Search Results (868)

Discrete production planning under integer constrained resource framework is a challenging issue which requires simultaneous consideration of output maximization, resource efficiency, and balanced resource. This research focuses on simulation of an integer-driven production planning model for an automotive production system. It combines the genetic algorithm-based stochastic data generator with a precise Diophantine feasibility enumeration. The genetic algorithm is used as a constraint-aware stochastic specification generator to generate feasible production parameter sets within certain operational constraints. Its main purpose is to create representative production environments for feasibility analysis and not to optimize production. A normalized multi-objective scoring function is presented to address the imbalance in the scales of economic and operational measures. A total of 64,518 feasible automotive production plans were enumerated under engine, tire, labor and budget constraints using the proposed framework. The Pareto-efficient solutions to the cost–output space that were identified, formed a discrete, piecewise Pareto frontier. The best production plan had a total of 83 units with 99% of the labor and tire resources exploited, whereas the budget and engine capacities were not binding. The optimal strategy implies full saturation of the labor capacity (>99%) due to the binding nature of labor as an objective. In practice, a safety buffer can be imposed through the introduction of an upper-bound utilization policy (e.g., 95%), which moves the optimal solution marginally inwards along the Pareto frontier. The analysis of sensitivity to changes in resources of $\pm 10\%$ showed the preservation of the Pareto structure and resilient adaptability in the output, which validated the usefulness of the suggested strategy in discrete manufacturing decision support. Full article

To address renewable generation and load uncertainty in multi-campus integrated energy systems, this paper proposes a distributionally robust day-ahead–real-time coordinated scheduling model under a cloud-edge collaborative architecture. The studied system consists of photovoltaic, wind power, and combined heat and power campuses, each equipped with energy storage and transferable load resources. The cloud layer determines the day-ahead baseline dispatch plan, while the edge layer performs scenario-dependent real-time corrections. To improve adaptability to adverse operating conditions, bounded forecast-error scenarios are constructed, and a conditional value-at-risk-based distributionally robust objective is formulated. Meanwhile, a soft day-ahead–real-time energy-binding mechanism is introduced to maintain plan-execution consistency while allowing necessary real-time adjustments. Case studies show that, compared with the cases without peer-to-peer energy exchange, demand response, and energy storage, the proposed model reduces the objective value by 5.22%, 10.96%, and 5.05%, respectively. Sensitivity analysis and stress tests verify its feasibility and robustness under increased uncertainty and reduced flexible-resource capacities. Full article

The virtually coupled train set (VCTS) system is a promising urban rail transit paradigm that replaces physical couplers with train-to-train (T2T) wireless communication, enabling dynamic marshaling to achieve the precise matching of transportation demand and resources. However, existing VCTS control strategies either assume perfect leader state availability, rely on continuous communication, or lack guaranteed transient/steady-state performance under Denial-of-Service (DoS) attacks. To address these critical limitations, this paper proposes a unified finite-time resilient event-triggered cooperative control framework for VCTSs against malicious DoS attacks. The proposed framework integrates three synergistic components: a distributed finite-time leader state estimator to reconstruct leader information under intermittent communication interruptions, a prescribed performance finite-time controller to bound tracking error fluctuations and accelerate convergence, and an adaptive event-triggered communication protocol to reduce controller update frequency. The closed-loop system stability, finite-time convergence, and prescribed performance guarantees are rigorously proven via Lyapunov analysis, and Zeno behavior is strictly excluded. Extensive comparative simulations demonstrate that the proposed framework outperforms representative state-of-the-art methods in terms of tracking accuracy, attack resilience, and communication efficiency, achieving a significance reduction of approximately 70% in controller update frequency while maintaining system stability under the considered DoS attack scenarios. Full article

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

Wearable electrocardiogram (ECG) monitoring enables continuous cardiovascular assessment, yet signals acquired in ambulatory environments are inevitably corrupted by baseline wander, electrode motion artifacts, and muscle interference, which obscure diagnostically critical waveform features. Existing deep learning denoisers rely on heuristic attention mechanisms and time-domain-only losses, lacking principled control over what information the network retains or discards. To address this limitation, we propose EFIB-Net, an information bottleneck-guided multi-resolution network for robust ECG denoising. The framework introduces two complementary components: an efficient frequency-guided attention module that derives temporal attention weights directly from the energy distribution of parallel multi-resolution convolutional branches, requiring only four learnable parameters while providing physically interpretable feature selection that naturally highlights QRS complexes, and a variational information bottleneck constraint at the encoder–decoder bottleneck that forces the latent representation to retain only reconstruction-relevant information and discard noise, guided by a spectral–temporal composite loss. To the best of our knowledge, we are among the first to explicitly introduce the information bottleneck principle into deep-learning-based ECG signal denoising. Experiments on the MIT-BIH Arrhythmia Database show that EFIB-Net outperforms ten traditional and deep learning baselines across four standard metrics—signal-to-noise ratio (SNR), root mean square error, percentage root-mean-square difference, and correlation coefficient; at an input SNR of −5 dB it reaches $8.12$ dB output SNR, surpassing the strongest attention-based competitor by 1.77 dB ($p<0.01$) while using only 0.45 M parameters and 10.8 ms inference latency per segment; downstream evaluation further demonstrates that the denoised signals achieve 99.18% R-peak detection sensitivity and 91.26% heartbeat classification F1-score, both within approximately one percentage point of the clean-signal upper bound, making it practical for real-time cardiac monitoring on resource-constrained wearable devices. Zero-shot cross-database evaluation on the QT Database further confirms generalizability, with only 0.54 dB degradation without retraining. Full article

With the rapid growth of data center (DC) energy consumption and the large-scale integration of renewable energy, DCs increasingly face time-varying power upper-bound constraints jointly shaped by grid power supply capability, renewable energy fluctuations, and demand response mechanisms. Meanwhile, DC power consumption exhibits a typical information-load-driven characteristic. The computing tasks hosted by virtual machines affect server-side IT power consumption through resource utilization states such as CPU, memory, disk I/O, and network I/O, and are further coupled with non-IT auxiliary power consumption from cooling, power distribution, and networking equipment. In such cyber–physical operation scenarios, physical-layer sensing data and hypervisor-level virtualization monitoring data jointly provide the state basis for power estimation, power warning, and migration decisions. To address the mismatch between dynamic power upper bounds and time-varying information loads, this paper investigates the information load scheduling problem under constrained power loads and proposes a two-stage virtual machine (VM) migration optimization framework. In the VM selection stage, a Multi-Factor Balanced (MFB) algorithm is designed. By introducing a warning-line trend model based on the arctangent function, MFB comprehensively considers resource utilization, power load variation trends, and service level agreement (SLA) violation levels to dynamically identify candidate VMs for migration. In the VM placement stage, a Multi-Factor Equilibrium Ant Colony Optimization (MFEACO) algorithm incorporating a Random Roulette Wheel (RRW) selection mechanism is proposed. By constructing normalized multi-dimensional equilibrium factors, MFEACO coordinates the trade-off among energy consumption, load balancing, and SLA violations. Simulation experiments are conducted on an improved CloudSim platform using real-world cluster trace data from Google and Alibaba. The results show that, while satisfying dynamic power constraints, the proposed MFB–MFEACO framework achieves a favorable comprehensive trade-off among energy consumption control, SLA violation suppression, and migration reduction. Compared with traditional heuristic methods and a power-constrained genetic algorithm baseline, the proposed framework demonstrates better dynamic adaptability and scheduling stability. Full article

This paper addresses the consensus problem for networked robotic sensors characterized by general linear dynamics and strict communication bandwidth limitations. We propose a weighted sliding integration-based adaptive dynamic event-triggered control (WSI-ADETC) strategy. First, we design a bounded adaptive parameter using a nonlinear protocol to enhance sensitivity to changes in consensus error. To further alleviate the communication burden on the sensing network, we propose a weighted sliding integration-based event-triggering mechanism to reduce the number of triggers compared to traditional adaptive dynamic event-triggered control (ADETC) approaches. Using Lyapunov analysis, we establish sufficient conditions for asymptotic consensus and demonstrate that the proposed controller effectively eliminates Zeno behavior. Numerical simulations demonstrate that the proposed WSI-ADETC strategy significantly reduces communication frequency while maintaining satisfactory consensus performance. Compared with recent adaptive dynamic event-triggered methods, the proposed method reduces the total triggering number by more than 53%, providing a communication efficient solution for resource-constrained robotic sensing networks. Full article

Accurate tropical cyclone (TC) track prediction remains challenging, as numerical models suffer from high computational cost, substantial storage requirements, and physical parameterization uncertainties, while data-driven large AI models depend heavily on training data volume and high-resolution inputs, resulting in prohibitive computational overhead. To address these issues, this paper proposes TCN-GAN-DM, a three-stage deep learning framework based on the China Meteorological Administration (CMA) Tropical Cyclone Best Track Dataset. Specifically, a dual-stream temporal convolutional network (TCN) first extracts temporal features from track and meteorological sequences, respectively. A generative adversarial network (GAN) then takes these features and produces multiple physically plausible candidate tracks via noise injection. Finally, a conditional diffusion model (DM) refines the predicted positions through progressive denoising. Experimental results for TCs in 2024 show that under the fair deterministic comparison using a single fixed candidate, the model achieves a 6 h track error of 49.10 km, which is comparable to CMA-GFS (49.75 km) and HWRF (44.34 km), and substantially lower than the large AI model FuXi (120.44 km). When evaluating the oracle metric (best-of-K, K = 6) as an upper bound of coverage, the model achieves the smallest errors among all models at 6 h (24.04 km) and 12 h (55.81 km). In addition, the proposed model has advantages over CMA-GFS, HWRF, and FuXi in terms of computational resource consumption and hardware deployment cost. However, its mean track error increases more rapidly beyond 12 h, and at lead times of 18 h and 24 h the model is outperformed by HWRF, FuXi, and CMA-GFS, indicating that its current strength lies primarily in short-term prediction. Consequently, the practical utility of TCN-GAN-DM is currently demonstrated for 6–12 h TC track prediction, offering a new solution for disaster prevention and mitigation that balances accuracy and deployment cost at these specific time scales. Full article

Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support. Full article

This study presents a comprehensive data-limited stock assessment of bonga shad (Ethmalosa fimbriata) and lesser African threadfin (Galeoides decadactylus) in the Central Gulf of Guinea using complementary catch- and abundance-based approaches, including Abundance-based Maximum Sustainable Yield (AMSY), Catch-based Maximum Sustainable Yield ++ (CMSY++), and the Bayesian State-space Schaefer Model (BSM). These models were applied because they are suitable for evaluating stock status in data-limited fisheries using catch and abundance information. While AMSY primarily uses abundance information, CMSY++ integrates catch and productivity priors, whereas BSM incorporates state-space error structures to account for observation uncertainty. Catch time series (1990–2021) were extracted from Food and Agricultural Organization (FAO) FishstatJ accessed in 2023, with catch values for 2022–2023 cautiously extrapolated from recent trends due to the temporary absence of updated official statistics. Standardized and scaled relative abundance indices from Cameroonian and Nigerian EEZ were used to support model estimation and assess the stock status. For Ethmalosa fimbriata, the results from CMSY++ and BSM yielded an MSY estimate of 126 × 10³ t and 95.5 × 10³ t, respectively, while for G. decadactylus, MSY from CMSY++ and BSM were 9.1 × 10³ and 13.4 × 10³, respectively. Stock status indicators suggested the stock was fully exploited based on both AMSY (F/F_MSY = 0.83) and CMSY++ (F/F_MSY = 1.03) and overfished based on BSM (F/F_MSY = 1.77). For G. decadactylus, the analysis based on AMSY suggested an overfished stock state (F/F_MSY = 1.2), while under CMSY++ and BSM, the stock is fully exploited. The log scale CPUE was symmetrical within the expected bounds, and the posterior parameter distributions were constrained, indicating that the model passed the convergence test and had robust parameter estimates. The study recommends maintaining catches within MSY-based reference points as the total allowable catch (TAC) and emphasizes the need for improved data continuity, regional collaboration, and precautionary management for long-term sustainability of fisheries resources in the Central Gulf of Guinea. Full article

Object detection in unstructured agricultural environments remains challenging due to large scale variations, complex backgrounds, irregular obstacle shapes, and limited computational resources. To address these challenges, this paper proposes Agri-DETR, an efficient end-to-end detection framework based on the Real-Time Detection Transformer (RT-DETR), with coordinated improvements in feature perception, multi-scale representation, spatial reconstruction, and bounding box regression. Specifically, a lightweight backbone with a high-resolution feature branch is introduced to enhance the representation of small and fine-grained targets. A large selective feature fusion module is designed to strengthen multi-scale contextual modeling and improve feature discrimination under complex backgrounds. In addition, an attention-enhanced dynamic upsampling module refines high-resolution feature reconstruction, while a scale–shape–geometry-aware Intersection over Union (SSGIoU) loss improves localization stability for irregular and elongated objects. Experimental results show that Agri-DETR achieves 66.0% Average Precision (AP) on the self-constructed Agricultural Obstacle Dataset (AO-Dataset), outperforming representative detectors while reducing the parameter count by approximately 25% compared with RT-DETR-R18 baseline. In particular, small-object AP increases by 1.4%, demonstrating improved detection capability for small obstacles. Cross-dataset evaluation on COCO2017 further shows that Agri-DETR achieves 48.3% AP, demonstrating favorable generalization capability beyond the agricultural domain. These results indicate that Agri-DETR achieves an effective balance among detection accuracy, model complexity, and practical efficiency, making it a promising solution for real-world agricultural obstacle detection. Full article

AI-enabled infrastructure systems increasingly govern access to emergency services, disaster relief, and utility restoration, yet they routinely produce inequitable outcomes even when allocation algorithms apply procedurally neutral rules. The standard explanation locates the cause inside the algorithm. This paper argues instead that inequity arises from the interaction between the algorithm and the physical environment in which it operates: network topology, resource locations, and demand distribution jointly constrain what any policy can achieve, and when those constraints are sufficiently binding, ethical infeasibility is structural rather than algorithmic. We introduce a constraint-based formulation that embeds ethical requirements into the feasible region, and a hierarchical Irreducible Infeasible Subsystem (IIS) procedure that attributes infeasibility to rule design, algorithmic choice, or physical infrastructure. We further establish the Structural Infeasibility Theorem, deriving closed-form bounds on inter-group disparity across all feasible policies. The framework was applied to zone-decomposable infrastructure allocation problems generally, with a metropolitan ambulance-dispatch system serving as a concrete instantiation. The study delivers four findings. First, the minimum-service violation may not be caused by the allocation algorithm itself; rather, it may arise from the physical layout of the infrastructure. Second, the observed efficiency–equity trade-off may not be an unavoidable feature of equitable allocation, but may instead reflect the difficulty of achieving equity within an underbuilt system. Third, before new infrastructure is added, improvements in equity may represent harm redistribution rather than harm reduction. Fourth, the IIS certificate can be translated into a concrete capital-investment requirement, showing what physical change may be needed to restore ethical feasibility. Full article

The problem of bilevel decision-making under multi-expert uncertain information is addressed in this paper. Traditional fuzzy bilevel models are unable to accurately quantify expert consensus and capture evaluation hesitation. To overcome these limitations, a fully hesitant fuzzy bilevel linear programming model is proposed, in which all coefficients and decision variables are characterized by hesitant fuzzy numbers. By virtue of $(\alpha ,k)$-cuts, the original model is equivalently transformed into an interval-valued bilevel programming problem and further decomposed into best–best and worst–worst sub-models to derive the upper and lower bounds of optimal solutions. Under the Slater constraint qualification, Karush–Kuhn–Tucker (KKT) conditions are adopted to convert the two sub-models into single-level mathematical programs with complementarity constraints (MPCCs), thereby enabling efficient model solving. The proposed method is applied to the resource allocation problem in quantum communication networks. The numerical results demonstrate that the optimal solution interval converges to a unique core value as the membership-level $\alpha$ increases, while a larger consensus parameter k reduces the fuzzy support set without altering the core solution. Full article

This paper addresses a human resource allocation problem with spatial-temporal constraints (HRAP-SC) in the parallel assembly of complex products, such as satellites and aircraft. It involves coordinating a limited pool of multi-skilled workers across geographically distributed workstations, subject to rigorous constraints including team collaboration requirements, operation priorities, technological tail times (e.g., curing), and strict 8 h workdays. Existing exact approaches typically fail to converge due to the combinatorial explosion arising from the strong coupling of shared resources across workstations, while meta-heuristic methods often suffer from performance instability caused by hyper-parameter sensitivity. To overcome these limitations, we propose a pattern-based decomposition algorithm (PDA), a novel parameter-free exact solution framework. By exploiting the inherent symmetry of identical jobs and parallel workstations, PDA defines a set of canonical patterns to drastically reduce the search space. It employs an efficient traversal mechanism reinforced by rigorous mathematical bounds and pruning rules to eliminate unpromising solutions. Computational experiments demonstrate that PDA significantly outperforms state-of-the-art Mixed-Integer Programming (MIP) and Constraint Programming (CP) solvers. Unlike standard solvers, which frequently time out (3600 s), PDA strictly evaluates only a single pattern when proving optimality, and robustly scales to large industrial instances (e.g., six jobs comprising 78 operations) to provide high-quality schedules. By successfully solving complex scheduling problems that remain intractable for monolithic solvers, PDA provides a robust and automated decision-support tool for production management in complex manufacturing systems. Full article

Background/Objectives: Human papillomavirus (HPV) infection is the leading cause of cervical cancer and is associated with several anogenital and oropharyngeal malignancies. Although licensed HPV vaccines are highly effective, access remains limited in many low- and middle-income countries due to cost, supply shortages, and implementation barriers. In this study, we evaluated the immunogenicity and safety of Gardisun, a newly developed quadrivalent prophylactic HPV vaccine, compared with Gardasil^®. Methods: This Phase III randomized, double-blind, active-controlled, parallel-group non-inferiority trial enrolled 450 healthy participants stratified by sex and randomized (1:1) to receive three 0.5 mL intramuscular doses of Gardisun or Gardasil^® on Days 0, 60, and 180. Participants were followed through to Day 210. The primary endpoint was the geometric mean titer (GMT) of antibodies against HPV types 6, 11, 16, and 18 one month after the administration of the third dose. Non-inferiority was defined as the lower bound of the 95% confidence interval (CI) for the GMT ratio exceeding 0.67. Safety was assessed through adverse event monitoring. Results: Of the 450 randomized participants, 422 completed the Month 7 visit and 429 received all three doses. Both vaccines induced antibody responses and seroconversion rates for all HPV types. The primary analysis met the non-inferiority criterion for HPV-6, while prespecified sensitivity analyses supported the existence of non-inferiority across all evaluated HPV types. Most adverse events were mild and transient, with no vaccine-related serious adverse events reported. Conclusions: Gardisun demonstrated robust immunogenicity and a safety profile comparable to that of Gardasil^®, supporting its potential as an accessible alternative quadrivalent HPV vaccine for broader vaccination programs in resource-limited settings. Full article