[ Objective] The aim was to construct drought and saline-alkaline resistance plant expression vector with mannose as selective agent, and further breed unmarked resilient varieties. [ Method] The plant expression vect...[ Objective] The aim was to construct drought and saline-alkaline resistance plant expression vector with mannose as selective agent, and further breed unmarked resilient varieties. [ Method] The plant expression vector was constructed by using Chimonanthus praecox（ L. ）Link aquapor.in CpTIP cDNA and Escherichia coli pmi gene, combined stress resistance gene with mannose positive selection system. [ Result] The test successfully constructed the plant expression vector pPMI：：CpTIP. [ Conclusion] The constructed vector linked advantages of stress resistance gene and mannose positive selection system.展开更多
Large‐scale underground hydrogen storage(UHS)provides a promising method for increasing the role of hydrogen in the process of carbon neutrality and energy transition.Of all the existing storage deposits,salt caverns...Large‐scale underground hydrogen storage(UHS)provides a promising method for increasing the role of hydrogen in the process of carbon neutrality and energy transition.Of all the existing storage deposits,salt caverns are recognized as ideal sites for pure hydrogen storage.Evaluation and optimization of site selection for hydrogen storage facilities in salt caverns have become significant issues.In this article,the software CiteSpace is used to analyze and filter hot topics in published research.Based on a detailed classification and analysis,a“four‐factor”model for the site selection of salt cavern hydrogen storage is proposed,encompassing the dynamic demands of hydrogen energy,geological,hydrological,and ground factors of salt mines.Subsequently,20 basic indicators for comprehensive suitability grading of the target site were screened using the analytic hierarchy process and expert survey methods were adopted,which provided a preliminary site selection system for salt cavern hydrogen storage.Ultimately,the developed system was applied for the evaluation of salt cavern hydrogen storage sites in the salt mines of Pingdingshan City,Henan Province,thereby confirming its rationality and effectiveness.This research provides a feasible method and theoretical basis for the site selection of UHS in salt caverns in China.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
Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).Ho...Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).However,the detection and screening of transgenic lines remain major bottlenecks,being time-consuming,labor-intensive,and inefficient during transformation and subsequent mutation identification.A simple and efficient visual marker system plays a critical role in addressing these challenges.Recent studies demonstrated that the GmW1 and RUBY reporter systems were used to obtain visual transgenic soybean(Glycine max) plants(Chen L et al.2023;Chen et al.2024).展开更多
Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embed...Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.展开更多
In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication a...In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.展开更多
Agricultural land development is a pivotal strategy for addressing the global food security crisis.Barren grassland,especially those in mountainous regions,constitutes critical areas where cultivation can substantiall...Agricultural land development is a pivotal strategy for addressing the global food security crisis.Barren grassland,especially those in mountainous regions,constitutes critical areas where cultivation can substantially enhance land resources.This study highlights the necessity for a precise correlation between land development initiatives and constraints in order to optimize efficiency and enhance the effectiveness of such projects,with the core being the seamless integration of land development engineering and techniques to eliminate agricultural constraints.This study employs a systems engineering approach to classify improvement factors into mobile and fixed categories,elucidating the integration methods of constraint factors.Adhering to the Wooden Barrel Principle,these constraints were rigorously analyzed based on soil quality,land topography,water availability,and agricultural infrastructure.An innovative method of engineering type combination is proposed,which effectively explains the correlation between natural factors combination,project type combination,and target factors combination.It provides a convenient way for the selection of barren grassland development projects and lays a foundation for land planning,development project establishment,program selection,engineering design,and budget preparation.Taking Tang County of China as an example,it is divided into 19 factor improvement areas,a quick reference table of engineering types is established,and 14 main types of engineering combinations are obtained,which lays a foundation for the application of theoretical framework in practice.展开更多
Non-orthogonal multiple access(NOMA)is a promising technology for the next generation wireless communication networks.The benefits of this technology can be further enhanced through deployment in conjunction with mult...Non-orthogonal multiple access(NOMA)is a promising technology for the next generation wireless communication networks.The benefits of this technology can be further enhanced through deployment in conjunction with multiple-input multipleoutput(MIMO)systems.Antenna selection plays a critical role in MIMO–NOMA systems as it has the potential to significantly reduce the cost and complexity associated with radio frequency chains.This paper considers antenna selection for downlink MIMO–NOMA networks with multiple-antenna basestation(BS)and multiple-antenna user equipments(UEs).An iterative antenna selection scheme is developed for a two-user system,and to determine the initial power required for this selection scheme,a power estimation method is also proposed.The proposed algorithm is then extended to a general multiuser NOMA system.Numerical results demonstrate that the proposed antenna selection algorithm achieves near-optimal performance with much lower computational complexity in both two-user and multiuser scenarios.展开更多
In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update ...In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO.展开更多
With the development of More Electric Aircraft(MEA),the Permanent Magnet Synchronous Motor(PMSM)is widely used in the MEA field.The PMSM control system of MEA needs to consider the system reliability,and the inverter ...With the development of More Electric Aircraft(MEA),the Permanent Magnet Synchronous Motor(PMSM)is widely used in the MEA field.The PMSM control system of MEA needs to consider the system reliability,and the inverter switching frequency of the inverter is one of the impacting factors.At the same time,the control accuracy of the system also needs to be considered,and the torque ripple and flux ripple are usually considered to be its important indexes.This paper proposes a three-stage series Model Predictive Torque and Flux Control system(three-stage series MPTFC)based on fast optimal voltage vector selection to reduce switching frequency and suppress torque ripple and flux ripple.Firstly,the analytical model of the PMSM is established and the multi-stage series control method is used to reduce the switching frequency.Secondly,selectable voltage vectors are extended from 8 to 26 and a fast selection method for optimal voltage vector sectors is designed based on the hysteresis comparator,which can suppress the torque ripple and flux ripple to improve the control accuracy.Thirdly,a three-stage series control is obtained by expanding the two-stage series control using the P-Q torque decomposition theory.Finally,a model predictive torque and flux control experimental platform is built,and the feasibility and effectiveness of this method are verified through comparison experiments.展开更多
In this study,a solution based on deep Q network(DQN)is proposed to address the relay selection problem in cooperative non-orthogonal multiple access(NOMA)systems.DQN is particularly effective in addressing problems w...In this study,a solution based on deep Q network(DQN)is proposed to address the relay selection problem in cooperative non-orthogonal multiple access(NOMA)systems.DQN is particularly effective in addressing problems within dynamic and complex communication environ-ments.By formulating the relay selection problem as a Markov decision process(MDP),the DQN algorithm employs deep neural networks(DNNs)to learn and make decisions through real-time interactions with the communication environment,aiming to minimize the system’s outage proba-bility.During the learning process,the DQN algorithm progressively acquires channel state infor-mation(CSI)between two nodes,thereby minimizing the system’s outage probability until a sta-ble level is reached.Simulation results show that the proposed method effectively reduces the out-age probability by 82%compared to the two-way relay selection scheme(Two-Way)when the sig-nal-to-noise ratio(SNR)is 30 dB.This study demonstrates the applicability and advantages of the DQN algorithm in cooperative NOMA systems,providing a novel approach to addressing real-time relay selection challenges in dynamic communication 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
Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts o...Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts on synanthropic biodiversity are poorly understood.We investigated how attached sunspaces,a widely adopted energy-saving technology in rural China,filter the distribution of two ecologically important aerial insectivores,the Barn Swallow(Hirundo rustica)and Red-rumped Swallow(Cecropis daurica).We surveyed 106 villages during the 2024 and 2025 breeding seasons and recorded a total of 2323 nests(612 Barn Swallow,1711 Red-rumped Swallow).Using Generalized Linear Models,we assessed their responses to building characteristics,landscape composition and the prevalence of sunspaces.Barn Swallow nests preferred perches at the base and single attachment faces,while Red-rumped Swallow nests favored multiple attachment faces and avoided long shelters.The proportion of buildings with sunspaces acted as a strong positive filter for Barn Swallow nest abundance(+24%)but as a significant negative filter for Red-rumped Swallow(-51%).Other landscape variables(e.g.,human population density,NDVI,Human Footprint Index)were not significant.This study demonstrates that specific architectural innovations can act as powerful ecological filters,leading to divergent distributional outcomes for sympatric species reliant on anthropogenic structures.Our findings reveal a critical trade-off in sustainable development:energy efficiency gains may inadvertently reduce habitat suitability for certain species.To reconcile climate and biodiversity goals in rural landscapes,we advocate integrating species-specific habitat requirements into building design.We propose actionable modifications to sunspaces to support swallows without compromising energy savings.These principles provide a template for mitigating the distributional impacts of green infrastructure globally.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.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
Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequ...Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.展开更多
Over the past decade,the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks.Traditional detection techniques,while still in use,often fall short when...Over the past decade,the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks.Traditional detection techniques,while still in use,often fall short when confronted with modern threats that use advanced evasion strategies.This systematic review critically examines recent developments in malware detection,with a particular emphasis on the role of artificial intelligence(AI)and machine learning(ML)in enhancing detection capabilities.Drawing on literature published between 2019 and 2025,this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore,SpringerLink,ScienceDirect,and ACM Digital Library.In doing so,it explores the evolution of malware,evaluates detection methods,assesses the quality and limitations of widely used datasets,and identifies key challenges facing the field.Unlike existing surveys,this work offers a structured comparison of AI-driven frameworks and provides a detailed account of emerging techniques such as hybrid detection frameworks and image-based analysis.The findings indicate that AIbased models trained on diverse,high-quality datasets consistently outperform conventional methods,particularly when supported by feature engineering,explainable AI and a multi-faceted strategy.The review concludes by outlining future research directions,including the need for standardized datasets,enhanced adversarial robustness,and the integration of privacy-preserving mechanisms in malware detection systems.展开更多
Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.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
Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(...Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(2DOF)modal pushover-based scaling procedure(2DMPS)has been developed for a nonlinear dynamic analysis of asymmetric in-plan buildings.The 2DMPS procedure scales ground motions to approach close enough to a target value of the inelastic displacement of the first-mode inelastic 2DOF modal stick,extended for structures with significant contributions of higher modes.Further,4-,6-and 13-story RC SMRF buildings were selected for analyses using ground motion records scaled by the 2DMPS procedure,the modal pushover-based scaling method(MPS),and ASCE/SEI 7-16 scaling procedures.The median values of EDPs on scaled records closely matched the benchmark results.The bias in the EDP values due to the scaled records in every group regarding their median value was lower than the dispersion of the 21 unscaled records.These results generally demonstrate the accuracy and efficiency of the 2DMPS method.Additionally,the 2DOF modal stick’s inelastic response spectra are better suited for calculating seismic demands for one-way asymmetric-plan structures than the SDOF inelastic response spectra.展开更多
基金Supported by Sub-project of Special Fund in Ministry of Agriculture of Transgenic Plants " Cultivation of New Varieties of Anti-adversity Transgenic Soybeans"(2008ZX08004-2)~~
文摘[ Objective] The aim was to construct drought and saline-alkaline resistance plant expression vector with mannose as selective agent, and further breed unmarked resilient varieties. [ Method] The plant expression vector was constructed by using Chimonanthus praecox（ L. ）Link aquapor.in CpTIP cDNA and Escherichia coli pmi gene, combined stress resistance gene with mannose positive selection system. [ Result] The test successfully constructed the plant expression vector pPMI：：CpTIP. [ Conclusion] The constructed vector linked advantages of stress resistance gene and mannose positive selection system.
基金supported by the Henan Institute for Chinese Development Strategy of Engineering&Technology(Grant No.2022HENZDA02)the Since&Technology Department of Sichuan Province Project(Grant No.2021YFH0010)the High‐End Foreign Experts Program of the Yunnan Revitalization Talents Support Plan of Yunnan Province.
文摘Large‐scale underground hydrogen storage(UHS)provides a promising method for increasing the role of hydrogen in the process of carbon neutrality and energy transition.Of all the existing storage deposits,salt caverns are recognized as ideal sites for pure hydrogen storage.Evaluation and optimization of site selection for hydrogen storage facilities in salt caverns have become significant issues.In this article,the software CiteSpace is used to analyze and filter hot topics in published research.Based on a detailed classification and analysis,a“four‐factor”model for the site selection of salt cavern hydrogen storage is proposed,encompassing the dynamic demands of hydrogen energy,geological,hydrological,and ground factors of salt mines.Subsequently,20 basic indicators for comprehensive suitability grading of the target site were screened using the analytic hierarchy process and expert survey methods were adopted,which provided a preliminary site selection system for salt cavern hydrogen storage.Ultimately,the developed system was applied for the evaluation of salt cavern hydrogen storage sites in the salt mines of Pingdingshan City,Henan Province,thereby confirming its rationality and effectiveness.This research provides a feasible method and theoretical basis for the site selection of UHS in salt caverns in China.
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supported by the Jilin Science and Technology Development Program,China (20240602032RC)the Jilin Agricultural Science and Technology Innovation Project,China (CXGC2024ZD001)+1 种基金the Jilin Agricultural Science and Technology Innovation Project,China (CXGC2024ZY012)the Jilin Province Development and Reform Commission-Project for Improving the Independent Innovation Capacity of Major Grain Crops,China (2024C002)。
文摘Emerging and powerful genome editing tools,particularly CRISPR/Cas9,are facilitating functional genomics research and accelerating crop improvement(Jiang et al.2021;Cao et al.2023;Chen C et al.2023;Liu et al.2023a).However,the detection and screening of transgenic lines remain major bottlenecks,being time-consuming,labor-intensive,and inefficient during transformation and subsequent mutation identification.A simple and efficient visual marker system plays a critical role in addressing these challenges.Recent studies demonstrated that the GmW1 and RUBY reporter systems were used to obtain visual transgenic soybean(Glycine max) plants(Chen L et al.2023;Chen et al.2024).
文摘Feature selection(FS)plays a crucial role in medical imaging by reducing dimensionality,improving computational efficiency,and enhancing diagnostic accuracy.Traditional FS techniques,including filter,wrapper,and embedded methods,have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data.Deep learning-based FS methods,particularly Convolutional Neural Networks(CNNs)and autoencoders,have demonstrated superior performance but lack interpretability.Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution,offering improved accuracy and explainability.Furthermore,integratingmulti-modal imaging data(e.g.,MagneticResonance Imaging(MRI),ComputedTomography(CT),Positron Emission Tomography(PET),and Ultrasound(US))poses additional challenges in FS,necessitating advanced feature fusion strategies.Multi-modal feature fusion combines information fromdifferent imagingmodalities to improve diagnostic accuracy.Recently,quantum computing has gained attention as a revolutionary approach for FS,providing the potential to handle high-dimensional medical data more efficiently.This systematic literature review comprehensively examines classical,Deep Learning(DL),hybrid,and quantum-based FS techniques inmedical imaging.Key outcomes include a structured taxonomy of FS methods,a critical evaluation of their performance across modalities,and identification of core challenges such as computational burden,interpretability,and ethical considerations.Future research directions—such as explainable AI(XAI),federated learning,and quantum-enhanced FS—are also emphasized to bridge the current gaps.This review provides actionable insights for developing scalable,interpretable,and clinically applicable FS methods in the evolving landscape of medical imaging.
基金supported by the National Key R&D Program of China (2023YFB2905605)the National Natural Science Foundation of China (62072229)。
文摘In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.
基金funded by Science and Technology Project of Hebei Education Department[QN2023085].
文摘Agricultural land development is a pivotal strategy for addressing the global food security crisis.Barren grassland,especially those in mountainous regions,constitutes critical areas where cultivation can substantially enhance land resources.This study highlights the necessity for a precise correlation between land development initiatives and constraints in order to optimize efficiency and enhance the effectiveness of such projects,with the core being the seamless integration of land development engineering and techniques to eliminate agricultural constraints.This study employs a systems engineering approach to classify improvement factors into mobile and fixed categories,elucidating the integration methods of constraint factors.Adhering to the Wooden Barrel Principle,these constraints were rigorously analyzed based on soil quality,land topography,water availability,and agricultural infrastructure.An innovative method of engineering type combination is proposed,which effectively explains the correlation between natural factors combination,project type combination,and target factors combination.It provides a convenient way for the selection of barren grassland development projects and lays a foundation for land planning,development project establishment,program selection,engineering design,and budget preparation.Taking Tang County of China as an example,it is divided into 19 factor improvement areas,a quick reference table of engineering types is established,and 14 main types of engineering combinations are obtained,which lays a foundation for the application of theoretical framework in practice.
文摘Non-orthogonal multiple access(NOMA)is a promising technology for the next generation wireless communication networks.The benefits of this technology can be further enhanced through deployment in conjunction with multiple-input multipleoutput(MIMO)systems.Antenna selection plays a critical role in MIMO–NOMA systems as it has the potential to significantly reduce the cost and complexity associated with radio frequency chains.This paper considers antenna selection for downlink MIMO–NOMA networks with multiple-antenna basestation(BS)and multiple-antenna user equipments(UEs).An iterative antenna selection scheme is developed for a two-user system,and to determine the initial power required for this selection scheme,a power estimation method is also proposed.The proposed algorithm is then extended to a general multiuser NOMA system.Numerical results demonstrate that the proposed antenna selection algorithm achieves near-optimal performance with much lower computational complexity in both two-user and multiuser scenarios.
文摘In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO.
基金co-supported by the National Natural Science Foundation of China(No.52477063)the National Key Research and Development Program of China(No.2023YFF0719100)。
文摘With the development of More Electric Aircraft(MEA),the Permanent Magnet Synchronous Motor(PMSM)is widely used in the MEA field.The PMSM control system of MEA needs to consider the system reliability,and the inverter switching frequency of the inverter is one of the impacting factors.At the same time,the control accuracy of the system also needs to be considered,and the torque ripple and flux ripple are usually considered to be its important indexes.This paper proposes a three-stage series Model Predictive Torque and Flux Control system(three-stage series MPTFC)based on fast optimal voltage vector selection to reduce switching frequency and suppress torque ripple and flux ripple.Firstly,the analytical model of the PMSM is established and the multi-stage series control method is used to reduce the switching frequency.Secondly,selectable voltage vectors are extended from 8 to 26 and a fast selection method for optimal voltage vector sectors is designed based on the hysteresis comparator,which can suppress the torque ripple and flux ripple to improve the control accuracy.Thirdly,a three-stage series control is obtained by expanding the two-stage series control using the P-Q torque decomposition theory.Finally,a model predictive torque and flux control experimental platform is built,and the feasibility and effectiveness of this method are verified through comparison experiments.
基金supported by the National Natural Science Foundation of China(Nos.61841107 and 62061024)Gansu Natural Sci-ence Foundation(Nos.22JR5RA274 and 23YFGA0062)Gansu Innovation Foundation(No.2022A-215).
文摘In this study,a solution based on deep Q network(DQN)is proposed to address the relay selection problem in cooperative non-orthogonal multiple access(NOMA)systems.DQN is particularly effective in addressing problems within dynamic and complex communication environ-ments.By formulating the relay selection problem as a Markov decision process(MDP),the DQN algorithm employs deep neural networks(DNNs)to learn and make decisions through real-time interactions with the communication environment,aiming to minimize the system’s outage proba-bility.During the learning process,the DQN algorithm progressively acquires channel state infor-mation(CSI)between two nodes,thereby minimizing the system’s outage probability until a sta-ble level is reached.Simulation results show that the proposed method effectively reduces the out-age probability by 82%compared to the two-way relay selection scheme(Two-Way)when the sig-nal-to-noise ratio(SNR)is 30 dB.This study demonstrates the applicability and advantages of the DQN algorithm in cooperative NOMA systems,providing a novel approach to addressing real-time relay selection challenges in dynamic communication 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funded by the National Natural Science Foundation of China(No.32201304)the Fundamental Research Funds for the Central Universities(2412022QD026)。
文摘Human-modified landscapes serve as ecological filters,determining species distributions and persistence.Energy-efficient technologies,while crucial for climate change mitigation,represent novel filters whose impacts on synanthropic biodiversity are poorly understood.We investigated how attached sunspaces,a widely adopted energy-saving technology in rural China,filter the distribution of two ecologically important aerial insectivores,the Barn Swallow(Hirundo rustica)and Red-rumped Swallow(Cecropis daurica).We surveyed 106 villages during the 2024 and 2025 breeding seasons and recorded a total of 2323 nests(612 Barn Swallow,1711 Red-rumped Swallow).Using Generalized Linear Models,we assessed their responses to building characteristics,landscape composition and the prevalence of sunspaces.Barn Swallow nests preferred perches at the base and single attachment faces,while Red-rumped Swallow nests favored multiple attachment faces and avoided long shelters.The proportion of buildings with sunspaces acted as a strong positive filter for Barn Swallow nest abundance(+24%)but as a significant negative filter for Red-rumped Swallow(-51%).Other landscape variables(e.g.,human population density,NDVI,Human Footprint Index)were not significant.This study demonstrates that specific architectural innovations can act as powerful ecological filters,leading to divergent distributional outcomes for sympatric species reliant on anthropogenic structures.Our findings reveal a critical trade-off in sustainable development:energy efficiency gains may inadvertently reduce habitat suitability for certain species.To reconcile climate and biodiversity goals in rural landscapes,we advocate integrating species-specific habitat requirements into building design.We propose actionable modifications to sunspaces to support swallows without compromising energy savings.These principles provide a template for mitigating the distributional impacts of green infrastructure globally.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
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 National Key Research and Development Plan of China(2021YFD2200202)the Key Research and Development Project of Jiangsu Province,China(BE2021366).
文摘Populus species,important economic species combining rapid growth with broad ecological adaptability,play a critical role in sustainable forestry and bioenergy production.In this study,we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism(SNP)markers and constructed a high-density genetic linkage map of 19 linkage groups.The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM.By integrating multidimensional phenotypic data,89 quantitative trait loci(QTL)associated with growth,wood physical and chemical properties,disease resistance,and leaf morphology traits were identified,with logarithm of odds(LOD)scores ranging from 3.13 to 21.72 Notably,pleiotropic analysis revealed significant colocaliza and phenotypic variance explained between 1.7% and 11.6%.-tion hotspots on chromosomes LG1,LG5,LG6,LG8,and LG14,with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits.Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched,suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways.Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55.Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1% to 21%,with Bayesian ridge regression as the best predictive model.This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.
文摘Over the past decade,the landscape of cybersecurity has been increasingly shaped by the growing sophistication and frequency of malware attacks.Traditional detection techniques,while still in use,often fall short when confronted with modern threats that use advanced evasion strategies.This systematic review critically examines recent developments in malware detection,with a particular emphasis on the role of artificial intelligence(AI)and machine learning(ML)in enhancing detection capabilities.Drawing on literature published between 2019 and 2025,this study reviews 105 peer-reviewed contributions from prominent digital libraries including IEEE Xplore,SpringerLink,ScienceDirect,and ACM Digital Library.In doing so,it explores the evolution of malware,evaluates detection methods,assesses the quality and limitations of widely used datasets,and identifies key challenges facing the field.Unlike existing surveys,this work offers a structured comparison of AI-driven frameworks and provides a detailed account of emerging techniques such as hybrid detection frameworks and image-based analysis.The findings indicate that AIbased models trained on diverse,high-quality datasets consistently outperform conventional methods,particularly when supported by feature engineering,explainable AI and a multi-faceted strategy.The review concludes by outlining future research directions,including the need for standardized datasets,enhanced adversarial robustness,and the integration of privacy-preserving mechanisms in malware detection systems.
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
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Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(2DOF)modal pushover-based scaling procedure(2DMPS)has been developed for a nonlinear dynamic analysis of asymmetric in-plan buildings.The 2DMPS procedure scales ground motions to approach close enough to a target value of the inelastic displacement of the first-mode inelastic 2DOF modal stick,extended for structures with significant contributions of higher modes.Further,4-,6-and 13-story RC SMRF buildings were selected for analyses using ground motion records scaled by the 2DMPS procedure,the modal pushover-based scaling method(MPS),and ASCE/SEI 7-16 scaling procedures.The median values of EDPs on scaled records closely matched the benchmark results.The bias in the EDP values due to the scaled records in every group regarding their median value was lower than the dispersion of the 21 unscaled records.These results generally demonstrate the accuracy and efficiency of the 2DMPS method.Additionally,the 2DOF modal stick’s inelastic response spectra are better suited for calculating seismic demands for one-way asymmetric-plan structures than the SDOF inelastic response spectra.
