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The parameter variations of magnetically coupled resonant wireless power transmission (MCR‐WPT) system cause frequency splitting and resonant frequency shift, which lead to the system detuning and reduce the transmission efficiency. To solve the problems, a frequency tracking control method based on the phase difference between the primary‐side voltage and the secondary‐side current (Frequency Tracking Control‐the primary‐side voltage and the secondary‐side current, FTC‐PS) in this paper. According to the characteristics that the phase difference between the voltage of transmitting side and the current of receiving side is not affected by frequency splitting, the phase difference is controlled at 90°, so that the system can achieve the maximum transmission efficiency under the condition of frequency splitting and resonant frequency shift. The experimental results show that the method can make the system always work at the natural resonant frequency when the parameters change, and the transmission efficiency is not affected by the frequency splitting phenomenon. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice. Plain Language Summary: Antarctic sea ice has changed significantly since 2016, leading to a higher demand for sea ice forecasts. However, forecasting Antarctic sea ice has not received enough attention. Limited observations and lack of understanding of ice‐related physical mechanisms result in significant errors in sea ice predictions in dynamical models, particularly at the subseasonal timescale. We utilized a deep‐learning model to predict Antarctic sea ice at this timescale to fill this gap. Results showed that the deep‐learning model performed skillfully 1–8 weeks in advance, with the Weddell Sea being the best‐predicted region and the West Pacific being the worst. Furthermore, our study found that the model significantly outperformed mainstream dynamic models and a conventional statistical model. These findings build a foundation for developing more advanced prediction models at high resolutions for operational applications. Key Points: A deep‐learning model outperforms dynamic models, filling a subseasonal Antarctic sea ice prediction gap by bypassing physical mechanismsUnlike a Markov model that predicts time series of fixed spatial patterns, our model can extract ice synchronized spatiotemporal evolutionsThe deep‐learning model captures climate signals in sea ice, delivering the best prediction in fall season and the Weddell Sea region [ABSTRACT FROM AUTHOR]

Unsupervised clustering is an essential step in identifying cell types from single‐cell RNA sequencing (scRNA‐seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single‐cell molecular heterogeneity. In particular, a silhouette coefficient‐based indicator is developed to determine the optimization direction of the bi‐objective function. In addition, a hierarchical autoencoder is employed to project the high‐dimensional data onto multiple low‐dimensional latent space sets, and then a clustering ensemble is produced in the latent space by the basic clustering algorithm. Following that, a bi‐objective fruit fly optimization algorithm is designed to prune dynamically the low‐quality basic clustering in the ensemble. Multiple experiments are conducted on 28 real scRNA‐seq datasets and one large real scRNA‐seq dataset from diverse platforms and species to validate the effectiveness of the DEPF. In addition, biological interpretability and transcriptional and post‐transcriptional regulatory are conducted to explore biological patterns from the cell types identified, which could provide novel insights into characterizing the mechanisms. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. Results To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. Availability and implementation The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

To improve the ability of natural language processing (NLP) in mobile communication networks, a mobile communication network NLP system based on a recurrent neural network is designed in the eleventh paper, "A Novel Natural Language Processing Model in Mobile Communication Networks", authored by Gautam Srivastava from the Department of Mathematics and Computer Science, Brandon University, Canada. First of all, image and visual processing in mobile networks are the key technology in the research of information processing because 90% of human information got from world comes from vision [[1]]. Then, the varied applications of information processing under mobile network environment are other key topics in research because the application ecology determines the human acceptability of the digital world [[5]]. It is a kind of huge virtual digital world to people that the theories and applications of information processing have been changed to complex multimodal information processing. [Extracted from the article]

Arecoline, the dominant alkaloid existing in areca nuts, is an addictive substance and classified as a Group 2B potential human carcinogen. Currently, the detection of arecoline is mostly dependent on chromatography-based approaches, which are time-consuming and expensive. We used arecaidine as a hapten to produce a highly specific monoclonal antibody (mAb) against arecoline. An indirect competitive enzyme-linked immunosorbent assay (icELISA) was developed using the mAb-A5H12, to detect arecoline in traditional Chinese medicines and fresh areca nuts. The icELISA indicated that the half maximum inhibition concentration (IC50) for arecoline was 67.9 ng/mL, with a working range of 10.1–502.6 ng/mL and a limit of detection (LOD) of 3.6 ng/mL. High-performance liquid chromatographic (HPLC) confirmed the accuracy and the working range of icELISA, suggesting that the icELISA approach based on the arecoline specific antibody could be a widely applicable and easy operation method in detection of arecoline in foods and medicines. [ABSTRACT FROM AUTHOR]

This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of efficient deep learning techniques running on cost-effective hardware, a number of methods have been developed to learn compact neural networks. Most of these works aim to slim down filters in different ways, e.g., investigating small, sparse or quantized filters. In contrast, we treat filters from an additive perspective. A series of secondary filters can be derived from a primary filter with the help of binary masks. These secondary filters all inherit in the primary filter without occupying more storage, but once been unfolded in computation they could significantly enhance the capability of the filter by integrating information extracted from different receptive fields. Besides spatial versatile filters, we additionally investigate versatile filters from the channel perspective. Binary masks can be further customized for different primary filters under orthogonal constraints. We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced. Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters, but require less memory and computation cost. [ABSTRACT FROM AUTHOR]

Deterioration trend prediction of hydropower units helps to detect abnormal conditions of hydropower units and can prevent early failures. The reliability and accuracy of the prediction results are crucial to ensure the safe operation of the units and promote the stable operation of the power system. In this paper, the long short-term neural network (LSTM) is introduced, a comprehensive deterioration index (CDI) trend prediction model based on the time–frequency domain is proposed, and the prediction accuracy of the situation trend of hydropower units is improved. Firstly, the time–domain health model (THM) is constructed with back-propagation neural network (BPNN) and condition parameters of active power, guide vane opening and blade opening and the time–domain indicators. Subsequently, a frequency-domain health model (FHM) is established based on ensemble empirical mode decomposition (EEMD), approximate entropy (ApEn), and k-means clustering algorithm. Later, the time–domain degradation index (TDI) is developed according to THM, the frequency-domain degradation index (FDI) is constructed according to FHM, and the CDI is calculated as a weighted sum by TDI and FDI. Finally, the prediction model of LSTM is proposed based on the CDI to achieve degradation trend prediction. In order to validate the effectiveness of the CDI and the accuracy of the prediction model, the vibration waveform dataset of a hydropower plant in China is taken as a case study and compared with four different prediction models. The results demonstrate that the proposed model outperforms other comparison models in terms of predicting accuracy and stability. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing is a transformative technology that enables us to study the heterogeneity of the tissue at the cellular level. Clustering is used as the key computational approach to group cells under the transcriptome profiles from single-cell RNA-seq data. However, accurate identification of distinct cell types is facing the challenge of high dimensionality, and it could cause uninformative clusters when clustering is directly applied on the original transcriptome. To address such challenge, an evolutionary multiobjective deep clustering (EMDC) algorithm is proposed to identify single-cell RNA-seq data in this study. First, EMDC removes redundant and irrelevant genes by applying the differential gene expression analysis to identify differentially expressed genes across biological conditions. After that, a deep autoencoder is proposed to project the high-dimensional data into different low-dimensional nonlinear embedding subspaces under different bottleneck layers. Then, the basic clustering algorithm is applied in those nonlinear embedding subspaces to generate some basic clustering results to produce the cluster ensemble. To lessen the unnecessary cost produced by those clusterings in the ensemble, the multiobjective evolutionary optimization is designed to prune the basic clustering results in the ensemble, unleashing its cell type discovery performance under three objective functions. Multiple experiments have been conducted on 30 synthetic single-cell RNA-seq datasets and six real single-cell RNA-seq datasets, which reveal that EMDC outperforms eight other clustering methods and three multiobjective optimization algorithms in cell type identification. In addition, we have conducted extensive comparisons to effectively demonstrate the impact of each component in our proposed EMDC. [ABSTRACT FROM AUTHOR]

In recent years, although research on person re-identification (ReID) has made significant progress, occluded person ReID remains a major challenge. In real-world scenes, persons are often occluded by various obstacles such as vehicles, umbrellas, and other persons. This leads to the noisy interference and the loss of visual information, which result in poor ReID performance of occluded persons. To address this issue, we propose an end-to-end Mask-guided Discriminative Feature Network (MDFNet). First, MDFNet adopts a dual-branch architecture with a shared encoder as the Feature Extraction module for paired images. Each pair of images consist of one image from the training set and its corresponding occluded image generated through an occlusion augmentation strategy. Second, MDFNet utilizes a Mask-guided Discriminative Feature Enhancement and Fusion (MDFEF) module to achieve the fusion and enhancement of global and local features for high-quality person representations. MDFEF module effectively suppresses the interference of occlusion, enriches the representation capacity of person features, and enables the model to focus more on the important features in non-occluded regions. Furthermore, MDFNet introduces a sparse pairwise loss for enabling the model to dynamically adapt to intra-class variations and reduce the negative impact of complex occlusions. The experimental results on four challenging person ReID datasets demonstrate the effectiveness of the proposed method. • Designed Mask-guided Discriminative Feature Enhancement and Fusion (MDFEF) module to balance global and local information, suppressing occlusion noise. • Proposed end-to-end Mask-guided Discriminative Feature Network (MDFNet) for occluded person re-identification (ReID) without pre-training, enhancing feature discriminability and alignment. • Evaluated MDFNet and state-of-the-art methods on four challenging person ReID datasets, demonstrating superior performance in occluded person ReID. [ABSTRACT FROM AUTHOR]

Background: Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. Results: Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. Conclusions: In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize. [ABSTRACT FROM AUTHOR]

Background: Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. Results: Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. Conclusions: In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize. [ABSTRACT FROM AUTHOR]

Revealing the changes in the Fram Strait (FS) multiyear ice (MYI) export is crucial due to their climate relevance in the context of the loss rate of MYI being faster than that of the total ice in the Arctic. Here, we estimated winter (October–April) MYI area export through the FS over the last 2 decades by using updated MYI concentration data retrieved from active and passive microwave satellite observations. We divided the period into two regimes relative to the ice index: D1 (2002/03–2010/11) and D2 (2012/13–2019/20). The observed variations of winter MYI exports D2 were compared with those of the previous decade D1. The results show that the MYI area exports display strong interannual variability. A significant decrease in MYI export for the periods between D1 and D2 is noted. On average, the wintertime MYI area exports declined sharply by 22% from 3.82 × 105 km2 in D1 to 3.00 × 105 km2 in D2. In addition, the percentage of MYI in the total sea ice outflow through the FS (PCM) also decreased distinctly from 72% in D1 to 59% in D2. Statistics show that weekly sea ice drift across the strait can explain 76% of the MYI area export variability. Furthermore, the dominant atmospheric drivers contributing to the decline in MYI area export during D2 were examined. In the last decade (D2), the strengthened low pressure in the North Atlantic sector, combined with an eastward shift in the axis of dipole anomaly (DA), resulted in reduced MYI advection from the Beaufort Sea and Siberian Coast toward the FS. Moreover, weakened cyclonic activity south of the FS also contributed to the reduction in MYI export during D2. [ABSTRACT FROM AUTHOR]

Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the limited memory and computation resources. We aim to design efficient neural networks for heterogeneous devices including CPU and GPU, by exploiting the redundancy in feature maps, which has rarely been investigated in neural architecture design. For CPU-like devices, we propose a novel CPU-efficient Ghost (C-Ghost) module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed C-Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. C-Ghost bottlenecks are designed to stack C-Ghost modules, and then the lightweight C-GhostNet can be easily established. We further consider the efficient networks for GPU devices. Without involving too many GPU-inefficient operations (e.g., depth-wise convolution) in a building stage, we propose to utilize the stage-wise feature redundancy to formulate GPU-efficient Ghost (G-Ghost) stage structure. The features in a stage are split into two parts where the first part is processed using the original block with fewer output channels for generating intrinsic features, and the other are generated using cheap operations by exploiting stage-wise redundancy. Experiments conducted on benchmarks demonstrate the effectiveness of the proposed C-Ghost module and the G-Ghost stage. C-GhostNet and G-GhostNet can achieve the optimal trade-off of accuracy and latency for CPU and GPU, respectively. MindSpore code is available at https://gitee.com/mindspore/models/pulls/1809, and PyTorch code is available at https://github.com/huawei-noah/CV-Backbones. [ABSTRACT FROM AUTHOR]

In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Pacific-Arctic sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the sea ice concentration (SIC) anomaly correlation coefficient (ACC) between predictions and observations, increased by 32 % in the Bering Sea and 18 % in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. SIC trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. We find that subsurface ocean heat content (OHC) provides a crucial source of prediction skill in all seasons, especially in the cold season, and adding sea ice thickness (SIT) to the regional Markov model has a substantial contribution to the prediction skill in the warm season but a negative contribution in the cold season. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model. [ABSTRACT FROM AUTHOR]

• A novel L-shaped composite shear wall composed of a series of columns was proposed. • An experimental investigation of L-shaped composite shear walls under cyclic lateral and axial loads was conducted. • A 3D finite element model, considering improved confinement, plastic hardening, and damage accumulation, can simulate seismic behavior well. • Plastic stress distributed methods were proposed to predict the lateral load strength of an L-shaped steel composite shear wall. Behavior of L-shaped multi-partition steel–concrete composite shear walls (MP-SC-CSWs) subjected to the cyclic lateral load is experimentally and numerically evaluated. Such composite shear walls possess proper capacity, stiffness, and ductility, as the steel tube strengthened by inner steel plates considerably confines the infill concrete. Four large-scale L-shaped MP-SC-CSW specimens were designed, fabricated, and tested. The L-shaped specimens were exposed to lateral hysteretic loading and axial compression loading. The experimental parameters were axial load ratios and height-to-depth ratios (wall aspect ratios). The experimental results indicated that the L-shaped MP-SC-CSWs had high lateral capacities, energy dissipations, and good ductility. Based on the experimental observations, specimens failed in flexure mode. Increasing the axial load ratio enhances the capacity to dissipate energy but decreases the ductility. The tested specimens could meet the drift requirement of the specification even when subjected to a high design axial load ratio of 0.76. Additionally, the wall aspect ratios had little influence on the ductility. 3D nonlinear finite element models of tested specimens were established and verified with test results to conduct further numerical studies. A simplified method based on the plastic stress distribution was developed and confirmed by the experimental and finite element analysis to calculate the flexural capacities of MP-SC-CSWs. [ABSTRACT FROM AUTHOR]

Transcription is a core step in gene expression. Regulation of transcription is achieved at the level of transcription machinery, local chromatin environment as well as higher-order chromatin organization. Our understanding of transcriptional regulation was advanced by recent introduction of transcription and chromatin-associated condensates, which typically arise via phase separation of proteins and nucleic acids. While studies from mammalian cells are unveiling the mechanisms of phase separation in transcription regulation, those in plants further broaden and deepen our understanding of this process. In this review, we discuss recent progress in plants how phase separation operates in RNA-mediated chromatin silencing, transcription activity, and chromatin compartmentalization. [ABSTRACT FROM AUTHOR]

• A new type of l -shaped steel–concrete composite shear wall using multi-partition steel tube was proposed. • The behavior under the eccentric load was investigated by the means of experiments and finite element analysis. • The proposed model using improved constitutional relationship of concrete simulated the eccentric behavior reasonably. • Simplified formulas were proposed to predict the N - M curves, which could consider the influence of load angle and slenderness. An l -shaped composite shear wall, comprised of a multi-partition steel tube and concrete, is developed to improve the behavior of the l -shaped component. It possesses high load-resisting strength and good ductility as it makes better use of steel and concrete. Eleven samples were tested under eccentric load to determine the impacts of the depth, breadth, and load angle. The experimental results showed that the eccentric strength of the specimen under a load angle π was higher than that of the specimen under a load angle of 0. The specimens failed in out-of-plane and in-plane flexural modes when the depth-to-width ratios were different. Then, the finite element (FE) model using an improved constitutional relation of concrete was established to analyze the eccentric mechanism of the l -shaped composite shear walls. Furthermore, parametric analysis using the FE model was taken to determine the key factors of eccentric behavior. Compared to the design methods specified in EC4, AISC, and CECS, it was found that the calculated capacity for the stub was underestimated while the calculated capacity of the slender components was overestimated. The simplified formulas were presented for predicting the N - M curves based on the experimental and FE analysis and it could conservatively include the influence of the load angle and the relative slenderness. [ABSTRACT FROM AUTHOR]

• The sub-cycle voltage disturbance caused by the incipient fault is obtained through experiment. • The waveform and occurrence frequency characteristics of the voltage disturbance are analyzed. • The model and disturbance characteristics indexes of incipient fault sub-cycle disturbance are proposed. Incipient fault is the precursors of permanent cable fault. Analyzing and modeling the transient power disturbances caused by incipient fault is an important prerequisite for accurately identifying incipient cable fault. In this paper, a large number of sub-cycle voltage disturbances caused by the incipient fault are obtained by performing an applying voltage test on a 10 kV cable with local insulation defects. Then, for the voltage signal with disturbance, the time domain analysis method is used to extract the disturbance, and Hilbert transform method and curve fitting method are used to analyze the variation trend of the disturbance. Three stages of the disturbance are obtained: normal stage, fault excitation stage, disturbance attenuation stage. Then an incipient fault sub-cycle voltage disturbance model is proposed and sub-cycle disturbance characteristic indexes which are fault excitation starting time and fault excitation duration are extracted; The validity of the model is analyzed by the reproduced experimental waveform, and the disturbance propagation characteristics along the line are analyzed by PSCAD simulation. [ABSTRACT FROM AUTHOR]

Flowering is the transition from vegetative to reproductive growth and is critical for plant adaptation and reproduction. FLOWERING LOCUS C (FLC) plays a central role in flowering time control, and dissecting its regulation mechanism provides essential information for crop improvement. Here, we report that DECAPPING5 (DCP5), a component of processing bodies (P-bodies), regulates FLC transcription and flowering time in Arabidopsis (Arabidopsis thaliana). DCP5 and its interacting partner SISTER OF FCA (SSF) undergo liquid–liquid phase separation (LLPS) that is mediated by their prion-like domains (PrDs). Enhancing or attenuating the LLPS of both proteins using transgenic methods greatly affects their ability to regulate FLC and flowering time. DCP5 regulates FLC transcription by modulating RNA polymerase II enrichment at the FLC locus. DCP5 requires SSF for FLC regulation, and loss of SSF or its PrD disrupts DCP5 function. Our results reveal that DCP5 interacts with SSF, and the nuclear DCP5–SSF complex regulates FLC expression at the transcriptional level. The P-body component DCP5 regulates FLC transcription and is dependent on SSF in Arabidopsis thaliana. [ABSTRACT FROM AUTHOR]

The unmanned combat aerial vehicle (UCAV) path planning problem is a complex global optimization problem that aims to seek an optimal flight route that avoids the threats and constraints on the battlefield. However, as the number and degree of the path threats increases, the recent UCAV path planning algorithms all suffer from being trapped into local optima with a low convergence rate, which leads to the poor performance. Therefore, to resolve these problems, we propose a Cooperative co-Evolution (CE)-based Spider Monkey Optimization (SMO) algorithm, named CESMO to address UCAV path planning problem for avoiding obstacles. First, a cooperative co-evolution strategy is proposed to prevent the algorithms from falling into local optima easily, enhancing the coherence of the path. After that, a further division is applied to the algorithm for diversifying the search direction of each individual in the sub-groups. Finally, the spider monkey optimization-based search mechanism is designed to further strengthen the search efficiency and boost the convergence rate. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and stability of the final paths. [ABSTRACT FROM AUTHOR]

Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability when dealing with large-scale feature selection dilemmas. To address these challenges, this paper proposes a novel weighted differential evolution algorithm based on self-adaptive mechanism, named SaWDE, to solve large-scale feature selection. First, a multi-population mechanism is adopted to enhance the diversity of the population. Then, we propose a new self-adaptive mechanism that selects several strategies from a strategy pool to capture the diverse characteristics of the datasets from the historical information. Finally, a weighted model is designed to identify the important features, which enables our model to generate the most suitable feature-selection solution. We demonstrate the effectiveness of our algorithm on twelve large-scale datasets. The performance of SaWDE is superior compared to six non-EC algorithms and six other EC algorithms, on both training and test datasets and on subset size, indicating that our algorithm is a favorable tool to solve the large-scale feature selection problem. Moreover, we have experimented SaWDE with six EC algorithms on twelve higher-dimensional data, which demonstrates that SaWDE is more robust and efficient compared to those state-of-the-art methods. SaWDE source code is available on Github at https://github.com/wangxb96/SaWDE. [ABSTRACT FROM AUTHOR]

The research has analyzed the axial behavior of an innovative L-shaped composite column. The L-shaped composite columns could work as series rectangular composite columns, which could take advantage of the steel tubes and the inner concrete. Seven specimens have been tested to analyze the failure progress, load-carrying capacity and failure mode. Main parameters were the length-to-width ratio and length-to-depth ratio. The shear failure mode accompanied by local buckling appeared for the tested specimens. The results showed that such columns possessed high strength and good ductility. A finite element model was established and verified by the experimental results. Results showed that the finite element model could simulate the axial behavior of the L-shaped composite columns well. Parametric analysis was conducted to analyze the influence on the axial behavior of composite columns. Furthermore, a theoretical model was proposed to calculate the axial strength of such component and was in good agreement with the experimental results. A simplified formula has been proposed for determining the axial strength according to the results of parametric analysis of FEA and the theoretical model. In addition, the calculated results based on design methods specified in EC4, CECS and AISC have been compared with the experimental results, the results of theoretical model and the results of simplified formula. Among them, the results of EC4 were the closest to the experimental results. [Display omitted] • An innovative multi-partition steel-concrete composite column is proposed. • The axial experiments and finite element analysis have been done to study the axial behavior. • A simplified method has been proposed for the design of the axial strength. • An improved stability factor is added in the simplified formula. [ABSTRACT FROM AUTHOR]

Generalized predictive control (GPC), which has excellent control performance and robustness, is only applicable to the control object that can be expressed in the transfer function and is difficult to be directly applied to the hydraulic turbine regulation system (HTRS) that contain extremely complex nonlinearity. Therefore, considering the practical application of GPC in a hydropower station containing the Francis turbine, the double models, real-time feedback linearization module, and a nonlinear characteristic compensator are designed, and a generalized predictive control strategy combining these modules (GPC-DM-RFLM-NCC) is proposed. Firstly, the salp swarm algorithm (SSA) is improved based on the chaotic mapping and Levy flight, an accurate nonlinear hydraulic turbine model is constructed based on the backpropagation neural network (BPNN) and improved SSA (ISSA), and an HTRS simulation platform with nonlinear characteristics is built by combining modules such as water diversion system, servo system, and generator. Then, the irrationality of the commonly used linear hydraulic turbine models in HTRS dynamic process simulation is verified through quantitative calculation. Furthermore, by combining the real-time feedback linearization method and ISSA-based nonlinear characteristic compensator, the GPC-DM-RFLM-NCC is proposed. Finally, the HTRS simulation platform, which consists of GPC, double models, real-time feedback linearization module, and nonlinear characteristic compensator, is built, and the applicability, reliability, and excellent robustness of the proposed GPC-DM-RFLM-NCC are verified by the real data of a hydropower station. • The salp swarm algorithm is optimized based on the chaotic mapping and Levy flight. • A refined model of the hydraulic turbine regulation system containing the precise hydro-turbine model is established. • The irrationality of the popular linear hydraulic turbine model is revealed through quantitative calculation. • A generalized predictive control strategy is proposed considering the nonlinearity of the hydraulic turbine. • Model mismatch caused by feedback linearization is estimated and corrected. [ABSTRACT FROM AUTHOR]

Background: Townes-Brocks syndrome (TBS) is a rare genetic disorder characterized by imperforate anus, dysplastic ears, thumb malformations, and other abnormalities. Previous studies have revealed that mutations in the SALL1 gene can disrupt normal development, resulting in the characteristic features of Townes-Brocks syndrome. Spalt-like transcription factors (SALLs) are highly conserved proteins that play important roles in various cellular processes, including embryonic development, cell differentiation, and cell survival. Over 400 different variants or mutations have been reported in the SALL1 gene in individuals with TBS. Most of these variants lead to the formation of premature termination codons (PTCs), also known as nonsense mutations. The majority of these PTCs occur in a specific region of the SALL1 gene called the "hotspot region", which is particularly susceptible to mutation. Methods: In this study, we conducted whole-exome sequencing on a three-generation Chinese family with anorectal malformations. Results: We identified a novel heterozygous mutation (chr16:51175376:c.757 C > T p.Gln253*) in the SALL1 gene. Molecular analysis revealed a heterozygous C to T transition at nucleotide position 757 in exon 2 of the SALL1 (NM_002968) gene. This mutation is predicted to result in the substitution of the Gln253 codon with a premature stop codon (p.Gln253*). The glutamine-rich domain forms a long alpha helix, enabling the mutant protein to interact with the wild-type SALL1 protein. This interaction may result in steric hindrance effects on the wild-type SALL1 protein. Conclusions: Our findings have expanded the mutation database of the SALL1 gene, which is significant for genetic counseling and clinical surveillance in the affected family. Furthermore, our study enhances the understanding of Townes-Brocks syndrome and has the potential to improve its diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Motivation Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this biomolecular data is to cluster their subtypes into subpopulations to facilitate subsequent downstream analysis. Recently, many clustering methods have been developed to address the biomolecular data. However, the computational methods often suffer from many limitations such as high dimensionality, data heterogeneity and noise. Results In our study, we develop a novel Graph-based Multiple Hierarchical Consensus Clustering (GMHCC) method with an unsupervised graph-based feature ranking (FR) and a graph-based linking method to explore the multiple hierarchical information of the underlying partitions of the consensus clustering for multiple types of biomolecular data. Indeed, we first propose to use a graph-based unsupervised FR model to measure each feature by building a graph over pairwise features and then providing each feature with a rank. Subsequently, to maintain the diversity and robustness of basic partitions (BPs), we propose multiple diverse feature subsets to generate several BPs and then explore the hierarchical structures of the multiple BPs by refining the global consensus function. Finally, we develop a new graph-based linking method, which explicitly considers the relationships between clusters to generate the final partition. Experiments on multiple types of biomolecular data including 35 cancer gene expression datasets and eight single-cell RNA-seq datasets validate the effectiveness of our method over several state-of-the-art consensus clustering approaches. Furthermore, differential gene analysis, gene ontology enrichment analysis and KEGG pathway analysis are conducted, providing novel insights into cell developmental lineages and characterization mechanisms. Availability and implementation The source code is available at GitHub: https://github.com/yifuLu/GMHCC. The software and the supporting data can be downloaded from: https://figshare.com/articles/software/GMHCC/17111291. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Aliphatic aldehydes are a class of organic compounds containing aldehyde groups, which are widespread, and closely related to people's daily life and health. In this work, a series of terpenes based hydrophobic deep eutectic solvents were designed and synthesized using hexafluoroisopropanol as hydrogen bond donor and menthol/thymol as hydrogen bond acceptor. Then they are used as extraction solvent in dispersive liquid-liquid microextraction for extracting and determining seven aliphatic aldehydes from drinking water and alcoholic beverage combined with high performance liquid chromatography-ultraviolet. Due to the fact that these hydrophobic deep eutectic solvents are liquid at the room temperature, a density greater than that of water, a lower viscosity (≤26.10 mPa s, 25 °C), after extraction and centrifugation, the microvolume DES-rich phase in the bottom is convenient for collection and direct analysis without further dissolution or dilution with organic solvents. Some factors affecting the extraction recovery were optimized by one-variable-at-a-time and response surface methodology. Under the optimal conditions, the enrichment factors for the seven aliphatic aldehydes were 48–56. The method had good performance: linear ranges of 1.0–200, 0.5–200, 0.2–200, 0.4–400, 1.0–400, 0.4–400 and 0.4–400 μg L−1 for seven aliphatic aldehydes (r 2 ≥ 0.9949), limits of detection of 0.1–0.5 μg L−1, intra-day and inter-day precisions <4.9%. The recoveries of seven aliphatic aldehydes ranged from 76.0 to 119.0%. The proposed dispersive liquid-liquid microextraction method is simple, rapid, highly efficient, and green, which effectively reduces the amount of toxic chemical reagents used and their impact on the environment. Rapid and efficient detection of aliphatic aldehydes helps ensure a healthy diet and has great application prospects in food safety analysis. [Display omitted] • Terpenes based HDESs with low viscosity and high density were designed and prepared. • A terpenes HDESs based DLLME was established for enrichment of aliphatic aldehydes. • The DES-rich phase in the bottom is easy to be collected and directly analyzed by HPLC. • The method is simple, rapid, effective and green for detecting aliphatic aldehydes. • The green profile of the method was assessed by AES and AGREE. [ABSTRACT FROM AUTHOR]

[Display omitted] • Amphiphilic PS-PMMA Janus particles with a distinct morphology were synthesized. • Stabilized Pickering emulsions were prepared using Janus particles. • PS-PMMA is easy to synthesize, inexpensive, and ideal for large-scale production. In this study, two kinds of Polystyrene - Poly(methyl methacrylate) (PS-PMMA) Janus particles with different particle sizes were synthesized via emulsion phase separation using a rotary evaporator. These Janus particles exhibit a distinctive morphology; one side is smooth and crater-like while the other is concave and sprouting. An interplay between device rotation, system stabilization, material hydrophilicity, and strength variation produces this unique morphology. These particles are cost-effective and easy to synthesize, making them ideal for versatile applications in advanced materials. They can form stable Pickering emulsions with liquid paraffin and distilled water, showing remarkable stability and suitability for large-scale production. [ABSTRACT FROM AUTHOR]

To better reveal the nature of gas flow in raw coal and tectonic coal composite seams during gas extraction, experiments on the gas desorption and diffusion kinetic characteristics of raw coal and tectonic coal under different gas adsorption equilibrium pressures were carried out. Meanwhile, the applicability of Airey empirical formula was analysed, and the unipore model and bidisperse model were used to fit the experimental results and calculate the diffusion coefficient. The results show that the initial gas desorption rate and gas desorption volume of raw coal are much smaller than those of tectonic coal at the initial stage of gas desorption; at the later stage, the gas desorption rate of tectonic coal decays rapidly and the ultimate gas desorption volume tends to a stable value, however, the gas desorption rate of raw coal decays slowly and the ultimate gas desorption volume continues to increase. Airey empirical formula has some limitations in characterizing the gas desorption law in coal and is more suitable for fracture-rich "block" coal, but for the tectonic coal, the fitted ultimate gas desorption volume has a significant error from the actual one. The unipore model is suitable for describing some specific periods in the initial stage of the gas desorption process of raw coal, and the bidisperse model makes up for the defects of the unipore model and can accurately describe the gas diffusion law of tectonic coal. The study provides a theoretical basis for the scientific planning of coal and coalbed methane mining. • Compare the differences in gas desorption law of raw coal and tectonic coal. • Airey empirical formula better fits gas desorption in fracture-rich "block" coal. • The limit for using the unipore model and the bidisperse model is studied. [ABSTRACT FROM AUTHOR]

The cross-shaped concrete-filled steel tube (CFST) is a new shape of column, which avoids convex corners and increases the interior area of the room. Cross-shaped CFSTs possess high bearing capacity, good seismic performance and efficient construction speed. In this study, cross-shaped stub CFSTs under axial loading were tested. The key parameter is the width/thickness ratio of the cross-section. Experiments revealed that local buckling could be delayed and section capacity enhanced by reducing the tube width/thickness ratio. Moreover, a finite-element model was established using the commercial program Abaqus to simulate the compressive performance of the specimens. The results were used to confirm the precision of the finite-element model. Further, a parametric study was completed to inspect the effects of the tube width/thickness ratio, concrete strength and steel yield strength on the mechanical properties of specimens. Relying on the constitutive theoretical model of restrained concrete, an equation is recommended to calculate the section capacity of cross-shaped CFSTs. Finally, the practical outcomes and the calculated outcomes obtained from different design codes (AISC, AIJ, EC4, CECS and GB) were compared. The comparison results revealed that all recommended design codes underrate the section capacity of cross-shaped CFSTs. [ABSTRACT FROM AUTHOR]

Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been developed. However, most of those computational methods suffer from study bias, experimental noise and instability, resulting in unsatisfactory identification of haploinsufficient genes. To address those challenges, we propose a deep forest model, called HaForest, to identify haploinsufficient genes. The multiscale scanning is proposed to extract local contextual representations from input features under Linear Discriminant Analysis. After that, the cascade forest structure is applied to obtain the concatenated features directly by integrating decision-tree-based forests. Meanwhile, to exploit the complex dependency structure among haploinsufficient genes, the LightGBM library is embedded into HaForest to reveal the highly expressive features. To validate the effectiveness of our method, we compared it to several computational methods and four deep learning algorithms on five epigenomic data sets. The results reveal that HaForest achieves superior performance over the other algorithms, demonstrating its unique and complementary performance in identifying haploinsufficient genes. The standalone tool is available at https://github.com/yangyn533/HaForest. [ABSTRACT FROM AUTHOR]

The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1. [ABSTRACT FROM AUTHOR]

The lepidopteran crop pest Plutella xylostella causes severe constraints on Brassica cultivation. Here, we report a novel role for RPX1 (resistance to P. xylostella) in resistance to this pest in Arabidopsis thaliana. The rpx1‐1 mutant repels P. xylostella larvae, and feeding on the rpx1‐1 mutant severely damages the peritrophic matrix structure in the midgut of the larvae, thereby negatively affecting larval growth and pupation. This resistance results from the accumulation of defence compounds, including the homoterpene (3E)−4,8‐dimethyl‐1,3,7‐nonatriene (DMNT), due to the upregulation of PENTACYCLIC TRITERPENE SYNTHASE 1 (PEN1), which encodes a key DMNT biosynthetic enzyme. P. xylostella infestation and wounding induce RPX1 protein degradation, which may confer a rapid response to insect infestation. RPX1 inactivation and PEN1 overexpression are not associated with negative trade‐offs for plant growth but have much higher seed production than the wild‐type in the presence of P. xylostella infestation. This study offers a new strategy for plant molecular breeding against P. xylostella. Summary statement: Plutella xylostella pests show severely negative influence on plant growth and reproduction, and the identification of resistant genes is essential. This study reveals that inactivation of RPX1 confers significant resistance to the pest by enhancing PEN1 expression and accumulating the homoterpene DMNT in Arabidopsis thaliana. [ABSTRACT FROM AUTHOR]

Due to the stochastic grid loads and intermittent renewable energy sources in today's power grid, hydro-generator and grid system stability is increasingly dependent on the hydro-turbine governing system (HTGS). For the HTGS with external disturbances and system uncertainties, an intelligent nonlinear robust controller (INRC) based on state-dynamic-measurement feedback linearization (SDMFL) is proposed to enhance the HTGS's control performance in this paper. This strategy establishes a dynamic output function including actuator displacement, generator rotor angle, and turbine parameters. The relationship between dynamic output and control input is derived based on this function to establish the corresponding linear state-space model. Then, the mixed H 2 / H ∞ robust controller based on the linear model is designed to suppress the effects of external disturbances and system uncertainties on the system performance. In addition, the controller performance is optimized by levy flight and chaos theory-based gravitational search algorithm (LCGSA), resulting in the INRC. Simulations are performed in MATLAB, and the results are compared with the nonlinear robust controller (NRC) and PID controller. The results show that the proposed controller INRC improves the control performance of nonlinear HTGS with smaller overshoot and shorter adjustment time in all cases. • Design of nonlinear robust controller based on SDMFL method for HTGS. • Application of LCGSA algorithm for optimal tuning of NRC parameters. • Damping the speed oscillations against large fluctuations in grid power. • Excellent damping efficiency, especially, low overshoot, steady-state error, and settling time. • Study of robustness of HTGS against variations of system parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel, green and environmental benign TC-AALLME-SFDES technique was developed. • Solid DES was used as extraction solvent. • The method was applied for the determination of TCS and APs from water samples. • The green profile of the method was assessed by analytical Eco-Scale and ComplexGAPI. • The method opened up the application of solid DESs in LLME technique. A novel and green microextraction technique, temperature-controlled air-assisted liquid–liquid microextraction based on the solidification of floating deep eutectic solvents (DESs), was developed and applied for the extraction of triclosan and alkylphenols in different environmental waters. Hydrophobic DESs composed of menthol and myristic acid with melting points slightly higher than room temperature (25 ± 2 °C) were prepared and used as extraction solvent. First, the solid DES was transformed into liquid DES while the aqueous sample was heated in a water bath. Then, fine droplets were formed by withdrawing and injecting the mixture of aqueous sample and DES with a 1 mL pipette several times. After extraction, the solidified DES phase was obtained in an ice water bath. And then the analyses were performed by HPLC-UV. The microextraction parameters were investigated by the Plackett-Burman design and central composite design. The analytical parameters of this method were investigated and good linear ranges (0.5/1.0–400 μg/L, R2 ≥ 0.9950), limits of detection (0.1–0.5 μg/L) and precision (≤5.9 %) were obtained. This method was successfully applied for the determination of triclosan and alkylphenols from five water samples with satisfactory recoveries (80.0–104.3 %). Subsequently, the green profile of the method was assessed by analytical Eco-Scale and Complementary Green Analytical Procedure Index. This method has the advantages of simplicity, rapidity, low cost, high sensitivity and green, which expands the application of solid solvents in liquid–liquid microextraction and has a wide application prospect. [ABSTRACT FROM AUTHOR]

Background: Intracardiac echocardiography (ICE) technology has been increasingly accepted as an integral part of atrial fibrillation (AF) ablation procedures. It is still unknown whether ICE can routinely replace transesophageal echocardiography (TEE) for routine thrombus screening in non‐selective AF patients. Objective: To assess whether ICE can routinely replace TEE in screening for left atrial (LA)/left atrial appendage (LAA) thrombus in general patients undergoing catheter ablation for AF. Methods: A total of 2003 consecutive patients undergoing AF ablation were included. 1155 patients (ICE group) received intra‐procedural ICE examination for LA/LAA thrombus screening, while 848 patients (TEE group) received pre‐procedure TEE examination. The incidence of thrombus, peri‐procedure complications, and hospital efficiency were assessed. Results: The LA and LAA were adequately visualized in all patients. Five patients in the ICE group and 15 patients in the TEE group were found to have LAA thrombus. The incidence of major periprocedural thrombo‐embolic events was comparable between two groups (0.2% vs. 0.1%, p =.76), none were due to undetected LA/LAA thrombus. Other major periprocedural complications occurred at similar rates in both groups, while post‐procedure fever was less common in the ICE group (12.7% vs. 17.4%, p <.001). Procedure times and hospital length of stay were both shorter in the ICE group (142 min [87–197 min] vs. 150 min [95–205 min], and 3[2–4] day vs. 4[3–5] day, respectively, both p <.001). Conclusions: ICE can replace TEE for atrial thrombus screening in AF patients undergoing ablation without increased complications. An "ICE replacing TEE" workflow can also reduce the incidence of postoperative fever and improve hospital efficiency. [ABSTRACT FROM AUTHOR]

• One-step derivatization and TC-VA-LLME-SFDES method was developed. • Hydrophobic DESs were used as both reducing agent and extraction solvent. • Only 2 min was needed for derivatization and extraction process. • The method was applied for analysis of total Fe in water and food samples. • The method opened up the application of solid DESs in LLME technique. In our work, hydrophobic thymol-based deep eutectic solvents (DESs) with strong reducibility, a lower density than water, and a slightly higher melting point than room temperature were synthesized. Based on these solid DESs, one-step derivatization and temperature-controlled vortex-assisted liquid-liquid microextraction based on the solidification of a floating DES (TC-VA-LLME-SFDES) via UV–Vis spectrophotometry for the rapid determination of total iron was developed. The derivatization and TC-VA-LLME were carried out simultaneously by the addition of DES as both the reducing agent and extraction solvent. After optimization, the calibration curve (5–250 μg L−1, R2 = 0.9982), limit of detection (1.5 μg L−1), limit of quantitation (5.0 μg L−1), precision (≤4.0%) and enrichment factor (92) was obtained. This method was applied for the determination of total iron in water and food samples with satisfactory recoveries (85.4–106.2%). One-step derivatization and TC-VA-LLME only required 2 min. Furthermore, this method opened up the application of solid DESs in liquid–liquid microextraction. [ABSTRACT FROM AUTHOR]

In recent years, contrastive learning has made great progress in the field of computer vision, which shows great potential in action representation learning. Current contrastive learning methods usually employ contrastive loss function to learn video motion representation, which attend to capturing the similar background appearance but ignore similar motion information. This problem is called background bias, which restricts the model from exploring motion patterns. The background bias can be divided into wide background bias and micro background bias. The wide background bias refers to the statistically significant background bias, while the micro background bias refers to the background bias directly interacting with the moving object. To tackle these problems, this paper first proposes a semi-negative pair merging foreground–background, which mainly decouples the dynamic factor with obvious motion and the static factor with stable invariants in the video frame sequence. Then the dynamic factor of the original video is fused with other static factors to obtain a random background image where the foreground is more similar than the background to solve the wide background bias. Secondly, a pixel-level motion aware representation decomposition module calculates the pixel-level intensity variations from the feature space of adjacent frames, and these variations are further accumulated to obtain the salient map that focuses on the boundaries of moving objects, so the model attends to the motion pattern rather than the background to solve the micro background bias. Furthermore, a new dual loss function is proposed based on joint wide and micro background bias to better capture both static and dynamic features. The heat map shows that the proposed method effectively can solve background bias and achieve better performance in public datasets compared with other existing methods. [ABSTRACT FROM AUTHOR]

T-shaped welded multi-partition composite members, which are made of several rectangular concrete-filled steel tubes, are proposed to speed up the fabrication procedure. Seven half-scale T-shaped specimens were tested by considering the section height-to-width ratio and slenderness ratio. In the test, a slight torsion angle was measured when the instability failure along the asymmetric axis occurred, indicating the specimens had good torsion resistance. When the relative slenderness ratio of the specimen was larger than 0.44, the specimens failed due to the instability along the asymmetric axis. Parametric numerical analysis is conducted by considering the slenderness ratio, cross-section dimension, steel ratio, and material strength. The results show that the strength of concrete negatively impacts the ductility of the specimens, leading to a decrease of 43%, while the strength of the material positively influences the stability of the specimens. The formulas from different traditional codes are verified by the parametric numerical analysis results, indicating most codes can conservatively predict the sectional bearing capacity, but overestimate the stability coefficient of the specimens by neglecting the reduction effect of the excessive height-to-depth ratio. Recommendations are provided for calculating the stability coefficient under axial compression applicable to T-shaped welded multi-partition composite members. • Axial compression of T-shaped welded multi-partition composite members was studied through experiments and FE analysis. • The measured torsion angle confirms the good torsional capacity of T-shaped composite members. • Both sectional bearing capacity and stability bearing capacity have been discussed. • A suggestion for calculating stability coefficients is proposed, which is suitable for T-shaped columns and shear walls. [ABSTRACT FROM AUTHOR]

Single measures of adiposity markers, such as body mass index (BMI) and waist circumference (WC), are associated with adverse mental health outcomes; however, long-term patterns of adiposity and their health effects remain unclear. The current study assessed adiposity trajectories during a 14-year span beyond middle age and their relevance to mental well-being in late life, and the contribution of genetic and lifestyle factors to the trajectories. Based on a nationally representative sample with longitudinal anthropometric measures, adiposity trajectories were identified by latent mixture modeling, and logistic regression model was used to estimate their associations with mental well-being, with adjustment for confounders. Of the 3491 eligible participants included (mean [SD] age, 69.5 [8.9] years), five discrete BMI and four WC trajectory patterns were identified over 14 years. Compared with the low-stable BMI group (range, 22.8 to 22.9 kg/m²; representing stable healthy body weight), the high-stable group (range, 34.3 to 35.4 kg/m²; stable obese) was associated with increased risk of depression (odds ratio [OR], 1.63; 95 % CI, 1.28–2.07) and low subjective well-being (OR, 1.35; 95 % CI, 1.02–1.79). Compared with the low-stable WC group (range, 75 to 79 cm healthy WC), the high-increasing group (range, 114 to 121 cm) was associated with increased risk of depression (odds ratio [OR], 1.64; 95 % CI, 1.19–2.25) and low well-being (OR, 1.48; 95 % CI, 1.01–2.16). The adiposity trajectories, especially the high-stable/increasing groups, were driven by genetic factors in a dose-response manner, whereas the high/moderate-increasing groups were also behaviorally related. This longitudinal cohort study reveals that stably high trajectory patterns of central and general adiposity during middle age were associated with higher risk of depression and low well-being in late life. The findings indicate the importance of weight management beyond middle age, such as adherence to a healthy lifestyle, in promoting mental health and well-being. [ABSTRACT FROM AUTHOR]

Neural architecture search (NAS) is to automatically design task-specific neural architectures, whose performance has already surpassed those of many manually designed neural networks. Existing NAS techniques focus on searching for the neural architecture and training the optimal network weights from the scratch. Nevertheless, it could be essential to study how to tune a given neural architecture instead of producing a completely new neural architecture in some scenarios, which may lead to a more optimal solution by combining human experience and the advantages of the machine's automatic searching. This paper proposes to learn to tune the architectures at hand to achieve better performance. The proposed Neural Architecture Tuning (NAT) algorithm trains a deep Q-network to tune neural architectures given a random architecture so that we can achieve better performance on a reduced space. We then apply adversarial autoencoder to make the learned policy be generalized to a different searching space in real-world applications. The proposed algorithm is evaluated on the NAS-Bench-101 dataset. The results indicate that our NAT framework can achieve state-of-the-art performance on the NAS-Bench-101 benchmark, and the learned policy can be adapted to a different search space while maintaining the performance. [ABSTRACT FROM AUTHOR]

The satellite observations unveiled that the July sea ice extent of the Arctic shrank to the lowest value, since 1979, in 2020 with a major ice retreat in the Eurasian shelf seas including Kara, Laptev, and East Siberian seas. Based on the ERA-5 reanalysis products, we explored the impacts of warm and moist air-mass transport on this extreme event. The results revealed that anomalously high energy and moisture converged into these regions in the spring months (April to June) of 2020, leading to a burst of high moisture content and warming within the atmospheric column. The convergence is accompanied by local enhanced downward longwave surface radiation and turbulent fluxes, which is favorable for initiating an early melt onset in the region with severe ice loss. Once the melt begins, solar radiation plays a decisive role in leading to further sea ice depletion due to ice–albedo positive feedback. The typical trajectories of the synoptic cyclones that occurred on the Eurasian side in spring 2020 agree well with the path of atmospheric flow. Assessments suggest that variations in characteristics of the spring cyclones are conducive to the severe melt of sea ice. We argue that large-scale atmospheric circulation and synoptic cyclones acted in concert to trigger the exceptional poleward transport of total energy and moisture from April to June to cause this record minimum of sea ice extent in the following July. [ABSTRACT FROM AUTHOR]

The combustion mechanism and gaseous products of asphalt binder at five different oxygen concentrations (21%, 18%, 15%, 12%, and 10%) were studied by thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR). The evolution of the functional groups and microscopic morphology of condensed phase were also analyzed by infrared spectroscopy (IR) and scanning electron microscopy (SEM). The semi quantitative research was realized by calculating the absorption peak area of IR, proximate and ultimate analyses. Results show that the oxygen concentration influences both the pyrolysis and combustion stages of asphalt, and the influence on the combustion of heavy components is more significant. With the decrease of oxygen concentration, the residual mass after the combustion of asphalt binder increases. Additionally, the temperature zone of combustion shifts higher, and a thermal reaction lag effect becomes more obvious. The concentration of oxygen only changes the concentration of gaseous products, instead of the types. Moreover, the concentrations of CO 2 , CO, H 2 O, SO 2 , etc. all decrease, and there is the biggest drop in CO 2. The low oxygen concentration inhibits the dehydrogenation reaction of asphalt in the aerobic pyrolysis stage, while the complete combustion of asphalt residues is inhibited in the combustion of heavy components stage. These will all provide more scientific basis for the analysis of asphalt combustion under low oxygen conditions in tunnel fires. • Combustion mechanism of asphalt binder under different oxygen concentration was discussed. • Dynamic evolution of volatiles released from asphalt binder combustion at different oxygen concentration was described. • Evolution of functional groups in the condensed phase of asphalt binder was analyzed. • Combustion mechanism of asphalt binder under low oxygen conditions was further understood. [ABSTRACT FROM AUTHOR]

This study experimentally and numerically explores the axial-compression behaviours of tapered recycled aggregate concrete-filled high-strength double skin steel tubular (RAC-FHDST) stub column. Twelve specimens were fabricated and tested, considering the effects of tapered angle, hollow ratio and replacement level of coarse recycled aggregate (CRA). The experimental results, consisting of deformation patterns, axial load versus strain responses and confining effect from external high-strength steel tube were obtained and analyzed. Failure mode showed that all stub columns exhibited good ductile behaviour. The elastic stiffness and ultimate strength of RAC-FHDSTs decreased with rising tapered angles, while the ductile index increased. As the replacement levels of CRA varied from 0% to 100%, the elastic stiffness and ductility index obviously reduced. Larger confining effect from outer steel tube to RAC occurred close to the top section of specimens, and the occurrence of confinement was advanced with decreasing hollow ratio and increasing CRA content. Additionally, detailed numerical models were constructed and validated using experiment results. The validated finite element (FE) models were then employed to analyze the working mechanism of specimen, encompassing the load distributions and the interaction between the sandwiched RAC and steel tubes. Afterwards, a parametric study was performed to reveal the influences of essential parameters on the axial compressive performance. Finally, the existing design approaches to normal CFST and CFDST stub columns were employed to assess their applicability for estimating the ultimate strengths of tapered RAC-FHDSTs. • Axial-load tests conducted on 12 RAC-FHDST stub columns. • Effects of tapered angle, hollow ratio and CRA content on the axial response of RAC-FHDSTs. • Confinement effect from outer high-strength steel tube. • The applicability of existing design provisions to tapered RAC-FHDSTs. [ABSTRACT FROM AUTHOR]

Perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), as emerging endocrine-disrupting chemicals (EDCs), pose adverse effects on aquatic organisms. Conventional ecological risk assessment (ERA) not fully considering the mode of toxicity action of PFOS and PFOA, may result in an underestimation of risks and confuse decision-makers. In the study, we developed species sensitivity weighted distribution (SSWD) models based on adverse outcome pathway (AOP) networks for deriving predicted no-effect concentrations (PNECs). Three kinds of weighting criteria (intraspecies variation, trophic level abundance, and data quality) and weighted log-normal distribution methods were adopted. The developed models considered the inter/intraspecies variation and integrated nontraditional endpoints of endocrine-disrupting effects. The PNECs of endocrine disruption effects were derived as 2.52 μg/L (95% confidence intervals 0.667–9.85 μg/L) for PFOS and 18.7 μg/L (5.40–71.0 μg/L) for PFOA, which were more conservative than those derived from the SSD method and were comparable with the values in the literature based on the chronic toxicity data. For PFOS, the effect of growth and development was the most sensitive; however, for PFOA, the effect of reproduction was the most sensitive in the effects of growth and development, reproduction, biochemistry and genetics, and survival. The endocrine-disrupting effects of PFOS and PFOA are significant and need to be fully recognized in the ERA. This study provided an ERA framework that can improve the ecological relevance and reduce the uncertainty of PNECs of EDCs. [Display omitted] • SSWD considers the inter/intraspecies variation and data quality of toxicity. • AOP network integrates endocrine disruption effects of muti-biological levels. • PNECs of endocrine disruption effects as 2.52 (PFOS) and 18.7 (PFOA) μg/L. • SSWD-based PNECs are more conservative than those from SSD models. • AOP-SSWD improves ecological relevance and reduce uncertainty of PNEC. [ABSTRACT FROM AUTHOR]

• Impact-resistant behaviours of steel–concrete composite wall with recycled aggregate concrete. • Time histories of impact force and deformation, and failure mode. • Effects of CRA content, material strength, steel plate ratio, tie-bar spacing, impact energy and axial load ratio on the impact response. • Single degree of freedom approach used to predict the displacement response. This paper investigates the impact-resistant behaviours of steel–concrete (SC) composite walls experimentally and numerically, which consist of two outer steel plates and infilled recycled aggregate concrete (RAC). Totally eight SC walls were designed and fabricated with different coarse recycled aggregate (CRA) content and steel plate thicknesses. Drop-hammer impact testing was first performed to obtain the failure mode, impact force and deformation. Test results indicated that the impact resistance behaviour of SC walls enhanced considerably with a decrease in CRA content. The tie bars combined with studs were effective to avoid the separation of steel plate from the concrete core. Afterwards, the finite element (FE) models were developed and calibrated, and then used to obtain more information on the full-range motion response, development of stress and strain, and energy absorption. In addition, the influences of different parameters, including the CRA content, material strength, steel plate ratio, tie-bar spacing, impact energy and axial load ratio on the impact resistance performance were assessed. Finally, the displacement response of SC composite walls with RAC was predicted using single-degree of freedom (SDOF) approach. [ABSTRACT FROM AUTHOR]

Five specimens have been tested under cyclic lateral loads to explore the seismic behavior of T-shaped multi-partition steel–concrete shear walls along the asymmetrical axis. The loading has been applied by considering the torsion effect from non-coincidence between the lateral loads and the shear center in this study. The main parameters of the test are the axial compression ratio and section height-to-breadth ratio aiming to study the vertical load and web constraint effect. The test results indicate that the failure mode of all specimens along the asymmetric axis is bending failure with minor shear deformation measured during loading. The yield drift (1/138) and the ultimate drift (1/41) of the T-shaped multi-partition steel–concrete shear walls could meet the requirement of the corresponding code, namely 1/350 and 1/70, respectively. The increase in axial compression ratio increases the out-of-plane displacement but decreases the ductility of the specimens, whilst the increase in section height-to-breadth ratio decreases the out-of-plane displacement of the specimens. The relatively small torsion angle of less than 0.6° indicates that the T-shaped multi-partition steel–concrete shear walls have good torsion resistance. Under the cyclic lateral loads along the asymmetrical axis, the out-of-plane displacement of the T-shaped specimen will occur on the side without the web. The web elastic stress distribution model can predict the lateral resistance more accurately, whilst the full-section plastic stress distribution model overestimates the lateral resistance of T-shaped multi-partition steel–concrete shear walls. • T-shaped multi-partition steel–concrete shear walls were tested under asymmetrical loads. • The ductility of T-shaped composite shear walls meets the requirement in Chinese code. • The measured torsion angle confirms the good torsional capacity of T-shaped composite shear walls. • The flange of T-shaped walls is under additional loads due to the out-of-plane deformation. • The web elastic stress distribution model is more suitable to predict the lateral resistance of the specimens. [ABSTRACT FROM AUTHOR]

• Coupling the traditional mechanical rolling and high frequency ultrasonic vibration process. • Numerical analysis and research on the residual stress and surface roughness of the processed components. • Explore the influence of different process parameters on component residual stress and surface roughness. In order to improve the surface performance of the key components of Ti-6Al-4 V titanium alloy and meet its requirements for surface performance and life in complex service environments, this study combines traditional mechanical rolling with high-frequency ultrasonic vibration, and achieves high-speed impact on the surface of titanium alloy and traditional rolling at the same time. Numerical simulation of ultrasonic rolling processing of titanium alloy workpiece is carried out, the residual stress and surface roughness of the workpiece after processing are analyzed, and the influence of different process parameters on it is explored. The results show that ultrasonic rolling processing can effectively improve the surface properties of Ti-6Al-4 V titanium alloy, so that the residual stress distribution of the workpiece is more uniform and increases with the increase of static force and amplitude. When the static force is 1000 N and the amplitude is 8 μm, the surface finishing effect is the best. [ABSTRACT FROM AUTHOR]

Materials and structures with enhanced energy-absorbing and impact perception capabilities are widely deployed for crash mitigation, protective packaging of sensitive elements, and personal impact protection. However, conventional lightweight structures with a monolithic constitutive material cannot simultaneously achieve exceptional energy-absorbing capacity and electromechanical sensitivity. Here we proposed a new class of architected ceramic-polymer composites with improved energy-absorbing capacity and piezoelectric performance. An interconnected porous lead zirconate titanate ([Pb(Zr 0.52 Ti 0.48)O 3 ], PZT) skeleton with uniformly distributed cellular-like pores in the transverse section and directionally aligned porous structure in the longitudinal section was fabricated using a facial camphene-templated freeze-casting method. Subsequently, the polymeric polydimethylsiloxane (PDMS) was impregnated into the skeleton to form the three-dimensional (3-D) interpenetrating-phase piezoelectric composite (IP3C). The as-fabricated interpenetrating architecture with each phase interconnected has endowed the proposed IP3C with an unprecedented combination of mechanical-damping (energy-absorption efficiency ∼ 7.71 MJ m−3) and electromechanical-conversion (piezoelectric constant d 33 ∼ 146 pC N−1) properties, which are 9 times and 7 times higher than the conventional counterpart 0–3 piezoelectric composite, respectively. As evidenced by numerical simulations, this remarkable enhancement is attributed to the high stress transfer efficiency within the IP3C, which is intrinsically controlled by the rationally designed interpenetrating architecture. The findings reported here demonstrate that multifunction, e.g., exceptional energy absorption and high sensitivity, can be achieved in one composite with architecture design, thereby driving forward and expanding the fundamental understanding in the area of multifunctional materials in hostile loading environments. • An interconnected porous lead zirconate titanate ([Pb(Zr 0.52 Ti 0.48)O 3 ], PZT) skeleton with uniformly distributed cellular-like pores in the transverse section and directionally aligned porous structure in the longitudinal section was fabricated using a facial camphene-templated freeze-casting method. • The as-fabricated interpenetrating architecture with each phase is interconnected has endowed the proposed IP3C with an unprecedented combination of mechanical-damping and electromechanical-conversion properties, which are 9 times and 7 times higher than the conventional counterpart 0–3 piezoelectric composite, respectively. • The findings reported here demonstrate that multifunction, e.g., exceptional energy absorption and high sensitivity, can be achieved in one composite with architecture design, thereby driving forward and expanding the fundamental understanding in the area of multifunctional materials in hostile loading environments. [ABSTRACT FROM AUTHOR]