Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-...Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs.Therefore,feasible and advanced non-noble-metal elec-trocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction.In this review,we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field.Then,we discussed the work-ing mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design,crystal structure tuning,interface strategy,and atomic engineering.We also included theoretical studies,machine learning,and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions.Finally,we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.展开更多
In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature sel...In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.展开更多
In the continuous development of the modern highway and bridge engineering industry,the reasonable selection of mega highway bridges and their design is crucial.Based on this,this paper takes the actual bridge project...In the continuous development of the modern highway and bridge engineering industry,the reasonable selection of mega highway bridges and their design is crucial.Based on this,this paper takes the actual bridge project as an example,and analyses the overall selection design of such highway bridges,including the basic overview of the project,the basic selection principle of mega highway bridge project structure and its design strategy,etc.,to provide scientific reference for its selection design.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
Molten-alkali etching has been widely used to reveal dislocations in 4H silicon carbide(4H-SiC),which has promoted the identification and statistics of dislocation density in 4H-SiC single crystals.However,the etching...Molten-alkali etching has been widely used to reveal dislocations in 4H silicon carbide(4H-SiC),which has promoted the identification and statistics of dislocation density in 4H-SiC single crystals.However,the etching mechanism of 4H-SiC is limited misunderstood.In this letter,we reveal the anisotropic etching mechanism of the Si face and C face of 4H-SiC by combining molten-KOH etching,X-ray photoelectron spectroscopy(XPS)and first-principles investigations.The activation energies for the molten-KOH etching of the C face and Si face of 4H-SiC are calculated to be 25.09 and 35.75 kcal/mol,respectively.The molten-KOH etching rate of the C face is higher than the Si face.Combining XPS analysis and first-principles calculations,we find that the molten-KOH etching of 4H-SiC is proceeded by the cycling of the oxidation of 4H-SiC by the dissolved oxygen and the removal of oxides by molten KOH.The faster etching rate of the C face is caused by the fact that the oxides on the C face are unstable,and easier to be removed with molten alkali,rather than the C face being easier to be oxidized.展开更多
The Sustainable Development Goals(SDGs)are significantly off-course as we reach the midpoint of their 2030 deadline.From a scientific perspective,the critical challenge in achieving the SDGs lies in the need for more ...The Sustainable Development Goals(SDGs)are significantly off-course as we reach the midpoint of their 2030 deadline.From a scientific perspective,the critical challenge in achieving the SDGs lies in the need for more scientific principles to understand the complex socio-ecological systems(SES)and their interactions influencing the 17 SDGs.Here,we propose a scientific framework to clarify the common scientific principles and the rational treatment of diversity under these principles.The framework’s core is revealing the complex mechanisms underlying the achievement of each Sustainable Development Goal(SDG)and SDG interactions.Building upon the identified mechanisms,complex SES models can be established,and the implementation of SDGs can be formulated as a multi-objective optimization problem,seeking a compromise in competition between essential costs and desired benefits.Our framework can assist countries,and even the world in accelerating progress towards the SDGs.展开更多
Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of si...Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.展开更多
Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We...Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.展开更多
Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is...Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is essential for the survival of sea cucumbers.Six MnSODs were identified from the transcriptomes of deep and shallow-sea sea cucumbers.To explore their environmental adaptation mechanism,we conducted environmental selection pressure analysis through the branching site model of PAML software.We obtained night positive selection sites,and two of them were significant(97F→H,134K→V):97F→H located in a highly conservative characteristic sequence,and its polarity c hange might have a great impact on the function of MnSOD;134K→V had a change in piezophilic a bility,which might help MnSOD adapt to the environment of high hydrostatic pressure in the deepsea.To further study the effect of these two positive selection sites on MnSOD,we predicted the point mutations of F97H and K134V on shallow-sea sea cucumber by using MAESTROweb and PyMOL.Results show that 97F→H,134K→V might improve MnSOD’s efficiency of scavenging superoxide a nion and its ability to resist high hydrostatic pressure by moderately reducing its stability.The above results indicated that MnSODs of deep-sea sea cucumber adapted to deep-sea environments through their amino acid changes in polarity,piezophilic behavior,and local stability.This study revealed the correlation between MnSOD and extreme environment,and will help improve our understanding of the organism’s adaptation mechanisms in deep sea.展开更多
The electronic structure,magnetic,and optical properties of two-dimensional(2D)GaSe doped with rare earth elements X(X=Sc,Y,La,Ce,Eu)were calculated using the first-principles plane wave method based on den-sity funct...The electronic structure,magnetic,and optical properties of two-dimensional(2D)GaSe doped with rare earth elements X(X=Sc,Y,La,Ce,Eu)were calculated using the first-principles plane wave method based on den-sity functional theory.The results show that intrinsic 2D GaSe is a p-type nonmagnetic semiconductor with an indi-rect bandgap of 2.6611 eV.The spin-up and spin-down channels of Sc-,Y-,and La-doped 2D GaSe are symmetric,they are non-magnetic semiconductors.The magnetic moments of Ce-and Eu-doped 2D GaSe are 0.908μ_(B)and 7.163μ_(B),which are magnetic semiconductors.Impurity energy levels appear in both spin-up and spin-down chan-nels of Eu-doped 2D GaSe,which enhances the probability of electron transition.Compared with intrinsic 2D GaSe,the static dielectric constant of the doped 2D GaSe increases,and the polarization ability is strengthened.The ab-sorption spectrum of the doped 2D GaSe shifts in the low-energy direction,and the red-shift phenomenon occurs,which extends the absorption spectral range.The optical reflection coefficient of the doped 2D GaSe is improved in the low energy region,and the improvement of Eu-doped 2D GaSe is the most obvious.展开更多
Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predic...Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited.Therefore,we aimed to develop a model that can effectively predict prognosis,differentiate microenvironment signatures,and optimize drug selection for patients with glioma.Materials and Methods:The CIBERSORT algorithm,bulk sequencing analysis,and single-cell RNA(scRNA)analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues.A predictive model was constructed based on cross-talk gene expression,and its effect on prognosis,recurrence prediction,and microenvironment characteristics was validated in multiple cohorts.The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments.Results:A high abundance of M2 macrophages in glioma tissues indicates poor prognosis,and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment.Eight genes involved in the cross-talk between macrophages and cancer cells were identified.Among them,periostin(POSTN),chitinase 3 like 1(CHI3L1),serum amyloid A1(SAA1),and matrix metallopeptidase 9(MMP9)were selected to construct a predictive model.The developed model demonstrated significant efficacy in distinguishing patient prognosis,recurrent cases,and characteristics of high inflammation,hypoxia,and immunosuppression.Furthermore,this model can serve as a valuable tool for guiding the use of trametinib.Conclusions:In summary,this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma;utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis,recurrence instances,and microenvironment characteristics;and aids in optimizing the application of trametinib in glioma patients.展开更多
With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety...With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety and efficiency.However,the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks,thereby resulting in low energy transmission efficiency.To enhance the energy transmission efficiency of IoT devices in energy harvesting,this paper focuses on the utilization of collaborative communication,along with pricing-based incentive mechanisms and auction strategies.We propose a dynamic relay selection scheme,including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism,to encourage more IoT devices to participate in the collaboration process.Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.展开更多
Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it...Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.展开更多
The year 2024 marks the 70th anniversary of the Five Principles of Peaceful Coexistence initiated by China.As an anchor foreign policy of New China and its first contribution of public product for the international co...The year 2024 marks the 70th anniversary of the Five Principles of Peaceful Coexistence initiated by China.As an anchor foreign policy of New China and its first contribution of public product for the international community,the Five Principles have made a historic contribution to the building of a new type of just and equitable international relations,advancing the purposes and principles of the Charter of the United Nations,and becoming universally applicable basic norms for international relations and key fundamental principles of international law.展开更多
Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In...Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.展开更多
Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accurac...Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accuracy.This module enables a central server to select a subset of participants to performmodel training based on data and device information.By doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are excluded.However,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection honestly.This lack of trustworthiness problem,which can demotivate participants,has received little attention.Another problem that has received little attention is the leakage of participants’private information during the selection process.We will therefore propose a federated learning framework with auditable participant selection.It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy.Considering the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors.Finally,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.展开更多
The grain protein content(GPC)is the key parameter for wheat grain nutritional quality.This study conducted a resampling GWAS analysis using 406 wheat accessions across eight environments,and identified four previousl...The grain protein content(GPC)is the key parameter for wheat grain nutritional quality.This study conducted a resampling GWAS analysis using 406 wheat accessions across eight environments,and identified four previously reported GPC QTLs.An analysis of 87 landraces and 259 modern cultivars revealed the loss of superior GPC haplotypes,especially in Chinese cultivars.These haplotypes were preferentially adopted in different agroecological zones and had broad effects on wheat yield and agronomic traits.Most GPC QTLs did not significantly reduce yield,suggesting that high GPC can be achieved without a yield penalty.The results of this study provide a reference for future GPC breeding in wheat using the four identified QTLs.展开更多
Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is ext...Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.展开更多
Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the tra...Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the transmitter and receiver must be parallel and coaxial;otherwise,the accuracy of mode detection at the receiver can be seriously influenced.In this paper,we design an OAM millimeter-wave communication system for overcoming the above limitation.Specifically,the first contribution is that the power distribution between different OAM modes and the capacity of the system with different mode sets are analytically derived for performance analysis.The second contribution lies in that a novel mode selection scheme is proposed to reduce the total interference between different modes.Numerical results show that system performance is less affected by the offset when the mode set with smaller modes or larger intervals is selected.展开更多
Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin ...Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.展开更多
基金the Natural Science Foundation of China(Grant No:22309180)Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No:XDB0600000,XDB0600400)+3 种基金Liaoning Binhai Laboratory,(Grant No:LILBLB-2023-04)Dalian Revitalization Talents Program(Grant No:2022RG01)Youth Science and Technology Foundation of Dalian(Grant No:2023RQ015)the University of Waterloo.
文摘Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs.Therefore,feasible and advanced non-noble-metal elec-trocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction.In this review,we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field.Then,we discussed the work-ing mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design,crystal structure tuning,interface strategy,and atomic engineering.We also included theoretical studies,machine learning,and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions.Finally,we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.
基金supported in part by the Natural Science Youth Foundation of Hebei Province under Grant F2019403207in part by the PhD Research Startup Foundation of Hebei GEO University under Grant BQ2019055+3 种基金in part by the Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant KLIGIP-2021A06in part by the Fundamental Research Funds for the Universities in Hebei Province under Grant QN202220in part by the Science and Technology Research Project for Universities of Hebei under Grant ZD2020344in part by the Guangxi Natural Science Fund General Project under Grant 2021GXNSFAA075029.
文摘In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.
文摘In the continuous development of the modern highway and bridge engineering industry,the reasonable selection of mega highway bridges and their design is crucial.Based on this,this paper takes the actual bridge project as an example,and analyses the overall selection design of such highway bridges,including the basic overview of the project,the basic selection principle of mega highway bridge project structure and its design strategy,etc.,to provide scientific reference for its selection design.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
基金This work is supported by the Natural Science Foundation of China(Grant Nos.62274143&62204216)Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(Grant Nos.LHZSD24E020001)+4 种基金the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Grant Nos.2022C0102&2023C01010)Partial support was provided by the Leading Innovative and Entrepreneur Team Introduction Program of Hangzhou(Grant No.TD2022012)Fundamental Research Funds for the Central Universities(Grant No.226-2022-00200)the Natural Science Foundation of China for Innovative Research Groups(Grant No.61721005)the Open Fund of Zhejiang Provincial Key Laboratory of Wide Bandgap Semiconductors,Hangzhou Global Scientific and Technological Innovation Center,Zhejiang University.
文摘Molten-alkali etching has been widely used to reveal dislocations in 4H silicon carbide(4H-SiC),which has promoted the identification and statistics of dislocation density in 4H-SiC single crystals.However,the etching mechanism of 4H-SiC is limited misunderstood.In this letter,we reveal the anisotropic etching mechanism of the Si face and C face of 4H-SiC by combining molten-KOH etching,X-ray photoelectron spectroscopy(XPS)and first-principles investigations.The activation energies for the molten-KOH etching of the C face and Si face of 4H-SiC are calculated to be 25.09 and 35.75 kcal/mol,respectively.The molten-KOH etching rate of the C face is higher than the Si face.Combining XPS analysis and first-principles calculations,we find that the molten-KOH etching of 4H-SiC is proceeded by the cycling of the oxidation of 4H-SiC by the dissolved oxygen and the removal of oxides by molten KOH.The faster etching rate of the C face is caused by the fact that the oxides on the C face are unstable,and easier to be removed with molten alkali,rather than the C face being easier to be oxidized.
基金the National Key Research and Development Programme of China(Grant No.2023YFC3804903).
文摘The Sustainable Development Goals(SDGs)are significantly off-course as we reach the midpoint of their 2030 deadline.From a scientific perspective,the critical challenge in achieving the SDGs lies in the need for more scientific principles to understand the complex socio-ecological systems(SES)and their interactions influencing the 17 SDGs.Here,we propose a scientific framework to clarify the common scientific principles and the rational treatment of diversity under these principles.The framework’s core is revealing the complex mechanisms underlying the achievement of each Sustainable Development Goal(SDG)and SDG interactions.Building upon the identified mechanisms,complex SES models can be established,and the implementation of SDGs can be formulated as a multi-objective optimization problem,seeking a compromise in competition between essential costs and desired benefits.Our framework can assist countries,and even the world in accelerating progress towards the SDGs.
文摘Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.
基金Major Project for New Generation of AI Grant No.2018AAA0100400)the Scientific Research Fund of Hunan Provincial Education Department,China(Grant Nos.21A0350,21C0439,22A0408,22A0414,2022JJ30231,22B0559)the National Natural Science Foundation of Hunan Province,China(Grant No.2022JJ50051).
文摘Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.
基金Supported by the Guangdong Province Basic and Applied Basic Research Fund Project(No.2020A1515110826)the National Natural Science Foundation of China(No.42006115)the Major Scientific and Technological Projects of Hainan Province(No.ZDKJ2021036)。
文摘Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is essential for the survival of sea cucumbers.Six MnSODs were identified from the transcriptomes of deep and shallow-sea sea cucumbers.To explore their environmental adaptation mechanism,we conducted environmental selection pressure analysis through the branching site model of PAML software.We obtained night positive selection sites,and two of them were significant(97F→H,134K→V):97F→H located in a highly conservative characteristic sequence,and its polarity c hange might have a great impact on the function of MnSOD;134K→V had a change in piezophilic a bility,which might help MnSOD adapt to the environment of high hydrostatic pressure in the deepsea.To further study the effect of these two positive selection sites on MnSOD,we predicted the point mutations of F97H and K134V on shallow-sea sea cucumber by using MAESTROweb and PyMOL.Results show that 97F→H,134K→V might improve MnSOD’s efficiency of scavenging superoxide a nion and its ability to resist high hydrostatic pressure by moderately reducing its stability.The above results indicated that MnSODs of deep-sea sea cucumber adapted to deep-sea environments through their amino acid changes in polarity,piezophilic behavior,and local stability.This study revealed the correlation between MnSOD and extreme environment,and will help improve our understanding of the organism’s adaptation mechanisms in deep sea.
文摘The electronic structure,magnetic,and optical properties of two-dimensional(2D)GaSe doped with rare earth elements X(X=Sc,Y,La,Ce,Eu)were calculated using the first-principles plane wave method based on den-sity functional theory.The results show that intrinsic 2D GaSe is a p-type nonmagnetic semiconductor with an indi-rect bandgap of 2.6611 eV.The spin-up and spin-down channels of Sc-,Y-,and La-doped 2D GaSe are symmetric,they are non-magnetic semiconductors.The magnetic moments of Ce-and Eu-doped 2D GaSe are 0.908μ_(B)and 7.163μ_(B),which are magnetic semiconductors.Impurity energy levels appear in both spin-up and spin-down chan-nels of Eu-doped 2D GaSe,which enhances the probability of electron transition.Compared with intrinsic 2D GaSe,the static dielectric constant of the doped 2D GaSe increases,and the polarization ability is strengthened.The ab-sorption spectrum of the doped 2D GaSe shifts in the low-energy direction,and the red-shift phenomenon occurs,which extends the absorption spectral range.The optical reflection coefficient of the doped 2D GaSe is improved in the low energy region,and the improvement of Eu-doped 2D GaSe is the most obvious.
基金funded by the Scientific Research Project of the Higher Education Department of Guizhou Province[Qianjiaoji 2022(187)]Department of Education of Guizhou Province[Guizhou Teaching and Technology(2023)015]+1 种基金Guizhou Medical University National Natural Science Foundation Cultivation Project(22NSFCP45)China Postdoctoral Science Foundation Project(General Program No.2022M720929).
文摘Background:The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics.However,biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited.Therefore,we aimed to develop a model that can effectively predict prognosis,differentiate microenvironment signatures,and optimize drug selection for patients with glioma.Materials and Methods:The CIBERSORT algorithm,bulk sequencing analysis,and single-cell RNA(scRNA)analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues.A predictive model was constructed based on cross-talk gene expression,and its effect on prognosis,recurrence prediction,and microenvironment characteristics was validated in multiple cohorts.The effect of the predictive model on drug selection was evaluated using the OncoPredict algorithm and relevant cellular biology experiments.Results:A high abundance of M2 macrophages in glioma tissues indicates poor prognosis,and cross-talk between macrophages and cancer cells plays a crucial role in shaping the tumor microenvironment.Eight genes involved in the cross-talk between macrophages and cancer cells were identified.Among them,periostin(POSTN),chitinase 3 like 1(CHI3L1),serum amyloid A1(SAA1),and matrix metallopeptidase 9(MMP9)were selected to construct a predictive model.The developed model demonstrated significant efficacy in distinguishing patient prognosis,recurrent cases,and characteristics of high inflammation,hypoxia,and immunosuppression.Furthermore,this model can serve as a valuable tool for guiding the use of trametinib.Conclusions:In summary,this study provides a comprehensive understanding of the interplay between M2 macrophages and cancer cells in glioma;utilizes a cross-talk gene signature to develop a predictive model that can predict the differentiation of patient prognosis,recurrence instances,and microenvironment characteristics;and aids in optimizing the application of trametinib in glioma patients.
基金funded by the Researchers Supporting Project Number RSPD2024R681,King Saud University,Riyadh,Saudi Arabia.
文摘With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety and efficiency.However,the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks,thereby resulting in low energy transmission efficiency.To enhance the energy transmission efficiency of IoT devices in energy harvesting,this paper focuses on the utilization of collaborative communication,along with pricing-based incentive mechanisms and auction strategies.We propose a dynamic relay selection scheme,including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism,to encourage more IoT devices to participate in the collaboration process.Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.
基金supported by the earmarked fund for China Agriculture Research System(CARS-35)the National Natural Science Foundation of China(32022078)supported by the National Supercomputer Centre in Guangzhou。
文摘Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.
文摘The year 2024 marks the 70th anniversary of the Five Principles of Peaceful Coexistence initiated by China.As an anchor foreign policy of New China and its first contribution of public product for the international community,the Five Principles have made a historic contribution to the building of a new type of just and equitable international relations,advancing the purposes and principles of the Charter of the United Nations,and becoming universally applicable basic norms for international relations and key fundamental principles of international law.
基金This research was funded by the Short-Term Electrical Load Forecasting Based on Feature Selection and optimized LSTM with DBO which is the Fundamental Scientific Research Project of Liaoning Provincial Department of Education(JYTMS20230189)the Application of Hybrid Grey Wolf Algorithm in Job Shop Scheduling Problem of the Research Support Plan for Introducing High-Level Talents to Shenyang Ligong University(No.1010147001131).
文摘Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.
基金supported by the Key-Area Research and Development Program of Guangdong Province under Grant No.2020B0101090004the National Natural Science Foundation of China under Grant No.62072215,the Guangzhou Basic Research Plan City-School Joint Funding Project under Grant No.2024A03J0405+1 种基金the Guangzhou Basic and Applied Basic Research Foundation under Grant No.2024A04J3458the State Archives Administration Science and Technology Program Plan of China under Grant 2023-X-028.
文摘Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accuracy.This module enables a central server to select a subset of participants to performmodel training based on data and device information.By doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are excluded.However,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection honestly.This lack of trustworthiness problem,which can demotivate participants,has received little attention.Another problem that has received little attention is the leakage of participants’private information during the selection process.We will therefore propose a federated learning framework with auditable participant selection.It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy.Considering the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors.Finally,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.
基金supported by the“Integration of Two Chains”Key Research and Development Projects of Shaanxi Province“Wheat Seed Industry Innovation Project”,Chinathe Key R&D of Yangling Seed Industry Innovation Center,China(Ylzy-xm-01)。
文摘The grain protein content(GPC)is the key parameter for wheat grain nutritional quality.This study conducted a resampling GWAS analysis using 406 wheat accessions across eight environments,and identified four previously reported GPC QTLs.An analysis of 87 landraces and 259 modern cultivars revealed the loss of superior GPC haplotypes,especially in Chinese cultivars.These haplotypes were preferentially adopted in different agroecological zones and had broad effects on wheat yield and agronomic traits.Most GPC QTLs did not significantly reduce yield,suggesting that high GPC can be achieved without a yield penalty.The results of this study provide a reference for future GPC breeding in wheat using the four identified QTLs.
文摘Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.
基金supported in part by The National Natural Science Foundation of China(62071255,62171232,61771257)The Major Projects of the Natural Science Foundation of the Jiangsu Higher Education Institutions(20KJA510009)+3 种基金The Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications),Ministry of Education(JZNY201914)The open research fund of National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology,Nanjing University of Posts and Telecommunications(KFJJ20170305)The Research Fund of Nanjing University of Posts and Telecommunications(NY218012)Henan province science and technology research projects High and new technology(No.182102210106).
文摘Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the transmitter and receiver must be parallel and coaxial;otherwise,the accuracy of mode detection at the receiver can be seriously influenced.In this paper,we design an OAM millimeter-wave communication system for overcoming the above limitation.Specifically,the first contribution is that the power distribution between different OAM modes and the capacity of the system with different mode sets are analytically derived for performance analysis.The second contribution lies in that a novel mode selection scheme is proposed to reduce the total interference between different modes.Numerical results show that system performance is less affected by the offset when the mode set with smaller modes or larger intervals is selected.
文摘Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.