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In this paper, we investigate the Bloch bands and develop a linear response theory for nonlinear systems, where the interplay between topological parameters and nonlinearity leads to new band structures. The nonlinear system under consideration is described by the Qi–Wu–Zhang model with Kerr-type nonlinearity, which can be treated as a nonlinear version of Chern insulator. We explore the eigenenergies of the Hamiltonian and discuss its Bloch band structures as well as the condition of gap closing. A cone structure in the ground Bloch band and tubed structure in the excited Bloch band is found. We also numerically calculate the linear response of the nonlinear Chern insulator to external fields, finding that these new band structures break the condition of adiabatic evolution and make the linear response not quantized. This feature of response can be understood by examining the dynamics of the nonlinear system. [ABSTRACT FROM AUTHOR]

To elucidate the mechanisms underlying photoperiodic responses, we investigated the genomic and metabolomic responses of two broomcorn millet (Panicum miliaceum L.) genotypes. For this purpose, light-insensitive (D32) and light-sensitive (M51) genotypes were exposed to a 16 h photoperiod (long-day (LD) conditions) and an 8 h photoperiod (short-day (SD) conditions), and various transcriptomic and metabolomic changes were investigated. A total of 1664, 2564, 13,017, and 15548 DEGs were identified in the SD-D, LD-D, LD-M, and SD-M groups, respectively. Furthermore, 112 common DEGs were identified as well. Interestingly, most DEGs in the different groups were associated with photosynthesis and phenylpropanoid and carotenoid biosynthesis. In addition, 822 metabolites were identified under different treatments. The main metabolites, including L-malic and fumaric acids, were identified in the negative mode, whereas brucine and loperamide were identified in the positive mode. KEGG analysis revealed that the metabolites in the different groups were enriched in the same metabolic pathway of the TCA cycle. Furthermore, in negative mode, the metabolites of M51 were mainly D-glucose, whereas those of D32 were mainly L-malic and fumaric acids. One photoperiod candidate gene (C2845_PM11G01290), annotated as ATP6B, significantly increased the levels of L-malic and fumaric acids. In conclusion, our study provides a theoretical basis for understanding the molecular mechanisms of photoperiodic response regulation and can be used as a reference for marker development and resource identification in Panicum miliaceum L.. [ABSTRACT FROM AUTHOR]

The article focuses on improving Named Entity Recognition (NER) within Information Extraction (IE), crucial for enhancing precision in Information Retrieval (IR) systems. It introduces Single-Stream Reasoner (SSR), a type-agnostic solution for NER tasks, addressing challenges such as unconventional predictions and inefficient processing, achieving state-of-the-art results across multiple benchmarks and demonstrating efficiency in convergence and low-resource scenarios.

Traumatic brain injury (TBI)‐induced post‐traumatic epilepsy (PTE) is a serious brain disease that causes the loss of motor functions, cognitive impairments, and even death. Real‐time and long‐term electrophysiological signal monitoring has significant implication for the treatment of patient with TBI. Brain–computer interfaces (BCIs) utilizing flexible electrodes enable accurate epilepsy identification by electrocorticography (ECoG) signal monitoring. In this study, a customized flexible osteoid electrode is developed, which contributes to demands and personal differentiation among patients with TBI. The end product of osteoid electrode after full osseointegration will be a stable and firmly integrated structure within the bone tissue, providing a secure platform for continuous electrophysiological signal monitoring. The flexibility and conductivity of the osteoid electrode enables high‐quality and long‐term epilepsy monitoring. Biocompatibility of the osteoid electrode is also demonstrated by in vitro cell experiments. Simultaneously, the porous osteoid electrode also exhibits osseointegration ability based on potential long‐term ECoG signal monitoring, which suggests its potential for the comprehensive treatment of TBI patients. This work not only introduces a potential therapeutic scaffold for TBI‐related diseases, but also provides promising techniques for clinical ECoG monitoring in patients with TBI. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a promising solution for the VRPTW. However, the challenge of addressing the VRPTW with many conflicting objectives (MOVRPTW) still remains for DRL. The MOVRPTW considers five conflicting objectives simultaneously: minimizing the number of vehicles required, the total travel distance, the travel time of the longest route, the total waiting time for early arrivals, and the total delay time for late arrivals. To tackle the MOVRPTW, this study introduces the MTMO/DRP-AT, a multi-task multi-objective evolutionary search algorithm, by making full use of both DRL and the multitasking mechanism. In the MTMO/DRL-AT, a two-objective MOVRPTW is constructed as an assisted task, with the objectives being to minimize the total travel distance and the travel time of the longest route. Both the main task and the assisted task are simultaneously solved in a multitasking scenario. Each task is decomposed into scalar optimization subproblems, which are then solved by an attention model trained using DRL. The outputs of these trained models serve as the initial solutions for the MTMO/DRL-AT. Subsequently, the proposed algorithm incorporates knowledge transfer and multiple local search operators to further enhance the quality of these promising solutions. The simulation results on real-world benchmarks highlight the superior performance of the MTMO/DRL-AT compared to several other algorithms in solving the MOVRPTW. [ABSTRACT FROM AUTHOR]

Imidazole is a five-membered heterocyclic ring containing three carbon atoms, two nitrogen atoms, and two double bonds. Among two nitrogen atoms, one of which carries with a hydrogen atom is a pyrrole-type nitrogen atom, another is a pyridine type nitrogen atom. Hence, the imidazole ring belongs to the π electron-rich aromatic ring and can accept strong suction to the electronic group. Moreover, the nitrogen atom of the imidazole ring is coordinated with metal ions to form metal-organic frameworks. In recent years, because of imidazole compounds' unique optical properties, their applications have attracted more and more attention in optical analysis. Thus, this review has summarized the synthesis, characterization, and application with emphasis on the research progress of imidazole compounds in optical analysis, including fluorescence probe, colorimetric probe, electrochemiluminescence sensor, fiber optical sensor, surface plasmon resonance, etc. This paper will suggest the direction for the development of imidazole-containing sensors with high sensitivity and selectivity. [ABSTRACT FROM AUTHOR]

Implementation of the state‐of‐the‐art understanding of the mechanics of unsaturated soils into geotechnical engineering practice is partly limited due to the lack of quick, reliable, and economical techniques for matric suction measurement. Matric suction is one of the key stress state variables that significantly influences the hydro‐mechanical behavior of unsaturated soils. In this paper, to address this objective, two artificial intelligence (AI) models were developed for estimating matric suction in unsaturated soils based on the particle swarm optimization support vector regression (PSO‐SVR) and multivariate adaptive regression spline (MARS) algorithms. The results suggest that both these models can reasonably estimate matric suction. Compared to the MARS model, the PSO‐SVR model can achieve higher accuracy. Nonetheless, the MARS model facilitates the sensitivity analysis and the selection of essential inputs. A novel integrated framework is proposed and validated, leveraging the strengths, and alleviating the limitations of the PSO‐SVR and MARS algorithms for reliable and rapid estimation of matric suction in the range of 0–1500 kPa for low plastic soils (0 < Ip ≤ 7). Six inputs are required to use this model successfully; some can be measured using conventional laboratory tests, and others can be calculated from mass‐volume relationships. [ABSTRACT FROM AUTHOR]

In this paper, we present a new module to predict the potential surface reconstruction configurations of given surface structures in the framework of our machine learning and graph theory assisted universal structure searcher. In addition to random structures generated with specific lattice symmetry, we made full use of bulk materials to obtain a better distribution of population energy, namely, randomly appending atoms to a surface cleaved from bulk structures or moving/removing some of the atoms on the surface, which is inspired by natural surface reconstruction processes. In addition, we borrowed ideas from cluster predictions to spread structures better between different compositions, considering that surface models of different atom numbers usually have some building blocks in common. To validate this newly developed module, we tested it with studies on the surface reconstructions of Si (100), Si (111), and 4H–SiC (1 1 ̄ 02) − c (2 × 2) , respectively. We successfully gave the known ground states, as well as a new SiC surface model, in an extremely Si-rich environment. [ABSTRACT FROM AUTHOR]

Alternative splicing is a crucial process in multicellular eukaryote, facilitated by the assembly of spliceosomal complexes comprising numerous small ribonucleoproteins. At an early stage, U1C is thought to be required for 5′ splice site recognition and base pairing. However, a systematic analysis of the U1C gene family in response to developmental cues and stress conditions has not yet been conducted in plants. This study identified 114 U1C genes in 72 plant species using basic bioinformatics analyses. Phylogenetic analysis was used to compare gene and protein structures, promoter motifs, and tissue- and stress-specific expression levels, revealing their functional commonalities or diversity in response to developmental cues, such as embryonic expression, or stress treatments, including drought and heat. Fluorescence quantitative expression analysis showed that U1C gene expression changed under salt, low temperature, drought, and Cd stress in rice seedlings. However, gene expression in shoots and roots was not consistent under different stress conditions, suggesting a complex regulatory mechanism. This research provides foundational insights into the U1C gene family's role in plant development and stress responses, highlighting potential targets for future studies. [ABSTRACT FROM AUTHOR]

In low-power electronics, there is a substantial demand for high-performance p-type oxide thin-film transistors (TFTs) that are capable of efficient operation at low voltages. In this study, we employ anodization to form an aluminum oxide gate dielectric layer, enabling the fabrication of p-type tin oxide (SnO) TFTs that effectively operate at a low voltage of 1 V. Under optimal device fabrication conditions, the SnO TFT demonstrates an on/off current ratio exceeding 103 and a saturation mobility of 1.94 cm2 V−1 s−1 at 1 V operation. The optimal SnO TFT fabrication conditions are subsequently used to fabricate a complementary inverter, comprising a SnO TFT and an n-type indium gallium zinc oxide TFT, achieving a gain of up to 38 at a 1 V supply voltage. Notably, the inverter's switching point voltage is finely tuned to the ideal value, precisely half of the supply voltage. This oxide-based complementary inverter showcases promising potential in low-power electronics. [ABSTRACT FROM AUTHOR]

Gravitational effects on premixed flame propagation, coupled with Darrieus–Landau instability, are investigated through direct numerical simulations under both thermal-diffusive (TD) stable and unstable situations. During the early stage of propagation, the dispersion relation between the linear growth rate σ and wavenumber k of perturbations exhibits good symmetry, allowing it to be well-fitted by the polynomial σ = c + a k − b k 2 . The modified Clavin–Garcia relation derived from asymptotic analysis aligns with numerical results only in the low-wavenumber region, since the significant error in the second-order coefficient bg renders it inapplicable for the high-wavenumber region. In the nonlinear regime, three morphological effects of gravity on the flame propagation have been identified: flattening the flame front, suppressing the cusp fusion rate, and promoting the new cusp generation (front splitting). The first two effects slow down the flame propagation, respectively, by reducing the overall flame front length and delaying the appearance of velocity peaks. Nevertheless, the promotion of front splitting accelerates the flame by facilitating more frequent velocity peaks arising from both cusp generation and fusion events. These various morphological effects, along with their distinct impacts on either accelerating or decelerating the flame propagation, result in significant variations in the behavior of large-scale flame propagation under different gravitational levels, despite gravity being a large-scale stabilizing effect. [ABSTRACT FROM AUTHOR]

Forest canopy height is a fundamental parameter of forest structure, and plays a pivotal role in understanding forest biomass allocation, carbon stock, forest productivity, and biodiversity. Spaceborne LiDAR (Light Detection and Ranging) systems, such as GEDI (Global Ecosystem Dynamics Investigation), provide large-scale estimation of ground elevation, canopy height, and other forest parameters. However, these measurements may have uncertainties influenced by topographic factors. This study focuses on the calibration of GEDI L2A and L1B data using an airborne LiDAR point cloud, and the combination of Sentinel-2 multispectral imagery, 1D convolutional neural network (CNN), artificial neural network (ANN), and random forest (RF) for upscaling estimated forest height in the Guangxi Gaofeng Forest Farm. First, various environmental (i.e., slope, solar elevation, etc.) and acquisition parameters (i.e., beam type, Solar elevation, etc.) were used to select and optimize the L2A footprint. Second, pseudo-waveforms were simulated from the airborne LiDAR point cloud and were combined with a 1D CNN model to calibrate the L1B waveform data. Third, the forest height extracted from the calibrated L1B waveforms and selected L2A footprints were compared and assessed, utilizing the CHM derived from the airborne LiDAR point cloud. Finally, the forest height data with higher accuracy were combined with Sentinel-2 multispectral imagery for an upscaling estimation of forest height. The results indicate that through optimization using environmental and acquisition parameters, the ground elevation and forest canopy height extracted from the L2A footprint are generally consistent with airborne LiDAR data (ground elevation: R2 = 0.99, RMSE = 4.99 m; canopy height: R2 = 0.42, RMSE = 5.16 m). Through optimizing, ground elevation extraction error was reduced by 45.5% (RMSE), and the canopy height extraction error was reduced by 30.3% (RMSE). After training a 1D CNN model to calibrate the forest height, the forest height information extracted using L1B has a high accuracy (R2 = 0.84, RMSE = 3.13 m). Compared to the optimized L2A data, the RMSE was reduced by 2.03 m. Combining the more accurate L1B forest height data with Sentinel-2 multispectral imagery and using RF and ANN for the upscaled estimation of the forest height, the RF model has the highest accuracy (R2 = 0.64, RMSE = 4.59 m). The results show that the extrapolation and inversion of GEDI, combined with multispectral remote sensing data, serve as effective tools for obtaining forest height distribution on a large scale. [ABSTRACT FROM AUTHOR]

Flexible bioelectronics have a large range of applications in the field of human health monitoring and human‐machine interfaces. Among various bioelectronics, flexible electrodes have been widely studied. However, it is still lack of investigation in the development of electrodes with high signal quality and long service life. Here, a sponge inspired flexible electrode based on cellulose aerogel is developed via thermally induced phase separation. Due to its flexibility, the electrode can easily conform to skin and achieve a higher signal‐to‐noise ratio (SNR) than the commercial rigid electrode. At the same time, the aerogel electrode with a sponge‐like structure can absorb solutions several times their own weight (≈800%). In addition, the dope of polypyrrole coated silver nanowires (AgNWs@PPy) improves its intrinsic bactericidal activity (antibacterial rate above 97.8%) and the cellulose‐based material itself offers good skin compatibility. Therefore, all these features contribute to a longer service life, while has been proven in electrophysiological signals recording. The SNR of electrocardiogram (ECG) signals recorded by aerogel electrode in 24 h is ≈15% higher than that of commercial electrode. Apart from that, when the commercial electrode is unavailable for eye‐open (EO) and eye‐closed (EC) paradigm in 12 h, the aerogel electrode can still record clear electroencephalogram (EEG) features. [ABSTRACT FROM AUTHOR]

Combination therapy has emerged as an efficacy strategy for treating complex diseases. Its potential to overcome drug resistance and minimize toxicity makes it highly desirable. However, the vast number of potential drug pairs presents a significant challenge, rendering exhaustive clinical testing impractical. In recent years, deep learning‐based methods have emerged as promising tools for predicting synergistic drug combinations. This review aims to provide a comprehensive overview of applying diverse deep‐learning architectures for drug combination prediction. This review commences by elucidating the quantitative measures employed to assess drug combination synergy. Subsequently, we delve into the various deep‐learning methods currently employed for drug combination prediction. Finally, the review concludes by outlining the key challenges facing deep learning approaches and proposes potential challenges for future research. [ABSTRACT FROM AUTHOR]

Alternaria alternata, as a main decay fungus of goji berry, can produce mycotoxins such as alternariol (AOH), alternariol monomethyl ether (AME), and tenuazonic acid (TeA). Carvacrol (CVR) has exhibited a broad-spectrum antifungal activity in vitro. We assumed that CVR can also be applied to control Alternaria rot on goji berries and mycotoxins produced by the pathogens. To investigate whether CVR impacts the accumulation of mycotoxins and cell membrane damage of A. alternata, the antifungal activity of CVR on the fungal growth and mycotoxin production was evaluated in this study. The results showed that the minimum inhibitory concentration (MIC) of CVR against A. alternata was 0.12 µL/mL. Meanwhile, the destruction of plasma membrane integrity, cytoplasmic leakage, intracellular oxidative damage, and inhibitory effect in vivo were also observed in A. alternata treated with CVR. Moreover, CVR significantly reduced the accumulation of AOH, AME, and TeA. Transcriptomic profiling was performed by means of comparative RNA-Seq analysis to research the gene expression level of A. alternata, which attested to significant changes in nitrogen metabolism, carbon utilization, fatty acid oxidation, and antioxidant enzymes in CVR-treated A. alternata. This study suggests a new understanding of the molecular mechanism of response to CVR treatment in A. alternata, indicating that CVR is a novel antifungal agent with the potential to be applied to various fungi. [ABSTRACT FROM AUTHOR]

Period-permanent-magnet (PPM) electromagnetic acoustic transducer (EMAT) has been widely used in shear horizontal (SH) ultrasonic guided wave testing owing to its advantages, such as non-contact coupling, and convenient to excite SH waves. However, its poor transduction efficiency leads to weak signals and limits the lift-off performance. This article investigates how to improve the signal amplitude by adjusting the number of turns of the racetrack coil. The inductive coupling process of the PPM-EMAT receiver is first studied using the equivalent circuit method, and the corresponding equivalent model is obtained. Aiming at the effects of coil configurations, the equivalent impedance parameters of multilayer racetrack coils are analyzed by calculations and measurements. The proposed model can be used to predict the receiving frequency response of PPM-EMAT receivers with different coil structures, and it has been verified experimentally. It can be obtained that by choosing an appropriate coil configuration and matching resistance, the SH wave signal amplitude can be increased by 3 times. [ABSTRACT FROM AUTHOR]

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields. [ABSTRACT FROM AUTHOR]

To achieve commercial application of organic solar cells (OSCs), it is necessary to reduce material costs and improve device efficiency. This paper reports on the utilization of a multifunctional building block, namely 3‐cyanoesterthiophene, which exhibits simple structure and accessibility of synthetic for cost‐effective and high‐performance polymer donors (PDs). Meanwhile, ternary and terpolymerization strategies have been studied. Two similar PDs, PBTCl0‐TCA and PBTCl100‐TCA, are synthesized, and the devices exhibit less‐than‐satisfactory efficiency of 13.21% and 11.53% due to mismatching energy level and imperfect morphology. The two PDs with comparable structures and commendable compatibility easily form alloy‐like phase in active layer, which can effectively boost the efficiency of ternary devices to 14.17% with retained high JSC and significant improved open‐circuit voltage (VOC) and fill factor (FF). Encouraged by the ternary blending phenomenon, a polymer donor (PBTCl50‐TCA) with same ratio by random terpolymerization is designed. And over 17% efficiency binary OSCs using terpolymerization donor are demonstrated. The synergies of incorporation of the cyanoester‐group and terpolymer endow the developed PDs with deep‐lying energy levels, face‐on orientation, thermodynamic miscibility with the prevailing nonfullerene acceptor and appropriate polymer crystallinity. The findings study provide valuable insights and support for the advancement of cost‐effective and high‐performance PDs. [ABSTRACT FROM AUTHOR]

Obtaining high-added value compounds from agricultural waste receives increasing attention, as it can both improve resource utilization efficiency and reduce waste generation. In this study, polysaccharides are extracted from the discarded roots of Abelmoschus manihot (L.) by the high-efficiency ultrasound-assisted extraction (UAE). The optimized condition was determined as solid–liquid ratio SL ratio = 1:20, temperature T = 30 °C and time T = 40 min, achieving an extraction yield of 13.41%. Composition analysis revealed that glucose (Glc, 44.65%), rhamnose (Rha, 26.30%), galacturonic acid (GalA, 12.50%) and galactose (Gal, 9.86%) are the major monosaccharides of the extract. The extract showed a low degree of esterification (DE) value of 40.95%, and its Fourier-transform infrared (FT−IR) spectrum exhibited several characteristic peaks of polysaccharides. Inspired by the wide cosmetic applications of polysaccharides, the skincare effect of the extract was evaluated via the moisture retention, total phenolic content (TPC) quantification, 2,2-Diphenyl-1-picrylhydrazyl (DPPH)-free radical scavenging activity, anti-hyaluronidase and anti-elastase activity experiments. The extract solutions demonstrated a 48 h moisture retention rate of 10.75%, which is superior to that of commercially available moisturizer hyaluronic acid (HA). Moreover, both the TPC value of 16.16 mg GAE/g (dw) and DPPH-free radical scavenging activity of 89.20% at the concentration of 2 mg/mL indicated the strong anti-oxidant properties of the extract. Furthermore, the anti-hyaluronidase activity and moderate anti-elastase activity were determined as 72.16% and 42.02%, respectively. In general, in vitro skincare effect experiments suggest moisturizing, anti-oxidant, anti-radical and anti-aging activities of the A. manihot root extract, indicating its potential applications in the cosmetic industry. [ABSTRACT FROM AUTHOR]

Networks have been widely employed to reflect the relationships of entities in complex systems. In a weighted network, each node corresponds to one entity while the edge weight between two nodes can represent the number of interactions between two associated entities. More and more schemes have been established to monitor the networks, which help identify the possible changes or anomalies in corresponding systems. However, limited works can comprehensively reflect the community structure, node heterogeneity, interaction sparsity and direction of weighted networks in the literature. This article proposes a log-linear stochastic block model with latent features of nodes based on the mixture of Bernoulli distribution and Poisson distribution to characterize the sparse directional interaction counts within network systems. Explicit matrices and vectors are designed to incorporate community structure and enable straightforward maximum likelihood estimation of parameters. We further construct a monitoring statistic based on the generalized likelihood ratio test for change detection of sparse weighted networks. Comparative studies based on simulations and real data are conducted to validate the high efficiency of proposed model and monitoring scheme. [ABSTRACT FROM AUTHOR]

The integration of graph embedding technology and collaborative filtering algorithms has shown promise in enhancing the performance of recommendation systems. However, existing integrated recommendation algorithms often suffer from feature bias and lack effectiveness in personalized user recommendation. For instance, users' historical interactions with a certain class of items may inaccurately lead to recommendations of all items within that class, resulting in feature bias. Moreover, accommodating changes in user interests over time poses a significant challenge. This study introduces a novel recommendation model, RCKFM, which addresses these shortcomings by leveraging the CoFM model, TransR graph embedding model, backdoor tuning of causal inference, KL divergence, and the factorization machine model. RCKFM focuses on improving graph embedding technology, adjusting feature bias in embedding models, and achieving personalized recommendations. Specifically, it employs the TransR graph embedding model to handle various relationship types effectively, mitigates feature bias using causal inference techniques, and predicts changes in user interests through KL divergence, thereby enhancing the accuracy of personalized recommendations. Experimental evaluations conducted on publicly available datasets, including "MovieLens-1M" and "Douban dataset" from Kaggle, demonstrate the superior performance of the RCKFM model. The results indicate a significant improvement of between 3.17% and 6.81% in key indicators such as precision, recall, normalized discount cumulative gain, and hit rate in the top-10 recommendation tasks. These findings underscore the efficacy and potential impact of the proposed RCKFM model in advancing recommendation systems. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM) metasurfaces comprising artificially designed subwavelength unit cells have drawn considerable attention due to the EM properties beyond the limits of natural materials. As one of the representative structures, active polarization conversion metasurface (APCM) is switchable by loading active components. It provides great freedom to manipulate the polarization state of EM waves. However, the current research mainly focused on the application of communication and paid less attention to the radar effect of APCM. APCM redistributes electromagnetic wave energy in multi-polarization channels, so it will have great application potential in polarimetric radar. Herein, based on the fully polarimetric radar one-dimensional high resolution range profile, the radar effect of time-modulated metasurface is studied. For this purpose, a method of target scattering mechanisms manipulation and a polarization-insensitive structure of APCM are proposed. The amplitude-phase joint modulation method is specifically analyzed in detail. The distance transformation and virtual multi-target phenomena are further discovered. Virtual targets along the distance dimension are generated in multi-polarization channels, while the scattering mechanisms of k-order targets are effectively manipulated. The relationship between the target scattering matrix and the modulation parameters is obtained. It may provide an effective method for the application of active metasurface in fully polarimetric radars. [ABSTRACT FROM AUTHOR]

Low-carbon conversion of hydrocarbon fuels and feedstocks, as well as the macroscopic production of H 2 , are central focus in the advancement of high-value and efficient conversion of CH 4. The effects of Ar addition ratio, discharge power and temperature on the results of plasma methane cracking were investigated on a dielectric barrier discharge (DBD) plasma experimental system. The results show that the addition of Ar can improve the methane conversion, and the optimal ratio of CH 4 conversion to H 2 production after the effect is CH 4 : Ar = 1:1, at which the methane conversion reaches 49.62% and the H 2 selectivity reaches 52.66%. The plasma at 25 °C can make the methane conversion close to the effect of catalytic cracking at 600 °C, while the H 2 selectivity is lower, the H 2 generation is 32.59% lower than the latter, and the products have more C 2 –C 4 hydrocarbon impurities. The solid byproduct resulting from the DBD plasma cracking of methane primarily consisted of carbon black, possessing an average particle size of 62.21 nm. The incorporation of Ar and the amplification of power augment the CH* radical intensities, which suggests both conditions contribute positively to the dissociation of the CH 4 molecule. [Display omitted] • The optimal ratio of CH 4 to Ar stands at 1:1. • Plasma operating at 25 °C can mimic the catalytic thermal cracking effect at 600 °C. • The generation of the solid-phase product, carbon black is induced by the plasma. • An increase in the C 2 –C 4 product is observed under plasma activity. [ABSTRACT FROM AUTHOR]

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with unknown etiology and treatment options for it were limited. Whether Gastroesophageal reflux disease (GERD) could affect the occurrence of IPF remains unclear. Methods : Using available data from FINNGEN and IEU OpenGWAS, we performed Two-sample mendelian randomization (MR) to explore the causal relationship between GERD and IPF. Results: Using 65 GERD-related SNPs, we found the association between GERD and the risk of IPF was not statistically significant with IVW approach (OR = 1.20, 95% CI = 0.84-1.70, p =.32), MR-Egger regression (OR = 1.65, 95% CI = 0.19-14.43, p =.65) and weighted median approaches (OR = 1.44, 95% CI = 0.94-2.23, p =.09). However, heterogeneity was observed with MR-Egger (p =.001) and IVW (p =.001) analysis. Similar results were obtained with MR-PRESSO (global heterogeneity test p value <.01). After removing one outlier (rs9636202), with weighted median method, we found GERD increased the risk of IPF (OR 1.55, 95% CI: 1.01-2.36, p =.045) while not with the IVW (OR: 1.27, 95% CI: 0.91-1.78, p =.16) and MR-Egger method (OR: 2.01, 95% CI: 0.26-15.8, p =.51). Hence, we set a stricter instrument p value threshold to a level of <1 × 10−8, there was no statistical significance with MR estimates. Additionally, there was no directional pleiotropy observed (intercept = −0.01; SE = 0.036. p =.772). To validate causal effect of GERD on IPF, we identified three SNPs (rs79348626, rs12759463 and rs4269485) from another GWAS data that were significantly associated with GERD independently. The analysis showed no evidence of causality between GERD and IPF using the IVW method (OR = 0.91, 95% CI = 0.42-1.97, p =.814), MR-Egger regression (OR = 0.43, 95% CI = 0.06-3.07, p =.553) and weighted median approaches (OR = 0.90, 95% CI = 0.36-2.27, p = 0 0.819). Conclusions: Our analysis using MR does not support GERD could significantly increase the incidence of IPF, which suggests that treating GERD cannot reduce the risk of developing IPF. [ABSTRACT FROM AUTHOR]

In order to improve cost competitiveness and the further application of metal-supported cells, it is important to explore cathodes suitable for low-temperature sintering. However, decreasing cathode sintering temperature while maintaining robust interface of cathode/electrolyte and decent electrochemical performances is challenging for solid oxide fuel cells. Herein, by combining the Pechini method with one-pot method, La 0.6 Sr 0.4 CoO 3-δ @ Gd 0.1 Ce 0.9 O 2-δ (LSC@GDC) composite cathodes are prepared and investigated, compared with mechanically mixed LSC-GDC cathodes. At the cathode sintering temperature of 800 °C, the LSC@GDC cathode single cells perform better performances, with deceased resistances, improved power density outputs and better feasibility to wide oxygen partial pressures from 0.21 atm down to 0.03 atm. The optimum composition of 70:30 wt% for LSC@GDC is proposed. The anode supported single cell with LSC@GDC30 cathode exhibits a maximum power density of 0.789 W/cm2 and 0.545 W/cm2 at 750 °C, under cathode oxygen partial pressure of 0.21 atm and 0.03 atm, respectively. Moreover, the low temperature sintered LSC@GDC50 cathode performs decent durability during repeatedly controlled thermal cycling over 10 times. • LSC@GDC cathode is successfully prepared by novel two-step one-pot method. • The cathode sintering activity improves as well as interface contact. • The cell performance in harsh condition is dramatically enhanced. • The cell exhibit decent long-term durability and thermal cycling stability. [ABSTRACT FROM AUTHOR]

Cellular automata (CA) are computational systems that exhibit complex global behavior arising from simple local rules, making them a fascinating candidate for various research areas. However, challenges such as limited flexibility and efficiency on conventional hardware platforms still exist. In this study, we propose a memristor-based circuit for implementing elementary cellular automata (ECA) by extending the stateful three-memristor logic operations derived from material implication (IMP) logic gates. By leveraging the inherent physical properties of memristors, this approach offers simplicity, minimal operational steps, and high flexibility in implementing ECA rules by adjusting the circuit parameters. The mathematical principles governing circuit parameters are analyzed, and the evolution of multiple ECA rules is successfully demonstrated, showcasing the robustness in handling the stochastic nature of memristors. This approach provides a hardware solution for ECA implementation and opens up new research opportunities in the hardware implementation of CA. [ABSTRACT FROM AUTHOR]

The utilization of mobile sensing through the deployment of sensors on third‐party vehicles that pass by an area offers notable benefits in terms of spatiotemporal coverage, precision, timeliness, and cost‐effectiveness for monitoring urban environments. However, the diverse travel behaviors exhibited by different types of vehicle fleets result in varying levels of sensing powers. Drawing inspiration from these phenomena, this study quantifies and compares the travel patterns and sensing powers of bus and taxi fleets. The experimental findings unveiled several noteworthy laws, such as a diminishing rate of increase in sensing power with the stable increasing vehicle number, the high sensing power exhibited by taxis, the comparable spatial coverage between buses, and the relatively low uncertainty associated with buses' sensing power. Furthermore, the impact of travel patterns (i.e., semi‐random behavior of taxis and deterministic behavior of buses) on the powers of mobile sensing was revealed. The aforementioned findings provide valuable insights for the development of efficient mobile sensing schemes and hold significant relevance for the implementation of smart city infrastructure. [ABSTRACT FROM AUTHOR]

Spectrum conversion involves the movement of a signal from one frequency to another, and it is well-studied in materials interacting with electromagnetic waves from microwave to optical frequencies. In this paper, a time-modulated active frequency selective surface (AFSS) absorber/reflector is proposed to manipulate the spectral distribution of the echo signal. The discrete harmonics based on the period modulation and the continuous spectrum based on the random coding modulation are analyzed in detail. In addition, a simple digital-coding control network based on field programmable gate array (FPGA) in the time-modulated AFSS absorber/reflector (TMAAR) is used to switch between absorbing and reflecting states flexibly. By coding "0" and "1" elements with the controlled sequences, the TMAAR is able to achieve the predefined modulation function for spectrum conversion. On this basis, a prototype of the designed TMAAR is manufactured. The linear frequency modulation radar echo experiments are performed to verify the required function of spectrum conversion. [ABSTRACT FROM AUTHOR]

Directed self‐assembly of materials into patterned structures is of great importance since the performance of them depends remarkably on their multiscale hierarchical structures. Therefore, purposeful structural regulation at different length scales through crystallization engineering provides an opportunity to modify the properties of polymeric materials. Here, an epitaxy‐directed self‐assembly strategy for regulating the pattern structures including phase structure as well as crystal modification and orientation of each component for both copolymers and polymer blends is reported. Owing to the specific crystallography registration between the depositing crystalline polymers and the underlying crystalline substrate, not only order phase structure with controlled size at nanometer scale but also the crystal structure and chain orientation of each component within the separated phases for both copolymers and polymer blend systems can be precisely regulated. [ABSTRACT FROM AUTHOR]

Background: Brachytherapy (BT) is a critical component of radiotherapy for locally advanced cervical cancer (LACC), and it has rapidly developed in recent decades. Since the advent of three-dimensional image-guided adaptive brachytherapy (3D-IGABT), magnetic resonance imaging (MRI) has emerged as the primary modality for image guidance. Meanwhile, other imaging modalities, such as computed tomography, 18F-fluorodeoxyglucose positron emission tomography, ultrasound, and their combinations have also been widely studied. Materials and methods: We reviewed studies on different imaging modalities utilized for target delineation and planning. Emerging techniques in IGABT like real-time image guidance and 3D printing were also included. We summarized research on their feasibility and concentrated on their clinical outcomes. Results: MRI-guided BT was the gold standard, and CT-guided BT was the most widely applied. Other modalities have shown feasibility and promising efficacy in dosimetry studies and preliminary outcomes. The longer-term clinical outcomes associated with these approaches require further elucidation. Conclusions: As 3D-IGABT was validated by promising clinical outcomes, the future of BT for LACC is expected to progress toward the refinement of more effective image-guided procedures. Moreover, achieving operational consensus and driving technological advancements to mitigate the inherent limitations associated with different imaging modes remain essential. [ABSTRACT FROM AUTHOR]

Accurate measurement of trans-stenotic pressure drop is vital for risk stratification in coronary artery disease. Currently, in vivo pressure measurement relies mostly on a pressure guidewire inserted into the artery, which inevitably alters local hemodynamics. To precisely assess the impact of guidewire insertion on the accuracy of pressure measurement, this study conducts numerical simulations with both an idealized straight-tube model and a patient-specific model. Results with and without a guidewire model are compared and analyzed. Results from the idealized model reveal that the insertion of a guidewire shifts velocity distribution, increases resistance, and amplifies the pressure drop across the stenosis. The patient-specific model also demonstrates that the guidewire causes non-negligible flow redistribution among the arterial branches, but the influence on pressure drop remains mostly localized. An analytical model for trans-stenotic pressure drop that takes the guidewire into consideration is also proposed and validated against the 3D simulation results. It is observed that the maximum relative error is around 3.0% in the patient-specific model, indicating the effectiveness of the analytical model in physiologically accurate settings. The proposed model can be used to align computed tomography-derived fractional flow reserve values with clinically recognized FFR standards established through guidewire measurements. [ABSTRACT FROM AUTHOR]

Leaching in cement-based materials and dissolution in rocks are important problems in civil engineering. In the past century, concrete damage caused by leaching have occurred worldwide. And, rock dissolution is usually the main cause of karst rock erosions. This paper provides a review of the causes, influencing factors, and effects on engineering properties of dissolution of rocks and leaching of cement-based materials. The applied experimental methods for leaching and dissolution have been sorted out and discussed. In situ field experiments can be used to study dissolution under natural conditions, while the laboratory experiments can effectively shorten the experiment time length (by changing pH, temperature, pressure or other factors that affect the leaching or dissolution) to quickly investigate the mechanism of dissolution and leaching. Micro tests including XRD, SEM, EDS, and other testing methods can obtain the changes in material properties and microstructures under leaching and dissolution. In addition, with the advances in technologies and updated instruments, more and more new testing methods are being used. The factors affecting the leaching and dissolution include environmental factors, materials, and solvent parameters. The mechanisms and deterioration processes of leaching and dissolution varies according to the types of material and the compositions. [ABSTRACT FROM AUTHOR]

B‐ and N‐embedded multiple resonance (MR) type thermally activated delayed fluorescence (TADF) emitters usually suffer from slow reverse intersystem crossing (RISC) process and aggregation‐caused emission quenching. Here, we report the design of a sandwich structure by placing the B−N MR core between two electron‐donating moieties, inducing through‐space charge transfer (TSCT) states. The proper adjusting of the energy levels brings about a 10‐fold higher RISC rate in comparison with the parent B−N molecule. In the meantime, a high photoluminescence quantum yield of 91 % and a good color purity were maintained. Organic light‐emitting diodes based on the new MR emitter achieved a maximum external quantum efficiency of 31.7 % and small roll‐offs at high brightness. High device efficiencies were also obtained for a wide range of doping concentrations of up to 20 wt % thanks to the steric shielding of the B−N core. A good operational stability with LT95 of 85.2 h has also been revealed. The dual steric and electronic effects resulting from the introduction of a TSCT state offer an effective molecular design to address the critical challenges of MR‐TADF emitters. [ABSTRACT FROM AUTHOR]

B‐ and N‐embedded multiple resonance (MR) type thermally activated delayed fluorescence (TADF) emitters usually suffer from slow reverse intersystem crossing (RISC) process and aggregation‐caused emission quenching. Here, we report the design of a sandwich structure by placing the B−N MR core between two electron‐donating moieties, inducing through‐space charge transfer (TSCT) states. The proper adjusting of the energy levels brings about a 10‐fold higher RISC rate in comparison with the parent B−N molecule. In the meantime, a high photoluminescence quantum yield of 91 % and a good color purity were maintained. Organic light‐emitting diodes based on the new MR emitter achieved a maximum external quantum efficiency of 31.7 % and small roll‐offs at high brightness. High device efficiencies were also obtained for a wide range of doping concentrations of up to 20 wt % thanks to the steric shielding of the B−N core. A good operational stability with LT95 of 85.2 h has also been revealed. The dual steric and electronic effects resulting from the introduction of a TSCT state offer an effective molecular design to address the critical challenges of MR‐TADF emitters. [ABSTRACT FROM AUTHOR]

The non-reciprocal transmission was investigated in a one-dimensional plasmonic waveguide coupled to multiple two-level quantum emitters' chain with non-reciprocal hopping coupling. The non-reciprocal transmission is originated from breaking time-reversal symmetry via the non-reciprocal hopping coupling between quantum emitters. The bidirectional non-reciprocal transmission was observed by appropriately modulating the phase shift and non-reciprocal hopping coupling between the two quantum emitters. In addition, unidirectional and dual-band non-reciprocal transmission could be achieved in the case of multiple quantum emitters. Our results could have potential applications in non-reciprocal optical devices, such as switches, single-photon transistors, isolators, and diodes. [ABSTRACT FROM AUTHOR]

The fractional Laplacian operator is a very important fractional operator that is often used to describe several anomalous diffusion phenomena. In this paper, we develop some numerical schemes, including a finite difference scheme and finite volume scheme for the fractional Laplacian operator, and apply the resulting numerical schemes to solve some fractional diffusion equations. First, the fractional Laplacian operator can be characterized as the weak singular integral by an integral operator with zero boundary condition. Second, because the solutions of fractional diffusion equations are usually singular near the boundary, we use a fractional interpolation function in the region near the boundary and use a classical interpolation function in other intervals. Then, we apply a finite difference scheme to the discrete fractional Laplacian operator and fractional diffusion equation with the above fractional interpolation function and classical interpolation function. Moreover, it is found that the differential matrix of the above scheme is a symmetric matrix and strictly row-wise diagonally dominant in special fractional interpolation functions. Third, we show a finite volume scheme for a discrete fractional diffusion equation by fractional interpolation function and classical interpolation function and analyze the properties of the differential matrix. Finally, the numerical experiments are given, and we verify the correctness of the theoretical results and the efficiency of the schemes. [ABSTRACT FROM AUTHOR]

Background: The personality-brain association mechanism has been a topic of interest in the field of neuroscience. Usually, the previous research strategy was to first group the population based on different personality traits, and then explore the brain mechanisms corresponding to different personality groups. At present, a "brain-first" research strategy, which uses data-driven approaches instead of personality traits to first group the population, has been adopted to further enhance study objectivity. Methods: Here, we used a data-driven approach following the "brain-first" research strategy to deeply mine the resting-state brain functional magnetic resonance imaging data of 119 healthy participants, classified subjects into different groups based on brain image characteristics, and used the Sixteen Personality Factor Questionnaire to explain the variabilities of resting-state brain characteristics between different groups. Results: We have identified 3 personality–brain connections, including the privateness–left frontoparietal network, liveliness–sensory–motor network, and vigilance–sensory–motor network. Conclusion: We conclude that the above-mentioned three personality factors are based on brain neural activity, independent of the subjective experience of the personality scale creator, and have stronger explanatory power of brain imaging features. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a simplified diagnostic tool for assessing sarcopenia and myosteatosis in gastrointestinal cancer patients, focusing on the creatinine to cystatin C ratio (CCR) as an evaluation marker. Methods: 955 patients were split into training (n = 671) and validation (n = 284) cohorts. Using logistic regression, risk factors for sarcopenia and myosteatosis were identified. The predictive capacity of the developed model was examined. The association between CCR and muscle imaging parameters, along with its impact on clinical outcomes, was analyzed. Results: No significant differences were observed in baseline traits between cohorts. CCR emerged as a significant risk factor for both sarcopenia and myosteatosis. Nomograms for diagnosing these conditions demonstrated strong predictive ability, with AUC values indicating high accuracy (sarcopenia AUC: 0.865–0.872; myosteatosis AUC: 0.848–0.849). The clinical utility of the nomograms was confirmed through decision curve analysis. CCR showed significant association with muscle imaging parameters and was a reliable indicator for assessing the risk of sarcopenia, myosteatosis, and cachexia. Moreover, CCR was able to differentiate between patient survival and disease progression rates. Conclusion: A diagnostic tool for sarcopenia and myosteatosis in gastrointestinal cancer patients was developed, with CCR being a pivotal biomarker for disease diagnosis and prognosis prediction. [ABSTRACT FROM AUTHOR]

As legislative restrictions on the use of hazardous lead-containing materials are approaching and the demand for environmentally-benign piezoelectric materials continues to grow, there is an urgent need for viable lead-free piezoelectric alternatives. (K,Na)NbO 3 (KNN)-based ceramics are environmentally-friendly, however, their piezoelectric properties still fall short of those of commercial lead-based ceramics. Herein, we report an alternative KNN material with the composition of (100- x)(K 0.49 Na 0.49 Li 0.02)(Nb 0.8 Ta 0.2)O 3 - x BaZrO 3 + 2 wt % MnO ceramics prepared by a conventional solid-reaction approach. The addition of BaZrO 3 produces finer domains and thus improves the piezoelectric and dielectric properties. MnO acts as sintering aid to reduce alkaline elements volatilization by lowering the sintering temperature. The mitigated volatilization of alkaline elements significantly reduces the concentration of oxygen vacancies, and thus contributes to enhanced piezoelectric properties. An optimal normalized strain (d 33 *) up to 476 pm/V at room temperature with a low driving electric field of 1.5 kV/mm is achieved at x = 4. More encouragingly, the ceramics also possess excellent temperature stability and fatigue resistance. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a simplified diagnostic tool for assessing sarcopenia and myosteatosis in gastrointestinal cancer patients, focusing on the creatinine to cystatin C ratio (CCR) as an evaluation marker. Methods: 955 patients were split into training (n = 671) and validation (n = 284) cohorts. Using logistic regression, risk factors for sarcopenia and myosteatosis were identified. The predictive capacity of the developed model was examined. The association between CCR and muscle imaging parameters, along with its impact on clinical outcomes, was analyzed. Results: No significant differences were observed in baseline traits between cohorts. CCR emerged as a significant risk factor for both sarcopenia and myosteatosis. Nomograms for diagnosing these conditions demonstrated strong predictive ability, with AUC values indicating high accuracy (sarcopenia AUC: 0.865–0.872; myosteatosis AUC: 0.848–0.849). The clinical utility of the nomograms was confirmed through decision curve analysis. CCR showed significant association with muscle imaging parameters and was a reliable indicator for assessing the risk of sarcopenia, myosteatosis, and cachexia. Moreover, CCR was able to differentiate between patient survival and disease progression rates. Conclusion: A diagnostic tool for sarcopenia and myosteatosis in gastrointestinal cancer patients was developed, with CCR being a pivotal biomarker for disease diagnosis and prognosis prediction. [ABSTRACT FROM AUTHOR]

The ducted coaxial propeller (DCP) is highly advantageous in the design of eVTOL aircraft due to its safety, compactness, and low noise levels. To study the aerodynamic characteristics of DCP in hovering, a novel eVTOL was used, and a slip grid model was established to solve the three-dimensional unsteady N-S equation. The aerodynamic characteristics of DCP were compared to those of the free coaxial propeller (FCP) and ducted single propeller (DSP) to reveal the interaction mechanism of unsteady flow between the duct and propellers. The results indicate that the duct significantly mitigates the intensity of tip vortexes by changing the characteristics of propeller tip winding, which reduces the corresponding energy loss. Additionally, the static pressure loss is decreased by the reduced radical-induced velocity in the slipstream area. Finally, the induced power loss is reduced by the decreased axial-induced velocity and suppressed wake contraction, resulting in DCP having 39% higher aerodynamic efficiency than FCP and the duct accounting for 41.7% of the total lift. Although DCP generates 1.77 times more lift than DSP, its aerodynamic efficiency is only 91.08% of DSP. [ABSTRACT FROM AUTHOR]

Background: Early identification of intravenous immunoglobulin (IVIG) resistance contributes to better management of Kawasaki disease (KD). This study aims to establish an effective prediction model for IVIG resistance in the Chinese population. Methods: A total of 658 eligible patients diagnosed with KD were enrolled in this study, with 461 in the training cohort and 197 in the validation cohort. The demographic characteristics and potential risk factors were compared between IVIG-responsive and resistant groups. Predictors were selected by the Akaike information criterion. The nomogram's performance was evaluated by calibration curve, decision curve analysis, and operating characteristic curve. Results: White blood cell counts (WBC), neutrophil-lymphocyte ratio (N/L ratio), hematocrit (HCT), albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), and creatinine (Cr) were detected as predictors of IVIG resistance. A predictive nomogram incorporating these predictors was constructed using the training cohort. The calibration curve and decision curve analysis showed good discrimination and calibration of the proposed nomogram in both training and validation sets, and the area under the receiver operating characteristic curve (AUROC) in both sets was 75.8% and 74.2%, respectively. Conclusion: This study identified WBC, N/L ratio, HCT, ALB, TBIL, LDH, and Cr as predictors for IVIG resistance in patients with KD. The proposed novel nomogram with a high level of accuracy and reliability may benefit clinical decision-making upon treatment initiation. [ABSTRACT FROM AUTHOR]

Quercetin, a flavonoid compound rich in hydroxyl groups, possesses antioxidant properties, whereas its poor water solubility limits its bioavailability. In pursuit of addressing the water solubility of quercetin and comprehending the impact of nanocrystal particle size on antioxidant efficacy, we prepared three different-sized quercetin nanocrystals, namely small (50 nm), medium (140 nm), and large (360 nm), using a nanosuspension method in this study. Within the in vitro setting, assessments employing solubility and radical scavenging assays revealed that quercetin nanocrystals displayed superior solubility (26, 21, and 13 fold corresponding to small, medium, and large particle sizes) and antioxidant performance compared to the coarse quercetin. Furthermore, quercetin nanocrystals of three particle sizes all demonstrated significant protection effects on the survival rate of H2O2-treated zebrafish at 72 h (77.78%, 73.33%, and 66.67% for small, medium, and large particle sizes, respectively), while the coarse quercetin group exhibited a low survival rate (53.3%) similar to the H2O2-treated group (47.8%). Moreover, all quercetin nanocrystals exhibited potent antioxidant capacity on both the antioxidants and enzymatic antioxidant system in H2O2-treated zebrafish to restore zebrafish to a normal state under oxidative stress. For instance, the levels of reactive oxygen species were reduced to 101.10%, 108.83%, and 109.77% of the normal levels for small, medium, and large particle-sized quercetin nanocrystals, respectively. In conclusion, quercetin nanocrystals demonstrated enhanced solubility, robust antioxidant capacity, and protective effects in zebrafish compared to coarse quercetin. [ABSTRACT FROM AUTHOR]

Resilience is an important concept for measuring a system's ability to cope with various disruptions. This study proposes an application-oriented framework for measuring the dynamic functional resilience (FR) of a transport network responding to supply and demand disruptions without external interventions. On the conceptual side, three complementary capacity-related dimensions, namely, robustness, adaptability, and recoverability, are incorporated in the single FR framework from the perspective of physical laws. On the applied side, we suggest a measurement model given certain network indices and apply it to the Beijing subway network (BSN). The results indicate the measurement model can capture the dynamics of network performances, identify the time-varying bottlenecks, and predict the influence of the dynamic capacity expansions on network resilience. The findings are useful for policy-making regarding the dynamic design, operation, and reconstruction of the transport infrastructure. • An application-oriented framework is proposed for measuring the functional resilience of transport networks. • Three complementary capacity-related dimensions are incorporated in the measurement framework. • The resilience dimensions are interpreted from the perspective of physical laws. • The measurement model can capture the dynamic network performances and identify the time-varying bottlenecks. [ABSTRACT FROM AUTHOR]

Understanding the size dependence of quasi-brittle materials' fracture behavior is useful in assessing the safety of flawed rock engineering structures. In this study, a set of uniaxial compression tests were conducted to scrutinize the mechanical properties and crack morphology of cubic specimens with sizes of 75 mm, 100 mm, 125 mm and 150 mm. The results showed that the specimens had significant size dependence in terms of strength and crack initiation angle. The crack type and fracture pattern were not affected by the specimen size and were mainly controlled by the pre-existing crack inclination angle. Next, the test results were compared with the maximum tangential stress criterion considering the T-stress. The results also showed that the crack initiation angles of specimens with different sizes were in good agreement with the theoretical calculations. The non-singular compressive stress perpendicular to the crack surface inhibited the generation of the fracture process zone (FPZ). In addition, the stress required for tensile failure was smaller for large-size specimens, leading to a decrease in the length of the FPZ with increasing specimen size. The FPZ variation law of the uniaxial compression tests was greatly different from that of the mode I loading. [ABSTRACT FROM AUTHOR]

Subarachnoid hemorrhage (SAH) denotes a serious type of hemorrhagic stroke that often leads to a poor prognosis and poses a significant socioeconomic burden. Timely assessment of the prognosis of SAH patients is of paramount clinical importance for medical decision making. Currently, clinical prognosis evaluation heavily relies on patients' clinical information, which suffers from limited accuracy. Non-contrast computed tomography (NCCT) is the primary diagnostic tool for SAH. Radiomics, an emerging technology, involves extracting quantitative radiomics features from medical images to serve as diagnostic markers. However, there is a scarcity of studies exploring the prognostic prediction of SAH using NCCT radiomics features. The objective of this study is to utilize machine learning (ML) algorithms that leverage NCCT radiomics features for the prognostic prediction of SAH. Retrospectively, we collected NCCT and clinical data of SAH patients treated at Beijing Hospital between May 2012 and November 2022. The modified Rankin Scale (mRS) was utilized to assess the prognosis of patients with SAH at the 3-month mark after the SAH event. Based on follow-up data, patients were classified into two groups: good outcome (mRS ≤ 2) and poor outcome (mRS > 2) groups. The region of interest in NCCT images was delineated using 3D Slicer software, and radiomic features were extracted. The most stable and significant radiomic features were identified using the intraclass correlation coefficient, t-test, and least absolute shrinkage and selection operator (LASSO) regression. The data were randomly divided into training and testing cohorts in a 7:3 ratio. Various ML algorithms were utilized to construct predictive models, encompassing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perceptron (MLP). Seven prediction models based on radiomic features related to the outcome of SAH patients were constructed using the training cohort. Internal validation was performed using five-fold cross-validation in the entire training cohort. The receiver operating characteristic curve, accuracy, precision, recall, and f-1 score evaluation metrics were employed to assess the performance of the classifier in the overall dataset. Furthermore, decision curve analysis was conducted to evaluate model effectiveness. The study included 105 SAH patients. A comprehensive set of 1316 radiomics characteristics were initially derived, from which 13 distinct features were chosen for the construction of the ML model. Significant differences in age were observed between patients with good and poor outcomes. Among the seven constructed models, model_SVM exhibited optimal outcomes during a five-fold cross-validation assessment, with an average area under the curve (AUC) of 0.98 (standard deviation: 0.01) and 0.88 (standard deviation: 0.08) on the training and testing cohorts, respectively. In the overall dataset, model_SVM achieved an accuracy, precision, recall, f-1 score, and AUC of 0.88, 0.84, 0.87, 0.84, and 0.82, respectively, in the testing cohort. Radiomics features associated with the outcome of SAH patients were successfully obtained, and seven ML models were constructed. Model_SVM exhibited the best predictive performance. The radiomics model has the potential to provide guidance for SAH prognosis prediction and treatment guidance. [ABSTRACT FROM AUTHOR]

Although non‐fused ring electron acceptors (NFREAs) have received increasing attention due to their relatively low synthetic costs, the achievement of high efficiencies strongly depends on tedious pre‐ or/and post‐treatments to refine the active layers, which in turn greatly increase fabrication complexity and expense of organic solar cells (OSCs). Nowadays most of the available as‐cast devices based on NFREAs are below 12% efficiencies. Herein, phenylalkyl category side groups (CnPh) are employed to construct new NFREAs named BOR‐CnPh (n = 3, 4, and 6), which exhibit inherently decent molecular aggregation and thus exclude additional treatments from device fabrication. The modified alkyl spacers of CnPh side groups not only trigger different aggregation of the acceptors, but also regulate the interaction conformations of donor (D) and acceptor (A), and thus D/A interactions. Encouragingly, the pristine PBDB‐T:BOR‐C4Ph blend delivers intrinsic fibrous networks with dominating face‐on orientation, which yields an optimal efficiency up to 13.12%, and ranks as the highest value among as‐cast OSCs based on NFREAs. This research provides a practical strategy to control molecular aggregations, interactions, and pristine heterojunction morphologies for easily available and high‐performance organic photovoltaics. [ABSTRACT FROM AUTHOR]

Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications in systems with a large number of atoms, especially the complexity of conformational space and the cost of local optimizations for big systems. Here, we introduce a crystal structure prediction method, MAGUS, based on the evolutionary algorithm, which addresses the above challenges with machine learning and graph theory. Techniques used in the program are summarized in detail and benchmark tests are provided. With intensive tests, we demonstrate that on-the-fly machine-learning potentials can be used to significantly reduce the number of expensive first-principles calculations, and the crystal decomposition based on graph theory can efficiently decrease the required configurations in order to find the target structures. We also summarized the representative applications of this method on several research topics, including unexpected compounds in the interior of planets and their exotic states at high pressure and high temperature (superionic, plastic, partially diffusive state, etc.); new functional materials (superhard, high-energy-density, superconducting, photoelectric materials), etc. These successful applications demonstrated that MAGUS code can help to accelerate the discovery of interesting materials and phenomena, as well as the significant value of crystal structure predictions in general. [ABSTRACT FROM AUTHOR]

A graph G is called fractional [ a , b ] -covered if for every edge e of G there is a fractional [ a , b ] -factor with the indicator function h such that h (e) = 1. In this paper, we provide a tight spectral radius condition for graphs being fractional [ a , b ] -covered. [ABSTRACT FROM AUTHOR]

Crown width (CW) is an important individual tree variable commonly used to assess tree vigor and the production efficiency of stands. However, our understanding of the effects of climate and the combined effects of climate with competition on the variation of CW remains unknown. Therefore, this study developed CW models by nonlinear mixed-effects (NLME) to explore these effects. Data were obtained from Korean pine (Pinus koraiensis Siebold and Zucc.) plantations in five forestry agencies in northeastern China. The results showed that stand basal area (BA), height to crown base (HCB), isothermality (BIO3), and annual precipitation (BIO12) were significantly related to the variations of CW. CW increased with increasing BIO12, while decreased with increasing BA, HCB, and BIO3. The hierarchical partitioning (HP) analysis showed that the relative importance of BIO3 was larger than BA, HCB, and BIO12. In addition, we found that competition altered the variations of CW responses to climate. Competition, tree size, and climate CW model (CC-CWM) performed the best performance in model fitting and prediction accuracy. Therefore, CC-CWM was selected to predict CW under future climate change. By comparing the eleven sampling methods with eighteen sample sizes, it was reasonable to select six medium-trees to estimate the random effect parameters. Our study provides evidence of the effects of climate and the combined effects of competition and climate on the variations of CW, potentially useful for the development of rational and scientific forest management decisions under future climate changes. [ABSTRACT FROM AUTHOR]