Search Results (1,242)

This paper introduces an enhanced speed control strategy for permanent magnet synchronous motors (PMSMs) through the application of sliding mode control (SMC) optimized by the Chess Optimization Algorithm (COA). Additionally, the proposed method is compared with other validated algorithms, such as Harris Hawk Optimization (HHO) and the Whale Optimization Algorithm (WOA). The SMC parameters are optimized using Integral of Time-weighted Absolute Error (ITAE) and Integral of Time-weighted Square Error (ITSE) as objectives function, and the controllers are validated in MATLAB/Simulink analyzing transient response, speed variation, and load disturbance conditions. The results show that COA-tuned SMC provides better performance, with the lowest overshoot of 0.1929% for ITAE and 3.4800% for ITSE, the fastest settling times of 0.00307 s for ITSE, and the lowest MAE and RMSE in most evaluated scenarios, offering the most balanced overall performance among the optimization algorithms tested. Comparison to PI and PID controllers demonstrates that SMC provides enhanced accuracy, robustness, and disturbance rejection. COA-SMC provides an efficient and stable solution for PMSM applications that demand quick and accurate speed regulation. Full article

Air refueling extends aircraft range and endurance, but its operational value hinges on where the refueling airspace is placed and how tanker missions are sequenced. This paper addresses the joint optimization of refueling airspace planning and tanker scheduling, in which each receiver selects a refueling point from a continuous feasible interval along a fixed route. The upper level determines refueling point locations (continuous variables), while the lower level schedules multiple heterogeneous tankers (discrete combinatorial variables); the two levels are tightly coupled through spatiotemporal constraints and fuel propagation. We propose a bottleneck-driven decoupled update (BDDU) strategy built on the Whale Optimization Algorithm (WOA). BDDU extracts bottleneck states from lower-level scheduling feedback and applies per-dimension step-size control to damp the coupling amplification effect inherent in bi-level optimization. Across three scenarios of varying coupling intensities and scales, BDDU-WOA raises the feasibility rate from 50% (WOA baseline) to 90% (+40 percentage points; $p<0.05$, Fisher’s exact test). The gain stems from a bottleneck-aware, dimension-wise step-size control mechanism with an adaptive, parameter-free classification threshold and only two tunable parameters, adding roughly 10% computational overhead. The method is intended for pre-mission planning of large-scale air refueling operations. Full article

The stranding network of the Basque Country (SAREUS) attended a stranded juvenile female Kogia breviceps on Zarautz (Gipuzkoa, Spain) on 2 October 2025. The necropsy revealed severe pathological findings: (1) plastic and plant debris occupying most of the volume of the main stomach, (2) pulmonary congestion, pink froth in respiratory airways and presence of sand in the oesophagus, and (3) haemorrhagic liver and meninges together with serosanguineous liquid in the pericardium. Results indicate that the individual stranded alive and drowned because of a debilitated condition most likely caused by plastic and plant material ingestion. To understand what happened in the last days of the animal, and to identify behaviours that could lead to plastic ingestion, the stomach content was analysed, including the chemical characterization of the plastic debris through ATR-FTIR and the identification of vegetal and prey species. One possible explanation of the stranding event could have been that reduced foraging ability in a juvenile animal contributed to ingestion of foreign material, although this hypothesis cannot be confirmed from the available evidence. Full article

Cryopreservation of semen is an important interdisciplinary field at the intersection of reproductive biotechnology and cryobiology, and holds significant value for the genetic rescue of endangered species, long-term preservation of germplasm resources, and management of reproduction in livestock and aquaculture. Although this technology is relatively well established in fish and terrestrial mammals, it still faces numerous challenges in marine mammals, particularly cetaceans. The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis), a rare and endemic freshwater porpoise of China, remains at an early stage of research regarding semen cryopreservation, and there is an urgent need to develop a technical system tailored to its unique physiological characteristics. Literature was searched in PubMed, Web of Science, and CNKI from database inception to May 2025. This structured narrative review was based on 77 core publications, including eight original studies on cetacean semen cryopreservation. This structured narrative review synthesizes evidence from cetacean semen cryopreservation research, with particular emphasis on technical insights from the bottlenose dolphin (Tursiops truncatus), Pacific white-sided dolphin (Lagenorhynchus obliquidens), killer whale (Orcinus orca), and beluga (Delphinapterus leucas). Additionally, it incorporates advances in extender formulations and cryoprotectants from studies on other species to summarize key technical parameters suitable for the cryopreservation of Yangtze finless porpoise semen, aiming to provide a theoretical basis and practical framework for establishing an efficient and safe cryopreservation system for this species. Full article

The East Sea (Sea of Japan) is one of the world’s most rapidly warming marginal seas, a sensitive setting in which to examine how cetacean communities are structured by, and respond to, ocean temperature. Using visual sighting records from line-transect surveys off the Korean east coast (2015–2024) and analyses designed to be robust to heterogeneous survey effort rather than to estimate abundance, we matched 177 sightings of six species to satellite sea-surface temperature (SST) and tested whether the species form distinct thermal guilds. Cold-water species (Dall’s porpoise and Pacific white-sided dolphin) occurred in water averaging 11.6 °C and warm-water species (common, Risso’s, and bottlenose dolphins and false killer whale) in water averaging 17.8 °C—a 6.2 °C separation that was highly significant, very large (Cohen’s d = 1.84), and independent of location, defining a temperature-structured community. Over the same period, spring regional mean SST rose about 2 °C (0.22 °C yr⁻¹). Strikingly, the cold-water guild was absent during the spring surveys in 2022 and 2024: it was absent in the two warmest-spring years despite the highest survey effort and full spatial coverage, its encounter rate fell with spring SST (ρ = −0.78), and—unlike the warm-water guild, which persisted unchanged—its loss was guild-specific. National bycatch statistics and local knowledge independently corroborate this decline. A sharply temperature-structured community, rapid warming, and the guild-specific loss of cold-water species together indicate that climate-driven reorganization of this assemblage is already underway, underscoring the need for sustained, all-season monitoring. Full article

Coiling temperature is a pivotal process parameter for hot-rolled strip steel, which directly determines the microstructure and mechanical properties of final products. Affected by the coupling of multiple process variables, coiling temperature presents strong nonlinearity and complex time-varying characteristics. Traditional heat transfer mechanism models, Random Forest (RF), Extreme Learning Machine (ELM) and single Long Short-Term Memory (LSTM) networks fail to fully explore the deep correlation among variables. In addition, their hyperparameters are generally selected by manual trial-and-error, leading to unsatisfactory prediction accuracy and poor robustness in practical production. To address the above limitations, this paper proposes a novel prediction model named WOA-CNN-GRU-SE, where the Whale Optimization Algorithm (WOA) is adopted for parameter optimization. Firstly, Convolutional Neural Network (CNN) is utilized to extract local coupling features from various working condition parameters. Secondly, the Squeeze-and-Excitation (SE) attention mechanism is applied to adaptively recalibrate channel weights, which enhances key features closely related to temperature variation and suppresses redundant interference information. Afterwards, Gated Recurrent Unit (GRU) is employed to conduct in-depth learning of temporal features. Furthermore, WOA is used to globally optimize critical hyperparameters, including learning rate, the number of GRU hidden units and L2 regularization coefficient, so as to eliminate the drawbacks of manual parameter tuning. Comparative experiments are conducted on actual production data from a hot rolling line. The results demonstrate that the proposed model outperforms CNN-GRU, CNN-GRU-SE, LSTM, RF and ELM in prediction performance. Its hit rate reaches 92.56% within the industrial error range of ±6 °C. This model effectively realizes accurate prediction of coiling temperature under complex working conditions and possesses great application potential in industrial practice. Full article

Battery management systems (BMSs) in electric vehicles (EVs) are instrumented with an increasing number of heterogeneous sensors, many of which contribute redundant or noisy measurements that increase computational cost without improving diagnostic accuracy. This paper proposes a Binary Hybrid Particle Whale Optimization Algorithm (BHPWOA) for multi-objective feature selection targeting three-class BMS fault diagnosis: OK, Warning, and Critical. The method is evaluated using an 18-feature EV charging dataset with $n=500$ samples. BHPWOA encodes candidate feature subsets as binary masks in a continuous $\left[\mathrm{0,1}\right]$ position space. It executes a Binary Particle Swarm Optimization (BPSO) phase during the first 50 iterations to rapidly identify a promising subset region, then transfers the global-best mask as the Whale Optimization Algorithm (WOA) leader for the remaining 50 iterations of bubble-net exploitation. A multi-objective fitness function simultaneously penalises classifier error and subset size, directly optimising the accuracy–cost trade-off. BHPWOA selects four features out of 18, corresponding to a $77.8\mathrm{\%}$ reduction, and achieves accuracy $=0.710$ and macro-F1 $=0.4455$ on the held-out test set. It outperforms all-feature KNN $\left(F_1=0.2997\right)$, standalone BPSO with six selected features $\left(F_1=0.4603\right)$, BWOA with two selected features $\left(F_1=0.4026\right)$, and BSFSA with five selected features $\left(F_1=0.4216\right)$ on the Pareto-dominant combined fitness objective. The selected subset $\left\{{\mathrm{C}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{V}\mathrm{o}\mathrm{l}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}}_V,{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{C}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}}_A,{\mathrm{S}\mathrm{O}\mathrm{C}}_{\mathrm{\%}},{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}}_{kW}\right\}$ achieves the best fitness score of $-0.5555$, enabling a $77.8\mathrm{\%}$ sensor-cost reduction while improving fault detection. Stability analysis across five independent random seeds confirms a mean feature count of $4.0\pm 0.7$ and a mean macro-F1 of $0.441\pm 0.021$, demonstrating algorithmic robustness. Full article

Quishing, a form of phishing conducted through QR codes, has emerged as a critical threat to user information security in mobile environments. Quishing attacks exploit the QR scanning workflow by opening malicious URLs in WebView and impersonating legitimate services to steal user credentials. Recent variants further evade static inspection by exposing credential-harvesting behavior only after user interaction, form submission, redirection, or page-state changes. In this paper, we propose WebView-Based Hybrid Analysis of Link and Event for On-Device QR Phishing Detection (WHALE), an On-Device multi-stage phishing detection framework based on an isolated Sandbox WebView. WHALE first loads the QR-decoded URL into the Sandbox WebView instead of directly delivering it to the User WebView, thereby separating the analysis process from the user session. In the static stage, WHALE extracts 54 features from the URL string, initial HTML, and DOM snapshot, and computes a static phishing risk score using a lightweight model. Inputs with uncertain static scores are forwarded to the dynamic stage. In the dynamic stage, WHALE inserts decoy credentials instead of real user credentials, triggers a controlled submit event, and analyzes 59 credential-flow state-transition features extracted before and after submission. The static model achieved an accuracy of 93.86%, precision of 93.08%, recall of 94.78%, and F1-score of 93.92%. The dynamic model achieved an accuracy of 0.915, precision of 0.895, recall of 0.946, specificity of 0.882, and F1-score of 0.920 on a source-group-disjoint independent test set. Real-device evaluation on a Samsung Galaxy S23 Ultra showed that WHALE maintains practical mobile overhead, with an average static internal runtime of 58.75 ms, dynamic internal runtime of 4165.42 ms, combined model inference time of 0.088 ms, and model asset size of 0.681 MB. These results demonstrate that WHALE can detect QR-based phishing threats On-Device while reducing user credential exposure through sandboxed credential-flow analysis. Full article

Soil temperature wireless sensor networks provide essential data for continuous soil temperature monitoring in cotton fields and support agricultural environmental regulation and crop growth management. However, conventional sensor node deployment methods often lead to coverage blind spots, redundant coverage, and insufficient utilization of sensing resources, which restrict network monitoring performance. To address these issues, this study proposes an Adaptive Chaotic Lévy Flight Prairie Dog Optimization algorithm, named ACLFPDO, for optimizing node deployment in soil temperature wireless sensor networks. The incremental novelty of ACLFPDO does not lie in the individual use of chaotic initialization, adaptive parameter adjustment, or Lévy-flight perturbation, which have been widely used in metaheuristic optimization, but in coupling these strategies with a stage-based Prairie Dog Optimization (PDO) position-updating framework and a coverage-oriented fitness design tailored to the STWSN area-coverage problem. An idealized two-dimensional simulation model was established, in which the cotton-field monitoring region was simplified as a regular square area and each sensor node was modeled using a fixed-radius circular binary sensing model. Coverage rate and node utilization efficiency were used as the main evaluation metrics. Comparative simulations were conducted against Prairie Dog Optimization, Snake Optimization (SO), and Whale Optimization algorithms (WO). Under a monitoring area side length of 50, sensing radius of 5, and 42 sensor nodes, ACLFPDO achieved a coverage rate of 98.49% and a node utilization efficiency of 74.65%. Compared with PDO, SO, and WO, the coverage rate increased by 16.88, 9.52, and 10.86 percentage points, respectively. The results indicate that ACLFPDO can improve coverage performance and sensing resource utilization under idealized simulation conditions. However, practical cotton-field deployment still requires further consideration of irregular boundaries, ridges, furrows, obstacles, burial-depth differences, communication connectivity, energy consumption, and soil spatial heterogeneity. Full article

When addressing high-dimensional complex optimization problems, the vanilla Dung Beetle Optimizer (DBO) suffers from slow convergence, frequent stagnation in local optima, and progressive degradation of population diversity. To overcome the above inherent defects, this paper proposes a multi-strategy hybrid improved DBO variant named the SWDBO, which incorporates three targeted enhancement modules. First, an adaptive population proportion strategy is developed to dynamically adjust the population sizes of rolling beetles, brood beetles, small beetles and thief beetles throughout iterations. More individuals are allocated for extensive global exploration at the early evolutionary stage, while more search agents are reserved for delicate local exploitation in later iterations, which maintains stable population diversity over the entire optimization process. Second, the bubble-net encircling and spiral predation mechanisms of the Whale Optimization Algorithm (WOA) are embedded into the position update formula of rolling beetles. This integration strengthens fine local search performance and accelerates the overall convergence rate. Third, a modified seagull optimization operator combined with Lévy random perturbation is introduced into the position updating rule of thief beetles. This improved jump mechanism optimizes individual movement trajectories and enables the algorithm to effectively escape local optimal traps. Numerical experiments are implemented on the 100-dimensional benchmark functions of CEC2017 and CEC2020. Moreover, the proposed SWDBO is validated on three classical constrained engineering optimization tasks, including three-bar truss design, ten-bar truss design and cantilever beam sizing optimization. Wilcoxon rank-sum tests statistically verify significant performance disparities between the SWDBO and competing optimizers. For the three structural engineering cases, the design solutions obtained by the SWDBO produce lighter structural mass while satisfying all constraint requirements. Overall experimental evidence proves that the proposed multi-strategy improvement framework can efficiently tackle high-dimensional numerical optimization and constrained engineering design problems, and the SWDBO exhibits prominent performance in balancing global exploration and local exploitation. Full article

Accurate prediction of battery thermal runaway (TR) is a critical requirement for electric vehicle (EV) battery management systems (BMSs), as TR remains one of the most severe failure modes in lithium-ion batteries. Conventional neural network training methods may suffer from local optimum entrapment, slow convergence, and unstable performance when applied to nonlinear battery safety data. To address these limitations, this paper proposes a Hybrid Particle Whale Optimization Algorithm-optimized feedforward neural network (HPWOA-FNN) for continuous TR probability prediction and binary high-risk event classification using multivariate EV charging sensor data. The proposed HPWOA combines the rapid convergence capability of Particle Swarm Optimization (PSO) during the initial exploration phase with the exploitation and refinement capability of the Whale Optimization Algorithm (WOA) during the second phase. A global-best transfer mechanism is introduced at the PSO-WOA phase boundary to preserve the best solution identified during exploration and initialize the WOA leader, thereby improving convergence continuity and reducing premature stagnation. The model is evaluated using a 500-sample EV battery-charging dataset containing 12 electrothermal, electrical, mechanical, and environmental features. The proposed HPWOA-FNN outperforms standalone PSO-, WOA-, and Stochastic Fractal Search Algorithm (SFSA)-optimized FNN models across all regression metrics, achieving MSE = 0.000989, RMSE = 0.031442, MAE = 0.027250, R² = 0.9702, and MAPE = 3.8075%. For binary high-risk event detection, HPWOA-FNN achieves the highest AUC of 0.9817 and the lowest false-negative count, reducing missed high-risk events to 7 compared with 9 for PSO, 12 for WOA, and 17 for SFSA. Feature-importance analysis identifies maximum temperature and internal resistance as the dominant predictors, consistent with established thermal runaway mechanisms. The results demonstrate that HPWOA-FNN provides an accurate, interpretable, and computationally practical framework for EV battery thermal runaway prediction and BMS decision support. Full article

Complex three-dimensional path planning requires a planner to generate collision-free, short, and smooth paths within limited computation time, but traditional RRT-based methods often suffer from unguided sampling, repeated expansion failures in dense obstacle regions, redundant initial paths, and collision-prone post-processing. To address this problem, this study defines the planning task as efficient path generation in a bounded three-dimensional obstacle space and proposes an environment feedback hybrid sampling bidirectional RRT method integrated with a path-aware improved whale optimization algorithm. In the initial search stage, the algorithm uses the collision rate of each random tree to switch among open-space exploration, heuristic convergence, and blocked region escape sampling. Local obstacle density estimation is further introduced to fuse the sampling direction, goal direction, opposite tree attraction, and obstacle repulsion, while adaptive dual step sizes, backtracking safe step size adjustment, and local rewiring reduce invalid expansions and improve the quality of the first feasible path. In the post-processing stage, the whale optimization algorithm is used to optimize key path nodes rather than all nodes, with path corridor constraints, dynamic fitness weighting, collision repair, elastic band refinement, and B-spline smoothing to shorten the path and improve smoothness while maintaining feasibility. Tested independently 100 times in each of four MATLAB three-dimensional obstacle environments and compared with the best-performing comparison algorithm in each environment, the proposed method reduced planning time by 64.4%, 83.4%, 80.1%, and 39.5%, respectively, and shortened path length by 4.9%, 7.1%, 13.4%, and 10.1%. The success rate reached 100% in the first three environments and 97% in the most complex dense obstacle environment. These results show that the proposed framework improves search efficiency, path quality, and robustness for three-dimensional collision-free path planning under complex obstacle constraints. Full article

Small Remotely Piloted Aircraft Systems (RPAS) are increasingly being developed with non-conventional geometries to enhance their performance, often drawing inspiration from nature. Among the most promising bio-inspired concepts are the wing geometries of dragonflies and the tubercles found on the ventral fins of humpback whales. Dragonflies are notable for their independent forewing and hindwing motion, which enable exceptional flight maneuvers and even gliding, a rare feature among insects. Their wings exhibit complex aerodynamics due to their undulating structure, which contributes to stability and lift generation. Similarly, the tubercles along the leading edges of whale fins have been shown to enhance lift and improve stall characteristics, particularly during high-agility maneuvers. This paper presents a computational analysis of the aerodynamic performance of non-conventional airfoils inspired by these natural features, comparing them with a conventional small RPA (INTA and ETSIAE-UPM). The results aim to highlight the potential benefits of employing bio-inspired airfoils in improving aerodynamic efficiency and flow control in small RPAS applications. Full article

Hellenic Open University has developed Onlabs, a virtual biology laboratory for its students to be trained before they use its on-site lab. The evaluation of the user’s performance in the virtual lab with respect to a particular experimental procedure is done with a scoring algorithm specifically designed for this purpose. For the calculation of the user’s overall progress score, an Artificial Neural Network (ANN) is used. The ANN, trained with data from random plays evaluated by biology experts, achieves significant convergence. Yet, when the trained ANN is used for the real-time evaluation of the user’s performance, it produces unrealistic scores, that is, incompatible with human experience, such as unscaled score values as well as a high increase in score with the execution of secondary actions. To overcome this problem, the ANN’s weights are fine-tuned with the use of a Genetic Algorithm (GA) and two algorithms of Swarm Intelligence (SI), Whale Optimization Algorithm (WOA) and Firefly Algorithm (FA). Among those, GA achieves successful optimization of the ANN’s weights, resulting in a more realistic score mechanism. Full article

The accurate prediction of parturition in managed beluga whales (Delphinapterus leucas) is fundamental for optimizing maternal and neonatal care; however, reliable predictive indicators remain limited. Here, eight pregnancies (five live births and three adverse pregnancy outcomes) monitored over 25 years at a single facility were retrospectively analyzed. The rectal temperatures, serum progesterone concentrations, gestation lengths, food intake, behavioral changes, and fetal heart rates of the whales were evaluated, particularly prepartum. Five successful pregnancies exhibited consistent seasonal timing and reproducible endocrine and physiological trajectories. The mean gestation length was 466 ± 8.4 days. The rectal temperatures of dams that delivered live offspring decreased by 1.6 ± 0.5 °C approximately 1.3 ± 0.5 days before parturition. In successful pregnancies, serum progesterone concentrations declined prepartum but typically remained detectable until parturition. In contrast, a concentration of approximately 1 ng/mL prior to parturition was observed in the pregnancy that resulted in stillbirth. Adverse pregnancy outcomes were associated with deviations from the patterns observed in successful pregnancies, including abnormal gestation length, notably reduced progesterone concentrations, altered fetal heart rate trajectories, and ultrasonographic evidence of fetal cranial asymmetry. These findings highlight the importance of integrated multimodal monitoring in predicting parturition and identifying abnormal pregnancy progression in managed beluga whales. Full article