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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]

Class-imbalanced biological datasets pose significant challenges in machine learning and data analysis tasks. Prior methods to handle imbalance rely on data oversampling, which increases computational costs and overfitting. While feature selection and ensemble learning are promising techniques, current applications in imbalanced contexts are limited. To address these challenges, we present a novel framework called Hybrid Sampling Nature-Inspired Optimization Ensemble (HSNOE) to enhance the identification of hidden responders in imbalanced biological datasets. Our contributions are three-fold: 1) A hybrid undersampling and oversampling technique to mitigate class-imbalance; 2) Integrate an ant colony optimization-based feature selection that identifies informative feature subsets; 3) An ensemble classifier integrating diverse models trained on optimized features to improve performance. The experiments conducted on the five biological datasets demonstrate that HSNOE exhibits more stable comprehensive performance across six evaluation metrics compared to ten benchmark methods. We also conducted a biological analysis specifically on the Pan-cancer dataset. Moreover, the HSNOE method has been made publicly available on GitHub. 1 1 https://github.com/wangxb96/HSNOE. • HSNOE can mitigate the effects of data skew when training the model. • A nature-inspired feature selection method is used to identify informative features. • HSNOE combines a nature-inspired optimization method to realize ensemble. • A case study was conducted to show the generalization capabilities of HSNOE. [ABSTRACT FROM AUTHOR]

Atmospheric circulation associated with the Arctic dipole (AD) pattern plays a crucial role in modulating the variations of summertime sea ice concentration (SIC) within the Pacific Arctic sector (PAS). Based on reanalysis data and satellite observations, we found that the impacts of atmospheric circulation associated with a positive AD (AD+) on SIC change over different regions of the PAS [including the East Siberian Sea (ESS), Beaufort and Chukchi Seas (BCS), and Canadian Arctic Archipelago (CAA)] are dependent on the phase shifts of Pacific decadal oscillation (PDO). Satellite observations reveal that SIC anomalies, influenced by AD+ during PDO− relative to that during PDO+, varies significantly in summer by 4.9%, −7.3%, and −6.4% over ESS, BCS, and CAA, respectively. Overall, the atmospheric anomalies over CAA and BCS in terms of specific humidity, air temperature, and thereby downward longwave radiation (DLR), are enhanced (weakened) in the atmospheric conditions associated with AD+ during PDO− (PDO+). In these two regions, the larger (smaller) increases in specific humidity and air temperature, associated with AD+ during PDO− (PDO+), are connected to the increased (decreased) poleward moisture flux, strengthened (weakened) convergence of moisture and heat flux, and in part to adiabatic heating. As a consequence, the DLR and surface net energy flux anomalies over the two regions are reinforced in the atmospheric scenarios associated with AD+ during PDO− compared with that during PDO+. Therefore, smaller SIC anomalies are identified over CAA and BCS in the cases related to AD+ during PDO− than during PDO+. Essentially, the changes of the DLR anomaly in CAA and BCS are in alignment with geopotential height anomalies, which are modulated by the anticyclonic circulation pattern in association with AD+ during varying PDO phases. In contrast, the SIC changes over ESS is primarily attributed to the variations in mechanical wind forcing and sea surface temperature (SST) anomalies. The cloud fraction anomalies associated with AD+ during different PDO phases are found not to be a significant contributor to the variations of sea ice anomaly in the studied regions. Given the oscillatory nature of PDO, we speculate that the recent shift to the PDO+ phase may temporarily slow the observed significant decline trend of the summertime SIC within PAS of the Arctic. [ABSTRACT FROM AUTHOR]

High dimensionality and sample imbalance of gene expression data promote the development of effective algorithms for classifying gene expression data. To improve the ability to distinguish different subtypes of gene expression data, we devise a hypervolume-based discrete evolutionary optimization algorithm (HYBDEOA) in this paper. Four objectives, namely the number of genes, the accuracy, the relevance, and the redundancy, are optimized simultaneously to guide the evolution. Firstly, binary encoding is used to choose some features, projecting data onto different subspaces. After that, a discrete neighborhood operation is conducted to generate a new binary-mapped population. Combining the new population with the current population, we employ the hypervolume-based mechanism to select the Pareto solutions. Finally, a discrete mutation method is proposed to find promising solutions in the binary search space. To demonstrate the performance of HYBDEOA, we apply HYBDEOA to 55 synthetic datasets and 35 cancer gene expression datasets. Extensive experiments are also conducted to reveal the effectiveness and efficiency of HYBDEOA. The experimental results demonstrate that our proposed method is a parameter-less and robust algorithm, which can group gene expression data with a finer and more informative classification. [ABSTRACT FROM AUTHOR]

Heterodera avenae, as an obligate endoparasite, causes severe yield loss in wheat (Triticum aestivum). Investigation on the mechanisms how H. avenae perceives wheat roots is limited. Here, the attractiveness of root exudates from eight plant genotypes to H. avenae were evaluated on agar plates. Results showed that the attraction of H. avenae to the root exudates from the non-host Brachypodium distachyon variety Bd21-3 was the highest, approximately 50 infective second-stage juveniles (J2s) per plate, followed by that from three H. avenae-susceptible wheat varieties, Zhengmai9023, Yanmai84 and Xiangmai25, as well as the resistant one of Xinyuan958, whereas the lowest attractive activity was observed in the two H. avenae-resistant wheat varieties, Xianmai20 (approximately 12 J2s/plate) and Liangxing66 (approximately 11 J2s/plate). Then Bd21-3, Zhengmai9023 and Heng4399 were selected for further assays as their different attractiveness and resistance to H. avenae, and attractants for H. avenae in their root exudates were characterized to be heat-labile and low-molecular compounds (LM) by behavioral bioassay. Based on these properties of the attractants, a principle of identifying attractants for H. avenae was set up. Then LM of six root exudates from the three plants with and without heating were separated and analyzed by HPLC-MS. Finally, dihydroxyacetone (DHA), methylprednisolone succinate, embelin and diethylpropionin in the root exudates were identified to be putative attractants for H. avenae according to the principle, and the attraction of DHA to H. avenae was validated by behavioral bioassay on agar. Our study enhances the recognition to the orientation mechanism of H. avenae towards wheat roots. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNNs) are successfully used in a number of applications. However, their storage and computational requirements have largely prevented their widespread use on mobile devices. Here we present a series of approaches for compressing and speeding up CNNs in the frequency domain, which focuses not only on smaller weights but on all the weights and their underlying connections. By treating convolution filters as images, we decompose their representations in the frequency domain as common parts (i.e., cluster centers) shared by other similar filters and their individual private parts (i.e., individual residuals). A large number of low-energy frequency coefficients in both parts can be discarded to produce high compression without significantly compression romising accuracy. Furthermore, we explore a data-driven method for removing redundancies in both spatial and frequency domains, which allows us to discard more useless weights by keeping similar accuracies. After obtaining the optimal sparse CNN in the frequency domain, we relax the computational burden of convolution operations in CNNs by linearly combining the convolution responses of discrete cosine transform (DCT) bases. The compression and speed-up ratios of the proposed algorithm are thoroughly analyzed and evaluated on benchmark image datasets to demonstrate its superiority over state-of-the-art methods. [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]

Abstract A facile hydrothermal process is used to prepare hybrid graphene-MoO 3 particles to be used as hole transport layers (HTLs) in organic solar cells (OSCs). The OSCs with active layer donor/acceptor combinations of Poly[N-9″-hepta-decanyl-2,7-carbazolealt-5,5-(4′,7′-di-2-thienyl-2′,1′,3′-benzothiadiazole)] (PCDTBT) and fullerene derivative [6,6]-phenyl-C71-butyric acid methylester (PC 71 BM) exhibit an enhanced power conversion efficiency (PCE) of 7.07%, an increase by 19% with the hybrid HTL compared to those devices with only MoO 3 HTLs. Through investigating the optical and electrical properties of the devices, we found that the superior PCE originates from an enhanced hole transport property resulting from the extraction capabilities of G-MoO 3. Comparing with thermal evaporated MoO 3 , the G-MoO 3 exhibits a higher optical transmittance, improved electrical conductivity and enhanced hole mobility. Moreover, the work function of the hybrid G-MoO 3 was close to the highest occupied molecular orbital (HOMO) level of PCDTBT, which reduced the energy barrier for the carriers and was suited for hole transport. Graphical abstract Image 1028359 Highlights • Hybrid graphene-MoO 3 (G-MoO 3) particles were synthesized via a simple hydrothermal method. • The G-MoO 3 was introduced into organic solar cells as hole transportion layers. • The organic solar cells with G-MoO 3 hole transportion layers exhibited higher conductivity and hole mobility. [ABSTRACT FROM AUTHOR]

Graphical abstract Solution-processed Cu-doped NiO x nanoparticles is an efficient hole transport layer in polymer solar cells. The Cu doping enhances the electrical conductivity and improves the interface contacts with the active layer, thereby improving the device efficiency. Abstract Polymer solar cells (PSCs) are considered promising energy power suppliers due to their light weight, printability, low-energy fabrication and roll-to-roll processability. Recently, the solution-processed NiO x nanoparticles have been a desirable interfacial material for hole transport in the PSCs, instead of organic semiconductors. However, pure NiO x films restrain the high performance of PSCs due to their poor electrical characteristics caused by the localized orbital distribution at the top of valence band. Therefore, metal ion doping has been explored as a method to endow NiO x nanoparticles with the appropriate electrical characteristics. Herein, we applied solution-processed Cu-doped NiO x (Cu:NiO x) nanoparticles as an efficient hole transport layer (HTL) in PSCs. The Cu-doped NiO x enhanced the electrical conductivity of the material and improved the interface contact with the active layer, which remarkably facilitated the hole extraction and effectively suppressed the carrier recombination at the interface. Thus, a higher power conversion efficiency of 7.05%, corresponding to an approximately 30% efficiency improvement compared with that of a pristine NiO x interlayer (5.44%) in poly[N- 9′'-hepta-decanyl-2,7-carbazolealt-5,5-(4′,7′-di-2-thienyl-2′,1′,3′-ben-zothiadiazole)]:[6,6]-phenyl-C 71 -butyric acid methyl ester (PCDTBT:PC 71 BM)-based PSCs, was achieved by the proposed device. The developed solution-processed Cu:NiO x nanoparticles may be an excellent alternative for interfacial materials in PSCs or other optoelectronic devices requiring HTLs. [ABSTRACT FROM AUTHOR]

Arctic sea ice cover has decreased dramatically over the last three decades. This study quantifies the sea ice concentration (SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and temporal variations. During each month the SIC trends are negative over the Arctic Ocean, wherein the largest (smallest) rate of decline found in September (March) is -0.48%/a (-0.10%/a). The summer (-0.42%/a) and autumn (-0.31%/a) seasons show faster decrease rates than those of winter (-0.12%/a) and spring (-0.20%/a) seasons. Regional variability is large in the annual SIC trend. The largest SIC trends are observed for the Kara (-0.60%/a) and Barents Seas (-0.54%/a), followed by the Chukchi Sea (-0.48%/a), East Siberian Sea (-0.43%/a), Laptev Sea (-0.38%/a), and Beaufort Sea (-0.36%/a). The annual SIC trend for the whole Arctic Ocean is -0.26%/a over the same period. Furthermore, the infl uences and feedbacks between the SIC and three climate indexes and three climatic parameters, including the Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Dipole anomaly (DA), sea surface temperature (SST), surface air temperature (SAT), and surface wind (SW), are investigated. Statistically, sea ice provides memory for the Arctic climate system so that changes in SIC driven by the climate indices (AO, NAO and DA) can be felt during the ensuing seasons. Positive SST trends can cause greater SIC reductions, which is observed in the Greenland and Barents Seas during the autumn and winter. In contrast, the removal of sea ice (i.e., loss of the insulating layer) likely contributes to a colder sea surface (i.e., decreased SST), as is observed in northern Barents Sea. Decreasing SIC trends can lead to an in-phase enhancement of SAT, while SAT variations seem to have a lagged infl uence on SIC trends. SW plays an important role in the modulating SIC trends in two ways: by transporting moist and warm air that melts sea ice in peripheral seas (typically evident inthe Barents Sea) and by exporting sea ice out of the Arctic Ocean via passages into the Greenland and Barents Seas, including the Fram Strait, the passage between Svalbard and Franz Josef Land (S-FJL), and the passage between Franz Josef Land and Severnaya Zemlya (FJL-SZ). [ABSTRACT FROM AUTHOR]

In this study, cone calorimeter and thermogravimetric analyses were used to simulate the asphalt combustion process under the conditions of fire radiation and programmed temperature increase. The gaseous compositions and release rules were analyzed by infrared spectroscopy to investigate the influence of hydrated lime on the smoke suppression mechanism in the asphalt combustion process. The experimental results show that hydrated lime can promote the asphalt mastic surface to form a barrier layer during the combustion process. This barrier layer can reduce the burning intensity of asphalt. Although the compositions of gaseous products do not change much, the rates of CO production and smoke release are decreased. In addition, hydrated lime is alkaline and can thus neutralize acidic gases such as SO2 and reduce the toxicity of gaseous products. With the addition of 40 wt.% hydrated lime, the total smoke release and the CO release rate both decrease by more than 20% relative to the addition of the same amount of limestone fillers and decrease by more than 10% relative to the addition of the same amount of magnesium hydroxide flame retardant. [ABSTRACT FROM AUTHOR]

Multi-partition composite shear walls have the strong points, such as high load-carrying capacity, good ductility, excellent energy dissipation capacity and fast construction speed. Even if they were used in the buildings, there was a little research work on this kind of composite shear walls. To study the axial behavior and cyclic behavior of multi-partition composite shear walls, six axially loaded specimens were tested, and two specimens were tested under horizontal cyclic load. Through testing of specimens, the damage progression, failure mechanism, stiffness and energy dissipation of composite shear walls were investigated. The experimental results showed that this kind shear walls under axial load had high load-carrying capacity and good ductility. The steel section could confine the core concrete, whose capacity was higher than that the summation capacity of steel section and core concrete. The shear walls exhibited excellent energy dissipation ability under high axial load ratio. Because of the confinement of steel section, the specimen showed higher energy dissipation capacity with the increasing of axial load ratio. However, the ductility of the specimen under cyclic load decreased with the increasing of axial load ratio. The experimental results were compared with the calculated results calculated by EC4, AISC and CECS. The results showed that all design codes underestimated the axial compressive capacity of the specimens. For specimens subjected to cyclic load, the results based on EC4 were the nearest to the experimental results. [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]

Here we study the extreme visual recovery problem, in which over 90% of pixel values in a given image are missing. Existing low rank-based algorithms are only effective for recovering data with at most 90% missing values. Thus, we exploit visual data’s smoothness property to help solve this challenging extreme visual recovery problem. Based on the discrete cosine transform (DCT), we propose a novel DCT regularizer that involves all pixels and produces smooth estimations in any view. Our theoretical analysis shows that the total variation regularizer, which only achieves local smoothness, is a special case of the proposed DCT regularizer. We also develop a new visual recovery algorithm by minimizing the DCT regularizer and nuclear norm to achieve a more visually pleasing estimation. Experimental results on a benchmark image data set demonstrate that the proposed approach is superior to the state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity. [ABSTRACT FROM AUTHOR]

The gradual withdraw of several broadly used nematicides from market has enhanced the need to develop sustainable and eco-friendly alternatives with nematicidal properties. Furfural is one of the promising alternatives to fill this need. Baseline information about the impact of furfural on egg hatch, penetration potential and ultrastructure of nematode is lacking. In this study, the reagent-grade (purity ≥ 99.0%) of furfural was applied against Meloidogyne incognita . In vitro tests showed gradual reduction in either the rate of egg hatch or second stage juvenile (J 2 ) viability of M. incognita when immersed in concentrations ranging from 0 to 10.0 μl/ml furfural. The mean EC 50 for J 2 and egg hatch was 0.37 and 0.27 μl/ml furfural, respectively. Furfural, even at low concentrations, resulted in a considerable suppression in egg hatch. Hatch was <5% after 8 days at 0.63 μl/ml furfural. The same furfural concentrations after 12 h caused 57.25% loss of viability in J 2 . Moreover, the penetration rate of juveniles to pea roots was suppressed when furfural was even applied at low rates. In pot experiments, furfural was applied as liquid (direct) or vapor (indirect) treatments at rates of 0–1.5 ml/kg soil. Significant reduction in galling, egg production and population density of M. incognita observed when furfural was applied at rates >0.2 ml/kg soil. No adverse effect was detected on plants or free-living nematodes as a result of furfural application. Liquid furfural proved to have superior juvenile-suppressive effect whereas its vapor has such superiority against eggs. Scanning electron microscope (SEM) study showed irregular appearance of the body surface accompanied with some cuticle disfigurement of furfural-treated juveniles. These results indicated that furfural can adversely affect egg hatch, juvenile viability, penetration potential and ultrastructure of M. incognita . Furfural may therefore be of a considerable potential as an appropriate alternative for class I nematicides. [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 transition set semantics (Wang and Jiao, LNCS 6128:84-103, ) partitions the Petri net behaviors in a canonical way such that behaviors in an equivalence class have the same canonical transition set sequence. This article extends the semantics in two ways: firstly, the semantics is parameterized by the basic relation on the structural transitions to define different variants; secondly, the semantics for the infinite firing sequences of the net is defined. We prove that these extensions still preserve the well-definedness, soundness and completeness of the semantics. Furthermore, we show how to recognize some infinite sequences called back-loops in the view of this new semantics. [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]

• DESs with low viscosity, density and melting point near room temperature were designed and used. • A centrifugation-free AA-LLME-SFDES method was developed. • Placket-burman design and central composite design were used for the method optimization. • This method was used for simultaneous analysis of bisphenols and PAHs in 15 tea infusions samples. • This method may be developed as a fully automated AA-LLME method. Deep eutectic solvents (DESs) are a new class of green "designer solvent"; its physicochemical properties can be easily tuned by adjusting DES′ constituents, chemical ratio and water content. In this study, three hydrophobic DESs with low viscosity, low density, and melting points close to room temperature were designed and synthesized. Based on these DESs, an air-assisted liquid–liquid microextraction technique was developed based on the solidification of floating DESs for the simultaneous determination of bisphenols and polycyclic aromatic hydrocarbons (PAHs) via HPLC. The microextraction parameters were optimized via the Plackett-Burman design and response surface methodologies. The method shows satisfactory linearity (R2 ≥ 0.9928), a low limit of detection (0.16–0.75 μg L−1) and satisfactory precision (≤2.3%), and was successfully applied for the simultaneous determination of bisphenols and PAHs from tea infusions with satisfactory recoveries (82.0–116.6%). This method is simple, rapid, economical, environmentally compatible, dispersive solvent-frees and centrifugation-free, and has promising applications in food safety. [ABSTRACT FROM AUTHOR]

The following information is missing from the Funding statement: Yannong Xiao is supported by the National Key R & D Program of China (2018YFD0200500). Hazem Abdelnabby is also affiliated with Department of Plant Protection, College of Agriculture, Benha University, Qaliubia, Egypt. [Extracted from the article]

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]

Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs. [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]