Search Results (1,068)

The Flexible Job Shop Scheduling Problem (FJSP) is central to smart manufacturing, yet standard algorithms often prioritize productivity (makespan) at the expense of cost and reliability. This paper introduces t-MOHHO, a collaborative optimization framework designed to equilibrate machine load, processing costs, and delivery timeliness alongside throughput. By incorporating an adaptive Student’s t-distribution mutation operator and a non-linear energy escape mechanism, t-MOHHO effectively navigates high-dimensional search spaces. Extensive validation on 10 MK benchmark instances reveals that t-MOHHO demonstrates significant advantages over classic HHO, MOPSO, and MOEA/D across most metrics. Notably, in comparison to the state-of-the-art NSGA-III, t-MOHHO executes a clear trade-off: it trades marginal makespan efficiency for substantial reductions in cost and tardiness. Specifically, on the large-scale MK10 instance, t-MOHHO reduces total tardiness by 56.2% and lowers processing costs by 3.4% compared to NSGA-III. These results demonstrate that t-MOHHO can strategically sacrifice maximum speed to secure superior punctuality and cost-efficiency, making it a robust decision-support tool for Just-in-Time (JIT) production environments. Full article

The lack of scientific methods for selecting monitoring indicators and equipment undermines the efficiency of forest fire emergency response. To address this gap, we developed a novel evaluation model for emergency monitoring indicators based on the spatiotemporal evolution of forest fires. The model, comprising four primary and eight secondary factors, leverages a hybrid TriFAHP and DBN approach to objectively determine factor weights based on survey data from 20 domain experts. The results indicate that the primary factor weights rank as follows: Monitorability (0.3807) > Timeliness (0.3353) > Sensitivity (0.1874) > Feasibility (0.0966). Four indicators (wind speed, temperature, flame, and gas) were identified as the most suitable for core monitoring. Furthermore, stage-specific monitoring strategies were proposed, prioritizing different core indicators across the ignition, spread, and fully developed fire stages. An indicator and equipment association was established, recommending optimal configurations such as UAV-mounted thermal imagers and lidar anemometers. The practical applicability of the proposed framework was successfully validated through real-world case studies, including the 2019 to 2020 Australia bushfires. This study provides a standardized framework aligning indicators, equipment, and scenarios, offering theoretical and practical guidance for optimizing emergency monitoring systems. Full article

Human error in evacuation guidance systems can significantly affect evacuation performance, particularly in complex underground environments where large numbers of occupants are concentrated. While previous studies have focused on optimizing evacuation routes and modeling crowd dynamics, the direct quantitative impact of human error in evacuation guidance has not been sufficiently addressed. This study aims to evaluate the effects of human error on evacuation efficiency in underground stations using a node–link-based evacuation model. A virtual three-level underground station was modeled, and evacuation simulations were conducted using two representative pathfinding algorithms, Dijkstra and A*, to compare classical and heuristic routing approaches under both normal and error conditions. Three scenarios were considered: a normal condition with accurate guidance, a misguidance scenario with incorrect information on exit availability, and a delayed evacuation scenario in which a subset of evacuees started evacuation later than others. In addition, congestion effects were incorporated by adjusting walking speeds based on crowd density. The results show that human error significantly increases evacuation time and alters congestion patterns. Compared to the normal condition, the misguidance scenario increased evacuation time by approximately 17.6%, while the delayed evacuation scenario resulted in an increase of up to 37.9%, indicating that delayed response has the most critical impact due to the interaction between late-starting evacuees and existing congestion. Although the A* algorithm demonstrated higher computational efficiency, its advantage did not consistently translate into improved evacuation performance under dynamic conditions. These findings highlight that evacuation performance is highly sensitive to the accuracy and timing of evacuation guidance, rather than being determined solely by optimal pathfinding. Therefore, improving the reliability and timeliness of evacuation guidance systems is essential for enhancing safety in underground environments. Full article

Background: Pathogenic variants in the NEXMIF gene have been linked to a broad neurodevelopmental phenotype, encompassing autism spectrum disorder, intellectual disability, and epilepsy. Among epileptic manifestations, Jeavons Syndrome was observed in 24% of affected females in the largest cohort of NEXMIF-related disorders reported to date, but long-term adult outcomes remain poorly documented. Methods and Results: We report a 25-year-old Italian woman with drug-resistant Jeavons syndrome in which the combined approach of next-generation sequencing and chromosomal microarray analysis allowed us to identify, after a 13-year diagnostic odyssey, a de novo ~350 Kb microdeletion at Xq13.2q13.3 encompassing the entire NEXMIF coding region, with no other OMIM genes involved. To our knowledge, this is the first reported case of a patient harboring a deletion restricted to the entire coding sequence of the NEXMIF gene. The patient presented with moderate intellectual disability and seizure onset at age 10 years. Her epilepsy proved refractory to multiple antiseizure medications. Video-EEG/polygraphic monitoring at age 23 years confirmed epilepsy with eyelid myoclonia, demonstrating characteristic eyelid myoclonia with absences triggered by eye closure. Conclutions: This case provides a detailed clinical description of an adult patient useful for genetic counseling regarding adult outcomes and prognostic expectations. Furthermore, this study underscores the diagnostic value of chromosomal microarray analysis alongside next-generation sequencing in individuals with intellectual disability and drug-resistant epilepsy, in order to expedite the diagnostic pathway and enable timelier and more appropriate patient management. Full article

Mobile data collectors (MDCs) play a very important role in Internet of Things (IoT) sensing networks. However, ensuring their trustworthiness against insider threats, such as on–off attacks and spatiotemporal fabrication, remains a critical challenge. Existing trust evaluation methods frequently struggle with these threats due to insufficient evidence dimensions and the inability to quantify behavioral stability. To address these limitations, this paper proposes an enhanced proactive trust evaluation system based on stability sequence extraction (E-PTES-S). E-PTES-S improves the evaluation accuracy by integrating five factors of evidence, stability-computation mechanisms, and an adaptive weight allocation scheme to maintain robustness even when proactive verification data is scarce. In addition to the usual interaction and proactive verification indicators, regional consistency (${\mathcal{T}}_{RC}$) and task timeliness (${\mathcal{T}}_{TT}$) are introduced to mitigate location falsification and transmit-time deviations more rigorously. Then, a sliding window technique is used to obtain an integrated evidence sequence, which includes a new continuous stability sequence (${\mathcal{F}}_{CSS}$) and traditional credible, untrustworthy, and uncertain sequences. This continuous stability sequence adds a variance-based incentive scheme to measure behavioral stability. Finally, the normalized trust value is derived from multiple indicators including multidimensional spatiotemporal evidence and stability metrics. Experimental results show that the proposed E-PTES-S achieves a normal node detection rate of 98.7% under complex dynamic conditions, outperforming the baseline PTES and Trust-SIoT algorithms by approximately 9% and 1%, respectively, while also improving the cumulative data collection profit by 4.8%. Furthermore, robustness analysis demonstrates that E-PTES-S exhibits excellent robustness against physical-layer uncertainties, successfully sustaining an 84.4% detection rate even under severe environmental shadowing. Full article

With the increasing demand for privacy-preserving and real-time personalized services in large-scale video platforms, designing robust federated recommendation frameworks over practical communication networks has become increasingly important. To this end, this paper proposes a bias-corrected federated learning framework tailored for video recommendation over stochastic communication links. At the local training stage, a bias-corrected mechanism is introduced to explicitly account for video duration and user activity, mitigating feature-level bias and enabling the learned representations to more accurately reflect users’ intrinsic preferences. To meet the timeliness requirements of real-time federated learning, the successful upload probability of local model transmission is analytically characterized under time-varying channel conditions. Building upon this probabilistic model, a statistically corrected global aggregation strategy is designed to preserve the unbiasedness of the global update with respect to the ideal fully reliable FedAvg scheme, even when a subset of local nodes fails to upload their models within the specified delay constraint. Comprehensive experimental evaluations validate that the proposed framework significantly improves recommendation accuracy and maintains robustness against communication unreliability in practical distributed environments. Full article

In multi-turn conversational AI, individually innocuous personally identifiable information (PII) fragments disclosed across successive turns can accumulate into a re-identification risk that no single utterance reveals on its own. Existing PII detectors operate on isolated utterances and therefore cannot track this cross-turn evidence build-up. We propose a stateful middleware guardrail whose core design principle is speaker-attributed entity isolation: every extracted PII fragment is attributed to its originating conversational participant, and evidence is accumulated in entity-isolated subgraphs that prevent cross-entity contamination. The system signals re-identification onset $t_{\mathrm{pred}}$ at the earliest turn where combination-based rules grounded in the uniqueness literature are satisfied. On a 184-record template-synthetic evaluation corpus, the gated NER configuration leads on primary timeliness (OW@5 $= 73.4\%$, MAE$= 1.357$ turns); the full system achieves OW@5 $= 70.7\%$ with MAE $= 2.442$ turns as an alternative operating mode for ambiguity-sensitive disclosure patterns. We further evaluate behavior on a 300-record mutation stress set, test RULE_B on the ABCD external corpus, and supplement RULE_A evaluation with both a proxy-labeled transfer analysis on PersonaChat and a manual annotation study on 151 Switchboard dialogues. The reported results should be interpreted as an initial empirical reference point rather than a sufficient endpoint for autonomous runtime enforcement. Full article

In the existing research on information propagation modeling in social networks, hypergraphs have been widely applied to characterize the high-order interaction relationships involving multiple nodes. However, most models are still based on the assumption of undirected connections, which leads to certain limitations in depicting the information flow direction and the structural characteristics of propagation chains. To address the above problems, a social hypernetwork propagation model with directional constraints is constructed in this paper by introducing the directed hypergraph structure and combining it with the improved SEIR model. The strength of social relationships is measured by intimacy in the model, and a comprehensive characterization of the information propagation process is achieved by integrating the threshold mechanism of the directed hypergraphs with the attenuation function of information timeliness. In addition, the effectiveness of the proposed model is verified by taking the event of “imposing additional tariffs” as an example, and the evolutionary characteristics of propagation in different network structures, as well as the impacts of user confidence and information timeliness, are analyzed using simulation experiments. The results indicate that the model is applicable to characterizing the information propagation trends and dynamic characteristics in real social networks, and can provide theoretical references and methodological support for the prediction and regulation of network public opinion. Full article

In this paper, a triple-clutch power shift transmission is proposed as a means of enhancing the performance of a dual-clutch transmission in a tractor. However, it should be noted that the oil temperature and the wear of the clutch have a detrimental effect on the performance of the proposed transmission. Consequently, an adaptive control strategy is proposed for the clutch in terms of oil temperature and wear. In order to address the issue of the timeliness of temperature compensation, an adaptive fuzzy control strategy is proposed with a view to improving the control of the peak oil filling pressure, pre-filling time, and bonding time. This paper sets out a proposed adaptive iterative control strategy for the compensation of wear, with a view to regulating the pressure at different stages of the filling process. The two proposed control strategies are then subjected to rigorous testing in a test bench to ascertain their effectiveness. The findings indicate that the proposed strategy is capable of effectively mitigating the deviation of clutch binding resulting from fluctuations in oil temperature and wear, thereby enhancing the performance of agricultural machinery transmissions. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

To address the issues of suboptimal recovery performance, low timeliness, and poor economic efficiency associated with traditional fault recovery methods following large-scale power outages in active distribution networks (ADNs) caused by extreme weather, this paper proposes a dynamic fault recovery strategy for ADNs based on a two-layer hybrid algorithm under extreme ice and snow conditions. First, a line fault rate model considering the thermal effect of current under extreme ice and snow conditions is constructed, and an information entropy-based typical scenario screening method is introduced to filter the fault scenarios. Second, a photovoltaic (PV) output model and a time-varying load model under the influence of extreme ice and snow conditions are established. Subsequently, a multi-objective dynamic fault recovery model is formulated, incorporating island partitioning and integration constraints based on the concept of single-commodity flow, alongside tightened relaxation constraints. To achieve an accurate and rapid solution for the fault recovery model, a two-layer hybrid algorithm is proposed. This algorithm combines an outer-layer improved binary grey wolf optimizer (IBGWO) and an inner-layer second-order cone relaxation (SOCR) algorithm to solve the discrete and continuous decision variables within the model, respectively. Finally, the effectiveness and superiority of the proposed method are verified using the PG&E 69-bus and IEEE 123-bus systems. Full article

Background: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Survival outcomes are strongly stage-dependent. Many patients are diagnosed at advanced stages due to pre-clinical and diagnostic delays. While advances in imaging, bronchoscopic techniques, molecular diagnostics, and systemic therapies have improved individualized treatment, system-level delays continue to limit their impact. Aim of the study: The aim of this narrative review is a synthesis with an implementation-oriented framework proposal. Part I synthesizes the peer-reviewed literature, Part II presents an operational framework integrating a Fast Trac Clinic (FTC) and a network of Lung Cancer Units (LCUs) including proposed turnaround-time (TAT) goals. Methods: A narrative review of the literature of selected European policy documents addressing diagnostic delays, rapid-access lung cancer pathways, and coordinated care models was conducted. Results: European models demonstrate that structured referral criteria, centralized coordination, and predefined interval targets can achieve the first specialist assessment within 7–10 days and the completion of diagnostics within 21–28 days in optimized settings. Key determinants of timeliness include: direct primary care referral, parallel diagnostic processes, prioritized pathology and molecular testing, and multidisciplinary team (MDT) assessment. We propose operational TAT targets for chest CT, PET-CT, histopathology, NGS, PFTs, and MDT decision-making. Conclusions: Reducing avoidable diagnostic and therapeutic delays in LC requires a coordinated, system-level approach. A standardized FTC-LCU pathway with explicit TAT benchmarks, multidisciplinary governance, and digital support infrastructure may improve diagnostic efficiency, increase the proportion of patients treated at earlier stages, and enhance patient experience. Prospective evaluation of implementation impact on stage distribution and survival is advised. Full article

Dairy cows typically respond to stressors by altering their behavior, such as reducing eating time (ET) and rumination time (RT). Although declines in milk yield (MY) have been extensively studied, models to quantify perturbations in ET and RT are still lacking. This study adopts a smoothing approach to identify and characterize perturbations in MY, ET, and RT in response to the main primary stressors, heat stress (HS), lameness (L), and mastitis (M), while evaluating the influences of parity and stage of lactation. A total of 350 Italian Simmental cows were monitored in farms equipped with automatic milking systems and accelerometers. Within this population, cows with a lactation period of at least 150 days were selected. A double-curve smoothing model (λ = 100 and λ = 10,000) was applied to calculate response and recovery times and to quantify production and feeding behavior losses. The results indicate that L causes the longest (30.6 d and 28.8 d, respectively) perturbations for both MY and ET. While L caused the greatest loss in milk production (14.7 kg), HS resulted in the greatest losses regarding feeding behavior (ET: 175.2 min and RT: 210.3 min). In general, M had a lower impact, likely due to the timeliness of treatments. Primiparous cows showed faster responses to stress but slower recovery times compared to multiparous ones. However, multiparous cows exhibited greater total MY losses. The method proved effective for quantifying resilience and opens new perspectives in health monitoring, allowing for the identification of both economic loss and each animal’s capacity to cope with pathological and environmental events, improving the overall sustainability of the dairy farm. Full article

Water accumulation in goafs in Chinese coal mines is a major hidden hazard that can trigger water inrush accidents and may also affect aquifer integrity and regional water security. Reliable delineation of goaf water distribution and identification of water-source types are therefore essential for mine water-hazard control and groundwater protection. This paper reviews the main technical routes for goaf groundwater investigation, including geophysical prospecting, hydrogeochemical and isotopic identification, direct inspection tools, and data-driven intelligent workflows. For geophysical detection, the mechanisms, engineering applicability, and key constraints of the Transient Electromagnetic Method (TEM), Surface Nuclear Magnetic Resonance (NMR), the High-Density Resistivity Method (HDRM), and the Coherent Frequency Component (CFC) electromagnetic wave reflection coherence method are synthesized, with emphasis on interpretation boundaries and uncertainty sources under complex geological conditions. For source identification, conventional hydrochemistry, stable isotopes, and laser-induced fluorescence are summarized, and intelligent recognition models such as neural networks and support vector machines are discussed in terms of workflow positioning and practical performance limits. A unified evaluation rationale is established and a semi-quantitative method–metric matrix is constructed to compare techniques in terms of reliability, deployability, cost level, environmental adaptability, and information value, thereby clarifying their functional roles and complementarities within staged engineering workflows. The synthesis indicates that major bottlenecks include limited deep capability under strong interference, pronounced interpretational non-uniqueness caused by complex geology and irregular goaf geometries, and constrained timeliness and generalization for mixed-source identification. Future directions are summarized as multi-method integration with fusion-driven interpretation, intelligent and quantitative decision support with quality control, and sensor–platform advances enabling more practical three-dimensional investigation, aiming to improve the reliability and engineering usability of goaf groundwater hazard assessment. Full article

Diagnostic testing of foot-and-mouth disease virus (FMDV) currently utilizes reverse transcription quantitative PCR (RT-qPCR) to detect the presence of viral RNA and double antibody sandwich ELISAs (DAS-ELISAs) to determine viral serotype. Serotype identification is critical to support informed vaccine selection to combat outbreaks. While DAS-ELISAs are capable of serotype identification, the test suffers from low sensitivity and requires a viral isolate for successful detection. In this study, we developed FMDV-ONTAPS: an Oxford Nanopore Technologies Amplicon P1 Sequencing protocol involving reverse transcription-PCR to amplify P1 of the FMDV genome, and Nanopore sequencing of the amplicons to provide genetic data for serotype and subtype/topotype identification. FMDV isolates representing all seven serotypes were successfully sequenced with this method. Additionally, the protocol successfully provided serotype identification from a variety of specimen matrices obtained from experimentally infected animals that included milk, serum, oral and nasal swabs, tissue suspensions, vesicular fluid, and oral fluid. The limit of detection for FMDV cell culture isolates was comparable for both sequencing and RT-qPCR detection. RT-qPCR Cq values for clinical samples evaluated ranged from 8 to 28.21. Sequencing was successful for all samples except for a single tissue suspension sample (Cq of 28.21). Identification of FMDV serotype in clinical samples is critical for effective outbreak response, and Nanopore sequencing offers a timelier and more sensitive alternative to DAS-ELISAs. Full article