The top 10 major science and technology achievements were chosen by 485 members of the Chinese Academy of Sciences (CAS) or Chinese Academy of Engineering (CAE) from 20 options. The results of the survey were released...The top 10 major science and technology achievements were chosen by 485 members of the Chinese Academy of Sciences (CAS) or Chinese Academy of Engineering (CAE) from 20 options. The results of the survey were released in early January, 2001 in Beijing. The top 10 scientific achievements for the year 2000 are as follows:展开更多
Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and pea...Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.展开更多
Selective separation of amino acids and proteins is crucial in various areas of research,including proteomics,protein structure and function studies,protein purification and drug development,and biosensing and biodete...Selective separation of amino acids and proteins is crucial in various areas of research,including proteomics,protein structure and function studies,protein purification and drug development,and biosensing and biodetection.A nanocomposite film is formed by combining layer-by-layer self-assembled gold nanospheres(Au NPs)driven by cucurbit[7]uril(CB[7])and polymethyl methacrylate(PMMA)film.Due to the host-vip interactions,the selective transmission of l-tryptophan in the nanocomposite film is confirmed by the current-voltage measurements using a picoammeter.Furthermore,by adjusting the particle size of Au NPs to increase channel size,lysozyme containing multiple tryptophan residues can selectively pass through the nanocomposite film,indicating the high versatility and adaptability of the nanocomposite film.This study will provide a new direction for the selective separation of amino acids and proteins.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via lo...Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via low-temperature coprecipitation,exhibiting excellent performance for the selective hydrogenation of 5-hydroxymethylfurfural(HMF).A linear correlation is first observed between solvent polarity(E_(T)(30))and product selectivity within both polar aprotic and protic solvent classes,suggesting that solvent properties play a vital role in directing reaction pathways.Among these,1,4-dioxane(aprotic)favors the formation of 2,5-bis(hydroxymethyl)furan(BHMF)with 97.5%selectivity,while isopropanol(iPrOH,protic)promotes 2,5-dimethylfuran production with up to 99.5%selectivity.Mechanistic investigations further reveal that beyond polarity,proton-donating ability is critical in facilitating hydrodeoxygenation.iPrOH enables a hydrogen shuttle mechanism where protons assist in hydroxyl group removal,lowering the activation barrier.In contrast,1,4-dioxane,lacking hydrogen bond donors,stabilizes BHMF and hinders further conversion.Density functional theory calculations confirm a lower activation energy in iPrOH(0.60 eV)compared to 1,4-dioxane(1.07 eV).This work offers mechanistic insights and a practical strategy for solvent-mediated control of product selectivity in biomass hydrogenation,highlighting the decisive role of solvent-catalyst-substrate interactions.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
The precise design and fabrication of biomaterial scaffolds is necessary to provide a systematic study for bone tissue engineering. Biomaterial scaffolds should have sufficient stiffness and large porosity. These two ...The precise design and fabrication of biomaterial scaffolds is necessary to provide a systematic study for bone tissue engineering. Biomaterial scaffolds should have sufficient stiffness and large porosity. These two goals generally contradict since larger porosity results in lower mechanical properties. To seek the microstructure of maximum stiffness with the constraint of volume fraction by topology optimization method, algorithms and programs were built to obtain 2D and 3D optimized microstructure and then they were transferred to CAD models of STL format. Ti scaffolds with 30% volume fraction were fabricated using a selective laser melting （SLM） technology. The architecture and pore shape in the metallic biomaterial scaffolds were relatively precise reproduced and the minimum mean pore size was 231μm. The accurate fabrication of intricate microstructure has verified that the SLM process is suitable for fabrication of metallic biomaterial scaffolds.展开更多
Understanding the feeding selectivity on phytoplankton by shellfi sh is currently a big challenge. In order to investigate the feeding behavior of bay scallop ( Argopecten irradians ) on phytoplankton, we compared its...Understanding the feeding selectivity on phytoplankton by shellfi sh is currently a big challenge. In order to investigate the feeding behavior of bay scallop ( Argopecten irradians ) on phytoplankton, we compared its compositions of phytopigments in digestive glands with those in the surrounding seawater, and conducted fi ve consecutive investigations between July and November 2016 in a bay scallop culture area along coast of Qinghuangdao City, northwest of the Bohai Sea, China. Phytopigments in four-size fractionated phytoplankton of seawater (micro-(20-200 μm);nano(L)-[10-20 μm];nano(S)-[2.7-10 μm], and pico-[<2.7 μm]) and digestive glands of A . irradians were examined to investigate the selective feeding of A . irradians . Results show that fucoxanthin and peridinin constituted the major part of taxonomically diagnostic carotenoids (TDCs) in the micro- and nano(L)-phytoplankton in seawater. Compared with total phytoplankton biomass of seawater (TPB, sum of the four sizes), a substantial decrease of fucoxanthin proportion to total DCs in digestive glands was observed while that of peridinin, 19′-butanoyloxyfucoxanthin, alloxanthin and 19′-hexanoyloxy-fucoxanthin showed an obvious increase when those pigments were mainly confi ned to micro-sized phytoplankton (20-200 μm). However, zeaxanthin and prasinoxanthin were mainly confi ned to nano(s)- and pico-phytoplankton, of which the proportions in digestive glands were usually lower in TPB. The contribution of lutein to total DCs in digestive glands (with an average of 7.23%) increased compared with TPB of seawater (with an average of 0.63%) during all fi ve sampling times.展开更多
To quantify the response to selection, heritability and genetic correlations between weight and size ofLitopenaeus vannamei, the body weight （BW）, total length （TL）, body length （BL）, first abdominal segment dep...To quantify the response to selection, heritability and genetic correlations between weight and size ofLitopenaeus vannamei, the body weight （BW）, total length （TL）, body length （BL）, first abdominal segment depth （FASD）, third abdominal segment depth （TASD）, first abdominal segment width （FASW）, and partial carapace length （PCL） of 5-month-old parents and of offspring were measured by calculating seven body measurings of offspring produced by a nested mating design. Seventeen half-sib families and 42 full-sib families ofL. vannamei were produced using artificial fertilization from 2-4 dams by each sire, and measured at around five months post-metamorphosis. The results show that heritabilities among various traits were high： 0.515±0.030 for body weight and 0.394±0.030 for total length. After one generation of selection, the selection response was 10.70% for offspring growth. In the 5th month, the realized heritability for weight was 0.296 for the offspring generation. Genetic correlations between body weight and body size were highly variable. The results indicate that external morphological parameters can be applied during breeder selection for enhancing the growth without sacrificing animals for determining the body size and breed ability; and selective breeding can be improved significantly, simultaneously with increased production.展开更多
Facility layout selection is a multi-criteria decision making (MCDM) problem,since it has a strategic impact on the efficiency of manufacturing system.In view of the interdependency among selection criteria,analytic n...Facility layout selection is a multi-criteria decision making (MCDM) problem,since it has a strategic impact on the efficiency of manufacturing system.In view of the interdependency among selection criteria,analytic network process (ANP) is proposed to analyze the structure of the facility layout selection problem and determine the weights for each criterion.A network structure is constructed that shows all elements and clusters and their interactions.Limit priorities are also calculated which help decision maker evaluate the relative importance among criterion in the alternative selection process.Moreover,a hybrid MCDM approach that employs ANP and technique for order preference by similarity to an ideal solution (TOPSIS)method to rank the optimal facility layout alternatives.Finally,an application of a new aeronautic component assembly workshop facility layout selection is conducted.To further illustrate the advantage of the proposed approach,the difference between ANP-TOPSIS and AHP-TOPSIS methods are compared and discussed.Results have demonstrated the effectiveness and feasibility of the proposed method.展开更多
Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding ...Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value(GEBV).This study is a fi rst attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits.The performance of GS models in L.vannamei was evaluated in a population consisting of 205 individuals,which were genotyped for 6 359 single nucleotide polymorphism(SNP)markers by specifi c length amplifi ed fragment sequencing(SLAF-seq)and phenotyped for body length and body weight.Three GS models(RR-BLUP,Bayes A,and Bayesian LASSO)were used to obtain the GEBV,and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes.The mean reliability of the GEBVs for body length and body weight predicted by the dif ferent models was 0.296 and 0.411,respectively.For each trait,the performances of the three models were very similar to each other with respect to predictability.The regression coeffi cients estimated by the three models were close to one,suggesting near to zero bias for the predictions.Therefore,when GS was applied in a L.vannamei population for the studied scenarios,all three models appeared practicable.Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.展开更多
Supplier selection can be regarded as a typical multiple attribute decision-making problem. In real-world situation, the values of the alternative attributes and their weights are always being nondeterministic, and as...Supplier selection can be regarded as a typical multiple attribute decision-making problem. In real-world situation, the values of the alternative attributes and their weights are always being nondeterministic, and as a result of this, the values are considered interval numbers. In addition, the common approach to measure the similarity between alternatives through their distance suffers from some minor shortcomings. To address these problems, this study develops a novel hybrid decision-making method by combining the technique for order preference by similarity to an ideal solution （TOPSIS） with grey relational analysis （GRA） for supplier selection with interval numbers. By introducing the intervals theory, the extensions of Euclidean distance and grey relational grade are defined. And then a new comprehensive closeness coefficient is constituted for supplier alternatives evaluation based on the interval Euclidean distance and the interval grey relational grade, which could indicate the distance-based similarity and the shape-based similarity simultaneously. A mtmerical example is taken to validate the flexibility of the proposed method, and result shows that this method can tackle the uncertainty in real-world supplier selection and also help decision makers to effectively select optimal suppliers.展开更多
This paper deals with network selection problem for users in heterogeneous network environment. The main context is to improve the TOPSIS( Technique for Order Preference by Similarity to Ideal Solution) network scheme...This paper deals with network selection problem for users in heterogeneous network environment. The main context is to improve the TOPSIS( Technique for Order Preference by Similarity to Ideal Solution) network scheme by combining the network properties and the users' requirement accurately and decrease ping-pong effect. The method of entropy and FAHP( Fuzzy Analytic Hierarchy Process) are used to calculate weight value and the sojourn time calculation is used to avoid ping-pang effect. The simulation results show that the improved scheme enhances the more accuracy of network selection than the existing methods and reduces the number of ping-pang effect.展开更多
Objective:To explore the core acupoints and combination rules of acupuncture therapy for postoperative urinary retention after hemorrhoidectomy,and,further to analyze the characteristics of the prescription of acupunc...Objective:To explore the core acupoints and combination rules of acupuncture therapy for postoperative urinary retention after hemorrhoidectomy,and,further to analyze the characteristics of the prescription of acupuncture therapy for postoperative urinary retention after hemorrhoidectomy.Methods:Relevant clinical studies in seven databases were searched systematically from database inception to 1 st October 2020.Based on data mining techniques,the core acupoints,characteristics and combination rules of acupuncture therapy for postoperative urinary retention after hemorrhoidectomy were analyzed comprehensively.Statistics collected and analysis of acupoint selection included the frequency of usage of acupoints,the meridian of acupoints,the location of acupoints and the combination of acupoints.Cluster and association analysis were performed utilizing Excel 13.0 and SPSS 26.0.Results:In the 36 studies included,the most frequently adopted acupoint was Sānyīnjiāo(三阴交 SP6);the most frequently adopted meridian was the foot-taiyin spleen meridian;the most frequently involved location was the lower limbs;and the most frequently adopted combination of acupoints was SP6 and Zhōngjí(中极CV 3).Cluster analysis showed that the acupoints could be divided into three categories:the first group comprised SP6 alone;the second group included CV3,Guānyuán(关元CV4),Yīnlíngquán(阴陵泉SP9) and Zúsānlǐ(足三里ST36);and the third group included Pángguāngshū(膀胱俞BL28),Chéngshān(承山BL57),Tàichōng(太冲LR3),Zhìbiān(秩边 BL54),Tàixī(太溪KI6),Hégǔ(合谷 LI4),Xuèhǎi(血海SP10),Qìhǎi(气海CV6) and Shǔidào(水道ST28).Conclusion:It was found that acupuncture therapy for postoperative urinary retention after hemorrhoidectomy mainly follows the four characteristic rules:the selection of acupoints following the meridian,selection of acupoints at local points,selection of acupoints at distal points and selection of acupoints according to special symptoms.展开更多
Pacific white shrimp has become a major aquaculture and fishery species worldwide.Although a large scale EST resource has been publicly available since 2008,the data have not yet been widely used for SNP discovery or ...Pacific white shrimp has become a major aquaculture and fishery species worldwide.Although a large scale EST resource has been publicly available since 2008,the data have not yet been widely used for SNP discovery or transcriptome-wide assessment of selective pressure.In this study,a set of 155 411 expressed sequence tags(ESTs) from the NCBI database were computationally analyzed and 17 225 single nucleotide polymorphisms(SNPs) were predicted,including 9 546 transitions,5 124 transversions and 2 481 indels.Among the 7 298 SNP substitutions located in functionally annotated contigs,58.4%(4 262) are non-synonymous SNPs capable of introducing amino acid mutations.Two hundred and fifty nonsynonymous SNPs in genes associated with economic traits have been identified as candidates for markers in selective breeding.Diversity estimates among the synonymous nucleotides were on average 3.49 times greater than those in non-synonymous,suggesting negative selection.Distribution of non-synonymous to synonymous substitutions(Ka/Ks) ratio ranges from 0 to 4.01,(average 0.42,median 0.26),suggesting that the majority of the affected genes are under purifying selection.Enrichment analysis identified multiple gene ontology categories under positive or negative selection.Categories involved in innate immune response and male gamete generation are rich in positively selected genes,which is similar to reports in Drosophila and primates.This work is the first transcriptome-wide assessment of selective pressure in a Penaeid shrimp species.The functionally annotated SNPs provide a valuable resource of potential molecular markers for selective breeding.展开更多
Every spring a huge number of passerines cross the Sahara Desert and the Mediterranean Sea on their way to their breeding grounds. Stopover sites after such extended barriers where birds can rest, refuel, and find she...Every spring a huge number of passerines cross the Sahara Desert and the Mediterranean Sea on their way to their breeding grounds. Stopover sites after such extended barriers where birds can rest, refuel, and find shelter from adverse weather, are of crucial importance for the outcome of their migration. Stopover habitat selection used by migrating birds depends on landscape context, habitat patch characteristics, as well as on the particular energetic conditions and needs of individ- ual birds, but it is still poorly investigated. We focused on a long-distance migrating passerine, the woodchat shrike, in order to investigate for the first time the species＇ habitat selection at a spring stopover site （island of Antikythira, Greece） after the crossing of the Sahara Desert and Mediterranean Sea. We implemented radio-tracking, color-ringing, and visual behavioral observa- tions to collect data on microhabitat use. Generalized Linear Mixed Models were developed to identify the species＇ most preferred microhabitat during its stopover on this low human disturbed island. We found that high maquis vegetation surrounded by low vegetation was chosen as perches for hunting. Moreover, high maquis vegetation appeared to facilitate hunting attempts toward the ground, the most frequently observed foraging strategy. Finally, we discuss our find- ings in the context of conservation practices for the woodchat shrike and their stopover sites on Mediterranean islands.展开更多
文摘The top 10 major science and technology achievements were chosen by 485 members of the Chinese Academy of Sciences (CAS) or Chinese Academy of Engineering (CAE) from 20 options. The results of the survey were released in early January, 2001 in Beijing. The top 10 scientific achievements for the year 2000 are as follows:
基金supported by the US Appalachian Regional Commission(ARC)under Grant MU-21579-23。
文摘Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.
基金financially supported by National Natural Science Foundation of China(Nos.22371086,22071074,21772055)the National Key Research and Development Program(No.2021YFA0716702)+3 种基金the 111 Project(No.B17019)the China Postdoctoral Science Foundation(No.2020 M672388)Self-determined research funds of CCNU from the colleges’basic research and operation of MOE,National Natural Science Foundation of China(No.22006055)the State Key Laboratory of Environmental Chemistry and Ecotoxicology,RCEES,CAS(No.KF2017-4)。
文摘Selective separation of amino acids and proteins is crucial in various areas of research,including proteomics,protein structure and function studies,protein purification and drug development,and biosensing and biodetection.A nanocomposite film is formed by combining layer-by-layer self-assembled gold nanospheres(Au NPs)driven by cucurbit[7]uril(CB[7])and polymethyl methacrylate(PMMA)film.Due to the host-vip interactions,the selective transmission of l-tryptophan in the nanocomposite film is confirmed by the current-voltage measurements using a picoammeter.Furthermore,by adjusting the particle size of Au NPs to increase channel size,lysozyme containing multiple tryptophan residues can selectively pass through the nanocomposite film,indicating the high versatility and adaptability of the nanocomposite film.This study will provide a new direction for the selective separation of amino acids and proteins.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金the National Nature Science Foundation of China for Excellent Young Scientists Fund(32222058)Fundamental Research Foundation of CAF(CAFYBB2022QB001).
文摘Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via low-temperature coprecipitation,exhibiting excellent performance for the selective hydrogenation of 5-hydroxymethylfurfural(HMF).A linear correlation is first observed between solvent polarity(E_(T)(30))and product selectivity within both polar aprotic and protic solvent classes,suggesting that solvent properties play a vital role in directing reaction pathways.Among these,1,4-dioxane(aprotic)favors the formation of 2,5-bis(hydroxymethyl)furan(BHMF)with 97.5%selectivity,while isopropanol(iPrOH,protic)promotes 2,5-dimethylfuran production with up to 99.5%selectivity.Mechanistic investigations further reveal that beyond polarity,proton-donating ability is critical in facilitating hydrodeoxygenation.iPrOH enables a hydrogen shuttle mechanism where protons assist in hydroxyl group removal,lowering the activation barrier.In contrast,1,4-dioxane,lacking hydrogen bond donors,stabilizes BHMF and hinders further conversion.Density functional theory calculations confirm a lower activation energy in iPrOH(0.60 eV)compared to 1,4-dioxane(1.07 eV).This work offers mechanistic insights and a practical strategy for solvent-mediated control of product selectivity in biomass hydrogenation,highlighting the decisive role of solvent-catalyst-substrate interactions.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金Project (51275179) supported by the National Natural Science Foundation of ChinaProject (2010A090200072) supported by Industry,University and Research Institute Combination of Ministry of Education, Ministry of Science and Technology and Guangdong Province,China+1 种基金Project (2012M511797) supported by China Postdoctoral Science FoundationProject (2012ZB0014) supported by FundamentalResearch Funds for the Central Universities of China
文摘The precise design and fabrication of biomaterial scaffolds is necessary to provide a systematic study for bone tissue engineering. Biomaterial scaffolds should have sufficient stiffness and large porosity. These two goals generally contradict since larger porosity results in lower mechanical properties. To seek the microstructure of maximum stiffness with the constraint of volume fraction by topology optimization method, algorithms and programs were built to obtain 2D and 3D optimized microstructure and then they were transferred to CAD models of STL format. Ti scaffolds with 30% volume fraction were fabricated using a selective laser melting （SLM） technology. The architecture and pore shape in the metallic biomaterial scaffolds were relatively precise reproduced and the minimum mean pore size was 231μm. The accurate fabrication of intricate microstructure has verified that the SLM process is suitable for fabrication of metallic biomaterial scaffolds.
基金Supported by the Qingdao National Laboratory for Marine Science and Technology Program(No.2016ASKJ02)the Central Publicinterest Scientific Institution Basal Research Fund,YSFRI,CAFS(No.20603022018002)the National Natural Science Foundation of China(Nos.41676103,41476098)
文摘Understanding the feeding selectivity on phytoplankton by shellfi sh is currently a big challenge. In order to investigate the feeding behavior of bay scallop ( Argopecten irradians ) on phytoplankton, we compared its compositions of phytopigments in digestive glands with those in the surrounding seawater, and conducted fi ve consecutive investigations between July and November 2016 in a bay scallop culture area along coast of Qinghuangdao City, northwest of the Bohai Sea, China. Phytopigments in four-size fractionated phytoplankton of seawater (micro-(20-200 μm);nano(L)-[10-20 μm];nano(S)-[2.7-10 μm], and pico-[<2.7 μm]) and digestive glands of A . irradians were examined to investigate the selective feeding of A . irradians . Results show that fucoxanthin and peridinin constituted the major part of taxonomically diagnostic carotenoids (TDCs) in the micro- and nano(L)-phytoplankton in seawater. Compared with total phytoplankton biomass of seawater (TPB, sum of the four sizes), a substantial decrease of fucoxanthin proportion to total DCs in digestive glands was observed while that of peridinin, 19′-butanoyloxyfucoxanthin, alloxanthin and 19′-hexanoyloxy-fucoxanthin showed an obvious increase when those pigments were mainly confi ned to micro-sized phytoplankton (20-200 μm). However, zeaxanthin and prasinoxanthin were mainly confi ned to nano(s)- and pico-phytoplankton, of which the proportions in digestive glands were usually lower in TPB. The contribution of lutein to total DCs in digestive glands (with an average of 7.23%) increased compared with TPB of seawater (with an average of 0.63%) during all fi ve sampling times.
基金Supported by National Higb Technology Research and Development Program of China (863 Program) (No. 2006AA10A406)the Key Laboratory of Marine Biology,Instihite of Oceanology,Chinese Academy of Sciences (No. IaF201002)
文摘To quantify the response to selection, heritability and genetic correlations between weight and size ofLitopenaeus vannamei, the body weight （BW）, total length （TL）, body length （BL）, first abdominal segment depth （FASD）, third abdominal segment depth （TASD）, first abdominal segment width （FASW）, and partial carapace length （PCL） of 5-month-old parents and of offspring were measured by calculating seven body measurings of offspring produced by a nested mating design. Seventeen half-sib families and 42 full-sib families ofL. vannamei were produced using artificial fertilization from 2-4 dams by each sire, and measured at around five months post-metamorphosis. The results show that heritabilities among various traits were high： 0.515±0.030 for body weight and 0.394±0.030 for total length. After one generation of selection, the selection response was 10.70% for offspring growth. In the 5th month, the realized heritability for weight was 0.296 for the offspring generation. Genetic correlations between body weight and body size were highly variable. The results indicate that external morphological parameters can be applied during breeder selection for enhancing the growth without sacrificing animals for determining the body size and breed ability; and selective breeding can be improved significantly, simultaneously with increased production.
基金supported by the National Natural Science Foundation of China (No.51575274)the National Defense Basic Scientific Research Program of China (No.JCKY2016605B006)the Key Research and Development Project of Jiangxi Province in China (No.20161ACE50004)
文摘Facility layout selection is a multi-criteria decision making (MCDM) problem,since it has a strategic impact on the efficiency of manufacturing system.In view of the interdependency among selection criteria,analytic network process (ANP) is proposed to analyze the structure of the facility layout selection problem and determine the weights for each criterion.A network structure is constructed that shows all elements and clusters and their interactions.Limit priorities are also calculated which help decision maker evaluate the relative importance among criterion in the alternative selection process.Moreover,a hybrid MCDM approach that employs ANP and technique for order preference by similarity to an ideal solution (TOPSIS)method to rank the optimal facility layout alternatives.Finally,an application of a new aeronautic component assembly workshop facility layout selection is conducted.To further illustrate the advantage of the proposed approach,the difference between ANP-TOPSIS and AHP-TOPSIS methods are compared and discussed.Results have demonstrated the effectiveness and feasibility of the proposed method.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA10A404)the National Natural Science Foundation of China(No.31502161)Financially Supported by Qingdao National Laboratory for Marine Science and Technology(No.2015ASKJ02)
文摘Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value(GEBV).This study is a fi rst attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits.The performance of GS models in L.vannamei was evaluated in a population consisting of 205 individuals,which were genotyped for 6 359 single nucleotide polymorphism(SNP)markers by specifi c length amplifi ed fragment sequencing(SLAF-seq)and phenotyped for body length and body weight.Three GS models(RR-BLUP,Bayes A,and Bayesian LASSO)were used to obtain the GEBV,and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes.The mean reliability of the GEBVs for body length and body weight predicted by the dif ferent models was 0.296 and 0.411,respectively.For each trait,the performances of the three models were very similar to each other with respect to predictability.The regression coeffi cients estimated by the three models were close to one,suggesting near to zero bias for the predictions.Therefore,when GS was applied in a L.vannamei population for the studied scenarios,all three models appeared practicable.Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.
基金Project(51505488)supported by the National Natural Science Foundation of China
文摘Supplier selection can be regarded as a typical multiple attribute decision-making problem. In real-world situation, the values of the alternative attributes and their weights are always being nondeterministic, and as a result of this, the values are considered interval numbers. In addition, the common approach to measure the similarity between alternatives through their distance suffers from some minor shortcomings. To address these problems, this study develops a novel hybrid decision-making method by combining the technique for order preference by similarity to an ideal solution （TOPSIS） with grey relational analysis （GRA） for supplier selection with interval numbers. By introducing the intervals theory, the extensions of Euclidean distance and grey relational grade are defined. And then a new comprehensive closeness coefficient is constituted for supplier alternatives evaluation based on the interval Euclidean distance and the interval grey relational grade, which could indicate the distance-based similarity and the shape-based similarity simultaneously. A mtmerical example is taken to validate the flexibility of the proposed method, and result shows that this method can tackle the uncertainty in real-world supplier selection and also help decision makers to effectively select optimal suppliers.
基金Sponsored by the National Natural Science Foundation of China for Young Scholar(Grant No.61302080)the National Natural Science Foundation of China(Grant No.61271182)the National High Technology Research and Development Program of China(863 Program)(Grant No.2012AA01A508)
文摘This paper deals with network selection problem for users in heterogeneous network environment. The main context is to improve the TOPSIS( Technique for Order Preference by Similarity to Ideal Solution) network scheme by combining the network properties and the users' requirement accurately and decrease ping-pong effect. The method of entropy and FAHP( Fuzzy Analytic Hierarchy Process) are used to calculate weight value and the sojourn time calculation is used to avoid ping-pang effect. The simulation results show that the improved scheme enhances the more accuracy of network selection than the existing methods and reduces the number of ping-pang effect.
文摘Objective:To explore the core acupoints and combination rules of acupuncture therapy for postoperative urinary retention after hemorrhoidectomy,and,further to analyze the characteristics of the prescription of acupuncture therapy for postoperative urinary retention after hemorrhoidectomy.Methods:Relevant clinical studies in seven databases were searched systematically from database inception to 1 st October 2020.Based on data mining techniques,the core acupoints,characteristics and combination rules of acupuncture therapy for postoperative urinary retention after hemorrhoidectomy were analyzed comprehensively.Statistics collected and analysis of acupoint selection included the frequency of usage of acupoints,the meridian of acupoints,the location of acupoints and the combination of acupoints.Cluster and association analysis were performed utilizing Excel 13.0 and SPSS 26.0.Results:In the 36 studies included,the most frequently adopted acupoint was Sānyīnjiāo(三阴交 SP6);the most frequently adopted meridian was the foot-taiyin spleen meridian;the most frequently involved location was the lower limbs;and the most frequently adopted combination of acupoints was SP6 and Zhōngjí(中极CV 3).Cluster analysis showed that the acupoints could be divided into three categories:the first group comprised SP6 alone;the second group included CV3,Guānyuán(关元CV4),Yīnlíngquán(阴陵泉SP9) and Zúsānlǐ(足三里ST36);and the third group included Pángguāngshū(膀胱俞BL28),Chéngshān(承山BL57),Tàichōng(太冲LR3),Zhìbiān(秩边 BL54),Tàixī(太溪KI6),Hégǔ(合谷 LI4),Xuèhǎi(血海SP10),Qìhǎi(气海CV6) and Shǔidào(水道ST28).Conclusion:It was found that acupuncture therapy for postoperative urinary retention after hemorrhoidectomy mainly follows the four characteristic rules:the selection of acupoints following the meridian,selection of acupoints at local points,selection of acupoints at distal points and selection of acupoints according to special symptoms.
基金Supported by the Key Program of National Natural Science Foundation of China (No.30730071)the National High Technology R&D Program of China(863 Program)(No.2012AA10A404)the Agricultural Science and Technology Achievements Transformation Funds Project(No.2010GB24910700)
文摘Pacific white shrimp has become a major aquaculture and fishery species worldwide.Although a large scale EST resource has been publicly available since 2008,the data have not yet been widely used for SNP discovery or transcriptome-wide assessment of selective pressure.In this study,a set of 155 411 expressed sequence tags(ESTs) from the NCBI database were computationally analyzed and 17 225 single nucleotide polymorphisms(SNPs) were predicted,including 9 546 transitions,5 124 transversions and 2 481 indels.Among the 7 298 SNP substitutions located in functionally annotated contigs,58.4%(4 262) are non-synonymous SNPs capable of introducing amino acid mutations.Two hundred and fifty nonsynonymous SNPs in genes associated with economic traits have been identified as candidates for markers in selective breeding.Diversity estimates among the synonymous nucleotides were on average 3.49 times greater than those in non-synonymous,suggesting negative selection.Distribution of non-synonymous to synonymous substitutions(Ka/Ks) ratio ranges from 0 to 4.01,(average 0.42,median 0.26),suggesting that the majority of the affected genes are under purifying selection.Enrichment analysis identified multiple gene ontology categories under positive or negative selection.Categories involved in innate immune response and male gamete generation are rich in positively selected genes,which is similar to reports in Drosophila and primates.This work is the first transcriptome-wide assessment of selective pressure in a Penaeid shrimp species.The functionally annotated SNPs provide a valuable resource of potential molecular markers for selective breeding.
文摘Every spring a huge number of passerines cross the Sahara Desert and the Mediterranean Sea on their way to their breeding grounds. Stopover sites after such extended barriers where birds can rest, refuel, and find shelter from adverse weather, are of crucial importance for the outcome of their migration. Stopover habitat selection used by migrating birds depends on landscape context, habitat patch characteristics, as well as on the particular energetic conditions and needs of individ- ual birds, but it is still poorly investigated. We focused on a long-distance migrating passerine, the woodchat shrike, in order to investigate for the first time the species＇ habitat selection at a spring stopover site （island of Antikythira, Greece） after the crossing of the Sahara Desert and Mediterranean Sea. We implemented radio-tracking, color-ringing, and visual behavioral observa- tions to collect data on microhabitat use. Generalized Linear Mixed Models were developed to identify the species＇ most preferred microhabitat during its stopover on this low human disturbed island. We found that high maquis vegetation surrounded by low vegetation was chosen as perches for hunting. Moreover, high maquis vegetation appeared to facilitate hunting attempts toward the ground, the most frequently observed foraging strategy. Finally, we discuss our find- ings in the context of conservation practices for the woodchat shrike and their stopover sites on Mediterranean islands.
