Herd Of Horses Research Articles

FECUNDITY OF INTESTINAL STRONGYLATA IN HORSES IN CRITICAL CONDITIONS OF CENTRAL YAKUTIA

Horse herd farming based on the year-round pasture management of horses has become one of the main branches of animal husbandry and the basis of the lifestyle and economy of the Yakutia population. Herds are formed according to their sex and age: stallions, mares, and foals are kept in the herd. Helminth infections are widespread among herd horses and are chronic in most cases without pronounced clinical manifestations. Horses seem to be completely healthy from the point of view of the usual visions of the disease. Therefore, no preventive or treatment measures are often applied. In cases of asymptomatic helminth infections (subclinical forms), the huge economic damage caused by them is determined not so much by the death of animals, but it is very difficult to endure wintering in Yakutia with a high invasion degree, and loss of fatness and weight. The Authors of the Article studied the interspecific relationships of equine intestinal nematodes in mixed invasion, and detected changes in the population density of individual nematode species in horses of different age and in different seasons of the year. The Strongylata incidence and infection rate in herd horses was studied based on the results of quantitative coproovoscopic and larvoscopic studies of faeces from herd horses, and on the results of incomplete helminthological dissections on horse ranches of Central Yakutia.

Management and hygiene measures during an outbreak of herpes, influenza, strangles or infections with multidrug resistant bacteria

The aim of this review is to describe general guidelines of hygiene measures in the horse stable as well as to provide current recommendations for an outbreak of a common infectious disease. General cleanliness, hand hygiene, avoidance of stress, regular deworming, and vaccinations belong to the basic hygiene measures in a horse herd. All new or returning equids should be submitted to a quarantine period as an important prevention measure. Repeated washing and disinfection of hands may prevent spreading of infectious agents to people and horses.The conception of a hygiene plan, including general biosecurity procedures and standard operating procedures in a case of an outbreak of an infectious disease, zoonosis, or colonization with multi-resistant bacteria is strongly recommended. As soon as the disease is suspected, extended hygiene measures including protective clothing, cleaning, disinfection, and isolation of potentially infected animals should be implemented. Prompt confirmation of the causative agent by examination of appropriate samples is crucial. It is important to adjust all safety measures based on the contagious nature of the respective pathogen and its major transmission routes. Apart from a lock-down of the stable, clinic or show grounds, the segregation of horses plays an important role. Implementation of the "traffic light system" is recommended. In this, the red group ("infected") include animals with clinical signs of the disease or that have been tested positive. All horses with possible pathogen contact should be allocated to a yellow group ("suspected") and regularly controlled for the signs of infection and fever. Clinically normal horses without contact to the infected animals belong to the green group ("healthy"). A change of protective clothing and an extensive disinfection should be performed when moving between the groups.The extended hygiene measures are to be maintained until all animals have been tested negative or fail to exhibit clinical signs of the disease for a certain time period.

A novel approach for spam detection using horse herd optimization algorithm

Communication via email has expanded dramatically in recent decades due to its cost-effectiveness, convenience, speed, and utility for a variety of contexts, including social, scientific, cultural, political, authentication, and advertising applications. Spam is an email sent to a large number of individuals or organizations without the recipient's desire or request. It is increasingly becoming a harmful part of email traffic and can negatively affect the usability of email systems. Such emails consume network bandwidth as well as storage space, causing email systems to slow down, wasting time and effort scanning and eliminating enormous amounts of useless information. Spam is also used for distributing offensive and harmful content on the Internet. The objective of the current study was to develop a new method for email spam detection with high accuracy and a low error rate. There are several methods to recognize, detect, filter, categorize, and delete spam emails, and almost the majority of the proposed methods have some extent of error rate. None of the spam detection techniques, despite the optimizations performed, have been effective alone. A step in text mining and message classification is feature selection, and one of the best approaches for feature selection is the use of metaheuristic algorithms. This article introduces a new method for detecting spam using the Horse herd metaheuristic Optimization Algorithm (HOA). First, the continuous HOA was transformed into a discrete algorithm. The inputs of the resulting algorithm then became opposition-based and then converted to multiobjective. Finally, it was used for spam detection, which is a discrete and multiobjective problem. The evaluation results indicate that the proposed method performs better compared to other methods such as K-nearest neighbours-grey wolf optimisation, K-nearest neighbours, multilayer perceptron, support vector machine, and Naive Bayesian. The results show that the new multiobjective opposition-based binary horse herd optimizer, running on the UCI data set, has been more successful in the average selection size and classification accuracy compared with other standard metaheuristic methods. According to the findings, the proposed algorithm is substantially more accurate in detecting spam emails in the data set in comparison with other similar algorithms, and it shows lower computational complexity.

Monitoring Body Length of Hokkaido Native Horse’s Foals Using a Drone

Monitoring Body Length of Hokkaido Native Horse’s Foals Using a Drone

A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions

The inconsistent irradiance, temperature, and unexpected behavior of the weather affect the output of photovoltaic (PV) systems, classified as partial or complex partial shading conditions. Under these circumstances, obtaining the maximum output power from PV systems becomes problematic. This paper proposes a population-based optimization model, the horse herd optimization algorithm (HOA), inspired by natural behavior, to solicit the maximum power under partial or complex partial shading conditions. It is an intelligent strategy inspired by the surprise pounce-chasing style of the horse herd model. The proposed technique outperforms the standard in different weather conditions, needs less computational time, and has a fast convergence speed and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the HOA is achieved with conventional techniques, such as “perturb and observe” (P&O), the bio-inspired adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), and the dragonfly algorithm (DA). The following comparison of the presented scheme with the other techniques shows its better performance with respect to fast tracking and efficiency, as well as stability under disparate weather conditions and the ability to obtain maximum power with negligible oscillation under partial and complex shading.

Where We Fit in the Firmament

Where We Fit in the Firmament

Horse herd optimization algorithm for economic dispatch problems

This article applies the horse herd optimization (HHO) algorithm to convoluted economic dispatch (ED) problems. HHO mimics the social behaviour of horses of different ages using six significant traits: grazing, hierarchy, sociability, imitation, defence mechanism and roam. The efficacy of the HHO method is demonstrated on five different ED problems, namely, valve-point effects, prohibited feasible area, ramp rate limits and multiple fuels. The simulated outcomes of the recommended method are comparable to those obtained by established artificial intelligence methods. Comparative and statistical analyses demonstrate that the proposed HHO algorithm performs well and can produce superior results to some other well-known and established algorithms, namely, differential evolution (DE), success-history based adaptive differential evolution with linear population size reduction (L-SHADE) and covariance matrix adaptation–evolution strategy (CMA-ES).

Fuel constrained combined heat and power dynamic dispatch using horse herd optimization algorithm

Due to gradually reduction of fossil fuel, the cost-effective use of available fuel for electric power generation has turn out to be a very significant concern of electric power utilities. This work recommends horse herd optimization algorithm (HOA) to solve fuel constrained combined heat and power dynamic economic dispatch with demand side management integrating wind turbine generators, solar PV plants and pumped storage hydro plant for three different scenarios. The effectiveness of the recommended method is divulged on an archetypal system. Numerical results of archetypal system for three different scenarios are compared with those obtained from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, fast convergence evolutionary programming, differential evolution and real-coded genetic algorithm. It has been seen from numerical results, that the total cost with fuel constraints is more than the cost without fuel constraints. It has been also observed from the comparison that the recommended HOA has the capability to confer with superior-quality solution.

Recent metaheuristic algorithms with genetic operators for high-dimensional knapsack instances: A comparative study

As a new attempt to effectively tackle the high-dimensional 0–1 knapsack (01KP) instances with uncorrelated, weakly-correlated, and strongly-correlated characteristics, in this paper, five lately-proposed meta-heuristic algorithms: horse herd optimization algorithm (HOA), gradient-based optimizer (GBO), red fox search optimizer (RFSO), golden eagle optimizer (GEO), and Bonobo optimizer (BO) have been transformed into binary ones by investigating the various V-shaped and S-shaped transfer functions to be applied to those high-dimensional 01KP problems, which are discrete ones; these binary variants are named BHOA, BGEO, BBO, BRFSO, and BBO. Furthermore, some genetic operators such as the one-point crossover operator and mutation operators have been borrowed to discover more permutations as a trying to avoid stuck into local minima for reaching better outcomes. These two operators are effectively integrated with those binary variants to propose other ones with better performance for achieving further improvements for tackling the high-dimensional 01KP instances called BIHOA, BIGEO, BIGBO, BIBO, and BIRFSO. Those genetic operators and recently-developed meta-heuristic algorithms-based high dimensional binary techniques have been extensively validated using 21 uncorrelated, weakly-correlated, and strongly-correlated 01KP instances with high-dimensions ranging between 100 and 10000, and the obtained outcomes were compared even witnessing which algorithm is the best. The experimental findings show the superiority of BIRFSO for the instances with dimensions greater than 500, and its competitivity for the others.

Wind farm incorporated optimal power flow solutions through multi-objective horse herd optimization with a novel constraint handling technique

Optimal power flow plays an important role in integrating wind power into electric power networks. Because of its complexities, standard formulae are insufficient for the present scenario. Therefore, the multi-objective optimal power flow problems for wind farm incorporated power systems have been explored in this paper. The objectives are to minimize the generation costs, pollutant emissions, power losses, and voltage deviations. This paper proposes the development of a multi-objective meta-heuristic horse herd optimization for solving multi-objective optimal power flow problems. For this, a decomposition concept is introduced to the proposed algorithm leading to decomposition based multi-objective horse herd optimization algorithm. The constraint handling techniques in the previous papers have been found to be inefficient for optimal power flow problems. Therefore, a novel constraint handling technique is proposed in this paper to effectively control the variables out of bounds. In order to validate the performance and suitability of the proposed algorithm, seven case studies are examined. The algorithm is tested on wind farm incorporated IEEE-30, IEEE-57, and IEEE-118 bus test systems to demonstrate the efficiency in solving multi-objective optimal power flow problem for various problem sizes. The experimental results are demonstrating the efficiency of the proposed algorithm in solving complex optimization problems of various scales with multiple objectives.

Effects of extensive grazing on the vegetation of a landscape-scale restoration site

The Wicken Fen Vision (Cambridgeshire, UK) is a landscape-scale habitat restoration project that uses process-driven, open-ended approaches to develop habitats on highly degraded and drained peat soils of former intensive arable land. The project land is extensively grazed with herds of free-roaming, minimally managed herds of Highland cattle and Konik horses. In one 119 ha area, seven 25m x 25 m grazing exclosures were erected and vascular plant species were recorded from 2007 to 2017. Plant species data were analysed to (1) compare changes in plant species composition and diversity in grazed and ungrazed areas; (2) use plant species traits and plant-environment associations to explore the nature of changes in plant composition; (3) use remote sensing to explore changes in vegetation structure; (4) examine the influence of land use histories on grazing outcomes in different parts of the site.There was a clear divergence through time between grazed and ungrazed areas, attributed to significantly greater canopy height, Ellenberg L (Light) and Ellenberg N (fertility) values within the exclosures. Species richness was significantly higher in grazed compared with ungrazed areas and species assemblages separated through the study period. After ten years, extensive free-roaming grazing has had significant impacts on vegetation structure and species richness but effects varied across the study site because of differing historical land use.

BinHOA: Efficient Binary Horse Herd Optimization Method for Feature Selection: Analysis and Validations

In the domains of data mining and machine learning, feature selection (FS) is an essential preprocessing step that has a significant effect on the machine learning model’s performance. The primary purpose of FS is to eliminate unnecessary features, resulting in time-space reduction as well as improved the corresponding learning model performance. Horse herd optimization algorithm (HOA) is a new metaheuristic algorithm that mimics the herding behavior of horses. Within a wrapper-based approach, a binary version of HOA is proposed in this study to select the optimal subset of features for classification purposes. The transfer function is the most important aspect of the binary version. Eight transfer functions, S-shaped and V-shaped, are tested to map the continuous search space into binary search space. Two main enhancements are integrated into the standard HOA to strengthen its performance. A Levy flight operator is added to improve the HOA’s exploring behavior and alleviate local minimal stagnation. Secondly, a local search algorithm is integrated to enhance the best solution obtained after each iteration of HOA. The purpose of the second enhancement is to increase the exploitation capability by looking for the most promising places discovered by HOA. Large-scaled, middle-scaled, and low-scaled datasets from reputable data repositories are used to validate the performance of the proposed algorithm (BinHOA). Comparative tests with state-of-the-art algorithms reveal that the Levy flight with the local search algorithm have a significant favorable impact on the performance of HOA. An enhancement of the population diversity is observed with avoidance of being trapped in local optima.

An Optimal Sizing Framework for Autonomous Photovoltaic/Hydrokinetic/Hydrogen Energy System Considering Cost, Reliability and Forced Outage Rate Using Horse Herd Optimization

An Optimal Sizing Framework for Autonomous Photovoltaic/Hydrokinetic/Hydrogen Energy System Considering Cost, Reliability and Forced Outage Rate Using Horse Herd Optimization

Reduction in growth rates of wild horse populations treated with the controlled-release immunocontraceptive PZP-22 in the western United States

Context Management of wild horses (Equus caballus) on public lands in the western United States by gathering and removing surplus animals has historically failed to keep up with annual population growth. Research has yielded several effective contraceptive agents but these agents have not been widely applied, especially on difficult-to-access horses occupying expansive ranges. Aims This study examined wild horse population growth reduction associated with controlled-release PZP-22 immunocontraceptive vaccine treatments in two challenging herd management areas (HMA) in the western United States. Methods Populations of wild horse herds at Cedar Mountain HMA, Utah (CM), and Sand Wash Basin HMA, Colorado (SWB), USA, were primed with PZP-22 in gathers conducted in 2008 and 2012 (CM only) and boosted with PZP-22 or native PZP administered by dart (SWB in 2010) or by hand (CM in 2012). The two herds were intensively observed between 2008 and 2015. Population size, mortality, reproductive rates and growth rates were calculated from exhaustive inventories of known individuals. Key results Adult and foal mortality were low at both sites. Consequently, both reproductive rates and population growth rates were strongly predicted by a fertility control index, the product of proportion of mares treated and vaccine efficacy. Following the 2012 CM gather, at which 69% of the mares present were treated, only 20% of mares in the herd foaled, and population growth rate decreased by 74% relative to baseline levels, emphasising the importance of treating a large majority of mares. Conclusions Contraception can significantly reduce rates of population growth in wild horse herds that occupy large areas and are challenging to access. Multiple-year efforts and recruitment of new mares into the treatment population beyond the initial application are needed to achieve a biologically significant impact. Implications The strong quantitative relationship between proportion of mares treated and vaccine efficacy and population growth rates should allow managers to better forecast the effort and number of removals (if any) needed to cost-effectively regulate wild horse population numbers and protect the range ecosystem.

A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method

BackgroundPredicting severe respiratory failure due to COVID-19 can help triage patients to higher levels of care, resource allocation and decrease morbidity and mortality. The need for this research derives from the increasing demand for innovative technologies to overcome complex data analysis and decision-making tasks in critical care units. Hence the aim of our paper is to present a new algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the intubation risk of hospitalized COVID-19 patients. MethodsIn this retrospective single-center study, the data of 1225 COVID-19 patients from February 9, 2020, to July 20, 2021, were analyzed by several ML algorithms which included, Decision Tree(DT), Support Vector Machine (SVM), Multilayer perceptron (MLP), and K-Nearest Neighbors(K-NN). First, the most important predictors were identified using the Horse herd Optimization Algorithm (HOA). Then, by comparing the ML algorithms' performance using some evaluation criteria, the best performing one was identified. ResultsPredictive models were trained using 12 validated features. Also, it found that proposed DT-based predictive model enables a reasonable level of accuracy (=93%) in predicting the risk of intubation among hospitalized COVID-19 patients. ConclusionsThe experimental results demonstrate the effectiveness of the proposed meta-heuristic feature selection technique in combining with DT model in predicting intubation risk for hospitalized patients with COVID-19. The proposed model have the potential to inform frontline clinicians with quantitative and non-invasive tool to assess illness severity and to identify high risk patients.

Binary Horse herd optimization algorithm with crossover operators for feature selection

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are also suggested to ensure the efficiency of the proposed method for FS domain. The performance of the proposed fifteen BHOA versions is examined using 24 real-world FS datasets. A set of six metric measures was used to evaluate the outcome of the optimization methods: accuracy, number of features selected, fitness values, sensitivity, specificity and computational time. The best-formed version of the proposed versions is BHOA with S-shape and one-point crossover. The comparative evaluation was also accomplished against 21 state-of-the-art methods. The proposed method is able to find very competitive results where some of them are the best-recorded. Due to the viability of the proposed method, it can be further considered in other areas of machine learning.

THE CURRENT STATE OF THE POLESIE HORSE POPULATION AND THE TARGET STANDARD OF THE MAIN TRAITS OF ITS CONFORMATION

THE CURRENT STATE OF THE POLESIE HORSE POPULATION AND THE TARGET STANDARD OF THE MAIN TRAITS OF ITS CONFORMATION

Spatial structure and motor activity of horses of Zabaikalsky breed

The total influence of external environmental factors by seasons on the spatial structure and motor activity of herd horses of Zabaikalsky breed with year-round grazing was studied. The scientific and economic research was carried out in Trans-Baikal Territory. Grazing of 10 herds of horses (20-27 heads) was carried out on the area of 301.5 km2 at different distances from each other. The distance from the horse-breeding camp was 2300-7900 m. According to the motor activity assessment of horses in the spring and summer seasons during 14 hours of grazing, the average speed was 1.4 and 1.1 km/h, the distance traveled was 16828 and 14827 m. The average air temperature during these periods was 10.4 and 12.2 °С, average air velocity was 4.5 and 2.1 m/s, average relative air humidity was 32.2 and 53.3%. In the autumn period, during 10 hours of grazing, the distance was 13879 ± 195.7 m at a horse speed of 1.2 ± 0.07 km/h, average air temperature of minus 1.1 °C, air speed of 2.3 m/s, and air humidity of 54.4%. In winter, the grazing time was 8 hours, the horses in the experiment covered the distance of 12190 ± 142.3 m at a speed of 1.8 ± 0.03 km/h with an average temperature of minus 24.7 °C, wind speed of 1.5 m/s and relative humidity of 64.7%. In the spring, the horses covered the distance greater than in summer, by 13.5% (p < 0.01), in autumn - by 21.2% (p < 0.001) and in winter - by 38.0% (p < 0.001). The highest nutritional value of pasture herbage was noted in the summer (0.60 feed units/kg of dry weight, 102.3 g of digestible protein/ feed unit). In the spring, the nutritional value of pasture grass was 0.35 feed units/kg of dry weight with 31.5 g of digestible protein.

Behavioural and Physiological Changes in a Herd of Arabian Mares after the Separation of Individuals Differently Ranked within the Dominance Hierarchy.

Simple SummaryThere are many situations in riding facilities and studs in which horses have to be separated out of a group, for example, when they are needed for a riding lesson, training purposes or veterinary treatments. Such a group may form a social herd in which a dominance hierarchy with dominant and submissive members is established. In the study, we investigated how mares respond to a short separation of some of the herd members and whether the response differs according to the rank order of the mares separated. The response was determined with rates of different behaviours showing agitation or calmness, times of locomotion and physiological parameters. The results of the experiment show that the separation of some mares increases the agitation in the remaining herd. In spite of the fact that such situations are constantly repeated in practice, they are stressful for the horses. The reaction of the remaining herd does not depend strictly on the composition of the mares separated regarding their rank in the dominance hierarchy, i.e., it does not differ consistently when a dominant, mixed or submissive group of mares is separated.Horses in a herd develop and maintain a dominance hierarchy between all individuals. There are many situations in riding facilities and studs in which horses have to be separated out of a group. The aim of the study was to determine the rate of behaviours, level of locomotor activity and cardiac activity variables in a herd of horses during a short social separation of individuals differently ranked in the dominance hierarchy. Twelve adult Arabian mares were involved. A behavioural test had been performed before the main experiment to determine the rank order of the mares in this social herd. Three tests were performed when a dominant, mixed and submissive three-member group of mares was separated for 10 min. The response of the remaining herd was determined by a rate of behaviours, time of locomotor activity and cardiac parameters. The results of the experiment reveal evident changes towards emotional arousal in the social herd elicited by a short separation of some conspecifics. The herd created by humans preserves the sensitivity to a temporary loss of its members. The response of the remaining herd does not depend strictly on the composition of the separated mares regarding their rank in the dominance hierarchy.

Horse Herd Optimization Algorithm for Fuel Constrained Day-ahead Scheduling of Isolated Nanogrid

ABSTRACT Day-ahead scheduling of isolated nanogrid (NG) is an important task in power system. Owing to slowly lessening of fossil fuel, the profitable use of available fuel for electric power generation has turn out to be an extremely important concern of electric power utilities. This paper suggests horse herd optimization algorithm (HOA) to solve fuel constrained day-ahead scheduling of isolated nanogrid (NG) for five neighboring homes. NG comprises diesel generators, solar PV plants, battery energy storage system (BESS) and plug-in electric vehicles (PEVs). Simulation results of the test system have been compared with those obtained from social group entropy optimization, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, fast convergence evolutionary programming and differential evolution. It has been observed from the comparison that the suggested HOA has the capacity to give better solution.