12}$CO$_2$ stretching mode (4.27 ${\mu}$m), the $^{13}$CO$_2$ stretching mode (4.39 ${\mu}$m), the $^{12}$CO$_2$ bending mode (15.2 ${\mu}$m), the $^{12}$CO stretching mode (4.67 ${\mu}$m) and the $^{13}$CO stretching mode (4.78 ${\mu}$m) using JWST observations. We also report a detection of the $^{12}$CO overtone mode at 2.35 ${\mu}$m. The $^{12}$CO$_2$/$^{13}$CO$_2$ ratios are in agreement and we find mean ratios of 85 $\pm$ 23, 76 $\pm$ 12 and 97 $\pm$ 17 for the 2.70 ${\mu}$m, 4.27 ${\mu}$m and the 15.2 ${\mu}$m bands, respectively. The main source of uncertainty stem from the error on the band strengths. The $^{12}$CO/$^{13}$CO ratios derived from the 4.67 ${\mu}$m bands are consistent, albeit elevated with respect to the $^{12}$CO$_2$/$^{13}$CO$_2$ ratios and we find a mean ratio of 165 $\pm$ 52. These findings indicate that ices leave the pre-stellar stage with elevated carbon isotope ratios relative to the interstellar medium and that fractionation becomes significant during the later stages.

This study proposes a machine learning-based Model Predictive Control (MPC) approach for controlling Air Handling Unit (AHU) systems by employing an Internet of Things (IoT) framework. The proposed framework utilizes an Artificial Neural Network (ANN) to provide dynamic-linear thermal model parameters considering building information and disturbances in real time, thereby facilitating the practical MPC of the AHU system. The proposed framework allows users to establish new setpoints for a closed-loop control system, enabling customization of the thermal environment to meet individual needs with minimal use of the AHU. The experimental results demonstrate the cost benefits of the proposed machine-learning-based MPC-IoT framework, achieving a 57.59\% reduction in electricity consumption compared with a clock-based manual controller while maintaining a high level of user satisfaction. The proposed framework offers remarkable flexibility and effectiveness, even in legacy systems with limited building information, making it a pragmatic and valuable solution for enhancing the energy efficiency and user comfort in pre-existing structures.

This paper presents the hardware implementation of two variants of projection-aggregation-based decoding of Reed-Muller (RM) codes, namely unique projection aggregation (UPA) and collapsed projection aggregation (CPA). Our study focuses on introducing hardware architectures for both UPA and CPA. Through thorough analysis and experimentation, we observe that the hardware implementation of UPA exhibits superior resource usage and reduced energy consumption compared to CPA for the vanilla IPA decoder. This finding underscores a critical insight: software optimizations, in isolation, may not necessarily translate into hardware cost-effectiveness.

Utilization of intracranial electroencephalography (iEEG) is rapidly increasing for clinical and brain-computer interface applications. iEEG facilitates the recording of neural activity with high spatial and temporal resolution, making it a desirable neuroimaging modality for studying neural dynamics. Despite its benefits, iEEG faces challenges such as inter-subject variability in electrode implantation, which makes the development of unified neural decoder models across different patients difficult. In this research, we introduce a novel decoder model that is robust to inter-subject electrode implantation variability. We call this model RISE-iEEG, which stands for Robust Inter-Subject Electrode Implantation Variability iEEG Classifier. RISE-iEEG employs a deep neural network structure preceded by a patient-specific projection network. The projection network maps the neural data of individual patients onto a common low-dimensional space, compensating for the implantation variability. In other words, we developed an iEEG decoder model that can be applied across multiple patients' data without requiring the coordinates of electrode for each patient. The performance of RISE-iEEG across multiple datasets, including the Audio-Visual dataset, Music Reconstruction dataset, and Upper-Limb Movement dataset, surpasses that of state-of-the-art iEEG decoder models such as HTNet and EEGNet. Our analysis shows that the performance of RISE-iEEG is 10\% higher than that of HTNet and EEGNet in terms of F1 score, with an average F1 score of 83\%, which is the highest result among the evaluation methods defined. Furthermore, the analysis of projection network weights in the Music Reconstruction dataset across patients suggests that the Superior Temporal lobe serves as the primary encoding neural node. This finding aligns with the auditory processing physiology.

This paper introduces an advanced stochastic hybrid system modeling framework for modern power systems (MPS) to identify, classify, and evaluate hidden contingencies, which cannot be detected by normal observation sensors. The stochastic hybrid system (SHS) model is designed to capture the dynamics of the internal states of individual nodes, considering their structural properties, and coupling variables under various local and network-level contingencies. Hidden contingencies are identified using a probing approach that measures changes in the eigenvalues of the SHS model and detects deviations from normal operation. Next, contingencies are categorized into three distinct groups according to their impact on MPS: physical contingencies, control network contingencies, and sensing and measurement network contingencies. This classification enables a proactive evaluation of contingencies. The practicality and efficacy of the proposed methodology are validated through simulation experiments on the electrical network of two real-world systems. These simulations underscore the approach's capacity to enhance the resilience of power systems against a spectrum of hidden contingencies., Comment: Under review in IEEE Transactions on Power Systems

A rich inventory of complex organic molecules (COMs) has been observed in high abundances in the gas phase toward Class 0 protostars. These molecules are suggested to be formed in ices and sublimate in the warm inner envelope close to the protostar. However, only the most abundant COM, methanol (CH3OH), has been firmly detected in ices before the era of James Webb Space Telescope (JWST). Now it is possible to detect the interstellar ices of other COMs and constrain their ice column densities quantitatively. We aim to determine the column densities of several oxygen-bearing COMs (O-COMs) in both gas and ice for two low-mass protostellar sources, NGC 1333 IRAS 2A and B1-c, as case studies in our JWST Observations of Young protoStars (JOYS+) program. By comparing the column density ratios w.r.t. CH3OH between both phases measured in the same sources, we can probe into the evolution of COMs from ice to gas in the early stages of star formation. We are able to fit the fingerprints range of COM ices between 6.8 and 8.8 um in the JWST/MIRI-MRS spectra of B1-c using similar components as recently used for IRAS 2A. We claim detection of CH4, OCN-, HCOO-, HCOOH, CH3CHO, C2H5OH, CH3OCH3, CH3OCHO, and CH3COCH3 in B1-c, and upper limits are estimated for SO2, CH3COOH, and CH3CN. The comparison of O-COM ratios w.r.t CH3OH between ice and gas shows two different cases. 1) the column density ratios of CH3OCHO and CH3OCH3 match well between the two phases, which may be attributed to a direct inheritance from ice to gas or strong chemical links with CH3OH. 2) the ice ratios of CH3CHO and C2H5OH w.r.t. CH3OH are higher than the gas ratios by 1-2 orders of magnitudes. This difference can be explained by the gas-phase reprocessing following sublimation, or different spatial distributions of COMs in the envelope., Comment: 42 pages (22 main text, 20 appendix); 27 figures (12 in main text, 15 in appendix); 5 tables (2 in main text, 3 in appendix) Accepted for publication in A&A

Sonomyography (SMG) is a novel human-machine interface that controls upper-limb prostheses by monitoring forearm muscle activity using ultrasonic imaging. SMG has been investigated for controlling upper-limb prostheses during the last two decades. The results show that this method, in combination with artificial intelligence, can classify different hand gestures with an accuracy of more than 90%, making it a great alternative control system compared to electromyography (EMG). However, up to now there are few reports of a system integrating SMG together with a prosthesis for testing on amputee subjects to demonstrate its capability in relation to daily activities. In this study, we developed ProRuka, a novel low-cost 6-degree-of-freedom prosthetic hand integrated with the control provided by a SMG system with a wearable ultrasound imaging probe. The classification of hand gestures using different machine learning classification/regression algorithms including KNN, nearest neighbor regression, random forest, decision tree classifier, decision tree regression, support vector regression and support vector machine in combination with a transfer learning model (VGG16) was first evaluated off-line to determine its reliability and precision. Additionally, the developed controlling system were evaluated on two amputees, in real-time experiments using a variety of hand function test kits. The results from an off-line study including ten healthy participants indicated that nine different hand motions can be classified with a success rate of 100%. In addition, the hand function test in real time (using 4 different hand gestures) confirmed that the designed prosthesis with the SMG controlling system can assist amputees to perform a variety of hand movements needed in daily activities.

We investigate the chemical history of interstellar OCS and SO2 by deriving a statistically-significant sample of gas-phase column densities towards massive protostars and comparing to observations of gas and ices towards other sources spanning from dark clouds to comets. We analyze a subset of 26 line-rich massive protostars observed by ALMA as part of the ALMAGAL survey. Column densities are derived for OCS and SO2 from their rare isotopologues O13CS and 34SO2 towards the compact gas around the hot core. We find that gas-phase column density ratios of OCS and SO2 with respect to methanol remain fairly constant as a function of luminosity between low- and high-mass sources, despite their very different physical conditions. The derived gaseous OCS and SO2 abundances relative to CH3OH are overall similar to protostellar ice values, with a significantly larger scatter for SO2 than for OCS. Cometary and dark-cloud ice values agree well with protostellar gas-phase ratios for OCS, whereas higher abundances of SO2 are generally seen in comets compared to the other sources. Gaseous SO2/OCS ratios are consistent with ices toward dark clouds, protostars, and comets, albeit with some scatter. The constant gas-phase column density ratios throughout low and high-mass sources indicate an early stage formation before intense environmental differentiation begins. Icy protostellar values are similar to the gas phase medians, compatible with an icy origin of these species followed by thermal sublimation. The larger spread in SO2 compared to OCS ratios w.r.t. CH3OH is likely due to a more water-rich chemical environment associated with the former, as opposed to a CO-rich origin of the latter. Post-sublimation gas-phase processing of SO2 can also contribute to the large spread. Comparisons to ices in dark clouds and comets point to a significant inheritance of OCS from earlier to later evolutionary stages., Comment: Accepted for publication in Astronomy and Astrophysics on July 17th 2024

Minimally invasive robotic surgery has gained significant attention over the past two decades. Telerobotic systems, combined with robot-mediated minimally invasive techniques, have enabled surgeons and clinicians to mitigate radiation exposure for medical staff and extend medical services to remote and hard-to-reach areas. To enhance these services, teleoperated robotic surgery systems incorporating master and follower devices should offer transparency, enabling surgeons and clinicians to remotely experience a force interaction similar to the one the follower device experiences with patients' bodies. This paper presents the design and development of a three-degree-of-freedom master-follower teleoperated system for robotic catheterization. To resemble manual intervention by clinicians, the follower device features a grip-insert-release mechanism to eliminate catheter buckling and torsion during operation. The bidirectionally navigable ablation catheter is statically characterized for force-interactive medical interventions. The system's performance is evaluated through approaching and open-loop path tracking over typical circular, infinity-like, and spiral paths. Path tracking errors are presented as mean Euclidean error (MEE) and mean absolute error (MAE). The MEE ranges from 0.64 cm (infinity-like path) to 1.53 cm (spiral path). The MAE also ranges from 0.81 cm (infinity-like path) to 1.92 cm (spiral path). The results indicate that while the system's precision and accuracy with an open-loop controller meet the design targets, closed-loop controllers are necessary to address the catheter's hysteresis and dead zone, and system nonlinearities., Comment: 24 pages, 7 figures, 6 tables

A novel method for decomposing a nonstationary signal into amplitude- and frequency-modulated (AM-FM) oscillations and discontinuous (jump) components is proposed. Current nonstationary signal decomposition methods are designed to either obtain constituent AM-FM oscillatory modes or the discontinuous and residual components from the data, separately. Yet, many real-world signals of interest simultaneously exhibit both behaviors i.e., jumps and oscillations. Currently, no available method can extract jumps and AM-FM oscillatory components directly from the data. In our novel approach, we design and solve a variational optimization problem to accomplish this task. The optimization formulation includes a regularization term to minimize the bandwidth of all signal modes for effective oscillation modeling, and a prior for extracting the jump component. Our method addresses the limitations of conventional AM-FM signal decomposition methods in extracting jumps, as well as the limitations of existing jump extraction methods in decomposing multiscale oscillations. By employing an optimization framework that accounts for both multiscale oscillatory components and discontinuities, our methods show superior performance compared to existing decomposition techniques. We demonstrate the effectiveness of our approaches on synthetic, real-world, single-channel, and multivariate data, highlighting their utility in three specific applications: Earth's electric field signals, electrocardiograms (ECG), and electroencephalograms (EEG)., Comment: 11 pages

In this study, we investigate two widely recognized Interacting Dark Energy(IDE) models and assess their compatibility with observational data, focusing on the growth rate of matter perturbations. We explore IDE models with different equations of state(EoS) parameters for Dark Energy (DE), including the CPL parameterization and a constant value for $w_{\mathrm{de}}$. To constrain the parameters of the IDE models using background data, we employ a Markov Chain Monte Carlo(MCMC) analysis. Our results show that both IDE-I and IDE-II models are Compatible with observational data, although with slight variations influenced by the homogeneity or clustering of DE. Following that, we investigate the growth of matter perturbations and perform a comprehensive statistical analysis utilizing both the background and growth rate data. The growth rate in IDE models exhibits deviations compared to the $\Lambda\mathrm{CDM}$ model due to the impact of homogeneity or clustering of DE, as well as the selection of the EoS parameter. However, we find that the IDE models show good compatibility with the growth rate data. Furthermore, we explore how the clustering or homogeneity of DE and the selection of the EoS parameter affect the evolution of the relative difference in the growth rate of IDE models, $\Delta f$, in comparison to the $\Lambda\mathrm{CDM}$ model. Lastly, we employ the AIC and BIC criteria to evaluate and identify the best model that is compatible with the observational data. The selection of the model depends on the homogeneity or clustering of DE, the EoS parameter, and the dataset used. Overall, the IDE-I and IDE-II models exhibit agreement with the data, with slight deviations depending on specific scenarios and parameters., Comment: 16 pages, 9 figures, Accepted in Physical Review D

Acoustic Soft Tactile (AST) skin is a novel sensing technology which derives tactile information from the modulation of acoustic waves travelling through the skin's embedded acoustic channels. A generalisable data-driven calibration model maps the acoustic modulations to the corresponding tactile information in the form of contact forces with their contact locations and contact geometries. AST skin technology has been highlighted for its easy customisation. As a case study, this paper discusses the possibility of using AST skin on a custom-built robotic end effector finger for strawberry handling. The paper delves into the design, prototyping, and calibration method to sensorise the end effector finger with AST skin. A real-time force-controlled gripping experiment is conducted with the sensorised finger to handle strawberries by their peduncle. The finger could successfully grip the strawberry peduncle by maintaining a preset force of 2 N with a maximum Mean Absolute Error (MAE) of 0.31 N over multiple peduncle diameters and strawberry weight classes. Moreover, this study sets confidence in the usability of AST skin in generating real-time tactile feedback for robot manipulation tasks., Comment: TAROS 2024

Deep neural network-based voice authentication systems are promising biometric verification techniques that uniquely identify biological characteristics to verify a user. However, they are particularly susceptible to targeted data poisoning attacks, where attackers replace legitimate users' utterances with their own. We propose an enhanced framework using realworld datasets considering realistic attack scenarios. The results show that the proposed approach is robust, providing accurate authentications even when only a small fraction (5% of the dataset) is poisoned.

This study sequentially investigates the enhancement of fracture behavior in adhesive joints. Initially, mechanical sanding techniques were explored, revealing significant improvements of 55% in fracture energy and 38% in load-bearing capacity with optimal 240-grit sanding. Subsequently, in a second stage, Multi-Walled Carbon Nanotubes (MWCNTs) were introduced at varying weight percentages (0.1%, 0.3%, and 0.5% by weight) into the adhesive joints. This combined approach aimed to synergistically examine the impact of surface pretreatments and nanoparticle integration on fracture behavior. Results highlight the potential for fracture resistance and enhanced load-carrying capabilities in reinforced specimens, with maximum load and fracture energy improvements of up to 92 % and 50 %, respectively, compared to unreinforced specimens and those subjected solely to sanding treatments. Furthermore, the fracture mode shifted from adhesive failure to cohesive substrate failure in reinforced specimens. This comprehensive investigation underscores the intricate interplay between mechanical preparation and nanomaterial incorporation in significantly improving adhesive joint performance

Current imaging methods for diagnosing BC are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve breast cancer (BC) diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to improve the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current hand-crafted radiomics methods in the diagnosis and classification of BC based on most recent studies on different imaging modalities, e.g. MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.

Software Defined Networking (SDN) has brought significant advancements in network management and programmability. However, this evolution has also heightened vulnerability to Advanced Persistent Threats (APTs), sophisticated and stealthy cyberattacks that traditional detection methods often fail to counter, especially in the face of zero-day exploits. A prevalent issue is the inadequacy of existing strategies to detect novel threats while addressing data privacy concerns in collaborative learning scenarios. This paper presents P3GNN (privacy-preserving provenance graph-based graph neural network model), a novel model that synergizes Federated Learning (FL) with Graph Convolutional Networks (GCN) for effective APT detection in SDN environments. P3GNN utilizes unsupervised learning to analyze operational patterns within provenance graphs, identifying deviations indicative of security breaches. Its core feature is the integration of FL with homomorphic encryption, which fortifies data confidentiality and gradient integrity during collaborative learning. This approach addresses the critical challenge of data privacy in shared learning contexts. Key innovations of P3GNN include its ability to detect anomalies at the node level within provenance graphs, offering a detailed view of attack trajectories and enhancing security analysis. Furthermore, the models unsupervised learning capability enables it to identify zero-day attacks by learning standard operational patterns. Empirical evaluation using the DARPA TCE3 dataset demonstrates P3GNNs exceptional performance, achieving an accuracy of 0.93 and a low false positive rate of 0.06.

Human Activity Recognition (HAR) stands as a pivotal technique within pattern recognition, dedicated to deciphering human movements and actions utilizing one or multiple sensory inputs. Its significance extends across diverse applications, encompassing monitoring, security protocols, and the development of human-in-the-loop technologies. However, prevailing studies in HAR often overlook the integration of human-centered devices, wherein distinct parameters and criteria hold varying degrees of importance compared to other applications. Notably, within this realm, curtailing the sensor observation period assumes paramount importance to safeguard the efficiency of exoskeletons and prostheses. This study embarks on the optimization of this observation period specifically tailored for HAR using Inertial Measurement Unit (IMU) sensors. Employing a Deep Convolutional Neural Network (DCNN), the aim is to identify activities based on segments of IMU signals spanning durations from 0.1 to 4 seconds. Intriguingly, the outcomes spotlight an optimal observation duration of 0.5 seconds, showcasing an impressive classification accuracy of 99.95%. This revelation holds immense significance, elucidating the criticality of precise temporal analysis within HAR, particularly concerning human-centric devices. Such findings not only enhance our understanding of the optimal observation period but also lay the groundwork for refining the performance and efficacy of devices crucially relied upon for aiding human mobility and functionality., Comment: This paper is accepted for publication in the proceedings of the 18th Annual IEEE International Systems Conference (SysCon 2024) \c{opyright} 2024 IEEE. Personal use of this material is permitted

Sleep is characterized by non-rapid eye movement (nREM) sleep, originating from widespread neuronal synchrony, and REM sleep, with neuronal desynchronization akin to waking behavior. While these were thought to be global brain states, recent research suggests otherwise. Using time-frequency analysis of mesoscopic voltage-sensitive dye recordings of mice in a urethane-anesthetized model of sleep, we find transient neural desynchronization occurring heterogeneously across the cortex within a background of synchronized neural activity, in a manner reminiscent of a critical spreading process and indicative of an "edge-of-synchronization phase" transition.

For $0<\rho\leq 1$, a $\rho$-happy vertex $v$ in a coloured graph $G$ has at least $\rho\cdot \mathrm{deg}(v)$ same-colour neighbours, and a $\rho$-happy colouring (aka soft happy colouring) of $G$ is a vertex colouring that makes all the vertices $\rho$-happy. A community is a subgraph whose vertices are more adjacent to themselves than the rest of the vertices. Graphs with community structures can be modelled by random graph models such as the stochastic block model (SBM). In this paper, we present several theorems showing that both of these notions are related, with numerous real-world applications. We show that, with high probability, communities of graphs in the stochastic block model induce $\rho$-happy colouring on all vertices if certain conditions on the model parameters are satisfied. Moreover, a probabilistic threshold on $\rho$ is derived so that communities of a graph in the SBM induce a $\rho$-happy colouring. Furthermore, the asymptotic behaviour of $\rho$-happy colouring induced by the graph's communities is discussed when $\rho$ is less than a threshold. We develop heuristic polynomial-time algorithms for soft happy colouring that often correlate with the graphs' community structure. Finally, we present an experimental evaluation to compare the performance of the proposed algorithms thereby demonstrating the validity of the theoretical results., Comment: 34 pages, 15 figures, 2 tables

Obfuscation stands as a promising solution for safeguarding hardware intellectual property (IP) against a spectrum of threats including reverse engineering, IP piracy, and tampering. In this paper, we introduce Obfus-chat, a novel framework leveraging Generative Pre-trained Transformer (GPT) models to automate the obfuscation process. The proposed framework accepts hardware design netlists and key sizes as inputs, and autonomously generates obfuscated code tailored to enhance security. To evaluate the effectiveness of our approach, we employ the Trust-Hub Obfuscation Benchmark for comparative analysis. We employed SAT attacks to assess the security of the design, along with functional verification procedures to ensure that the obfuscated design remains consistent with the original. Our results demonstrate the efficacy and efficiency of the proposed framework in fortifying hardware IP against potential threats, thus providing a valuable contribution to the field of hardware security.