Wolf Pack Research Articles

The application of new media communication technology in vocational education teaching under the background of big data

Abstract Exploring the application of new media communication technology in vocational education teaching is better for improving the skill level of vocational school students. In this paper, starting from the KNN algorithm in the context of big data, the basic principle and process of the algorithm are explained, and the principle and model of the wolf pack optimization algorithm are introduced. The parameters of the KNN algorithm are optimized using the wolf pack search algorithm, and the definition of the objective function, initialization and solution of the optimal K value are given, which leads to the WPOA-KNN analysis model. The new media communication technology is analyzed, including teaching media, new media teaching technology and the advantages of new media communication technology applied to vocational education teaching. The WPOA-KNN analysis model was used to analyze and demonstrate the teaching of new media technology for vocational education in a higher education institution. From the satisfaction evaluation, the percentage of those who said they liked it was 76.06%, and the percentage of those who disliked it was only 9.09%. In terms of students’ learning behavior, the overall percentage of those who rated above C grade was 92.37%. This shows that using new media communication technology for vocational education teaching can enhance students’ learning enthusiasm, make them have a more exquisite technical level, and meet the diversified talent needs of employing enterprises.

Arrhythmia Classification Based on Adaptive Refined Composite Multiscale Fluctuation Dispersion Entropy

To improve the accuracy of electrocardiography (ECG) signal classification and identify abnormal heart rhythms, an arrhythmia classification algorithm based on adaptive refined composite multiscale fluctuation dispersion entropy (ARCMFDE) is proposed. First, an improved QRS complex detection algorithm named the improved Pan-Tompkins algorithm (IPTA) is used. The QRS wave is detected, and the waveform is further processed; then, the signal is decomposed into multiple modal components using variational mode decomposition with the optimized number of decomposition layers (K). Subsequently, the RCMFDE is extracted from the different modal components as a classification feature. Finally, differential evolution (DE) and grey wolf optimization (GWO) are combined to form the hybrid differential evolution-grey wolf pack optimization (DE-GWO) algorithm to optimize the penalty factor c and the kernel function parameter g of the support vector machine for performing pattern recognition. Experimental results show that compared with other methods such as variational mode decomposition (VMD), fluctuation dispersion entropy (FDE), genetic algorithms (GA), and support vector machine (SVM). The proposed classification model has superior performance, with an average accuracy of 96.1%, a sensitivity of 95.9%, and a specificity of 98.7% for four types of heart rhythm recognition. Thus, accurate classification of ECG signals can be achieved using the proposed ARCMFDE-based DE-GWO method.

Wolf contact in horses at permanent pasture in Germany.

Wolves returned to Germany in 2000, leading to fear in German horse owners that their horses could be in danger of wolf attacks or panic-like escapes from pastures when sighting wolves. However, reports from southern European countries indicate that wolf predation on horses diminishes with increasing presence of wildlife. Therefore, we conducted a long-term, filed observation between January 2015 and July 2022 on 13 non breeding riding horses, mares and geldings, kept permanently on two pastures within the range of wildlife and a stable wolf pack with annual offspring. Wildlife cameras at the fences of the pastures made 984 times recordings of wolves and 3151 times recordings of wildlife in and around the pastures. Between 1 January 2022 and 23 March 2022 we observed two stable horse groups. Pasture 1 was grazed by five horses of mixed breed, four mares and one gelding, with the median age of 8 years (min. = 6y, max. = 29y). Pasture 2 was grazed by eight heavy warmbloods and draught horses, three mares and five geldings, with the median age of 16 years (min. = 13y, max. = 22y). During this period no wolf was recorded at pasture 2, but wild boar several times, whereas at pasture 1, wolves were recorded 89 times, and for the wildlife mostly hare. Wolves may have avoided pasture 2 because of the presence of wild boar or because the large group of older, heavy breed horses may have formed a stable, protective group. The latter needs to be confirmed in a follow-up field observation, which records anti-predator behavior and welfare indicators in horses. In conclusion, wolves did not attack the mature horses on pastures with plenty of wildlife and the horses did not respond to the presence of wolves with visible signs of reduced welfare or panic. This indicates that wolves may prefer to prey on easily accessible wildlife around and at horse pastures and that Central European horses become accustom to the presence of non-hunting wolves.

Optimal Configuration of Reactive Power in Distribution Network with Power Electronic Transformer

Traditional means of reactive power regulation are limited to adjusting the ratio of the On-Load Tap Changer or changing the access position of the reactive power compensation device and its access capacity. In order to save the cost of putting in reactive power compensation devices, this paper proposes to use the characteristics of the reactive power output function on both sides of the power electronic transformer to regulate the system reactive power distribution. For this purpose, a reactive power optimization model including distributed power supplies and power electronic transformers is constructed with the objectives of minimizing voltage excursions, maximizing voltage stability margins and minimizing active losses in the network. At the same time, based on the characteristics of decision variables, cross-feedback hybrid optimization algorithm is used to solve different types of variables, and an improved wolf pack algorithm using Tent chaotic map and Levy flight strategy is proposed to improve the solution efficiency of the algorithm. The experimental results conclude that the proposed model and algorithm are valid, and the algorithm has significant advantages in terms of model solution time and global search capability when compared with the traditional Wolf Pack algorithms and Particle Swarm Optimization algorithms.

Flexible Job Shop Scheduling Optimization for Green Manufacturing Based on Improved Multi-Objective Wolf Pack Algorithm

Green manufacturing has become a new production mode for the development and operation of modern and future manufacturing industries. The flexible job shop scheduling problem (FJSP), as one of the key core problems in the field of green manufacturing process planning, has become a hot topic and a difficult issue in manufacturing production research. In this paper, an improved multi-objective wolf pack algorithm (MOWPA) is proposed for solving a multi-objective flexible job shop scheduling problem with transportation constraints. Firstly, a multi-objective flexible job shop scheduling model with transportation constraints is established, which takes the maximum completion time and total energy consumption as the optimization objectives. Secondly, an improved wolf pack algorithm is proposed, which designs individual codes from two levels of process and machine. The precedence operation crossover (POX) operation is used to improve the intelligent behavior of wolves, and the optimal Pareto solution set is obtained by introducing non-dominated congestion ranking. Thirdly, the Pareto solution set is selected using the gray relational decision analysis method and analytic hierarchy process to obtain the optimal scheduling scheme. Finally, the proposed algorithm is compared with other algorithms through a variety of standard examples. The analysis results show that the improved multi-objective wolf pack algorithm is superior to other algorithms in terms of solving speed and convergence performance of the Pareto solution, which shows that the proposed algorithm has advantages when solving FJSPs.

Omni-Directional Capture for Multi-Drone Based on 3D-Voronoi Tessellation

This paper addresses the multi-drone formation capture in three-dimensional (3D) environments. The omni-directional minimum volume (ODMV) 3D-Voronoi diagram algorithm is proposed for the first time to achieve the two goals of (1) forming and keeping a capture and (2) planning the control action within its safe, collision region for each drone. First, we extend the traditional 2D Voronoi diagram to the 3D environment and use the non-overlapping spatial division property of 3D Voronoi diagram to inherently avoid the collision between drones. Second, we make improvements to the problem of capture angle in our minimum area strategy and propose an omni-directional minimum volume strategy to accomplish the effective capture of a target by constraining the capture angle. Finally, the wolf pack algorithm (WPA) with variable step size is introduced to provide a movement strategy for multi-drone formations. Thus, the proposed ODMV can also achieve dynamic target and multi target capture in environments with obstacles. The Optitrack motion capture system and Crazyflie drones are used to conduct the multi-drone capture experiment. Both simulation and experimental results are included to demonstrated the effectiveness of the proposed ODMV method.

Assessment the Air Pollution at the some Areas in Salahaddin Governorate - Iraq

The study was conducted in Salah al-Din Governorate - northern Iraq, during the period from 1/11/2021 to 30/3/2022, with the aim of evaluating the level of pollutants in the atmospheric air for three areas: Al-Boutama (rural area), Baiji refinery (oil industry area), and the city of Tikrit (a densely populated area), the concentrations of toxic gases were measured: sulfur dioxide (SO2), nitrogen oxide (NO), nitrite (NO2), hydrochloric acid (HCl), hydrogen fluoride (HF), carbon dioxide (CO2) and carbon monoxide. (CO), using the Wolf Pack Modular Area Monitor. The results were as follows: Significant differences were found between the study sites for all the variables tested, and Baiji refinery recorded the highest values for each of the variables SO2, NO, NO2, HCl and HF, which amounted to 2.54, 8.28, 6.4, 10.62 ppm and 0.42 mg/ m3, respectively. November 2021 recorded the highest concentrations of HCl and CO compounds with averages of 7.6 ppm and 18.37 mg/m3, respectively, while January 2022 recorded the highest averages of NO2, HF and CO2, which amounted to 5.27 ppm and 0.4 mg/m3 and 791 ppm, respectively, the interaction between Baiji refinery site and the months of November, December and January recorded the highest concentrations of pollutants amounting to 4.4 ppm for SO2 concentration, 11.2 ppm for NO concentration, 7.9 ppm for NO2 concentration, 13.8 ppm for HCl concentration, and 0.7 mg/m3 for HF concentration, and 16.5 ppm for VOCs concentration.

Behavior automatic analysis for wolf pack hunting: making fast behavior analysis of massive data possible

Automatic wolf pack hunting behavior analysis contributes to the identification of behavioral mechanism and the development of bio-swarm intelligence engineering. However, wolf pack hunting commonly involves a complicated background and high-speed motion, where the frequent interactions with each other lead to frequent visual occlusion of the individual wolves. These difficulties make the automatic analysis of group hunting behavior significantly more challenging. Hence, we develop an automatic wolf pack hunting behavior detection scheme appropriate for videos, comprising a four-level individual feature map (frame, video, space, and semantic) and a group feature map. We propose a temporal–spatial feature fusion-based motion state recognition algorithm appropriate for scale-varied and occlusion-frequent individuals to obtain a robust semantic-level feature. Based on this individual feature map, we propose a video-based wolf pack hunting automatic behavior detection method. The developed scheme is validated on our Wolf2022 dataset, while the proposed motion state recognition and group behavior detection algorithms are further tested through ablation experiments. The results revealed that the motion state recognition accuracy reaches 88%, correctly detecting 15 out of 17 group behavior video clips.Significance statementIt is difficult to fast extract quantitative analysis results of wolf pack hunting behavior from video data. Our research focused on the research of the automatic analysis method for wolf pack hunting. Using the proposed method, the statical individual and group behavior attributes can be automatically generated from video data, which contributes to the building of a conceptual bridge between the wolf pack hunting behavior and bio-swarm intelligence engineering.

African Vernacular Symbols of Black Intersex Children in Sinethemba Ngubane's Installations (2007-2016)

African Vernacular Symbols of Black Intersex Children in Sinethemba Ngubane's Installations (2007-2016)

Street Guide as a Literary Genre: La Manada City

This study thoroughly examines La Manada (The Wolf Pack) City, an artwork that illuminates the various forms of violence and oppression experienced by urban communities, particularly women and marginalized groups. Our research specifically focuses on the literary elements of this painted map which demonstrates the transition from defensive to artistic strategies as a means of survival. Initially, we aim to provide a comprehensive background of the artwork, including its title, social context, the incidents that inspired the idea, and the author’s activism. Subsequently, we scrutinize the literary resources of the 257 items that comprise the street guide of the map. By analysing the various names given to locations on the map, including literary devices and semantic fields, we observe reminiscences of classic surrealist paintings and the artist’s ability to protect herself while revealing the violence hidden behind the guise of antithesis, alliteration, metaphor, and other literary devices. To demonstrate the effectiveness of our research, we compared it to two other maps with different intentions. Our findings confirm the strength of La Manada City, which operates both locally and globally. As a representation of the former emporium city of Seville, our map exposes the adverse impact of dominant capitalistic strategies on community life, perpetuating inequalities for countless “poor owners of the world” by disregarding nature and culture.

Performance enhanced geometrically shaped 32-ary quadrature amplitude modulation enabled by swarm intelligence algorithm

The geometrically shaped 32-ary quadrature amplitude modulation (GS-32QAM) signal enabled by swarm intelligence algorithm is proposed in high-speed and long-haul coherent optical communication system. The geometric shaping (GS) is obtained by constructing a generic constellation optimization scheme applying particle swarm algorithm (PSO), wolf pack algorithm (WPA), and marine predator algorithm (MPA), respectively. The complexity of three algorithms applied to GS-32QAM is systematically analyzed, where MPA can obtain better search results with slightly higher complexity than PSO. The results show that the optimized constellation is significantly better than the uniform signal in terms of reducing bit error rate and increasing generalized mutual information (GMI). The maximum GMI gain of GS signal with PSO and WPA are both 0.25 bit / sym, and the maximum GMI gain of GS signal with MPA is up to 0.3 bit / sym. At the hard-decision forward error correlation limitation of 3.8 × 10 − 3, compared with uniform signal, the GS signal with PSO, WPA, and MPA can provide the optical signal-to-noise ratio gain of 1, 1.1, and 1.6 dB tolerance, respectively. In terms of data rate, compared with uniform signal, three GS signals can increase by 33, 36, and 43 Gbit / s, respectively.

Trajectory planning and prescribed performance tracking control based on fractional-order observers for unmanned helicopters with unmeasurable states

The trajectory planning and tracking control problem is studied in this paper based on prescribed performance method (PPM) for the small-scale unmanned autonomous helicopter (UAH) with wind-gust disturbances (WGDs) and unmeasurable states. For the purpose, the nonlinear model with flapping dynamics is established, and the transformation performance function is used to ensure that the errors of trajectory tracking satisfy the corresponding performance. A fractional-order observer is designed for unmeasurable states to estimate the flapping angles in actual flight, and the fractional-order extended state observer (ESO) is constructed to estimate the WGDs, respectively. On the basis of PPM and the designed observers, the fractional-order theory-based backstepping trajectory tracking control scheme is developed for the UAH system, and the three-dimensional trajectory is planned by the improved wolf pack algorithm. Then the stability of the entire system is proven through strict theoretical analysis. Finally, the simulation analysis on the UAH is presented to demonstrate the efficiency of the designed method.

RETRACTED ARTICLE: Research on reliability of sports intelligent training system based on hybrid wolf pack algorithm and IoT

To expand the application scope of the WCA algorithm in the actual process, this research has optimized the problems in the algorithm in two aspects: The first aspect further improves the operating mechanism of the WCA algorithm and improves the performance of optimization problem. In the second aspect, other optimization strategy mechanisms of the WCA algorithm are introduced to enable the algorithm to optimize multi-objective and multidimensional problems. This paper studies a physical test system and sports intelligent based on hybrid wolf pack algorithm and IoT. The system collects and sends data from the data collection terminal of the Web server, receives the data through the wireless module, and sends it to the Web server through the network. The Web server processes and stores the data information to generate a database, and users can view their own sports information by logging in to the Web service program with the account password. In addition, the teacher and administrator accounts have the ability to view all users' exercise information. The system adds three sports items, pull-ups, squats, and standing long jumps. At the same time, the system uses a general motion recognition algorithm, which can effectively reuse and add new sports items. According to the actual needs of intelligent sports training, this paper combines somatosensory technology, bone tracking technology, and motion recognition algorithm to realize a high-precision, low-latency intelligent sports training system.

How to Choose? Comparing Different Methods to Count Wolf Packs in a Protected Area of the Northern Apennines.

Despite a natural rewilding process that caused wolf populations in Europe to increase and expand in the last years, human-wolf conflicts still persist, threatening the long-term wolf presence in both anthropic and natural areas. Conservation management strategies should be carefully designed on updated population data and planned on a wide scale. Unfortunately, reliable ecological data are difficult and expensive to obtain and often hardly comparable through time or among different areas, especially because of different sampling designs. In order to assess the performance of different methods to estimate wolf (Canis lupus L.) abundance and distribution in southern Europe, we simultaneously applied three techniques: wolf howling, camera trapping and non-invasive genetic sampling in a protected area of the northern Apennines. We aimed at counting the minimum number of packs during a single wolf biological year and evaluating the pros and cons for each technique, comparing results obtained from different combinations of these three methods and testing how sampling effort may affect results. We found that packs' identifications could be hardly comparable if methods were separately used with a low sampling effort: wolf howling identified nine, camera trapping 12 and non-invasive genetic sampling eight packs. However, increased sampling efforts produced more consistent and comparable results across all used methods, although results from different sampling designs should be carefully compared. The integration of the three techniques yielded the highest number of detected packs, 13, although with the highest effort and cost. A common standardised sampling strategy should be a priority approach to studying elusive large carnivores, such as the wolf, allowing for the comparison of key population parameters and developing shared and effective conservation management plans.

Fast Area Optimization Approach for XNOR/OR-based Fixed Polarity Reed-Muller Logic Circuits based on Multi-strategy Wolf Pack Algorithm

Area optimization is one of the most important contents of circuits logic synthesis. The smaller area has stronger testability and lower cost. However, searching for a circuit with the smallest area in a large-scale space of polarity is a combinatorial optimization problem. The existing optimization approaches are inefficient and do not consider the time cost. In this paper, we propose a multi-strategy wolf pack algorithm (MWPA) to solve high-dimension combinatorial optimization problems. MWPA performs global search based on the proposed global exploration strategy, extends the search area based on the Levy flight strategy, and performs local search based on the proposed deep exploitation strategy. In addition, we propose a fast area optimization approach (FAOA) for fixed polarity Reed-Muller (FPRM) logic circuits based on MWPA, which searches the best polarity corresponding to a FPRM circuit. The experimental results confirm that FAOA is highly effective and can be used as a promising EDA tool.

Quantification of collective behaviour via causality analysis

Terms such as leader, mediator, and follower sound equal in the description of a pack of wolves, a street protest crowd, or a business team and have very similar meanings. This indicates the presence of some general law or structure that governs collective behaviour. To reveal this, we selected the most common parameter for most levels of the organisation—motion. A causality analysis of distance correlations was performed to obtain follow-up networks that show who follows whom and how strongly. These networks characterise an observed system in general and work as an automation bridge between the biological experiment and the broad field of network analysis. The proposed method was tested on 3D image data from a controlled experiment on a 6-member school of aquarium fish of Tiger Barb. The network patterns can be easily ethologically interpreted and agreed with expected behaviour.

An improved monarch butterfly spectrum allocation algorithm for multi-source data stream in complex electromagnetic environment

In the era of the Internet of Everything, various wireless devices and sensors use spectrum, which is a precious and non-renewable resource, to communication. Due to the characteristics of massive, heterogeneous, and multi-source, the generated multi-source data stream brings difficulties to spectrum cognition. As a result, unreasonable spectrum allocation strategy leads to low utilization of spectrum resources. Optimizing spectrum allocation strategy can effectively improve spectrum utilization. Aiming at the problem of trapped local optimum solution in the genetic algorithm (GA) and particle swarm optimization algorithm (PSO), an improved monarch butterfly algorithm is proposed. Firstly, this paper employs the simulated annealing algorithm to select the migration rate, which increases the diversity of monarch butterfly population. Secondly, chaos mapping algorithm is utilized to improve the optimization ability and convergence speed. Finally, in the view of the problem that the monarch butterfly algorithm is easy to fall into the local optimal solution, there is no better way to escape from the local optimal solution. The Wolf pack updating operator is selected to improve the diversity of the population to generate new monarch butterflies. This method updates the population by generating new monarch butterfly individuals, so as to increasing the diversity of the population. The experimental results show that the improved monarch butterfly algorithm outperforms the other two algorithms in terms of convergence speed and system revenue.

‘Like a piece of meat in a pack of wolves’: gay/bisexual men and sexual racialization

Human geographers have analyzed the co-constitutive relationship between race, gender and sexualities across different spaces and social contexts and have called for intersectional approaches in discussions of identities, power and space. This article applies an intersectional framework to the processes of sexualization, racialization and exoticization that shape the daily lives and erotic/romantic encounters experienced and narrated by participants to two different projects: gay and bisexual men from a North African background living in Belgium; Italian gay men living in England; non-White gay men living in Italy. By discussing qualitative data collected during interviews with these men, and through a continued dialogue about this data between the authors, the paper explores both the effects of these processes on the lives of participants, and the strategies they enact to navigate their social worlds. The focus is on two elements, central to participants’ narratives: the specificity of the intersectional experience of encountering men who expect a specifically gendered and racialized performance based on ‘roughness’ and ‘wildness’, and the capitalization on these exoticizing and racializing images to increase one’s desirability on the dating/hook-up scene and everyday social and work life. By highlighting these elements, this paper shows the importance of applying an intersectional approach to analyses of the entanglements of racialization and sexualization in order to complicate linear accounts of these processes.

Management and Behavior of Livestock Guarding Dogs in a Multiuse Rural Landscape in Northern Israel

The efficacy of livestock guarding dogs (LGDs; Canis familiaris) on modern ranches in the dense multiuse landscape of rural northern Israel is controversial. Minimal time is spent by the ranchers with the herd and LGDs, and the LGDs are known to wander to nearby army bases, homesteads, villages, and other facilities. Thirteen Akbash and six mixed-breed LGDs guarding cattle in a paddock were observed through direct observations and tracking with Global Positioning System collars. Predator presence and interaction with LGDs were recorded using telemetry and trail cameras. In addition, 10 local ranchers were interviewed to assess the effects of management and LGD use on depredation. We expected Akbash LGDs to exhibit behavioral traits consistent with depredation mitigation and that differing ranch-LGD management methods would impact depredation levels. At the study ranch, depredations were at a minimum despite the presence of three wolf packs (Canis lupus) and jackals (Canis aureus) in the vicinity of the observed ranch. The Akbash LGDs spatially displaced wolves and temporally displaced jackals, but they also regularly roamed away from the paddock and returned. In addition, Akbash LGDs were more likely to follow the herd than mixed-breed LGDs; the total number of LGDs in the paddock increased with the number of newborn calves; and LGD intra-aggression increased with the number of LGDs present in the paddock. In the survey, we found little relationship between ranch management type and depredation outcomes. In particular, the use of protective enclosures by some ranches did not mitigate depredation compared with ranches without enclosures. In modern multiuse landscapes, Akbash LGDs show potential for being an important component of depredation mitigation, but their relative contribution needs to be studied further. In addition, the use of protective enclosures, which are known to have negative ecological impacts, should be reconsidered. Several improvements to LGD management are suggested.

Territorial scent-marking and proestrus in a recolonizing wild Gray Wolf (<i>Canis lupus</i>) population in central Wisconsin

Gray Wolf (Canis lupus) uses scent-marking to communicate breeding status, dominance, and territorial boundaries. Despite its importance for reproduction and pack dynamics, information on scent-marking and proestrus in wild wolf populations is limited to a handful of locations. We estimated the rate of territorial scent-marking and the probability of proestrus in a recolonizing Gray Wolf population near the species southern range extent in eastern North America. An analysis of 221 pack-winters of tracking data show that the incremental addition of one wolf pack increased marking rates by 3.4%, whereas increasing the number of wolves in a pack decreased marking rates by 12.1%. Scent-marking rates subsequently increased from 1.9 times/km during recolonization to 3.0 times/km once the population was saturated. We observed evidence of proestrus from 19 December to 14 March with the highest probability of proestrus occurring around 6 February, after peak marking rates around 26 January. Repeated observations of bloody urinations within individual packs suggest proestrus averages 27.9 days. Our study reveals the role of population growth on territorial behaviours and provides a foundation for studies exploring the role of geographic and temporal variation on territorial and reproductive behaviours in wolves.