Search Results (69)

Advances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remains challenging due to the variability in machine operations and the lack of access to internal control data. This paper introduces a lightweight, embedded-compatible framework for health status signature extraction based on empirical mode decomposition (EMD), leveraging only data from a single triaxial accelerometer. The core of the proposed method is a cycle-synchronized segmentation strategy that uses accelerometer-derived velocity profiles and cross-correlation to align signals with machining cycles, eliminating the need for controller or encoder access. This ensures process-aware decomposition that preserves the operational context across diverse and dynamic machining conditions to address the inadequate segmentation of unstable process data that often fails to capture the full scope of the process, resulting in misinterpretation. The performance is evaluated on a challenging real-world manufacturing benchmark where the extracted intrinsic mode functions (IMFs) are analyzed in the frequency domain, including quantitative evaluation. As results show, the proposed method shows its effectiveness in detecting subtle degradations, following a low computational footprint, and its suitability for deployment in embedded predictive maintenance systems on brownfield or controller-limited machinery. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the $\tau$-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the $\tau$-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments. Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for collaborative search missions in complex environments. However, in the presence of interference, communication disruptions between UAVs and ground control stations can severely degrade coordination efficiency, leading to prolonged search times and reduced mission success rates. To address these challenges, this paper proposes a novel multi-agent deep reinforcement learning (MADRL) framework for joint spectrum and search collaboration in multi-UAV systems. The core problem is formulated as a combinatorial optimization task that simultaneously optimizes channel selection and heading angles to minimize the total search time under dynamic interference conditions. Due to the NP-hard nature of this problem, we decompose it into two interconnected Markov decision processes (MDPs): a spectrum collaboration subproblem solved using a received signal strength indicator (RSSI)-aware multi-agent proximal policy optimization (MAPPO) algorithm and a search collaboration subproblem addressed through a target probability map (TPM)-guided MAPPO approach with an innovative action-masking mechanism. Extensive simulations demonstrate superior performance compared to baseline methods (IPPO, QMIX, and IQL). Extensive experimental results demonstrate significant performance advantages, including 68.7% and 146.2% higher throughput compared to QMIX and IQL, respectively, along with 16.7–48.3% reduction in search completion steps versus baseline methods, while maintaining robust operations under dynamic interference conditions. The framework exhibits strong resilience to communication disruptions while maintaining stable search performance, validating its practical applicability in real-world interference scenarios. Full article

Driven by growing environmental awareness and supportive regulatory frameworks, electric vehicles (EVs) are witnessing accelerating market penetration. However, a key consumer concern remains: the economic impact of battery degradation, manifesting as vehicle depreciation and diminished driving range. To alleviate this concern, EV manufacturers commonly offer performance-guaranteed free-replacement warranties, under which batteries are replaced at no cost if capacity falls below a specified threshold within the warranty period. This paper develops a symmetry-informed analytical framework to forecast time-varying aggregate warranty replacement demand (AWRD) and to design optimal battery inventory strategies. By coupling stochastic EV sales dynamics with battery performance degradation thresholds, we construct a demand forecasting model that presents structural symmetry over time. Based on this, two inventory optimization models are proposed: the Service-Level Symmetry Model (SLSM), which prioritizes reliability and customer satisfaction, and the Cost-Efficiency Symmetry Model (CESM), which focuses on economic balance and inventory cost minimization. Comparative analysis demonstrates that CESM achieves superior cost performance, reducing total cost by 20.3% while maintaining operational stability. Moreover, incorporating CESM-derived strategies into SLSM yields a hybrid solution that preserves service-level guarantees and achieves a 3.9% cost reduction. Finally, the applicability and robustness of the AWRD forecasting framework and both symmetry-based inventory models are validated using real-world numerical data and Monte Carlo simulations. This research offers a structured and symmetrical perspective on EV battery warranty management and inventory control, aligning with the core principles of symmetry in complex system optimization. Full article

The growing deployment of photovoltaic (PV) and energy storage systems (ESSs) in power grids has amplified concerns over component degradation, which undermines efficiency, increases costs, and shortens system lifespan. This paper proposes a quantum-enhanced optimization framework to mitigate degradation impacts in PV–storage systems through real-time adaptive energy dispatch. The framework combines quantum-assisted Monte Carlo simulation, quantum annealing, and reinforcement learning to model and optimize degradation pathways. A predictive maintenance module proactively adjusts charge–discharge cycles based on probabilistic forecasts of degradation states, improving resilience and operational efficiency. A hierarchical structure enables real-time degradation assessment, hourly dispatch optimization, and weekly long-term adjustments. The model is validated on a 5 MW PV array with a 2.5 MWh lithium-ion battery using real degradation profiles. Results demonstrate that the proposed framework reduces battery wear by 25% and extends PV module lifespan by approximately 2.5 years compared to classical methods. The hybrid quantum–classical implementation achieves scalable optimization under uncertainty, enabling faster convergence across high-dimensional solution spaces. This study introduces a novel paradigm in degradation-aware energy management, highlighting the potential of quantum computing to enhance both the sustainability and real-time control of renewable energy systems. Full article

Sustainable agricultural practices are essential for eradicating global hunger, especially in light of the growing world population. Utilizing natural antagonists, such as fungi and bacteria, to combat plant diseases, rather than relying solely on synthetic chemical pesticides, which pose significant risks to the environment and human health, is known as biocontrol. Microbial biological control agents (MBCAs) have proven effective against phytopathogens and are increasingly embraced in agricultural practices. MBCAs possess several beneficial traits, including antagonistic potential, rhizosphere competence, and the ability to produce lytic enzymes, antibiotics, and toxins. These biocontrol mechanisms directly target soil-borne pathogens or indirectly stimulate a plant-mediated resistance response. The effectiveness of MBCAs in managing plant diseases depends on various mechanisms, such as hyperparasitism, antibiosis, competition for nutrients or space, disruption of quorum-sensing signals, production of siderophores, generation of cell wall-degrading enzymes, and the induction and priming of plant resistance. Formulating effective biopesticides requires optimal conditions, including selecting effective strains, considering biosafety, appropriate storage methods, and ensuring a prolonged shelf life. Therefore, formulation is crucial in developing pesticide products, particularly concerning efficacy and production costs. However, several challenges must be addressed to ensure the successful application of biological control, including the shelf life of biopesticides, slower efficacy in pest management, inadequate awareness and understanding of biocontrol methods, regulatory registration for commercialization, and suitable agricultural applications. This review clarifies the principles of plant disease biocontrol, highlighting the mechanisms of action and functionality of MBCAs in biocontrol activities, the formulation of biopesticides derived from microorganisms, and the challenges and barriers associated with the development, registration, commercialization, and application of biopesticides. Full article

The non-uniform memory access (NUMA) architecture is the de facto norm in modern server processors. Applications running on NUMA processors may suffer significant performance degradation (NUMA effect) due to the non-uniform memory accesses, including data and page table accesses. Recent studies show that the NUMA effect of long-running memory-intensive workloads can be mitigated by replicating or migrating page tables to nodes that require accesses to remote page tables. However, this technique cannot adapt to the situation where other applications compete for the memory controller. Furthermore, it was only implemented on x86 processors and cannot be readily applied on ARM server processors, which are becoming increasingly popular. To address this issue, we designed the page table access latency aware (PTL-aware) page table auto-migration (Auto-PTM) mechanism. Then we implemented it on Linux ARM64 (the Linux kernel name for AArch64) by identifying the differences between the ARM architecture and the x86 architecture in terms of page table structure and the implementation of the Linux kernel source code. We evaluate it on real ARM NUMA servers. The experimental results demonstrate that, compared to the state-of-the-art PTM mechanism, our PTL-aware mechanism significantly enhances the performance of workloads in various scenarios (e.g., GUPS by 3.53x, XSBench by 1.77x, Hashjoin by 1.68x). Full article

The rapid adoption of electric vehicles (EVs) and the increasing reliance on renewable energy sources necessitate innovative charging infrastructure solutions to address key challenges in energy efficiency, grid stability, and sustainable transportation. Dynamic wireless charging systems, which enable EVs to charge while in motion, offer a transformative approach to mitigating range anxiety and optimizing energy utilization. However, these systems face significant operational challenges, including dynamic traffic conditions, uncertain EV arrival patterns, energy transfer efficiency variations, and renewable energy intermittency. This paper proposes a novel quantum computing-assisted optimization framework for the modeling, operation, and control of wireless dynamic charging infrastructure in urban highway networks. Specifically, we leverage Variational Quantum Algorithms (VQAs) to address the high-dimensional, multi-objective optimization problem associated with real-time energy dispatch, charging pad utilization, and traffic flow coordination. The mathematical modeling framework captures critical aspects of the system, including power balance constraints, state-of-charge (SOC) dynamics, stochastic vehicle arrivals, and charging efficiency degradation due to vehicle misalignment and speed variations. The proposed methodology integrates quantum-inspired optimization techniques with classical distributionally robust optimization (DRO) principles, ensuring adaptability to system uncertainties while maintaining computational efficiency. A comprehensive case study is conducted on a 50 km urban highway network equipped with 20 charging pad segments, supporting an average traffic flow of 10,000 EVs per day. The results demonstrate that the proposed quantum-assisted approach significantly enhances energy efficiency, reducing energy losses by up to 18% compared to classical optimization methods. Moreover, traffic-aware adaptive charging strategies improve SOC recovery by 25% during peak congestion periods while ensuring equitable energy allocation among different vehicle types. The framework also facilitates a 30% increase in renewable energy utilization, aligning energy dispatch with periods of high solar and wind generation. Key insights from the case study highlight the critical impact of vehicle alignment, speed variations, and congestion on wireless charging performance, emphasizing the need for intelligent scheduling and real-time optimization. The findings contribute to advancing the integration of quantum computing into sustainable transportation planning, offering a scalable and robust solution for next-generation EV charging infrastructure. Full article

Environmental degradation and climate change are of paramount concern and require urgent action. Physical education holds an important role in addressing environmental issues and contributing to environmental sustainability. The present study assesses the effectiveness of an intervention program to cultivate environmental awareness in fifth-grade children through physical activities and games in physical education. The participants were 74 children (39 girls and 35 boys) divided into two groups, the experimental and control group. Participants completed a scale assessing their knowledge of environmental issues, which had been shown by previous research to be valid and reliable for this population. The experimental procedure of the study included three stages: completing the questionnaire before the implementation of the intervention and at the end, as well as a retention measurement after four months. The results indicated that the students of the experimental group maintained a significantly higher level of knowledge of fundamental environmental concepts at the end of the program compared to their counterparts in the control group (p < 0.05). In addition, children of the experimental group seemed to retain their knowledge as a result of the intervention program (p > 0.05). The findings suggest that interventions using physical education can serve as a meaningful strategy for promoting environmental care and sustainability and turn young tourists into tomorrow’s responsible consumers and travelers who respect and protect nature. Finally, such a process could potentially aim to increase environmental awareness as a learning experience through the provision of physical activity and recreation. Full article

Proton exchange membrane fuel cell system (PEMFCS)-based battery-hybridized turboprop regional aircraft emerge as a promising solution to the urgency of reducing the environmental impact of such airplanes. The development of integrated simulation frameworks consisting of versatile and easily adaptable models and control strategies is deemed highly strategic to guarantee proper component sizing and in-flight, onboard energy management. This need is here addressed via a novel efficiency-driven PEMFCS model and a degradation-aware battery-PEMFCS unit specification-independent control algorithm. The proposed model simplifies stack voltage and current estimation while maintaining accuracy so as to support, in conjunction with the afore-introduced versatile control strategy, the development of architectures appropriate for subsequent fully integrated (i.e., at the entire aircraft design level) simulation frameworks. The model also allows assessing the balance of plant impact on the fuel cell system’s net power, as well as the heat generated by the stack and related cooling demand. Finally, the multi-stack configuration meeting the DC bus line 270 V constraint, as currently assumed by the aviation industry, is determined. Full article

This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) and use routing protocols to forward the sensed data of target(s) to an aerial base station (BS) in real-time through multihop communication, which can then transmit the data to a control center. However, the unpredictability of target locations and the highly dynamic nature of autonomous, decentralized UAV networks result in frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and can incur large control overhead and delays. In addition, their performance suffers from poor network connectivity in sparse networks with multiple objectives (exploration and monitoring of targets), which results in frequent route unavailability. To address these challenges, we propose two routing schemes: Pipe routing and TC-Pipe routing. Pipe routing is a mobility-, congestion-, and energy-aware scheme that discovers routes to the BS on-demand and proactively switches to alternate high-quality routes within a limited region around the routes (referred to as the “pipe”) when needed. TC-Pipe routing extends this approach by incorporating a decentralized topology control mechanism to help maintain robust connectivity in the pipe region around the routes, resulting in improved route stability and availability. The proposed schemes adopt a novel approach by integrating the topology control with routing protocol and mobility model, and rely only on local information in a distributed manner. Comprehensive evaluations under diverse network and traffic conditions—including UAV density and speed, number of targets, and fault tolerance—show that the proposed schemes improve throughput by reducing flow interruptions and packet drops caused by mobility, congestion, and node failures. At the same time, the impact on coverage performance (measured in terms of coverage and coverage fairness) is minimal, even with multiple targets. Additionally, the performance of both schemes degrades gracefully as the percentage of UAV failures in the network increases. Compared to schemes that use dedicated UAVs as relay nodes to establish a route to the BS when the UAV density is low, Pipe and TC-Pipe routing offer better coverage and connectivity trade-offs, with the TC-Pipe providing the best trade-off. Full article

To enhance maintenance endeavors, it is imperative to gain a deep understanding of system degradation. In systems with degradation-aware control, observing degradation becomes particularly challenging. Even with sensors, such controllers continuously mitigate deviations to ensure the system operates within optimal limits. Here, we propose a framework explicitly tailored for degradation-aware control systems, built upon two main components: (1) degradation modeling to estimate and track hidden degradation over time and (2) a Long Short-Term Memory Autoencoder-Degradation Stage Detector (A-LSTMA-DSD) to define alarm and failure thresholds for enabling condition-based maintenance. In degradation modeling, the framework utilizes actuator measurements to model hidden degradation. Next, an A-LSTMA-DSD model is developed to flag anomalies, based on which alarm and failure thresholds are assigned. These dynamic thresholds are defined to ensure sufficient time for addressing maintenance requirements. Working with real data from a boiler unit in an oil refinery and focusing on steam leakages, our proposed framework successfully identified all failures and on average triggered alarm and failure thresholds 15 and 8 days in advance of failures, respectively. In addition to triggering these thresholds, our system outperforms baseline models, such as CNN, LSTM, ANN, ARIMA, and Facebook Profit, in identifying failures by 60% and 95%, respectively. Full article

Ozone depletion, global warming, soil degradation, etc., could be, to a great extent, instrumental in making our Earth an unsafe place. Therefore, to prevent further damage, Article 6 of the United Nations Framework Convention on Climate Change (UNFCCC) emphasizes spreading awareness among the members of the planetary community to protect the planet. The study aims to identify teaching pedagogies that can effectively develop awareness and responsibility among university youth for a sustainable future. The study adopts an exploratory triangulation approach and uses three instruments: a closed-ended questionnaire, a focus group interview, and a comparative performance of control and experimental groups. Fifty-one faculties from two government universities of Saudi Arabia: Qassim University, Qassim, and Prince Sattam bin Abdulaziz University, Alkharj along with 47 students pursuing conversation courses at Level Three in Prince Sattam University participated in the study. JASP 0.9 open-source software was used for statistical analysis. The results revealed that constructivist inquiry-based approaches promoted sustainable development education. Full article

Land degradation is a critical issue in Ethiopia, exacerbating food insecurity by reducing agricultural productivity. Soil and water conservation (SWC) practices are essential to control erosion and increase food production. However, there is a lack of comprehensive evaluations on the impact of Ethiopia’s Productive Safety Net Program (PSNP) on SWC practices. This study aimed to assess the contribution of the PSNP to SWC in the Amhara Sayint Woreda. The researchers used a mixed-method approach, combining quantitative and qualitative data. Multistage sampling was used to select households, and data were collected through questionnaires, interviews, focus groups, and observations. The study provided empirical evidence that the PSNP has a positive impact on SWC practices. Key factors influencing SWC participation include age, family size, education, plot size, livestock ownership, credit service, and access to extension services. The results suggest that the PSNP should improve payment for public work participants implementing SWC, undertake institutional reform, and increase public awareness of the benefits of SWC in reversing land degradation and improving food security. This study uniquely contributes to the understanding of how the PSNP influences the varying degrees of participation in SWC practices, filling a critical research gap. The findings can inform policymakers and program managers to enhance the PSNP’s effectiveness in promoting sustainable land management and food security in Ethiopia. Full article