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
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
Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve thro...Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.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
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
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
Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from nume...Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.展开更多
False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail...False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.展开更多
The theory of ecological speciation suggests that assortative mating evolves most easily when mating preferences aredirectly linked to ecological traits that are subject to divergent selection. Sensory adaptation can ...The theory of ecological speciation suggests that assortative mating evolves most easily when mating preferences aredirectly linked to ecological traits that are subject to divergent selection. Sensory adaptation can play a major role in this process,because selective mating is often mediated by sexual signals: bright colours, complex song, pheromone blends and so on. Whendivergent sensory adaptation affects the perception of such signals, mating patterns may change as an immediate consequence.Alternatively, mating preferences can diverge as a result of indirect effects: assortative mating may be promoted by selectionagainst intermediate phenotypes that are maladapted to their (sensory) environment. For Lake Victoria cichlids, the visual environmentconstitutes an important selective force that is heterogeneous across geographical and water depth gradients. We investigatethe direct and indirect effects of this heterogeneity on the evolution of female preferences for alternative male nuptial colours(red and blue) in the genus Pundamilia. Here, we review the current evidence for divergent sensory drive in this system, extractgeneral principles, and discuss future perspectives [Current Zoology 56 (3): 285-299, 2010].展开更多
Global temperatures are increasing rapidly affecting species globally.Understanding if and how different species can adapt fast enough to keep up with increasing temperatures is of vital importance.One mechanism that ...Global temperatures are increasing rapidly affecting species globally.Understanding if and how different species can adapt fast enough to keep up with increasing temperatures is of vital importance.One mechanism that can accelerate adaptation and promote evolutionary rescue is sexual selection.Two different mechanisms by which sexual selection can facilitate adaptation are pre-and postcopulatory sexual selection.However,the relative effects of these different forms of sexual selection in promoting adaptation are unknown.Here,we present the results from an experimental study in which we exposed fruit flies Drosophila melanogaster to either no mate choice or 1 of 2 different sexual selection regimes(pre-and postcopulatory sexual selection)for 6 generations,under different thermal regimes.Populations showed evidence of thermal adaptation under preco-pulatory sexual selection,but this effect was not detected in the postcopulatory sexual selection and the no choice mating regime.We further demonstrate that sexual dimorphism decreased when flies evolved under increasing temperatures,consistent with recent theory predicting more sexually concordant selection under environmental stress.Our results suggest an important role for precopulatory sexual selection in promoting thermal adaptation and evolutionary rescue.展开更多
Background:Floods and other extreme events have disastrous effects on wetland breeding birds.However,such events and their consequences are difficult to study due to their rarity and unpredictable occurrence.Methods:H...Background:Floods and other extreme events have disastrous effects on wetland breeding birds.However,such events and their consequences are difficult to study due to their rarity and unpredictable occurrence.Methods:Here we compared nest-sites chosen by Reed Parrotbills(Paradoxornis heudei) during June-August 2016 in Yongnianwa Wetlands,Hebei Province,China,before and after an extreme flooding event.Results:Twenty-three nests were identified before and 13 new nests after the flood.There was no significant difference in most nest-site characteristics,such as distance from the road,height of the reeds in which nests were built,or nest volume before or after the flood.However,nests after the flood were located significantly higher in the vegetation compared to before the flood(mean ± SE:1.17 ± 0.13 m vs.0.75 ± 0.26 m,p < 0.01).However,predation rate also increased significantly after the flood(67% vs.25%,p = 0.030).Conclusions:Our results suggested that Reed Parrotbills demonstrated behavioral plasticity in their nest-site selection.Thus,they appeared to increase the height of their nests in response to the drastically changing water levels in reed wetlands,to reduce the likelihood that their nests would be submerged again by flooding.However,predation rate also increased significantly after the flood,suggesting that the change in nest height to combat the threat of flooding made the nests more susceptible to other threats,such as predation.Animals' response to rare climatic events,such as flooding,may produce ecological traps if they make the animals more susceptible to other kinds of threats they are more likely to continue to encounter.展开更多
The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are c...The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.展开更多
Sparse identification of nonlinear dynamics(SINDy)has made significant progress in data-driven dynamics modeling.However,determining appropriate hyperparameters and addressing the time-consuming symbolic regression pr...Sparse identification of nonlinear dynamics(SINDy)has made significant progress in data-driven dynamics modeling.However,determining appropriate hyperparameters and addressing the time-consuming symbolic regression process remain substantial challenges.This study proposes the adaptive backward stepwise selection of fast SINDy(ABSS-FSINDy),which integrates statistical learning-based estimation and technical advancements to significantly reduce simulation time.This approach not only provides insights into the conditions under which SINDy performs optimally but also highlights potential failure points,particularly in the context of backward stepwise selection(BSS).By decoding predefined features into textual expressions,ABSS-FSINDy significantly reduces the simulation time compared with conventional symbolic regression methods.We validate the proposed method through a series of numerical experiments involving both planar/spatial dynamics and high-dimensional chaotic systems,including Lotka-Volterra,hyperchaotic Rossler,coupled Lorenz,and Lorenz 96 benchmark systems.The experimental results demonstrate that ABSS-FSINDy autonomously determines optimal hyperparameters within the SINDy framework,overcoming the curse of dimensionality in high-dimensional simulations.This improvement is substantial across both lowand high-dimensional systems,yielding efficiency gains of one to three orders of magnitude.For instance,in a 20D dynamical system,the simulation time is reduced from 107.63 s to just 0.093 s,resulting in a 3-order-of-magnitude improvement in simulation efficiency.This advancement broadens the applicability of SINDy for the identification and reconstruction of high-dimensional dynamical systems.展开更多
This study focuses on the characters of public perceptions on climate and cryosphere change,which are based on a questionnaire survey in the(U|¨)r(u|¨)mqi River Basin.In comparison with scientific observatio...This study focuses on the characters of public perceptions on climate and cryosphere change,which are based on a questionnaire survey in the(U|¨)r(u|¨)mqi River Basin.In comparison with scientific observation results of climate and cryosphere change,this paper analyzes the possible impact of the change on water resources and agriculture production in the area.Perceptions of most respondents on climate and cryosphere changes confirm the main objective facts.For the selection of adaptation measures addressing the shortage of water resource,the results are as follows:most people preferred to choose the measures like "policy change" and "basic facility construction" which are mostly implemented by the government and the policy-making department;some people showed more preference to the measures of avoiding unfavorable natural environment,such as finding job in or migrating to other places.The urgency of personal participation in the adaptation measures is still inadequate.Some adaptation measures should be implemented in line with local conditions and require the organic combination of "resource-development" with "water-saving".展开更多
Ulvophytes are attractive model systems for understanding the evolution of growth,development,and environmental stress responses.They are untapped resources for food,fuel,and high-value compounds.The rapid and abundan...Ulvophytes are attractive model systems for understanding the evolution of growth,development,and environmental stress responses.They are untapped resources for food,fuel,and high-value compounds.The rapid and abundant growth of Ulva species makes them key contributors to coastal biogeochemical cycles,which can cause significant environmental problems in the form of green tides and biofouling.Until now,the Ulva mutabilis genome is the only Ulva genome to have been sequenced.To obtain further insights into the evolutionary forces driving divergence in Ulva species,we analyzed 3905 single copy ortholog family from U.mutabilis,Chlamydomonas reinhardtii and Volvox carteri to identify genes under positive selection(GUPS)in U.mutabilis.We detected 63 orthologs in U.mutabilis that were considered to be under positive selection.Functional analyses revealed that several adaptive modifications in photosynthesis,amino acid and protein synthesis,signal transduction and stress-related processes might explain why this alga has evolved the ability to grow very rapidly and cope with the variable coastal ecosystem environments.展开更多
The Mariana Trench,the deepest trench on the earth,is ideal for deep-sea adaptation research due to its unique characters,such as the highest hydrostatic pressure on the Earth,constant ice-cold temperature,and eternal...The Mariana Trench,the deepest trench on the earth,is ideal for deep-sea adaptation research due to its unique characters,such as the highest hydrostatic pressure on the Earth,constant ice-cold temperature,and eternal darkness.In this study,tissues of a the hadal holothurian(Paelopatides sp.)were fi xed with RNA later in situ at~6501-m depth in the Mariana Trench,which,to our knowledge,is the deepest in-situ fi xed animal sample.A high-quality transcript was obtained by de-novo transcriptome assembly.A maximum likelihood tree was constructed based on the single copy orthologs across nine species with their available omics data.To investigate deep-sea adaptation,113 positively selected genes(PSGs)were identifi ed in Paelopatides sp.Some PSGs such as microphthalmia-associated transcription factor(MITF)may contribute to the distinct phenotype of Paelopatides sp.,including its translucent white body and degenerated ossicles.At least eight PSGs(transcription factor 7-like 2[TCF7L2],ETS-related transcription factor Elf-2-like[ELF2],PERQ amino acid-rich with GYF domain-containing protein[GIGYF],cytochrome c oxidase subunit 7a,[COX7A],type I thyroxine 5′-deiodinase[DIO1],translation factor GUF1[GUF1],SWI/SNF related-matrix-associated actin-dependent regulator of chromatin subfamily C and subfamily E,member 1[SMARCC]and[SMARCE1])might be related to cold adaptation.In addition,at least nine PSGs(cell cycle checkpoint control protein[RAD9A],replication factor A3[RPA3],DNA-directed RNA polymerases I/II/III subunit RPABC1[POLR2E],putative TAR DNA-binding protein 43 isoform X2[TARDBP],ribonucleoside-diphosphate reductase subunit M1[RRM1],putative serine/threonine-protein kinase[SMG1],transcriptional regulator[ATRX],alkylated DNA repair protein alkB homolog 6[ALKBH6],and PLAC8 motif-containing protein[PLAC8])may facilitate the repair of DNA damage induced by the high hydrostatic pressure,coldness,and high concentration of cadmium in the upper Mariana Trench.展开更多
Cattle are central to the lives and diverse cultures of African people.It has played a crucial role in providing valuable protein for billions of households and sources of income and employment for producers and other...Cattle are central to the lives and diverse cultures of African people.It has played a crucial role in providing valuable protein for billions of households and sources of income and employment for producers and other actors in the livestock value chains.The long-term natural selection of African cattle typically signals signatures in the genome,contributes to high genetic differentiations across breeds.This has enabled them to develop unique adaptive traits to cope with inadequate feed supply,high temperatures,high internal and external parasites,and diseases.However,these unique cattle genetic resources are threatened by indiscriminate cross-breeding,breed replacements with exotic cosmopolitan breeds,and climate change pressures.Although there are no functional genomics studies,recent advancements in genotyping and sequencing technologies have identified and annotated limited functional genes and causal variants associated with unique adaptive and economical traits of African cattle populations.These genome-wide variants serve as candidates for breed improvement and support conservation efforts for endangered cattle breeds against future climate changes.Therefore,this review plans to collate comprehensive information on the identified selection footprints to support genomic studies in African cattle to confirm the validity of the results and provide a framework for further genetic association and QTL fine mapping studies.展开更多
The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,a...The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.展开更多
Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally c...Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production(carcass,wool and milk yield).Therefore,eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation(EAS),are generally considered as multipurpose breeds(milk,meat and wool),not specialised for a particular type of production,but known for their robustness and resistance to certain environmental conditions.Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures,decipher their biological and productive functionality,and provide a"genomic"characterization of EAS adaptation and determine its production type.Results We identified positive selection signatures in EAS using several methods based on reduced local variation,linkage disequilibrium and site frequency spectrum(eROHi,iHS,nSL and CLR).Our analyses identified numerous genomic regions and genes(e.g.,desmosomal cadherin and desmoglein gene families)associated with environmental adaptation and economically important traits.Most candidate genes were related to meat/production and health/immune response traits,while some of the candidate genes discovered were important for domestication and evolutionary processes(e.g.,HOXa gene family and FSIP2).These results were also confirmed by GO and QTL enrichment analysis.Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type,ultimately providing a new opportunity for future breeding programmes.At the same time,the numerous genes identified will improve our understanding of ruminant(sheep)robustness and resistance in the harsh and specific Mediterranean environment.展开更多
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.
基金Supported by Chongqing Health Commission and Chongqing Science and Technology Bureau,No.2023MSXM182。
文摘Rheumatoid arthritis(RA)patients face significant psychological challenges alongside physical symptoms,necessitating a comprehensive understanding of how psychological vulnerability and adaptation patterns evolve throughout the disease course.This review examined 95 studies(2000-2025)from PubMed,Web of Science,and CNKI databases including longitudinal cohorts,randomized controlled trials,and mixed-methods research,to characterize the complex interplay between biological,psychological,and social factors affecting RA patients’mental health.Findings revealed three distinct vulnerability trajectories(45%persistently low,30%fluctuating improvement,25%persistently high)and four adaptation stages,with critical intervention periods occurring 3-6 months postdiagnosis and during disease flares.Multiple factors significantly influence psychological outcomes,including gender(females showing 1.8-fold increased risk),age(younger patients experiencing 42%higher vulnerability),pain intensity,inflammatory markers,and neuroendocrine dysregulation(48%showing cortisol rhythm disruption).Early psychological intervention(within 3 months of diagnosis)demonstrated robust benefits,reducing depression incidence by 42%with effects persisting 24-36 months,while different modalities showed complementary advantages:Cognitive behavioral therapy for depression(Cohen’s d=0.68),mindfulness for pain acceptance(38%improvement),and peer support for meaning reconstruction(25.6%increase).These findings underscore the importance of integrating routine psychological assessment into standard RA care,developing stage-appropriate interventions,and advancing research toward personalized biopsychosocial approaches that address the dynamic psychological dimensions of the disease.
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%.
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.
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supported by Fundamental Research Program of Shanxi Province(Nos.202203021211088,202403021212254,202403021221109)Graduate Research Innovation Project in Shanxi Province(No.2024KY616).
文摘Data collected in fields such as cybersecurity and biomedicine often encounter high dimensionality and class imbalance.To address the problem of low classification accuracy for minority class samples arising from numerous irrelevant and redundant features in high-dimensional imbalanced data,we proposed a novel feature selection method named AMF-SGSK based on adaptive multi-filter and subspace-based gaining sharing knowledge.Firstly,the balanced dataset was obtained by random under-sampling.Secondly,combining the feature importance score with the AUC score for each filter method,we proposed a concept called feature hardness to judge the importance of feature,which could adaptively select the essential features.Finally,the optimal feature subset was obtained by gaining sharing knowledge in multiple subspaces.This approach effectively achieved dimensionality reduction for high-dimensional imbalanced data.The experiment results on 30 benchmark imbalanced datasets showed that AMF-SGSK performed better than other eight commonly used algorithms including BGWO and IG-SSO in terms of F1-score,AUC,and G-mean.The mean values of F1-score,AUC,and Gmean for AMF-SGSK are 0.950,0.967,and 0.965,respectively,achieving the highest among all algorithms.And the mean value of Gmean is higher than those of IG-PSO,ReliefF-GWO,and BGOA by 3.72%,11.12%,and 20.06%,respectively.Furthermore,the selected feature ratio is below 0.01 across the selected ten datasets,further demonstrating the proposed method’s overall superiority over competing approaches.AMF-SGSK could adaptively remove irrelevant and redundant features and effectively improve the classification accuracy of high-dimensional imbalanced data,providing scientific and technological references for practical applications.
基金supported by National Key Research and Development Plan of China(No.2022YFB3103304).
文摘False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.
基金funded by the Swiss National Science Foundation (SNSF)the Netherlands Foundation for Scientific Research (NWO-ALW and NWO-WOTRO)
文摘The theory of ecological speciation suggests that assortative mating evolves most easily when mating preferences aredirectly linked to ecological traits that are subject to divergent selection. Sensory adaptation can play a major role in this process,because selective mating is often mediated by sexual signals: bright colours, complex song, pheromone blends and so on. Whendivergent sensory adaptation affects the perception of such signals, mating patterns may change as an immediate consequence.Alternatively, mating preferences can diverge as a result of indirect effects: assortative mating may be promoted by selectionagainst intermediate phenotypes that are maladapted to their (sensory) environment. For Lake Victoria cichlids, the visual environmentconstitutes an important selective force that is heterogeneous across geographical and water depth gradients. We investigatethe direct and indirect effects of this heterogeneity on the evolution of female preferences for alternative male nuptial colours(red and blue) in the genus Pundamilia. Here, we review the current evidence for divergent sensory drive in this system, extractgeneral principles, and discuss future perspectives [Current Zoology 56 (3): 285-299, 2010].
基金E.S.was financially supported by the Erasmus ProgrammeE.I.S.was financially supported by research grants from Stina Werners Fond,Gyllenstiernska Krapperupsstiftelsen,Olle Engqvist Byggmastare Foundation and the Swedish Research Council(VR,grant no.2016-03356).
文摘Global temperatures are increasing rapidly affecting species globally.Understanding if and how different species can adapt fast enough to keep up with increasing temperatures is of vital importance.One mechanism that can accelerate adaptation and promote evolutionary rescue is sexual selection.Two different mechanisms by which sexual selection can facilitate adaptation are pre-and postcopulatory sexual selection.However,the relative effects of these different forms of sexual selection in promoting adaptation are unknown.Here,we present the results from an experimental study in which we exposed fruit flies Drosophila melanogaster to either no mate choice or 1 of 2 different sexual selection regimes(pre-and postcopulatory sexual selection)for 6 generations,under different thermal regimes.Populations showed evidence of thermal adaptation under preco-pulatory sexual selection,but this effect was not detected in the postcopulatory sexual selection and the no choice mating regime.We further demonstrate that sexual dimorphism decreased when flies evolved under increasing temperatures,consistent with recent theory predicting more sexually concordant selection under environmental stress.Our results suggest an important role for precopulatory sexual selection in promoting thermal adaptation and evolutionary rescue.
基金supported by the National Natural Science Foundation of China(Nos.31672303 to CY,31472013 and 31772453 to WL)
文摘Background:Floods and other extreme events have disastrous effects on wetland breeding birds.However,such events and their consequences are difficult to study due to their rarity and unpredictable occurrence.Methods:Here we compared nest-sites chosen by Reed Parrotbills(Paradoxornis heudei) during June-August 2016 in Yongnianwa Wetlands,Hebei Province,China,before and after an extreme flooding event.Results:Twenty-three nests were identified before and 13 new nests after the flood.There was no significant difference in most nest-site characteristics,such as distance from the road,height of the reeds in which nests were built,or nest volume before or after the flood.However,nests after the flood were located significantly higher in the vegetation compared to before the flood(mean ± SE:1.17 ± 0.13 m vs.0.75 ± 0.26 m,p < 0.01).However,predation rate also increased significantly after the flood(67% vs.25%,p = 0.030).Conclusions:Our results suggested that Reed Parrotbills demonstrated behavioral plasticity in their nest-site selection.Thus,they appeared to increase the height of their nests in response to the drastically changing water levels in reed wetlands,to reduce the likelihood that their nests would be submerged again by flooding.However,predation rate also increased significantly after the flood,suggesting that the change in nest height to combat the threat of flooding made the nests more susceptible to other threats,such as predation.Animals' response to rare climatic events,such as flooding,may produce ecological traps if they make the animals more susceptible to other kinds of threats they are more likely to continue to encounter.
文摘The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets,particularly in industrial contexts where efficient data handling and process innovation are critical.Feature selection,an essential step in data-driven process innovation,aims to identify the most relevant features to improve model interpretability,reduce complexity,and enhance predictive accuracy.To address the limitations of existing feature selection methods,this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization(APO)algorithm.Specifically,we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete,binary feature selection problems.Moreover,we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox.This parallel design significantly improves runtime efficiency and scalability,particularly for high-dimensional feature spaces.Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features.These findings highlight the robustness and effectiveness of APO,validating its potential for advancing process innovation,economic productivity and smart city application in real-world machine learning scenarios.
基金Project supported by the National Natural Science Foundation of China(Nos.12172291,12472357,and 12232015)the Shaanxi Province Outstanding Youth Fund Project(No.2024JC-JCQN-05)the 111 Project(No.BP0719007)。
文摘Sparse identification of nonlinear dynamics(SINDy)has made significant progress in data-driven dynamics modeling.However,determining appropriate hyperparameters and addressing the time-consuming symbolic regression process remain substantial challenges.This study proposes the adaptive backward stepwise selection of fast SINDy(ABSS-FSINDy),which integrates statistical learning-based estimation and technical advancements to significantly reduce simulation time.This approach not only provides insights into the conditions under which SINDy performs optimally but also highlights potential failure points,particularly in the context of backward stepwise selection(BSS).By decoding predefined features into textual expressions,ABSS-FSINDy significantly reduces the simulation time compared with conventional symbolic regression methods.We validate the proposed method through a series of numerical experiments involving both planar/spatial dynamics and high-dimensional chaotic systems,including Lotka-Volterra,hyperchaotic Rossler,coupled Lorenz,and Lorenz 96 benchmark systems.The experimental results demonstrate that ABSS-FSINDy autonomously determines optimal hyperparameters within the SINDy framework,overcoming the curse of dimensionality in high-dimensional simulations.This improvement is substantial across both lowand high-dimensional systems,yielding efficiency gains of one to three orders of magnitude.For instance,in a 20D dynamical system,the simulation time is reduced from 107.63 s to just 0.093 s,resulting in a 3-order-of-magnitude improvement in simulation efficiency.This advancement broadens the applicability of SINDy for the identification and reconstruction of high-dimensional dynamical systems.
基金funded by the "973" National Social Development Research Program "Dynamic process of cryosphere,the mechanism of cryospheric impacts on climate, hydrology and ecologyadaptation measures" (Grant No.2007CB411507)Science of state key laboratory open fund of "The research of typical basin of cryosphere change and its threshold level,adaptation and strategy"(SKLCS08-04)
文摘This study focuses on the characters of public perceptions on climate and cryosphere change,which are based on a questionnaire survey in the(U|¨)r(u|¨)mqi River Basin.In comparison with scientific observation results of climate and cryosphere change,this paper analyzes the possible impact of the change on water resources and agriculture production in the area.Perceptions of most respondents on climate and cryosphere changes confirm the main objective facts.For the selection of adaptation measures addressing the shortage of water resource,the results are as follows:most people preferred to choose the measures like "policy change" and "basic facility construction" which are mostly implemented by the government and the policy-making department;some people showed more preference to the measures of avoiding unfavorable natural environment,such as finding job in or migrating to other places.The urgency of personal participation in the adaptation measures is still inadequate.Some adaptation measures should be implemented in line with local conditions and require the organic combination of "resource-development" with "water-saving".
基金Foundation item:The National Key Research and Development Program of China under contract No.2016YFC1402102the Central Public-interest Scientific Institution Basal Research Fund,CAFS under contract Nos 2020TD19 and 2020TD27+3 种基金the Major Scientific and Technological Innovation Project of Shandong Provincial Key Research and Development Program under contract No.2019JZZY020706the National Natural Science Foundation of China under contract No.31770393the Earmarked Fund for China Agriculture Research System under contract No.CARS-50the Taishan Scholars Funding of Shandong Province.
文摘Ulvophytes are attractive model systems for understanding the evolution of growth,development,and environmental stress responses.They are untapped resources for food,fuel,and high-value compounds.The rapid and abundant growth of Ulva species makes them key contributors to coastal biogeochemical cycles,which can cause significant environmental problems in the form of green tides and biofouling.Until now,the Ulva mutabilis genome is the only Ulva genome to have been sequenced.To obtain further insights into the evolutionary forces driving divergence in Ulva species,we analyzed 3905 single copy ortholog family from U.mutabilis,Chlamydomonas reinhardtii and Volvox carteri to identify genes under positive selection(GUPS)in U.mutabilis.We detected 63 orthologs in U.mutabilis that were considered to be under positive selection.Functional analyses revealed that several adaptive modifications in photosynthesis,amino acid and protein synthesis,signal transduction and stress-related processes might explain why this alga has evolved the ability to grow very rapidly and cope with the variable coastal ecosystem environments.
基金Supported by the National Key Research and Development Program of China(Nos.2018YFC0309804,2016YFC0304905)the Major Scientifi c and Technological Projects of Hainan Province(No.ZDKJ2019011)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA22040502)。
文摘The Mariana Trench,the deepest trench on the earth,is ideal for deep-sea adaptation research due to its unique characters,such as the highest hydrostatic pressure on the Earth,constant ice-cold temperature,and eternal darkness.In this study,tissues of a the hadal holothurian(Paelopatides sp.)were fi xed with RNA later in situ at~6501-m depth in the Mariana Trench,which,to our knowledge,is the deepest in-situ fi xed animal sample.A high-quality transcript was obtained by de-novo transcriptome assembly.A maximum likelihood tree was constructed based on the single copy orthologs across nine species with their available omics data.To investigate deep-sea adaptation,113 positively selected genes(PSGs)were identifi ed in Paelopatides sp.Some PSGs such as microphthalmia-associated transcription factor(MITF)may contribute to the distinct phenotype of Paelopatides sp.,including its translucent white body and degenerated ossicles.At least eight PSGs(transcription factor 7-like 2[TCF7L2],ETS-related transcription factor Elf-2-like[ELF2],PERQ amino acid-rich with GYF domain-containing protein[GIGYF],cytochrome c oxidase subunit 7a,[COX7A],type I thyroxine 5′-deiodinase[DIO1],translation factor GUF1[GUF1],SWI/SNF related-matrix-associated actin-dependent regulator of chromatin subfamily C and subfamily E,member 1[SMARCC]and[SMARCE1])might be related to cold adaptation.In addition,at least nine PSGs(cell cycle checkpoint control protein[RAD9A],replication factor A3[RPA3],DNA-directed RNA polymerases I/II/III subunit RPABC1[POLR2E],putative TAR DNA-binding protein 43 isoform X2[TARDBP],ribonucleoside-diphosphate reductase subunit M1[RRM1],putative serine/threonine-protein kinase[SMG1],transcriptional regulator[ATRX],alkylated DNA repair protein alkB homolog 6[ALKBH6],and PLAC8 motif-containing protein[PLAC8])may facilitate the repair of DNA damage induced by the high hydrostatic pressure,coldness,and high concentration of cadmium in the upper Mariana Trench.
基金The authors are grateful for the financial support by the Agricultural Science and Technology Innovation Program,China(CAAS-ASTIP-2014-LIHPS-01)the China Agriculture Research System of MOF and MARA(CARS-37)+1 种基金the Foundation for Innovation,Groups of Basic Research in Gansu Province,China(20JR5RA580)the Key Research and Development Programs of Science and Technology of Gansu Province,China(20YF8WA031)are duly acknowledged.
文摘Cattle are central to the lives and diverse cultures of African people.It has played a crucial role in providing valuable protein for billions of households and sources of income and employment for producers and other actors in the livestock value chains.The long-term natural selection of African cattle typically signals signatures in the genome,contributes to high genetic differentiations across breeds.This has enabled them to develop unique adaptive traits to cope with inadequate feed supply,high temperatures,high internal and external parasites,and diseases.However,these unique cattle genetic resources are threatened by indiscriminate cross-breeding,breed replacements with exotic cosmopolitan breeds,and climate change pressures.Although there are no functional genomics studies,recent advancements in genotyping and sequencing technologies have identified and annotated limited functional genes and causal variants associated with unique adaptive and economical traits of African cattle populations.These genome-wide variants serve as candidates for breed improvement and support conservation efforts for endangered cattle breeds against future climate changes.Therefore,this review plans to collate comprehensive information on the identified selection footprints to support genomic studies in African cattle to confirm the validity of the results and provide a framework for further genetic association and QTL fine mapping studies.
基金funded by the University of Jeddah,Jeddah,Saudi Arabia,under Grant No.(UJ-23-DR-26)。
文摘The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.
基金supported by Croatian Science Foundation project IP-2018–01-8708-Application of NGS methods in the assessment of genomic variability in ruminants–“ANAGRAMS”the EU Operational Program Competitiveness and Cohesion 2014–2020 project KK.01.1.1.04.0058—Potential of microencapsulation in cheese productionthe project No.QK1919156 of the Ministry of Agriculture,Czech Republic.
文摘Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production(carcass,wool and milk yield).Therefore,eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation(EAS),are generally considered as multipurpose breeds(milk,meat and wool),not specialised for a particular type of production,but known for their robustness and resistance to certain environmental conditions.Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures,decipher their biological and productive functionality,and provide a"genomic"characterization of EAS adaptation and determine its production type.Results We identified positive selection signatures in EAS using several methods based on reduced local variation,linkage disequilibrium and site frequency spectrum(eROHi,iHS,nSL and CLR).Our analyses identified numerous genomic regions and genes(e.g.,desmosomal cadherin and desmoglein gene families)associated with environmental adaptation and economically important traits.Most candidate genes were related to meat/production and health/immune response traits,while some of the candidate genes discovered were important for domestication and evolutionary processes(e.g.,HOXa gene family and FSIP2).These results were also confirmed by GO and QTL enrichment analysis.Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type,ultimately providing a new opportunity for future breeding programmes.At the same time,the numerous genes identified will improve our understanding of ruminant(sheep)robustness and resistance in the harsh and specific Mediterranean environment.
