Herd Of Horses Research Articles

A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability and uncertainty

In this study, stochastic multi-objective allocation of wind turbines (WTs) in radial distribution networks is performed using a new multi-objective improved horse herd optimizer (MOIHHO) and an unscented transformation (UT) method for modeling the uncertainties of WTs power and network load. The objective function aims to minimize power loss, improve reliability, and reduce the costs associated with wind turbines (WTs), presenting these goals as a three-dimensional function. The Multi-Objective Improved Horse Herd Optimizer (MOIHHO) is derived from an enhanced version of the traditional horse herd optimizer. This enhancement utilizes mirror imaging based on convex lens principles to address issues of premature convergence. Additionally, the decision-making process is designed to identify the final fuzzy solution among the non-dominant solutions within the Pareto front set. The simulation results are presented with and without considering uncertainty in two scenarios of deterministic and stochastic WT allocation on 33- and 69-bus distribution networks and different objectives are compared. Also, the effect of incorporating uncertainties are evaluated on power loss and reliability using the MOIHHO. Moreover, the superiority of the MOIHHO is investigated in achieving better objective function value compared with conventional MOHHO, multi-objective particle swarm optimization (MOSPO), multi-objective gray wolf optimizer (MOGWO), and multi-objective gazelle optimization algorithm (MOGOA). The obtained results demonstrated that considering the UT-based stochastic scenario, the power losses cost is increased, and the reliability is weakened for 33- and 69-bus networks in comparison with the deterministic scenario.

Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images

Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and gradually exceeds the entire body; this kind of cancer originates in both sexes. Prompt recognition of this disorder is most significant in this phase, and it is measured by providing patients with the essential treatment so their efficient lifetime can be protected. Scientists and researchers in numerous studies have initiated techniques to identify tumours in early phases. Still, misperception in classifying skeptical lesions can be due to poor image excellence and dissimilar breast density. BC is a primary health concern, requiring constant initial detection and improvement in analysis. BC analysis has made major progress recently with combining multi-modal image modalities. These studies deliver an overview of the segmentation, classification, or grading of numerous cancer types, including BC, by employing conventional machine learning (ML) models over hand-engineered features. Therefore, this study uses multi-modality medical imaging to propose a Computer Vision with Fusion Joint Transfer Learning for Breast Cancer Diagnosis (CVFBJTL-BCD) technique. The presented CVFBJTL-BCD technique utilizes feature fusion and DL models to effectively detect and identify BC diagnoses. The CVFBJTL-BCD technique primarily employs the Gabor filtering (GF) technique for noise removal. Next, the CVFBJTL-BCD technique uses a fusion-based joint transfer learning (TL) process comprising three models, namely DenseNet201, InceptionV3, and MobileNetV2. The stacked autoencoders (SAE) model is implemented to classify BC diagnosis. Finally, the horse herd optimization algorithm (HHOA) model is utilized to select parameters involved in the SAE method optimally. To demonstrate the improved results of the CVFBJTL-BCD methodology, a comprehensive series of experimentations are performed on two benchmark datasets. The comparative analysis of the CVFBJTL-BCD technique portrayed a superior accuracy value of 98.18% and 99.15% over existing methods under Histopathological and Ultrasound datasets.

Spatial data mining-focused research on image processing techniques for remotely sensed images

ABSTRACT Spatial data mining is an important approach for collecting useful data from big datasets, especially remotely sensed images. This study tackles issues in environmental monitoring and management using sophisticated image processing. The Horse Herd Optimization-based VGG19 (HHO-VGG19) is proposed to improve land cover classification, recognition of objects, detection of changes, and detection of anomalies. The study used the BCDD dataset, which was scaled to 512 × 512 pixels, then applied Z-score normalization and extracted features using Principal Component Analysis (PCA). The VGG19 architecture was enhanced by utilizing Horse Herd Optimization to enhance image classification efficiency. The HHO-VGG19 model surpasses conventional techniques, with F1-score of 92%, a recall of 94%, an accuracy of 98.5%, and a 30-second execution time reduction. The findings indicate the efficiency of integrating sophisticated image processing with spatial data mining, giving an effective tool for remote sensing image processing in environmental uses including tracking ecosystems and handling of natural resources.

Neuro connect: Integrating data-driven and BiGRU classification for enhanced autism prediction from fMRI data

ABSTRACT Autism Spectrum Disorder (ASD) poses a significant challenge in early diagnosis and intervention due to its multifaceted clinical presentation and lack of objective biomarkers. This research presents a novel approach, termed Neuro Connect, which integrates data-driven techniques with Bidirectional Gated Recurrent Unit (BiGRU) classification to enhance the prediction of ASD using functional Magnetic Resonance Imaging (fMRI) data. This study uses both structural and functional neuroimaging data to investigate the complex brain underpinnings of autism spectrum disorder (ASD). They use an Auto-Encoder (AE) to efficiently reduce dimensionality while retaining critical information by learning and compressing important characteristics from high-dimensional data. We treat the feature-extracted data using a BiGRU model for the classification task of predicting ASD. They provide a new optimization strategy, the Horse Herd Algorithm (HHA), and show that it outperforms other established optimizers, such SGD and Adam, in order to improve classification accuracy. The model’s performance is greatly enhanced by the HHA’s novel optimization technique, which more precisely refines weight modifications made during training. The proposed ASD and EEG dataset accuracy value is 99.5%, and 99.3 compared to the existing method the proposed has a high accuracy value.

Influence of Sires on Population Substructure in Dülmen Wild Horses.

The objectives of the present study were to analyze the influence of the stallions employed in the Dülmen wild horses on the genetic diversity and population substructure using Bayesian cluster analysis. The Dülmen wild horse is maintained as a unique horse population exposed to the natural conditions all year round in the Merfelder Bruch near Dülmen in Westphalia, Germany. Stallions selected for breeding have to prove their abilities to survive under this harsh environment. We used multilocus genotypic information from a set of 29 autosomal microsatellites to determine the paternity of 185 male foals sired by nine stallions. As females could not be sampled, we could not make inferences on all yearlings and test whether there are differences in the genetic population parameters between both sexes. The mean number of progeny was 19.92 with a range of 2-32, caused by the length of the service period per stallion. The average observed and unbiased expected heterozygosity was 0.688 and 0.631, the mean number of alleles was 4.448, and Wright's FIS was -0.173. Pairwise genetic distances (FST and Nei's unbiased genetic distances) were significant and varied between 0.038 to 0.091 and 0.085 to 0.290, respectively. Neighbor-joining dendrogram plots clustered a large proportion of the paternal progeny groups in different branches. Posterior Bayesian analyses using seven paternal half-sib groups with 10-74 members supported a maximum of six clusters, with two paternal progeny groups not differing, and a median of five clusters, with two groups of two sires each falling into the same clusters. When sires were employed in non-consecutive years, progeny from these different years of the same sires were grouped in the same cluster, whereas the progeny of one sire from two consecutive years were in different clusters. We were able to distinguish male progeny from Dülmen wild horse stallions and to show the effects of stallion use on the genetic substructure in the Dülmen wild horse herd. In conclusion, the analyses showed the genetic potential of the Dülmen wild horse stallions to maintain a high genetic diversity and also the effects in which breeding seasons and for how long stallions are used to sire foals. The selection of stallions may be sensitive for the further development of genetic diversity and preserve this closed population as a valuable resource for further studies on the evolution of the horse.

Horse Herd optimization with deep learning based intrusion detection in cloud computing environment

Horse Herd optimization with deep learning based intrusion detection in cloud computing environment

Optimal design of hybrid green energy powered reduced switch converter based shunt active power filter using horse herd algorithm

Renewable energy sources are playing a leading role in today's world. However, integrating these sources into the distribution network through power electronic devices can lead to power quality (PQ) challenges. This work addresses PQ issues by utilizing a shunt active power filter in combination with an Energy Storage System (ESS), a Wind Energy Generation System (WEGS), and a Solar Energy System. While most previous research has relied on complex methods like the synchronous reference frame (SRF) and active-reactive power (pq) approaches, this work proposes a simplified approach by using a neural network (NN) for generating reference signals, along with the design of a five-level reduced switch voltage source converter. The gain values of the proportional-integral controller (PIC), as well as the parameters for the shunt filter, boost, and buck-boost converters in the WEGS and ESS, are optimally selected using the horse herd optimization algorithm. Additionally, the weights and biases for the neural network (NN) are also determined using this method. The proposed system aims to achieve three key objectives: (1) stabilizing the voltage across the DC bus capacitor; (2) reducing total harmonic distortion (THD) and improving the power factor; and (3) ensuring superior performance under varying demand and PV irradiation conditions. The system's effectiveness is evaluated through three different testing scenarios, with results compared against those obtained using the genetic algorithm, biogeography-based optimization (BBO), as well as conventional SRF and pq methods with PIC. The results clearly demonstrate that the proposed method achieves THD values of 3.69%, 3.76%, and 4.0%, which are lower than those of the other techniques and well within IEEE standards. The method was developed using MATLAB/Simulink version 2022b.

Enhancing Health Data Assessment Performance: A Multi-Sensor Focused Study for Public Health

The term health data assessment performance described the process of assessing efficacy of acquiring and reporting health data. Multi sensor approach for predicting diabetes in public health scenarios, multi sensor for public health assimilates assorted data source and addresses community health challenges widely and efficiently. In this study, we proposed a Horse Herd Fused Discriminant Generative Adversarial Network (HHF-DGAN) algorithm for diabetes prediction. Sensor-based diabetes data is gathered from kaggle, and then data preprocessing using z-score normalization is inaccurate records from a dataset. To extract feature from the pre-processed data, Short-Time Fourier Transform (STFT) is used for dimensionality reduction. HHF-DGAN is a public health technique that addresses diabetes prevention and management while also including multi sensor assessment data. The proposed methods are to evaluate F1-score (86%), precision (92%), recall (93%), and accuracy (93%). The effectiveness of the suggested strategy in improving prediction reliability for diabetes detection in public health circumstances has been established by comparing it with existing methods. The study concludes field of healthcare assessments with a robust framework for leveraging multi sensor data and deep learning methods in disease prediction.

Influence of climatic changes on respiratory health in a teaching herd of outdoor-housed horses

Horses maintained outdoors may experience a lower-allergenic environment compared to their stabled counterparts. This study hypothesizes that climatic changes in southern Brazil can influence respiratory status. To test this hypothesis, we evaluated the lower airways of 17 horses from a teaching herd in southern Brazil, maintained outdoors, during winter, spring, and summer. Except for one mare with a history of severe asthma, all horses were considered healthy and underwent a physical examination. Airway endoscopic evaluation included scoring for tracheal mucus (0−5) and bronchial septum thickness (1−5). Bronchoalveolar lavage fluid (BALF) was collected at three time points, while bronchial septum biopsies were performed during spring and summer for airway epithelial investigation. Data analysis involved repeated measures ANOVA and Wilcoxon tests (p < 0.05). Tracheal mucus score and septal thickness did not differ across investigation periods. In BALF cytology, the mean percentage of neutrophils was higher in spring than summer (7.9 ± 13.4 % vs. 4.5 ± 11.7 %, P = 0.037), and eosinophil count was higher in winter than spring (0.64 ± 1.29 % vs. 0.03 ± 0.13 %, P = 0.034) and summer (0.64 ± 1.29 % vs. 0.14 ± 0.60 %, P = 0.023). Histopathological observations showed no differences between time points, and no correlations were observed with BALF analyses (P > 0.05). This study demonstrates that, even in an outdoor environment, horses' airways exhibit cytological modifications associated with different seasons, indicating a need for deeper investigation; endobronchial biopsy did not contribute to the clinical diagnosis.

Towards sustainable regional energy solutions: An optimized operational model for integrated energy systems with price-responsive planning

With dynamic changes in the energy market and continuous advances in smart energy technology, regional integrated energy systems (RIESs) have emerged as a vital direction in the evolution of energy systems. They are increasingly gaining significance in the realm of energy supply (ES) and demand response initiatives. In this context, this article introduces a novel optimal operational model for an integrated energy system (IES) that incorporates energy price responsiveness. The paper commences by delivering a comprehensive overview of the typical structure of an IES, presenting detailed models for each system module, encompassing the electrical energy supplier, heating, and cooling components. The proposed system model comprises three distinct subsystems, each catering to specific energy requirements. The primary emphasis is on devising an efficient planning scheme tailored to the energy consumption patterns and operational aspects of the IES. Through the presented model, there is substantial potential for significantly reducing the overall system cost without inducing a noteworthy upsurge in environmental pollution. Moreover, the energy efficiency of the system can experience considerable enhancement. The desired optimization problem has been solved using the proposed multi-objective Horse Herd Optimization Algorithm (MOHHOA). Additionally, in this strategy, Pareto front and fuzzy selection are employed to make better decisions among all Pareto solutions. The optimization strategy devised in this research not only enables the integrated operation of the IES but also ensures its compatibility with ongoing energy development initiatives. The proposed method yields a diverse set of Pareto solutions across the objective space, providing various trade-offs between economic, environmental, and reliability considerations. Utilizing MOHHOA, the algorithm efficiently balances exploration and exploitation during optimization, resulting in informed decision-making. In optimal planning, Scenario 2 exhibits lower operational costs (8.2 % reduction) and a dramatic improvement in LESP (1.24 %–1.176 %) compared to Scenario 1. Sensitivity analysis reveals an 11.2 % increase in environmental costs in Scenario 1 with a 36 % rise in electricity prices. Conversely, in Scenario 2, a 10 % reduction in natural gas prices leads to a 24.5 % increase in system running costs. These findings underscore the significance of considering energy price fluctuations for optimizing integrated energy system performance.

Enhancing energy efficiency in distributed systems with hybrid energy storage

This paper presents a pioneering approach to enhance energy efficiency within distributed energy systems by integrating hybrid energy storage. Unlike prior research, our study focuses on synergizing two distinct energy storage systems while concurrently optimizing system configuration and operational strategies to enhance performance and reduce costs. The employed distributed energy system incorporates hybrid energy storage, merging thermal energy storage with power storage technologies such as supercapacitors and lithium batteries. We conduct a comprehensive investigation into the impact of this innovative system on distributed energy systems, employing a dual-objective cooperative optimization method that addresses energy efficiency and economic factors alongside environmental considerations. This approach is applied to an almost zero-energy community, demonstrating its efficacy in improving load profiles and optimizing energy management through demand-side management. Additionally, we incorporate stochastic energy management, renewable energy resource planning, and dynamic storage to account for uncertainties in energy sources and demand, utilizing the Two-Point Estimate Method (2P + E PEM). To address the complexity of the problem, we introduce an advanced multi-objective optimization algorithm, the Horse Herd Optimization Algorithm (HOA), inspired by the movement behavior of horses in a herd. This algorithm exhibits superior performance with various operators including elite selection and crossover. In conclusion, our contributions include the introduction of a distributed energy system with hybrid storage, a dual-objective cooperative optimization method, and the application of advanced algorithms. Our results demonstrate significant reductions, with primary energy consumption decreasing by nearly 54.8 % and equivalent pollutant emissions by 63.6 %, highlighting the significance and effectiveness of our proposed approach.

Cost-Effective Planning of Hybrid Energy Systems Using Improved Horse Herd Optimizer and Cloud Theory under Uncertainty

In this paper, an intelligent stochastic model is recommended for the optimization of a hybrid system that encompasses wind energy sources, battery storage, combined heat and power generation, and thermal energy storage (Wind/Battery/CHP/TES), with the inclusion of electric and thermal storages through the cloud theory model. The framework aims to minimize the costs of planning, such as construction, maintenance, operation, and environmental pollution costs, to determine the best configuration of the resources and storage units to ensure efficient electricity and heat supply simultaneously. A novel meta-heuristic optimization algorithm named improved horse herd optimizer (IHHO) is applied to find the decision variables. Rosenbrock’s direct rotational technique is applied to the conventional horse herd optimizer (HHO) to improve the algorithm’s performance against premature convergence in the optimization due to the complexity of the problem, and its capability is evaluated with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) methods. Also, the cloud theory-based stochastic model is recommended for solving problems with uncertainties of system generation and demand. The obtained results are evaluated in three simulation scenarios including (1) Wind/Battery, (2) Wind/Battery/CHP, and (3) Wind/Battery/CHP/TES systems to implement the proposed methodology and evaluate its effectiveness. The results show that scenario 3 is the best configuration to meet electrical and thermal loads, with the lowest planning cost (12.98% less than scenario 1). Also, the superiority of the IHHO is proven with more accurate answers and higher convergence rates in contrast to the conventional HHO, PSO, and MRFO. Moreover, the results show that when considering the cloud theory-based stochastic model, the costs of annual planning are increased for scenarios 1 to 3 by 4.00%, 4.20%, and 3.96%, respectively, compared to the deterministic model.

Combining dynamic adaptive snake algorithm with perturbation and observation for MPPT in PV systems under shading conditions

Photovoltaic (PV) systems play an increasingly vital role in the global new energy landscape. However, when PV arrays are affected by partial shading, their power-voltage (P-U) characteristic curves exhibit dynamic multi-peak features, posing challenges to traditional Maximum Power Point Tracking (MPPT) techniques, including slow convergence speed, low tracking efficiency, and oscillations around the Maximum Power Point (MPP), leading to unnecessary power losses. Therefore, this study proposes a dual-layer control MPPT technology that integrates dynamic adaptive snake optimization algorithm with variable step perturbation and observation (ISO-IP&O) to address the MPP extraction problem of PV systems under complex weather conditions. This study compares and analyzes the proposed ISO-IP&O controller with current renowned MPPT techniques, including Grey Wolf Optimization combined with P&O (GWO-P&O), Improved Squirrel Search Algorithm (ISSA), Original Snake Optimization (SO), Horse Herd Optimization (HHO), and Adaptive Factor Selection Multi-Swarm Particle Swarm Optimization (FMSPSO), validating the effectiveness of the proposed ISO-IP&O controller. Simulation results demonstrate that the ISO-IP&O technology achieves an average tracking efficiency of 99.91 % under various shading conditions, with an average convergence speed of only 0.07 seconds. Compared with other existing methods, the ISO-IP&O technology exhibits superior performance in terms of GMPP tracking speed, maximum power tracking efficiency, and stability. Experimental validation on the HIL+RCP physical experimental platform shows that under eight partial shading conditions(PSC), the proposed method still achieves the highest average tracking efficiency of 99.68 %, with the fastest average tracking speed of 0.66 seconds. The ISO-IP&O controller achieves rapid, robust, and efficient GMPP tracking under various complex shading conditions, contributing to the enhancement of PV system performance.

Widespread horse-based mobility arose around 2200 bce in Eurasia

Horses revolutionized human history with fast mobility1. However, the timeline between their domestication and their widespread integration as a means of transport remains contentious2–4. Here we assemble a collection of 475 ancient horse genomes to assess the period when these animals were first reshaped by human agency in Eurasia. We find that reproductive control of the modern domestic lineage emerged around 2200 bce, through close-kin mating and shortened generation times. Reproductive control emerged following a severe domestication bottleneck starting no earlier than approximately 2700 bce, and coincided with a sudden expansion across Eurasia that ultimately resulted in the replacement of nearly every local horse lineage. This expansion marked the rise of widespread horse-based mobility in human history, which refutes the commonly held narrative of large horse herds accompanying the massive migration of steppe peoples across Europe around 3000 bce and earlier3,5. Finally, we detect significantly shortened generation times at Botai around 3500 bce, a settlement from central Asia associated with corrals and a subsistence economy centred on horses6,7. This supports local horse husbandry before the rise of modern domestic bloodlines.

93 Evaluating the efficacy of anthelmintics in an upper Midwest horse herd

Abstract Cyathostomins in the gastrointestinal tract are ubiquitous in horses around the world and remain a target in parasite control programs. Resistance to anthelmintics has risen in recent years with widespread resistance to benzimidazoles and pyrantels and emerging resistance to macrocyclic lactones. Thus, investigating the efficacy of anthelmintics in different locations is important to direct future parasite control programs. This 2-yr study evaluated the efficacy of three anthelmintic drugs in a university-owned equine herd in the upper Midwest using fecal egg count reduction tests (FECRT). Additionally, the effect of age group and body condition score (BCS) on fecal egg shedding was evaluated. We hypothesized that drug resistance is present in the parasite population of this equine herd. Fecal egg counts (FEC) of strongelid eggs were determined using the McMaster method. The efficacy of anthelmintics was determined by calculating the percentage of reduction in FEC between d 0 and d 14 post-treatment. Eighteen horses of various breeds were used in the study. The horses were grouped by age (&amp;lt; 10, 10-16, and &amp;gt; 16 yr; n = 6 in each group) and BCS using the Henneke system (5.0-5.5, n = 9; 6.0-6.5., n = 6; and 7.0-8.0, n = 3). In yr 1, horses within age group were randomly assigned to anthelmintic treatment: pyrantel pamoate, fenbendazole, or ivermectin. In yr 2, all horses received ivermectin. Individual FEC were performed on the day of anthelmintic treatment (d 0) using freshly voided feces. FEC were repeated 14 d after treatment. FEC were log transformed before analysis. Efficacy of the anthelmintics were compared using a mixed model ANOVA with repeated measures (SAS 9.4). Results were considered significant when P &amp;lt; 0.05. The FECRT was greater when treated with ivermectin and pyrantel pamoate (94.8 ± 11.6 % and 96.6 ± 14.3 %, respectively) compared with fenbendazole (54.4 ± 10.8 %; P &amp;lt; 0.05). The FECRT increased from 54.4% when treated with fenbendazole to 100% when the same horses were treated with ivermectin (P &amp;lt; 0.05). Age tended to affect egg shedding in horses (P = 0.0548). Young horses (&amp;lt; 10 yr) had a greater FEC compared with older horses (&amp;gt;16 yr; P &amp;lt; 0.05) but were not different from horses aged 10 to 16 yr (P &amp;gt; 0.05). There was no effect of BCS on strongelid egg shedding in this study (P = 0.9468). These results indicate the efficacy was within the expected range with no signs of resistance to pyrantel pamoate (93.3 to 100% reduction). There was evidence of emerging resistance to ivermectin (70.4 to 100% reduction) and significant resistance to fenbendazole (0 to 71.5% reduction) in the cyathostomin population in this horse herd. The findings of this study agree with previous research demonstrating resistance to anthelmintics. These results highlight the importance of designing improved parasite control programs to limit the spread of drug resistance with the ultimate goal of improving overall health of the horse.

Trypanosoma evansi Infection in Sumba Horses in East Sumba Regency : A Study at BBVet Denpasar

Background: Sumba is one of the original habitats of the Sandalwood Ponies and the presence of pony is an important element for the community. The Sumbanese herd their ponies in the savanna and rely on nature as a source of horse feed, consequently, during the dry season, the availability of the feed decreases. These conditions can potentially reduce the health status of ponies and increase morbidity or mortality from diseases caused by Trypanosoma evansi. Purpose: This study aims to determine the occurrence and intensity of Trypanosoma evansi infection in blood samples from horses examined at the Denpasar Bali Veterinary Centre. Methods: Thirty blood samples were collected from East Sumba Regency. Three milliliters of blood were taken from the jugular vein and immediately transferred into an ethylenediaminetetraacetic acid tube. Thin blood smears were subsequently prepared and examined using the Giemsa-stained blood smears method. The preparations of blood smear were examined under microscope to determine the Trypanosoma evansi infection and the intensity was calculated on the average number of Trypanosoma evansi in 100 red blood cells. Results: The result showed that six out of 30 horses were tested positive for Trypanosoma evansi. The infection intensity ranged from two to 18 parasites with an average number of 8 in the blood smear examination method. Conclusion: The incidence of trypanosomiasis in Sumba horses varies with each season in East Sumba Regency. The prophylactic measures that should be adopted in the particular herd of horses because this is the most significant blood protozoan parasite of equines.

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources.

Modified wild Horse Herd Optimization based Bhattacharyya error constraint (BEC) based L2-norm Linear discriminant analysis (LDA) method for the sentiment analysis

People's opinions are analyzed via sentiment analysis in all fields. Reviews and tweets, among other formats, are used to express opinions. Irony, sarcasm, and other difficult-to-discern hidden meanings can occasionally be found in viewpoints. Artificial intelligence must be used to examine the sentiments as a result. We suggest a unique Bhattacharyya error constraint (BEC) based L2-norm linear discriminant analysis (LDA) because some of the earlier efforts lack optimization. There are some overfitting and class disparity issues in this. To address this, we used a brand-new method called Modified Wild Horse Herd Optimization (MHHO). The experiment is run to evaluate the performance of the suggested strategy and to compare it to other approaches already in use. We have used performance measures for comparison, and the results demonstrate that the suggested method successfully assesses the sentiment from the acquired dataset.

A fuzzy rough set-based horse herd optimization algorithm for map reduce framework for customer behavior data

A fuzzy rough set-based horse herd optimization algorithm for map reduce framework for customer behavior data

Population genomics provide insight into ancestral relationships and diversity of the feral horses of Theodore Roosevelt National Park.

Theodore Roosevelt National Park (TRNP) manages a herd of feral horses (Equus caballus) which was present on the landscape prior to the establishment of the park. The population presents a unique scenario in that it has experienced fairly intensive and well-documented management since the park's establishment, including herd size reductions, intentional introduction of diversity, and subsequent attempts to remove introduced lineages. This provides an interesting case study on the genetic effects of diverse evolutionary forces on an isolated feral population. To explore the effects of these forces and clarify the relationship of this feral herd with other horses, we used genome-wide markers to examine the population structure of a combined dataset containing common established breeds. Using the Illumina Equine 70k BeadChip, we sampled SNPs across the genome for 118 TRNP horses and evaluated the inbreeding coefficient f and runs of homozygosity (RoH). To identify breed relationships, we compared 23 representative TRNP samples with 792 horses from 35 different breeds using genomic population structure analyses. Mean f of TRNP horses was 0.180, while the mean f for all other breeds in the dataset was 0.116 (SD 0.079). RoH analysis indicates that the TRNP population has experienced recent inbreeding in a timeframe consistent with their management. With Bayesian clustering, PCA, and maximum likelihood phylogeny, TRNP horses show genetic differentiation from other breeds, likely due to isolation, historical population bottlenecks, and genetic drift. However, maximum likelihood phylogeny places them with moderate confidence (76.8%) among draft breeds, which is consistent with the known history of breeds used on early North Dakota ranches and stallions subsequently introduced to the park herd. These findings will help resolve speculation about the origins of the herd and inform management decisions for the TRNP herd.