Search Results (51,079)

Inter-hub feeder service is a promising application for electric vertical takeoff and landing aircraft (eVTOL), especially for passengers connecting to subsequent flights under tight time constraints. We develop an optimization framework that accounts for heterogeneous passenger behavior. Using stated-preference survey data, we incorporate delay-risk perception under remaining connection time constraints, identify heterogeneous preference classes, and formulate a bilevel optimization model. The upper level selects eVTOL schedules under given resource and fare configurations. The lower level captures the stochastic user equilibrium of heterogeneous passengers choosing among eVTOL and external transport alternatives. To solve the resulting mixed-integer nonlinear bilevel problem, we propose a Neural Bilevel Optimization and generalized Benders decomposition (Neur2BiLO-GBD) hybrid algorithm. Numerical experiments on the Shanghai Hongqiao–Pudong corridor show that the baseline profit-maximizing plan also generates positive social net utility for the feeder system. Fleet size, charging infrastructure, and fare affect operator profit and social net utility differently, so their high-value regions do not fully coincide. When external transport has larger potential delays and remaining connection time is short, eVTOL is more likely to achieve both high operator profit and high social net utility. Full article

In-transit vehicle malfunctions during material transportation in water conservancy construction can cause delivery delays, disrupt subsequent tasks, and lead to duplicate material requisitions. To address these issues, this study develops a material transportation scheduling optimization model that explicitly incorporates vehicle-malfunction response. The model minimizes total transportation cost subject to vehicle capacity, resource compatibility, time-window, and non-duplicate-requisition constraints. Two response strategies are considered: continued transportation after on-site repair and relay transportation using an external temporary emergency vehicle. The failed task and its affected subsequent tasks are dynamically rescheduled. An adaptive hill-climbing genetic algorithm (AHCGA) is employed to coordinate transportation-batch generation, task sequencing, and resource assignment. The case study shows that, under the no-malfunction baseline scenario, all transportation batches complete unloading within their acceptable time windows. Across 30 runs, AHCGA achieves a mean total cost of CNY 9179.86; Wilcoxon signed-rank tests indicate statistically significant cost differences between AHCGA and both GA and HCGA (p < 0.001). The best AHCGA run yields a total transportation cost of CNY 8794.40 with zero total delay. Multi-scenario comparisons and sensitivity analyses further show that neither on-site repair nor external relay is universally dominant. Their relative suitability depends jointly on failed-task characteristics, on-site response and repair time, emergency-vehicle response time, transshipment efficiency, and call-out cost. The proposed model provides quantitative decision support for material transportation organization, vehicle-malfunction response, and post-malfunction rescheduling in water conservancy construction. Full article

The goal of this project is to identify ways to improve the performance of the bushing–flange–circlip connection on the V-arm suspension components of heavy-duty commercial vehicles. Failures related to circlip ejection and flange loosening identified from customer relationship management (CRM) data motivated the development of this computational optimization framework. This study involved generating a parametric dataset, using Latin Hypercube Sampling (LHS) with synthetic data, for the development of the artificial neural networks (ANNs). The networks use the surrogate model to optimize solutions through a combination of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The ANN provided an overall test set of R² = 0.968 across the three objective functions. The hybrid optimization method produced 11 surrogate-predicted Pareto-optimal candidate designs, simultaneously minimizing the micro-displacement and stiffness loss while maximizing the fatigue life. The present results are based on analytically derived synthetic training data. Simcenter 3D Version 2506 Finite Element Analysis (FEA) integration and prototype validation constitute the planned next phase. The methods described here can be configured for other components of the suspension and are expected to be compatible with similar applications depending on future enhancements. Full article

Underwater glider (UG) formations exhibit significant trajectory deviations when subjected to non-uniform ocean currents, complicating spatial reconfiguration upon exiting complex marine environments and compromising environmental monitoring fidelity. To solve these problems, we develop a rapid formation reconfiguration algorithm based on distributed model predictive control (DMPC). The algorithm combines a flexible boundary-triggering mechanism for autonomous control suspension and rapid activation, a nonlinear vector heading pre-compensation scheme to counteract strong current disturbances, and a multi-objective leader capability assessment with a dynamic rotation strategy for optimized leader selection. The predictive plant model is calibrated utilizing empirical sea trial datasets, and its control performance is validated through comparative simulations against conventional MPC and active disturbance rejection control (ADRC). The results demonstrate that the proposed algorithm reduces the standard deviation of lateral inter-glider distance by 42% and 17% relative to ADRC and conventional MPC, shortens the reconfiguration time to 13.3 h, and lowers the maximum relative deflection, lateral distance standard deviation and average energy consumption. This framework significantly improves formation stability, reconfiguration efficiency, and energy efficiency, providing a robust methodology for persistent swarm deployments. Full article

Background: Major spine surgery carries an inherent risk of hemodynamic instability due to prone positioning, significant blood loss, and the strict necessity to maintain adequate spinal cord perfusion. Hemodynamic management mainly relies on a reactive approach, treating hypotension only after it occurs, which increases the risk of postoperative complications such as acute kidney injury and ischemic events. This narrative review evaluates the clinical impact, current evidence, and future perspectives of integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into perioperative hemodynamic care. Methods: A comprehensive literature search was conducted through PubMed, EMBASE, and the Cochrane Library, spanning from inception to January 2026. The search strategy employed combinations of Medical Subject Headings terms and keywords related to “Artificial Intelligence,” “Machine Learning,” “Hypotension Prediction Index,” “hemodynamic monitoring,” and “major spine surgery.” Studies were selected based on their relevance to predictive hemodynamic algorithms, goal-directed fluid therapy (GDFT), and automated closed-loop systems within the perioperative setting of complex spinal interventions. Results: Five studies show that AI/ML tools can improve hemodynamic management in spine surgery: an hypotension prediction index (HPI)-guided algorithm reduced intraoperative hypotension during prone spinal fusion; a machine learning model accurately predicted massive blood loss in metastatic spinal disease; an AutoML framework linked intraoperative hypertension to worse neurological recovery after spinal cord injury (SCI); a case report showed HPI-guided goal-directed therapy enabled safe, transfusion-free major spine surgery; and topological network analysis identified a narrow optimal mean arterial pressure (MAP) range for neurological recovery after SCI. Collectively, these preliminary findings suggest a potential role for AI/ML in reducing hemodynamic instability and enabling more individualized perioperative management in spine surgery. Rather than converging on a single verdict, these five studies fall into three distinct evidentiary categories when appraised using a structured model-validation (V1–V4) and clinical-translation (T0–T4) framework applied within each category: a real-time monitoring technology (HPI) with a substantial extra-spinal evidence base but a limited spine-specific replication record; a single, externally validated but clinically unproven preoperative prediction model; and two retrospective, hypothesis-generating discovery frameworks that remain exploratory irrespective of surgical domain. Conclusions: The evidence identified does not support a single, unified statement about “AI/ML in spine surgery.” Instead, it points to three distinct situations that warrant separate research priorities: consolidating spine-specific replication of an otherwise mature monitoring technology (HPI); externally confirming the clinical utility, rather than only the discriminative accuracy, of a single preoperative prediction model; and prospectively testing the retrospectively derived targets generated by discovery-oriented analytic frameworks. Considered together, these findings should inform hypothesis-driven research design rather than a single implementation-readiness judgment. Full article

Surface plasmon resonance (SPR) sensors translate refractive-index (RI) changes near an interface into measurable angular shifts, but a large shift is useful only when the resonance remains sufficiently narrow and deep. Conventional single-metal SPR structures typically force a trade-off in which sensitivity gains come at the cost of broader resonances or shallower reflectance dips. Here, a BAK1/Cu/Al/BaTiO₃/FASnI₃/BP multilayer SPR refractive-index sensor is proposed and optimized using the transfer matrix method (TMM) coupled with a genetic algorithm (GA). The Cu/Al bimetallic region provides a plasmonic metal core, BaTiO₃ and FASnI₃ progressively enhance the evanescent field, and black phosphorus (BP) forms the analyte-facing sensing interface. To avoid sensitivity-only optimization, the GA uses a composite sensitivity figure (CSF) that integrates angular sensitivity, resonance dip depth, and full width at half maximum as the fitness function. At an analyte refractive index (RI) of 1.355, the sensor reaches a maximum sensitivity of 510.11°/RIU and a CSF of 76.39 RIU⁻¹. These results establish the GA-CSF framework as a generalizable route to the balanced design of multilayer SPR refractive-index sensors and provide a computationally guided starting point for experimental implementation. Full article

In smart manufacturing, efficient job shop scheduling (JSS) and resource management remain critical challenges, especially in dynamic production environments. Traditional methods often struggle to adapt to real-time changes and unexpected events. To address these limitations, this paper proposes a novel Generative Adversarial Network (GAN)-based generative AI framework that augments scheduling data with realistic synthetic scenarios and integrates Local Outlier Factor (LOF)-enhanced Q-learning-based reinforcement learning (QRL) for adaptive JSS optimization in Industry 5.0 environments. The GAN is trained to generate realistic synthetic scheduling scenarios, which are combined with real-world data from a state-of-the-art Festo Didactics Cyber Physical Lab to augment the diversity and coverage of training samples. The LOF algorithm enables real-time bottleneck detection, while the QRL agent learns robust scheduling policies that minimize makespan and prioritize bottleneck mitigation. Experimental results demonstrate that the proposed GAN-LOF-QRL approach achieves an average makespan reduction of 70.8% across varying production volumes (12, 15, and 18 orders), significantly improving scheduling efficiency and resource utilization compared to traditional RL and heuristic methods. This research advances smart manufacturing initiatives and Industry 5.0 goals by providing a scalable, adaptive scheduling solution that leverages generative AI to address the complexities of modern supply networks. Full article

Accurate vibration prediction is critical for condition monitoring and predictive maintenance of hydropower units, yet it remains challenging due to strong noise interference, severe non-stationarity, and multi-scale coupling characteristics of raw vibration signals. This paper proposes a novel hybrid prediction framework that integrates cooperative denoising, multi-scale signal decomposition, and deep learning-based sequential modeling to achieve high-precision long-term vibration forecasting. First, a two-stage cooperative denoising stategy combining wavelet threshold denoising (WTD) and singular spectrum analysis (SSA) is designed to suppress high-frequency noise while effectively preserving the global trend and critical transient features. Then the denoised signal is decomposed into a set of physically interpretable intrinsic mode functions (IMFs) and a residual component via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), which alleviates mode mixing and improves decomposition completeness. Subsequently, each IMF component is independently predicted using a gated recurrent unit (GRU) network optimized by the Adam algorithm with adaptive learning rate decay, enabling efficient capture of nonlinear temporal dependencies. The proposed framework is validated using 3.5-year real-world vibration data from an lower guide bearing of a pumped-storage hydropower unit. Experimental results demonstrate that the model achieves MAE = 0.3831, RMSE = 0.6964, MAPE = 0.2832%, and $R^2=0.9911$, compared to 0.6666 for the conventional CEEMDAN-GRU model, a 32.45 percentage point increase and a 54% reduction in unexplained variance. Ablation studies and comparative analyses verify the superiority and statistical significance of the cooperative denoising mechanism and the overall hybrid architecture. This work provides a reliable, interpretable, and deployable tool for the condition monitoring and predictive maintenance of hydropower units, supporting proactive operation and reducing unplanned downtime in clean energy systems. Full article

Autonomous driving systems often exhibit trajectory tracking behavior that differs markedly from human drivers, a phenomenon intuitively described as “mechanicalness.” This study moves beyond the traditional focus on tracking accuracy to systematically analyze these behavioral differences through a real-vehicle comparative experiment. Using a steer-by-wire vehicle equipped with the open-source Autoware platform, trajectory data were collected on a closed campus road under straight and curved conditions. A five-dimensional evaluation framework is established to quantify control continuity, prediction horizon, error response mode, style adaptability, and interaction friendliness. Results show that Autoware exhibits a “high-precision, low-smoothness, zero-tolerance” mechanical style, characterized by high-frequency micro-corrections, reactive curve entry, rigid speed tracking, and segmented braking. Human drivers, in contrast, employ an organic mode with discrete corrections, elastic path tolerance, and anticipatory coordination. The technical origins of mechanicalness are identified as four algorithmic paradigms: geometric tracking, decoupled control, error-driven logic, and limited prediction horizon. The findings further reveal a systematic safety–comfort trade-off inherent to mechanical control, and suggest optimization directions including adaptive dead-zone mechanisms, extended prediction horizons, and lateral–longitudinal coordination. These insights provide theoretical and engineering foundations for developing more human-like autonomous driving control strategies. Full article

This work addresses the post-combustion CO₂ capture demand, employing a single-atom solvent as the absorbent to conduct systematic research on process modeling, energy consumption analysis, and multi-objective optimization. The energy consumption of the single-atom solvent-enhanced CO₂ capture process was reduced to 2.864 GJ/t, representing a 26.9% reduction compared with that of the conventional solution. The effects of solvent flow rate, gas flow rate, rich solvent temperature, reflux ratio, and extraction ratio on the energy consumption, annual total utility consumption, CO₂ equivalent emissions, and total annual cost were systematically investigated. The results indicate that rich solvent temperature and reflux ratio are the most sensitive parameters affecting system energy consumption variations; increasing solvent flow rate linearly elevates the reboiler duty, whereas gas flow rate variations exert negligible influence on system performance. The energy consumption was further reduced to 1.84 GJ/t CO₂ after process parameter optimization. A multi-objective optimization approach coupling the NSGA-II with Aspen Plus process simulation was developed for economic–energy–environmental optimization. Annual total utility consumption was reduced by 12.75%, CO₂ equivalent emissions per unit of product were reduced by 47.87%, and total annual cost was reduced by 13.09% after optimization. The optimal operating conditions under multi-objective optimization were determined simultaneously. This study provides an optimization strategy for the industrial application of CO₂ capture technology. Full article

To address the disruption caused by vessel delays to the port’s initial operation plan, this study investigates the integrated rescheduling optimization of vessel sequencing and berth allocation under vessel delay impacts. Focusing on a port with a one-way channel, a rolling decision-making mechanism is designed to identify rescheduling points. The model accounts for the priority differences among various vessel types and integrates combined strategies such as berth reallocation, Normal Berth Shifting (NBS), Cross-Terminal Berth Shifting (CTBS), and Cargo Discharge-Only (CDO). To minimize total rescheduling costs, an integer linear programming model is developed, and an adaptive large neighborhood search (ALNS) algorithm is designed to solve it. The model outputs include the optimized vessel sequencing and berth allocation within a fixed planning horizon, as well as the optimal combined strategies for suitable vessels. Multiple case studies verify the effectiveness of the proposed framework and the superiority of the algorithm. Furthermore, a sensitivity analysis is conducted based on the number and duration of delayed vessels, as well as the proportion of international feeder vessels among the delayed ones. The results provide valuable decision support for ports in mitigating the impacts of vessel delays. Full article

Anaerobic digestion of agro-industrial residues supports waste valorization and renewable-energy production, but reliable prediction requires validation that accounts for repeated measurements within reactors. This study compared 16 regression models for predicting cumulative methane production from digestion time and banana peel–sugarcane molasses composition using 5007 observations from seven batch reactors. Models were evaluated by leave-one-reactor-out cross-validation (LORO-CV). Radial-basis-function support vector regression (SVR-RBF; C = 10, gamma = “scale”, epsilon = 0.1) achieved the lowest pooled RMSE (118.09 NmL CH₄), with R² = 0.9482 and MAE = 75.75 NmL CH₄, and was selected as the surrogate model. However, reactor-level Wilcoxon tests with Holm correction showed no significant differences between SVR-RBF and the other algorithms. Held-out-reactor R² values ranged from −1.366 to 0.928, indicating heterogeneous generalization. Differential Evolution consistently identified approximately 100% banana peel and 0% molasses as the optimal composition. Across 70 runs, the median optimum was 310.10 h and 1433.25 NmL CH₄. Bootstrap analysis placed 99% of composition optima at ≥99% banana peel, although uncertainty in optimal time was substantial. Kinetic benchmarking supported the slower, higher-volume methane production observed in complete banana-peel reactors. This boundary solution is therefore a model-supported candidate requiring experimental confirmation, not a universal co-digestion optimum. Full article

Vertical circulating rice seedling racks in cold regions of northeast China face limited natural radiation, uneven light distribution, and high energy demand. This study develops a digital twin optimization framework integrating dynamic light field reconstruction, crop physiological response, surrogate modeling, and multi-objective analysis. A solar position algorithm-based simulator reconstructs the spatiotemporal PPFD distribution along the circulating trajectory and couples it with stage-specific rice seedling light response curves. An XGBoost surrogate model is then used to accelerate parameter evaluation for stage-wise optimization. Preliminary measurements using a plant light analyzer showed limited PPFD deviations from simulation, with a mean deviation of −3.4% and a maximum deviation of +11.4%. The surrogate model achieved a test R² of 0.9955 and a five-fold cross-validation R² of 0.9933 ± 0.0024. Under the engineering constraint of CV ≤ 0.10, stage-wise optimization identified max yield, best efficiency, and balanced operating strategies. The balanced strategy predicted a 7.7% increase in YI, a 6.0% reduction in energy use, and a decrease in CV from 0.0353 to 0.0058 relative to the fixed-parameter baseline. SHAP analysis further identified growth stage as the dominant structural factor, while lighting duration and rack speed mainly regulated within-stage performance. This framework provides a quantitative basis for stage-aware light environment optimization and subsequent experimental validation. Full article

Prestressed and post-tensioned concrete structural elements constitute approximately 43.4% of modern bridge infrastructure, representing 58.2% of the total bridge surface area due to their long-span capabilities. Despite their structural efficiency, evaluating residual post-tensioning forces and diagnosing localized degradation within internally grouted tendons—such as localized stress corrosion cracking (SCC), grout voids, and moisture infiltration—remains a critical challenge due to geometric confinement and high material attenuation. This paper presents a non-destructive Structural Health Monitoring (SHM) methodology optimized for the continuous and periodic assessment of post-tensioned anchorage zones under operational traffic loads. The proposed Identification of Active Anomalies (IAA) system integrates the Acoustic Emission (AE) method with unsupervised machine learning to classify multi-mechanism structural degradation. By implementing a mathematically transparent k-means clustering framework initialized via the k-means++ heuristic, high-velocity multi-parameter AE data streams are partitioned within an n-dimensional Euclidean feature space. The scientific novelty of this work lies in its real-scale validation on an operational, highly complex cable-stayed bridge, establishing a previously unpublished acoustic signature database (the 2025 Signal Database). The empirical validity of the algorithm’s predictive boundaries was confirmed through forensic physical inspections and material sampling during a major structural rehabilitation in 2026, which corroborated the active corrosion states within heavily confined post-tensioned anchorage blocks. Furthermore, extracted AE pattern classes are explicitly correlated with structural crack opening widths, enabling real-time tracking of macro-defect propagation, anchorage slippage, and active micro-structural corrosion. Full article

To address the limitations of individual battery thermal management technologies, this study proposes a hybrid battery thermal management system coupling static immersion cooling with refrigerant-based direct cooling. The system employs parallel upper and lower direct cooling plates, with the refrigerant flow split regulated to enhance buoyancy-driven convection within the sealed immersion chamber. Numerical simulations are conducted to compare an R134a direct cooling system with a 50% ethylene glycol solution indirect cooling system over total flow rates of 6–18 L⋅min⁻¹ and upper plate flow ratios of 10–90%. The effects of the total flow rate and flow distribution on the pressure drop, battery temperature, temperature uniformity, flow characteristics, and pumping power are systematically evaluated. The R134a direct cooling system reduces the average battery temperature by approximately 0.5–1.0 °C compared with the indirect cooling system. Increasing the upper plate flow ratio strengthens the natural convection within the immersion chamber and alleviates vertical temperature non-uniformity, whereas excessive flow redistribution weakens the cooling capacity of the lower plate. A Kriging surrogate model coupled with a multi-objective genetic algorithm identifies the optimal condition at a total flow rate of 8.82 L⋅min⁻¹ and an upper plate flow ratio of 56.45%. Relative to the baseline condition of 9 L⋅min⁻¹ and an upper plate flow ratio of 10%, the optimized condition reduces the average battery temperature, maximum temperature difference, and pumping power by 10.1%, 7.2%, and 52.1%, respectively, while maintaining a low cell temperature standard deviation of 0.032 °C. Full article