Search Results (429)

Background/Objectives: Healthcare facilities are among the most energy-intensive public buildings, yet hospital decision-support models rarely integrate energy-related performance indicators alongside operational metrics. This study aims to address this gap by developing a discrete-event simulation framework capable of jointly evaluating clinical efficiency and energy consumption in elective orthopedic surgical pathways. Methods: A comprehensive discrete-event simulation model was developed to represent the diagnostic imaging and orthopedic surgical process. The model was parameterized using a hybrid data-collection approach that combined clinical activity data, scientific literature, and expert judgment. Energy consumption was modeled by differentiating fixed loads, such as heating, ventilation, and air-conditioning systems and lighting, from activity-dependent loads associated with diagnostic and surgical equipment. Baseline performance was assessed and compared with alternative scenarios for organizational and technological improvements. Results: The analysis showed that fixed infrastructural loads, particularly HVAC systems, were the main drivers of per-patient energy consumption, with inefficient space utilization and prolonged idle times. Scenario analysis demonstrated that organizational interventions, such as increasing operating room throughput and optimizing MRI scheduling, can substantially reduce energy intensity by diluting fixed loads and decreasing idle consumption. Technological interventions, such as replacing conventional surgical lamps with LED systems, produced smaller but still beneficial reductions. The combined implementation of organizational and technological strategies yielded the greatest overall improvement. Conclusions: Integrating energy metrics into discrete-event simulation provides effective support for hospital decision-making by revealing the interaction between workflow design, resource utilization, and environmental performance. The findings indicate that organizational redesign, particularly when combined with technological upgrades, can significantly improve both operational efficiency and sustainability in hospital settings. This study highlights discrete-event simulation as a promising tool for energy-aware healthcare planning. Full article

Intermittently operated, tankless reverse osmosis (RO) systems are widely used in decentralized and point-of-use applications, yet water stagnation during idle periods remains a critical challenge, leading to degraded water quality, accelerated fouling, and performance loss. This study presents an experimentally validated engineering solution that eliminates stagnant water in intermittently operated RO systems. A dual-membrane RO configuration with flexible series–parallel switching was developed, enabling membranes to alternate between production and flushing modes. An adaptive control strategy, integrated into the system hardware, regulates membrane switching and flushing based on real-time feed-water quality. The proposed configuration and control framework was evaluated under representative intermittent operating conditions. Experimental results show that the zero-stagnant-water strategy effectively prevents residual water accumulation during shutdown and maintains stable permeate quality, with total dissolved solids consistently below 10 mg/L. Long-term testing further demonstrates reduced membrane fouling and slower performance degradation compared with conventional fixed-operation schemes, resulting in enhanced desalination efficiency and operational stability. Owing to its modular design and simple control logic, the proposed approach is readily transferable to decentralized and point-of-use membrane water treatment systems requiring reliable, high-quality water under intermittent operation. Full article

This paper studies the reliability and availability of a k-out-of-(M+S) retrial machine repair system with two-way communication, consisting of M primary components and S warm standby components. The system incorporates the retrial behavior of failed components. When the repairman becomes idle, he initiates outgoing calls after a random period either to failed components in the orbit for repair or to components outside the orbit for preventive maintenance. The main contribution of this study is the incorporation of proactive repairman behavior, which more realistically captures operational practices in certain engineering systems. By employing the matrix analytic method together with a recursive approach, the steady-state probabilities of the system are obtained, and several important performance measures are derived. Furthermore, the Runge–Kutta method is used to evaluate the system reliability and the mean time to failure. A sensitivity analysis is conducted to investigate the effects of key system parameters, supported by numerical experiments and graphical illustrations. Finally, a cost–benefit model is formulated, and a genetic algorithm is implemented to determine the optimal values of the decision variables that minimize the cost–benefit ratio. Full article

Fleet electrification is increasingly recognized as a cornerstone of urban decarbonization in high-density megacities. This study introduces a multi-scenario simulation framework integrating high-resolution mobile signaling data with traffic modeling to quantify the systemic environmental and energy impacts of road-based battery electric vehicle (BEV) integration in Shanghai. By evaluating both a fixed-fleet baseline and dynamic-fleet growth scenarios focused on the urban road network, we find that aggressive fleet electrification leads to a profound reduction in aggregate carbon emissions and criteria pollutants, effectively decoupling transit-related environmental burdens from urban growth. However, results also highlight a significant energy trade-off: while fossil fuel displacement accelerates, grid-based electricity demand increases under fleet growth conditions. Within this context, the expanded vehicle population exacerbates urban congestion, which disproportionately inflates the fuel consumption of remaining internal combustion vehicles. Their operational efficiency is severely compromised by frequent stop-and-go cycles, leading to an intensification of idling losses. Ultimately, this research highlights the capability of the proposed simulation framework to provide granular insights into urban emission dynamics, offering a quantitative foundation for policymakers to harmonize electrification targets with proactive traffic management and grid infrastructure strengthening to evaluate the systemic trade-offs toward achieving long-term urban sustainability. Full article

Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article

While direct current (DC) ice-melting is currently adopted for some transmission lines, its application to 10 kV distribution transformers—often located in remote and rugged terrain—presents significant operational challenges. Disconnecting these transformers prior to ice-melting is a complex procedure that incurs substantial labor, material, and financial costs. Leaving transformers connected risks DC current flowing into idle windings, potentially causing damage. Furthermore, existing mobile DC ice-melting power supplies are bulky and impose stringent transportation requirements, rendering them unsuitable for use on mountain roads. To overcome these limitations, this paper proposes a compact, lightweight variable-frequency ice-melting device. The operating principle and output characteristics of the variable-frequency method are investigated in detail. Using Simulink, system modeling and simulation analyses are performed to obtain the voltage and current output characteristics, along with harmonic spectra. Simulation results demonstrate that the proposed device achieves significant miniaturization compared with conventional solutions: within the typical parameter range of conventional devices, the volume can be reduced by 44–58% and the weight by 43–52%. In addition, the selected LC filter parameters (L = 10.39 mH, C = 86.62 μF) represent an optimized compromise solution that effectively suppresses input harmonics while maintaining the output current total harmonic distortion (THD) within an acceptable limit of 3.6%. Experimental results further validate the feasibility of the variable-frequency ice-melting current. Based on a matrix converter topology, the proposed device enables flexible adjustment of the output melting voltage and frequency, exhibits excellent low-frequency performance and dynamic response, and maintains low output harmonic content—fully meeting the application requirements for variable-frequency ice-melting. The key novelty lies in a compact matrix-converter-based de-icing device with systematic low-frequency performance analysis, offering superior portability and adaptability over traditional DC solutions. Full article

Efficient task allocation and coordination are critical for heterogeneous multi-agent systems operating in dynamic field environments. This paper presents a closed-loop framework that integrates Large Language Models (LLMs) with graph-based optimisation to enable end-to-end task decomposition, allocation, and adaptive execution. High-level task scripts are initially parsed by an LLM into structured execution flows, which are transformed into Directed Acyclic Graphs (DAGs) capturing action-level dependencies. A Genetic Algorithm (GA) then optimises agent-to-task assignments by minimising makespan under capability and battery constraints. To ensure robustness, the framework incorporates an LLM-driven recovery module that enables localised replanning under execution failures without interrupting unaffected agents. System-level experiments in a high-fidelity agroforestry simulation demonstrate a 37% increase ($p<0.001$) in harvesting productivity and a 19% reduction in human idle time compared to manual baselines. Under mid-execution failures, the system maintains significantly higher performance, with replanning latencies averaging 24 s. The framework scales to large fleets (up to 1000 agents) and effectively enhances human–robot collaboration through structured, dependency-aware coordination. Full article

Respiratory rate (RR) is a critical indicator of physiological status, yet unobtrusive and continuous RR monitoring remains challenging, particularly in wearable applications that require soft, lightweight, and low-power sensing systems. This paper presents an integrated approach that combines a textile-embedded embroidered strain-gauge sensor with Tiny Machine Learning (TinyML) to enable real-time, on-device RR estimation. The sensing platform consists of a textile-integrated meander-pattern strain gauge and a fabric-mounted analog readout circuit, which together capture thoracic expansion during breathing. Two lightweight neural network models—a convolutional neural network (CNN) operating on raw respiratory waveforms and a dense neural network (DNN) operating on wavelet features—were developed and trained using a public strain-sensor dataset and a custom dataset collected with the textile system (TexHype dataset). Both models were optimized through 8-bit quantization and deployed to an STM32L4 microcontroller, where end-to-end on-device preprocessing, filtering, segmentation, normalization, and inference were performed. The CNN achieved the highest accuracy, with a mean absolute error (MAE) of 1.23 breaths per minute (BPM) on the TexHype dataset, but exhibited substantial inference latency (5.8–6.2 s) due to its computational complexity. In contrast, the wavelet-based DNN demonstrated lower accuracy (MAE 2.21 BPM) but achieved real-time performance with inference times of 18–96 ms, and a power overhead ($\Delta P=P_{\mathrm{active}}-P_{\mathrm{idle}}$) of approximately 3.3 mW during inference. Cross-dataset testing revealed limited generalization between different strain-sensor platforms. The findings highlight key trade-offs between accuracy, latency, and energy efficiency, and illustrate the potential of combining stretchable electronics with embedded intelligence to enable next-generation wearable respiratory monitoring systems. Full article

Food delivery logistics sustainability includes environmental impact, economic efficiency, and service quality. Traditional logistics models mainly rely on fixed “pickup buffer” policies (such as a set 10 min wait). These systems do not account for the changing nature of restaurant operations and delivery conditions, leading to higher operating costs, driver idle time, and poorer food quality. To move delivery systems from reactive decision-making to proactive, dynamically forecasted operations, an adaptive control mechanism is needed. In on-demand food delivery, this offers a clear path to sustainability through better dispatch accuracy, order prep, and pickup coordination. To resolve these bottlenecks, this study examines how a smart logistics framework based on a dynamic Gradient Boosting Regressor (GBR) and policy-sensitive GBR can provide more accurate estimates of drivers’ waiting times in light of contextual factors such as rush hour, time of day, and operational constraints. In last-mile food delivery, the proposed method aims to reduce operational costs, improve scheduling effectiveness, and maximize resource utilization by moving beyond static, predefined waiting periods to adaptive, context-aware decisions. The developed framework analyzes a proprietary dataset of 368,250 instant orders from a major Saudi Arabian logistics provider to evaluate the efficacy of static thresholds versus a proposed predictive, dynamic machine-learning-based approach. After rigorous data cleaning and temporal-logic adjustments, a “High-Fidelity Ground-Truth” subset of 1842 verified orders is used to simulate policy performance. This 99.5% reduction is necessitated by the widespread absence of the “Order Ready” timestamp in operational logs, which is the critical target variable for supervised learning; comparative analysis confirms the subset remains representative of the parent population’s spatiotemporal dynamics. The baseline analysis reveals severe inefficiencies in the static model, with a 61.67% violation rate for driver wait times, particularly in Riyadh ($p<0.001$) and during late-night operations. The simulation results demonstrate that the dynamic policy reduces the “Buffer Miss Rate” (premature driver arrivals) from 59.08% to 7.32%, resulting in a 68.5% reduction in total operational waste costs. Full article

This paper presents a deterministic embedded control architecture for an industrial electroplating line. The validated system includes two autonomous trolleys, 18 station-aligned process positions, shared-track motion, and redundant grouped baths. The proposed controller addresses the limitations of rigid sequential automation by combining asynchronous finite-state trolley execution, runtime allocation of equivalent technological stations, dwell-time-preserving retrieval, distributed thermal supervision, and layered fail-safe protection within a single ATmega2560-based implementation. The core contribution is the integration of virtual process groups and temporal FIFO logic into a compact plant-side embedded controller. This enables adaptive bath selection and process-completion-based retrieval without reliance on a real-time operating system or a computationally heavy supervisory runtime. The architecture also incorporates predictive pre-start validation, runtime software arbitration, hardware-wired interlocks, binary-coded trolley positioning, and a distributed 1-Wire thermal measurement network. Validation was performed in a controller-centered hardware-in-the-loop representation of an 18-station zinc electroplating line. Over a 100-batch horizon, the proposed architecture reduced makespan from 1642 min to 1244 min, corresponding to a 24.2% throughput improvement. Average trolley idle time decreased from 18.4 min/batch to 4.1 min/batch. Grouped-bath utilization increased from 64% to 91%, while tracked bottleneck incidents decreased from 18 to 2. These results show that adaptive, resource-aware, and safety-layered electroplating control can be realized effectively on a compact embedded platform in an industry-representative HIL setting, while preserving dwell-time integrity and controller-level safety invariants. Full article

The increasing need to reduce reliance on fossil-derived aviation fuels and mitigate environmental impacts has intensified research into renewable alternatives for aviation energy systems. The growing interest in ethanol-based fuels is primarily driven by their simple oxygen-rich molecular structure and advantageous physicochemical characteristics, yet experimental studies examining their application in hybrid power architectures, including micro-turboprop engine-based power sources, are still limited. This study presents an experimental investigation of ethanol–Jet A1 fuel blends used in a micro-turboprop engine operating as a power generation unit for unmanned aerial vehicle applications. Ethanol was blended with Jet A1 at volumetric fractions of 10%, 20% and 30% and the engine was tested under multiple operating regimes corresponding to different electrical power outputs. Exhaust gas temperature, electrical power output and gaseous emissions (CO and NO_x) were measured for each operating condition. The results indicate that low ethanol fractions (E10) provide performance comparable to neat kerosene, while higher ethanol fractions lead to a reduction in exhaust gas temperature at low-power regimes due to the lower heating value and high latent heat of vaporization of ethanol. Emission measurements showed a decrease in NO_x emissions with increasing ethanol content, associated with lower combustion temperatures, while CO emissions increased at low-power regimes due to incomplete combustion under lean conditions. Additionally, combustion instability was observed during rapid transitions from maximum to idle regime operation for higher ethanol blends, attributed to transient ultra-lean mixtures, evaporative cooling, and reduced reaction rates. The results demonstrate that ethanol–kerosene blends can be used in micro-turboprop systems at low blend ratios without major performance penalties, but transient operating conditions impose stability limits that must be considered in practical UAV power system applications. Full article

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism and a state-of-charge (SOC)-based dual closed-loop current control strategy. In the proposed topology, a Boost converter is introduced to actively enhance the effective voltage difference between cells, thereby improving the equalization current amplitude and accelerating the balancing process. A switched-inductor structure is further adopted to enable scalable inter-group energy transfer in multi-cell battery systems. To improve control accuracy, SOC is selected as the balancing variable, and a dual closed-loop control framework is designed, where the outer loop regulates SOC deviation, and the inner loop controls the equalization current via proportional–integral (PI) controllers. A MATLAB/Simulink model is established to evaluate the proposed method under multiple operating conditions, including idle, charging, and discharging states. The results show that the proposed topology significantly reduces the equalization time compared with conventional LC resonant circuits and improves balancing speed by approximately 49% under the dual closed-loop control strategy. In addition, the system maintains stable performance across different operating conditions. It should be noted that this study focuses on topology design and control strategy validation through simulation. Due to the focus on topology validation and control mechanism analysis, this study is limited to simulation-based verification. Experimental implementation will be conducted in future work. Full article

This paper presents a dual-core RISC-V architecture designed for energy-efficient AI acceleration at the edge, featuring dynamic MAC unit sharing, frequency scaling (DFS), and FIFO-based resource arbitration. The system comprises two RISC-V cores that compete for shared computational resources—a single Multiply–Accumulate (MAC) unit and a shared external memory subsystem—governed by a channel-based arbitration mechanism with CPU-priority semantics, while each core maintains private instruction and data caches. The architecture implements a tightly coupled Neural Processing Unit (NPU) with CONV, GEMM, and POOL operations that execute opportunistically in the background when the MAC unit is available. Dynamic frequency scaling (DFS) with three levels (100/200/400 MHz) is applied to the shared MAC unit, allowing the dynamic acceleration of CNN workloads. The arbitration mechanism uses SystemC sc_fifo channels with CPU-priority polling, ensuring that CPU execution is minimally impacted by background AI processing while the NPU makes progress during idle MAC slots. The NPU supports 3 × 3 convolutions, matrix multiplication (GEMM) with 10 × 10 tiles, and pooling operations. The implementation is cycle-accurate in SystemC, targeting FPGA deployment. Experimental evaluation demonstrates that the dual-core architecture achieves 1.87× speedup with 93.5% efficiency for parallel workloads, while DFS enables 70% power reduction at low frequency. The system successfully executes simultaneous CPU and AI workloads, with CPU-priority arbitration ensuring no CPU starvation under contention. The proposed design offers a practical solution for embedded AI applications requiring both general-purpose computation and neural network acceleration, validated through comprehensive SystemC simulation on modern FPGA platforms. Full article

To broaden the application of biomethanation for energy storage and renewable integration, this study investigates the performance of a trickle-bed reactor (TBR) for hydrogen (H₂) utilisation in biogas upgrading, using both pure Carbon dioxide (CO₂) and biogas-derived CO₂ as substrates for methane (CH₄) production. Renewable sources such as wind and solar are inherently variable, increasing the need for scalable storage solutions. Converting surplus electricity into H₂ and CH₄ via biological methanation offers an efficient and safer alternative to direct H₂ storage. By reducing CO₂ produced by biogas plants, methanogenic archaea produce CH₄, enabling H₂ valorisation and enhanced biogas yields. This study demonstrates that TBR technology can achieve CH₄ formation rates up to 15 L-CH₄/L-reactor/day under optimised conditions. Siporax carrier material supported dense biofilm formation and effective gas–liquid mass transfer, facilitating high conversion efficiency. The system showed operational robustness, with rapid recovery after prolonged idle periods and stable production rates of 10–12 L-CH₄/L/day. Wastewater was used as a realistic medium to assess reactor performance under complex, variable conditions. Reactor design focused primarily on enhancing gas–liquid mass transfer and supporting sustained microbial activity through adequate nutrient supply, ensuring sufficient buffer capacity to maintain pH stability. These results demonstrate the potential of TBR-based systems for high-rate, stable biomethanation and highlight their applicability in future energy infrastructures for integrating H₂ through decentralised biogas upgrading. Full article

Battery-powered edge systems must operate under tight energy budgets while facing growing computational demand from rapidly evolving edge workloads. Field-programmable gate arrays (FPGAs) offer middle ground when optimized for energy, especially flash-based FPGAs due to inherent low-power characteristics. Microchip flash-based SoC FPGAs further expose ultra-low-power (LP) modes including fabric Flash*Freeze (F*F), processor sleep and selectable standby clocks. Combining these modes with HW/SW partitioning and clock-frequency scaling can reduce energy for low-duty-cycle workloads; however, selecting an energy-efficient operating point in this multidimensional design space is non-trivial. This work explores the design space by measuring and analyzing LP modes across three architectural approaches (SW, co-design, and HW) under frequency scaling on a Microchip Smartfusion2 platform, using a low-duty-cycle heart-rate monitoring workload. Measurements indicate that, for low-duty-cycle workloads, total energy is dominated by the idle phase and is minimized by combining fabric-F*F with processor sleep. The results further show that main-clock downscaling reduces active-phase current but has limited impact on idle consumption once F*F and sleep are applied, while standby-clock selection trades idle current against LP entry/exit latency. Event-rate scaling further shows that the energy-optimal operating point can shift with duty cycle. We provide measurement-based guidelines for duty-cycle-aware energy-efficient operating point selection in similar flash-based SoC platforms. Full article