Search Results (400)

Background/Objectives: Oral cancer is one of the most common types of cancer, with high mortality rates if not detected early. Traditional diagnostic methods based on clinical examination rely on experience, leading to delays in early and reliable diagnosis. In recent years, medical imaging and AI-based computer-aided diagnostic systems have shown promising results in the automated identification of oral cancer. In particular, the efficient management of high-dimensional feature spaces in machine learning and deep learning approaches directly impacts classification performance. In this context, metaheuristic-based feature selection technics is a critical component because of eliminating redundant and irrelevant features. To address these challenges, this study proposes a metaheuristic-based feature selection method to reduce feature dimensionality and enhance the classification performance of oral cancer detection. Methods: This study proposes an improved Honey Badger Algorithm-based feature selection approach for the automated detection of oral cancer. In the proposed method, the distance vector used in the HBA method has been redefined to improve the balance between exploration and exploitation. Additionally, a new Cauchy mutation-based migration strategy was integrated into the proposed method to increase diversity in the search space and avoid getting stuck in local minima. The continuous-valued iHBA method was discretized with a modified sin–cos transfer function for feature selection. Oral cancer images were filtered using the CLAHE method, and after extracting deep features with the ResNet50 architecture, the proposed metaheuristic-based method was used to select discriminative features. Results: The proposed method was first tested for reliability and limitations through repeated runs on problems with different characteristics, such as unimodal and multimodal classical test functions. Then, the method was applied to extract significant features for oral cancer detection using a Histopathological Imaging Database containing 1224 histopathological oral tissue images at 100× and 400× magnification levels from 230 patients. The proposed approach was assessed in terms of accuracy, precision, recall, F1-score, and convergence curves in comparison with various classical feature selection techniques, such as wrapper-based, filter-based, and embedded-based methods, as well as other metaheuristic-based methods. The experimental results demonstrated that the suggested strategy outperformed both traditional feature selection techniques and alternative metaheuristic approaches. Conclusions: The effectiveness of the proposed method in improving diagnostic accuracy was evaluated through comprehensive experimental analyses. The obtained findings show that the proposed iHBA-based feature selection approach can reduce feature dimensionality, eliminate redundant and irrelevant features, and improve the classification performance of oral cancer detection. Therefore, the proposed method provides an effective and competitive computer-aided diagnostic framework for the automated classification of histopathological oral cancer images. Full article

Cold-chain supply systems depend on a sequence of linked production and logistics decisions. In prepared-food cold chains, quality may deteriorate not because one visible failure occurs, but because testing, traceability records, temperature monitoring, and abnormal-condition reporting are gradually weakened under cost pressure. Once such hidden effort reduction accumulates, external disturbances may push the system from strict assurance to weakened governance. To explain this nonlinear process, an asymmetric evolutionary game is built between prepared-food producers and cold-chain logistics providers, each choosing between strict and weakened quality assurance. White Gaussian noise is introduced to represent random operating shocks, and the two-population strategy system is projected onto a system-level quality-governance coordinate, $q$. This projection is used as a transparent baseline coordinate rather than as an assumption of linear system evolution. The reduced system is then transformed into a stochastic cusp catastrophe model, with a resilience indicator used to measure the distance from critical transition conditions. Numerical simulations show that quality assurance costs and short-term cost-saving benefits move the system toward a weakened-governance basin, whereas external incentives, coordination degree, and credible accountability mechanisms support recovery toward strict collaboration. The framework offers a scenario-based resilience diagnosis approach for identifying threshold effects in cold-chain quality governance. Digital traceability, temperature-data sharing, incentive alignment, and accountability rules are further interpreted as operational innovations that improve resilience and reduce avoidable quality losses in sustainable cold-chain operations. Full article

Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and avoiding local optima. To deal with this issue, this paper proposes a new method called the Blackcap Optimization Algorithm (BCOA), which is inspired by the navigation and migration behavior of Blackcap birds. Instead of using complicated distance calculations, the proposed method is based on angular movement vectors. The movement of each search agent is controlled by an angle-based mathematical model that combines the global best angle, a successful neighboring angle, and an adaptive exponential disturbance factor. In addition, the algorithm uses a quasi-genetic path transition mechanism to combine successful parent paths together, along with a territorial competition stage. This structure helps reduce computational cost and improves the balance between exploration and exploitation. The performance of the proposed algorithm is tested on 32 benchmark functions and seven engineering and network optimization problems. The simulation results show that BCOA has a good ability to avoid local optima and can achieve acceptable convergence speed and cost reduction compared to several existing methods. Full article

In industrial applications such as in situ leaching and uranium mining, permanent magnet synchronous motors (PMSMs) for submersible pumps are frequently connected to frequency converters via long cables. During this long-distance transmission, traveling wave reflections induced by high-frequency pulse width modulation (PWM) generate severe transient overvoltages that threaten motor insulation. Because installation space at deep-well motor terminals is severely restricted, overvoltage suppression must be implemented at the inverter output. Here, the parameter design and optimization of a passive LC filter specifically developed for 250 Hz high-frequency PMSMs are presented. The optimal inductance and capacitance parameters were determined by balancing multiple operational constraints, including fundamental voltage drop, high-frequency harmonic attenuation, and the avoidance of low-order harmonic resonance. Furthermore, the anti-saturation performance of the magnetic core material, evaluated thermal characteristics through electromagnetic-thermal co-simulation, and analyzed the risk of self-excited oscillation between the filter capacitors and the motor was analyzed. Finally, hardware experiments conducted on a 20 m cable test bench validate that the designed LC filter effectively mitigates terminal overvoltage. The peak terminal voltage was reduced from 900 V to 505 V, and total harmonic distortion (THD) was limited to below 5%. This design provides a highly reliable, space-efficient solution for overvoltage suppression in high-speed, long-cable motor drive systems. Full article

This study evaluated the effects of locally sourced ingredient dietary, with or without supplementation of black soldier fly (Hermetia illucens; BSF) larvae, on laying hen welfare. A total of 150 Lohman White hens aged 30 weeks were assigned to three treatments over ten weeks: a standard corn–soybean diet (CONTROL), an alternative diet incorporating triticale, faba beans and rapeseed meal (ALTER), and the ALTER diet supplemented with 5% dried BSF larvae provided separately (ALTER + BSF). Welfare assessments included larvae consumption time, a novel object test, an avoidance distance test, body condition scoring, and ethological observation of natural behaviors. Hens fed ALTER diet initially showed increased incidence of comb pecking wounds, which declined over the trial, along with reduced morning grooming compared to the CONTROL group (p = 0.009). However, the ALTER diet significantly improved plumage cleanliness (p < 0.001). Supplementation with BSF larvae partially mitigated early stress responses, maintained plumage cleanliness, and improved exploratory behavior and habituation to novelty (p < 0.001). Hens showed sustained and increased motivation to consume BSF larvae with an average consumption time of 5.5 min. Additionally, BSF supplementation was associated with increased resting and the emergence of dustbathing behavior during the afternoon (p < 0.05). No aggressive behaviors were observed, and no significant dietary effects were found for human fearfulness throughout the trial. In conclusion, dried BSF larvae can serve as effective environmental enrichment, improving hens’ adaptability to locally sourced diets in rural farming systems. Full article

Harmful algal blooms (HABs) are increasing in frequency in the Chesapeake Bay, posing risks to marine ecosystems, water quality, and public health. Chlorophyll-a (Chl-a) is a widely used indicator of algal biomass, and satellite observations such as Sentinel-3 Ocean and Land Color Instrument (OLCI) enable large-scale monitoring of bloom dynamics. However, cloud cover and atmospheric interference frequently introduce missing pixels in daily satellite products, reducing temporal continuity and limiting monitoring reliability. Satellite-derived chlorophyll-a (Chl-a) data exhibit substantial missingness, with daily pixel gaps ranging from approximately 52.30% to 100% (mean ≈ 88.95%). This study evaluates spatial interpolation, EOF-based, supervised machine-learning, deep-learning, and convolutional autoencoder approaches for reconstructing missing Chl-a values. Sentinel-3 OLCI Chl-a data from 2023–2024 were used for model training, while data from 2025 served as a temporally independent test set to avoid spatiotemporal leakage. To simulate cloud-induced data gaps, artificial missingness scenarios ranging from 50% to 90% were applied for the Inverse Distance Weighting (IDW) and Data Interpolating Empirical Orthogonal Functions (DINEOF) baseline approaches, while machine-learning, deep-learning, and convolutional autoencoder models were evaluated using real satellite-derived missing observations. The evaluated models include IDW, DINEOF, K-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), XGBoost, a Long Short-Term Memory (LSTM) network, and a Temporal Data Interpolating Convolutional Autoencoder (Temporal DINCAE). Model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), prediction bias, and the coefficient of determination (R²). Results indicate that tree-based ensemble models outperform spatial interpolation and EOF-based methods, with XGBoost achieving the best overall performance (R² ≈ 0.86; RMSE ≈ 9.61 mg m⁻³). The LSTM model achieved lower prediction errors (RMSE ≈ 5.87 mg m⁻³; MAE ≈ 2.16 mg m⁻³), highlighting the benefit of incorporating temporal dependencies, although with slightly reduced variance capture. The convolutional autoencoder-based Temporal DINCAE model achieved strong reconstruction performance (R² ≈ 0.84; RMSE ≈ 11.15 mg m⁻³). Uncertainty quantification shows that Extra Trees tends to underestimate uncertainty with narrower prediction intervals, whereas XGBoost provides better-calibrated but wider intervals. Full article

With the expansion of transmission networks and the increasing penetration of inverter-based resources (IBRs), fixed offline distance-protection settings face increasing difficulty in balancing selectivity, sensitivity, and operating speed. This problem is particularly evident in Zone III remote-backup protection, where conservative load-avoidance settings may create blind zones. This paper proposes an adaptive three-zone distance-protection setting method based on explicit sensitivity constraints and a disturbance-domain model. The method has two main features. First, online recalculation is restricted to the local disturbance domain affected by topology changes, thereby avoiding network-wide recomputation. Second, Zone II and Zone III settings are determined by a constrained model that incorporates real-time branch coefficients, load impedance, sensitivity requirements, and downstream coordination limits. A fallback mechanism is also included to maintain security under data loss or abnormal measurements. In a 220 kV case study, the proposed method increases the Zone II sensitivity coefficient from 1.92 to 1.95 and the Zone III remote-backup sensitivity coefficient from 0.83 to 1.35. Additional tests under high-resistance faults, measurement errors, volatile load, and inverter-based resource integration show that the method preserves selectivity while reducing backup protection blind zones. The disturbance-domain strategy also reduces the average recalculation time from 820 ms to 18 ms in the tested regional setting-calculation scenario. Full article

The reinforcement and retrofit of damaged steel buildings has emerged as a primary focus in civil engineering. It should be noted that completing the reasonable strengthening design for avoiding the sudden collapse of a structure in extreme engineering conditions was an urgent task, while the existing method required a long time which significantly influenced the reinforcing practice. In the present study, an improved explainable machine learning (ML) framework was developed for the rapid assessment of the bending property of repaired laminated channel beams. Firstly, a comprehensive database of 192 samples combining experimental and finite element data was established. The Mahalanobis distance analysis and Pearson correlation analysis were sequentially performed to evaluate the singularity of the samples and the dependencies between the variables. Secondly, the adversarial tests were conducted on the randomly selected 10 pairs of training and testing sets to determine the database with the best distribution consistency. Then, three machine-learning models of artificial neural networks (ANN), random forest (RF), and extreme gradient boosting tree (XGBoost) were respectively trained and validated. Finally, the explainability analysis of the XGBoost model was carried out in the global and local perspectives based on the SHAP method. The prediction accuracy (R²) of all ML models exceeded 90%, demonstrating good accuracy and providing a useful reference within the current database for the reinforcement design of damaged steel beams in emergency situations. In addition, the XGBoost model achieved superior prediction accuracy (R² = 97.98%) and stability (CoV = 0.82%) compared to ANN and RF. The explainability analysis revealed that boundary conditions and load type had the most significant influence on bending capacity. The proposed ML approach enabled efficient and reliable bending capacity estimation, supporting rapid decision-making in emergency reinforcement scenarios for damaged steel structures. Full article

Autonomous ground vehicle navigation in dynamic real-world environments demands path planning systems that simultaneously accommodate real-time environmental hazards and diverse user-defined objectives requirements that classical algorithms, with their static, single-objective cost functions, cannot fulfil. This paper presents the Semantic Personalised Path Planning (SPPP) system, centred on a novel Semantic Personalised Cost (SPC) algorithm that augments the A* search framework with a dynamically computed personalised cost term. The SPC function integrates eight real-time semantic obstacle categories including traffic congestion, weather severity, road surface conditions, and construction activity with eight user-defined preference dimensions spanning safety, travel time, emergency response, comfort, and battery efficiency. An adaptive scaling mechanism amplifies obstacle penalties near the goal, and a gradient-based weight evolution rule refines preference weights iteratively over successive route segments. The user-defined preference activation directly personalises the routing objective to individual operational needs, with the gradient-based evolution further refining preference alignment over successive route segments. Experiments were conducted in two phases: 500 randomised obstacle configurations on a controlled $8\times 8$ grid, and a real 847-node road graph extracted from OpenStreetMap around SRM Institute of Science and Technology, Kattankulathur, representing a single 1.4 km urban corridor, with obstacle scores derived from live Mapbox Traffic and OpenWeatherMap application programming interface data. Under the full emergency preference scenario, SPPP achieves 94.3% obstacle avoidance versus 31.7% for the Euclidean distance threshold A* baseline, a difference statistically significant at $p < 0.001$ under the Wilcoxon signed-rank test with Cohen’s d ≈ 18.9. Real-world computation time of 1.91 ms on a standard laptop and 3.76 ms on a Raspberry Pi 4 confirms deployability on embedded autonomous vehicle hardware. Full article

Ensuring the safety of sustainably managed medicinal plants is closely linked to the quality of plant raw materials, including the presence of heavy metals within safe limits. Sustainable management in the context of herbal raw materials therefore entails responsible management of herbal plant resources, integrating environmental protection with ensuring long-term economic profitability. The aim of this study was to analyze selected biochemical parameters and to determine metal concentrations in soils and leaves of Plantago lanceolata L. collected from natural habitats at increasing distances from traffic routes. The content of Zn, Cu, Ni, and Pb was determined in the soils and leaves of Plantago lanceolata L. Assessing the content of these elements in plant raw materials allows for: the prevention of harmful substances in final products, adaptation of raw materials to applicable safety standards (avoiding toxicity), and protection of consumer health. This promotes sustainable development by building a safe supply chain. The leaves of Plantago lanceolata L. were also tested for biochemical enzymatic (catalase (CAT) and superoxide dismutase (SOD)) and non-enzymatic (chlorophyll a and b (Chl a and b), carotenoids (Car), ascorbic acid (AAC)), and mechanisms regulating the activity of reactive oxygen species (ROS) were determined in the leaves of Plantago lanceolata L. Based on the results of leaf pH, relative water content (RWC), ascorbic acid content, and total chlorophyll content, the air pollution tolerance index (APTI) was calculated. The distance from the road has a significant impact on the concentration of the heavy metals analyzed. The soils were found to be free of Zn, Cu, Pb, and Ni contamination. However, analysis of Plantago lanceolata L. leaves revealed exceedances of acceptable lead limits for herbal plants. The content of pigments, the ratio of Chl a/b, and Chl (a + b)/Car in the leaves of Plantago lanceolata L. was significantly dependent on the distance from the road. The activity of CAT and SOD in the leaves of Plantago lanceolata L. growing closest to the road was significantly higher compared to the others. APTI values suggest that Plantago lanceolata L. exhibits sensitivity to pollution, independent of its distance from the emission source. Full article

The ability to continuously learn over time by incorporating new information while holding onto previously acquired expertise is known as incremental learning (IL). Although this concept is fundamental to human learning, existing machine learning techniques have a significant propensity to forget prior experience by overwriting previously learned patterns from classes. The continuous learning of new information in K-nearest neighbor (KNN) with lazy learning strategies compounds to loss of old knowledge upon learning new information and stability-plasticity dilemma. The change in new data points and data distributions in unforeseen ways impacts KNN’s ability to adapt to changes in class label distribution, leading to concept drift. This experiment models a hybrid 3WDKNN-based incremental learning algorithm (ILA) designed for application in a heterogeneous and dynamically changing environment. This model addresses the limitations of KNN by overcoming computational costs and inefficiencies associated with loss of information in classes, while facilitating incremental learning to attain high predictive accuracy in crop yield datasets. The algorithm employs weighted voting to identify optimal assigned classes for the nearest neighbor and uses memory reconstruction strategy for class incremental learning until the memory is full without forgetting. Using weighted voting for the best assigned classes for the nearest neighbor, the algorithm uses a local mean vector to determine the best distances for the shortest-term incremental learning to achieve the highest performance accuracy in a concept drift environment. The hybrid 3WDKNN_ILA was developed and evaluated alongside advanced algorithms within the same dataset context. The model improves performance in incremental learning contexts by utilizing current concepts and minimizing errors on both current and recent data to avoid parameterization. The model achieves optimal efficient incremental learning by mitigating intentional loss and minimizing errors associated with valuable class information derived from aggregated mean values through class rectification and transfer. The hybrid model achieves the best efficient performance accuracy in all the tested weighted averages of 200W, 500W, and 1000W with tested set K values of 5, 9, and 13K. This hybrid model demonstrates performance accuracy of 97% at a value of 13K, whereas 3WD_KNN achieves 96% at 9K, HoKNN attains 89% at 13K, and 1IKNN reaches 88% at 9K accuracy, respectively. The integrated novelty in the hybrid 3WDKNN_ILA proves superior in terms of computational efficiency, accuracy, and high-level incremental performance and learning in comparison with other tested models of algorithms. Full article

This paper presents research on a complete, closed-loop environmental perception strategy at the system level and on standardized sea trial verification methods for unmanned surface vehicles (USVs) in typical maritime mission scenarios. Existing research on USV perception mostly focuses on optimizing discrete functional algorithms, such as object detection and tracking, with verification performed only in specific scenarios. These works generally lack task-matched perception schemes covering the full operation cycle and corresponding standardized, reproducible verification systems, making them difficult to adapt to the full-cycle execution requirements of real, complex maritime tasks. To address the above issues, this paper proposes a systematic environmental perception strategy for typical USV operation tasks, establishes a complete workflow from object detection to obstacle avoidance perception and decision-making, and designs two sets of standardized sea trial schemes for core tasks. These schemes provide unified specifications and an evaluation benchmark for verifying the real-world performance of USV environmental perception systems. Finally, full-cycle sea trials on a real ship are completed, and the test results verify the engineering practicability of the proposed perception strategy, as well as the reproducibility, standardization, and extensibility of the established test system, with the laser positioning hit rate improved by 82% and 54% under green-water and foggy conditions compared with the conventional miss distance-based method. Full article

As global energy demand continues to grow, the inherent safety requirements for natural gas long-distance pipelines are becoming increasingly stringent. Therefore, accurately analyzing the trends in pipeline defects using multi-round internal inspection data is of great significance for enhancing pipeline inherent safety levels and reducing the risk of pipeline medium leakage. However, existing pipeline in-line inspection data alignment methods for long-distance multi-round pipeline data alignment suffer from cumbersome alignment procedures and low computational efficiency. This paper proposes an adaptive threshold dynamic time warping defect alignment method (Adaptive Dynamic Threshold-Dynamic Time Warping, ADTH-DTW) for rapidly matching multi-round in-line inspection data. A new multi-round in-line inspection data alignment framework based on valve-weld-defect is established. By integrating the DTW algorithm into each alignment stage, unnecessary manual effort is avoided, significantly improving data alignment efficiency. First, the ADTH method is used to clean redundant weld seam data in the in-line inspection data. By dynamically generating expected values and combining an intelligent point selection strategy, the method accurately identifies and removes interfering data. Additionally, valve chamber data is used to correct the overall mileage, providing a data foundation for subsequent defect alignment. Second, the dynamic time warping algorithm is used to align weld seam data and establish a data mapping table. Finally, relative displacement methods are employed to achieve defect matching. The validation results from three rounds of in-vehicle inspection data tested on-site indicate that the ADTH-DTW algorithm achieves an average 23.08% improvement in alignment accuracy compared to methods such as DTW, KL divergence, JS divergence, and linear interpolation, with computational efficiency nearly tripled. This effectively addresses the issue of incompatible computational efficiency and accuracy in existing data alignment algorithms, thereby enhancing the intrinsic safety level of natural gas long-distance pipelines. Full article

Fishways play a critical role in restoring river connectivity and conserving fishery resources, yet their efficiency is often limited by mismatches between hydraulic conditions and species-specific behavioral traits. To quantify the upstream migration behavior of fish under the combined influence of flow heterogeneity and schooling effects, this study examined the endangered species L. elongata in the Yangtze River Basin. Volitional swimming behavior was tested in an open-channel flume under three spatially heterogeneous flow regimes (I: Low–Moderate–High; II: High–Moderate–Low; III: Moderate–High–Low). A video monitoring system recorded the upstream movement of solitary fish and three-individual schools. Swimming trajectories, upstream migration time, preferred flow velocities, and schooling metrics—including nearest neighbor distance (NND) and mean pairwise distance (MPD)—were analyzed. Linear mixed-effects models were employed to account for repeated measures and individual variability. Results showed that schooling behavior significantly enhanced upstream migration efficiency: schooling fish arrived at the target area on average 8.93 s earlier than solitary individuals (p < 0.01), while flow condition alone had no detectable effect on arrival time. L. elongata consistently preferred low-velocity zones (0.20–0.50 m/s) and avoided high-velocity regions (0.75–1.25 m/s), with meandering upstream trajectories predominating. NND showed no significant differences across flow conditions (p > 0.05), indicating stable schooling cohesion. However, MPD increased significantly under Flow III compared to Flows I and II (p < 0.01), suggesting that higher flow heterogeneity leads to more dispersed group spacing while overall cohesion is maintained. Distinct movement strategies were observed: solitary fish predominantly utilized boundary regions as hydraulic refuges (wall-following: 63.8–80.5%), whereas schools exhibited greater spatial exploration and reduced wall-following. These findings demonstrate that schooling enhances migration efficiency while preserving a cohesive group structure and that flow heterogeneity influences within-group spatial organization. To optimize fishway performance for L. elongata, we recommend maintaining flow velocities within 0.20–0.50 m/s. This study provides scientific guidance for hydraulic regulation in fishway design and habitat restoration, emphasizing the combined effects of flow heterogeneity and schooling behavior on migration performance. Full article

Selecting the best multi-objective algorithms for photovoltaic energy storage system (PV-ESS) design remains challenging due to limited benchmarking across renewable energy studies. This study addresses this gap through a systematic evaluation of four widely used multi-objective optimization algorithms: NSGA-II, Multi-Objective Particle Swarm Optimization (MOPSO), weighted-sum scalarization, and ε-constraint methods. Performance assessment utilized three Pareto front quality metrics: Inverted Generational Distance (IGD) for convergence quality, hypervolume (HV) for objective-space coverage, and spacing for solution distribution uniformity. The algorithms were tested on PV-ESS design problems in three developing economies (Nigeria, South Africa, India) under identical problem formulations and computational resources. NSGA-II achieved superior performance across all metrics in all three case studies. For convergence quality, NSGA-II attained a mean IGD of 0.0083, outperforming MOPSO by 29%, ε-constraint by 64%, and weighted-sum by 131%. For objective-space coverage, NSGA-II achieved a mean HV of 0. 700, representing 10–16% better coverage than other methods. For solution distribution, NSGA-II showed a mean spacing of 0.076, indicating 30–117% more uniform Pareto fronts. Computational efficiency analysis revealed that NSGA-II’s runtime is between 5.5 and 7.8 h per case, providing better quality–time ratios compared to ε-constraint methods (which are 18 times slower), while avoiding MOPSO’s premature convergence. Statistical validation confirmed NSGA-II’s superiority, with p < 0.01 across all quality metrics. These results establish NSGA-II as the best algorithm for lifecycle-aware PV-ESS optimization, offering quantitative, evidence-based guidance for practitioners selecting optimization tools for renewable energy system design. The demonstrated performance leads to $ 45,000–$ 60,000 lifecycle cost savings per MW/MWh of system capacity through improved Pareto front identification. Full article