Wolf Research Articles

Research and application of a novel grey multivariable model in port scale prediction under the impact of Free Trade Zone

Purpose Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.Design/methodology/approach Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.Findings In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.Practical implications The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.Originality/value Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.

A novel model for optimal selection of relay bus with maximum link reliability in VANET using hybrid fuzzy niching grey wolf optimization

A novel model for optimal selection of relay bus with maximum link reliability in VANET using hybrid fuzzy niching grey wolf optimization

Data-driven optimization for microgrid control under distributed energy resource variability

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

A Hybrid Discrete Grey Wolf Optimization Algorithm Imbalance-ness Aware for Solving Two-dimensional Bin-packing Problems

A Hybrid Discrete Grey Wolf Optimization Algorithm Imbalance-ness Aware for Solving Two-dimensional Bin-packing Problems

Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?

The potential of advanced tree-based models and optimized deep learning algorithms to predict fluvial bedload transport was explored, identifying the most flexible and accurate algorithm, and the optimum set of readily available and reliable inputs. Using 926 datasets for 20 rivers, the performance of three groups of models was tested: (1) standalone tree-based models Alternating Model Tree (AMT) and Dual Perturb and Combine Tree (DPCT); (2) ensemble tree-based models Iterative Absolute Error Regression (IAER), ensembled with AMT and DPCT; and (3) optimized deep learning models Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) ensembled with Grey Wolf Optimizer. Comparison of the predictive performance of the models with that of commonly used empirical equations and sensitivity analysis of the driving variables revealed that: (i) the coarse grain-size percentile D 90 was the most effective variable in bedload transport prediction (where D x is the xth percentile of the bed surface grain size distribution), followed by D 84, D 50, flow discharge, D 16, and channel slope and width; (ii) all tree-based models and optimized deep learning algorithms displayed ‘very good’ or ‘good’ performance, outperforming empirical equations; and (iii) all algorithms performed best when all input parameters were used. Thus, a range of different input variable combinations must be considered in the optimization of these models. Overall, ensemble algorithms provided more accurate predictions of bedload transport than their standalone counterpart. In particular, the ensemble tree-based model IAER-AMT performed most strongly, displaying great potential to produce robust predictions of bedload transport in coarse-grained rivers based on a few readily available flow and channel variables.

Fine tuning deep learning models for breast tumor classification

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958–2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000–2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Molecular Detection of Anaplasma phagocytophilum in Small Mammals and Infesting Ticks in Laikipia County, Kenya

Anaplasmosis is a set of disease conditions of various mammals caused by bacteria species of the genus Anaplasma. These are sub-microscopic, Gram-negative, obligate intracellular pathogens that infect both vertebrate and invertebrate hosts. Significant species that infect domestic and wildlife animals include Anaplasma marginale, Anaplasma ovis, Anaplasma mesaeterum, Anaplasma platys, and Anaplasma phagocytophilum. Although A. phagocytophilum has a widespread distribution, there are only a few epidemiological reports from sub-Saharan Africa. This study focused on molecular detection and characterization of A. phagocytophilum in small mammals and their infesting ticks in Laikipia County, Kenya. A total of 385 blood and 84 tick archival samples from small mammals (155 females and 230 males) were analyzed. The blood samples were subjected to a nested PCR-HRM melt analysis using species-specific primers to amplify the 16S ribosomal RNA genes. The ticks were also subjected to nested PCR-HRM involving 16S rRNA gene primers. Anaplasma phagocytophilum DNA was detected in 19 out of 385 samples using species-specific 16S rRNA gene primers giving a prevalence of 4.9% for A. phagocytophilum. Analysis of the tick’s samples using 16S rRNA gene species-specific primers also detected A. phagocytophilum in 3 samples from Haemaphysalis leachi ticks (3/84) equivalent to prevalence of 3.6%. Sequencing of 16S rRNA PCR products confirmed A. phagocytophilum in small mammals and ticks’ samples. Phylogenetic analysis of the haplotype from this study demonstrated a close ancestral link with strains from Canis lupus familiaris, Alces alces, Apodemus agrarius, and ticks (Haemaphysalis longicornis) reported in Europe, China, and Africa. Comparison was also made with a known pathogenic A. phagocytophilum variant HA and a nonpathogenic variant 1 that were clustered into a distinctive clade different form haplotypes detected in this study. All the haplotype sequences for A. phagocytophilum from this study were submitted and registered in GenBank under the accession numbers OQ308965–OQ308976. Our study shows that small mammals and their associated ticks harbor A. phagocytophilum. The vector competence for H. leachi in A. phagocytophilum transmission should further be investigated.

MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

Optimal Rotor Design and Analysis of Energy-Efficient Brushless DC Motor-Driven Centrifugal Monoset Pump for Agriculture Applications

The agricultural sector emphasizes sustainable development and energy efficiency, particularly in optimizing water pumping systems for irrigation. Brushless DC (BLDC) motors are the preferred prime mover over induction motors due to their high efficiency in such applications. This article details the rotor design and analysis of an energy-efficient BLDC motor with specifications of 1 hp, 3000 rpm, and 48 V, specifically tailored for a centrifugal monoset pump for irrigation. The focus lies in achieving optimal energy efficiency through grey wolf optimization (GWO) algorithm in the rotor design to determine optimal dimensions of the Neodymium Iron Boron (NdFeB) magnet as well as its grade. The finite element method analysis software, MagNet, is used to model and analyze the BLDC motor. The motor parameters, such as speed, torque, flux functions, temperature, and efficiency, are analyzed. For performance comparison, the same model with different magnet models is also analyzed. Validation via 3D finite element analysis highlights improvements in magnet flux linkage, stator tooth flux density, and rotor inertia with increased magnet thickness. Simulation results affirm the consistent performance of the designed BLDC motor, preferably when efficiency is increased. This efficiency and the constant speed lead to an improvement in the overall conversion efficiency of 7% within its operating range, affirming that the motor pump system is energy-efficient.

Multi-strategy enhanced Grey Wolf Optimizer for global optimization and real world problems

Multi-strategy enhanced Grey Wolf Optimizer for global optimization and real world problems

Chaos-infused wind power integration in the grey wolf optimal paradigm for combine thermal-wind power plant systems

This research presents a novel methodology for tackling the combined thermal-wind economic load dispatch (ELD) issue in contemporary power system. The proposed approach involves hybridizing active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) into grey wolf optimization (GWO) algorithm, while effectively incorporating the intricacies associated with renewable energy sources (RES). A more accurate model is made possible by hybridization for complex systems with memory and hereditary characteristics. The GWO is used as a tool for global search while ASA, IPA and SQP methods are used for rapid local optimization mechanism. The performance evaluation of the design heuristics is carried out on 37 thermal and 3 wind power generating units and outcomes endorse the effectiveness of the proposed scheme over state-of-the-art counterparts. The worthy performance is further validated on statistical assessments in case of thermal-wind integrated ELD problem in terms of measure of central tendency and variation on cost and complexity indices.

Multi-objective optimization for electric discharge drilling of waspaloy: A comparative analysis of NSGA-II, MOGA, MOGWO, and MOPSO

This study investigates the impact of pulse-on-time (TON), current (I), pulse-off-time (TOFF), voltage (V), and tool electrode speed (TES) on material removal rate (MRR), average surface roughness (Ra), power consumption (PC) and surface defects for electrical discharge drilling (EDD) of Waspaloy. Based on a Taguchi design with five factors and four levels, experimental trials reveal TOFF, TON, and TOFF as the most influential factors, contributing 50.45%, 34.29%, and 33.09% to MRR, Ra, and PC, respectively. To address conflicting conditions and optimize responses, non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective genetic algorithm (MOGA), multi-objective grey wolf optimizer (MOGWO), and multi-objective particle swarm optimization (MOPSO) are employed. Comparative analysis of 8 optimal solutions provided by four optimization techniques, which exhibit less than 10% errors in confirmatory experiments, has been done, ensuring better prediction accuracy. MOPSO is preferable to the rest of the optimization techniques due to its lower percentage error. NSGA-II, MOGA, MOGWO, and MOPSO exhibit relative improvement in responses based on confirmatory experiments, establishing them as effective tools for exploring EDD applications. Higher current is confirmed as a primary contributor to increased surface damage through microscopic image analysis, and EDX analysis exposes carbon deposition migration on both the tool and workpiece.

Moving towards an ecological management of overabundant ungulates: insights from wildlife-vehicle collisions and hunting bag data

Increasing abundance of large ungulates is raising human-wildlife impacts and the effectiveness of recreational hunting to reduce their population growth is increasingly questioned. We report on long-term trends (> 15 years) in wildlife-vehicle collisions (WVC) and hunting bags, and on associations between the annual growth rate of WVC and that of hunting bags for three ungulates – the wild boar, the red deer, and the roe deer – and the grey wolf in northwest Spain to evaluate the regulating capacity of recreational hunting at large spatial scale. Wildlife-vehicle collisions increased by 332% in 16 years and 91% of all traffic accidents were caused by collisions with these three ungulates. All ungulate species showed significant positive trends in WVC and hunting bags, but we did not observe a negative association between annual growth rate of hunting bags and that of WVC except for the wild boar. Results suggest that recreational hunting was unable to reduce ungulate population growth at the regional scale. There was no upward trend of vehicle collisions with wolves over the study period, possibly reflecting stable wolf populations. Natural mortality due to predation could be promoted through the protection of apex predators, but the lethal management of apex predators, often based on sociopolitical pressures rather than damage levels, can conflict with the strategy for mitigating ungulate impacts. Ungulate management needs to be reconsidered from an ecological perspective that integrates human management measures, including recreational hunting, based on the population dynamics and the recovery of predator–prey interactions by favoring the expansion of apex predators.

Scheduling of Container Transportation Vehicles in Surface Coal Mines Based on the GA–GWO Hybrid Algorithm

The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA−GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm’s global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively.

Conservation and mitigation approaches for human–gray wolf (Canis lupus) conflicts in Shigar Valley, Northern Pakistan

AbstractHuman‐gray wolf conflict over livestock depredation and absence of mitigation measures is a key threat leading to the decline of wolf populations in Pakistan, where gray wolves are endangered. In this study, we collected data from January 2019 to September 2021, and human–wolf conflict and mitigation measures were investigated by interviews with 200 shepherds that were randomly selected from 43 different human settlements. We used binary logistic GLM models to predict landscape features related to gray wolf conflict patterns. Over the 3 years of the study, the gray wolf was responsible for 963 livestock losses and 244.50 million rupees (1,381,388.14 USD) in economic loss in summer, and 798 livestock losses along with 169.31 million rupees (956,608.1356 USD) economic loss in winter for the same 200 shepherds in Shigar valley. Our questionnaires revealed that shepherds hold negative attitudes toward the species when there is inadequate compensation by the several government department.

Domestication constrains the ability of dogs to convey emotions via facial expressions in comparison to their wolf ancestors

Dogs (Canis lupus familiaris) are the domestically bred descendant of wolves (Canis lupus). However, selective breeding has profoundly altered facial morphologies of dogs compared to their wolf ancestors. We demonstrate that these morphological differences limit the abilities of dogs to successfully produce the same affective facial expressions as wolves. We decoded facial movements of captive wolves during social interactions involving nine separate affective states. We used linear discriminant analyses to predict affective states based on combinations of facial movements. The resulting confusion matrix demonstrates that specific combinations of facial movements predict nine distinct affective states in wolves; the first assessment of this many affective facial expressions in wolves. However, comparative analyses with kennelled rescue dogs revealed reduced ability to predict affective states. Critically, there was a very low predictive power for specific affective states, with confusion occurring between negative and positive states, such as Friendly and Fear. We show that the varying facial morphologies of dogs (specifically non-wolf-like morphologies) limit their ability to produce the same range of affective facial expressions as wolves. Confusion among positive and negative states could be detrimental to human–dog interactions, although our analyses also suggest dogs likely use vocalisations to compensate for limitations in facial communication.

Lifetime reproductive characteristics of gray wolves

Abstract Female and male cooperative breeders can use different strategies to maximize reproduction and fitness over their lifetimes. Answering questions about fitness in cooperative breeders requires long-term studies as well as complete data on group composition and size which can be exceedingly difficult to obtain. Using a long-term genetic data set of complete group pedigrees, I asked how lifetime reproductive characteristics of female and male gray wolves (Canis lupus) differed. I predicted that genetic relatedness to helpers would be higher for females than males due to philopatric behavior of female wolves, group size would be similar between the sexes, females would inherit breeding positions from within groups more often than males due to differences in dispersal strategies between the sexes, males would have more lifetime mates and produce more young than females because of polygamy, and females would breed for more years than males due to the likelihood that females would still breed (with a new partner) after a mate died or was expelled from the group. I documented complete lifetime breeding histories for 11 male and 18 female wolves in Idaho, United States, 2008 to 2018. Genetic relatedness to helpers, group size, number of mates, pups, and years breeding did not differ between the sexes. Females, however, inherited breeding positions within groups far more often than males. Individuals who secured breeding positions generally reproduced for 2 seasons and commonly had more than 1 partner during their lifetimes if they were able to maintain their breeding position longer. Direct fitness varied greatly within female and male breeding wolves.

Complex hilly terrain agricultural UAV trajectory planning driven by Grey Wolf Optimizer with interference model

Complex hilly terrain agricultural UAV trajectory planning driven by Grey Wolf Optimizer with interference model