Search Results (12)

The Pyrenean capercaillie (Tetrao urogallus aquitanicus) is a forest obligate grouse that has experienced a marked population decline in recent decades owing to the lack of optimal habitats. However, the effect of forest structure on potential predators and habitat competitors has not been well-studied. We conducted a camera-trapping study at three conservation areas in Huesca province (northeastern Spain), which were classified as ‘optimal’, ‘favorable’, and ‘unfavorable’ based on habitat suitability for the capercaillie. This study was conducted for 3417 days at a total of 130 camera locations in autumn–winter and spring–summer, capturing 8757 valid photos. In total, 36 different species were recorded. The most frequently detected species were Southern chamois (Rupicapra pyrenaica pyrenaica; 32.6%), roe deer (Capreolus capreolus; 18%), wild boar (Sus scrofa; 9.6%), red squirrel (Sciurus vulgaris; 6.1%), mustelids (5.6%), and red fox (Vulpes vulpes; 4.8%). Capercaillies were photographed in the optimal and favorable habitat areas. Nest predators, such as mustelids and red fox, were more frequently detected in the favorable area during autumn–winter and in the optimal area in spring–summer, while corvids were more frequently detected in the unfavorable habitat area during both periods. No clear pattern was found for wild boar (nest predator and habitat competitor) or cervids (competitors). As capercaillie coexist with a wide range of predators and competitors, and habitat structure may not always explain species relative abundance, factors such as disturbance and food resources should be also taken into account when aiming to develop targeted management for the benefit of the capercaillie. Full article

Pavement markings, as a crucial component of traffic guidance and safety facilities, are subject to degradation and abrasion after a period of service. To ensure traffic safety, retroreflectivity and diffuse illumination should be above the minimum thresholds and required to undergo inspection periodically. Therefore, an onboard light detection and ranging (LiDAR) and camera deployment optimization method is proposed for pavement marking distress detection to adapt to complex traffic conditions, such as shadows and changing light. First, LiDAR and camera sensors’ detection capability was assessed based on the sensors’ built-in features. Then, the LiDAR–camera deployment problem was mathematically formulated for pavement marking distress fusion detection. Finally, an improved red fox optimization (RFO) algorithm was developed to solve the deployment optimization problem by incorporating a multi-dimensional trap mechanism and an improved prey position update strategy. The experimental results illustrate that the proposed method achieves 5217 LiDAR points, which fall on a 0.58 m pavement marking per data frame for distress fusion detection, with a relative error of less than 7% between the mathematical calculation and the field test measurements. This empirical accuracy underscores the proposed method’s robustness in real-world scenarios, effectively mitigating the challenges posed by environmental interference. Full article

This work aims to constitute a framework dataflow based on the prediction, optimization, and characterization of optimal solutions. To this purpose, a metaheuristic optimization method is used to obtain the optimal design solutions for discrete plane frame structures considering as objective function the minimization of their maximum resultant displacement, subjected to side and behavioral constraints. The design variables that lead to the optimal solutions are constituted into datasets which are subsequently submitted to a clustering analysis. The results obtained provide pertinent insights about the optimal solutions clusters’ ranges, giving effective support to a specific solution selection. Full article

The rise of direct current (DC) distribution networks, driven by distributed energy storage and large-scale photovoltaic integration, has significantly altered distribution network configurations. In DC networks, short-circuit faults cause a sharp drop in voltage and a rapid increase in current, negatively impacting system stability. To solve this problem, we used an improved red fox optimization (IRFO) algorithm to calculate the distance to failure of the protection device. The algorithm shows higher convergence and accuracy compared to conventional methods. The isolated forest algorithm rejects anomalous data, while an adjustable feedback factor and genetic crossover operator further improve performance. Adaptive interpolation is employed to address low sampling frequency issues, enhancing fault localization precision. Simulations performed in Simulink show that the method is highly resistant to interference with minimal localization error. It is also resistant to changes in system parameters, highlighting its robustness and usefulness in fault localization. Full article

This paper presents an optimized nonlinear controller, the Integral Super-Twisting Sliding Mode Control (ISTSMC), for acute leukemia therapy. To enhance its performance, we introduce the RedFox Optimization Algorithm, a nature-inspired technique based on the hunting behavior of red foxes. This algorithm is utilized to fine-tune the controller parameters, ensuring optimal achievement of control objectives. We discuss the fundamentals of ISTSMC, Sliding Mode Control, and Synergetic Control, detailing their optimization methodology using the RedFox Algorithm. The effectiveness of ISTSMC is evaluated through numerical simulations and compared with traditional Sliding Mode Control (SMC) and Synergetic Control (SC). The results demonstrate that ISTSMC achieves superior performance with a steady state error of 53.85, a settling time of 59.60, and a transient time of 4.7942, significantly outperforming SMC and SC. Additionally, ISTSMC reduces leukemic cell levels to a safe threshold more efficiently while maintaining healthy cell populations within acceptable limits. These improvements highlight the potential of ISTSMC in optimizing chemotherapy administration, ensuring better patient outcomes while minimizing side effects. Full article

Effective data fragmentation is essential in enhancing the performance of distributed database management systems (DDBMS) by strategically dividing extensive databases into smaller fragments distributed across multiple nodes. This study emphasizes horizontal fragmentation and introduces an advanced machine learning algorithm, Red Fox Optimization-based Support Vector Machine (RFO-SVM), designed for optimizing the data fragmentation process. The input database undergoes meticulous pre-processing to address missing data concerns, followed by analysis through RFO-SVM. This algorithm efficiently classifies features and target labels based on class labels. The RFO algorithm optimizes critical SVM parameters, including the kernel, kernel parameter, and boundary parameter, leveraging the accuracy metric. The resulting classified data serves as fragments for the fragmentation process. To ensure precision in fragmentation, a Genetic Algorithm (GA) allocates these fragments to diverse nodes within the DDBMS, optimizing the total allocation cost as the fitness function. The proposed model, implemented in Python, significantly contributes to the efficient fragmentation and allocation of databases in distributed systems, thereby enhancing overall performance and scalability. Full article

The growing applicability of functionally graded materials is justified by their ability to contribute to the development of advanced solutions characterized by the material customization, through the selection of the best parameters that will confer the best mechanical behaviour for a given structure under specific operating conditions. The present work aims to attain the optimal design solutions for a set of illustrative 2D and 3D discrete structures built from functionally graded materials using the Red Fox Optimization Algorithm, where the design variables are material parameters. From the results achieved one concludes that the optimal selection and distribution of the different materials’ mixture and the different exponents associated with the volume fraction law significantly influence the optimal responses found. To note additionally the good performance of the coupling between this optimization technique and the finite element method used for the linear static and free vibration analyses. Full article

Recently, various metaheuristic (MH) optimization algorithms have been presented and applied to solve complex engineering and optimization problems. One main category of MH algorithms is the naturally inspired swarm intelligence (SI) algorithms. SI methods have shown great performance on different problems. However, individual MH and SI methods face some shortcomings, such as trapping at local optima. To solve this issue, hybrid SI methods can perform better than individual ones. In this study, we developed a boosted version of the reptile search algorithm (RSA) to be employed for different complex problems, such as intrusion detection systems (IDSs) in cloud–IoT environments, as well as different optimization and engineering problems. This modification was performed by employing the operators of the red fox algorithm (RFO) and triangular mutation operator (TMO). The aim of using the RFO was to boost the exploration of the RSA, whereas the TMO was used for enhancing the exploitation stage of the RSA. To assess the developed approach, called RSRFT, a set of six constrained engineering benchmarks was used. The experimental results illustrated the ability of RSRFT to find the solution to those tested engineering problems. In addition, it outperformed the other well-known optimization techniques that have been used to handle these problems. Full article

In modern trends, wireless sensor networks (WSNs) are interesting, and distributed in the environment to evaluate received data. The sensor nodes have a higher capacity to sense and transmit the information. A WSN contains low-cost, low-power, multi-function sensor nodes, with limited computational capabilities, used for observing environmental constraints. In previous research, many energy-efficient routing methods were suggested to improve the time of the network by minimizing energy consumption; sometimes, the sensor nodes run out of power quickly. The majority of recent articles present various methods aimed at reducing energy usage in sensor networks. In this paper, an energy-efficient clustering/routing technique, called the energy and distance based multi-objective red fox optimization algorithm (ED-MORFO), was proposed to reduce energy consumption. In each communication round of transmission, this technique selects the cluster head (CH) with the most residual energy, and finds the optimal routing to the base station. The simulation clearly shows that the proposed ED-MORFO achieves better performance in terms of energy consumption (0.46 J), packet delivery ratio (99.4%), packet loss rate (0.6%), end-to-end delay (11 s), routing overhead (0.11), throughput (0.99 Mbps), and network lifetime (3719 s), when compared with existing MCH-EOR and RDSAOA-EECP methods. Full article

Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data. Full article

Due to the prospect of using walking robots in an impassable environment for tracked or wheeled vehicles, walking locomotion is one of the most remarkable accomplishments in robotic history. Walking robots, however, are still being deeply researched and created. Locomotion over irregular terrain and energy consumption are among the major problems. Walking robots require many actuators to cross different terrains, leading to substantial consumption of energy. A robot must be carefully designed to solve this problem, and movement parameters must be correctly chosen. We present a minimization of the hexapod robot’s energy consumption in this paper. Secondly, we investigate the reliance on power consumption in robot movement speed and gaits along with the Cost of Transport (CoT). To perform optimization of the hexapod robot energy consumption, we propose two algorithms. The heuristic algorithm performs gait switching based on the current speed of the robot to ensure minimum energy consumption. The Red Fox Optimization (RFO) algorithm performs a nature-inspired search of robot gait variable space to minimize CoT as a target function. The algorithms are tested to assess the efficiency of the hexapod robot walking through real-life experiments. We show that it is possible to save approximately 7.7–21% by choosing proper gaits at certain speeds. Finally, we demonstrate that our hexapod robot is one of the most energy-efficient hexapods by comparing the CoT values of various walking robots. Full article

Knowledge about the dispersal and gene flow patterns in wild animals are important for our understanding of population ecology and the connectedness of populations. It is also important for management relating to disease control and the transmission of new and emerging diseases. Our study aimed to evaluate the genetic structuring among comparative samples of red foxes in a small part of Scandinavia and to estimate the gene flow and potential directionality in the movements of foxes using an optimized set of microsatellite markers. We compared genetic samples of red foxes (Vulpes vulpes) from two areas in Sweden and two areas in Norway, including red fox samples from areas where the occurrence of the cyclophyllic tapeworm Echinococcus multilocularis has been documented, and areas without known occurrence of the parasite. Our results show a high level of gene flow over considerable distances and substantiates migration from areas affected with E. multilocularis into Norway where the parasite is not yet detected. The results allow us to better understand the gene flow and directionality in the movement patterns of red foxes, which is important for wildlife management authorities regarding the spread of E. multilocularis. Full article