Search Results (25)

Three new Mn(II) complexes with imidazo[1,2-a]pyridine derivatives were synthesized and structurally characterized in a solid state by single crystal X-ray diffraction, FT-IR and Raman spectroscopy, and thermal analyses. The investigated compounds include [Mn(3-Climpy)₂Cl₂(MeOH)₂] (1), [Mn(3-Brimpy)₂Cl₂(MeOH)₂] (2), and a rare double chloro-bridged coordination polymer [Mn(impy)₂Cl₂]_n (3). Spectroscopic studies were used to assess their potential stability in DMEM (Dulbecco’s Modified Eagle Medium), and encapsulation in Pluronic P-123 micelles improved their solubility in aqueous solution, as well as cellular uptake and selectivity. Biological evaluation revealed negligible cytotoxicity against most cancer and control cell lines, but unexpectedly high activity against pancreatic adenocarcinoma (PANC-1), exceeding that of cisplatin. Complex 2, bearing a bromine substituent in the imidazole ring, showed the strongest effects, correlating with enhanced intracellular accumulation, reactive oxygen species (ROS) generation, and mitochondrial membrane potential disruption. Molecular docking and protein binding assays demonstrated moderate affinity toward human serum albumin (HSA) and transferrin, whereas DNA interaction was weak and non-damaging. These results highlight the structure–activity relationship of Mn(II) imidazo[1,2-a]pyridine complexes and support their potential as targeted redox-active agents against pancreatic cancer, with polymeric encapsulation providing an effective strategy to enhance biological performance. Full article

Keeping the levels of soil major nutrients (total nitrogen, TN; available phosphorous, AP; and available potassium, AK) in optimum condition is important to achieve the goals of precision agriculture systems. To address the issues of slow speed and low accuracy in soil nutrient detection, this study developed a prediction model for soil major nutrients content based on an improved Extreme Learning Machine (ELM) algorithm. This model utilizes a soil major nutrients detection system integrating pyrolysis and artificial olfaction. First, the Bootstrap Aggregating (Bagging) ensemble strategy was introduced during the model integration phase to effectively reduce prediction variance through multi-submodel fusion. Second, Generative Adversarial Networks (GAN) were employed for sample augmentation, enhancing the diversity and representativeness of the dataset. Subsequently, a multi-scale convolutional and Efficient Lightweight Attention Network (ELA-Net) was embedded in the feature mapping layer to strengthen the representation capability of soil gas features. Finally, adaptive hyperparameter tuning was achieved using the Adaptive Chaotic Bald Eagle Optimization Algorithm (ACBOA) to enhance the model’s generalization capability. Results demonstrate that this model achieves varying degrees of performance improvement in predicting total nitrogen (R² = 0.894), available phosphorus (R² = 0.728), and available potassium (R² = 0.706). Overall prediction accuracy surpasses traditional models by 8–12%, with significant reductions in both RMSE and MAE. These results demonstrate that the method can rapidly, accurately, and non-destructively estimate key soil nutrients, providing theoretical guidance and practical support for field fertilization, soil fertility assessment, and on-site decision-making in precision agriculture. Full article

This paper presents a multi-strategy enhanced bald eagle search algorithm (EAB-BES) for 3D UAV path planning in urban environments. EAB-BES addresses key limitations of the traditional bald eagle search (BES) algorithm, including slow convergence, susceptibility to local optima, and poor adaptability in complex urban scenarios. The algorithm enhances solution space exploration through elite opposition-based learning, balances global search and local exploitation via an adaptive weight mechanism, and refines local search directions using block-based elite-guided differential mutation. These innovations significantly improve BES’s convergence speed, path accuracy, and adaptability to urban constraints. To validate its effectiveness, six high-density urban environments with varied obstacles were used for comparative experiments against nine advanced algorithms. The results demonstrate that EAB-BES achieves the fastest convergence speed and lowest stable fitness values and generates the shortest, smoothest collision-free 3D paths. Statistical tests and box plot analysis further confirm its superior performance in multiple performance metrics. EAB-BES has greater competitiveness compared with the comparative algorithms and can provide an efficient, reliable and robust solution for UAV autonomous navigation in complex urban environments. Full article

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article

The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resource-constrained devices, and dynamic network environments. These challenges include denial-of-service (DoS) attacks, anomalous network behaviors, and data manipulation, which threaten the security and reliability of IoT ecosystems. New methods based on machine learning have been reported in the literature, addressing topics such as intrusion detection and prevention. This paper proposes an advanced anomaly detection framework for IoT networks expressed in several phases. In the first phase, data preprocessing is conducted using techniques like the Median-KS Test to remove noise, handle missing values, and balance datasets, ensuring a clean and structured input for subsequent phases. The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. This approach identifies the most significant features, reduces dimensionality, and enhances computational efficiency without sacrificing accuracy. In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. This multi-step methodology ensures adaptability and scalability in handling diverse IoT scenarios. The evaluation results demonstrate the superiority of the proposed framework over existing methods. It achieves a 12.5% improvement in accuracy (98%), a 14% increase in detection rate (95%), a 9.3% reduction in false positive rate (10%), and a 10.8% decrease in false negative rate (5%). These results underscore the framework’s effectiveness, reliability, and scalability for securing real-world IoT networks against evolving cyber threats. Full article

In this study, we propose an innovative inner–outer loop control framework for a quadcopter unmanned aerial vehicle (UAV) that significantly enhances the trajectory-tracking speed and accuracy while enhancing robustness against external disturbances. The inner loop employs a Linear Active Disturbance Rejection Controller (LADRC) and the outer loop a proportion integral differential (PID) controller, unified within a fuzzy control scheme. We introduce a Self-Adaptive Bald Eagle Search Optimization algorithm to optimize the initial controller settings, thereby accelerating convergence and improving parameter-tuning precision. Simulation results show that our proposed controller outperforms the conventional two-loop cascade PID configuration, as well as alternative strategies combining an outer-loop PID with a second-order inner-loop LADRC or a fuzzy-enhanced PID-LADRC approach. Moreover, the system maintains the desired position and attitude under external perturbations, underscoring its superior disturbance-rejection capability and stability. Full article

Protected areas are crucial for the conservation of West Africa’s increasingly imperiled wildlife, but are under unprecedented pressure associated with exponential human population growth in the region. Using birds as biodiversity indicators, we investigated the conservation status of Togo’s Fazao-Malfakassa National Park, which was managed by a private foundation from 1990 to 2015, and since 2015 has been managed by the state. Between 2022 and 2024, we conducted 90 days of bird surveys in the park and documented a total of 240 bird species. Our findings include 34 species new to the park, including the first record of Emin’s Shrike (Lanius gubernator) in Togo, the first sightings of the Great Blue Turaco (Corythaeola cristata) since 1990, and first observations of the Abyssinian Ground-Hornbill (Bucorvus abyssinicus) since 2019. Many such species survive in Togo only in Fazao-Malfakassa National Park, but its exceptional biodiversity has come under increasing assault from illegal activities, including poaching, logging, road construction, charcoal production, cattle grazing, and land clearance to establish agricultural plantations. We were unable to document 91 bird species previously reported for the park during our surveys, suggesting a possible ~31% decline in avian species richness in the park compared to historical records. Apparent extirpations of globally-threatened raptors such as the Critically Endangered White-backed Vulture (Gyps africanus) and Hooded Vulture (Necrosyrtes monachus), and declines of the Endangered Bateleur (Terathopius ecaudatus) and Martial Eagle (Polemaetus bellicosus) further indicate that current conservation strategies are failing to adequately protect wildlife in the park. Togo’s two other historical national parks have already been almost entirely destroyed by human activities, and unless urgent conservation action is taken, there is a high risk that Fazao-Malfakassa National Park will share the same fate. We urgently recommend improving support for law enforcement capacity and park staff, conducting community conservation outreach, and ongoing monitoring of wildlife in the park to assess its conservation success. Full article

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the novel Improved Eagle Strategy (IES) hybrid model, which uniquely integrates bio-inspired optimization with clustering techniques to drastically reduce computation time while ensuring highly accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model’s application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model, marking a significant advancement in optimizing cloud computing processes, particularly within the IoT ecosystem. Full article

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm’s robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms. Full article

The inverse kinematics of robotic manipulators involves determining an appropriate joint configuration to achieve a specified end-effector position. This problem is challenging because the inverse kinematics of manipulators are highly nonlinear and complexly coupled. To address this challenge, the bald eagle search optimization algorithm is introduced. This algorithm combines the advantages of evolutionary and swarm techniques, making it more effective at solving nonlinear problems and improving search efficiency. Due to the tendency of the algorithm to fall into local optima, the Lévy flight strategy is introduced to enhance its performance. This strategy adopts a heavy-tailed distribution to generate long-distance jumps, thereby preventing the algorithm from becoming trapped in local optima and enhancing its global search efficiency. The experiments first evaluated the accuracy and robustness of the proposed algorithm based on the inverse kinematics problem of manipulators, achieving a solution accuracy of up to ${10}^{-18}$ m. Subsequently, the proposed algorithm was compared with other algorithms using the CEC2017 test functions. The results showed that the improved algorithm significantly outperformed the original in accuracy, convergence speed, and stability. Specifically, it achieved over 70% improvement in both standard deviation and mean for several test functions, demonstrating the effectiveness of the Lévy flight strategy in enhancing global search capabilities. Furthermore, the practicality of the proposed algorithm was verified through two real engineering optimization problems. Full article

This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm’s performance with other swarm intelligence algorithms using commonly used test functions. The algorithm’s performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm. Full article

The current seepage prediction model of the sluice gate is rarely used. To solve the problem, this paper selects the bidirectional long and short-term neural network (BiLSTM) with high information integration and accuracy, which can well understand and capture the temporal pattern and dependency relationship in the sequence and uses the multi-strategy improved Harris Hawks optimization algorithm (MHHO) to analyze its two hyperparameters: By optimizing the number of forward and backward neurons, the overfitting and long-term dependence problems of the neural network are solved, and the convergence rate is accelerated. Based on this, the MHHO-BiLSTM statistical prediction model of sluice seepage is established in this paper. To begin with, the prediction model uses water pressure, rainfall, and aging effects as input data. Afterward, the bidirectional long short-term memory neural network parameters are optimized using the multi-strategy improved Harris Hawks optimization algorithm. Then, the statistical prediction model based on the optimization algorithm proposed in this paper for sluice seepage is proposed. Finally, the seepage data of a sluice and its influencing factors are used for empirical analysis. The calculation and analysis results indicate that the optimization algorithm proposed in this paper can better search the optimal parameters of the bidirectional long short-term memory neural network compared with the original Harris Eagle optimization algorithm, optimizing the bidirectional long short-term memory neural network (HHO-BiLSTM) and the original bidirectional long short-term memory neural network (BiLSTM). Meanwhile, the bidirectional long and short-term neural network (BiLSTM) model shows higher prediction accuracy and robustness. Full article

A novel approach called the nonlinear convex decreasing weights golden eagle optimization technique based on a global optimization strategy is proposed to overcome the limitations of the original golden eagle algorithm, which include slow convergence and low search accuracy. To enhance the diversity of the golden eagle, the algorithm is initialized with the Arnold chaotic map. Furthermore, nonlinear convex weight reduction is incorporated into the position update formula of the golden eagle, improving the algorithm’s ability to perform both local and global searches. Additionally, a final global optimization strategy is introduced, allowing the golden eagle to position itself in the best possible location. The effectiveness of the enhanced algorithm is evaluated through simulations using 12 benchmark test functions, demonstrating improved optimization performance. The algorithm is also tested using the CEC2021 test set to assess its performance against other algorithms. Several statistical tests are conducted to compare the efficacy of each method, with the enhanced algorithm consistently outperforming the others. To further validate the algorithm, it is applied to the cognitive radio spectrum allocation problem after discretization, and the results are compared to those obtained using traditional methods. The results indicate the successful operation of the updated algorithm. The effectiveness of the algorithm is further evaluated through five engineering design tasks, which provide additional evidence of its efficacy. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems. Full article

Lepidopteran species are mostly pests, causing serious annual economic losses. High-quality genome sequencing and assembly uncover the genetic foundation of pest occurrence and provide guidance for pest control measures. Long-read sequencing technology and assembly algorithm advances have improved the ability to timeously produce high-quality genomes. Lepidoptera includes a wide variety of insects with high genetic diversity and heterozygosity. Therefore, the selection of an appropriate sequencing and assembly strategy to obtain high-quality genomic information is urgently needed. This research used silkworm as a model to test genome sequencing and assembly through high-coverage datasets by de novo assemblies. We report the first nearly complete telomere-to-telomere reference genome of silkworm Bombyx mori (P50T strain) produced by Pacific Biosciences (PacBio) HiFi sequencing, and highly contiguous and complete genome assemblies of two other silkworm strains by Oxford Nanopore Technologies (ONT) or PacBio continuous long-reads (CLR) that were unrepresented in the public database. Assembly quality was evaluated by use of BUSCO, Inspector, and EagleC. It is necessary to choose an appropriate assembler for draft genome construction, especially for low-depth datasets. For PacBio CLR and ONT sequencing, NextDenovo is superior. For PacBio HiFi sequencing, hifiasm is better. Quality assessment is essential for genome assembly and can provide better and more accurate results. For chromosome-level high-quality genome construction, we recommend using 3D-DNA with EagleC evaluation. Our study references how to obtain and evaluate high-quality genome assemblies, and is a resource for biological control, comparative genomics, and evolutionary studies of Lepidopteran pests and related species. Full article