BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To inv...BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To investigate the ERP characteristics of conditional reasoning in MD patients and explore the neural mechanism of cognitive processing.METHODS Thirty-four patients with MD and 34 healthy controls(HCs)completed ERP measurements while performing the Wason selection task(WST).The clusterbased permutation test in FieldTrip was used to compare the differences in the mean amplitudes between the patients with MD and HCs on the ERP components under different experimental conditions.Behavioral data[accuracy(ACC)and reaction times(RTs)],the ERP P100 and late positive potentials(LPPs)were analyzed.RESULTS Although the mean ACC was greater and the mean of RTs was shorter in HCs than in MD patients,the differences were not statistically significant.However,across both groups,the ACC in the precautionary WST was greater than that in the other tasks,and the RTs in the abstract task were greater than those in the other tasks.Importantly,compared with that of HCs,the P100 of the left centroparietal sites was significantly increased,and the early LPP was attenuated at parietal sites and increased at left frontocentral sites;the medium LPP and late LPP were increased at the left frontocentral sites.CONCLUSION Patients with MD have conditional reasoning dysfunction and exhibit abnormal ERP characteristics evoked by the WST,which suggests neural correlates of abnormalities in conditional reasoning function in MD patients.展开更多
Thanks to its abundant reserves,relatively high energy density,and low reduction potential,potassium ion batteries(PIBs)have a high potential for large-scale energy storage applications.Due to the large radius of pota...Thanks to its abundant reserves,relatively high energy density,and low reduction potential,potassium ion batteries(PIBs)have a high potential for large-scale energy storage applications.Due to the large radius of potassium ions,most conventional anode materials undergo severe volume expansion,making it difficult to achieve stable and reversible energy storage.Therefore,developing high-performance anode materials is one of the critical factors in developing PIBs.In this sense,antimony(Sb)-based anode materials with high theoretical capacity and safe reaction potentials have a broad potential for application in PIBs.However,overcoming the rapid capacity decay induced by the large radius of potassium ions is still an issue that needs to be focused on.This paper reviews the latest research on different types of Sb-based anode materials and provides an in-depth analysis of their optimization strategies.We focus on material selection,structural design,and storage mechanisms to develop a detailed description of the material.In addition,the current challenges still faced by Sb-based anode materials are summarized,and some further optimization strategies have been added.We hope to provide some insights for researchers developing Sb-based anode materials for next-generation advanced PIBs.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
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
Surgical resection of colorectal liver metastases(CRLM) has a well-documented improvement in survival. To benefit from this intervention, proper selection of patients who would be adequate surgical candidates becomes ...Surgical resection of colorectal liver metastases(CRLM) has a well-documented improvement in survival. To benefit from this intervention, proper selection of patients who would be adequate surgical candidates becomes vital. A combination of imaging techniques may be utilized in the detection of the lesions. The criteria for resection are continuously evolving; currently, the requirements that need be met to undergo resection of CRLM are: the anticipation of attaining a negative margin(R0 resection), whilst maintaining an adequate functioning future liver remnant. The timing of hepatectomy in regards to resection of the primary remains controversial; before, after, or simultaneously. This depends mainly on the tumor burden and symptoms from the primary tumor. The role of chemotherapy differs according to the resectability of the liver lesion(s); no evidence of improved survival was shown in patients with resectable disease who received preoperative chemotherapy. Presence of extrahepatic disease in itself is no longer considered a reason to preclude patients from resection of their CRLM, providing limited extra-hepatic disease, although this currently is an area of active investigations. In conclusion, we review the indications, the adequate selection of patients and perioperative factors to be considered for resection of colorectal liver metastasis.展开更多
Abstract Performance optimization of cyber-physical systems （CPS） calls for co-design strategies that handle the issues in both computing domain and physical domain. Periods of controller tasks integrated into a uni...Abstract Performance optimization of cyber-physical systems （CPS） calls for co-design strategies that handle the issues in both computing domain and physical domain. Periods of controller tasks integrated into a uniprocessor system are related to both control performance and real-time schedu- lability analysis simultaneously. System performance improvement can be achieved by optimizing the periods of controller tasks. This paper extends an existing model to select task periods in real-time for CPS with fixed priority controller tasks scheduled by rate-monotonic algorithm. When all the tasks can be integrated, the analytic solution of the problem is derived by using the method of Lagrange multipliers and gradient descent method is evaluated to be suitable online. To further deal with the condition that the system is overloaded, an integrated method is proposed to select periods of tasks online by selecting a subset of tasks first and then optimizing the periods for them. Experimental results demonstrate that our method yields near-optimal result with a short running time.展开更多
Prostate cancer cells demonstrate a remarkable ＂addiction＂ to androgen receptor （AR） signaling in all stages of disease progression. As such, suppression of AR signaling remains the therapeutic goal in systemic tr...Prostate cancer cells demonstrate a remarkable ＂addiction＂ to androgen receptor （AR） signaling in all stages of disease progression. As such, suppression of AR signaling remains the therapeutic goal in systemic treatment of prostate cancer. A number of molecular alterations arise in patients treated with AR-directed therapies. These molecular alterations may indicate the emergence of treatment resistance and may be targeted for the development of novel agents for prostate cancer. The presence of functional androgen receptor splice variants may represent a potential explanation for resistance to abiraterone and enzalutamide, newer AR-directed agents developed to treat metastatic castration-resistant prostate cancer （mCRPC）. In the last 8 years, many androgen receptor splice variants have been identified and characterized. Among these, androgen receptor splice variant-7 （AR-V7） has been investigated extensively. In AR-V7, the entire COOH-terminal ligand-binding domain of the canonical AR is truncated and replaced with a variant-specific peptide of 16 amino acids. Functionally, AR-V7 is capable of mediating constitutive nuclear localization and androgen receptor signaling in the absence of androgens, or in the presence of enzalutamide. In this review, we will focus on clinical translational studies involving detection/measurement of AR-V7. Methods have been developed to detect AR-V7 in clinical mCRPC specimens. AR-V7 can be reliably measured in both tissue and circulating tumor cells derived from mCRPC patients, making it possible to conduct both cross-sectional and longitudinal clinical correlative studies. Current evidence derived from studies focusing on detection of AR-V7 in mCRPC support its potential clinical utility as a treatment selection marker.展开更多
Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes.However,such procedures are highly innovative,technology intensive,interdisciplinary,and multi-par...Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes.However,such procedures are highly innovative,technology intensive,interdisciplinary,and multi-party engineering projects,making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment.In this study,we consider three factors,namely,functional structure,task granularity,and task feasibility in task decomposition of high-end equipment development.Based on the principles of systems engineering,a method of task decomposition is proposed.As for decomposition scheme selection,a method based on the superiority and inferiority ranking(SIR)method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility,uncertainty risk and task integration complexity.To verify the proposed method,development of a military electric vehicle is used as an example to demonstrate the calculation process.展开更多
Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the respons...Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the response of clustering performance to different features subsets. In the present paper, we analyzed the performance differences between k-means, fuzzy c-means, and spectral clustering algorithms in the conditions of different feature subsets of soil data sets. The experimental results demonstrated that the performances of spectral clustering algorithm were generally better than those of k-means and fuzzy c-means with different features subsets. The feature subsets containing environmental attributes helped to improve clustering performances better than those having spatial attributes and produced more accurate and meaningful clustering results. Our results demonstrated that combination of spectral clustering algorithm with the feature subsets containing environmental attributes rather than spatial attributes may be a better choice in applications of soil data clustering.展开更多
Darwin’s theory of evolution believes that biological evolution is a process of natural selection. This theory has been supported by much evidence, but the internal biological mechanism is not clear. Here, I elaborat...Darwin’s theory of evolution believes that biological evolution is a process of natural selection. This theory has been supported by much evidence, but the internal biological mechanism is not clear. Here, I elaborate on the cycle of potassium resources on the earth and the biological utilization and efficiency, which may be the core mechanism of natural selection and affect the evolution of organisms and the development of human society.展开更多
Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collectio...Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.展开更多
Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanc...Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating formula.The proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction tasks.iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets.It ranked first in 11 out of 17 datasets in terms of average classification accuracy.Moreover,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets.The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.展开更多
Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it under...Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.展开更多
Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,...Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.展开更多
In this paper,the metastableβTB8 titanium alloy with nanocrystallineαphase is achieved by electric pulse treatment.The morphology evolution and variant selection of nanocrystallineαphase in metastableβTB8titanium ...In this paper,the metastableβTB8 titanium alloy with nanocrystallineαphase is achieved by electric pulse treatment.The morphology evolution and variant selection of nanocrystallineαphase in metastableβTB8titanium alloy were investigated by using scanning electron microscope(SEM),electron backscattered diffraction(EBSD)and transmission electron microscope(TEM)analysis.The results indicated that the morphologies of the nanocrystallineαphase were mainly triangular clusters and needle-like at the pressure of 0 MPa.With increasing pressure from 20 to 50 MPa,the volume fraction of needlelikeαphase decreased,and a large amount of V-shapedαphase formed in the interior ofβgrains.Based on the EBSD data,the parentβphase was reconstructed by MTEX software.In the interior of theβgrains,12 variants can form for the samples electric pulse treated at 0 and20 MPa,while only 3 and 6 variants can form for the samples electric pulse treated at 30 and 50 MPa.In the grain boundary of theβgrains,one or more grain boundaryαvariants can be generated for the samples electric pulse treated at different pressures as long as one of the neighborβgrains follows the Burgers orientation relationship.展开更多
基金Supported by Wuxi Taihu Talent Project,No.WXTTP 2021the General Scientific Research Program of Wuxi Municipal Health Commission,No.M202447.
文摘BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To investigate the ERP characteristics of conditional reasoning in MD patients and explore the neural mechanism of cognitive processing.METHODS Thirty-four patients with MD and 34 healthy controls(HCs)completed ERP measurements while performing the Wason selection task(WST).The clusterbased permutation test in FieldTrip was used to compare the differences in the mean amplitudes between the patients with MD and HCs on the ERP components under different experimental conditions.Behavioral data[accuracy(ACC)and reaction times(RTs)],the ERP P100 and late positive potentials(LPPs)were analyzed.RESULTS Although the mean ACC was greater and the mean of RTs was shorter in HCs than in MD patients,the differences were not statistically significant.However,across both groups,the ACC in the precautionary WST was greater than that in the other tasks,and the RTs in the abstract task were greater than those in the other tasks.Importantly,compared with that of HCs,the P100 of the left centroparietal sites was significantly increased,and the early LPP was attenuated at parietal sites and increased at left frontocentral sites;the medium LPP and late LPP were increased at the left frontocentral sites.CONCLUSION Patients with MD have conditional reasoning dysfunction and exhibit abnormal ERP characteristics evoked by the WST,which suggests neural correlates of abnormalities in conditional reasoning function in MD patients.
基金financially supported by the National Natural Science Foundation of China(No.22209057)the Guangzhou Basic and Applied Basic Research Foundation(No.2024A04J0839)。
文摘Thanks to its abundant reserves,relatively high energy density,and low reduction potential,potassium ion batteries(PIBs)have a high potential for large-scale energy storage applications.Due to the large radius of potassium ions,most conventional anode materials undergo severe volume expansion,making it difficult to achieve stable and reversible energy storage.Therefore,developing high-performance anode materials is one of the critical factors in developing PIBs.In this sense,antimony(Sb)-based anode materials with high theoretical capacity and safe reaction potentials have a broad potential for application in PIBs.However,overcoming the rapid capacity decay induced by the large radius of potassium ions is still an issue that needs to be focused on.This paper reviews the latest research on different types of Sb-based anode materials and provides an in-depth analysis of their optimization strategies.We focus on material selection,structural design,and storage mechanisms to develop a detailed description of the material.In addition,the current challenges still faced by Sb-based anode materials are summarized,and some further optimization strategies have been added.We hope to provide some insights for researchers developing Sb-based anode materials for next-generation advanced PIBs.
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.
文摘Surgical resection of colorectal liver metastases(CRLM) has a well-documented improvement in survival. To benefit from this intervention, proper selection of patients who would be adequate surgical candidates becomes vital. A combination of imaging techniques may be utilized in the detection of the lesions. The criteria for resection are continuously evolving; currently, the requirements that need be met to undergo resection of CRLM are: the anticipation of attaining a negative margin(R0 resection), whilst maintaining an adequate functioning future liver remnant. The timing of hepatectomy in regards to resection of the primary remains controversial; before, after, or simultaneously. This depends mainly on the tumor burden and symptoms from the primary tumor. The role of chemotherapy differs according to the resectability of the liver lesion(s); no evidence of improved survival was shown in patients with resectable disease who received preoperative chemotherapy. Presence of extrahepatic disease in itself is no longer considered a reason to preclude patients from resection of their CRLM, providing limited extra-hepatic disease, although this currently is an area of active investigations. In conclusion, we review the indications, the adequate selection of patients and perioperative factors to be considered for resection of colorectal liver metastasis.
基金supported by State Administration of Science,Technology and Industry for National Defense,China(No.1000-GEAC0001)
文摘Abstract Performance optimization of cyber-physical systems （CPS） calls for co-design strategies that handle the issues in both computing domain and physical domain. Periods of controller tasks integrated into a uniprocessor system are related to both control performance and real-time schedu- lability analysis simultaneously. System performance improvement can be achieved by optimizing the periods of controller tasks. This paper extends an existing model to select task periods in real-time for CPS with fixed priority controller tasks scheduled by rate-monotonic algorithm. When all the tasks can be integrated, the analytic solution of the problem is derived by using the method of Lagrange multipliers and gradient descent method is evaluated to be suitable online. To further deal with the condition that the system is overloaded, an integrated method is proposed to select periods of tasks online by selecting a subset of tasks first and then optimizing the periods for them. Experimental results demonstrate that our method yields near-optimal result with a short running time.
文摘Prostate cancer cells demonstrate a remarkable ＂addiction＂ to androgen receptor （AR） signaling in all stages of disease progression. As such, suppression of AR signaling remains the therapeutic goal in systemic treatment of prostate cancer. A number of molecular alterations arise in patients treated with AR-directed therapies. These molecular alterations may indicate the emergence of treatment resistance and may be targeted for the development of novel agents for prostate cancer. The presence of functional androgen receptor splice variants may represent a potential explanation for resistance to abiraterone and enzalutamide, newer AR-directed agents developed to treat metastatic castration-resistant prostate cancer （mCRPC）. In the last 8 years, many androgen receptor splice variants have been identified and characterized. Among these, androgen receptor splice variant-7 （AR-V7） has been investigated extensively. In AR-V7, the entire COOH-terminal ligand-binding domain of the canonical AR is truncated and replaced with a variant-specific peptide of 16 amino acids. Functionally, AR-V7 is capable of mediating constitutive nuclear localization and androgen receptor signaling in the absence of androgens, or in the presence of enzalutamide. In this review, we will focus on clinical translational studies involving detection/measurement of AR-V7. Methods have been developed to detect AR-V7 in clinical mCRPC specimens. AR-V7 can be reliably measured in both tissue and circulating tumor cells derived from mCRPC patients, making it possible to conduct both cross-sectional and longitudinal clinical correlative studies. Current evidence derived from studies focusing on detection of AR-V7 in mCRPC support its potential clinical utility as a treatment selection marker.
基金supported by the National Natural Science Foundation of China(7169023371901214)the National Key R&D Program of China(2017YFC1405005)。
文摘Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes.However,such procedures are highly innovative,technology intensive,interdisciplinary,and multi-party engineering projects,making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment.In this study,we consider three factors,namely,functional structure,task granularity,and task feasibility in task decomposition of high-end equipment development.Based on the principles of systems engineering,a method of task decomposition is proposed.As for decomposition scheme selection,a method based on the superiority and inferiority ranking(SIR)method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility,uncertainty risk and task integration complexity.To verify the proposed method,development of a military electric vehicle is used as an example to demonstrate the calculation process.
文摘Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the response of clustering performance to different features subsets. In the present paper, we analyzed the performance differences between k-means, fuzzy c-means, and spectral clustering algorithms in the conditions of different feature subsets of soil data sets. The experimental results demonstrated that the performances of spectral clustering algorithm were generally better than those of k-means and fuzzy c-means with different features subsets. The feature subsets containing environmental attributes helped to improve clustering performances better than those having spatial attributes and produced more accurate and meaningful clustering results. Our results demonstrated that combination of spectral clustering algorithm with the feature subsets containing environmental attributes rather than spatial attributes may be a better choice in applications of soil data clustering.
文摘Darwin’s theory of evolution believes that biological evolution is a process of natural selection. This theory has been supported by much evidence, but the internal biological mechanism is not clear. Here, I elaborate on the cycle of potassium resources on the earth and the biological utilization and efficiency, which may be the core mechanism of natural selection and affect the evolution of organisms and the development of human society.
文摘Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.
基金supported by the Deanship of Scientific Research and Innovation at Al-Balqa Applied University in Jordan.
文摘Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational costs.This paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating formula.The proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction tasks.iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets.It ranked first in 11 out of 17 datasets in terms of average classification accuracy.Moreover,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets.The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.
文摘Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research has been partly supported by National Natural Science Foundation of China No. 61272528 and No. 61034005, and the Central University Fund （ID-ZYGX2013J073）.
文摘Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.
基金financially supported by the National Natural Science Foundation of China(Nos.51804087 and 52161010)the Science and Technology Programs of Guiyang(No.[2021]1-7)the Breeding Programs of Guizhou University(Nos.[2019]16 and[2020]21)。
文摘In this paper,the metastableβTB8 titanium alloy with nanocrystallineαphase is achieved by electric pulse treatment.The morphology evolution and variant selection of nanocrystallineαphase in metastableβTB8titanium alloy were investigated by using scanning electron microscope(SEM),electron backscattered diffraction(EBSD)and transmission electron microscope(TEM)analysis.The results indicated that the morphologies of the nanocrystallineαphase were mainly triangular clusters and needle-like at the pressure of 0 MPa.With increasing pressure from 20 to 50 MPa,the volume fraction of needlelikeαphase decreased,and a large amount of V-shapedαphase formed in the interior ofβgrains.Based on the EBSD data,the parentβphase was reconstructed by MTEX software.In the interior of theβgrains,12 variants can form for the samples electric pulse treated at 0 and20 MPa,while only 3 and 6 variants can form for the samples electric pulse treated at 30 and 50 MPa.In the grain boundary of theβgrains,one or more grain boundaryαvariants can be generated for the samples electric pulse treated at different pressures as long as one of the neighborβgrains follows the Burgers orientation relationship.
