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.

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.

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.

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.

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.

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.

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.

Multi-Objective Optimization of Fused Deposition Modeling for Mechanical Properties of Biopolymer Parts Using the Grey-Taguchi Method

The urgent need to develop customized functional products only possible by 3D printing had realized when faced with the unavailability of medical devices like surgical instruments during the coronavirus-19 disease and the ondemand necessity to perform surgery during space missions.Biopolymers have recently been the most appropriate option for fabricating surgical instruments via 3D printing in terms of cheaper and faster processing.Among all 3D printing techniques,fused deposition modelling(FDM)is a low-cost and more rapid printing technique.This article proposes the fabrication of surgical instruments,namely,forceps and hemostat using the fused deposition modeling(FDM)process.Excellent mechanical properties are the only indicator to judge the quality of the functional parts.The mechanical properties of FDM-processed parts depend on various process parameters.These parameters are layer height,infill pattern,top/bottom pattern,number of top/bottom layers,infill density,flow,number of shells,printing temperature,build plate temperature,printing speed,and fan speed.Tensile strength and modulus of elasticity are chosen as evaluation indexes to ascertain the mechanical properties of polylactic acid(PLA)parts printed by FDM.The experiments have performed through Taguchi’s L27orthogonal array(OA).Variance analysis(ANOVA)ascertains the significance of the process parameters and their percent contributions to the evaluation indexes.Finally,as a multiobjective optimization technique,grey relational analysis(GRA)obtains an optimal set of FDM process parameters to fabricate the best parts with comprehensive mechanical properties.Scanning electron microscopy(SEM)examines the types of defects and strong bonding between rasters.The proposed research ensures the successful fabrication of functional surgical tools with substantial ultimate tensile strength(42.6 MPa)and modulus of elasticity(3274 MPa).

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.

A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network.Additionally,the nodes are dis-carding and modifying the data packets according to the requirements of the sys-tem.The assault modifies the fundamental concept of the WSNs,which is that different devices should communicate with one another.In the proposed system,there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices.The currently available model is unable to recognise the myriad of different kinds of attacks.The fuzzy engine identified suspicious actions by utilising the rules that were gen-erated to make a prediction about the malicious node that would halt the process.Experiments conducted using simulation are used to determine delay,accuracy,energy consumption,throughput,and the ratio of packets successfully delivered.It stands in contrast to the model that was suggested,as well as the methodologies that are currently being used,and analogue behavioural modelling.In comparison to the existing method,the proposed model achieves an accuracy rate of 45 per-cent,a packet delivery ratio of 79 percent,and a reduction in energy usage of around 35.6 percent.These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the net-work’s capability of detecting grey hole assaults.

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

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.

Materials Selection of Thermoplastic Matrices of Natural Fibre Composites for Cyclist Helmet Using an Integration of DMAIC Approach in Six Sigma Method Together with Grey Relational Analysis Approach

Natural fibre reinforced polymer composite(NFRPC)materials are gaining popularity in the modern world due to their eco-friendliness,lightweight nature,life-cycle superiority,biodegradability,low cost,and noble mechanical properties.Due to the wide variety of materials available that have comparable attributes and satisfy the requirements of the product design specification,material selection has become a crucial component of design for engineers.This paper discusses the study’s findings in choosing the suitable thermoplastic matrices of Natural Fibre Composites for Cyclist Helmet utilising the DMAIC,and GRA approaches.The results are based on integrating two decision methods implemented utilising two distinct decision-making approaches:qualitative and quantitative.This study suggested thermoplastic polyethylene as a particularly ideal matrix in composite cyclist helmets during the selection process for the best thermoplastic matrices material using the 6σtechnique,with the decision based on the highest performance,the lightest weight,and the most environmentally friendly criteria.The DMAIC and GRA approach significantly influenced the material selection process by offering different tools for each phase.In the future study,selection technique may have been more exhaustive if more information from other factors had been added.

Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

At an early point,the diagnosis of pancreatic cancer is mediocre,since the radiologist is skill deficient.Serious threats have been posed due to the above reasons,hence became mandatory for the need of skilled technicians.However,it also became a time-consuming process.Hence the need for automated diagnosis became mandatory.In order to identify the tumor accurately,this research pro-poses a novel Convolution Neural Network(CNN)based superior image classi-fication technique.The proposed deep learning classification strategy has a precision of 97.7%,allowing for more effective usage of the automatically exe-cuted feature extraction technique to diagnose cancer cells.Comparative analysis with CNN-Grey Wolf Optimization(GWO)is carried based on varied testing and training outcomes.The suggested study is carried out at a rate of 90%–10%,80%–20%,and 70%–30%,indicating the robustness of the proposed research work.Outcomes show that the suggested method is effective.GWO-CNN is reli-able and accurate relative to other detection methods available in the literatures.

Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.

Prediction of Total Output Value of Construction Industry in Jiangxi Province Based on Grey Prediction Model

In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.

Mapping the Child’s Grey Interior:Authenticity of Plague Narrative in The Great Plague

The Great Plague:The Diary of Alice Paynton,London 1665-1666 adopts the realistic painting style and direct writing to the painful experience,describes the situation of the Great Plague in London truly from the perspective of children,and breaks people’s cognitive expectation of children’s literature,the construction of children’s Gray inner world is completed behind the true description of the Great Plague in London with the true brushwork,which presents a different world of childlike innocence.Despite the importance of epidemics writing in literature,the child’s diary perspective of them is one of the least developed of Plague Narrative.Only when authenticity and the child’s grey interior are integrated will we reveal a true picture of society as it was.

Multi-Criteria Group Decision-Making Method Based on an Improved Single-Valued Neutrosophic Hamacher Weighted Average Operator and Grey Relational Analysis

This paper proposes a multi-criteria decision-making (MCGDM) method based on the improved single-valued neutrosophic Hamacher weighted averaging (ISNHWA) operator and grey relational analysis (GRA) to overcome the limitations of present methods based on aggregation operators. First, the limitations of several existing single-valued neutrosophic weighted averaging aggregation operators (i.e. , the single-valued neutrosophic weighted averaging, single-valued neutrosophic weighted algebraic averaging, single-valued neutrosophic weighted Einstein averaging, single-valued neutrosophic Frank weighted averaging, and single-valued neutrosophic Hamacher weighted averaging operators), which can produce some indeterminate terms in the aggregation process, are discussed. Second, an ISNHWA operator was developed to overcome the limitations of existing operators. Third, the properties of the proposed operator, including idempotency, boundedness, monotonicity, and commutativity, were analyzed. Application examples confirmed that the ISNHWA operator and the proposed MCGDM method are rational and effective. The proposed improved ISNHWA operator and MCGDM method can overcome the indeterminate results in some special cases in existing single-valued neutrosophic weighted averaging aggregation operators and MCGDM methods.

A hybrid machine learning optimization algorithm for multivariable pore pressure prediction

Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.