Search Results (80)

Przewalski’s gazelle (Procapra przewalskii) is an endangered ungulate endemic to the Qinghai–Tibet Plateau, with a small population size and exposure to multiple ecological pressures. Its gut microbiota may play a crucial role in host environmental adaptation. To investigate the functional divergence of gut microbial communities, we performed high-throughput metagenomic sequencing on 105 wild fecal samples collected from 10 geographic regions around Qinghai Lake. The results revealed significant regional differentiation in key functional modules related to metabolism, antibiotic resistance mechanisms, and virulence-associated pathways. All populations showed enrichment in core metabolic pathways such as carbohydrate and amino acid metabolism, with carbohydrate-active enzymes dominated by glycoside hydrolases (GHs) and glycosyltransferases (GTs), exhibiting overall functional conservation. Although populations shared many antibiotic- and virulence-related reference genetic markers, the marker composition associated with distinct resistance mechanisms and pathogenic processes exhibited clear population-specific patterns, suggesting differential microbial responses to local environmental pressures. Correlation network analysis further identified core taxa (e.g., Arthrobacter and Oscillospiraceae/Bacteroidales lineages) as key genera linking community structure with core metabolic, resistance-related, and virulence-associated marker functions. Overall, the gut microbiota of Przewalski’s gazelle exhibits a complex spatially structured functional differentiation, reflecting host–microbiome co-adaptation under region-specific ecological pressures. These findings provide critical methodological and theoretical support for microecological health assessment and regionally informed conservation management of this endangered species. Full article

Rapid urban expansion and increasing population density intensify the loss of open spaces, exacerbate flooding frequency and runoff pollution, increase the urban heat island effect, and deteriorate ecological resilience and human well-being. This study presents Gazelle Valley Park (GVP) in Jerusalem (Israel), a unique large-scale ecohydrological infrastructure within a dense Mediterranean city. GVP was established in 2015 following a public-led campaign and comprises a multifunctional nature-based solution designed to collect and circulate stormwater through a series of vegetated ponds, enhancing filtration, aeration, and pollutant removal, while sustaining a wetland ecosystem. Its design follows international ecological standards and embodies the principle “from nuisance to resource”, transforming urban runoff into an asset that supports rich biodiversity while offering recreational, cultural, and educational activities. During the dry summer, reclaimed wastewater is introduced in order to support a perennial aquatic habitat, which introduces various challenges due to increased salinity, oxygen demand, and contaminants. Hydrometric and geochemical monitoring demonstrates strong correlations between rainfall and runoff and point at the role of sedimentation and vegetation in reducing pollutant loads. The park benefits from its holistic operation, where hydrology, ecology, education, and public engagement are integrated, thus making the whole greater than the sum of its parts. Full article

Gut microbiota plays a vital role in host resilience but may be disrupted under environmental antibiotic pressure. The goitered gazelle (Gazella subgutturosa), a keystone ungulate in the Qaidam Basin, is crucial for ecosystem stability. We aimed to investigate how this species responds to antibiotic pressure through gut microbial adaptation. Using 16S rRNA sequencing and weighted gene co-expression network analysis (WGCNA) on fecal and soil samples from six regions, we identified 18 microbial modules, of which three were strongly associated with antibiotics (|r| ≥ 0.75, p < 0.05). Gut microbial α-diversity was lowest in the antibiotic-rich, vegetation-poor TGL region and highest in XRH, where diverse vegetation appeared to buffer antibiotic impact. Antibiotic pressure can reshape gut microbial communities, exerting both adaptive benefits and adverse effects. High-quality habitats may alleviate the negative impacts of antibiotic pressure. Full article

Mechanical failures frequently occur in On-Load Tap Changers (OLTCs) during operation, potentially compromising the reliability and stability of power systems. The goal of this study is to develop an intelligent and accurate diagnostic approach for OLTC mechanical fault identification, particularly under the challenge of non-stationary vibration signals. To achieve this, a novel hybrid method is proposed that integrates the Gazelle Optimization Algorithm (GOA), Feature Mode Decomposition (FMD), and a Transformer-based classification model. Specifically, GOA is employed to automatically optimize key FMD parameters, including the number of filters (K), filter length (L), and number of decomposition modes (N), enabling high-resolution signal decomposition. From the resulting intrinsic mode functions (IMFs), statistical time domain features—peak factor, impulse factor, waveform factor, and clearance factor—are extracted to form feature vectors. After feature extraction, the resulting vectors are utilized by a Transformer to classify fault types. Benchmark comparisons with other decomposition and learning approaches highlight the enhanced performance of the proposed framework. The model achieves a 95.83% classification accuracy on the test set and an average of 96.7% under five-fold cross-validation, demonstrating excellent accuracy and generalization. What distinguishes this research is its incorporation of a GOA–FMD and a Transformer-based attention mechanism for pattern recognition into a unified and efficient diagnostic framework. With its high effectiveness and adaptability, the proposed framework shows great promise for real-world applications in the smart fault monitoring of power systems. Full article

To combat the catastrophic effects of climate change, the usage of renewable energy sources (RESs) has increased dramatically in recent years. The main drivers of the increase in solar photovoltaic (PV) system grid integrations in recent years have been lowering energy costs and pollution. Active and reactive powers are controlled by a proportional–integral controller, whereas energy storage batteries improve the quality of energy by storing both current and voltage, which have an impact on steady-state error. Since traditional controllers are unable to maximize the energy output of solar systems, artificial intelligence (AI) is essential for enhancing the energy generation of PV systems under a variety of climatic conditions. Nevertheless, variations in the weather can have an impact on how well photovoltaic systems function. This paper presents an intelligent power management controller (IPMC) for obtaining power management with load and electric-vehicle applications. The architecture combines the solar PV, battery with electric-vehicle load, and grid system. Initially, the PV architecture is utilized to generate power from the irradiance. The generated power is utilized to compensate for the required load demand on the grid side. The remaining PV power generated is utilized to charge the batteries of electric vehicles. The power management of the PV is obtained by considering the proposed control strategy. The power management controller is a combination of the twisting sliding-mode controller (TSMC) and Modified Pufferfish Optimization Algorithm (MPOA). The proposed method is implemented, and the application results are matched with the Mountain Gazelle Optimizer (MSO) and Beluga Whale Optimization (BWO) Algorithm by evaluating the PV power output, EV power, battery-power and battery-energy utilization, grid power, and grid price to show the merits of the proposed work. Full article

This theoretical research study proposes a novel hybrid algorithm that integrates an improved quasi-dynamical oppositional learning mutation scheme into the Mountain Gazelle Optimization method, augmented with chaotic sequences, for the thermal and economical design of a shell-and-tube heat exchanger operating with nanofluids. The Mountain Gazelle Optimizer is a recently developed metaheuristic algorithm that simulates the foraging behaviors of Mountain Gazelles. However, it suffers from premature convergence due to an imbalance between its exploration and exploitation mechanisms. A two-step improvement procedure is implemented to enhance the overall search efficiency of the original algorithm. The first step concerns substituting uniformly random numbers with chaotic numbers to refine the solution quality to better standards. The second step is to develop a novel manipulation equation that integrates different variants of quasi-dynamic oppositional learning search schemes, guided by a novel intelligently devised adaptive switch mechanism. The efficiency of the proposed algorithm is evaluated using the challenging benchmark functions from various CEC competitions. Finally, the thermo-economic design of a shell-and-tube heat exchanger operated with different nanoparticles is solved by the proposed improved metaheuristic algorithm to obtain the optimal design configuration. The predictive results indicate that using water + SiO₂ instead of ordinary water as the refrigerant on the tube side of the heat exchanger reduces the total cost by 16.3%, offering the most cost-effective design among the configurations compared. These findings align with the demonstration of how biologically inspired metaheuristic algorithms can be successfully applied to engineering design. Full article

The growing demand for clean, reliable and efficient power supply has driven the adoption of renewable energy sources in the package of distributed generation (DG) at the distribution segment of the power system. Despite advancements in DG allocation methodologies, a significant research gap exists regarding the simultaneous evaluation of DG sizing, location and power factor optimization, and their economic implications. This study presents the Mountain Gazelle Optimizer (MGO), a recent optimization approach to address the challenges of sizing, locating, and optimizing the power factor of multi-type DG units in a radial distribution network (RDN). In this work, the MGO is employed to reduce voltage variations, reactive power losses, real power losses, and costs while improving the bus voltage in the RDNs. The methodology involves extensive simulations across multiple scenarios covering one to three DG allocations with varying power factors (unity, fixed, and optimal). Key performance metrics evaluated included real and reactive loss reductions, voltage profile index (VPI), voltage stability index (VSI), and cost reductions due to energy losses compared to base cases. The proposed approach was implemented on the standard 33- and 69-bus networks, and the findings demonstrate that the MGO much outperforms other optimization approaches in the existing literature, realizing considerable decreases in real power losses (up to 98.10%) and reactive power losses (up to 93.38%), alongside notable cost savings. This research showcases the critical importance of optimizing DG power factors, a largely neglected aspect in most prior studies. In conclusion, this work fills a vital gap by integrating power factor optimization into the DG allocation framework, offering a comprehensive approach to enhancing the electricity distribution networks’ dependability, efficacy, and sustainability. Full article

The Arabian sand gazelle (Gazella marica) is a native and highly adaptable species of the Arabian Peninsula. Due to drastic population declines, the species is listed as globally vulnerable. Very little is known about the behavioral ecology of this species in captivity; therefore, this study was designed to investigate the seasonal variations in the activity patterns of Arabian sand gazelles at Al Reem Biosphere Reserve, Qatar. Data were collected in two phases, i.e., summer (September–October 2021) and winter (December 2021–January 2022), for a total of 16 days. Results revealed that feeding and walking (p = 0.001) were the dominant activities in both seasons, yet these were higher in summer compared to winter. Likewise, standing, lying down and other activities (social interactions, defecating, maintenance, sexual behaviors) were also higher in summer compared to winter. All these findings suggest that Arabian sand gazelles are adaptable to harsh environments. However, we strongly recommend a year-round investigation on the impacts of humans, feed types and Arabian Oryx on the behavioral activities of Arabian sand gazelles. In addition, we suggest studying the behavior ecology of the wild scattered populations of Arabian sand gazelles for better management of captive breeding stocks. Full article

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods. Full article

Understanding the change in the habitat distributions and abundance of wildlife in space and time is critical for the conservation of biodiversity and mitigate human–wildlife conflicts (HWCs). Tibetan antelope or chiru (Pantholops hodgsonii), Tibetan gazelle or goa (Procapra picticaudata), Tibetan wild ass or kiang (Equus kiang), and Wild yak (Bos mutus) have been sympatric on the Qinghai–Tibetan plateau (QTP) for numerous generations. However, reviews on the habitat distributions and abundance of these four wild herbivores (WHs), as well as the methods examining the changes in these aspects, are still lacking. Here, we firstly review the distributions and abundance of four major WHs on the QTP across different periods, examining the underlying causes of changes and HWCs. Furthermore, we critically compare three aspects of methods: transect surveys, machine learning (ML), and deep learning (DL) methods of studying WHs. The results show that since the 1990s, the distributions and abundance of WHs have exhibited a trend of initial decline followed by recovery, largely attributed to global climate warming and a decrease in illegal hunting. However, in recent years, the primary challenge has shifted from wildlife protection to balancing the human and wildlife interests within the constraints of limited resources. In the future, we should focus on enhancing the ecological functions of habitats to achieve harmonious coexistence between humans and nature, as well as establishing a scientific compensation mechanism to mitigate human–wildlife conflicts. In order to accurately calculate the changes, we should select appropriate models to analyze the habitats of wildlife based on their specific characteristics and the environmental conditions. Additionally, with the advancement of large models, AI (artificial intelligence) should be utilized for precise and rapid wildlife conservation. The findings of this study also provide guidance and reference for addressing the issues related to wildlife habitats and abundance in other regions globally. Full article

The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utilization and low latency. To address these issues, we propose a hybrid bio-inspired algorithm, HMAO, combining the mountain gazelle optimizer (MGO) and the aquila optimizer (AO). HMAO improves cluster stability and enhances data delivery reliability in FANETs. The algorithm uses MGO for efficient cluster head (CH) selection, considering UAV energy levels, mobility patterns, intra-cluster distance, and one-hop neighbor density, thereby reducing re-clustering frequency and ensuring coordinated operations. For cluster maintenance, a congestion-based approach redistributes UAVs in overloaded or imbalanced clusters. The AO-based routing algorithm ensures reliable data transmission from CHs to the base station by leveraging predictive mobility data, load balancing, fault tolerance, and global insights from ferry nodes. According to the simulations conducted on the network simulator (NS-3.35), the HMAO technique exhibits improved cluster stability, packet delivery ratio, low delay, overhead, and reduced energy consumption compared to the existing methods. Full article

In recent years, Jiangsu Province has increasingly prioritized the coordinated regional development of innovation. Unicorn and gazelle companies, characterized by technological or business model innovation, serve as significant indicators of regional innovation capacity. Therefore, this study uses unicorn and gazelle companies recognized between 2020 and 2022 in Jiangsu Province as samples, employing ArcGIS and geographical detectors to investigate the spatial distribution characteristics and influencing factors of these companies, and to propose optimization strategies, with the aim of assessing the innovation landscape of Jiangsu Province. The key findings and conclusions are as follows: (1) over the past three years, the average nearest-neighbor distance decreased from 10.491 km to 1.706 km, indicating a significant spatial agglomeration; the peak core density of business clusters increased substantially, reflecting the growth in the number of companies as clustering deepened. (2) Unicorns and gazelles exhibit clear clustering characteristics. The standard deviation ellipse of unicorns is centered around Suzhou, while gazelles display a continuous distribution pattern in Southern and Central Jiangsu. (3) Geographical detector analysis reveals that the level of urban technological innovation is the most influential factor, with key determinants of distribution including total retail sales and patent grants. (4) To foster the development of unicorn and gazelle companies in Jiangsu, the government should focus on enhancing regional innovation capacity, ensuring the sustainable nurturing of innovative firms, and promoting the catalytic development of surrounding areas through core cities. Full article

The gut microbiota is integral to the health and adaptability of wild herbivores. Interactions with soil microbiota can shape the composition and function of the gut microbiota, thereby influencing the hosts’ adaptive strategies. As a result, soil microbiota plays a pivotal role in enabling wild herbivores to thrive in extreme environments. However, the influence of soil microbiota from distinct regions on host’s gut microbiota has often been overlooked. We conducted the first comprehensive analysis of the composition and diversity of gut and soil microbiota in goitered gazelles across six regions in the Qaidam Basin, utilizing source tracking and ecological assembly process analyses. Significant differences were observed in the composition and diversity of soil and gut microbiota among the six groups. Source tracking analysis revealed that soil microbiota in the GangciGC (GC) group contributed the highest proportion to fecal microbiota (8.94%), while the Huaitoutala (HTTL) group contributed the lowest proportion (1.80%). The GC group also exhibited the lowest α-diversity in gut microbiota. The observed differences in gut microbial composition and diversity among goitered gazelles from six regions in the Qaidam Basin were closely tied to their adaptive strategies. Ecological assembly process analysis indicated that the gut microbiota were primarily influenced by stochastic processes, whereas deterministic processes dominated most soil microbial groups. Both the differences and commonalities in gut and soil microbiota play essential roles in enabling these gazelles to adapt to diverse environments. Notably, the utilization pattern of soil microbiota by gut microbiota did not align with regional trends in gut microbial α-diversity. This discrepancy may be attributed to variations in environmental pressures and the gut’s filtering capacity, allowing gazelles to selectively acquire microbiota from soil to maintain homeostasis. This study highlights the significant regional variation in gut and soil microbiota diversity among goitered gazelle populations in the Qaidam Basin and underscores the critical role of soil-derived microbiota in their environmental adaptation. Full article

Ungulates are essential for maintaining the health of grassland ecosystems on the Tibetan plateau. Increased livestock grazing has caused competition for food resources, threatening ungulates’ survival. The survival risk of food resources for ungulates can be quantified by the grazing pressure index, which requires accurate grassland carrying capacity. Previous research on the grazing pressure index has rarely taken into account the influence of wild ungulates, mainly due to the lack of precise spatial data on their quantity. In this study, we conducted field investigations to construct high-resolution spatial distributions for the four endemic ungulates on the Tibetan plateau. By factoring in the grazing consumption of these ungulates, we recalculated the grassland carrying capacity to obtain the grazing pressure index, which allowed us to assess the survival risks for each species. The results show: (1) Quantity estimates for Tibetan antelope (Pantholops hodgsonii), Tibetan wild donkey (Equus kiang), Tibetan gazelle (Procapra picticaudata), and wild yak (Bos mutus) of the Tibetan plateau are 24.57 × 10⁴, 17.93 × 10⁴, 7.16 × 10⁴, and 1.88 × 10⁴, respectively; they mainly distributed in the northern and western regions of the Tibetan plateau. (2) The grassland carrying capacity of the Tibetan plateau is 69.98 million sheep units, with ungulate grazing accounting for 5% of forage utilization. Alpine meadow and alpine steppe exhibit the highest grassland carrying capacity. (3) The grazing pressure index on the Tibetan plateau grasslands is 2.23, indicating a heightened grazing pressure in the southern and eastern regions. (4) The habitat survival risk analysis indicates that the high survival risk (the grazing pressure index exceeds 1.2) areas for the four ungulate species account for the following proportions of their total habitat areas: Tibetan wild donkeys (49.76%), Tibetan gazelles (47.00%), Tibetan antelopes (40.76%), and wild yaks (34.83%). These high-risk areas are primarily located within alpine meadow and temperate desert steppe. This study provides a quantitative assessment of survival risks for these four ungulate species on the Tibetan plateau grasslands and serves as a valuable reference for ungulate conservation and grassland ecosystem management. Full article

The COVID-19 pandemic highlighted the urgent need for effective surface disinfection solutions, which has led to the use of mobile robots equipped with ultraviolet (UVC) lamps as a promising technology. This study aims to optimize the navigation of differential mobile robots equipped with UVC lamps to ensure maximum efficiency in disinfecting complex environments. Bio-inspired metaheuristic algorithms such as the gazelle optimization algorithm, whale optimization algorithm, bat optimization algorithm, and particle swarm optimization are applied. These algorithms mimic behaviors of biological beings such as the evasive maneuvers of gazelles, the spiral hunting patterns of whales, the echolocation of bats, and the collective behavior of flocks of birds or schools of fish to optimize the robot’s trajectory. The optimization process adjusts the robot’s coordinates and the time it takes to stops at key points to ensure complete disinfection coverage and minimize the risk of excessive UVC exposure. Experimental results show that the proposed algorithms effectively adapt the robot’s trajectory to various environments, avoiding obstacles and providing sufficient UVC radiation exposure to deactivate target microorganisms. This approach demonstrates the flexibility and robustness of these solutions, with potential applications extending beyond COVID-19 to other pathogens such as influenza or bacterial contaminants, by tuning the algorithm parameters. The results highlight the potential of bio-inspired metaheuristic algorithms to improve automatic disinfection and achieve safer and healthier environments. Full article