Significant genomic introgression from grey junglefowl(Gallus sonneratii)to domestic chickens(Gallus gallus domesticus)

Background Chicken is one of the most numerous and widely distributed species around the world,and many studies support the multiple ancestral origins of domestic chickens.The research regarding the yellow skin phenotype in domestic chickens(regulated by BCO2)likely originating from the grey junglefowl serves as crucial evidence for demonstrating the multiple origins of chickens.However,beyond the BCO2 gene region,much remains unknown about the introgression from the grey junglefowl into domestic chickens.Therefore,in this study,based on wholegenome data of 149 samples including 4 species of wild junglefowls and 13 local domestic chicken breeds,we explored the introgression events from the grey junglefowl to domestic chickens.Results We successfully detected introgression regions besides BCO2,including two associated with growth trait(IGFBP2 and TKT),one associated with angiogenesis(TIMP3)and two members of the heat shock protein family(HSPB2 and CRYAB).Our findings suggest that the introgression from the grey junglefowl may impact the growth performance of chickens.Furthermore,we revealed introgression events from grey junglefowl at the BCO2 region in multiple domestic chicken breeds,indicating a phenomenon where the yellow skin phenotype likely underwent strong selection and was retained.Additionally,our haplotype analysis shed light on BCO2 introgression event from different sources of grey junglefowl into domestic chickens,possibly suggesting multiple genetic flows between the grey junglefowl and domestic chickens.Conclusions In summary,our findings provide evidences of the grey junglefowl contributing to the genetic diversity of domestic chickens,laying the foundation for a deeper understanding of the genetic composition within domestic chickens,and offering new perspectives on the impact of introgression on domestic chickens.

Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.

Management of Strawberry Grey Mold Disease Using Biocontrol Agents and Plant Extracts

Strawberry (Fragaria × ananassa Duch.) is a significant global soft fruit crop, prized for its nutrient content and pleasant flavor. However, diseases, particularly grey mold caused by Botrytis cinerea Pers. Fr. poses major constraints to strawberry production and productivity. Grey mold severely impacts fruit quality and quantity, diminishing market value. This study evaluated five B. cinerea isolates from various locations in the Ri-Bhoi district of Meghalaya. All isolates were pathogenic, with isolate SGM 2 identified as highly virulent. Host range studies showed the pathogen-producing symptoms in the fava bean pods, marigold, gerbera, and chrysanthemum flowers and in the fava bean, gerbera, and lettuce leaves. In vitro tests revealed that neem extract (15% w/v) achieved the highest mycelial growth inhibition at 76.66%, while black turmeric extract (5% w/v) had the lowest inhibition at 9.62%. Dual culture methods with bio-control agents indicated that Bacillus subtilis recorded the highest mean inhibition at 77.03%, while Pseudomonas fluorescens had the lowest at 20.36% against the two virulent isolates. Pot evaluations demonstrated that B. subtilis resulted in the lowest percent disease index at 20.59%, followed by neem extract at 23.31%, with the highest disease index in the control group at 42.51%. Additionally, B. subtilis significantly improved plant growth, yielding an average of 0.32 kg compared to 0.14 kg in the control. The promising results of B. subtilis and neem leaf extract from this study suggest their potential for eco-friendly managing grey mold in strawberries under field conditions.

Optimization of Agricultural Industrial Structure in Changping District of Beijing Based on Grey Relational Analysis

In the economic development of Beijing,although the share of the total amount of agricultural industry in the overall economy is relatively low,it has an important impact on the daily life of residents,social stability and the development of other industries.Changping District,as an important agricultural production base of Beijing,its agricultural development has an indispensable strategic significance for the stability and growth of the entire regional economy.Therefore,it is very important to study the structure of agricultural industry in Changping District.Based on the detailed analysis of the agricultural industrial structure of Changping District,this paper uses the grey relation theory to analyze the different industries in the agricultural industrial structure of Changping District,including planting,forestry,animal husbandry,fishery and agricultural,forestry,service industries,in order to reveal the impact of these industries on the agricultural industrial structure of Changping District.Through this study,it comes up with specific and feasible suggestions for the optimization of agricultural industrial structure in Changping District,and provides valuable reference for the agricultural development of other areas in Beijing.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

基于Fuzzy Matter-Element模型的电力上市公司财务核心竞争能力评价

鉴于电力上市公司财务核心竞争能力评价的复杂性以及单项指标评价结果的不相容性,本文在模糊物元分析的基础上,将熵权的理论方法与海明贴近度的概念相结合,建立了电力上市公司财务核心竞争能力评价的模糊物元模型。把不同电力企业作为物元的事物,以它们的各项评价指标及相应的模糊量值构造模糊物元,通过计算与理想模糊物元之间的海明贴近度,实现对上市电力公司财务核心竞争能力的综合评价。

Evaluation model of venture investment based on fuzzy matter-element method of combined weight

An evaluation model of an international venture investment project on the basis of fuzzy matter-element and combined weight methods is introduced. First, the compound fuzzy matter-element of optimal subordinate degree is constructed on the principle of the bigger-more-optimal or the less-more-optimal depending on the actual evaluation indicators, and combined with standard fuzzy matter-element to form a difference-square fuzzy matter-element. Secondly, a combined weight is calculated by both information entropy and the expert grading method. Finally, the compound fuzzy matter-element of Euclidian approach degree by M(·,+)method is constituted and used to classify venture investment projects. Based on the model above, six venture investment projects in a company are evaluated, and the results show that the projects are all good, which is demonstrated by the good income of the projects. Therefore, the coincidence of evaluation results and actual operation status indicates that the model is of great value in practical application.

Evaluation on Urban Land Ecological Security Based on the PSR Model and Matter-Element Analysis: A Case Study of Zhuhai, Guangdong, China

As one of the Special Economic Zones since the reform and opening up, Zhuhai has developed during the past 30 years. Its economic development, industrial structure and ecological environment have undergone great changes. Research on changes in Zhuhai’s land ecological security is of great significance. Using relevant data from 2007-2012, this study established a land ecological security assessment system based on the PSR conceptual framework model. The system contained 18 indicators from 3 aspects according to the concrete features of Zhuhai. Then we used the matterelement analysis and the improved entropy weight to analyze and evaluate the land ecological security of Zhuhai. The results showed that: from 2007 to 2012, the levels of the land ecological security of Zhuhai were “secure”, and the value increased year by year;as the land ecological security response value increased, Zhuhai was capable of solving land ecosystem problems. However, it should be noted that the structure of land ecosystem in Zhuhai has not formed and that rapid expansion of construction land has caused the shortage of cultivated land and other issues. Measures should be taken to control the construction area, improve land intensive utilization and improve the land ecological security.

Application of Matter-element Model in Soil Nutrient Evaluation of Ecological Fragile Region

On the basis of the soil environment investigation in Da＇an City, Jilin Province, China, 40 soil samples from main land use types were obtained and tested by standard method. Soil organic matter （SOM）, total N （TN）, total P （TP）, total K （TK）, available N （AN）, available P （AP） and available K （AK） were chosen as the evaluation factors. A regional soil nutrient evaluation model was developed based on the matter-element model. The results show that the soil samples with nutrient grade Ⅱ-Ⅴ respectively account for 10%, 30%, 32.5% and 27.5%, and those with grade Ⅳ and Ⅴ account for 60% in all samples. The relationship between soil nutrients and land types indicates that the nutrients of farmland are relatively good, with 41.7% of soil samples with the nutrient grade Ⅳ and Ⅴ. The nutrients of saline-alkali land and sandy land are the worst, with 100% of soil samples with the nutrient grade IV and V. And the ratios of soil samples grade IV and V in grassland and wasteland are respectively 62.5 % and 54.55%. Generally speaking, the soil nutrients status in Da＇an City is poor, 60% of soil samples are in poor and extremely poor conditions, indicating that the soil has been severely eroded. Being a relatively superior evaluation method with more accurate resuits and spatial distribution consistency, matter-element analysis is more suitable for regional soil nutrient evaluation than previous models.

Evaluation of the Strategic Tolerance Human Resource Management Based on Matter-Element Analysis Method

The main features and connotation of the tolerance human resource management based on strategy are analyzed and summarized. The theory and method of systematic engineering is used to build the evaluation index systems for the tolerance human resource management. On the basis of it, by using the theory of matter-element analysis, the matter-element models are formulated for the evaluation of the tolerance human resource management. Regarding annual tolerance human resource management as the object of matter-dement, the tolerance human resource ：management for two years are analyzed and evaluated through using the matter. element models proposed. A ease study is carried out and the satisfactory results are obtained. A new scientific methed is provided for the evaluation of human resource management based on strategy.

Carrying capacity of water environment in public tourism resources based on matter-element model

When water environmental carrying capacity of public resource tourist attraction is studied, the two action subjects of tourists and local residents should be discussed, and comprehensive consideration must be given to the influence of these two on water environment. On the basis of water resource carrying capacity and water quality carrying capacity, water environmental carrying capacity index of public resource tourist attraction was constructed, the model for the water environmental carrying capacity of public resource tourist attraction was established on the basis of matter-element model and analytical hierarchy process. By applying this method, water environmental carrying capacity situation of a certain public resource tourist attraction can be gained, moreover, situations about several aspects of water environmental carrying capacity can be evaluated.

Application of matter-element analysis based on entropy right to evaluate the pavement condition in permafrost region

Evaluation of pavement performance is one of the most important issues in a pavement-management system.By employing the concept of entropy,the matter-element model for evaluating pavement is established,and the weights of the evaluation indices are obtained from sur-veying data.By calculating the degree of dependence of the matter-element model,the pavement performance evaluation can be obtained by this method.The results show that the matter-element model based on entropy right has good performance for evaluating the pavement condition in permafrost region.

Matter-elements model and application for prediction of ccoal and gas outburst

The theory and method of extenics were applied to establish classical field matterelements and segment field matter elements for coal and gas outburst.A matter-element model for prediction was established based on five matter-elements,which includedgas pressure,types of coal damage,coal rigidity,initial speed of methane diffusionand in-situ stress.Each index weight was given fairly and quickly through the improvedanalytic hierarchy process,which need not carry on consistency checks,so accuracy ofassessment can be improved.

Integrated Evaluation of Air Traffic Controller Workload Based on Matter-Element Analysis

A model for evaluating the controller workload was presented based on matter-element analysis, particularly from a mansystem engineering perspective. On the basis of a questionnaire survey, 18 kinds of indexes which influence the controller workload were determined. By establishing the classical field and node field of the controller workload, the correlation function of the controller workload grade was obtained; then the correlation degree and estimated grade of controller workload were given. A case study verifies the feasibility of the proposed evaluation method.

Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning.Each feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical methods.As a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this research.Instability can result when the selection of features is subject to metaheuristics,which can lead to a wide range of results.Thus,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more equitably.We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes.In the proposed method,the number of features selected is minimized,while classification accuracy is increased.To test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

Deformation critical threshold estimation of Xiaowan ultrahigh arch dam with time-varying grey model

The structural behavior of the Xiaowan ultrahigh arch dam is primarily influenced by external loads and time-varying characteristics of dam concrete and foundation rock mass during long-term operation. According to overload testing with a geological model and the measured time series of installed perpendicular lines, the space and time evolution characteristics of the arch dam structure were analyzed, and its mechanical performance was evaluated. Subsequently, the deformation centroid of the deflective curve was suggested to indicate the magnitude and unique distribution rules for a typical dam section using the measured deformation values at multi-monitoring points. The ellipse equations of the critical ellipsoid for the centroid were derived from the historical measured time series. Hydrostatic and seasonal components were extracted from the measured deformation values with a traditional statistical model, and residuals were adopted as a grey component. A time-varying grey model was developed to accurately predict the evolution of the deformation behavior of the ultrahigh arch dam during future operation. In the developed model, constant coefficients were modified so as to be time-dependent functions, and the prediction accuracy was significantly improved through introduction of a forgetting factor. Finally, the critical threshold was estimated, and predicted ellipsoids were derived for the Xiaowan arch dam. The findings of this study can provide technical support for safety evaluation of the actual operation of ultrahigh arch dams and help to provide early warning of abnormal changes.