Search Results (70)

With the rapid growth of electric vehicles, charging demand at highway service areas has increased sharply, while insufficient charging facilities have intensified the mismatch between supply and demand. Existing studies on photovoltaic–energy storage–charging systems mainly focus on urban scenarios and rarely consider the spatiotemporal characteristics of long-distance highway travel. To address this gap, this study proposes a capacity planning method for photovoltaic–energy storage–charging systems in highway service areas. EV charging load is simulated using a Monte Carlo approach considering travel characteristics and state of charge, while an M/M/c queuing model is used to quantify user waiting time. A multi-objective optimization model considering system costs and waiting-time costs is solved using a multi-objective genetic algorithm, and the Pareto solutions are ranked by VIKOR. Under the normal-load scenario, the optimized configuration yields a weighted average waiting time of 5.06 min and reduces the maximum waiting time from 52 min to 11.83 min, with a construction and maintenance cost of RMB 2.5758 million. Under the high-load scenario, the corresponding values are 5.35 min, 12.12 min, and RMB 3.3078 million, respectively. The results show that the proposed method can adapt system capacity to different traffic demand levels while maintaining charging service quality. Full article

Refined oil depots are critical nodes in energy supply chains. Tank truck queuing increases costs and idling carbon emissions while reducing customer satisfaction. This research proposes a modified finite capacity Markovian queuing system (M/M/c/N) with random service interruptions and establishes a multi-objective model covering expected cost, carbon emissions and customer satisfaction. We derive core metrics and formulate corresponding unit time objective functions. The Bayesian optimization-based NSGA-II (BO-NSGA-II) hybrid algorithm is adopted to solve the proposed model. Comparisons against four algorithms are conducted using four multi-objective evaluation metrics: hypervolume (HV), Inverted Generational Distance (IGD), generational distance (GD) and spacing, which confirm that BO-NSGA-II achieves a balanced overall performance. Its HV reaches 0.657, while IGD (0.0066), GD (0.0009) and spacing (0.0069) remain low, demonstrating broad Pareto front coverage, high convergence accuracy and uniform solution distribution. Trade-off analysis indicates that the algorithm entails a marginal expected cost increase of merely 1.15% compared with the minimum expected cost solution obtained by MOEA/D. Meanwhile, carbon emissions drop to 11.25 kgCO₂/h and customer satisfaction rises to 0.9769, achieving coordinated economic, environmental and service benefits. Sensitivity analysis further identifies the optimal loading bay configuration range of 2 to 4. Insufficient allocation leads to congestion, while excessive allocation causes equipment idling and higher emissions. Blind expansion also raises the expected cost. This study provides methodological references for the collaborative optimization of oil depot resource allocation, low-carbon scheduling and service performance. Full article

High-value aviation rotable parts require accurate demand forecasting because each failure simultaneously creates a replacement demand, a repair workload, and a potential aircraft on-ground (AOG) risk. This study develops a continuous-time Markov chain (CTMC) queuing framework for forecasting demand and optimizing target stock levels under realistic maintenance, repair, and overhaul (MRO) capacity constraints. Historical service-engineering records from B737 mechanical engine control (MEC) units are first benchmarked and filtered to retain inherent random failures during the useful-life phase. A Kolmogorov-Smirnov goodness-of-fit test is then used to verify the exponential time-between-failure assumption required by the Markov model. Based on this validated failure-pattern classification, the study derives steady-state probability models for both an ideal $M/M/\infty$ repair system and a finite-capacity $M/M/c$ repair system. The main contribution is not the isolated use of Weibull or exponential reliability models, Markov chains, or inventory optimization, which are established methods, but their auditable integration into an airline rotable-parts workflow that links failure-pattern screening, finite repair capacity, service-level constraints, and engineering validation. The empirical results show that repair bottlenecks shift probability mass toward higher numbers of failed units, create a fat-tailed backlog distribution, and double the required target stock from 6 to 12 MEC units under a 95% service-level requirement. Sensitivity experiments further show that repair turnaround time, fleet size, and MRO channel capacity jointly determine the inventory-capacity cost trade-off. The proposed framework provides an interpretable decision tool for airlines to align spare-part procurement with actual repair-system performance. Full article

Smartwatches are increasingly used in safety-critical scenarios, yet their optical heart-rate (HR) measurements often contain noise, artifacts, and missing data, undermining clinical trust. This paper presents Edge Beats, a data-curation layer and end-to-end architecture that enables the low-cost, open source PineTime smartwatch to function as a practical HR sensing node for distributed wearable systems. Heart-rate packets are streamed from PineTime to an ESP32 at the edge layer over Bluetooth Low Energy (BLE), then forwarded via an embedded Message Queuing Telemetry Transport (MQTT) broker to an edge server laptop for processing and visualization. A lightweight multi-stage algorithm cleans and smooths the HR stream using physiological boundary checks, a configurable data imputation technique, and exponential moving average (EMA) smoothing, all designed for real-time operation on resource-constrained hardware. We have evaluated the system over long monitoring sessions and compared the processed PineTime output against a commercial Huawei GT Pro 2 smartwatch. The system suppresses extreme spikes and short-term oscillations, yielding a more stable HR trace with qualitative agreement to the reference trends while keeping values in a physiologically plausible range. Network measurements show low latency (almost 3 ms one-way, 15 ms RTT) and stable throughput, and power measurements (100–450 mW for ESP32 and 3–70 mW for PineTime watch) confirm that continuous HR streaming over BLE and MQTT is feasible within the PineTime’s energy budget. These results imply that data stream processing combined with a modest publish–subscribe architecture improves the stability and usability of HR streams obtained from commodity wearable sensors, making PineTime a candidate as a complementary component for mission-critical health and safety systems. Full article

To optimize the freight distribution structure of ports and reduce carbon emissions from freight transportation, this paper develops a bi-level programming model for freight traffic shifting between roadway and waterway networks that incorporates carbon emissions. First, a complex freight network based on the roadway–water transport system is constructed, comprising roadway networks, inland waterway networks, maritime networks, and transshipment nodes. A traffic impedance model is then formulated within this complex network framework, integrating the roadway BPR function, the M/M/1 queuing model for lock passage time on inland waterways, and the M/M/c queuing model for port cargo handling into the impedance function. This allows micro-level congestion effects to be combined with macro-level traffic assignment. Next, a bi-level programming model for freight traffic shifting in the roadway–water network system is established, with carbon emissions incorporated. The NSGA-II algorithm is employed to determine the optimal carbon subsidy level, based on which the traffic distribution in the complex freight network is analyzed. Finally, the proposed model is applied to the roadway–waterway bimodal network in the Hangzhou Bay port area of Cixi. The results indicate that without subsidies, the waterway transport share is only 1.74%. The optimal subsidy efficiency frontier is identified at CNY 350,000/day, where the waterway share increases to 22.7% and carbon emissions decrease by 33.27 tons/day. The subsidy strategy evolves through three stages: first, prioritizing maritime shipping; second, jointly promoting inland and maritime shipping; and finally, shifting focus to infrastructure investment once subsidies reach saturation. This study offers a quantitative analytical tool for designing differentiated carbon subsidy policies to facilitate the road-to-waterway modal shift under fiscal constraints. Full article

This paper focuses on optimizing machine learning-based time-series forecasting models by constructing compact data. Compact Data Learning for Time Series (CDL-TS) is a novel framework aimed at minimizing forecasting model errors. By utilizing reduced sampling and robust comparison procedures, CDL-TS addresses the challenges of forecasting models on extensive real-time data systems. By strategically minimizing data size while maintaining accuracy, CDL-TS presents an innovative framework that facilitates robust predictions and enhances operational efficiency. The combined bivariate performance measure-based optimization effectively balances sampling frequency with the mean square error (MSE) to improve the trade operation performance in stock markets. Through a series of empirical applications, particularly involving the M/M/1 queuing system and various stock trade optimizations with global high-tech companies, the CDL-TS framework has proven its effectiveness by significantly minimizing both forecasting errors and operational costs. This accomplishment highlights the robust capabilities of CDL-TS in enhancing predictive accuracy while facilitating operation cost savings across different domains, including complex systems like queues and real-time financial markets. Full article

The escalating global rates of traffic accidents in urban areas and the growing demands of smart cities underscore the urgent need for advanced real-time monitoring solutions. This paper presents an EdgeML-based system for vehicle tracking that performs real-time speed and distance analysis and traffic violation detection. This is achieved by deploying a YOLOv8 object detection model on a Raspberry Pi 5 with a Coral USB Edge TPU accelerator. The system integrates computer vision and IoT technologies to enable real-time processing. It utilizes the Message Queuing Telemetry Transport (MQTT) protocol to allow scalable communication between distributed edge devices and a central MongoDB database, facilitating real-time storage and analysis of traffic data. A synthetic dataset generated via the Blender 3D modeling tool validates the system’s accuracy, demonstrating average speed and distance measurement errors of ±2.11 km/h and ±0.58 m, respectively. These findings are further supported by preliminary practical experiments in a real-world environment, where speed estimation errors remained within 0–2 km/h and distance errors stayed below 0.11 m. Key innovations of this work include license plate recognition, speeding and collision detection, and context analysis using Google’s Gemini-2.5-Flash API. A Streamlit dashboard provides real-time visualization of traffic metrics, violations, and aggregated data. A comparative evaluation of YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n identifies YOLOv8n as the most suitable model for embedded deployment, achieving 91.07 ± 0.61% mAP@0.5 without quantization, 88.77 ± 3.31% mAP@0.5 with quantization, while maintaining real-time performance of 30–43 frames per second (FPS) on the Edge TPU. The system’s modular architecture, low latency, and robust performance highlight its suitability for smart city applications, enhancing traffic safety and enabling data-driven urban mobility management. Full article

Agrivoltaics offer a sustainable solution to the growing competition between food and energy production. However, their adoption is often constrained by the design and operation challenges associated with optimising the complex trade-off between crop yield and photovoltaic (PV) output. Digital twins can mitigate these risks, yet most agricultural digital twins operate as fragmented digital shadows, lacking high-fidelity modelling, advanced simulation, and bidirectional control capabilities. This study presents a comprehensive, end-to-end digital twin framework to address these limitations. The framework integrates a high-resolution 3D orchard model, reconstructed via UAV photogrammetry, with a CesiumJS-based web interface linked to a modular IoT architecture built on Node-RED, Message Queuing Telemetry Transport (MQTT) protocol and InfluxDB for real-time monitoring and control. A PV simulation engine supports the design, simulation and optimisation of agrivoltaic systems. Bidirectional communication was validated through remote actuation of a physical solar tracker, demonstrating integration among the 3D environment, sensor data and control systems to achieve a closed-loop digital twin. Simulation analyses suggested that panel orientation and row spacing exert a dominant influence on crop-level light distribution. Simulation results demonstrated that a 90° azimuth configuration achieved the highest daily energy yield of 53.97 kWh but reduced peak crop-level irradiance to 205 W/m². In contrast, the baseline 0° configuration offered a balanced output of 40.86 kWh with a peak light availability of 338 W/m². The validated, interoperable digital twin architecture provides a reference model for the design, simulation, monitoring and control of an agrivoltaic system, reducing investment uncertainty and supporting sustainable food–energy co-production. Full article

To address water shortages, Libya has undertaken one of the most significant global civil infrastructure projects, the Great Man-Made River Project, aimed at transporting groundwater from southern well-fields to densely populated northern cities. The second phase of the project consisted of installing the water supply system, which comprises 479 well pumps, united to deliver around 2 million cubic meters of water to the north. Historical performance data for the water supply system were analyzed, and a model for the system’s reliability calculation was developed: a finite-source, multi-server queuing system with spares (M/M/c//N/Y). The model enabled the analytical calculation of the system’s reliability in different configurations. Each configuration was assessed using a unique risk score, defined as the product of reliability-based parameters and the consequences associated with water-shortage events resulting from system failures. The analysis identified two alternative configurations that satisfy all predefined performance and risk criteria. Both configurations require fewer resources than the original system design, with up to seven fewer spare pumps, allowing for decision-makers to select the most appropriate option based on additional operational and contextual considerations. Full article

This study analyses the operational efficiency of urban loading and unloading zones (LUZs) by applying queuing theory without waiting (Erlang B model) and incorporating weighted occupancy time as a fundamental metric. Six scenarios were evaluated in an urban block in Zaragoza, Spain: three using field data obtained through real world observation and three simulated. The system’s performance was compared under conditions of free access with a model that strictly enforces the municipal ordinance for Urban Goods Distribution, restricting access to authorized vehicles and maximum dwell times. The objective of this study is to evaluate the operational performance of different LUZ configurations, assessing how real versus regulation-compliant usage affects system capacity, estimated loss rates, and the spatial temporal productivity of the zones. The M/M/1/1 model in Kendall notation is suitable for representing this type of queuing-free urban environment, and weighted occupancy time proves to be a robust indicator for evaluating the performance of heterogeneous zones. The scenario assessment confirms that the sizing of these zones is correct if their proper use is guaranteed. The study concludes with recommendations and best practices for city governance in formulating urban policies aimed at developing more efficient and sustainable logistics to control land use in the LUZ. Full article

This paper presents a traffic-driven scaling framework for a digital twin proxy pool (DTPP) in vehicular edge computing (VEC), designed to eliminate the latency and synchronization issues inherent in conventional digital twin (DT) migration approaches. The core innovation lies in replacing the migration of vehicle DTs between edge servers (ESs) with instantaneous switching within a pre-allocated pool of DT proxies, thereby achieving zero migration latency and continuous synchronization. The proposed architecture differentiates between short-term DTs (SDTs) hosted in edge-side in-memory databases for real-time, low-latency services, and long-term DTs (LDTs) in the cloud for historical data aggregation. A queuing-theoretic model formulates the DTPP as an M/M/c system, deriving a closed-form lower bound for the minimum number of proxies required to satisfy a predefined queuing-delay constraint, thus transforming quality-of-service targets into analytically computable resource allocations. The scaling mechanism operates on a cloud–edge collaborative principle: a cloud-based predictor, employing a TCN-Transformer fusion model, forecasts hourly traffic arrival rates to set a baseline proxy count, while edge-side managers perform monotonic, 5 min scale-ups based on real-time monitoring to absorb sudden traffic bursts without causing service jitter. Extensive evaluations were conducted using the PeMS dataset. The TCN-Transformer predictor significantly outperforms single-model baselines, achieving a mean absolute percentage error (MAPE) of 17.83%. More importantly, dynamic scaling at the ES reduces delay violation rates substantially—for instance, from 13.57% under static provisioning to just 1.35% when the minimum proxy count is 2—confirming the system’s ability to maintain service quality under highly dynamic conditions. These findings shows that the DTPP framework provides a robust solution for resource-efficient and latency-guaranteed DT services in VEC. Full article

By using the $C_0$-semigroup theory, we study the asymptotic behavior of the time-dependent solution and the time-dependent indices of the ${\mathrm{M}}^{[\mathrm{X}]}/\mathrm{G}/1$ queuing model with feedback and optional server vacations based on a single vacation policy. This queuing model is described by infinitely many partial differential equations with integral boundary conditions in an unbounded interval. Under certain conditions, by studying spectrum of the underlying operator of this queuing model on the imaginary axis, we prove that the time-dependent solution of this queuing model strongly converges to its steady-state solution. Next, we prove that the time-dependent queuing length of this queuing system converges to its steady-state queuing length and the time-dependent waiting time of this queuing system converges to its steady-state waiting time as time tends to infinity. Our results extend the steady-state results of this queuing system. Full article

The utilization of high millimeter wave (mmWave, 30–100 GHz) in 5G New Radio (NR) systems and sub-terahertz (sub-THz, 100–300 GHz) in future 6G requires dense deployments of base stations (BSs) to provide uninterrupted connectivity to the users. 3GPP Integrated Access and Backhaul (IAB) deployments that utilize wireless relay nodes offer cost-efficient densification options for these systems. However, the infrastructure that is often scaled and deployed for busy-hour traffic conditions is not used efficiently during periods when traffic demands are lower, resulting in excessive power consumption. In this work, we consider the IAB roadside deployment option and demonstrate that the deployment designed to meet traffic demands during busy-hour traffic conditions can be efficiently controlled to provide large power savings during other times of the day. To demonstrate the feasibility of the solution, we will utilize the tools of stochastic geometry and queuing theory. Our numerical results show that the dynamic switching of IAB nodes may lead to power savings of up to 40% depending on the traffic and deployment specifics. The proposed methodology also allows us to maintain the specified upper bound on the transit delay and improve the utilization of active IAB nodes. Full article

The rapid adoption of electric vehicles (EVs) requires efficient charging infrastructure planning. This study proposes a multi-objective optimization model for siting and capacity planning of EV charging stations, distinguishing between fast and slow chargers. The model integrates investment, dynamic electricity costs, and user experience, factoring in congestion-adjusted travel distances, time-of-use pricing, and queuing delays using an enhanced M/M/c approach. A comparison of algorithm reveals that the simulated annealing (SA) algorithm outperforms the genetic algorithm (GA) and ant colony optimization (ACO) in minimizing total costs. A case study in Changchun’s urban core demonstrates the model’s applicability, resulting in an optimal plan of 15 stations with 110 fast and 40 slow chargers, providing 11,544 kVA capacity at an annual cost of 38.2651 million yuan. Compared to traditional models that ignore charger types and simplify delays, the proposed model reduces total system costs by 4.31%, investment costs by 5.31%, and user costs by 3%, while easing delays in high-demand areas. This framework provides practical insights for urban planners and policymakers, helping balance investment and user satisfaction, and promoting sustainable EV mobility. Full article

Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System/Enterprise Resource Planning (MES/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes. Full article