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The possibilities for assessing the impact of human activity on mammals using camera traps are demonstrated in Russia for the first time, three species of large carnivores taken as examples: the brown bear (Ursus arctos), the Eurasian lynx (Lynx lynx), and the gray wolf (Canis lupus). To do this, a single-season species-level occupancy analysis and an analysis of daily activity were utilized. This study was carried out in 2022 on the territory of the Southern Forestry of the Central Forest Nature Reserve, in western European Russia during the period of maximum human activity (July–August). The relative abundance indices of pedestrians, all-terrain vehicles, and the distance to villages were used as variables. The maximum predicted occupancy was found for the lynx (ψ = 0.97), while the minimum was found for the wolf (ψ = 0.78). Detection probability was the highest for the brown bear (p = 0.18), in contrast to both lynx and wolf (0.08 each). For the lynx and the brown bear, large degrees of overlap of daily activities with pedestrians ( = 0.57 and = 0.47) and all-terrain vehicles ( = 0.51 and = 0.41) were noted, in contrast to those for the wolf ( = 0.37 and = 0.35, respectively). Human activity had no noticeable effect on either the spatial or temporal distribution patterns of the brown bear and lynx. In contrast, the wolf showed a shift in both patterns away from human activity. The combined use of occupancy and daily activity analyses based on data from camera traps is the optimal method for assessing and regulating human activity in particularly strictly protected natural areas. [ABSTRACT FROM AUTHOR]

The recovery and expansion of formerly isolated wolf populations in Europe raise questions about the nature of their interactions and future consequences for population viability and conservation. Will fragmented populations fuse or maintain a certain level of isolation with migration? Central Europe is suitable for obtaining empirical data in this field as it represents a 'crossroad' with the potential for contact among several phylogeographic lineages. In this study, non‐invasive genetic samples obtained during population monitoring in the Bohemian and Bavarian Forest (BBF) mountain ranges in the Czech Republic and Germany (Bohemian Massif) were analysed at different neutral markers including mitochondrial sequence, nuclear autosomal microsatellites and gonosomal sex markers. Resultant genetic profiles were compared with reference data to study population ancestry. Both cluster analyses of microsatellite genotypes and syntopic occurrence of haplotypes HW01 and HW22 showed genetic admixture between Central European and Alpine populations. This represents secondary contact and interbreeding of formerly allopatric populations with different phylogeographic histories and distant expansion centres in different biomes in the Baltic region versus the Apennine peninsula and Alps. Moreover, the study describes the founding event and genealogy of this admixed deme, inhabiting intermediate environmental conditions compared to parental forms, and emphasises the role of protected areas as stepping stones in the range recolonization process in endangered large mammals. [ABSTRACT FROM AUTHOR]

An improved grey wolf optimization (GWO) is proposed for direct L1-norm optimization in rank deficient GNSS networks to detect outliers since the standard GWO and the global optimization (GO) algorithms cannot deal with such problems. In contrast to the traditional method for L1-norm minimization which is solved by a cumbersome search of linear programming (LP) problem, in this algorithm, one only requires to input the target function. Furthermore, it has a greater ability to detect gross errors of observables with low reliability compared to traditional methods such as LP. Moreover, it does not consider the correct observables as blunders in contrast to some algorithms such as the iteratively reweighted least-squares (IRLS) method, which may consider them as outliers. The numerical results of real and simulated GNSS networks approve the efficiency of the new algorithm. [ABSTRACT FROM AUTHOR]

Molecular methods are routinely used to estimate the effective size of populations (Ne). However, underlying model assumptions are frequently violated to an unknown extent. Although simulations can detect sources of bias and help to adjust sampling strategies and analyses methods, additional information from empirical data can also be used to calibrate methods and improve molecular Ne estimation methods. Here, we take advantage of long‐term genetic and ecological monitoring data of the grey wolf (Canis lupus) in Germany, and detailed population genetic studies in Poland, Spain and Portugal to improve Ne estimation strategies in this species, and species with similar life history traits. We first calculated Ne from average lifetime reproductive success and detailed census data from the German population, which served as a baseline to compare to molecular estimates based on linkage disequilibrium and sibship frequency. This yielded a robust Ne/Nc estimation that we used to calibrate molecular estimates of German, Polish and Iberian wolf populations. The linkage disequilibrium method was strongly influenced by spatial genetic structure, much more than the sibship frequency method. When Ne was estimated in local neighbourhoods, both methods yielded comparable results. Estimates of the metapopulation effective size seemed to correspond generally well with the sum of the estimates of local neighbourhoods. Overall, we found that the number of packs is a good proxy of the effective population size. Using this as a rule of thumb, we evaluated for all European wolf populations the Ne 500 indicator and concluded that half of the European wolf populations do not yet fulfil this criterion. [ABSTRACT FROM AUTHOR]

By implementing a multi‐objective optimization approach in forecasting, we introduce three optimization models grey wolf optimizer, genetic algorithm, and differential evolution algorithm combined with multilayer perceptron neural networks and support vector machines to predict electricity consumption in the UAE. The hybrid models' accuracy and efficiency were evaluated using various forecasting metrics. This study's contributions are threefold: it is the first to employ such a sophisticated hybrid approach, particularly using the recently introduced grey wolf optimizer, it compares optimization techniques with the established Pearson correlation‐based method for dimensionality reduction and it represents one of the most extensive macroeconomic forecasts in the UAE using multi‐objective heuristic hybrid optimization methods. Our findings indicate that the grey wolf optimizer significantly outperforms all other models, followed by the genetic algorithm. [ABSTRACT FROM AUTHOR]

On the Internet, online microblogging and social networks have experienced tremendous growth. Millions of individuals share opinions on social networking platforms including Twitter, Facebook, YouTube, and Microblogging sites. Millions of individuals express their thoughts on social platforms namely Twitter, Facebook, YouTube, and microblogging sites. As user‐generated content is growing to a huge extent on the internet, the analysis, extraction identification and classification of opinion and sentiment polarity of different aspects has now become a challenging issue in sentiment analysis. In sentiment analysis, aspect level opinion mining, opinion spam review detection, extraction and representation of most relevant accessed data from the user‐generated data is a difficult process. The word vector representation using existing deep learning techniques affects the performance in aspect level opinion mining. Spammers propagate false opinion for payment exchange, which leads to degradation of the financial growth, business, and fame of the organization. To overcome the limitations aspect level opinion mining is used to recognize sentiment and polarity of aspects in the specified text. This work presents various input feature vectors of deep learning approaches aiming to improve performance in aspect level opinion mining. Next, three meta‐heuristic algorithms with k‐means clustering approach for classification of opinion spam reviews on social media transit tweets is presented. The proposed approaches are evaluated and compared with other existing approaches as well as other benchmark datasets namely Restaurant and Amazon reviews. [ABSTRACT FROM AUTHOR]

AbstractCloud computing is a technique for developing systems that rely on dynamic sharing to enable the integration of several systems to provide services. For the purpose of completing their intended work, cloud users may literally access the cloud resources over the internet. The tasks that cloud users submit and the time it takes to do them determine the effectiveness and efficiency of cloud computing services. Task scheduling is essential to improving a cloud system’s efficacy and performance since it optimizes resource allocation and utilization. In this context, cloud computing offers a variety of advantages, including cost savings, security, mobility, flexibility, disaster recovery, quality control, automated software updates, and sustainability. Therefore, the requirement to control resource allocation has increased along with the number of cloud users. However, cloud task scheduling requires a quick and intelligent algorithm that can identify available resources and plan out tasks that different people desire. Therefore, a quick, effective work scheduling method is needed for improved resource allocation and scheduling. Grey Wolf Job Optimization (GWO) and Cuckoo Search Optimization (CSO) are used for the Optimized Efficient Job Scheduling Resource (OEJSR). The "grey wolf optimization" (GWO) ensemble with OEJSR has provided the best resource allocation models. The prior research was compared using computation time, make span, iteration-based performance, fitness, and success rate. Studies demonstrate the superiority of the suggested approach.

ABSTRACT Molecular methods are routinely used to estimate the effective size of populations (Ne). However, underlying model assumptions are frequently violated to an unknown extent. Although simulations can detect sources of bias and help to adjust sampling strategies and analyses methods, additional information from empirical data can also be used to calibrate methods and improve molecular Ne estimation methods. Here, we take advantage of long‐term genetic and ecological monitoring data of the grey wolf (Canis lupus) in Germany, and detailed population genetic studies in Poland, Spain and Portugal to improve Ne estimation strategies in this species, and species with similar life history traits. We first calculated Ne from average lifetime reproductive success and detailed census data from the German population, which served as a baseline to compare to molecular estimates based on linkage disequilibrium and sibship frequency. This yielded a robust Ne/Nc estimation that we used to calibrate molecular estimates of German, Polish and Iberian wolf populations. The linkage disequilibrium method was strongly influenced by spatial genetic structure, much more than the sibship frequency method. When Ne was estimated in local neighbourhoods, both methods yielded comparable results. Estimates of the metapopulation effective size seemed to correspond generally well with the sum of the estimates of local neighbourhoods. Overall, we found that the number of packs is a good proxy of the effective population size. Using this as a rule of thumb, we evaluated for all European wolf populations the Ne 500 indicator and concluded that half of the European wolf populations do not yet fulfil this criterion.

Abstract Grey Wolf optimization (GWO) is a newly developed stochastic meta-heuristic technique motivated by nature. It shows potential in diverse optimization challenges. It replicates grey wolf hunting behaviour and social hierarchy, exploring the solution space similar to their natural process. The algorithm efficiently explores and converges to the optimal solution. However, a drawback of the standard GWO is its limited exploitation capability due to its exploration-focused iterations. This may hinder finding the optimal solution nearby, leading to lower local convergence rates and degraded solution quality. To address this, the GWO-Employed-Onlooker model suggests incorporating the onlooker and scout bee operators from the artificial bee colony algorithm (ABC) during the position-changing stage of the grey wolves. This enhances exploitation capability, resulting in improved local convergence rates and better solution quality. The proposed method’s performance is evaluated on various optimization functions and compared their convergence rate to standard GWO, Genetic Algorithm (GA), Firefly Algorithm (FA), ABC, and Ant Colony Optimization (ACO) techniques. The results demonstrate that the proposed strategy GWO-Employed-Onlooker is better, indicating that it is valuable in solving optimization problems.

Emotion detection involves distinguishing the complex nature of emotions, cross-cultural variations, and ethical considerations. The proposed model addresses cross-cultural issues through diverse training and ensures ethical data usage through advanced machine-learning techniques to enhance accuracy. The research aims to develop a Dusky Canidae Optimization-based Deep Convolutional Neural Network model (DCO opt deep CNN) for emotion recognition in audio-visual data, benefiting various advancements. During preprocessing, audio and video are separated to enhance sound quality and eliminate data redundancy while retaining vital information. Features are then extracted and employed for training and testing using the automation and effectiveness of Deep CNNs. The concatenated features are optimized using a hybrid approach that combines Dusky Hawk and Canidae Wolf Optimization (CWO), offering simplicity and interpretability with optimal parameter settings. The proposed DCO model effectively classifies emotions within the data. Evaluating the database, the DCO model exhibits a performance enhancement of 1.28%, 2.91%, and 1.28% in accuracy, sensitivity, and specificity with respect to the existing learner memorizing optimization-based deep NN model. [ABSTRACT FROM AUTHOR]

Although lethal interactions between wolves and other apex carnivores have been reported for a handful of species, the killing or consumption of wolves by free-ranging golden eagles has rarely been reported. Here, we present four opportunistic field observations documenting the consumption of a wolf pup by free-ranging golden eagles and predatory behavior of eagles towards wolf pups. Although we could not confirm the killing and consumption of wolves by eagles in a single predatory event, actual predation seems plausible based on our observations. We hypothesize that these intraguild apex predators' interactions, consisting on a smaller raptor predator attacking or consuming a lager mammal and social predator, are rare events, but provide valuable information on potential relationships across keystone apex predators. [ABSTRACT FROM AUTHOR]

Large carnivores often exhibit high survival rates in protected areas, whereas intentional and unintentional human‐caused mortality may be greater in adjacent areas. These patterns can result in source‐sink dynamics and limit population expansion beyond protected areas.We used telemetry data from 438 canids in 141 packs collected from 2002 to 2020 to evaluate mortality risk for wolves, coyotes, and admixed canids in a 3‐species hybrid zone in and adjacent to a large protected area in Ontario, Canada. The hybrid zone is occupied by most of the remaining eastern wolves (Canis lycaon), a rare, threatened species that hybridizes with sympatric eastern coyotes (C. latrans) and Great Lakes grey wolves (C. lupus).Within Algonquin Provincial Park (APP), annual human‐caused mortality from harvest and vehicles was low (0.06, 95% CI [0.03, 0.08]), whereas annual human‐caused mortality was higher in adjacent areas (0.31, 95% CI [0.25, 0.37]). Smaller protected areas implemented to help protect eastern wolves did not significantly reduce mortality. Eastern wolves survived poorly relative to other canids and dispersing canids survived poorly relative to residents. Mortality risk was greater when canids were closer to roads. Mortality risk was also increased or reduced by the strength of individual‐level selection or avoidance of roads relative to their availability, respectively.Our results provide a comprehensive evaluation of factors influencing spatial variation in mortality risk for canids to inform eastern wolf recovery efforts. Additionally, we developed a novel modelling approach for investigating the influence of resource selection on mortality risk, which highlighted that individual‐level responses to risk can strongly influence population‐level mortality patterns.Synthesis and applications. Despite being listed as 'threatened' under the Ontario Endangered Species Act, eastern wolves are still legally trapped and shot outside protected areas in central Ontario. Eastern wolves and dispersing canids survive poorly outside of APP, primarily from human‐caused mortality. These results, along with the apparent inadequacy of the smaller protected areas, suggest that expanding the threatened eastern wolf population outside APP is unlikely under current management conditions. Protecting eastern wolves from human‐caused mortality is complicated as it would require a harvest ban for all canids, including coyotes. [ABSTRACT FROM AUTHOR]

The high rate of knee osteoarthritis has raised the need for accurate diagnostic methods. In this study, we propose a precise detection method using the center of pressure data obtained from the patients. The introduced automatic detection pipeline is based on the two modern algorithms of grey wolf and BAT. The extracted statistical features and the obtained data from healthy individuals and patients are processed with the grey wolf binary algorithm. The results are fed into the binary bat algorithm to select important features and increase the pipeline accuracy. Then the groups are classified using a four-layer neural network. We show that the proposed method with a simple four-layer neural network offers fantastic accuracy in high-speed processing large data and classifies the high-dimensional knee osteoarthritis center of pressure data with appropriate precision, recall, specificity, and F1 values. The proposed method has direct applications in knee osteoarthritis diagnostics in clinics. [ABSTRACT FROM AUTHOR]

Effective population size estimates are critical information needed for evolutionary predictions and conservation decisions. This is particularly true for species with social factors that restrict access to breeding or experience repeated fluctuations in population size across generations. We investigated the genomic estimates of effective population size along with diversity, subdivision, and inbreeding from 162,109 minimally filtered and 81,595 statistically neutral and unlinked SNPs genotyped in 437 grey wolf samples from North America collected between 1986 and 2021. We found genetic structure across North America, represented by three distinct demographic histories of western, central, and eastern regions of the continent. Further, grey wolves in the northern Rocky Mountains have lower genomic diversity than wolves of the western Great Lakes and have declined over time. Effective population size estimates revealed the historical signatures of continental efforts of predator extermination, despite a quarter century of recovery efforts. We are the first to provide molecular estimates of effective population size across distinct grey wolf populations in North America, which ranged between Ne ~ 275 and 3050 since early 1980s. We provide data that inform managers regarding the status and importance of effective population size estimates for grey wolf conservation, which are on average 5.2–9.3% of census estimates for this species. We show that while grey wolves fall above minimum effective population sizes needed to avoid extinction due to inbreeding depression in the short term, they are below sizes predicted to be necessary to avoid long‐term risk of extinction. [ABSTRACT FROM AUTHOR]

Assessing genetic diversity, population connectivity, demographic patterns, and phylogeographic relationships is vital for understanding the evolutionary history of species and thus aid in conservation management decisions. Indian wolves (currently, Canis lupus pallipes and Canis lupus chanco) are considered ancient, unique and divergent lineages among grey wolves, yet their population genetics are poorly understood. To void this knowledge gap, we collected samples from Indian peninsular (n = 77) and Himalayan wolves (n = 24) and used a combination of maternal (mtDNA CR and Cyt b) and bi-parental (nuclear microsatellites) markers to estimate levels of genetic diversity, examine the patterns of genetic structuring between them and within their distribution range, and assess their demographic histories. Both the wolf populations showed moderate levels of genetic variability, comparable to other grey wolves. Low levels of genetic differentiation were observed within both the Indian peninsular and Himalayan wolves indicating high levels of gene flow within their populations. On the other hand, high levels of genetic differentiation were observed between the two wolves indicating absence of gene flow. Molecular analysis highlighted the uniqueness of both the Indian wolves which was further supported by the presence of unique haplotypes indicating no admixture between them. Demographic analysis using both mtDNA and microsatellites revealed decline in population sizes of both the wolf lineages and both have undergone bottlenecks. Estimates of past effective population size revealed recent population declines of both lineages of Indian wolves at around 25–50 generations corresponding to about 100–200 years ago. Our results further support the designation of both lineages of Indian wolves as two distinct species Canis pallipes and Canis himalayensis and suggest increasing conservation efforts to save the unique and ancient wolf species from extinction. [ABSTRACT FROM AUTHOR]

Many mesocarnivores are fossorial and use burrow systems to avoid predators. But fossorial animals cannot stay safely underground forever; they must also risk emerging overground to forage and find mates. To make this trade‐off effectively and maximise their own fitness, it is imperative they assess how risk varies in space and time and adapt their denning behaviour accordingly. We used the badger in Białowieża Forest, Poland, as a model for investigating how the denning behaviour of a fossorial mesocarnivore varies in response to short‐term large carnivore risk. To this end, we experimentally simulated perceived wolf presence outside 10 badger setts using audio playbacks of wolves (their howls). We assayed two behavioural measures of fear: badger emergence time from setts on the day playbacks were broadcast and their presence in setts on the day after. We found that neither badger emergence time nor next‐day sett use varied in response to wolf playbacks. The results of the present study contrast with a previous study of ours that found badgers used setts in areas with high landscape level perceived wolf risk less often than those in lower‐risk areas. Together, these papers' findings suggest that different spatiotemporal scales of perceived risk can have differential effects on badger behaviour. We conclude that rather than take risk avoidance measures at all risky times and places, badgers likely display a diversity of reactions to large carnivore presence that depend on the context and spatiotemporal scale of the risk being perceived. [ABSTRACT FROM AUTHOR]

Cloud computing is a technique for developing systems that rely on dynamic sharing to enable the integration of several systems to provide services. For the purpose of completing their intended work, cloud users may literally access the cloud resources over the internet. The tasks that cloud users submit and the time it takes to do them determine the effectiveness and efficiency of cloud computing services. Task scheduling is essential to improving a cloud system's efficacy and performance since it optimizes resource allocation and utilization. In this context, cloud computing offers a variety of advantages, including cost savings, security, mobility, flexibility, disaster recovery, quality control, automated software updates, and sustainability. Therefore, the requirement to control resource allocation has increased along with the number of cloud users. However, cloud task scheduling requires a quick and intelligent algorithm that can identify available resources and plan out tasks that different people desire. Therefore, a quick, effective work scheduling method is needed for improved resource allocation and scheduling. Grey Wolf Job Optimization (GWO) and Cuckoo Search Optimization (CSO) are used for the Optimized Efficient Job Scheduling Resource (OEJSR). The "grey wolf optimization" (GWO) ensemble with OEJSR has provided the best resource allocation models. The prior research was compared using computation time, make span, iteration-based performance, fitness, and success rate. Studies demonstrate the superiority of the suggested approach. [ABSTRACT FROM AUTHOR]

Honey formation optimization (HFO) is originally proposed for design problems where the definitions of the objective functions are known priori or under design. HFO extends the Artificial Bee Colony (ABC) algorithm with the concept of multiple components in a source and the worker bees tending to collect components currently needed. However, the necessity of component design for a particular problem makes the HFO not applicable to optimize an arbitrary objective function. In this paper, HFO with single component (HFO-1) is proposed in order to remove this hardship of HFO for numerical function optimizations. Unlike the HFO, which only models the honey formation inside the bee, the HFO-1 further model the honey production process in the hive where sources are turned into honey-forms inside the bee and mature in time through various types of mixing processes using enzymes until the whole mixture becomes mature in the hive. During mixing process, the Pbest (population best) is used as primal catalyzer that metamorphoses other forms towards itself. When the current mixture is mature, a new mixture is started from a new site and saturated with the Gbest (global best) to fasten the maturity of the new mixture towards Gbest . HFO-1 is original in that it extends the formation phase of HFO with novel local search and importantly introduces 3 new phases, mixing, maturation, and saturation, specific to honey production. In this article, 6 algorithms (Whale Optimization, Differential Search, Particle Swarm Optimization, Improved Grey Wolf, Moth-Flame Optimization, HFO-1) are comparatively studied on the basis of 60 popular benchmark functions, containing CEC2019 functions. The results show that HFO-1 is superior to others according to mean absolute error, mean variance and Wilcoxon Rank-Sum Test analysis. [ABSTRACT FROM AUTHOR]

The traditional grey wolf (GW) positioning algorithm enjoys a good positioning effect, but its improvement of localization accuracy is limited due to the lack of ranging information between the labels. This paper presents a novel GW cooperative positioning algorithm that can improve the localization accuracy. Firstly, a new notion of fitness is established which takes the ranging information between the labels into account. Then the ranging information from the label to the base station is used to acquire a good initial location of the label, based on the traditional GW positioning algorithm. Finally, the entire ranging information and the acquired initial location are employed to obtain a precise positioning. Simulation results demonstrate that when compared to the traditional GW positioning algorithm, the positioning accuracy of the proposed algorithm shows different improvement effects under different conditions, but the improvement degree is more than 34%. [ABSTRACT FROM AUTHOR]

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Wolves have been the archetype of wildlife persecution by humans for centuries all over the world, and still are heavily persecuted in some regions. Facultative diurnal/nocturnal wild mammals are known to become more nocturnal when persecuted. Conversely, little is known regarding the possibility of wolves becoming more diurnal if not persecuted. We took advantage of a 9-year natural experiment of restricted human access to a restored coal mine debris dump to study the daily activity patterns of wolves under conditions of infrequent human presence. Results were compared with a paired control site with frequent human use. Circadian wolf activity was monitored using camera traps (3 years in human-restricted site; 2 years in control). Additionally, data from two GPS–GSM-collared wolves monitored in a second control site were also analyzed. In our control sites, wolves were nearly inactive during daylight hours. In contrast, in the human-restricted site wolves extended their activity toward noon, with a daily activity peak between 10:00 and 12:00, and showed some activity throughout the entire circadian 2-h interval cycle considered. Wolves clearly had higher diurnality in the human-restricted area with 78% greater incidence of capture with remote cameras during the day than in the control site. We suggest that the shift toward increased diurnality was related to the loss of fear of humans. Evidence in support of this hypothesis comes from flight initiation distance (FID) data. Wolves showed relatively short FIDs when faced with a human observer (range 70–183 m) in broad daylight at the human-restricted site, but were so afraid of humans in the control site that we were unable to conduct FID trials there. Based on these results, we suggest that wolves may increase their diurnality in those European countries with currently increasing movement of human populations from rural to urban areas and that do not conduct lethal control of wolves. This would represent a historical landmark for a species that has been persecuted for many centuries. However, such behavioral shifts could bring new human–wolf conflicts that would require new policies. [ABSTRACT FROM AUTHOR]

Phylogeographic patterns in large mammals result from natural environmental factors and anthropogenic effects, which in some cases include domestication. The grey wolf was once widely distributed across the Holarctic, but experienced phylogeographic shifts and demographic declines during the Holocene. In the 19th–20th centuries, the species became extirpated from large parts of Europe due to direct extermination and habitat loss. We reconstructed the evolutionary history of the extinct Western European wolves based on the mitogenomic composition of 78 samples from France (Neolithic‐20th century) in the context of other populations of wolves and dogs worldwide. We found a close genetic similarity of French wolves from ancient, medieval and recent populations, which suggests the long‐term continuity of maternal lineages. MtDNA haplotypes of the French wolves showed large diversity and fell into two main haplogroups of modern Holarctic wolves. Our worldwide phylogeographic analysis indicated that haplogroup W1, which includes wolves from Eurasia and North America, originated in Northern Siberia. Haplogroup W2, which includes only European wolves, originated in Europe ~35 kya and its frequency was reduced during the Holocene due to an expansion of haplogroup W1 from the east. Moreover, we found that dog haplogroup D, currently restricted to Europe and the Middle East, was nested within the wolf haplogroup W2. This suggests European origin of haplogroup D, probably as a result of an ancient introgression from European wolves. Our results highlight the dynamic evolutionary history of European wolves during the Holocene, with a partial lineage replacement and introgressive hybridization with local dog populations. [ABSTRACT FROM AUTHOR]

Contacts between humans and wild animals are common and frequent, but very rarely end in attacks by wild animals on humans. The aim of this paper is to identify the circumstances of wildlife attacks on humans in Croatia, while the idea behind the paper is that a better understanding of interactions between humans and wildlife should subsequently lead to fewer attacks. As there is no database on attacks by wild animals on humans in Croatia, the data for this analysis was collected from scientific publications, media, and relevant reports. The variables analysed refer to the type of attacks and the victims of the attacks. The results showed that there were 33 attacks by wild animals on humans in Croatia in the period from 2005 to 2023; the most frequent were attacks by wild boars, brown bears, and grey wolves. The characteristics of the attacks differ depending on the animal that perpetrated the attacks, but there are some common characteristics: the attacks mainly took place in rural areas; the victims were mostly male and over 40 years old; in almost all attacks they suffered minor injuries; and during the attacks the victims were in most cases hunting, walking dogs or herding livestock. The data obtained from this analysis can be used to develop preventive measures and define appropriate behaviour in areas with wildlife.

Abstract A 14‐year‐old male grey wolf (Canis lupus) with a history of severe facial swelling was submitted for necropsy. Clinical and radiological examination demonstrated an expansile neoplastic mass in the nasal and frontal sinuses. On necropsy, an amorphous neoplastic mass and extensive necrosis were observed in the nasal turbinate. Microscopic examination revealed a tumour principally composed of obvious clear tumour cells characterised by small hyperchromatic nuclei and abundant clear cytoplasm. These clear cells were positive for mucin with PAS, PAS‐D reaction, and alcian blue (pH 2.5) staining, but negative for PTAH staining. Immunohistochemically, some of tumour cells were strongly positive for mesenchymal cells (vimentin), whereas they were negative for myoepithelial antigen (alpha‐SMA) and cytokeratin. Based on the histopathological and immunohistochemical features, the present case was diagnosed as high‐grade clear cell variant mucoepidermoid carcinoma (MEC). This is the first description of clear cell variant MEC in a wolf.

Managing and conserving populations of large carnivores, including gray wolves Canis lupus, often involves making decisions that balance conflicting stakeholder desires. To help inform these decisions, we developed a microhaplotype amplicon sequencing panel consisting of 341 loci. Of these, 321 were selected from candidates identified through restriction-site associated DNA sequencing (RADseq) with 62 samples collected across Idaho, United States. These loci were selected for high expected heterozygosity in order to inform individual identification and relationship inference. The mean ± SD expected heterozygosity of these loci in a sample of 733 individuals collected in Idaho during 2018–2020 was 0.57 ± 0.09. The 20 additional loci were composed of ten selected for differentiation of western coyotes and wolves, nine selected for differentiation of wolves and domestic dogs, and one locus designed to amplify sry to identify genetic sex. This panel will facilitate monitoring, conservation, and management of gray wolves in the western United States. [ABSTRACT FROM AUTHOR]

We present information on the minimum average number of wolves per pack in late summer derived from fieldwork activities during the last three decades in north-western Spain, and on the mean minimum number of wolves travelling together in winter in the Cantabrian Mountains. The minimum average litter size observed between August and October (when the pups are between 3–6 months old) was 5.15 (CI 95%: 4.50–5.85); whereas the minimum mean number of adults/subadults observed was 3.85 (CI 95%: 3.30–4.45). Combined, the estimated minimum average pack size in late summer was 8.95 wolves (CI 95%: 8.10–9.80). In winter time (December-March), the mean number of wolves observed travelling together was 3.44 (CI 95%: 3.23–3.68). When using the average minimum pack size to convert wolf reproductions into an estimated number of wolves in late summer, an estimated number of wolves not linked to breeding packs should be also considered, which, in northern Spain, has been estimated between 16 and 25% of the total number of wolves at this time of the year. [ABSTRACT FROM AUTHOR]

Dirofilaria repens is an expanding vector-borne zoonotic parasite of canines and other carnivores. Sub-clinically infected dogs constitute the most important reservoir of the parasite and the source of infection for its mosquito vectors. However, occurrence of D. repens infection in wild animals may contribute to the transmission of the parasite to humans and may explain the endemicity of filariae in newly invaded regions. The aim of the current study was to determine the occurrence of D. repens in 511 blood and spleen samples from seven species of wild carnivores (wolves, red foxes, Eurasian badgers, raccoons, raccoon dogs, stone martens, and pine martens) from different regions of Poland by means of a PCR protocol targeting the 12S rDNA gene. Dirofilaria repens–positive hosts were identified in seven of fourteen voivodeships in four of the seven regions of Poland: Masovia, Lesser Poland, Pomerania and Warmia-Masuria. The highest prevalence was found in Masovia region (8%), coinciding with the highest previously recorded prevalence in dogs in Central Poland. The DNA of Dirofilaria was detected in 16 samples of three species (total prevalence 3.13%). A low and similar percentage of positive samples (1.9%, 4.2% and 4.8%) was recorded among badgers, red foxes, and wolves, respectively. Dirofilaria repens–positive hosts were identified in seven of fourteen voivodships. Based on detection in different voivodeships, D. repens–positive animals were recorded in four out of the seven regions of Poland: in Masovia, Lesser Poland, Pomerania, and Warmia-Masuria. The highest prevalence of filariae was found in Masovia region (8%), reflecting the highest previously recorded prevalence in dogs (12–50%) in Central Poland. In summary, we conducted the first comprehensive study on the epidemiology of D. repens in seven species of wild hosts in all seven regions of Poland and identified the first case of D. repens infection in Eurasian badgers in Poland and the second in Europe. [ABSTRACT FROM AUTHOR]

A SARS-CoV-2 virus has spread around the globe since March 2020. Millions of people infected worldwide with coronavirus. People from every country expressed their sentiments about coronavirus on social media. The aim of this work is to determine the general public opinion of Indian Twitter users about coronavirus. The Hindi tweets posted about COVID-19 is used as input data for sentiment analysis. The natural language processing is applied on input data for feature extraction. Further, the optimal features are selected from the pre-processed data using the metaheuristic based Grey wolf optimization technique. Finally, a hybrid of convolution neural network(CNN) and a long short-term memory (LSTM) model pair is employed to categorize the sentiments as positive, negative, and neutral. The outcome of the proposed model is compared with other machine learning techniques, namely, Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, Support vector machine (SVM), CNN, LSTM, LSTM–CNN, and CNN–LSTM. The highest accuracy of 87.75%, 88.41%, 87.89%, 85.54%, 89.11%, 91.46%, 88.72%, 91.54%, and 92.34% is obtained by Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, SVM, CNN, LSTM, LSTM–CNN, and CNN–LSTM, respectively. The proposed ensemble hybrid model gives the highest 95.54%, 91.44%, 89.63%, and 90.87% classification accuracy, precision, recall, and F-score, respectively. [ABSTRACT FROM AUTHOR]

Smartphones have now become an integral part of our everyday lives. User authentication on smartphones is often accomplished by mechanisms (like face unlock, pattern, or pin password) that authenticate the user’s identity. These technologies are simple, inexpensive, and fast for repeated logins. However, these technologies are still subject to assaults like smudge assaults and shoulder surfing. Users’ touch behavior while using their cell phones might be used to authenticate them, which would solve the problem. The performance of the authentication process may be influenced by the attributes chosen (from these behaviors). The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of smartphones. We began by concentrating on feature selection methods utilizing the grey wolf optimization strategy. The random forest classifier is used to evaluate these tactics. The testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public dataset. It achieved a 97.89% accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly; processing power of the device and memory to validate individuals. Simultaneously, the findings revealed that our approach has a lower equal error rate (EER) of 0.5104, a false acceptance rate (FAR) of 1.00, and a false rejection rate (FRR) of 0.0209 compared to the methods discussed in the literature. These promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources. [ABSTRACT FROM AUTHOR]

The aim of this research is to achieve the highest efficiency for a dye-sensitized solar cell (DSSC) before the fabrication process. For DSSC efficiency improvement, six different optimization algorithms are used for the DSSC parameter extraction. The algorithms used are the genetic algorithm, grey wolf algorithm, dragonfly algorithm, moth flame algorithm, ant-lion algorithm, and whale algorithm, developed based on MATLAB coding. The physical parameters for the DSSC are the electron lifetime, electrode thickness, ideality factor, absorption coefficient, and diffusion coefficient. A comparative study is carried out among the six algorithms based on the highest efficiency and computational speed. Finally, a sensitivity analysis of environmental conditions (solar irradiance and temperature) and physical parameters is implemented and analyzed to simulate the DSSC performance for different values of these parameters. The DSSC parameters studied are short-circuit current density, open-circuit voltage, fill factor, and efficiency. The optimal electron lifetime is 100 ms, and the optimal thickness of the photoanode layer is 1 μm, reaching maximum efficiency equal to 11.79%. [ABSTRACT FROM AUTHOR]

The process parameters involved in electron beam welding have major influences on spiking and penetration efficiency of the joint. In addition to this, the input–output relationships of electron beam welding are nonlinear and complex in nature. Therefore, adaptive neuro-fuzzy inference system (ANFIS)-based input–output modeling had been attempted to predict the spiking severity in electron beam welded joints. Swarm-based optimization algorithms, like grey wolf optimizer, particle swarm optimization, and bonobo optimizer (BO), were used for optimizing the ANFIS architecture and predicting the response precisely. Multi-objective bonobo optimization (MOBO), Multi-objective grey wolf optimization, and Multi-objective particle swarm optimization algorithms had been used for solving the conflicting multi-objective criteria problems associated with the study. The input process parameters were accelerating voltage, beam current, scan speed, focusing distance, and beam oscillation parameters, whereas mean weld-bead penetration and its standard deviation were considered as the responses of the system. The irregular penetration, that is, spiking was expressed in terms of the standard deviation of weld penetration. The accuracy level of optimization and modeling had been tested with some test cases obtained through the real experiments. MOBO had shown the better accuracy in predicting the optimized set of input parameters, which could satisfy both the spiking and penetration criteria, while BO-ANFIS had shown the superior efficiency in predicting the response with minimum error. [ABSTRACT FROM AUTHOR]

The new era of engineering society focuses on the utilization of the potential advantage of carbon nanomaterials. The machinability facets of nanocarbon materials are passing through an initial stage. This article emphasizes the machinability evaluation and optimization of Milling performances, namely Surface roughness (Ra), Cutting force (Fc), and Material removal rate (MRR) using a recently developed Grey wolf optimization algorithm (GWOA). The Taguchi theory-based L27 orthogonal array (OA) was employed for the Machining (Milling) of polymer nanocomposites reinforced by Multiwall carbon nanotube (MWCNT). The second-order polynomial equation was intended for the analysis of the model. These mathematical models were used as a fitness function in the GWOA to predict machining performances. The ANOVA outcomes efficiently explore the impact of machine parameters on Milling characteristics. The optimal combination for lower surface roughness value is 1.5 MWCNT wt.%, 1500 rpm of spindle speed, 50 mm/min of feed rate, and 3 mm depth of cut. For lower cutting force, 1.0 wt.%, 1500 rpm, 90 mm/min feed rate and 1 mm depth of cut and the maximize MRR was acquired at 0.5 wt.%, 500 rpm, 150 mm/min feed rate and 3 mm depth of cut. The deviation of the predicted value from the experimental value of Ra, Fc, and MRR are found as 2.5, 6.5 and 5.9%, respectively. The convergence plot of all Milling characteristics suggests the application potential of the GWO algorithm for quality improvement in a manufacturing environment. [ABSTRACT FROM AUTHOR]

Skulls from 392 grey wolves (Canis lupus) were examined macroscopically according to predefined criteria. Two hundred and seven skulls were included in this study, comprised of 124 young adults (59.9%) and 83 adults (40.1%); of these, 65 (31.4%) specimens were from male wolves and 104 (50.3%) were from females, with 38 (18.4%) of unknown sex. Out of 8,694 possible teeth, 8,339 (95.9%) were present for evaluation. Fifty-five teeth (15.5%) were absent congenitally, 30 (8.5%) were lost during life and 270 (76.1%) were lost artefactually post mortem. Skeletal or dental malocclusion was present in 37 specimens (17.9%), with level bite being the most commonly encountered malocclusion. Enamel hypoplasia was present in five skulls (2.4%), affecting eight teeth (0.1%) in total. An abnormal number of roots was found on 23 teeth (0.3%) on 13 skulls (6.3%). Persistent deciduous teeth occurred in two (1.0%) specimens, affecting one (0.01%) tooth each. Fenestration or dehiscence was found associated with 203 teeth (2.4%) in 72 skulls (34.8%). Periodontitis was noted on 115 skulls (55.6%) and 1,000 teeth (11.5%), affecting significantly more adults (n = 63, 75.0%) than young adults (n = 52, 41.9%; P

The performance of a gearbox is sensitive to failures, especially in the long-term high speed and heavy load field. However, the multi-fault diagnosis in gearboxes is a challenging problem because of the complex and non-stationary measured signal. To obtain fault information more fully and improve the accuracy of gearbox fault diagnosis, this paper proposes a feature extraction method, hierarchical refined composite multiscale fluctuation dispersion entropy (HRCMFDE) to extract the fault features of rolling bearing and the gear vibration signals at different layers and scales. On this basis, a novel fault diagnosis scheme for the gearbox based on HRCMFDE, ReliefF and grey wolf optimizer regularized extreme learning machine is proposed. Firstly, HRCMFDE is employed to extract the original features, the multi-frequency time information can be evaluated simultaneously, and the fault feature information can be extracted more fully. After that, ReliefF is used to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive features are inputted into the optimized regularized extreme learning machine to identify the fault states of the gearbox. Through three different types of gearbox experiments, the experimental results confirm that the proposed method has better diagnostic performance and generalization, which can effectively and accurately identify the different fault categories of the gearbox and outperforms other contrastive methods. [ABSTRACT FROM AUTHOR]

Simple Summary: There are a number of different animal species that can be infected by Hepatozoon canis, but the most common are domestic and wild carnivores. This study presents the first results of the occurrence, distribution, and diversity of H. canis in Serbia and contributes to the limited knowledge regarding the role of grey wolves in the enzootic cycles of this pathogen. Based on a comparison with previously published data, we found a higher prevalence and higher genetic diversity of H. canis. The obtained results showed an overall high prevalence of the pathogen, with 57.94% of tested animals positive for H. canis, while genetic analysis of a sequenced fragment of the 18S ssrRNA gene showed variability at five positions leading to five sequence types present in grey wolves, where only two were previously known of. In addition to the known patterns of transmission of this pathogen, through tick ingestion during grooming or the transplacental route, the high diversity of H. canis in Serbia could be explained by the diet of grey wolves in this area. Further studies are needed to determine the mechanism of transmission, the potential source of infection, and the impact of this pathogen on wild carnivores. Wild canids are globally recognised as hosts and reservoirs of a large number of ecto- and endoparasites. Data that reveal the importance of the grey wolf (Canis lupus L.1758) in the spread of hepatozoonosis are very scarce. There are a large number of different potential host species that can be infected by Hepatozoon canis, but the most common are domestic and wild carnivores, such as dogs, jackals, foxes, and wolves. In this study, the epidemiological significance of the grey wolf as a host for the pathogen was analysed for the first time in Serbia, as well as the genetic variability of H. canis. The presence of H. canis in wolf spleens has been demonstrated using molecular methods. A total of 107 wolf spleen samples from 30 localities in Serbia were analysed. The presence of H. canis was confirmed in 62 (57.94%) individuals from 26 out of 30 localities. According to the analysis, the sampled H. canis sequences were found to be characterised by a certain heterogeneity. Based on five mutated nucleotide sites in the sequences, H. canis could be divided into five sequence types, S1 to S5. The five sequence types can potentially circulate in grey wolf populations as well as among other domestic and wild canids. This study is the first confirmation of the presence of H. canis in grey wolf populations in Serbia. Considering that the role of this vector-borne disease is poorly researched in wild carnivores, it is very important to indicate the role of this species in the circulation of this pathogen in natural ecosystems. [ABSTRACT FROM AUTHOR]

A 14‐year‐old male grey wolf (Canis lupus) with a history of severe facial swelling was submitted for necropsy. Clinical and radiological examination demonstrated an expansile neoplastic mass in the nasal and frontal sinuses. On necropsy, an amorphous neoplastic mass and extensive necrosis were observed in the nasal turbinate. Microscopic examination revealed a tumour principally composed of obvious clear tumour cells characterised by small hyperchromatic nuclei and abundant clear cytoplasm. These clear cells were positive for mucin with PAS, PAS‐D reaction, and alcian blue (pH 2.5) staining, but negative for PTAH staining. Immunohistochemically, some of tumour cells were strongly positive for mesenchymal cells (vimentin), whereas they were negative for myoepithelial antigen (alpha‐SMA) and cytokeratin. Based on the histopathological and immunohistochemical features, the present case was diagnosed as high‐grade clear cell variant mucoepidermoid carcinoma (MEC). This is the first description of clear cell variant MEC in a wolf. [ABSTRACT FROM AUTHOR]

We studied the spontaneous vocal behaviour of captive wolves at the International Wolf Center (IWC) in Minnesota (spring 2019 and winter 2020), and the Centro del Lobo Ibérico Félix Rodríguez de la Fuente (CLIFRF) in Spain (winter 2020). We used AudioMoth recording devices to record wolf howling 24 h/day. We identified 412 solo howl series and 403 chorus howls and found differences between wolves at the two centres. Vocal rates for North American wolves at the IWC (7.8 chorus howls/day in spring and 4.8 chorus howls/day in winter) were higher than rates obtained for Iberian wolves from CLIFRF (3.8 chorus howls/day in winter). Howling rates obtained in our study were similar to those obtained for captive Mexican wolves and greater than those reported for wild wolves. Hourly distribution of howling was also different between centres. The greatest howling activity identified at IWC was at pre-sunrise, while at CLIFRF the peak occurred at sunset. Weather conditions had little influence on the vocal behaviour of the captive wolves we studied. We show the potential of passive recorders to study topics of animal acoustic communication, such as vocal rates and temporal patterns, that have not been deeply addressed due to technological constraints. [ABSTRACT FROM AUTHOR]

Two wolf species reported from Pakistan include the grey wolf (Canis lupus pallipes) and the Tibetan wolf (Canis lupus chanco). Limited studies have focused on grey wolf ecology in the country, therefore, scientific data on the species' ecological parameters and conservation are scanty. The species is also facing persecution threat in response to depredation on livestock in many parts of the country. We investigated occurrence and diet composition of grey wolf in Deosai National Park, Pakistan to record how much proportion of its diet comprises of livestock, leading to human-wolf conflict. Field surveys were made on monthly basis from July 2018 to December 2019 for data collection. The distribution of the species was determined by surveying the potential habitats and recording its direct and indirect field signs (such as scats, pug marks, prey remains, hairs), while diet composition was investigated by using noninvasive technique of scat analysis. Results showed grey wolf being distributed at an elevation range of 3921 m to 4282 m above sea level (asl) in the park. Scat analysis showed 7 wild and 6 domestic prey species in its diet, with approximately 47 % contribution from livestock and 53 % from wild prey. Most frequently consumed domestic prey were donkey, yalk, goat and sheep, while among wild prey golden marmot (Marmota caudata), house mouse (Mus musculus), Palm Civet (Paguma larvata), Royel's Pika (Ochotona roylei) and markhor (Capra falconeri) were frequently consumed. Winter diet of grey wolf contained more proportion of livestock (59%) compared to wild prey (41%) whereas its summer diet contained more proportion of wild prey (53%). The grey wolf was found as one of the major predators in the study area mainly attacking donkeys, cows, sheep, and goats. [ABSTRACT FROM AUTHOR]

Abstract Introgressive hybridization between domestic animals and their wild relatives is an indirect form of human‐induced evolution, altering gene pools and phenotypic traits of wild and domestic populations. Although this process is well documented in many taxa, its evolutionary consequences are poorly understood. In this study, we assess introgression patterns in admixed populations of Eurasian wolves and free‐ranging domestic dogs (FRDs), identifying chromosomal regions with significantly overrepresented hybrid ancestry and assessing whether genes located within these regions show signatures of selection. Although the dog admixture proportion in West Eurasian wolves (2.7%) was greater than the wolf admixture proportion in FRDs (0.75%), the number and average length of chromosomal blocks showing significant overrepresentation of hybrid ancestry were smaller in wolves than FRDs. In wolves, 6% of genes located within these blocks showed signatures of positive selection compared to 23% in FRDs. We found that introgression from wolves may provide a considerable adaptive advantage to FRDs, counterbalancing some of the negative effects of domestication, which can include reduced genetic diversity and excessive tameness. In wolves, introgression from FRDs is mostly driven by drift, with a small number of positively selected genes associated with brain function and behaviour. The predominance of drift may be the consequence of small effective size of wolf populations, which reduces efficiency of selection for weakly advantageous or against weakly disadvantageous introgressed variants. Small wolf population sizes result largely from human‐induced habitat loss and hunting, thus linking introgression rates to anthropogenic processes. Our results imply that maintenance of large population sizes should be an important element of wolf management strategies aimed at reducing introgression rates of dog‐derived variants.

In recent time, heart disease has become common leading to mortality of many individuals. Hence, early and accurate prediction of this disease is vital to reduce death rate and enhance people's lives. Concurrently, Artificial Intelligence has gained more attention at present as it permits deeper understanding of the healthcare data thereby providing accurate prediction results. This efficient prediction will solve complicated queries regarding heart diseases and hence assists clinical practitioners to adopt smart medical decisions. Hence, this study intends to predict heart disease with high accuracy by proposing an improved feature selection and enhanced classification approach. The paper employs Grey-wolf with Firefly algorithm for effective feature selection and using Differential Evolution Algorithm for tuning the hyper parameters of Artificial Neural Network (ANN). Hence, it is named as Grey Wolf Firefly algorithm with Differential Evolution (GF-DE) for better classification of the selected features. This proposed classification model trains the neural network to obtain optimal weights and tunes huge number of hyper parameters in an efficiently. To prove this, the proposed system is comparatively analysed with existing methods in terms of performance metrics like accuracy, precision, recall and F1 score for Cleveland and Statlog dataset. In addition, statistical analysis is also undertaken to analyse the significance of proposed system. Outcomes revealed the efficiency of proposed method which makes it highly suitable for heart disease prediction in an efficient manner. [ABSTRACT FROM AUTHOR]

Gray wolf Canis lupus predation on domestic dogs Canis familiaris is a considerable wolf-human conflict issue in several regions of Europe and North America but it has not been well documented in the scientific literature. Livestock depredations by wolves may be related to the abundance of wild prey. Regardless of the presumed motivations of wolves for attacking dogs (likely due to interference competition and predation), the abundance of wild prey populations may also influence the risk of wolf attacks on dogs. We examined whether the annual number of fatal attacks by wolves on dogs was related to the abundance of primary prey, including wild boar Sus scrofa and roe deer Capreolus capreolus in Estonia, as well as the abundance of moose Alces alces in Finland. Statistical models resulted in significant negative relationships, thus providing evidence that the risk of attacks in both house yards (Estonia) and hunting situations (Finland) was highest when the density of wild prey was low. Wild ungulates cause damage to agriculture and forestry, but they seem to mitigate conflicts between wolves and humans; therefore, it is necessary to develop a holistic, multispecies management approach in which the importance of wild ungulates for large carnivore conservation is addressed. [ABSTRACT FROM AUTHOR]