A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

Genomic signatures of selection,local adaptation and production type characterisation of East Adriatic sheep breeds

Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production(carcass,wool and milk yield).Therefore,eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation(EAS),are generally considered as multipurpose breeds(milk,meat and wool),not specialised for a particular type of production,but known for their robustness and resistance to certain environmental conditions.Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures,decipher their biological and productive functionality,and provide a"genomic"characterization of EAS adaptation and determine its production type.Results We identified positive selection signatures in EAS using several methods based on reduced local variation,linkage disequilibrium and site frequency spectrum(eROHi,iHS,nSL and CLR).Our analyses identified numerous genomic regions and genes(e.g.,desmosomal cadherin and desmoglein gene families)associated with environmental adaptation and economically important traits.Most candidate genes were related to meat/production and health/immune response traits,while some of the candidate genes discovered were important for domestication and evolutionary processes(e.g.,HOXa gene family and FSIP2).These results were also confirmed by GO and QTL enrichment analysis.Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type,ultimately providing a new opportunity for future breeding programmes.At the same time,the numerous genes identified will improve our understanding of ruminant(sheep)robustness and resistance in the harsh and specific Mediterranean environment.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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.

Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.

Cultural Adaptation of the Mental Health Literacy Scale

Background:Mental health literacy(MHL)refers to one’s knowledge and understanding of mental health disorders and their treatments.This literacy may be influenced by cultural norms and values that shape individuals’experiences,beliefs,attitudes,and behaviors regarding mental health.This study focuses on adapting the Mental health literacy scale(MHLS)for use in the multicultural context of Israel.Objectives include validating its construct,assessing its accuracy in measuring MHL in this diverse setting and examining and comparing levels of MHL across different cultural groups.Methods:The data collection included 1057 participants,representing all the ethnic groups of the Israeli population aged 18 and over.The tools included the MHLS and a demographic questionnaire.Confirmatory factor analysis(CFA)was employed to assess the original structure of the MHLS.Results:The results revealed that after evaluating the original MHLS,five items were excluded,leading to the validation of a modified version—Israeli mental health scale(IMHLS)with four factors and 25 items.CFA and reliability analyses supported an established and robust four-factor model.Significant ethnic differences in MHLS scores were identified,with Muslim participants showing the highest familiarity with mental disorders,followed by Druze and Christian participants,while Jewish participants had the lowest familiarity.Conclusion:The study concluded that the IMHLS is a valid and reliable tool for assessing MHL in Israel’s diverse and multicultural population.The revised scale better reflects the cultural nuances of the Israeli context.The significant ethnic differences that the study revealed in IMHLS emphasize the importance of culturally sensitive mental health interventions tailored to different ethnic groups in Israel.

Genomic selection for meat quality traits based on VIS/NIR spectral information

The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.

Early and accurate diagnosis and selection of appropriate treatment plans are crucial for patients with gastrointestinal hemangiomas

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.

On the Adaptability Range, Self-Selection, and Economic Nature of Biological Evolution

This article employs a combined approach of biology and economics to reveal that biological evolution has an economic nature, evolving towards improved energy efficiency. The orthodox Darwinian theory of evolution describes evolution as the random variation of organisms and their survival through natural selection. In fact, the natural environment itself is a constantly changing context, and the strategy to adapt to this change is to enhance behavioral capabilities, thereby expanding the range and dimensions of behavior. Therefore, the improvement of behavioral capabilities is an important aspect of evolution. The enhancement of behavioral capabilities expands the range of adaptation to the natural environment and increases the space for behavioral choices. Within this space of behavioral choices, some options are more effective and superior to others;thus, the ability to select is necessary to make the improved behavioral capabilities more beneficial to the organism itself. The birth and development of the brain serve the purpose of selection. By using the brain to make selections, at least the “better” behavior will be chosen between two alternatives. Once the better behavior yields better results, and the organism can associate these results with the corresponding behavior, it will persist in this behavior. The persistent repetition of a behavior over generations will form a habit. Habits passed down through generations constitute a new environment, causing the organism’s genes to activate or deactivate certain functions, ultimately leading to genetic changes that are beneficial to that habit. Since the brain’s selection represents the organism’s self-selection, it differs from random variation;it is also a rational selection, choosing behaviors that either obtain more energy or reduce energy consumption. Thus, this evolution possesses an economic nature.

FedAdaSS: Federated Learning with Adaptive Parameter Server Selection Based on Elastic Cloud Resources

The rapid expansion of artificial intelligence(AI)applications has raised significant concerns about user privacy,prompting the development of privacy-preserving machine learning(ML)paradigms such as federated learning(FL).FL enables the distributed training of ML models,keeping data on local devices and thus addressing the privacy concerns of users.However,challenges arise from the heterogeneous nature of mobile client devices,partial engagement of training,and non-independent identically distributed(non-IID)data distribution,leading to performance degradation and optimization objective bias in FL training.With the development of 5G/6G networks and the integration of cloud computing edge computing resources,globally distributed cloud computing resources can be effectively utilized to optimize the FL process.Through the specific parameters of the server through the selection mechanism,it does not increase the monetary cost and reduces the network latency overhead,but also balances the objectives of communication optimization and low engagement mitigation that cannot be achieved simultaneously in a single-server framework of existing works.In this paper,we propose the FedAdaSS algorithm,an adaptive parameter server selection mechanism designed to optimize the training efficiency in each round of FL training by selecting the most appropriate server as the parameter server.Our approach leverages the flexibility of cloud resource computing power,and allows organizers to strategically select servers for data broadcasting and aggregation,thus improving training performance while maintaining cost efficiency.The FedAdaSS algorithm estimates the utility of client systems and servers and incorporates an adaptive random reshuffling strategy that selects the optimal server in each round of the training process.Theoretical analysis confirms the convergence of FedAdaSS under strong convexity and L-smooth assumptions,and comparative experiments within the FLSim framework demonstrate a reduction in training round-to-accuracy by 12%–20%compared to the Federated Averaging(FedAvg)with random reshuffling method under unique server.Furthermore,FedAdaSS effectively mitigates performance loss caused by low client engagement,reducing the loss indicator by 50%.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

Mechanisms of species divergence through visual adaptation and sexual selection:Perspectives from a cichlid model system

The theory of ecological speciation suggests that assortative mating evolves most easily when mating preferences aredirectly linked to ecological traits that are subject to divergent selection. Sensory adaptation can play a major role in this process,because selective mating is often mediated by sexual signals: bright colours, complex song, pheromone blends and so on. Whendivergent sensory adaptation affects the perception of such signals, mating patterns may change as an immediate consequence.Alternatively, mating preferences can diverge as a result of indirect effects: assortative mating may be promoted by selectionagainst intermediate phenotypes that are maladapted to their (sensory) environment. For Lake Victoria cichlids, the visual environmentconstitutes an important selective force that is heterogeneous across geographical and water depth gradients. We investigatethe direct and indirect effects of this heterogeneity on the evolution of female preferences for alternative male nuptial colours(red and blue) in the genus Pundamilia. Here, we review the current evidence for divergent sensory drive in this system, extractgeneral principles, and discuss future perspectives [Current Zoology 56 (3): 285-299, 2010].

Adaptation or ecological trap? Altered nest-site selection by Reed Parrotbills after an extreme flood

Background:Floods and other extreme events have disastrous effects on wetland breeding birds.However,such events and their consequences are difficult to study due to their rarity and unpredictable occurrence.Methods:Here we compared nest-sites chosen by Reed Parrotbills(Paradoxornis heudei) during June-August 2016 in Yongnianwa Wetlands,Hebei Province,China,before and after an extreme flooding event.Results:Twenty-three nests were identified before and 13 new nests after the flood.There was no significant difference in most nest-site characteristics,such as distance from the road,height of the reeds in which nests were built,or nest volume before or after the flood.However,nests after the flood were located significantly higher in the vegetation compared to before the flood(mean ± SE:1.17 ± 0.13 m vs.0.75 ± 0.26 m,p < 0.01).However,predation rate also increased significantly after the flood(67% vs.25%,p = 0.030).Conclusions:Our results suggested that Reed Parrotbills demonstrated behavioral plasticity in their nest-site selection.Thus,they appeared to increase the height of their nests in response to the drastically changing water levels in reed wetlands,to reduce the likelihood that their nests would be submerged again by flooding.However,predation rate also increased significantly after the flood,suggesting that the change in nest height to combat the threat of flooding made the nests more susceptible to other threats,such as predation.Animals' response to rare climatic events,such as flooding,may produce ecological traps if they make the animals more susceptible to other kinds of threats they are more likely to continue to encounter.

Public Perceptions of Cryosphere Change and the Selection of Adaptation Measures in the rmqi River Basin

This study focuses on the characters of public perceptions on climate and cryosphere change,which are based on a questionnaire survey in the(U|¨)r(u|¨)mqi River Basin.In comparison with scientific observation results of climate and cryosphere change,this paper analyzes the possible impact of the change on water resources and agriculture production in the area.Perceptions of most respondents on climate and cryosphere changes confirm the main objective facts.For the selection of adaptation measures addressing the shortage of water resource,the results are as follows:most people preferred to choose the measures like "policy change" and "basic facility construction" which are mostly implemented by the government and the policy-making department;some people showed more preference to the measures of avoiding unfavorable natural environment,such as finding job in or migrating to other places.The urgency of personal participation in the adaptation measures is still inadequate.Some adaptation measures should be implemented in line with local conditions and require the organic combination of "resource-development" with "water-saving".

Identification of genes under positive selection reveals evolutionary adaptation of Ulva mutabilis

Ulvophytes are attractive model systems for understanding the evolution of growth,development,and environmental stress responses.They are untapped resources for food,fuel,and high-value compounds.The rapid and abundant growth of Ulva species makes them key contributors to coastal biogeochemical cycles,which can cause significant environmental problems in the form of green tides and biofouling.Until now,the Ulva mutabilis genome is the only Ulva genome to have been sequenced.To obtain further insights into the evolutionary forces driving divergence in Ulva species,we analyzed 3905 single copy ortholog family from U.mutabilis,Chlamydomonas reinhardtii and Volvox carteri to identify genes under positive selection(GUPS)in U.mutabilis.We detected 63 orthologs in U.mutabilis that were considered to be under positive selection.Functional analyses revealed that several adaptive modifications in photosynthesis,amino acid and protein synthesis,signal transduction and stress-related processes might explain why this alga has evolved the ability to grow very rapidly and cope with the variable coastal ecosystem environments.

Positive selection analysis reveals the deep-sea adaptation of a hadal sea cucumber ( Paelopatides sp.) to the Mariana Trench

The Mariana Trench,the deepest trench on the earth,is ideal for deep-sea adaptation research due to its unique characters,such as the highest hydrostatic pressure on the Earth,constant ice-cold temperature,and eternal darkness.In this study,tissues of a the hadal holothurian(Paelopatides sp.)were fi xed with RNA later in situ at~6501-m depth in the Mariana Trench,which,to our knowledge,is the deepest in-situ fi xed animal sample.A high-quality transcript was obtained by de-novo transcriptome assembly.A maximum likelihood tree was constructed based on the single copy orthologs across nine species with their available omics data.To investigate deep-sea adaptation,113 positively selected genes(PSGs)were identifi ed in Paelopatides sp.Some PSGs such as microphthalmia-associated transcription factor(MITF)may contribute to the distinct phenotype of Paelopatides sp.,including its translucent white body and degenerated ossicles.At least eight PSGs(transcription factor 7-like 2[TCF7L2],ETS-related transcription factor Elf-2-like[ELF2],PERQ amino acid-rich with GYF domain-containing protein[GIGYF],cytochrome c oxidase subunit 7a,[COX7A],type I thyroxine 5′-deiodinase[DIO1],translation factor GUF1[GUF1],SWI/SNF related-matrix-associated actin-dependent regulator of chromatin subfamily C and subfamily E,member 1[SMARCC]and[SMARCE1])might be related to cold adaptation.In addition,at least nine PSGs(cell cycle checkpoint control protein[RAD9A],replication factor A3[RPA3],DNA-directed RNA polymerases I/II/III subunit RPABC1[POLR2E],putative TAR DNA-binding protein 43 isoform X2[TARDBP],ribonucleoside-diphosphate reductase subunit M1[RRM1],putative serine/threonine-protein kinase[SMG1],transcriptional regulator[ATRX],alkylated DNA repair protein alkB homolog 6[ALKBH6],and PLAC8 motif-containing protein[PLAC8])may facilitate the repair of DNA damage induced by the high hydrostatic pressure,coldness,and high concentration of cadmium in the upper Mariana Trench.

Signatures of positive selection for local adaptation of African native cattle populations:A review

Cattle are central to the lives and diverse cultures of African people.It has played a crucial role in providing valuable protein for billions of households and sources of income and employment for producers and other actors in the livestock value chains.The long-term natural selection of African cattle typically signals signatures in the genome,contributes to high genetic differentiations across breeds.This has enabled them to develop unique adaptive traits to cope with inadequate feed supply,high temperatures,high internal and external parasites,and diseases.However,these unique cattle genetic resources are threatened by indiscriminate cross-breeding,breed replacements with exotic cosmopolitan breeds,and climate change pressures.Although there are no functional genomics studies,recent advancements in genotyping and sequencing technologies have identified and annotated limited functional genes and causal variants associated with unique adaptive and economical traits of African cattle populations.These genome-wide variants serve as candidates for breed improvement and support conservation efforts for endangered cattle breeds against future climate changes.Therefore,this review plans to collate comprehensive information on the identified selection footprints to support genomic studies in African cattle to confirm the validity of the results and provide a framework for further genetic association and QTL fine mapping studies.

On the Influence and Strategy Selection of Cross - cultural Adaptation

Different cultural subjects have different cultural and historical background, and thus inevitably bring difference in people's ideological values and behaviors etc,and even shocks. T oday,cross - cultural adaptation becomes a common social issue,and arouses general concern of the whole society. Influencing factors of cross - cultural adaptation include cultural distance,personality psychology,thinking pattern,values,social living environment,social support,know ledge & skills,pragmatic transfer etc. Based on making clear the problems,cross - cultural adaptation should be realized from multiple aspects.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

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.

Diabetic retinopathy identification based on multi-sourcefree domain adaptation

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

Double adaptive selection strategy for MOEA/D

Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection(DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS(MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.