This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independ...This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.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
Gastrointestinal hemangioma(GIH)is clinically rare,accounting for 7%-10%of benign gastrointestinal tumors and 0.5%of systemic hemangiomas.GIH can occur as either solitary or multiple lesions,with gastrointestinal blee...Gastrointestinal hemangioma(GIH)is clinically rare,accounting for 7%-10%of benign gastrointestinal tumors and 0.5%of systemic hemangiomas.GIH can occur as either solitary or multiple lesions,with gastrointestinal bleeding as a significant clinical manifestation.Understanding the clinical and endoscopic features of GIH is essential for improving diagnostic accuracy,particularly through endoscopy and selective arteriography,which are highly effective in diagnosing GIH and preventing misdiagnosis and inappropriate treatment.Upon confirmed diagnosis,it is essential to thoroughly evaluate the patient's condition to determine the most suitable treatment modality—whether surgical,endoscopic,or minimally invasive intervention.The minimally invasive interventional partial embolization therapy using polyvinyl alcohol particles,proposed and implemented by Pospisilova et al,has achieved excellent clinical outcomes.This approach reduces surgical trauma and the inherent risks of traditional surgical treatments.展开更多
Birds,a fascinating and diverse group occupying various habitats worldwide,exhibit a wide range of life-history traits,reproductive methods,and migratory behaviors,all of which influence their immune systems.The assoc...Birds,a fascinating and diverse group occupying various habitats worldwide,exhibit a wide range of life-history traits,reproductive methods,and migratory behaviors,all of which influence their immune systems.The association between major histocompatibility complex(MHC)genes and certain ecological factors in response to pathogen selection has been extensively studied;however,the role of the co-working molecule T cell receptor(TCR)remains poorly understood.This study aimed to analyze the copy numbers of TCR-V genes,the selection pressure(ωvalue)on MHC genes using available genomic data,and their potential ecological correlates across 93 species from 13 orders.The study was conducted using the publicly available genome data of birds.Our findings suggested that phylogeny influences the variability in TCR-V gene copy numbers and MHC selection pressure.The phylogenetic generalized least squares regression model revealed that TCR-Vαδcopy number and MHC-I selection pressure were positively associated with body mass.Clutch size was correlated with MHC selection pressure,and Migration was correlated with TCR-Vβcopy number.Further analyses revealed that the TCR-Vβcopy number was positively correlated with MHC-IIB selection pressure,while the TCR-Vγcopy number was negatively correlated with MHC-I peptide-binding region selection pressure.Our findings suggest that TCR-V diversity is significant in adaptive evolution and is related to species’life-history strategies and immunological defenses and provide valuable insights into the mechanisms underlying TCR-V gene duplication and MHC selection in avian species.展开更多
[Objective] The aim was to study the internal relationship of the four theories on natural selection unit. [Method] The value field of fitness of heterozygote was investigated by constructing mathematical models, to c...[Objective] The aim was to study the internal relationship of the four theories on natural selection unit. [Method] The value field of fitness of heterozygote was investigated by constructing mathematical models, to clarify the internal relations of the four theories on natural selection unit. [Result] According to mathematical modes constructed in the study, only the mutated genes meet the requirements of natural selection on heterozygous and homozygous aspects, as well as show high fitness in heterozygous condition, could the mutated genes be kept, giving consideration to both individual and population adaptation. Thus, this methodology theoretically inte- grates the theories of individual selection, collective selection, and genetic selection as well as Kimura＇s neutral theory of health information. [Conclusion] The result of this study suggested that the four theories on natural selection unit can co-exist, and share common premises.展开更多
The increasing prevalence of diabetes has led to a growing population of endstage kidney disease(ESKD)patients with diabetes.Currently,kidney transplantation is the best treatment option for ESKD patients;however,it i...The increasing prevalence of diabetes has led to a growing population of endstage kidney disease(ESKD)patients with diabetes.Currently,kidney transplantation is the best treatment option for ESKD patients;however,it is limited by the lack of donors.Therefore,dialysis has become the standard treatment for ESKD patients.However,the optimal dialysis method for diabetic ESKD patients remains controversial.ESKD patients with diabetes often present with complex conditions and numerous complications.Furthermore,these patients face a high risk of infection and technical failure,are more susceptible to malnutrition,have difficulty establishing vascular access,and experience more frequent blood sugar fluctuations than the general population.Therefore,this article reviews nine critical aspects:Survival rate,glucose metabolism disorder,infectious complications,cardiovascular events,residual renal function,quality of life,economic benefits,malnutrition,and volume load.This study aims to assist clinicians in selecting individualized treatment methods by comparing the advantages and disadvantages of hemodialysis and peritoneal dialysis,thereby improving patients’quality of life and survival rates.展开更多
The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques we...The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.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 vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amount...In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.展开更多
This review updates the present status of the field of molecular markers and marker-assisted selection(MAS),using the example of drought tolerance in barley.The accuracy of selected quantitative trait loci(QTLs),candi...This review updates the present status of the field of molecular markers and marker-assisted selection(MAS),using the example of drought tolerance in barley.The accuracy of selected quantitative trait loci(QTLs),candidate genes and suggested markers was assessed in the barley genome cv.Morex.Six common strategies are described for molecular marker development,candidate gene identification and verification,and their possible applications in MAS to improve the grain yield and yield components in barley under drought stress.These strategies are based on the following five principles:(1)Molecular markers are designated as genomic‘tags’,and their‘prediction’is strongly dependent on their distance from a candidate gene on genetic or physical maps;(2)plants react differently under favourable and stressful conditions or depending on their stage of development;(3)each candidate gene must be verified by confirming its expression in the relevant conditions,e.g.,drought;(4)the molecular marker identified must be validated for MAS for tolerance to drought stress and improved grain yield;and(5)the small number of molecular markers realized for MAS in breeding,from among the many studies targeting candidate genes,can be explained by the complex nature of drought stress,and multiple stress-responsive genes in each barley genotype that are expressed differentially depending on many other factors.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
Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can a...Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.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
Typhoons are becoming frequent and intense with ongoing climate change,threatening ecological security and healthy forest development in coastal areas.Eucalyptus of a predominant introduced species in southern China,f...Typhoons are becoming frequent and intense with ongoing climate change,threatening ecological security and healthy forest development in coastal areas.Eucalyptus of a predominant introduced species in southern China,faces significant growth challenges because of typhoon.Therefore,it is vital to investigate the variation of related traits and select superior breeding materials for genetic improvement.Variance,genetic parameter,and correlation analyses were carried out on wind damage indices and eight wood proper-ties in 88 families from 11 provenances of 10-year-old Euca-lyptus camaldulensis.The selection index equation was used for evaluating multiple traits and selecting superior prov-enances and family lines as future breeding material.The results show that all traits were highly significantly differ-ent at provenance and family levels,with the wind damage index having the highest coefficient of genetic variation.The heritability of each trait ranged from 0.48 to 0.87,with the wind damage index,lignin and hemicellulose contents,and microfibril angle having the highest heritabilities.The wind damage index had a positive genetic correlation with wood density,a negative correlation with lignin content,a negative phenotypic correlation and a negative genetic correlation with microfibril angle.Wind damage index and genetic progress in the selection of eight wood traits varied from 7.2%to 614.8%.Three provenances and 12 superior families were selected.The genetic gains of the wind damage index were 10.2%and 33.9%for provenances and families,and these may be starting material for genetic modification for wind resistance in eucalyptus and for their dissemination to typhoon-prone coastal areas of southern China.展开更多
Scallop culture is an important way of bottom-seeding marine ranching,which is of great significance to improve the current situation of fishery resources.However,there are some problems in site-selection evaluation o...Scallop culture is an important way of bottom-seeding marine ranching,which is of great significance to improve the current situation of fishery resources.However,there are some problems in site-selection evaluation of marine ranching,such as imperfect criteria system,complex structure,untargeted criteria quantification,etc.In addition,no site-selection evaluation method of bottom-seeding culture areas for scallops is available.Therefore,we established a hierarchy structure model according to the analytic hierarchy process(AHP)theory,in which social,physical,chemical,and biological environments are used as main criteria,and marine functional zonation,water depth,current,water temperature,salinity,substrate type,water quality,sediment quality,red tide,phytoplankton,and zooplankton are used as sub-criteria,on which a multi-parameter evaluation system is set up.Meanwhile,the dualism method,assignment method,and membership function method were used to quantify sub-criteria,and a quantitative evaluation for the entire criteria was added,including the evaluation and analysis of two types of unsuitable environmental situations.By overall consideration in scallop yield,quality,and marine ranching construction objectives,the weight of the main criteria could be determined.Five grades in the suitability corresponding to the evaluation result were divided,and the Python language was used to create an evaluation system for efficient calculation and intuitive presentation of the evaluation outcome.Eight marine cases were simulated based on existing survey data,and the results prove that the method is feasible for evaluating and analyzing the site selection of bottom-seeding culture areas for scallops under various environmental situations.The proposed evaluation method can be promoted for the site selection of bottom-seeding marine ranching.This study provided theoretical and methodological references for the site selection evaluation of other types of marine ranching.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
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
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classif...In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.展开更多
文摘This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
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Supported by Science and Technology Plan of Qinghai Province,No.2023-ZJ-787.
文摘Gastrointestinal hemangioma(GIH)is clinically rare,accounting for 7%-10%of benign gastrointestinal tumors and 0.5%of systemic hemangiomas.GIH can occur as either solitary or multiple lesions,with gastrointestinal bleeding as a significant clinical manifestation.Understanding the clinical and endoscopic features of GIH is essential for improving diagnostic accuracy,particularly through endoscopy and selective arteriography,which are highly effective in diagnosing GIH and preventing misdiagnosis and inappropriate treatment.Upon confirmed diagnosis,it is essential to thoroughly evaluate the patient's condition to determine the most suitable treatment modality—whether surgical,endoscopic,or minimally invasive intervention.The minimally invasive interventional partial embolization therapy using polyvinyl alcohol particles,proposed and implemented by Pospisilova et al,has achieved excellent clinical outcomes.This approach reduces surgical trauma and the inherent risks of traditional surgical treatments.
基金supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2022C04014)Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding(No.2021C02068-10).
文摘Birds,a fascinating and diverse group occupying various habitats worldwide,exhibit a wide range of life-history traits,reproductive methods,and migratory behaviors,all of which influence their immune systems.The association between major histocompatibility complex(MHC)genes and certain ecological factors in response to pathogen selection has been extensively studied;however,the role of the co-working molecule T cell receptor(TCR)remains poorly understood.This study aimed to analyze the copy numbers of TCR-V genes,the selection pressure(ωvalue)on MHC genes using available genomic data,and their potential ecological correlates across 93 species from 13 orders.The study was conducted using the publicly available genome data of birds.Our findings suggested that phylogeny influences the variability in TCR-V gene copy numbers and MHC selection pressure.The phylogenetic generalized least squares regression model revealed that TCR-Vαδcopy number and MHC-I selection pressure were positively associated with body mass.Clutch size was correlated with MHC selection pressure,and Migration was correlated with TCR-Vβcopy number.Further analyses revealed that the TCR-Vβcopy number was positively correlated with MHC-IIB selection pressure,while the TCR-Vγcopy number was negatively correlated with MHC-I peptide-binding region selection pressure.Our findings suggest that TCR-V diversity is significant in adaptive evolution and is related to species’life-history strategies and immunological defenses and provide valuable insights into the mechanisms underlying TCR-V gene duplication and MHC selection in avian species.
基金Supported by the Scientific Research Program of the Education Department of Guangxi Zhuang Autonomous Region of China (200807MS065)the Education Department of Guangxi Zhuang Autonomous Region of China (201106LX743)~~
文摘[Objective] The aim was to study the internal relationship of the four theories on natural selection unit. [Method] The value field of fitness of heterozygote was investigated by constructing mathematical models, to clarify the internal relations of the four theories on natural selection unit. [Result] According to mathematical modes constructed in the study, only the mutated genes meet the requirements of natural selection on heterozygous and homozygous aspects, as well as show high fitness in heterozygous condition, could the mutated genes be kept, giving consideration to both individual and population adaptation. Thus, this methodology theoretically inte- grates the theories of individual selection, collective selection, and genetic selection as well as Kimura＇s neutral theory of health information. [Conclusion] The result of this study suggested that the four theories on natural selection unit can co-exist, and share common premises.
基金Supported by Science and Technology Department of Jilin Province,No.YDZJ202201ZYTS110 and No.20200201352JC.
文摘The increasing prevalence of diabetes has led to a growing population of endstage kidney disease(ESKD)patients with diabetes.Currently,kidney transplantation is the best treatment option for ESKD patients;however,it is limited by the lack of donors.Therefore,dialysis has become the standard treatment for ESKD patients.However,the optimal dialysis method for diabetic ESKD patients remains controversial.ESKD patients with diabetes often present with complex conditions and numerous complications.Furthermore,these patients face a high risk of infection and technical failure,are more susceptible to malnutrition,have difficulty establishing vascular access,and experience more frequent blood sugar fluctuations than the general population.Therefore,this article reviews nine critical aspects:Survival rate,glucose metabolism disorder,infectious complications,cardiovascular events,residual renal function,quality of life,economic benefits,malnutrition,and volume load.This study aims to assist clinicians in selecting individualized treatment methods by comparing the advantages and disadvantages of hemodialysis and peritoneal dialysis,thereby improving patients’quality of life and survival rates.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant no.2019QZKK0904)Natural Science Foundation of Hebei Province(Grant no.D2022403032)S&T Program of Hebei(Grant no.E2021403001).
文摘The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.
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.
基金supported in part by the National Natural Science Foundation of China(No.61701197)in part by the National Key Research and Development Program of China(No.2021YFA1000500(4))in part by the 111 Project(No.B23008).
文摘In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.
基金supported by Bolashak International Fellowships,Center for International Programs,Ministry of Education and Science,KazakhstanAP14869777 supported by the Ministry of Education and Science,KazakhstanResearch Projects BR10764991 and BR10765000 supported by the Ministry of Agriculture,Kazakhstan。
文摘This review updates the present status of the field of molecular markers and marker-assisted selection(MAS),using the example of drought tolerance in barley.The accuracy of selected quantitative trait loci(QTLs),candidate genes and suggested markers was assessed in the barley genome cv.Morex.Six common strategies are described for molecular marker development,candidate gene identification and verification,and their possible applications in MAS to improve the grain yield and yield components in barley under drought stress.These strategies are based on the following five principles:(1)Molecular markers are designated as genomic‘tags’,and their‘prediction’is strongly dependent on their distance from a candidate gene on genetic or physical maps;(2)plants react differently under favourable and stressful conditions or depending on their stage of development;(3)each candidate gene must be verified by confirming its expression in the relevant conditions,e.g.,drought;(4)the molecular marker identified must be validated for MAS for tolerance to drought stress and improved grain yield;and(5)the small number of molecular markers realized for MAS in breeding,from among the many studies targeting candidate genes,can be explained by the complex nature of drought stress,and multiple stress-responsive genes in each barley genotype that are expressed differentially depending on many other factors.
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financial supports from National Natural Science Foundation of China(No.62205172)Huaneng Group Science and Technology Research Project(No.HNKJ22-H105)Tsinghua University Initiative Scientific Research Program and the International Joint Mission on Climate Change and Carbon Neutrality。
文摘Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.
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.
基金supported by the National Natural Science Foundation of China(Grant Number 32201527)National Key R&D Program of China(Grant No.2023YFD2201004).
文摘Typhoons are becoming frequent and intense with ongoing climate change,threatening ecological security and healthy forest development in coastal areas.Eucalyptus of a predominant introduced species in southern China,faces significant growth challenges because of typhoon.Therefore,it is vital to investigate the variation of related traits and select superior breeding materials for genetic improvement.Variance,genetic parameter,and correlation analyses were carried out on wind damage indices and eight wood proper-ties in 88 families from 11 provenances of 10-year-old Euca-lyptus camaldulensis.The selection index equation was used for evaluating multiple traits and selecting superior prov-enances and family lines as future breeding material.The results show that all traits were highly significantly differ-ent at provenance and family levels,with the wind damage index having the highest coefficient of genetic variation.The heritability of each trait ranged from 0.48 to 0.87,with the wind damage index,lignin and hemicellulose contents,and microfibril angle having the highest heritabilities.The wind damage index had a positive genetic correlation with wood density,a negative correlation with lignin content,a negative phenotypic correlation and a negative genetic correlation with microfibril angle.Wind damage index and genetic progress in the selection of eight wood traits varied from 7.2%to 614.8%.Three provenances and 12 superior families were selected.The genetic gains of the wind damage index were 10.2%and 33.9%for provenances and families,and these may be starting material for genetic modification for wind resistance in eucalyptus and for their dissemination to typhoon-prone coastal areas of southern China.
基金Supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42010203)the National Natural Science Foundation of China(No.42176090)。
文摘Scallop culture is an important way of bottom-seeding marine ranching,which is of great significance to improve the current situation of fishery resources.However,there are some problems in site-selection evaluation of marine ranching,such as imperfect criteria system,complex structure,untargeted criteria quantification,etc.In addition,no site-selection evaluation method of bottom-seeding culture areas for scallops is available.Therefore,we established a hierarchy structure model according to the analytic hierarchy process(AHP)theory,in which social,physical,chemical,and biological environments are used as main criteria,and marine functional zonation,water depth,current,water temperature,salinity,substrate type,water quality,sediment quality,red tide,phytoplankton,and zooplankton are used as sub-criteria,on which a multi-parameter evaluation system is set up.Meanwhile,the dualism method,assignment method,and membership function method were used to quantify sub-criteria,and a quantitative evaluation for the entire criteria was added,including the evaluation and analysis of two types of unsuitable environmental situations.By overall consideration in scallop yield,quality,and marine ranching construction objectives,the weight of the main criteria could be determined.Five grades in the suitability corresponding to the evaluation result were divided,and the Python language was used to create an evaluation system for efficient calculation and intuitive presentation of the evaluation outcome.Eight marine cases were simulated based on existing survey data,and the results prove that the method is feasible for evaluating and analyzing the site selection of bottom-seeding culture areas for scallops under various environmental situations.The proposed evaluation method can be promoted for the site selection of bottom-seeding marine ranching.This study provided theoretical and methodological references for the site selection evaluation of other types of marine ranching.
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 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.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
基金supported by National Natural Science Foundation of China(62371098)Natural Science Foundation of Sichuan Province(2023NSFSC1422)+1 种基金National Key Research and Development Program of China(2021YFB2900404)Central Universities of South west Minzu University(ZYN2022032).
文摘In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.
