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This research proposes an improved energy management strategy (EMS) for a fuel cell hybrid power system for an electric aircraft based on a recently developed coyote optimization algorithm (COA). The suggested hybrid system consists of fuel cells and an energy storage system (ESS) to supply the required load in stable conditions. The distribution and performance of the hybrid electrical power system are determined by various energy sources. Consequently, having the best energy management system is essential for completing this work. The suggested EMS’s main objectives are to reduce hydrogen energy utilization and increase power source longevity. The proposed coyote optimization algorithm with external energy maximization strategy (COA-EEMS) and coyote optimization algorithm with equivalent consumption minimisation strategy (COA-ECMS) are tested with the help of the Opal-RT 5700 real-time HIL simulator and MATLAB/Simulink. The proposed algorithms confirm their robustness and higher efficiency by minimizing hydrogen fuel consumption compared to existing algorithms. The merits of the proposed algorithms are presented in detailed and compared with existing algorithms.

The majority of residents in southern California live in urban areas. Therefore, working with cities to promote tolerance and coexistence with urban wildlife is crucial to the conservation and management of native species. Human conflicts with coyotes (Canis latrans) illustrate the importance of incorporating the social sciences, particularly knowledge of human behavior, communication, and education, in a coyote management strategy. Here, we review 199 cities across southern California to determine which localities have a coyote management website or a coyote management plan. We also included cities that have collaborated with the California Department of Fish and Wildlife in developing a “Wildlife Watch” program model. Wildlife Watch (based on the Neighborhood Watch national crime prevention program) uses conservation-oriented principles to empower local communities, agencies, and residents to remove wildlife attractants and to exclude or deter coyotes from neighborhoods. We examine how cities with coyote management websites and programs differ from cities without, based on U.S. census demographics. Using data from coyote conflict and sighting tools (Coyote Cacher, iNaturalist, and CDFW’s Wildlife Incident Reporting System) we compare coyote reports across cities with different management plans and websites. Finally, based on demographics from the US Census, we examine ways Wildlife Watch, or related programs, can be expanded and improved. An adaptive community-based program, like Wildlife Watch, offers a valuable toolkit to managers for navigating the diverse array of human perceptions, values, and attitudes regarding urban species and human-wildlife conflicts.

Renewable and distributed power generation have been acknowledged as options for the safe, secure, sustainable, and cost-effective production, delivery, and consumption of energy in future low-carbon cities. This research introduces the Dynamic Coyote Search Algorithm (DCSA)-based optimal scheduling of distributed energy systems for home energy management systems. According to the heat storage properties of the building, a smart building energy model is established and introduced into the optimal scheduling of the distributed energy system in order to optimize the adjustment of the room temperature within the user’s acceptable room temperature range. The DCSA algorithm used is to minimize the daily comprehensive operating cost, including environmental factors. According to the simulation results, the impact of smart energy storage on scheduling is analyzed, and the results show that the optimal scheduling of building smart energy storage participating in the system reduces the total cost by about 3.8%. In addition, the DCSA has a significantly faster convergence speed than the original coyote algorithm.

This study presents an exergy assessment methodology for a power production system defined by a high-temperature proton exchange membrane fuel cell. The evaluated structure has an organic Rankine cycle for recovering the lost heat. This study provides an optimum balanced model by optimizing the variables of the system. Here, a new improved metaheuristic, called Fractional-order Coyote Optimization Algorithm is proposed to the studied system to provide results with higher accuracy and precision. Three cost functions have been utilized for optimization: irreversibility, work, and exergy. Simulation results of the proposed method are then implemented in a case study and its results are validated by comparing with experimental data, the original COA, and the Genetic Algorithm (GA) from the literature. Final achievements indicate that the proposed algorithm gives the highest confirmation by the experimental data.

The increasing awareness that hybridization, and resultant gene flow, plays a major role in animal diversification has led to a growing number of studies that have focused on assessing the morphological consequences of this process. Analyses of mammalian hybrids have identified skeletal effects of hybridization, including a suite of anomalous dental and sutural traits on the skull that are present at high frequencies in hybrid populations. These studies have also detected consistent patterns of morphological shape and size differences between hybrids and parental taxa across a wide variety of organisms. However, more research is required to understand the universality of these traits and shape/size differences. Building on these previous studies, a sample of genetically determined canid hybrids was examined, specifically the eastern coyote (Canis latrans var.), a hybrid between coyotes, wolves, and dogs, to test whether this group exhibits a comparable pattern of anomalous nonmetric characters, and to assess differences in craniomandibular shape and size. First, specimens of C. latrans var., C. latrans, and C. lupus were scored for anomalous traits, including supernumerary and rotated teeth, dental crowding, and sutural anomalies. Geometric morphometric analyses were then conducted on a subset of these individuals to explore craniomandibular size and shape variation, as well as allometry. The results are largely consistent with other studies, indicating that the incidence of dental anomalies, dental crowding, and sutural anomalies is significantly higher in hybrids. However, differences are not significant for supernumerary teeth. The exploration of morphometric variation identifies intermediate morphology in the hybrids, and some indication of greater morphological variability in the mandible. When these results are combined with previous studies, they suggest that skeletal signatures of hybridization are common to different mammalian taxa across multiple generations; however, some traits such as supernumerary teeth may be lost after a few generations.

One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal series. Z-shape transfer functions (ZTF) are evaluated to verify their efficiency in improving BCOA performance in QSAR classification based on classification accuracy (CA), the geometric mean of sensitivity and specificity (G-mean), and the area under the curve (AUC). The Kruskal-Wallis test is also applied to show the statistical differences between the functions. The efficacy of the best suggested transfer function, ZTF4, is further assessed by comparing it to the most recent binary algorithms. The results prove that ZTF, especially ZTF4, significantly improves the performance of the original BCOA. The ZTF4 function yields the best CA and G-mean of 99.03% and 0.992%, respectively. It shows the fastest convergence behaviour compared to other binary algorithms. It takes the fewest iterations to reach high classification performance and selects the fewest descriptors. In conclusion, the obtained results indicate the ability of the ZTF4-based BCOA to find the smallest subset of descriptors while maintaining the best classification accuracy performance.

This article presents an improved version of the coyote optimization algorithm (COA) that is more compatible with nature. In the proposed algorithm, fitness-distance balance (FDB) and Levy flight were used to determine the social tendency of coyote packs and to develop a more effective model imitating the birth of new coyotes. The balanced search performance, global exploration capability, and local exploitation ability of the COA algorithm were enhanced, and the premature convergence problem resolved using these two methods. The performance of the proposed Levy roulette FDB-COA (LRFDBCOA) was compared with 28 other meta-heuristic search (MHS) algorithms to verify its effectiveness on 90 benchmark test functions in different dimensions. The proposed LRFDBCOA and the COA ranked, respectively, the first and the ninth, according to nonparametric statistical results. The proposed algorithm was applied to solve the AC optimal power flow (ACOPF) problem incorporating thermal, wind, and combined solar-small hydro powered energy systems. This problem is described as a constrained, nonconvex, and complex power system optimization problem. The simulation results showed that the proposed algorithm exhibited a definite superiority over both the constrained and highly complex real-world engineering ACOPF problem and the unconstrained convex/nonconvex benchmark problems. WOS:000625039600003 2-s2.0-85102027544

Due to the computational complexity of multilevel image thresholding, Swarm Intelligence Optimization Algorithm (SIOA) has been widely applied to improve the calculation efficiency. Therefore, more and more attention has been paid to exploring the application of the latest SIOA in multilevel segmentation. This article takes Otsu and fuzzy entropy as the objective functions, using Coyote Optimization Algorithm (COA) for multilevel thresholds optimization selection, through fuzzy median aggregation of local neighborhood information and then forms the Fuzzy Coyote Optimization Algorithm (FCOA), so that the thresholding image segmentation can be achieved in the end. To prevent the COA algorithm from falling into the local optimum, this article follows the differential evolution strategy adopted by the standard COA, using the number of iterations to construct the differential scaling factor to form the Improved Coyote Optimization Algorithm (ICOA). The experimental results show that fuzzy Kapur entropy and fuzzy median value aggregation-based ICOA(FICOA) achieves better image segmentation quality. Compared with Grey Wolf Optimizer (GWO), Fuzzy Modified Quick Artificial Bee Colony and Aggregation Algorithm (FMQABCA) and Fuzzy Modified Discrete Grey Wolf Optimizer and Aggregation Algorithm (FMDGWOA), FCOA and FICOA have certain advantages in visual effects of image segmentation and PSNR, FSIM evaluation indices. Particularly compared with GWO (also a wolf evolutionary algorithm), FICOA shows significant advantages.

This paper studies the parameter estimation of fractional order equivalent circuit model of lithium-ion batteries. Since intelligent optimization algorithms can achieve parameters with high accuracy by transforming the parameter estimation into optimization problem, coyote optimization algorithm is taken in this paper by modifying two key steps so as to improve the accuracy and convergence speed. Firstly, tent chaotic map is introduced to avoid falling into local optimum and enhance population diversity. Secondly, dual strategy learning is employed to improve the searching ability, accuracy and convergence speed. Non-parametric statistical significance is tested by 6 benchmark functions with the comparison of other 5 optimization algorithms. Furthermore, the proposed algorithm is applied to identify the fractional order model of the Samsung ICR18650 (2600 mAh) and compared with conventional coyote optimization algorithm and particle swarm algorithm, which declared the excellence in identification accuracy.

Canidae is a species-rich, abundant, and widespread family. Several wild canid species, in particular, have shown a significant range expansion and increased abundance in the last few decades or even in the last century. The grey wolf (Canis lupus), coyote (Canis latrans), and the red fox (Vulpes vulpes) are resident on whole continents or even on multiple continents. Although canids share common behavioural and ecological characteristics, the formula of species-specific elements contributes to their success. This review investigated which factors have contributed mainly to the expansion of the grey wolf, coyote, and red fox. Analysis of the literature review shows that the grey wolf has dramatically benefitted from legal protection, reintroduction programs, and the ability to colonise areas naturally because of its particular social system, early reproduction, high fecundity, and rapid physical development. As a meso-carnivore, the coyote has shown a rapid spread after the extermination of apex predators in several regions in North America. Along with changes in land use, their high adaptability and hybridisation with wolves have all contributed to their prolonged success. The red fox has shown the largest expansion among canids even though it is a solitary species. Their morphological, reproductive and behavioural traits have facilitated their expansion to all corners of the world. Moreover, the species benefitted from human-caused changes like land conversion and the almost complete eradication of rabies in Europe. Overall, it is crucial to change management policies for grey wolves and increase control measures to regulate the three species and mitigate (potential) human-carnivore conflicts.

This paper presents a new method based on coyote algorithm (COA) which is inspired from the social life of coyotes for the problem of simultaneous network reconfiguration and distributed generation (DG) placement to reduce real power loss. The effectiveness of the proposed COA method has been evaluated on two distribution systems consisting of 69-node and 119-node systems at two scenarios consisting of reconfiguration only and simultaneous reconfiguration and DG placement. The result analysis has indicated that network reconfiguration combination with optimization of location and size of distributed generation (DGs) is more effective for power loss reduction than network reconfiguration only. About the network reconfiguration only, for the 69-node and 119-node systems COA can search out the optimal solution that reduce power loss by 56.16% and 32.86%, respectively. Meanwhile, the optimal solution obtained by the network reconfiguration combination with optimization of location and size of DGs using COA helps to reduce power loss of two aforementioned systems by 84.37% and 55.31%, respectively. The result comparisons with other methods in the literature have also shown that COA has ability to obtain the better network configuration and location and size of DGs than other methods. Therefore, the proposed COA can be a promising method for the problem of simultaneous reconfiguration and DG placement.

Internet of Things (IoT) is an important technique in the modern wireless telecommunications field. It is based on a collection of sensor nodes connected through wireless sensor networks (WSNs). The lifetime of this network is affected by the battery power of the connected sensor nodes. Network clustering techniques are used to improve energy consumption and extend the lifetime of the WSN. These techniques divide the sensor nodes into clusters and every cluster has a unique cluster head (CH) node. Recently, clustering-based metaheuristic techniques are used to solve this problem and find the optimal CH nodes under certain considerations such as less energy consumption and high reliability. This paper proposes a new clustering scheme for heterogeneous WSN using Coyote Optimization based on a Fuzzy Logic (COFL) algorithm. It uses the coyote optimization algorithm (COA) in conjunction with fuzzy logic (FL) system to reinforce and balance the clustering process for increasing the wireless network lifetime and reducing energy consumption. FL based clustering is adapted to determine a tentative set of CHs. The output of the FL is added as a solution within the initial solutions of the COA. Furthermore, a new fitness function has been adapted to minimize the total intra-cluster distance between each CH node and its cluster members and minimize the inter-cluster distance between the CHs nodes and the base station. An extensive simulation with three different scenarios is performed. The performance of the proposed COFL algorithm is compared with the well-known algorithms; namely low-energy adaptive clustering hierarchy protocol (LEACH) and stable election protocol (SEP) as traditional protocols and also coyote optimization algorithm (COA), grey wolf optimization (GWO), and particle swarm optimization (PSO). The COFL algorithm outperforms other algorithms in terms of alive node analysis, energy consumption, throughput, and central tendency measurements for alive nodes and normalized energy.

The recycling of nutrients is a valuable process throughout all ecosystems, but it is particularly important in extreme environments like deserts since acquiring nutrients poses a greater challenge for survival due to their scarcity. In our study, we focused on animal scat (coyote and woodrat) and its influence on plant communities and growth since they aid in the recycling of nutrients. We collected data on plants surrounding three different types of sites: areas with presence of accrued coyote or woodrat scat and areas without. We found that the distribution of scat across the desert landscape does not greatly affect the distribution of annual plants and their overall height. However, we delineated a clear difference between the surrounding perennial communities of our three sites. Differences in pH between the soil found in woodrat and coyote sites highlight the ability of scat to alter soil and potentially affect vegetation. Our study demonstrates a need to better understand the role of scat in a nutrient-deficient environment., CEC Research Volume 6, Issue 1

In 1983, Savarie and colleagues did an extensive study of the toxicity of para-aminopropiophenone (PAPP) in multiple species of mammals and birds (Savarie et al. 1983). They tested 15 species and found that PAPP was toxic to coyotes: the LD50 for coyotes via oral gavage in cod liver oil was 5.6 mg/kg. However, they also found that coyotes (and numerous other species) vomited after consuming PAPP and this vomiting likely decreased the efficacy of PAPP. They concluded that the induction of vomiting by PAPP must be addressed before it could be effective as a chemical to control coyotes (Savarie et al. 1983). Huo Ping Pan further investigated the effects of PAPP to coyotes in studies compiled in an internal report [Pan, unpublished report, National Wildlife Research Center (NWRC)]. In the study of Savarie et al. (1983), coyotes that consumed 150 mg PAPP in beef tallow baits vomited and recovered. Pan hypothesized that if the dose of PAPP was increased slightly, coyotes may succumb to PAPP without vomiting. He dosed coyotes with 200, 300, or 400 mg PAPP; all the coyotes except two at the 200 mg dose succumbed to the effects of PAPP. These two animals both lapsed into unconsciousness but had to be euthanized. One of these two animals and one animal at the 300 mg dose “slightly” vomited. Pan went on to test a dose of 350 mg more thoroughly. Nine of ten coyotes died within 2.5 hours of dosing and one survived. None of the ten coyotes dosed with 350 mg of PAPP vomited (Pan, unpublished report, NWRC). By increasing the dose of PAPP, Pan was able to overcome the problem of emesis that Savarie et al. (1983) reported. With the data generated from these studies, Pan and colleagues concluded that work with PAPP should continue, as they thought that it could be developed as a “superior substitute for 1080 as coyote control toxicant.” PAPP has been registered in New Zealand for the control of stoats and feral cats and in Australia for the control of wild dogs and foxes. The USDA APHIS Wildlife Services National Wildlife Research Center (NWRC) has an interest in the development of PAPP for coyote control. NWRC investigated the efficacy of PAPP as a means of lethal control in coyotes using multiple routes of administration. In initial studies, animals were orally gavaged with PAPP in an inert carrier to determine LD50 values and test LD100 doses. When used on the landscape a toxicant must be efficacious under varied conditions such as full or empty stomach. Therefore, after the initial trials, studies were done to determine the efficacy of PAPP in coyotes when the animals consumed PAPP with other food. Coyotes were also offered PAPP in meatballs and allowed to freely consume the food bait. In further trials, NWRC researchers tested the effectiveness of two different doses of PAPP capsules (880 mg and 400 mg) placed in a spring-loaded ejector device (SLED) to lethally control coyotes. Both doses were lethal to all coyotes tested, supporting further research into the development of PAPP as a tool to control coyotes.

Die vorliegende Masterarbeit behandelt das Thema Wildnispädagogik und Naturverbindung im Kontext der Sozialen Arbeit. Die subjektiven Erlebensperspektiven von Fachkräften, welche wildnispädagogisch mit Kindern, Jugendlichen und Familien arbeiten, bildeten das grundlegende Forschungsinteresse. Dem Forschungsinteresse unterliegt die Annahme, dass Adressat*innen der Sozialen Arbeit, welche häufig belastenden Erfahrungen ausgesetzt sind, in besonderer Weise von Naturerfahrungen im Rahmen von wildnispädagogischen Angeboten profitieren könnten. Diese Arbeit ging daher der Frage nach, wie Fachkräfte ihr professionelles Handeln in der Schnittstelle von Wildnispädagogik und Sozialer Arbeit erleben. Dabei wurde zudem nach den Beweggründen, die der Arbeit unter-liegen und nach ihren subjektiven Wirkungszuschreibungen gefragt. Zur Beantwortung der Forschungsfrage wurden sieben narrative Interviews mit insgesamt elf Fachkräften aus pädagogischen und sozialarbeiterischen Handlungsfeldern durchgeführt. Diese wurden anschließend nach der Grounded Theory analysiert und ausgewertet. Der Forschungsprozess zeichnete sich durch einen zirkulären Vorgang in Datenerhebung, Analyse und Auswertung aus. Ein zentrales Ergebnis dieser Arbeit ist die Bedeutung von Beziehung. Beziehung zur natürlichen Welt, im Sinne einer Naturverbundenheit, zu sich selbst, zur Gemeinschaft, in welcher wildnispädagogische Angebote häufig stattfinden und zur Fachkraft/Mentor*in. Aus der Beziehung zu einer*einem begleitende*n*m Mentor*in kann Interesse und Begeisterung für Natur entstehen bzw. dieses gefördert werden. Aus der Sicht der Fachkräfte können wildnispädagogisch Angebote zu heilsamen und entwicklungsfördernden Prozessen führen. Sie beschrieben, dass die Adressat*innen selbstbewusster wurden, sich ihr Selbstbild verbesserte und sie eigene Ängste überwinden konnten. Zudem wird die Beziehung zu sich selbst durch Naturerfahrungen und wildnispädagogische Angebote gestärkt, wodurch eigene Bedürfnisse besser wahrgenommen werden können. In einer Gemeinschaft zu sein, ermöglichte außerdem die Erfahrung, sich auf ein soziales Umfeld verlassen und auf die Unterstützung der Gemeinschaft zurückgreifen zu können. Die Erkenntnisse dieser Arbeit leisten einen wissenschaftlichen Beitrag zu der noch kaum beachteten Disziplin der Wildnispädagogik im Feld der Sozialen Arbeit und ermöglichen Handlungsempfehlungen für die Praxis zu diskutieren. This master thesis focuses on the topic of Coyote-Mentoring, a nature-based educational approach and nature connection in the context of social work. The basic research interest was, to gain an insight into the subjective experience perspectives of professionals who work with Coyote-Mentoring with children, adolescents and families. The research interest is based on the assumption, that addressees of social work, who are often exposed to stressful experiences, could benefit in a special way from nature experiences in the context of Coyote-Mentoring. Therefore, this study investigated the question of how professionals experience their professional activities at the interface of Coyote-Mentoring and social work. They were also asked about the motivations underlying their work and their subjective attributions of impact. To answer the research question, seven narrative interviews were conducted with eleven professionals from educational and social work fields. Subsequently these were analyzed and evaluated according to grounded theory. The research process was characterized by a circular process in data collection, analysis and evaluation. This work stresses the importance of relationship, to the natural world, in the sense of a nature connection, to oneself, to the community, in which Coyote-Mentoring often take place, as well as to the specialist/mentor. During the relationship with a mentor an interest and enthusiasm for nature can arise or be encouraged. Professionals describe healing and developmental processes resulting from this nature-based educational approach. Among other things addressees became more self-confident, their self-image improved and they were able to overcome their own fears. In addition, the relationship to oneself is strengthened through nature experiences and Coyote-Mentoring, which makes it easier to perceive one's own needs. From the professionals’ point of view, being in a community also enables the experience of being able to rely on the support of the community. The findings of this work make a scientific contribution to the still little-noted discipline of Coyote-Mentoring in the field of social work and enable recommendations for action to be discussed for practice. Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers Masterarbeit Wien, FH Campus Wien 2022

Long-standing myths exist about the origins of wolf–coyote hybrids and related Canis species in Ontario. Specifically, there is a perceived controversy whether they are the product of natural hybridization that occurred between wolves and coyotes in the wild during the last century or the descendants of animals that escaped or were released from captive colonies or controlled breeding experiments. We review the relevant evidence and conclude that captive colonies and controlled breeding experiments were unlikely to have played any role in the origins of wolf–coyote hybrids and related Canis species in Ontario.

The coyote (Canis latrans) is a widespread predator with a high degree of adaptation to different ecosystems. The objective of this study was to estimate the relative abundance index, habitat selection, and diet of C. latrans through scent stations, in two types of vegetation located in northwestern Chihuahua. From April 2018 to March 2019, ten fixed scent stations (SS) were placed in pine-oak forest and other ten in semi-open grassland, with a linear separation of 500 meters between each station to achieve a five km transect in each vegetation during 12 samplings (two sampling night per month) for totaling 420 SS after discarding inactive SS. The relative abundance index of coyote showed that both types of vegetation, pine-oak forest (0.30) and semi-open grassland (0.23) were used in a similar way. The habitat selection test (Chi2) showed that coyote abundance and type of vegetation were independent (χ2 = 2.96, P > 0.05), not showing statistically significant differences in annual relative abundance index of coyotes between the two vegetation types. The food items detected in thirty-four scats collected belonged to mammals (55.9 %), fruits (35.3 %) and arthropods (8.8 %). Rodents and lagomorphs were the main source of food. Throughout the sampling period, the pine-oak forest showed the greatest relative abundance index of C. latrans. This may be because the forest provides them with shelter from climatic situations and a greater variety of seeds, fruits and insects. In this study the two seasons with the highest relative abundance were spring and autumn in both ecosystems, coinciding with an increase in rainfall, resulting in an abundance of potential prey (rodents and lagomorphs), and other food items like fruits. Regarding annual diet the most consumed prey were mammals. We found differences in diet between seasons, that can be explained by the variation in food availability among seasons.

Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547×10-4, 7.64801×10-4, and 7.59756×10-4 for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

In this paper, a novel PIλDND2N2 controller is designed for an Automatic Voltage Regulator (AVR) in a power system and is studied in this paper. AVR is responsible for keeping the terminal voltage of a synchronous generator at a specified interval. The proposed control design is achieved by deploying for the first-time the coyote optimization algorithm (COA) in an AVR system by optimizing a novel objective function. A comparison is made among the proposed controller and FOPID, PID, PIDA and PIDD2 controllers optimized by others metaheuristic algorithms in the literature. The comparison is deducted in terms of transient response and disturbance rejection. From the simulations it is concluded that the proposed method outperforms other methods in the literature. Furthermore, the stability of the proposed design is examined via the location of the closed-loop poles and frequency response. Moreover, the robustness of the controller is tested under model uncertainties. In both tests the proposed method displays an outstanding performance.

La terminología botánica registrada en el Códice de la Cruz Badiano no ha sido estudiada desde el trabajo precursor de Garibay en 1964. Un análisis etimológico de los nombres de las plantas, sustentado en las investigaciones recientes sobre la fonología, gramática y la composición léxica del náhuatl clásico, nos permite proponer nuevas identificaciones para algunas especies ilustradas en el manuscrito, cotejándolas con las designaciones que reciben en las variantes nahuas contemporáneas mejor documentadas. La lista de plantas que podemos determinar de manera confiable, con base en la evidencia léxica, iconográfica y etnobotánica, deja entrever un patrón biogeográfico inesperado: el número de géneros con afinidad meridional supera con mucho a los taxa de distribución boreal. Inferimos por ello que buena parte del conocimiento farmacológico de las élites precolombinas se originó en las tierras bajas tropicales, como lo indican en forma análoga las materias primas vegetales y animales empleadas en las artes suntuarias. Esta línea de investigación, que relaciona la historia natural de México con la medicina tradicional y la cultura material de los pueblos indígenas, puede aportar nuevas pistas para esclarecer la historia temprana de la civilización mesoamericana. Para concluir, examinamos el papel que jugó el Códice, como primer texto botánico que se conservó del Nuevo Mundo, en los proyectos de la Academia de los Linceos y el curso de la ciencia occidental en el siglo XVII.

An improved coyote optimization algorithm (ICOA) is proposed to meet the requirements of global search capability, convergence speed, and stability for mobile robot path planning problems. Firstly, the population is divided into the elite group and the general groups after initialization, and the coyote individuals in the general groups are evolved by introducing the strategy of the best leading the poor. Secondly, after returning to the original position, coyotes adopt a new growth mode, and each individual makes a certain contribution to improve the level of the whole group. Thirdly, different from the original greedy algorithm, the survival of the fittest is carried out after each group of coyotes grows up, and the individuals do not affect each other. Finally, ICOA and the other seven optimization algorithms are simulated on four maps for mobile robot path planning. The simulation results show that ICOA can keep the diversity of the population, and has a strong global search ability, better stability, fast convergence speed, which reflects the strong optimization ability.

A vital subject of engineering structures is mechanical oscillations. If the mechanical oscillation is uncontrolled, it can lead to structural failure due to large dynamic stresses developed, as the collapse occurred on the Broughton Suspension bridge due to soldiers walking in step. This paper addresses mechanical oscillation problems employing a proposed modified variant of coyote optimizer. Coyote optimization algorithm (COA) is a new meta-heuristic which mimics the social behavior of coyotes. COA suffers with stagnation problems and immature convergence while solving optimization problems. In this paper, the COA is hybridized with Laplace Crossover operator and new culture tendency strategies are adapted, modified variants of coyote optimization algorithm (MvCOA). The proposed MvCOA is presented to approximately solve mechanical oscillation problems independently of their order, form, and stated conditions. With the fundamental concepts of ordinary differential equations and Fourier series expansion, mechanical oscillation problems can be modeled as a problem of optimization whereby the optimization task is achieved using the proposed MvCOA. Five different ordinary differential equations and four mechanical oscillation problems are solved approximately and then compared with their corresponding exact solutions. The statistical analysis validates that the presented MvCOA is an effective algorithm for different optimization problems. However, from the empirical results, it is visible that the suggested MvCOA approximate approach was able to reach a successful performance solving different mechanical oscillation problems.

This study presents an initial demonstration of assimilating small Uncrewed Aircraft System (sUAS) data into an operational model with a goal to ultimately improve tropical cyclone (TC) analyses and forecasts. The observations, obtained using the Coyote sUAS in Hurricane Maria (2017), were assimilated into the operational Hurricane Weather Research and Forecast system (HWRF) as they could be in operations. Results suggest that the Coyote data can benefit HWRF forecasts. A single-cycle case study produced the best results when the Coyote observations were assimilated at greater horizontal resolution with more relaxed quality control (QC) than comparable flight-level high-density observations currently used in operations. The case study results guided experiments that cycled HWRF for a roughly four-day period that covered all Coyote flights into Maria. The cycled experiment that assimilated the most data improved initial inner-core structure in the analyses and better agreed with other aircraft observations. The average errors in track and intensity decreased in the subsequent forecasts. Intensity forecasts were too weak when no Coyote data was assimilated, and assimilating the Coyote data made the forecasts stronger. Results also suggest that a symmetric distribution of Coyote data around the TC center is necessary to maximize its benefits in the current configuration of operational HWRF. Although the sample size was limited, these experiments provide insight for potential operational use of data from newer sUAS platforms in future TC applications.

Agave coyote (Agave spp.) es una especie silvestre utilizada por su gran potencial para la elaboración de mezcal en Oaxaca (México). En este estudio se evaluó y comparó la respuesta en el crecimiento, nutrición y contenido de sólidos solubles totales (SST, azúcares) en agave coyote a la aplicación de fertilizantes de liberación lenta (FLL) en condiciones de campo. Bajo un diseño de bloques completamente al azar se evaluaron tres tratamientos: control (sin fertilización), Osmocote plus® (15-09-12) y Multicote Agri® (18-06-12), con cuatro repeticiones, en cada repetición se incluyeron 5 plantas por tratamiento. Después de 10 meses se midió altura de planta (AP); número de hojas desplegadas (NHD); circunferencia de tallo (CT); volumen radicular (VR); densidad radicular (DR); peso fresco de hojas (PFH), tallo (PFT) y raíz (PFR); peso seco de hojas (PSH), tallo (PST) y raíz (PSR); SST y contenido foliar de Ca2+, Na+, NO3- y K+. Con respecto a las plantas control, la AP incrementó 21.2%, NHD 28.4%, PFH 77.0%, PFT 62.8%, PSH 177.0%, PST 53.1% y PSR 39.1% con la aplicación de Osmocote. Multicote aumentó 15.3% la AP, así como el contenido foliar de K+ en 25.6%, el de NO3- en 26.2% y el de Na+ en 29.8%. CT, VR, DR, PFR, contenido foliar de Ca2+ y SST no respondieron significativamente a la fertilización. Los agaves son plantas con un ciclo de cultivo largo, por lo que es necesario realizar un mayor número de evaluaciones para corroborar la respuesta de este agave a la aplicación de FLL.

There is arguably no other North American species that better illustrates the complexities of the human-wildlife interface than the coyote. In this study, a melanistic coyote in metropolitan Atlanta, Georgia was exhibiting unusually bold behaviors that included encounters with humans, domestic dogs, and attempts to enter homes. After tracking this coyote (nicknamed Carmine) across a highly urbanized landscape with participatory science, including at least 80 publicly reported sightings, he was captured and relocated to a wildlife sanctuary. Genome-wide analyses revealed 92.8% coyote ancestry, 1.7% gray wolf ancestry, and 5.5% domestic dog ancestry. The dog alleles in Carmine’s genome were estimated to have been acquired by his ancestors 14–29 years ago. Despite his bold behavior, Carmine did not carry any mutations known to shape hypersociability in canines. He did, however, carry a single copy of the dominant mutation responsible for his melanistic coat color. This detailed study of Carmine dispels common assumptions about the reticent coyote personality and the origins of behavior. His unusual bold behavior created a higher level of human-coyote interaction. He now serves as a public ambassador for human-wildlife coexistence, urging the global community to reconsider mythologies about wildlife and promote coexistence with them in landscapes significantly altered by human activity in our rapidly changing world.

One of the most dangerous health diseases affecting the world's population is diabetes mellitus (DM), and its diagnosis is the key to its treatment. Several methods have been implemented to diagnose diabetes patients. In this work, a hybrid model which combines of coyote optimization algorithm (COA) and least squares support vector machine (LS-SVM) is proposed to classify of Type-II-DM patients. LS-SVM classifier is applied for classification process but it's very sensitive when its parameter values are changed. To overcome this problem, COA algorithm is implemented to optimize parameters of the LS-SVM classifier. This is the goal of the proposed model called the COA-LS-SVM. The proposed model is implemented and evaluated using Pima Indians Diabetes Dataset (PIDD). Also, it's compared with several classification algorithms that were implemented on the same PIDD. The experimental results demonstrated the effectiveness of the proposed model and its superiority over other algorithms, as it could accomplish an average classification accuracy of 98.811%.

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A unique dataset obtained from the Coyote small uncrewed aircraft system (sUAS) in the inner-core boundary layer of Hurricane Maria (2017) is assimilated using NOAA’s Hurricane Ensemble Data Assimilation System (HEDAS) for data assimilation and Hurricane Weather Research and Forecasting (HWRF) system for model advances. The case of study is 1800 UTC 23 September 2017 when Maria was a category-3 hurricane. In addition to the Coyote observations, measurements collected by the NOAA Lockheed WP-3D Orion and U.S. Air Force C-130 aircraft were also included. To support the assimilation of this unique dataset, a new online quality control (QC) technique in HEDAS scales the observation–background difference by the total uncertainty during data assimilation and uses the interquartile range outlier method to identify outlier observations. Experimental setup includes various very frequent cycling scenarios for a control that does not assimilate Coyote observations, assimilation of Coyote observations in addition to the control observations, and the application of online QC. Findings suggest progressively improved analyses with more-frequent cycling, Coyote assimilation, and application of online QC. This applies to verification statistics computed at the locations of both Coyote and non-Coyote observations. In terms of the storm structure, only experiments that assimilated the Coyote observations were able to reproduce the double-eyewall structure that was observed at the time of the analysis, which is more consistent with the intensity of the storm according to the observations that were collected. Limitations of the study and future plans are also discussed. Significance Statement Findings from this study illustrate the significant impact difficult-to-obtain, near-surface observations can have on improving the accuracy of tropical cyclone structure and intensity. Adding these novel measurements in a way that also includes advanced cycling and quality control techniques in data assimilation has the potential to improve public forecasts that are reliant upon detailed depictions of storm strength and boundary layer structure prior to landfall. The results speak to the importance of parallel and consistent advancements in modeling, data assimilation, and observational capabilities to improve the depiction of the tropical cyclone inner-core structure in numerical models.

Prior to 1900, coyotes (Canis latrans) were restricted to the western and central regions of North America, but by the early 2000s, coyotes became ubiquitous throughout the eastern United States. Information regarding morphological and genetic structure of coyote populations in the southeastern United States is limited, and where data exist, they are rarely compared to those from other regions of North America. We assessed geographic patterns in morphology and genetics of coyotes with special consideration of coyotes in the southeastern United States. Mean body mass of coyote populations increased along a west‐to‐east gradient, with southeastern coyotes being intermediate to western and northeastern coyotes. Similarly, principal component analysis of body mass and linear body measurements suggested that southeastern coyotes were intermediate to western and northeastern coyotes in body size but exhibited shorter tails and ears from other populations. Genetic analyses indicated that southeastern coyotes represented a distinct genetic cluster that differentiated strongly from western and northeastern coyotes. We postulate that southeastern coyotes experienced lower immigration from western populations than did northeastern coyotes, and over time, genetically diverged from both western and northeastern populations. Coyotes colonizing eastern North America experienced different selective pressures than did stable populations in the core range, and we offer that the larger body size of eastern coyotes reflects an adaptation that improved dispersal capabilities of individuals in the expanding range.

The application of the motion of a vertically suspended mass-spring system released under tension is studied focusing upon the delay timescale for the bottom mass as a function of the spring constants and masses. This ``hang-time", reminiscent of the Coyote and Road Runner cartoons, is quantified using the far-field asymptotic expansion of the bottom mass' Laplace transform. These asymptotics are connected to the short time mass dynamics through Tauberian identities and explicit residue calculations. It is shown, perhaps paradoxically, that this delay timescale is maximized in the large mass limit of the top ``boulder". Experiments are presented and compared with the theoretical predictions. This system is an exciting example for the teaching of mass-spring dynamics in classes on Ordinary Differential Equations, and does not require any normal mode calculations for these predictions.

Range expansion is a widespread biological process, with well‐described theoretical expectations associated with the colonization of novel ranges. However, comparatively few empirical studies address the genomic outcomes accompanying the genome‐wide consequences associated with the range expansion process, particularly in recent or ongoing expansions. Here, we assess two recent and distinct eastward expansion fronts of a highly mobile carnivore, the coyote (Canis latrans), to investigate patterns of genomic diversity and identify variants that may have been under selection during range expansion. Using a restriction‐associated DNA sequencing (RADseq), we genotyped 394 coyotes at 22,935 SNPs and found that overall population structure corresponded to their 19th century historical range and two distinct populations that expanded during the 20th century. Counter to theoretical expectations for populations to bottleneck during range expansions, we observed minimal evidence for decreased genomic diversity across coyotes sampled along either expansion front, which is likely due to hybridization with other Canis species. Furthermore, we identified 12 SNPs, located either within genes or putative regulatory regions, that were consistently associated with range expansion. Of these 12 genes, three (CACNA1C, ALK, and EPHA6) have putative functions related to dispersal, including habituation to novel environments and spatial learning, consistent with the expectations for traits under selection during range expansion. Although coyote colonization of eastern North America is well‐publicized, this study provides novel insights by identifying genes associated with dispersal capabilities in coyotes on the two eastern expansion fronts.

Despite the importance of biodiversity, humans value some species more highly than others. Placing different levels of value on species can impact nonhuman animal welfare and conservation. Feelings are central to value recognition. It’s critical to better understand how to foster positive feelings toward different species. The purpose of this study was to test the Transcendent Feelings of Animal Valuation scale and evaluate the influence of a wildlife sanctuary visit on feelings toward moose and coyotes. The scale was piloted with 29 visitors and demonstrated good reliability. It was then administered to 100 visitors. At baseline, participants had a significantly stronger emotional valuation of moose compared to coyote, consistent with negative social constructions of coyotes. Both moose and coyote scores increased significantly from pre- to post-visit, suggesting that exposure to wildlife in a sanctuary setting can increase feelings of valuation toward diverse species. The experience may also influence wildlife stewardship.

This paper describes the design and implementation of the open-source tool $$\textsc {Coyote} $$ C O Y O T E for testing concurrent programs written in the $$\textsc {C}{} \texttt {\#} $$ C # language. $$\textsc {Coyote} $$ C O Y O T E provides algorithmic capabilities to explore the state-space of interleavings of a concurrent program, with deterministic repro for any bug that it finds. $$\textsc {Coyote} $$ C O Y O T E encapsulates multiple ideas from the research community to offer state-of-the-art testing for $$\textsc {C}{} \texttt {\#} $$ C # programs, as well as an efficiently engineered implementation that has been shown robust enough to support industrial use.

Although the coyote (Canis latrans) is native to North America, we have limited understanding of its presence in Virginia. Coyote range expansion is linked to anthropogenic factors, including habitat fragmentation and the extirpation of apex predators. Information on coyote adaptations to Virginia habitats is scarce, and eastern coyotes may have unique adaptations for colonizing an area. Anecdotal evidence suggests that coyotes are abundant in Hanover County (north of Richmond, VA), but this has not been confirmed. This study was conducted over an 18-month period with multiple survey sites throughout Hanover County, each equipped with game camera stations and 20% with scent lures. Coyote detection probability and occupancy was estimated using the “Unmarked” package in “R”. With a low detection rate of 16.8%, the study found that coyotes occupied 76.8% of the County. Sixty-one percent of detections may support movement along powerlines, game trails, and fire roads versus movement in dense forest and open fields. This study is the first to provide insight into the occupancy, detection, and movement of the eastern coyote in Hanover County, Virginia. Some movement observations were made and are offered in this study.

Coyote (Canis latrans) management becomes increasingly necessary as the species' range expands, but some methods may be controversial in urban landscapes. Understanding why the public considers certain methods acceptable may help decrease conflict between residents and wildlife managers. We surveyed 4000 registered voters in New Hanover County, North Carolina, to evaluate attitudes toward three coyote management methods: no management, public education and trap/euthanasia. We used the expectancy-value model and multinomial logistic regression models to determine which public beliefs and desires regarding outcomes of each coyote management method predicted acceptance of each method. Attitudes of respondents who accepted a method differed from those who rejected the method. Positive attitudes toward no management were influenced by outcomes involving a natural death for coyotes and family, pet and personal safety. Positive attitudes toward public education were influenced by outcomes involving family safety, public participation, fewer coyotes and for coyotes to avoid an inhumane death. Positive attitudes toward trap/euthanasia were influenced by outcomes for coyotes avoiding an unnatural and inhumane death, pet safety and public participation. Understanding the public's beliefs and desires regarding coyote management methods will help wildlife managers tailor public education programs, resolve controversies regarding alternative management methods and improve future wildlife management decisions.

Management practices of nonhuman animals in nature (“wildlife”) are globally controversial. In some places, individuals believe it should be up to individual landowners to “manage” wildlife. In others, wildlife is seen as belonging to everyone and should be respected, or least hunted ethically. Wildlife killing contests are legal in most U.S. states. Coyote killing contests take place in many of them and several states have enacted legislation to ban them. In Oregon, efforts have failed three times. This paper is a critical discourse analysis of testimonies in the 2021 Oregon hearings. Opposition to the bill is analyzed according to five psychological rationalizations to unpack the pro-contest arguments as an example of rural resistance. The findings suggest unpacking these as more productive for activist groups when creating strategies to counter pro-killing beliefs.

In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.