Search Results (2,266)

This paper develops a mathematically grounded neuro-fuzzy control framework for IoT-enabled irrigation systems in precision agriculture. A discrete-time, physically motivated model of soil moisture is formulated to capture the nonlinear water dynamics driven by evapotranspiration, irrigation, and drainage in the crop root zone. A Mamdani-type fuzzy controller is designed to approximate the optimal irrigation strategy, and an equivalent Takagi–Sugeno (TS) representation is derived, enabling a rigorous stability analysis based on Input-to-State Stability (ISS) theory and Linear Matrix Inequalities (LMIs). Online parameter estimation is performed using a Recursive Least Squares (RLS) algorithm applied to real IoT field data collected from a drip-irrigated orchard. To enhance prediction accuracy and long-term adaptability, the fuzzy controller is augmented with lightweight artificial neural network (ANN) modules for evapotranspiration estimation and slow adaptation of membership-function parameters. This work provides one of the first mathematically certified neuro-fuzzy irrigation controllers integrating ANN-based estimation with Input-to-State Stability (ISS) and LMI-based stability guarantees. Under mild Lipschitz continuity and boundedness assumptions, the resulting neuro-fuzzy closed-loop system is proven to be uniformly ultimately bounded. Experimental validation in an operational IoT setup demonstrates accurate soil-moisture regulation, with a tracking error below 2%, and approximately 28% reduction in water consumption compared to fixed-schedule irrigation. The proposed framework is validated on a real IoT deployment and positioned relative to existing intelligent irrigation approaches. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

In response to the difficulties in multi-disciplinary coordination, the complexity of schedule management, and the weakness of risk control in the construction of high-voltage substations, and based on the current construction status and historical experience of high-voltage projects in Jilin Province, this paper, from the perspectives of schedule and risk management, proposes a multi-disciplinary coordination and risk control strategy that integrates the work breakdown structure (WBS), design structure matrix (DSM), critical chain project management (CCPM), and the fuzzy analytic hierarchy process (FAHP). First, the task flow is decomposed using WBS, and DSM-based coupling analysis is employed to identify interdependencies among disciplines, thereby optimizing task sequencing and parallel arrangements. Second, an optimized project schedule model is established using CCPM, with aggregated buffers that enhance the reliability and flexibility of schedule management. Finally, a risk register is developed based on field investigations, and a three-dimensional quality–schedule–safety risk assessment model is constructed using FAHP; targeted risk prevention and control measures are then proposed according to the quantitative evaluation results. A 500 kV substation project in Jilin Province is adopted as a case study for application and verification. Compared with traditional serial scheduling, the proposed schedule optimization strategy shortens the overall project duration by 29.1%. Furthermore, targeted management recommendations were proposed based on the risk assessment results of the project. The proposed optimization strategy can provide theoretical support and practical guidance for the construction of high-voltage substations and their associated projects, forming an effective technical solution that is scalable and replicable, and it is of great significance for improving the level of project construction management. Full article

This paper addresses Region-3 control of a 2.5 MW three-bladed HAWT using a data-driven workflow that links empirical modeling to implementable pitch control. To focus on fundamental regulation dynamics, the turbine is modeled as a rigid single-mass drivetrain driven by identified quasi-steady aerodynamics. First, we identify a compact shaft-power surface $P(\omega ,V,\beta )$ and recover the associated MPP condition, which clarifies why the optimal rotor speed rises with wind and motivates a comparison between capped-MPP operation and constant-speed regulation. We then synthesize a practical Region-3 loop—PI in rate with a first-order pitch servo and saturation handling—and evaluate proportional (P), PI, and PI + servo controllers under sinusoidal and Kaimal-turbulent inflow. Finally, we propose an adaptive PI variant that keeps a fixed acceleration feed-through but retunes the integral path online via ARX(1,1) + RLS to maintain a target closed-loop bandwidth. Performance metrics computed over the full simulation window (t ∈ [0, 50] s) show that P-only control exhibits large steady bias and cap violations; PI recenters speed and power around their targets; adding a pitch servo further trims peaks and ripple. In steady-state turbulent tests, PI + servo achieves tight regulation, Δω_peak ≈ 0.033% (0.079 rad/s), P_RMS ≈ 0.62%, while the adaptive PI maintains similar tightness with the lowest variability overall Δω_peak ≈ 0.0104% (0.025 rad/s), P_RMS ≈ 0.17. The workflow yields a practically implementable β(V) schedule and a lightweight adaptation mechanism that compensates for slow aerodynamic performance drift without changing the control structure. While structural loads and aeroelastic modes are not explicitly modeled, the proposed controller enforces strict speed and power constraints via a rigid-body dynamic analysis. Extensions to IPC, preview/forecast augmentation, and validation on higher-fidelity aeroelastic/drivetrain models are identified as future work. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

In modern Industry 4.0 environments, real-time scheduling presents a complex challenge requiring both formal correctness guarantees and optimal performance. Background: Traditional approaches fail to provide an optimal integration between formal correctness guaranteeing and optimization, and such failure either produces suboptimal results or a correct result lacking guarantee, and studies have indicated that poor scheduling decisions could cause productivity losses of up to 20–30% and increased operational costs of up to USD 2.5 million each year in medium-scale manufacturing facilities. Methods: This work proposes a new hybrid approach by integrating Extended Time Petri Nets (ETPNs) and Finite-State Automata (FSAs) with formal modeling, abstracting ETPNs by extending conventional Time Petri Nets to deterministic time and priority systems, accompanied by Genetic Algorithms (GAs) to optimize the solution to tackle a multi-objective optimization problem. Our solution tackles indeterministic problems by incorporating suitable priority resolution methods and GA to pursue optimal solutions to very complex scheduling problems and starting accurately from standard real-time scheduling-policy models such as DM, RM, and EDF-EDF. Results: Experimental evaluation has clearly verified performance gains up to 48% above conventional techniques, covering completely synthetic and practical case studies, including 31–48% improvement on synthetic benchmarks, 24% increase on resource allocation, and total elimination of constraint violations. Conclusions: The new proposed hybrid technique is, to a considerable extent, a dramatic advancement within real-time scheduling techniques and Industry 4.0, successfully and effectively integrating optimal correctness guaranteeing and favorable GA-aided optimization techniques, which particularly guarantee optimal correctness to safe-related applications and provide considerable improvements to support efficient and optimal performance, extremely helpful within Industry 4.0. Full article

With the advancement of industrial systems toward greater complexity and higher asset value, unexpected equipment failures now risk severe production interruptions, substantial economic costs, and critical safety hazards. Conventional maintenance strategies, which are primarily reactive or schedule-based, have proven inadequate in preventing unplanned downtime, underscoring a pressing demand for more intelligent monitoring solutions. In this context, intelligent spectral detection has arisen as a transformative methodology to bridge this gap. This review explores the integration of spectroscopic techniques with machine learning for equipment defect diagnosis and prognosis, with a particular focus on applications such as hydrogen leak detection and transformer oil aging assessment. Key aging indicators derived from spectral data are systematically evaluated to establish a robust basis for condition monitoring. The paper also identifies prevailing challenges in the field, including spectral data scarcity, limited model interpretability, and poor generalization across different operational scenarios. Future research directions emphasize the construction of large-scale, annotated spectral databases, the development of multimodal data fusion frameworks, and the optimization of lightweight algorithms for practical, real-time deployment. Ultimately, this work aims to provide a clear roadmap for implementing predictive maintenance paradigms, thereby contributing to safer, more reliable, and more efficient industrial operations. Full article

Reliability and safety of high-voltage transmission lines are essential for stable and continuous operation of a power system. Environmental factors such as pressure, temperature, surface contamination, humidity, etc., significantly affect the dielectric strength of air, often causing unpredictable voltage breakdowns. This research presents a novel machine learning-based predictive framework that integrates Paschen’s Law with simulated and empirical data to estimate the breakdown voltage $\left(V_{bk}\right)$ of transmission lines in various environmental conditions. The main contribution is to demonstrate that data-driven prediction of breakdown voltage $\left(V_{bk}\right)$ using a hybrid machine learning model is in agreement with physical discharge theory. The model achieved root mean square error (RMSE) of 5.2% and mean absolute error (MAE) of 3.5% when validated against field data. Despite the randomness of avalanche breakdown, model predictions strongly match experimental measurements. This approach enables early detection of insulation stress, real-time monitoring, and optimises maintenance scheduling to reduce outages, costs, and safety risks. Its robustness is confirmed experimentally. Overall, this work advances the prediction of avalanche breakdown behaviour using machine learning. Full article

Musculoskeletal disorders (MSDs) remain one of the most persistent occupational health challenges in the U.S. construction industry, where physically demanding tasks such as heavy lifting, kneeling, and working in awkward postures contribute to elevated injury rates. This study aims to identify significant job-related determinants of MSDs in construction-sector occupations. By integrating publicly available datasets from the Survey of Occupational Injuries and Illnesses (SOII) and the Occupational Information Network (O*NET) datasets, a stepwise multiple regression analysis was conducted on 344 occupation-condition observations representing 86 construction occupations, yielding a final model that explained 49% of the variance. Ten significant predictors of MSD events were identified and classified as either risk amplifiers or mitigators. Amplifiers included factors such as exposure to noise, disease, hazardous conditions, and time pressure, all of which heightened MSD risk, while mitigators—such as reduced cramped-space exposure and regulated work environments—were associated with lower risk. MSDs resulting from sprains, strains, or tears accounted for 62.8% of all cases, frequently leading to days away from work (36.3%) or job restrictions (26.5%). The findings underscore that ergonomic risk in construction extends beyond physical strain to include scheduling, equipment design, and work organization. These results provide actionable insights for employers and safety professionals to redesign tools, optimize task rotation, and implement realistic work pacing strategies, ultimately reducing MSD incidence and improving productivity in this high-risk sector. Full article

This paper introduces a novel, integrated optimization framework for sustainable multi-site logistics planning, which simultaneously addresses production, inventory, and distribution decisions. The proposed hybrid methodology combines a Genetic Algorithm (GA) with Linear Programming (LP) to minimize total logistics costs while proactively integrating environmental impact assessment. The model determines optimal production schedules across multiple facilities, manages inventory levels, and solves the Vehicle Routing Problem (VRP) for distribution. A key innovation is the incorporation of a CO₂ emission forecasting module directly into the optimization loop, allowing the algorithm to anticipate and mitigate the environmental consequences of logistics decisions during the planning phase, rather than performing a post-hoc evaluation. The framework was implemented in Python 3.13.4, utilizing the PuLP library for LP components and custom-developed GA routines. Its performance was validated through a numerical case study and a series of sensitivity analyses, which investigated the effects of fluctuating demand and key cost parameters. The results demonstrate that the inclusion of emission forecasting enables the identification of solutions that achieve a superior balance between economic and environmental objectives, leading to significant reductions in both total costs and predicted CO₂ emissions. This work provides practitioners with a scalable and practical decision-support tool for designing more sustainable and resilient multi-echelon supply chains. Full article

As one of the most widely used high-performance kernels, General Matrix Multiplication, or GEMM, plays a pivotal role in diverse application fields. With the growing prevalence of training for Convolutional Neural Networks (CNNs) and Large Language Models (LLMs), the design and implementation of high-efficiency, low-precision GEMM on modern Neural Processing Unit (NPU) platforms are of great significance. In this work, HGEMM for Ascend NPU is presented, which enables collaborative processing of different computation types by Cube units and Vector units. The major contributions of this work are the following: (i) dual-stream pipeline scheduling is implemented, which synchronizes padding operations, matrix–matrix multiplications, and element-wise instructions across hierarchical buffers and compute units; (ii) a suite of tiling strategies and a corresponding strategy selection mechanism are developed, comprehensively accounting for the impacts from M, N, and K directions; and (iii) SplitK as well as ShuffleK methods are raised to address the challenges of memory access efficiency and AI Core utilization. Extensive evaluations demonstrate that our proposed HGEMM achieves an average 3.56× speedup over the CATLASS template-based implementation under identical Ascend NPU configurations, and an average 2.10× speedup relative to the cuBLAS implementation on Nvidia A800 GPUs under general random workloads. It also achieves a maximum computational utilization exceeding 90% under benchmark workloads. Moreover, the proposed HGEMM not only significantly outperforms the CATLASS template-based implementation but also delivers efficiency comparable to the cuBLAS implementation in OPT-based bandwidth-limited LLM inference workloads. Full article

The dramatic shift in human behavior from hunter-gatherer to modern lifestyles has led to a systematic disruption of the human circadian cycle. Contributors include night-shift work, jet lag, and less intuitive but widespread factors, such as exposure to artificial light at night and irregular eating schedules. Circadian disruption is classified as a Group 2A carcinogen by the International Agency for Research on Cancer (IARC). Hepatocellular carcinoma (HCC) is the third most deadly cancer worldwide, with a rising prevalence in Western countries, largely driven by increasing rates of obesity and steatotic liver disease-associated hepatocarcinogenesis. Emerging evidence suggests that circadian disruption plays a significant role in HCC pathogenesis. Several genes involved in metabolism, cell survival, and immunosurveillance are under the control of circadian rhythms. Experimental preclinical data and epidemiological studies have indicated a strong association between circadian disruption and HCC development. Moreover, molecular signatures related to circadian regulation appear to accurately predict the prognosis of patients with HCC. The concept of chronotherapy is also gaining interest, with studies suggesting improved immunotherapy effectiveness when immune checkpoint inhibitors are administered in the morning. This review summarizes the current literature on the impact of circadian disruption on HCC pathogenesis, prognosis, and treatment. Full article

This paper considers the two parallel-machine scheduling problem with intree-precedence constraints where machines are subject to non-availability constraints. In the literature, this problem is considered to be an open problem of unknown complexity. The proposed solution proves that the problem under consideration has polynomial complexity. Periods of machine unavailability are predetermined, and both task execution and inter-task communication are modeled as requiring one unit of time. The optimization criterion central to this study is the minimization of the makespan. Such a scheduling challenge is directly applicable to manufacturing environments, where production equipment can be intermittently offline for reasons such as unscheduled repairs or planned preventative maintenance. Adopting a unit-time task model offers a valuable framework for subsequently scheduling larger, preemptable jobs.This work presents a new method, called Scheduling Intrees with Unavailability Constraints (SIwUC), which operates by aggregating tasks into distinct groups. The analysis establishes that the SIwUC algorithm produces optimal schedules and reveals how the underlying problem architecture and its solutions demonstrate a symmetrical property in the distribution of tasks across the two parallel machines. This paper demonstrates that the proposed SIwUC algorithm builds optimal schedules and highlight how the problem structure and its solutions exhibit a form of symmetry in balancing task allocation between the two parallel machines. Full article

People living in rural communities are typically underserved by public transportation services and face challenges in accessing healthcare, jobs, stores, and other destinations. Understanding the lived experiences of people who use public transportation in rural communities can help to inform a more equitable transportation system. This qualitative study gathered the perspectives of community health center employees about the public bus system for Hawai‘i Island, a rural county in the United States. Using a community-engaged research approach, the evaluation team interviewed 10 employees through either in-person small group interviews or online individual interviews between April and July 2023. Transcripts were coded and analyzed using a thematic analysis approach. While all study participants were selected for their interest in commuting to work by bus, most believed the bus was not a reliable or convenient option. Participants shared their experiences about not being able to rely on the bus schedule, feeling unsafe while walking to bus stops or waiting for the bus, and other barriers to using the bus system. Participants also shared their insights about how a reliable bus system would benefit community health center patients who needed transportation to more than just their medical appointments, but also to places like pharmacies, laboratory services, and grocery stores. These findings can be used to initiate discussions around the ways that community health centers can further address transportation as a social determinant of health and inform transportation providers about how to best plan and invest in transportation infrastructure and services to meet the needs of rural populations. Full article

Industrial wireless sensor and actuator networks (IWSANs) are central to Industry 4.0, supporting distributed sensing, actuation, and communication in cyber-physical production systems. Unlike previous studies, which focus on isolated constraints, this review synthesises recent work across eight coupled dimensions. These span reliability and fault tolerance, security and trust, time synchronisation, energy harvesting and power management, media access control (MAC) and scheduling, interoperability, routing and topology control, and real-world validation, within a unified comparative framework. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a Scopus search identified 60 primary publications published between 2022 and 2025. The analysis shows a clear shift from reactive designs to predictive approaches that incorporate learning methods and energy considerations. Fault detection now relies on deep learning (DL) and statistical modelling, security incorporates trust and intrusion detection, and new synchronisation and MAC schemes approach wired levels of determinism. Regarding applied contributions, the analysis notes that routing and energy harvesting advances extend network lifetime. However, gaps remain in mobility support, interoperability across protocol layers, and field validation. The present work outlines these open issues and highlights research directions needed to mature IWSANs into robust infrastructure for Industry 4.0 and the emerging Industry 5.0 vision. Full article