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Prairie dogs are notoriously difficult to enumerate, with previously methods including visual counts, mark‐resight, burrow counts, and catch per unit effort. Unlike those methods, spatial capture‐recapture (SCR) analyses allow for formal estimation of density along with associated estimates of uncertainty, detection probability, and the size of the average area over which an individual was detected during the study period (referred to as an activity center). Using SCR analyses, we compared density estimates as part of a field trial evaluating the effectiveness of an oral sylvatic plague vaccine in black‐tailed prairie dogs (Cynomys ludovicianus), Gunnison's prairie dogs (C. gunnisoni), white‐tailed prairie dogs (C. leucurus), and Utah prairie dogs (C. parvidens) at 11 study areas in the western United States. The study was designed as a matched pairs analysis that included 27 individual paired plots (54 plots), each consisting of a plot treated with vaccine baits and a plot treated with placebo baits. Overall, we captured >3,000 individuals each year on these plots, and recapture rates ranged from 5–87%. For black‐tailed prairie dogs, density estimates ranged from 2.7 individuals/ha (95% CI = 2.2–3.3/ha) to 77.3/ha (63.2–94.4/ha), and for Gunnison's prairie dogs, estimates ranged from 11.7/ha (10.6–12.8/ha) to 15.4/ha (14.4–16.7/ha). White‐tailed prairie dogs were at their lowest density (3.3/ha, 95% CI = 2.9–3.8/ha) during the first year of the study and their highest density (14.5/ha; 13.5–15.6/ha) during the last year of the study. Utah prairie dog density estimates ranged from a low of 4.0/ha (95% CI = 3.55–4.6/ha) to a high of 20.8/ha (16.8–25.8/ha). Best‐fitting models of prairie dog density indicated increasing patterns of density over time on most study plots, negative effects of plague, and positive effects of vaccination. Finally, we found low correlations between catch per unit effort estimates from previous published literature at these sites and our densities estimates. Spatial capture‐recapture estimates allowed us to consistently compare treatment effects across space and time, although some exceptions are noted where we observed significant movement between plots within a pair (3 pairs) and when trapping effort between plots or years was not consistent. [ABSTRACT FROM AUTHOR]

It is commonly known that a number of variables, including price, supply levels, time, and green level, affect how quickly certain things are in demand. Furthermore, the inventory carrying cost is considered to be a nonlinear representation of time and is subject to variation throughout time. More precisely, it rises with time since longer storage times necessitate more costly warehouse space. This study presents a fully backlogged situation inventory system for a single commodity where the product's selling price, green level, and time are used to simultaneously compute the demand rate in accordance with a power pattern. Purchase price is determined by the product's nonlinear green level. Complete backorders are available for shortages. The impact of the product's selling price, green level and time power function are combined to determine the product's demand. Moreover, the holding cost also rises as the product is stored for a longer period of time. The primary goal is to determine the best inventory policy to maximise total profit per unit of time. Though the problem is highly nonlinear in nature. Hence, we cannot solve it analytically. To overcome these difficulties, we have applied several well-known popular metaheuristic algorithms (Water Cycle Algorithm (WCA), Artificial Electric Field Algorithm (AEFA), Teaching Learning Based Optimization Algorithm (TLBOA), Grey Wolf Optimizer Algorithm (GWOA), Sparrow Search Algorithm (SSA), Whale Optimizer Algorithm (WOA), Prairie Dog Optimization Algorithm (PDOA), Gazelle Optimization Algorithm (GOA), A Sinh Cosh Optimizer Algorithm (SCHOA) and White Sherk Optimizer Algorithm (WSOA), Archimedes Optimization Paradigm Algorithm (AOPA), Marine Predator Optimization Algorithm (MPOA), Geyser Inspired Algorithm (GIA), Runge Kutta Optimization Algorithm (RKOA), Lungs Performance-based Optimization Algorithm (LPOA) and Dwarf Mongoose Optimization Algorithm (DMOA)). It is observed that WCA perform better than other algorithms with respect to the convergence rate. A numerical example is taken in order to validate the proposed model. Finally, a post optimality analysis is performed in order to make a fruitful conclusion. [ABSTRACT FROM AUTHOR]

This document is a compilation of scientific articles that explore the historical treatment of rodents in scientific research and the growing recognition of their cognitive abilities and welfare. It emphasizes the importance of considering animal suffering and the need for animal-friendly methods in rodent behavioral testing. The articles discuss various approaches and technologies, such as mirror-based tests and automated monitoring systems, that prioritize animal welfare while providing valid results. The research presented in these articles sheds light on the complex behaviors and capabilities of rodents, highlighting their similarities to humans and the potential for further study in this field. [Extracted from the article]

This article discusses the historical treatment of rodents in scientific research and the need for improved methods that prioritize animal welfare. It highlights the growing recognition of rodents' cognitive abilities and challenges the notion that their welfare is less important due to their perceived inferiority. The article introduces a collection of contributions from researchers around the world that explore various approaches to animal-friendly behavioral testing for rodents. It discusses the importance of designing behavioral tests that consider the specific characteristics of different animal species and prioritize animal welfare. The text also highlights the contributions of various researchers in developing animal-friendly behavioral tests, including the use of mirror-based tests, infrared thermography, robotic animals, and automated home-cage monitoring systems. The article concludes by emphasizing the need for further development and exploration of new approaches in animal-friendly behavioral testing. This document is a list of references from various scientific articles related to animal behavior, specifically focusing on rodents such as mice and rats. The articles cover a range of topics including empathy, social behavior, cognitive abilities, and experimental methods for studying animal behavior. [Extracted from the article]

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm's running time for the test function's ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. [ABSTRACT FROM AUTHOR]

Renewable energy systems (RESs) and electric vehicles (EVs) are critical components of current power systems for lowering greenhouse gas (GHG) emissions, combating climate change, and providing alternative energy sources. However, high initial costs and a lack of infrastructure, power storage, and power quality (PQ) are potential concerns for RESs and EVs in power systems. Among the numerous power quality (PQ) devices, the unified power quality conditioner (UPQC) can compensate for voltage and current-related PQ difficulties, correct the power factor, and is well suited for coordinative operation and control with RESs uncertainty. The position, size, and dynamic VAr control of UPQC, on the other hand, are key influencing variables for enhancing the performance and PQ of electrical distribution systems (EDSs). This paper describes an upgraded UPQC configuration known as open-UPQC (UPQCO) for reducing PQ difficulties caused by the inclusion of PVs and EVs. Further, an efficient gazelle optimization algorithm (GOA) is suggested to solve the multi-objective optimal allocation of the PV-UPQC-O issue with an emphasis on voltage quality, distribution losses, and voltage stability. Simulations were performed for various scenarios using a modified IEEE 33-bus test system. According to the comparison data, the PV-UPQC-O improved the feeder performance more effectively than the standard UPQC and UPQC-O. The proposed PV-UPQC-O results for total loss reduction of 76.15% when compared to base case without EV load penetration. On the other hand, it is around 76.52% reduction when compared to base case with EV load penetration. Furthermore, the proposed GOA outperforms the prairie dog optimization (PDO), pelican optimization algorithm (POA) and coati optimization algorithm (COA) in terms of global solution and convergence. [ABSTRACT FROM AUTHOR]

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars' defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis). That would cut down the cost of operating extra sensors and then it would be possible to move them to other vicinity. [ABSTRACT FROM AUTHOR]

Prairie dogs (Cynomys sp.) are recognized as keystone species because their colonies provide habitat heterogeneity and are inhabited by specialized plant and animal communities. While many studies have demonstrated the unique ecological properties of prairie dog colonies and their effects on local grassland bird communities, few have examined how their presence influences bird diversity at a landscape scale. We collected species diversity data across 192 points within 64 80-ha sampling cells. With 11 of those 64 cells containing an active prairie dog town, we tested if the presence of black-tailed prairie dog (Cynomys ludovicianus) colonies would result in higher landscape-scale avian biodiversity in a mixed-grass prairie and sagebrush steppe ecosystem in north-central Montana. Utilizing permutational multivariate analysis of variance, we observed significant dissimilarities between avian communities with and without prairie dog colonies, and colony presence explained a moderate proportion of variance in avian diversity. Indicator species analysis found 5 species significantly associated with colony cells: Horned Lark (Eremophila alpestris), Marbled Godwit (Limosa fedoa), Brewer's Blackbird (Euphagus cyanocephalus), Killdeer (Charadrius vociferus), and Burrowing Owl (Athene cunicularia). These findings suggest prairie dogs play an important role in maintaining high avian biodiversity at a landscape scale within grassland ecosystems. Further, our results emphasize the importance of incorporating prairie dogs into conservation efforts for grassland birds. [ABSTRACT FROM AUTHOR]

This article discusses the challenges posed by the Mpox outbreak in a clinical setting. The World Health Organization has declared Mpox a public health emergency due to its increased spread and higher fatality rate compared to previous outbreaks. The article highlights the various patterns of skin lesions associated with Mpox, including mucocutaneous involvement and the potential for unaesthetic scars. It also emphasizes the importance of dermoscopy for accurate diagnosis and long-term follow-up to manage the infection effectively. The article concludes by discussing the impact of Mpox on patients' quality of life, particularly in terms of residual scars. [Extracted from the article]

This article, published in the Journal of Raptor Research, discusses the occurrence of cooperative breeding by Golden Eagles in Colorado. Cooperative breeding is when more than two individuals participate in raising offspring from a single nest. While Golden Eagles are typically monogamous and defend their territory, this study documents a case in which three Golden Eagles, including paired adults and a yearling, engaged in cooperative breeding near Franktown, Colorado. The article provides details about the nesting behavior and observations of the eagles throughout the breeding season. The authors suggest that delayed natal dispersal and the availability of prey in the territory may have contributed to the cooperative breeding behavior. [Extracted from the article]

Cardiovascular disease (CVD) detection using deep learning (DL) includes leveraging advanced neural network (NN) models to analyze medical data, namely imaging, electrocardiograms (ECGs), and patient records. This study introduces a new Nature Inspired Metaheuristic Algorithm with Deep Learning for Healthcare Data Analysis (NIMADL-HDA) technique. The NIMADL-HDA technique examines healthcare data for the recognition and classification of CVD. In the presented NIMADL-HDA technique, Z-score normalization was initially performed to normalize the input data. In addition, the NIMADL-HDA method made use of a barnacle mating optimizer (BMO) for the feature selection (FS) process. For healthcare data classification, a convolutional long short-term memory (CLSTM) model was employed. At last, the prairie dog optimization (PDO) algorithm was exploited for the optimal hyperparameter selection procedure. The experimentation outcome analysis of the NIMADL-HDA technique was verified on a benchmark healthcare dataset. The obtained outcomes stated that the NIMADL-HDA technique reached an effectual performance over other models. The NIMADL-HDA method provides an adaptable and sophisticated solution for healthcare data analysis, aiming to improve the interpretability and accuracy of the algorithm in terms of medical applications. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network's status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs' capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO's exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Intraspecific resource partitioning may play a critical role in how predators optimize prey selection. The Black-footed Ferret (Mustela nigripes ; henceforth, ferret) is a highly specialized predator of prairie dogs (Cynomys spp.; henceforth, PDs). Adult ferrets are sexually dimorphic and PDs are of similar size making them a difficult prey item. PD young are born 6 to 8 weeks prior to births of ferrets, producing a crop of smaller prey items during a period when energetic needs of female ferrets are highest. We asked whether relatively small female ferrets select small PDs as prey. We examined survival rates from early to late summer for large and small black-tailed PDs (Cynomys ludovicianus) in Montana and South Dakota as a function of their distance to adult male and female ferrets using capture–mark–recapture of PDs and simultaneous summer monitoring of ferret locations. Survival of small PDs (<600 g) was low when a female ferret was nearby, but distance to nearest female ferret did not affect survival of large PDs. Distance to the nearest male ferret did not influence survival regardless of PD size. Reduced competition from males for a critical food resource needed by females rearing young would benefit fitness of both sexes. If female ferrets depend on young PDs during their reproductive period, existing habitat models may substantially overestimate ferret carrying capacity. [ABSTRACT FROM AUTHOR]

In western North American, black-tailed prairie dogs (Cynomys ludovicianus) construct aggregated burrow systems referred to as towns or colonies. We collected data on the location, number, density, and aggregation of burrow entrances over time for 15 randomly chosen towns across Boulder County, Colorado, from 2002 to 2018 to document how towns change over time. Across all towns the average rate of change was positive during this period of time, although rates of gain and loss of burrows were variable among towns and were sometimes variable across space within towns. Density and aggregation intensity scaled sublinearly relative to number of burrows. In general, the spatial scaling of growth among towns was similar over time. Our study revealed prairie dog towns are dynamic entities. En el oeste de América del Norte, los perritos de las praderas de cola negra (Cynomys ludovicianus) construyen sistemas de madrigueras agregados denominados pueblos o colonias. Utilizamos imágenes aéreas de teledetección de 2002 a 2018 para estimar el cambio en el tamaño y la estructura espacial de la ciudad. Recopilamos datos sobre la ubicación, el número, la densidad y la agregación de entradas de madrigueras a lo largo del tiempo para 15 pueblos elegidos al azar en todo el condado de Boulder, Colorado. Descubrimos pueblos cambiados en tamaño y complejidad como consecuencia de la construcción y destrucción (por abandono y erosión) de madrigueras. Las tasas de aumento y disminución en el número de madrigueras variaron entre los pueblos. En todos los pueblos, la tasa promedio de cambio fue positiva durante este período de tiempo, aunque las tasas de construcción y destrucción de madrigueras variaron entre pueblos y, a veces, variaron en el espacio dentro de los pueblos. Densidad e intensidad de agregación escaladas sublinealmente en relación con el número de madrigueras. Nuestro estudio reveló que los pueblos de perritos de la pradera son entidades dinámica. [ABSTRACT FROM AUTHOR]

To better understand the population of the Bald Eagle (Haliaeetus leucocephalus) nesting along northern Colorado’s Front Range, from 2016 to 2022 we studied 86 occupied nests within an area of 20,586 km2. From 2017 to 2020, 279 juveniles fledged from 237 nesting attempts in a smaller, main nest-study area with 68 nests. The nests’ success over these four years ranged from 52 to 70%, and their productivity varied from 1.1 to 1.3. The average nearest-nest distances for three discrete areas in the Front Range (5.03 to 7.26 km) are at least 2.8 to 4.0 times greater than these distances in four nesting populations in wetter regions but shorter than distances observed between nests in drier Arizona. In our study area the coverage of buildings within 400 m of Bald Eagle nests is relatively low by comparison to the coverage around randomly selected points, averaging 1344 m2; for 63% of the nests this coverage was less than 800 m2. We classified the 86 nest territories into eight categories that describe the dominant resource habitat and predicts the eagles’ reliance on Black-tailed Prairie Dogs (Cynomys ludovicianus) versus fish as prey. Predation on fish was predicted to be dominant at 51% (n = 44) of the nests, predation on prairie dogs at 32% (n = 28). [ABSTRACT FROM AUTHOR]

Nonbreeding raptors are vulnerable to many anthropogenic changes such as habitat fragmentation and increasing risk of collision (e.g., with wind turbines, powerlines, or vehicles). One aspect of habitat quality for raptors during winter has received comparably less attention: the role of colonial burrowing rodents such as prairie dogs (Cynomys spp.) as a reliable food source, and the effects of widespread reduction in prairie dog abundance and availability to nonbreeding raptors. We used a historical dataset (1998–2002) of roadside surveys for raptors and prairie dogs in the Southern and Central Great Plains, including Arizona, Colorado, New Mexico, and Texas, USA, and the Mexican state of Chihuahua, to provide the most spatially and temporally expansive analysis to date of the potential role of prairie dogs in influencing presence and abundance of raptors. We assessed patterns of co-occurrence among raptors and prairie dogs and modeled the response of selected species to weather, latitude, grassland cover, and prairie dog presence and abundance at multiple spatial scales. During the study period, we detected 19 species of raptors and identified nine raptor species that co-occurred with prairie dogs more than expected by chance. We also found evidence that occurrence of prairie dogs was related to presence and/or abundance of Ferruginous Hawks (Buteo regalis), Golden Eagles (Aquila chrysaetos), Bald Eagles (Haliaeetus leucocephalus), and Rough-legged Hawks (B. lagopus). Our work underscores the association of prairie dogs with many wintering raptor species, especially Ferruginous Hawks and Golden Eagles, and indicates that prairie dogs may also be an important resource for Rough-legged Hawks and Bald Eagles during the nonbreeding season. Las rapaces no reproductoras son vulnerables a muchos cambios antropogénicos, como la fragmentación del hábitat y el aumento del riesgo de colisión (por ejemplo, con aerogeneradores, líneas eléctricas o vehículos). Un aspecto de la calidad del hábitat para las rapaces durante el invierno ha recibido comparativamente menos atención: el papel de roedores coloniales que excavan madrigueras, como los perritos de las praderas (Cynomys spp.), como una fuente confiable de alimento, y los efectos de la reducción generalizada en la abundancia y disponibilidad de perritos de las praderas para las rapaces no reproductoras. Utilizamos una serie de datos histórica (1998–2002) de censos en carreteras de rapaces y perritos de las praderas en las Grandes Llanuras del sur, que incluyen Arizona, Colorado, Nuevo México y Texas, EEUU, y el estado mexicano de Chihuahua, para proporcionar el análisis espacial y temporal más amplio hasta la fecha sobre el papel potencial de los perritos de las praderas en la influencia de la presencia y abundancia de rapaces. Evaluamos patrones de coocurrencia entre rapaces y perritos de las praderas y modelamos la respuesta de especies seleccionadas a factores como el clima, la latitud, la cobertura de pastizales y la presencia y abundancia de perritos de las praderas considerando múltiples escalas espaciales. Durante el período de estudio, detectamos 19 especies de rapaces e identificamos nueve especies de rapaces que coocurrieron con perritos de las praderas más de lo esperado por azar. También encontramos evidencia de que la presencia de perritos de las praderas estaba relacionada con la presencia y/o abundancia de Buteo regalis, Aquila chrysaetos, Haliaeetus leucocephalus, y B. lagopus. Nuestro trabajo destaca la asociación de los perritos de las praderas con muchas especies de rapaces invernantes, especialmente con B. regalis y A. chrysaetos, e indica que los perritos de las praderas también pueden ser un recurso importante para B. lagopus y H. leucocephalus durante la temporada no reproductiva. [Traducción del equipo editorial] [ABSTRACT FROM AUTHOR]

An efficient task scheduling plays an important role in facilitating the virtual resource in a cloud computing environment by minimalizing make span and enhancing the allocation of resources. Requests for resources are treated as tasks, and appropriate resources are allocated based on user requirements. But, due to high demand and requests, the cloud has difficulty allocating resources. To overcome the issues, this research introduced an optimization-based task scheduling approach. The Multi-Objective Prairie Dog Optimization (MOPDO) algorithm is introduced which considers the makespan time and the execution time as the major objective while allocating resources in IoT. The proposed MOPDOA effectively allocates the resource to the Virtual Machines (VMs) by choosing the host with maximized resources. The search mechanism with the help of MOPDO helps to detect a suitable VM for resource allocation will be continued. After the process of allotting the resources to VMs, the load balancing process must be initiated to schedule the tasks for VMs. When the task count is assigned as 100, the makespan time of MOPDOA is 12s while Particle Swarm Gray Wolf Optimization (PSGWO) obtains a makespan time of the 20s. Similarly, for different VMs, the proposed approach is 175.45s for execution whereas the existing Improved Multi-Objective Multi-Verse Optimizer obtains 186.33s to execute for 10 VMs. [ABSTRACT FROM AUTHOR]

High-performance concrete (HPC) is a specialized type of concrete designed to meet stringent performance and uniformity standards that are difficult to achieve with conventional materials and standard mixing, placing, and curing methods. The testing process to determine the mechanical properties of HPC specimens is complex and time-consuming, and making improvements can be difficult after the test result does not meet the required properties. Anticipating concrete characteristics is a pivotal facet in the realm of High-Performance Concrete (HPC) manufacturing. Machine learning (ML)-driven models emerge as a promising avenue to tackle this formidable task within this context. This research endeavors to employ a synergy of ML hybrid and ensemble frameworks for the prognostication of the mechanical attributes within HPC, encompassing compressive strength (CS), slump (SL), and flexural strength (FS). The formulation of these hybrid and ensemble constructs was executed through the integration of Support Vector Regression (SVR) with three distinct meta-heuristic algorithms: Prairie Dog Optimization (PDO), Pelican Optimization Algorithm (POA), and Mountain Gazelle Optimizer (MGO). Some criteria evaluators were used in the training, validation, and testing phases to assess the robustness of the established models, and the best model was proposed for practical applications through comparative analysis of the results. As a result, the hybrid and ensemble models were the potential methods to predict concrete properties accurately and efficiently, thereby reducing the need for expensive and time-consuming testing procedures. In general, the ensemble model, i.e., SVPPM, had a more suitable performance with high values of R2 equal to 0.989 (MPa), 0.984 (mm), and 0.992 (MPa) and RMSE = 3.82 (MPa), 9.5 (mm), and 0.30 (MPa) for CS, SL, FS compared to other models, respectively. [ABSTRACT FROM AUTHOR]

Many studies assume that it is beneficial for individuals of a species to be heavier, or have a higher body condition index (BCI), without accounting for the physiological relevance of variation in the composition of different body tissues. We hypothesized that the relationship between BCI and masses of physiologically important tissues (fat and lean) would be conditional on annual patterns of energy acquisition and expenditure. We studied three species with contrasting ecologies in their respective natural ranges: an obligate hibernator (Columbian ground squirrel, Urocitellus columbianus), a facultative hibernator (black-tailed prairie dog, Cynomys ludovicianus), and a food-caching non-hibernator (North American red squirrel, Tamiasciurus hudsonicus). We measured fat and lean mass in adults of both sexes using quantitative magnetic resonance (QMR). We measured body mass and two measures of skeletal structure (zygomatic width and right hind foot length) to develop sex- and species-specific BCIs, and tested the utility of BCI to predict body composition in each species. Body condition indices were more consistently, and more strongly correlated, with lean mass than fat mass. The indices were most positively correlated with fat when fat was expected to be very high (pre-hibernation prairie dogs). In all cases, however, BCI was never better than body mass alone in predicting fat or lean mass. While the accuracy of BCI in estimating fat varied across the natural histories and annual energetic patterns of the species considered, measuring body mass alone was as effective, or superior in capturing sufficient variation in fat and lean in most cases. [ABSTRACT FROM AUTHOR]

The California Bearing Ratio (CBR) holds significant importance in the design of flexible pavements and airport runways, serving as a critical soil parameter. Moreover, it offers a means to gauge the soil response of subgrades through correlation, an aspect pivotal in soil engineering, particularly in shaping subgrade design for rural road networks. The CBR value of soil is influenced by numerous factors, encompassing variables like maximum dry density (MDD), optimum moisture content (OMC), liquid limit (LL), plastic limit (PL), plasticity index (PI), soil type, and soil permeability. The condition of the soil, whether soaked or unsoaked, also contributes to this value. It is worth noting that determining CBR is time-consuming and extensive. Acknowledging the gravity of this determination, the study introduces a pioneering approach employing machine learning. This innovative technique uses a foundational multi-layer perceptron model, harnessing the algorithm's robust capabilities in addressing regression challenges. A hybridization approach enhances the multi-layer perceptron's performance and achieves optimal results. This approach integrates the Bonobo Optimizer (BO), Smell Agent Optimization (SAO), Prairie Dog Optimization (PDO), and Gold Rush Optimizer (GRO). The hybrid models proposed in this study exhibit promising results in predicting CBR values. The MLAO3 hybrid model is particularly noteworthy, emerging as the most accurate predictor among the range of models, with an impressive R2 value of 0.994 and an RMSE value of 2.80. [ABSTRACT FROM AUTHOR]

We describe a case of a unique antipredator behavior sequence in response to rattlesnakes in a population of black-tailed prairie dogs in Fort Collins, Colorado. Our analysis revealed individuals across multiple social groups within the population engaged in novel behavioral responses to prairie rattlesnakes, including 'escorting' behavior, where a prairie dog would walk alongside the snake. Using Markov chain analysis, we also found that prairie dogs engaged in non-random behavioral transitions and that specific pairs of behaviors were contributing to this pattern. Digital video images related to the article are available at http://www.momo-p.com/showdetail-e.php?movieid=momo230929cl01a). [ABSTRACT FROM AUTHOR]

The activities presented in this article allowed students to conceptually explore and apply mathematical models of fitness through a hypothetical activity where black-footed ferrets eat prairie dogs over several generations. In addition, students examined an applet of butterflies being eaten by a bird, showing directional, stabilizing, and disruptive selection. Finally, students applied a mathematical model created in Excel by entering relative fitness values and interpreting the resulting graphs. [ABSTRACT FROM AUTHOR]

Bryce Andrews' memoir, "Holding Fire: A Reckoning with the American West," offers a thoughtful and perceptive exploration of the problematic connection between firearms and American masculinity. In contrast to the popular TV series "Yellowstone," which portrays Montana as a violent and brutal place, Andrews challenges the link between gun violence and masculine identity. He shares personal experiences and reflections, including his regret over shooting prairie dogs and his struggle with colonial ancestry. Andrews ultimately offers a solution by transforming his grandfather's gun into a tree-planting spade, suggesting that American men can move away from violence and embrace a more sustainable future. [Extracted from the article]

The prairie dog optimization (PDO) algorithm is a metaheuristic optimization algorithm that simulates the daily behavior of prairie dogs. The prairie dog groups have a unique mode of information exchange. They divide into several small groups to search for food based on special signals and build caves around the food sources. When encountering natural enemies, they emit different sound signals to remind their companions of the dangers. According to this unique information exchange mode, we propose a randomized audio signal factor to simulate the specific sounds of prairie dogs when encountering different foods or natural enemies. This strategy restores the prairie dog habitat and improves the algorithm's merit-seeking ability. In the initial stage of the algorithm, chaotic tent mapping is also added to initialize the population of prairie dogs and increase population diversity, even use lens opposition-based learning strategy to enhance the algorithm's global exploration ability. To verify the optimization performance of the modified prairie dog optimization algorithm, we applied it to 23 benchmark test functions, IEEE CEC2014 test functions, and six engineering design problems for testing. The experimental results illustrated that the modified prairie dog optimization algorithm has good optimization performance. [ABSTRACT FROM AUTHOR]

Objective: The Prairie Chub Macrhybopsis australis is a poorly studied minnow species endemic to the upper Red River basin and is of both state and federal conservation interest due to uncertainty about its life history and potential listing status. The upper Red River basin of Oklahoma and Texas is a harsh environment where drought and extreme flow events are exacerbated by human alterations. As an assumed pelagic‐broadcast‐spawning minnow, the Prairie Chub is capable of a protracted spawning season and larval fish survival is assumed to be linked to discharge and streamflow variability. Methods: We systematically collected 2,017 age‐0 Prairie Chub from seven sites (North Fork Red River, Salt Fork Red River, Pease River, Red River, Prairie Dog Town Fork Red River, North Wichita River, and South Wichita River) with variable flow patterns during April–September 2019 and May–August 2020. We used otolith age estimates and back calculations to determine successful spawning dates. We used a hurdle model framework to examine relationships between hatch probability and hatch frequency. Result: Hatch probability had a negative relationship with calendar day and declined as calendar day increased. Hatch counts peaked in late June and early July and declined thereafter in 2019 but showed no discernible peak during the spawning season in 2020. Hatch probability during the spawning season increased with relative flow and air temperature. Increased hatch counts were also positively related to discharge variability (CV) for the 10 d prior to hatch dates. Conclusion: Our findings indicated that successful hatches had considerable spatial and temporal variability, with some sites contributing minimally to the population during some years. Spatial and temporal variability of hatch probability and hatch frequencies pose a variety of considerations for future conservation and management efforts, particularly given the pending federal listing status of the species. [ABSTRACT FROM AUTHOR]

Summary: Vehicular ad hoc network (VANET) is most significant for supporting intelligent transportation system (ITS)‐based technologies, but it gets hurdled by sparse distribution of vehicles on highways, and dynamically challenging topology that arises due to increase in traffic. Hence, energy stable and optimized cluster construction maximizes the network lifetime. In this paper, Hybrid Prairie Dogs and Beluga Whale Optimization‐based Node Clustering (HPDBWOA‐NC) mechanism is proposed with the parameters of highway route, node velocity, number of vehicular nodes, and communication for achieving stable cluster construction in VANETs. It is proposed with the balanced exploration and exploitation potential of Prairie Dog Optimization Algorithm (PDOA) and Beluga Whale Optimization Algorithm (BWOA) for establishing optimal clusters that increase the network stability during the routing process. It integrated the exploration and exploitation capabilities of PDOA and BWOA and confirmed better optimized clusters which confirmed reliable data delivery by preventing the issue of premature convergence. It constructed clusters and selected cluster heads (CHs) depending on the fitness factors of energy, interdistance between vehicles, communication range, and vehicular density. The results of the proposed HPDBWOA‐NC generated optimal number of CHs in the network which is comparatively 34.21% better than the benchmarked mechanisms. The mean throughput and packet delivery ratio (PDR) achieved by the proposed HPDBWOA‐NC are identified to be significantly improved by 25.48% and 28.91% better than the investigated metaheuristic clustering protocols. The statistical study also guaranteed an increased factor of 81, during the processing of optimizing the clusters during the employment of ITS applications in VANETs. [ABSTRACT FROM AUTHOR]

Integrating cutting-edge technology with conventional farming practices has been dubbed "smart agriculture" or "the agricultural internet of things." Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks. [ABSTRACT FROM AUTHOR]

The article presents the parameter tuning of the Power System Stabilizer (PSS) using the hybrid method. The hybrid methods proposed in this article are Praire Dog Optimization (PDO) and Marine Predator Algorithm (MPA). The proposed method can be called PDOMPA. In the PDOMPA method, the marine predator algorithm (MPA) is able to search around optimal individuals when updating population positions. MPA is used to make the exploration and exploitation stages of PDO more valid and accurate. PDO is an algorithm inspired by the life of prairie dogs. Prairie dogs are adapted to colonizing in burrows underground. Prairie dogs have daily habits of eating, observing for predators, establishing fresh burrows, or preserving existing ones. Meanwhile, MPA is a duplication of marine predator life which is modeled mathematically. In order to validate the performance of the PDOMPA method, this article presents a comparative simulation of the objective function and the transient response of PSS. This research uses validation by comparing with conventional methods, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Marine Predator Algorithm (MPA), and Praire Dog Optimization (PDO). Based on the simulation results, PDOMPA presents fast convergence in some cases and shows optimal results compared to competitive algorithms. From the simulation results using load variations, it was found that the proposed method has the ability to reduce the average undershoot and overshoot of speed by 42.2% and 85.37% compared to the PSS-Lead Lag method. Meanwhile the average settling time value of speed is 50.7%. [ABSTRACT FROM AUTHOR]

The concept of interconnected multi-microgrids (MMGs) is presented as a promising solution for the improvement in the operation, control, and economic performance of the distribution networks. The energy management of the MMGs is a strenuous and challenging task, especially with the integration of renewable energy resources (RERs) and variation in the loading due to the intermittency of these resources and the stochastic nature of the load demand. In this regard, the energy management of the MMGs is optimized with optimal inclusion of a hybrid system consisting of a photovoltaic (PV) and a wind turbine (WT)-based distributed generation (DGs) under uncertainties of the generated powers and the load variation. A modified Capuchin Search Algorithm (MCapSA) is presented and applied for the energy management of the MMGs. The MCapSA is based on enhancing the searching abilities of the standard Capuchin Search Algorithm (CapSA) using three improvement strategies including the quasi-oppositional-based learning (QOBL), the random movement-based Levy flight distribution, and the exploitation mechanism of the prairie dogs in the prairie dog optimization (PDO). The optimized function is a multi-objective function that comprises of the cost and the voltage deviation reduction along with stability enhancement. The effectiveness of the proposed technique is verified on standard benchmark functions and the obtained results. Then, the proposed method is used for energy management of IEEE 33-bus and 69-bus MMGs at uncertainties conation. The results depict that the energy management with inclusion of WTs and PVs using the proposed technique can reduce the cost and summation of the VD by 46.41% and 62.54%, and the VSI is enhanced by 15.1406% for the first MMG. Likewise, for the second MMG, the cost and summation of the VD are reduced by 44.19% and 39.70%, and the VSI is enhanced by 4.49%. [ABSTRACT FROM AUTHOR]

In this article, the black box dynamic model is presented for forecasting the performance of the PEM (Proton-exchange membrane) fuel cell (FC). An optimized deep artificial neural network has been used to build the experimental nonlinear model of the polymer membrane FC series that functions with hydrogen and oxygen. This research investigates predictability for a gate recurrent unit (GRU) optimized by a modified Prairie Dog Optimizer in PEMFCs. The results obtained have been validated by applying a case study and then a comparison is conducted among the outcomes of the offered technique and 2 other published methods: modified relevance vector machine and Lattice Gated Recurrent Unit (LGRU). The voltage clearly changes significantly, as demonstrated by simulations, even though the FC is handled with a low starting temperature and current. Also, the voltage point distribution has become more concentrated when the current and temperature are high. In both the training and prediction phases, the MAPE is reduced to approximately 0.0043 and 0.0047, respectively, showing that the proposed GRU technique produces superior prediction results when the operational settings approach the optimum operating conditions. According to simulations, the proposed IPDO/GRU with a 0.004 root mean square has the least error, followed by the mRVM and GRU with 0.009 and 0.010 root mean square values. The outcomes show that using the offered procedure does provide the finest verification of the empirical data. • A method for forecasting PEMFC performance. • Optimized CNN to design an experimental nonlinear model of the PEMFC. • Using an optimized gate recurrent unit (GRU) for this purpose. • Optimizing GRU by a modified Prairie Dog Optimizer in PEMFCs. [ABSTRACT FROM AUTHOR]

Stressors activate the hypothalamic-pituitary-adrenal axis which produces glucocorticoid (GC) hormones. Social buffering, the lowering of glucocorticoid secretion with increased social ties, commonly mitigates the stress response following exposure to a stressor. Black-tailed prairie dogs (Cynomys ludovicianus) are ground-dwelling sciurids that vary their social behaviour over time, and provide an exciting opportunity to investigate social buffering and GC production. We observed social behaviours and measured GC hormones in prairie dogs during the spring (reproduction) and summer. We expected social buffering would reduce GC production in the summer, following the stressors experienced during the spring. However, we found social integration and GC deposition were inversely correlated during the spring, which suggests social buffering occurs during the highly stressful period of reproduction, instead of after. Our study shows that the relationship between social behaviour and GC production is context-dependent, and social buffering may be an adaptive strategy in this colonially-living species. [ABSTRACT FROM AUTHOR]

The growing demand for electricity driven by population growth and industrialization is met by integrating hybrid renewable energy sources (HRESs) into the grid. HRES integration improves reliability, reduces losses, and addresses power quality issues for safe and effective microgrid (MG) operation, requiring efficient controllers. In this regard, this article proposes a prairie dog optimization (PDO) algorithm for the photovoltaic (PV)-, fuel cell (FC)-, and battery-based HRESs designed in MATLAB/Simulink architecture. The proposed PDO method optimally tunes the proportional integral (PI) controller gain parameters to achieve effective compensation of load demand and mitigation of PQ problems. The MG system has been applied to various intentional PQ issues such as swell, unbalanced load, oscillatory transient, and notch conditions to study the response of the proposed PDO controller. For evaluating the efficacy of the proposed PDO algorithm, the simulation results obtained are compared with those of earlier popular methodologies utilized in the current literature such as bee colony optimization (BCO), thermal exchange optimization, and PI techniques. A detailed analysis of the results found emphasizes the efficiency, robustness, and potential of the suggested PDO controller in significantly improving the overall system operation by minimizing the THD, improving the control of active and reactive power, enhancing the power factor, lowering the voltage deviation, and keeping the terminal voltage, DC-link voltage, grid voltage, and grid current almost constant in the event of PQ fault occurrence. As a result, the proposed PDO method paves the way for real-time employment in the MG system. [ABSTRACT FROM AUTHOR]

Swarm-based metaheuristics have shown significant progress in solving different complex optimization problems, including the parameter identification of linear, as well as nonlinear, systems. Nonlinear systems are inherently stiff and difficult to optimize and, thus, require special attention to effectively estimate their parameters. This study investigates the parameter identification of an input nonlinear autoregressive exogenous (IN-ARX) model through swarm intelligence knacks of the nonlinear marine predators' algorithm (NMPA). A detailed comparative analysis of the NMPA with other recently introduced metaheuristics, such as Aquila optimizer, prairie dog optimization, reptile search algorithm, sine cosine algorithm, and whale optimization algorithm, established the superiority of the proposed scheme in terms of accurate, robust, and convergent performances for different noise and generation variations. The statistics generated through multiple autonomous executions represent box and whisker plots, along with the Wilcoxon rank-sum test, further confirming the reliability and stability of the NMPA for parameter estimation of IN-ARX systems. [ABSTRACT FROM AUTHOR]

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black‐tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross‐validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host–pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially explicit forecasting of disease‐induced population fluctuations for use in natural resource management decision‐making. [ABSTRACT FROM AUTHOR]

Acanthamoeba is well known to host a variety of microorganisms such as viruses, bacteria, protozoa, and yeast. Given the recent number of cases of monkeypox infection, we speculate that amoebae may be aiding viral transmission to the susceptible hosts. Although there is no confirmatory evidence to suggest that Acanthamoeba is a host to monkeypox (a double-stranded DNA virus), the recent discovery of mimivirus (another double-stranded DNA virus) from Acanthamoeba, suggests that amoebae may shelter monkeypox virus. Furthermore, given the possible spread of monkeypox virus from animals to humans during an earlier outbreak, which came about after patients came in contact with prairie dogs, it is likely that animals may also act as mixing vessel between ubiquitously distributed Acanthamoeba and monkeypox virus, in addition to the environmental habitat that acts as an interface in complex interactions between diverse microorganisms and the host. [ABSTRACT FROM AUTHOR]

Background: Host associated gut microbiota are important in understanding the coevolution of host-microbe, and how they may help wildlife populations to adapt to rapid environmental changes. Mammalian gut microbiota composition and diversity may be affected by a variety of factors including geographic variation, seasonal variation in diet, habitat disturbance, environmental conditions, age, and sex. However, there have been few studies that examined how ecological and environmental factors influence gut microbiota composition in animals' natural environments. In this study, we explore how host habitat, geographical location and environmental factors affect the fecal microbiota of Cynomys ludovicianus at a small spatial scale. We collected fecal samples from five geographically distinct locations in the Texas Panhandle classified as urban and rural areas and analyzed them using high throughput 16S rRNA gene amplicon sequencing. Results: The results showed that microbiota of these fecal samples was largely dominated by the phylum Bacteroidetes. Fecal microbiome diversity and composition differed significantly across sampling sites and habitats. Prairie dogs inhabiting urban areas showed reduced fecal diversity due to more homogenous environment and, likely, anthropogenic disturbance. Urban prairie dog colonies displayed greater phylogenetic variation among replicates than those in rural habitats. Differentially abundant analysis revealed that bacterial species pathogenic to humans and animals were highly abundant in urban areas which indicates that host health and fitness might be negatively affected. Random forest models identified Alistipes shahii as the important species driving the changes in fecal microbiome composition. Despite the effects of habitat and geographic location of host, we found a strong correlation with environmental factors and that- average maximum temperature was the best predictor of prairie dog fecal microbial diversity. Conclusions: Our findings suggest that reduction in alpha diversity in conjunction with greater dispersion in beta diversity could be indicative of declining host health in urban areas; this information may, in turn, help determine future conservation efforts. Moreover, several bacterial species pathogenic to humans and other animals were enriched in prairie dog colonies near urban areas, which may in turn adversely affect host phenotype and fitness. [ABSTRACT FROM AUTHOR]