Search Results (10,561)

Solid waste disposal scheduling coordinates waste generation, facility capacities, fleet resources, treatment compatibility, disposal priorities, and environmental costs over multiple periods. The deterministic model considered in this study is a continuous linear program with a convex polyhedral feasible region. To provide a domain-specific population-search framework for this constraint-intensive setting, this paper proposes an Adaptive Sparrow Search Algorithm with Dynamic Penalty and Repair Operators (ADP-SSA). Its distinctive feature is a closed-loop design: population diversity, feasibility ratio, violation type, and stagnation jointly regulate sparrow roles, penalty pressure, and schedule repair. The repair stage performs compatibility correction, capacity redistribution, fleet-resource adjustment, priority-preserving backlog control, terminal clearance, and feasible local refinement. Experiments on six benchmark instances constructed from public New York City and Philadelphia municipal-waste records compare ADP-SSA with SSA, PSO, GA, GWO, and DE under a common evaluation budget. The reported aggregate results indicate lower mean feasible cost and stronger feasibility for ADP-SSA. Friedman and repeated-measures ANOVA tests computed from the six matched instance means identify a global algorithm effect, while exact pairwise Wilcoxon tests are reported with multiplicity correction. The present formulation and evidence do not establish superiority over commercial linear-programming solvers or unexecuted recent optimizers; these comparisons are identified as limitations and priorities for future work. Full article

Engine noise is a predominant source of noise in the cabin of internal combustion engine vehicles and new energy hybrid vehicles. The conventional multi-channel active noise control (ANC) system, based on the adaptive notch filtered-X least mean square algorithm, is commonly employed to mitigate such multi-tonal noise. However, the computational efficiency and convergence performance of this system may be significantly hindered by the large estimated secondary path length and the frequency-dependent convergence behavior. To overcome these limitations, this paper proposes a computationally efficient and fast-converging multi-channel ANC system by incorporating a local secondary path (LSP) equalization method. The proposed method enhances the convergence speed by equalizing the magnitude responses of estimated secondary paths and reduces the computational complexity through an improved LSP modeling approach. Accordingly, a set of low-order equalized LSP models with normalized amplitude-frequency responses is generated and employed for reference filtering. A computational complexity analysis comparing the conventional system, a recent cost-effective system, and the proposed system is presented. Numerical simulations are conducted to evaluate the convergence speed and noise attenuation performance of these three systems. Additionally, real vehicle experiments are performed using a digital signal processing controller. The results demonstrate that the proposed multi-channel ANC system achieves a superior noise reduction effect. Under accelerated conditions, the average attenuation of the second-order noise component at the four error microphones is measured at 4.4 dB(A), 6.2 dB(A), 13.4 dB(A), and 10.0 dB(A). These findings confirm the practical effectiveness of the proposed multi-channel ANC system. Full article

Background/Objectives: The primary objective was to assess reference intervals for fetal Global Longitudinal Strain (GLS) using bidimensional speckle-tracking echocardiography (2D-STE), including only prospective studies specifically designed for this purpose. An additional objective was to evaluate studies’ methodological quality and reproducibility. Methods: This is a systematic review registered at PROSPERO (CRD420251038889). Five electronic databases (Web of Science, Scopus, MEDLINE/PubMed, EMBASE and LILACS) were searched, from inception to May 2025. Prospective studies specifically designed to establish 2D-STE GLS reference intervals in low-risk singleton pregnancies with normal fetuses were included. Data were independently extracted by two reviewers. Risk of bias was assessed using an adapted tool, including study design and statistical and reporting methods. Results: After the initial identification of 187 records, nine studies published between 2012 and 2025 were included. There was marked heterogeneity among the studies. Four articles achieved high-quality scores (>70%) and three of them reported similar left ventricular (LV) GLS at 24 weeks (−22%). Right ventricular absolute GLS values were slightly lower than LV numbers. Regression models for both ventricles showed GLS absolute values decreased with gestation across studies. The 2D-STE algorithm (endocardial versus myocardial) was the main source of discrepancy between studies. Conclusions: High-quality prospective studies show a consistent pattern of biventricular GLS variation with gestational age. However, technical heterogeneity, lack of standardization, operator subjectivity and vendor-specific algorithm differences currently limit the applicability of the method. Multicentric studies with large sample sizes, standardized protocols and artificial intelligence-assisted tools are needed to consolidate this technique. Full article

Background: Emergency aeromedical services, including helicopter emergency medical service (HEMS) and medical emergency evacuation (MEDEVAC), are critical to time-sensitive care. Dispatch decisions are complex and consequential, determining whether, which, and under what conditions to deploy aeromedical resources. This study reviews modelling approaches for emergency aeromedical dispatch. Methods: Following PRISMA, studies between 2003 and 2026 (June) were screened, yielding 42 studies. Models were classified as predictive and learning-based, sequential decision, and prescriptive optimisation-based, with solution techniques, operational applications, and policy contexts analysed. Results: Markov decision process and approximate dynamic programming models dominate the sequential decision literature, particularly in military MEDEVAC. Prescriptive models support resource allocation, base location, coverage planning, and dispatch optimisation, while predictive and AI/ML-based approaches remain limited but emerging. Key challenges include computational complexity, data uncertainty, policy fragmentation, and ethical concerns. Sequential models reflect dispatch’s dynamic, stochastic nature, where current deployments constrain future resource availability. Priority-aware policies outperform closest-unit rules, but limited real-world validation hinders adoption. Conclusions: This review maps methods and provides evidence-based guidance for researchers and dispatch organisations selecting decision support models. Future opportunities include AI-assisted dispatch, hybrid predictive–prescriptive modelling, real-time adaptive algorithms, sustainability-oriented optimisation, improved helicopter landing zone identification, and standardised ethical and regulatory frameworks. Full article

Space Station Operation Mission Planning (SSOMP) requires coordinated decisions across long-term activity allocation, mid-term logistics optimization, and short-term execution scheduling and is a key component of autonomous mission operations for high-precision space missions. Existing optimization methods have achieved substantial progress at individual planning levels, but their dependence on problem-specific models, limited support for semantic review of decision rationale, and computational cost restrict their adaptability to multi-level planning scenarios. This paper proposes a Large Language Model (LLM)-assisted framework for multi-level SSOMP. The framework combines Staged Fine-Tuning (Staged-FT), Reflective Constraint–Repair Prompting (RCRP), and LLM-Guided Evolutionary Variation (LGEV). Staged-FT uses a Cognitive-Load-Theory-informed curriculum with Low-Rank Adaptation to adapt general-purpose LLMs to SSOMP domain knowledge. RCRP couples a Deterministic Rule Engine with LLM-based semantic repair to improve hard constraint satisfaction. LGEV embeds the fine-tuned LLM into NSGA-III as a fitness-aware variation operator for multi-objective activity allocation. Three case studies are conducted on literature-derived benchmark scenarios of logistics optimization, emergency re-planning, and activity allocation with logistics design, corresponding to Flight Increment Planning, Short-Term Execution Planning, and Overall Operation Planning, respectively. Results show that Staged-FT produces solutions close to traditional algorithms, RCRP achieves full hard constraint satisfaction in the emergency re-planning and logistics planning cases, and LGEV reduces the convergence generations of NSGA-III while improving Pareto-front quality. The framework provides a constraint-aware approach with explicit reasoning traces that can support expert review of AI-assisted planning for autonomous space mission operations. Full article

This study addresses a large-scale location–allocation problem in a three-echelon automotive supply chain comprising 382 suppliers, candidate distribution centers, and six assembly plants. The planning task is to redesign the inbound consolidation network while minimizing transportation and distribution center operating costs, enforcing a 480 km supplier-to-center service radius, and achieving at least 90% demand-weighted coverage. We formulate a mixed discrete-continuous model with supplier-to-center assignment, center location, throughput, and flow decisions. A feasibility-oriented hybrid algorithm uses a genetic algorithm as the main search engine, ant colony construction to seed solutions near the feasible region, adaptive mutation and simulated annealing to preserve exploration and refine elite solutions, and an online neural surrogate to avoid a subset of costly exact fitness evaluations. The design differs from a simple collection of metaheuristics: all components share one variable-length encoding, the same feasibility metrics, and periodic exact reevaluation of candidate solutions. Using the competition case data, the redesigned network reduces total cost by 27.0% relative to the six-center baseline, decreases the demand-weighted average supplier-to-center distance from 461.3 km to 53.0 km, lowers the maximum distance from 2807.22 km to 441.78 km, and raises coverage from 45.0% to 100%. Across ten independent runs, the hybrid method obtains a mean cost 10.3% below that of a standard genetic algorithm, with lower run-to-run dispersion. The results show that feasibility-aware initialization, adaptive search, and selective surrogate evaluation can support practical redesign of a strongly constrained, national-scale inbound logistics network. The directly attached reproducibility package provides the MATLAB implementation and the seven supplied input workbooks used by the reported model. The evidence is limited to one deterministic competition instance, a fixed cost schedule, and fixed-topology sensitivity calculations; generalization under demand uncertainty, facility disruption, and alternative road conditions remains to be tested. Full article

In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix Plant Simulation model with a MATLAB R2025b-based optimization environment using a data exchange interface, allowing for the iterative assessment of complex manufacturing systems. The study examines an adaptive replication strategy designed to manage stochastic variability in simulation outcomes. In the proposed method, the required number of simulation runs are determined dynamically based on confidence interval estimation. The stopping criterion is specified using a 95% confidence interval, ensuring adequate statistical accuracy while decreasing excess computational effort. The framework allows multiple performance indicators, such as throughput, congestion levels, and machine failures, which are built into an objective function. The optimization is driven by a (1, λ)-evolution strategy with Gaussian mutation and adaptive step-size control, allowing robust search in noisy objective function. However, thanks to the framework presented, it is also possible to apply other optimization algorithms. A case study of a manufacturing system was built and modeled in Tecnomatix Plant Simulation to validate the proposed methodology. In comparison with the baseline production configuration in one of the simulation runs, the suggested framework reduced the objective function by 43.36%. Benchmark experiments demonstrated that the adaptive replication strategy achieved a solution quality comparable to fixed replication schemes while requiring fewer simulation evaluations on average, thereby reducing the computational effort without compromising statistical reliability. The benchmark comparison showed that the adaptive replication strategy improved the objective value by up to 17.20% compared with fixed-replication strategies while requiring substantially less computational time than the fixed-20 and fixed-30 strategies. The robustness analysis further demonstrates that the adaptive replication strategy produces consistent optimization results across independent runs despite the stochastic nature of both the simulation model and the optimization process. From an industrial perspective, the proposed framework provides a practical decision-support tool for the optimization of manufacturing systems under uncertainty, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin technologies. Full article

To address the problems of enhanced non-stationarity, significant feature distribution shift, and insufficient cross-condition generalization capability of traditional fault diagnosis methods under variable operating conditions such as varying speed and load, a rolling bearing fault diagnosis method integrating group sparse reconstruction and a multi-strategy improved quantum particle swarm optimization-based relevance vector machine (RVM) is proposed. First, group sparse representation learning is employed to reconstruct the original vibration signals, thereby suppressing background noise and enhancing fault-related impulsive components to improve signal separability and stability. Subsequently, a modal component selection criterion combining kurtosis and correlation coefficients is introduced to optimize and reconstruct the decomposed modal components, enabling the reconstructed signals to retain more fault-sensitive information. On this basis, multiple information entropy features are extracted from the reconstructed signals to construct high-dimensional state feature vectors for comprehensively characterizing the dynamic operating states of rolling bearings. To further enhance the parameter optimization capability, Chebyshev chaotic mapping is incorporated into the quantum particle swarm optimization (QPSO) algorithm to improve the uniformity of population initialization. Meanwhile, a Cauchy mutation strategy is introduced to strengthen the global search capability and avoid premature convergence, thereby forming a multi-strategy improved QPSO algorithm. Finally, the improved optimization algorithm is utilized to adaptively optimize the key hyperparameters of the RVM, resulting in a fault diagnosis model with high accuracy, strong generalization capability, and sparse characteristics. Experimental validation on the HUST and XJTU-SY bearing datasets demonstrates that the proposed MIQPSO-RVM framework achieves diagnostic accuracies of 96.70% and 94.83%, respectively. Compared with several representative intelligent diagnosis methods and deep learning models, the proposed method exhibits superior diagnostic performance, robustness, and generalization capability under complex operating conditions. Full article

Regenerative aluminum melting serves as a core process in recycled aluminum production. In the regenerative aluminum melting process, the furnace temperature is a key variable which affects product performance and energy costs. The extreme in-furnace temperature necessitates sensors equipped with protective jackets, which increases measurement costs and severely compromises real-time monitoring capability. Accordingly, accurate furnace temperature prediction is highly valuable for regenerative aluminum melting. In regenerative aluminum melting furnaces, periodic burner nozzle commutation and frequent material charging and discharging lead to complex and time-varying operating conditions, posing considerable challenges to high-precision furnace temperature prediction. To address these issues, a condition-adaptive multi-scale convolution-enhanced Transformer (CA-MC-Transformer) model is proposed for furnace temperature prediction. Firstly, an agglomerative hierarchical clustering algorithm based on the weighted dynamic time warping (WDTW) distance is designed to perform unsupervised clustering on historical process data, thereby extracting physically interpretable prior labels for macroscopic operating conditions. Secondly, multi-scale dilated causal convolutions are utilized to capture local dynamic features at diverse temporal resolutions. A soft attention mechanism is further introduced to dynamically assign fusion weights to condition embeddings and local features, enabling condition-adaptive feature reconstruction. Finally, the fused adaptive features are fed into an encoder-only Transformer network to capture the global long-range temporal dependencies and achieve accurate furnace temperature prediction. Comparative experiments conducted on real operational datasets from an aluminum plant verify that the proposed method effectively eliminates the inherent tracking lag of conventional deep learning models, and substantially improves prediction accuracy and anti-noise robustness under complex and variable operating conditions. Full article

This paper proposes a mismatch-index-driven coordinated flexible-step terminal-free distributed model predictive control (DMPC) scheme with an adaptive prediction horizon for asynchronous multi-agent systems (MASs) subject to bounded disturbances. The proposed approach explicitly exploits the inherent symmetry of the undirected communication topology among the agents, which ensures reciprocal information exchange, balanced cooperative interactions, and facilitates the rigorous analysis of consensus under asynchrony. By extending the generalized discrete-time control Lyapunov function (g-dclf) framework to the perturbed setting, we introduce a robust g-dclf together with a robust average decrease constraint that explicitly accounts for the worst-case effect of disturbances. A coordinated self-triggering mechanism, built upon the cost prediction mismatch index and the flexible-step execution strategy, is developed to simultaneously determine the inter-execution times and the number of control steps to be applied in each iteration. In addition, an adaptive shrinking prediction horizon strategy is incorporated to further reduce the computational complexity of the local optimization control problems (OCPs) as the agents approach consensus. The resulting robust flexible-step terminal-free DMPC (RFSTDMPC) algorithm is fully distributed, handles asynchronous communication, and operates without any stability-related terminal constraint. Recursive feasibility of each local OCP and input-to-state stability (ISS) of the overall closed-loop MAS are rigorously established under the symmetric network structure. Simulation results on the consensus problem of three perturbed nonholonomic vehicles demonstrate the effectiveness of the proposed scheme in achieving practical full-state stabilization while significantly alleviating the online computational burden. Full article

The digital transformation of financial services has increased the need for intelligent information systems capable of supporting credit risk assessment in dynamic and data-intensive environments. Traditional credit scoring approaches often face limitations when evaluating customers with limited financial histories, heterogeneous data sources, and rapidly evolving behavioral patterns. In response to these challenges, this study proposes an adaptive credit risk assessment framework that integrates machine learning, an Adaptive Champion–Challenger strategy, and MLOps practices within a unified information systems architecture. The proposed framework was evaluated using real operational data obtained from a Mexican FinTech company specializing in mobile phone financing. Three machine learning algorithms—Logistic Regression, XGBoost, and TabNet—were implemented and continuously evaluated through a rolling Champion–Challenger process supported by out-of-time validation and statistically validated model promotion criteria. Experimental results indicate that different algorithms became optimal during different evaluation periods, indicating that model effectiveness varied over time as customer behavior and portfolio characteristics evolved. While XGBoost served as the initial static baseline model, TabNet and Logistic Regression achieved superior performance during several evaluation periods, illustrating the potential benefits of adaptive model selection under changing data conditions. The proposed Adaptive Champion–Challenger Framework achieved a mean AUC of 0.817, compared with 0.798 obtained by the static baseline model. Statistical validation using the DeLong test for correlated ROC curves confirmed that the observed performance improvement was significant (p = 0.0021), providing evidence that the performance gains achieved by the adaptive strategy were unlikely to be attributable to random variation. From a Digital Transformation and Information Systems perspective, the findings suggest that maintaining predictive effectiveness in dynamic FinTech environments requires not only high-performing machine learning algorithms but also governance mechanisms that support continuous model evaluation, monitoring, traceability, and adaptive model selection. The results indicate that periodic model replacement based on statistically validated out-of-time performance can help maintain predictive effectiveness under changing data conditions while supporting model governance and operational reliability. Overall, the proposed framework provides a practical and scalable approach for implementing adaptive credit risk assessment systems that support continuous model governance, data-driven decision-making, and the management of machine learning models in alternative financing environments. Full article

Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%. Full article

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

With the rapid growth of social media, online news has become increasingly multimodal, combining textual and visual information, posing new challenges for fake news detection. Existing methods often suffer from redundant features, distribution differences across modalities, and insufficient modeling of semantic interactions. To address these issues, this paper proposes an Adaptive Binary Osprey Optimization Algorithm and Cross-modal Disentangled Fusion model (ABOOA-CDF). First, an Adaptive Binary Osprey Optimization Algorithm (ABOOA) is developed for multimodal feature selection by integrating chaotic initialization, adaptive search, and binary mapping strategies to identify informative feature subsets. Then, a Cross-modal Relation Disentanglement Module (CRDM) is introduced to decompose multimodal representations into shared, discrepant, and complementary components, thereby enhancing semantic relationship modeling. Furthermore, an Adaptive Semantic Fusion Module (ASFM) dynamically learns fusion weights to generate discriminative multimodal representations. Experimental results demonstrate that ABOOA-CDF effectively improves detection performance. Compared with MFO and OOA, the proposed method achieves Accuracy improvements of 1.02 and 2.66 percentage points, respectively, verifying its effectiveness in feature optimization, cross-modal relation modeling, and semantic fusion. Full article

Central heating systems often rely on uniform regulation, making it difficult to meet the differentiated comfort demands of users and frequently leading to overheating. This paper proposes an end-to-end, data-driven framework that systematically couples density-adaptive clustering with dynamic response modeling for precision heating control. First, an adaptive DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is developed, which automatically determines its parameters via k-distance graph initialization, differential evolution optimization, and hierarchical clustering post-processing. Without requiring a pre-set cluster number, it consistently identifies four typical daily room temperature fluctuation patterns. Validated on 120-day data from a residential community in Luoyang, the first four clusters cover over 80% of users, and the clustering quality approaches that of manually optimized conventional methods. Second, multi-input ARX (Autoregressive with Exogenous Inputs) models are built for the representative user of each cluster to characterize dynamic responses to supply water temperature, flow rate, and outdoor temperature. Rolling prediction for the entire community achieves an RMSE of 0.24 °C and an R² of 0.93. Finally, a differentiated regulation strategy combining main-cluster supply temperature control and small-cluster flow compensation is designed. Simulation results demonstrate that this strategy drives the room temperatures of all clusters significantly toward the 20 °C comfort target, with a marked reduction in standard deviation. The primary contribution of this study lies in the construction of a reproducible, closed-loop pipeline—from raw room temperature data to demand-based regulation logic—offering a quantitative basis for central heating systems transitioning from passive balancing to data-driven, classified control. Full article