Search Results (884)

Project control in cost estimation, time scheduling, and resource accounting remains challenging, particularly when using the open-source Industry Foundation Classes (IFCs) format. This study aims to define the state of the art in integrating these three domains. A systematic literature review was conducted, using a bibliometric analysis to map and interpret scientific knowledge and research trajectories, and an inductive analysis for a detailed examination of relevant studies. The analysis highlights a lack of clarity in applying the IFC standard across project control domains, as current practices often rely on non-standardized procedures, including incorrect use of classes or properties, creation of unneeded user-defined PropertySets and properties, or reliance on proprietary software. Integration of cost, time, and resource management remains limited, and proposed technological solutions generally require coding skills that typical professionals do not possess. Additional challenges include fragmented data across multiple databases, manual assignment of time, cost, and resource information, and limited collaboration, all of which are time-consuming and error-prone. There is a critical need for clearer guidelines on IFC usage to enable standardized procedures and facilitate the development of IFC-based tools. Automating these labor-intensive tasks could improve efficiency, reduce errors, and support broader adoption of integrated project control practices. Full article

The increasing penetration of electric vehicle (EV) fast-charging stations (FCSs) into distribution networks and microgrids poses considerable operational challenges, including voltage deviations, increased power losses, and peak load stress. This work proposes a novel hybrid optimization framework that integrates Lagrangian relaxation (LR) with adaptive sheep flock optimization (ASFO) to address the resource scheduling issues when EVs are penetrated and their impact on net load demand, total cost. Besides the impact of EV uncertainty on energy exchange cost and operational costs, voltage profile deviations were also studied. LR is employed to decompose the original problem and manage complex operational constraints, while ASFO is employed to solve the relaxed subproblems by efficiently exploring the high-dimensional, non-convex solution space. The proposed method is tested on an IEEE 33-bus distribution system with integrated PV and BESS under 24 h dynamic load and renewable scenarios. Results establish that the hybrid LR-ASFO method significantly outperforms conventional methods. Compared to standalone metaheuristics, the proposed framework reduces total cost by 5.6%, improves voltage profile deviations by 2.4%, and minimizes total operational cost by 4.3%. Furthermore, it safeguards constraint feasibility while avoiding premature convergence, thereby accomplishing better global optimality and system reliability. Full article

With the large-scale integration of new energy and power electronic devices into power systems, frequency stability has become an increasingly critical concern. To maintain frequency stability while mitigating the high capital expenditure of energy storage systems (ESSs), this paper develops a control framework centered on edge energy management terminals (EEMTs). The design is based on a demonstration project in which distributed energy resources (DERs) and flexible loads collaboratively provide frequency regulation. A monitoring station is implemented to make fast frequency response (FFR) resources dispatchable, detectable, measurable, and tradable. Furthermore, a control strategy tailored for building- and factory-level applications is proposed. This strategy enables real-time optimal scheduling of DERs and flexible loads through coordinated communication between EEMTs and net load units (NLUs). Two field tests further demonstrate the effectiveness and advantages of the proposed approach. In addition, this paper proposes a coordinated scheme in which wind farms and NLUs jointly participate in frequency regulation, aiming to mitigate the response delay of NLUs and the secondary frequency drop observed in wind farms. The feasibility and benefits of this scheme are validated through experimental tests. Full article

Growing EV adoption is reshaping how Distributed Energy Resources (DERs) interact with the grid, playing a pivotal role in global decarbonization efforts and the transition towards a sustainable energy future. This study built a Virtual Power Plant (VPP) operation framework centered on EV behavioral dynamics, connecting individual driving and charging behaviors with the physical and economic layers of energy management. The EV behavioral dynamic model quantifies the stochastic travel, parking, and charging behaviors of individual EVs through an Agent-Based Trip and Charging Chain (AB-TCC) simulation, producing a Behavioral Flexibility Trace (BFT) that represents time-resolved EV availability and flexibility. The Forecasting Model employs a Bi-directional Long Short-Term Memory (Bi-LSTM) network trained on historical meteorological data to predict short-term renewable generation and represent physical variability. The two-stage optimization model integrates behavioral and physical information with market price signals to coordinate day-ahead scheduling and real-time operation, minimizing procurement costs and mitigating imbalance penalties. Simulation results indicate that the proposed framework yielded an approximately 15% increase in revenue over 7 days through EV-based flexibility utilization. These findings demonstrate that the proposed approach effectively leverages EV flexibility to manage renewable generation variability, thereby enhancing both the profitability and operational reliability of VPPs in local distribution systems. This facilitates greater penetration of intermittent renewable energy sources, accelerating the transition to a low-carbon energy system. Full article

This paper addresses the challenge of optimizing cloudlet resource allocation in a code evaluation system. The study models the relationship between system load and response time when users submit code to an online code-evaluation platform, LambdaChecker, which operates a cloudlet-based processing pipeline. The pipeline includes code correctness checks, static analysis, and design-pattern detection using a local Large Language Model (LLM). To optimize the system, we develop a mathematical model and apply it to the LambdaChecker resource management problem. The proposed approach is evaluated using both simulations and real contest data, with a focus on improvements in average response time, resource utilization efficiency, and user satisfaction. The results indicate that adaptive scheduling and workload prediction effectively reduce waiting times without substantially increasing operational costs. Overall, the study suggests that systematic cloudlet optimization can enhance the educational value of automated code evaluation systems by improving responsiveness while preserving sustainable resource usage. Full article

Industry 5.0 involves the integration of advanced technologies, collaboration between humans and intelligent machines, resilience and sustainability, all of which are essential for the advancement of the food services industry. This analysis reviews the scientific literature on Industries 4.0 and 5.0 technologies, whether experimental or implemented, focused on producing large meals in food service. The review has been conducted through a systematic search, covering aspects from consumer ordering and the cooking process to distribution while considering management, quality control, and sustainability. A total of thirty-one articles, published between 2006 and 2025, were selected, with the majority focusing on Industry 5.0 (71%) and a significant proportion on testing phases (77.4%). In the context of Food Service Perspectives, the emphasis has been placed on customer service (32.3%), highlighting the use of Artificial Intelligence (AI)-powered robots for serving customers and AI for service personalization. Sustainability has also received attention (29%), focusing on AI and machine learning (ML) applications aimed at waste reduction. In management (22.6%), AI has been applied to optimize production schedules, enhance menu engineering, and improve overall management. Big Data (BD) and ML were utilized for sales analysis, while Blockchain technology was employed for traceability. Cooking innovations (9.7%) centered on automation, particularly the use of collaborative robots (cobots). For Quality Control (6.4%), AI, along with the Internet of Things (IoT) and Cloud Computing, has been used to monitor the physical aspects of food. The study underscores the importance of strategic investments in technology to optimize processes and resources, personalize services, and ensure food quality, thereby promoting balance and sustainability. Full article

Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static layouts may accumulate inefficiencies over time. This study introduces a decision support system (DSS) that evaluates the hydraulic adequacy of existing irrigation systems using two new concepts: the Resource Overutilization Ratio (ROR) and the Irrigation Ecolabel. The ROR quantifies the deviation between the actual discharge of an installed irrigation network and the theoretical discharge required from crop water needs and user-defined scheduling assumptions, while the ecolabel translates this value into an intuitive A+++–D scale inspired by EU energy labels. Crop water demand was estimated using the FAO-56 Penman–Monteith method and adjusted using canopy cover derived from UAV-based canopy height models. A vineyard case study in Galicia (Spain) serves an example to illustrate the potential of the DSS. Firstly, using a fixed canopy cover, the FAO-based workflow indicated moderate oversizing, whereas secondly, UAV-derived canopy measurements revealed substantially higher oversizing, highlighting the limitations of non-spatial or user-estimated canopy inputs. This contrast (A+ vs. D rating) illustrates the diagnostic value of integrating high-resolution geospatial information when canopy variability is present. The DSS, released as open-source software, provides a transparent and reproducible framework to help farmers, irrigation managers, and policymakers assess whether existing drip systems are hydraulically oversized and to benchmark system performance across fields or management scenarios. Rather than serving as an irrigation scheduler, the DSS functions as a standardized diagnostic tool for identifying oversizing and supporting more efficient use of water, energy, and materials in perennial cropping systems. Full article

Effectively managing multi-source heterogeneous data remains a critical challenge in distributed cyber-physical systems (CPS). To address this, we present a novel and edge-centric computing framework integrating four key technological innovations. Firstly, a hybrid OPC communication stack seamlessly combines Client/Server, Publish/Subscribe, and P2P paradigms, enabling scalable interoperability across devices, edge nodes, and the cloud. Secondly, an event-triggered adaptive Kalman filter is introduced; it incorporates online noise-covariance estimation and multi-threshold triggering mechanisms. This approach significantly reduces state-estimation error by 46.7% and computational load by 41% compared to conventional fixed-rate sampling. Thirdly, temporal asynchrony among edge sensors is resolved by a Dynamic Time Warping (DTW)-based data-fusion module, which employs optimization constrained by Mahalanobis distance. Ultimately, a content-aware deterministic message queue data distribution mechanism is designed to ensure an end-to-end latency of less than 10 ms for critical control commands. This mechanism, which utilizes a “rules first” scheduling strategy and a dynamic resource allocation mechanism, guarantees low latency for key instructions even under the response loads of multiple data messages. The core contribution of this study is the proposal and empirical validation of an architecture co-design methodology aimed at ultra-high-performance industrial systems. This approach moves beyond the conventional paradigm of independently optimizing individual components, and instead prioritizes system-level synergy as the foundation for performance enhancement. Experimental evaluations were conducted under industrial-grade workloads, which involve over 100 heterogeneous data sources. These evaluations reveal that systems designed with this methodology can simultaneously achieve millimeter-level accuracy in field data acquisition and millisecond-level latency in the execution of critical control commands. These results highlight a promising pathway toward the development of real-time intelligent systems capable of meeting the stringent demands of next-generation industrial applications, and demonstrate immediate applicability in smart manufacturing domains. Full article

In complex urban low-altitude (ULA) airspace, unmanned aerial vehicles (UAVs) face several safety challenges, such as building obstacles, airspace restrictions, and environmental uncertainties. In this study, these issues are addressed by adopting a novel quantitative method for evaluating UAV flight safety margins and integrating this method into a ULA airspace grid model. This method comprehensively considers critical factors such as airspace obstacles, environmental conditions, and UAV performance to compute a quantitative safety margin. Once safety buffer grids around restricted and potential conflict grids are introduced, dynamic constraints can be imposed on the trajectory planning process. The proposed model not only achieves a balance between path cost and safety redundancy but also significantly enhances UAV flight safety and the efficiency of airspace resource utilization in complex urban environments. The experimental results validate the effectiveness of this approach for planning multi-UAV trajectories, demonstrating its feasibility and potential for broader application. This research not only extends the safety implications of low-altitude airspace grid modeling but also provides a new technical pathway and theoretical foundation for future ULA airspace safety management, multi-UAV collaborative scheduling, and refined airspace governance. Full article

This study investigates the optimization of Pumped Storage Hydropower (PSH) integrated with Floating Photovoltaic (FPV) systems, with a focus on sustainable energy management. A nonlinear programming framework combined with scenario analysis was applied to a real hydropower system in Trentino, Italy. The optimization maximizes revenues through energy arbitrage while accounting for water resource and environmental objectives. Upgrading the traditional hydropower plant to PSH operation increases revenues by 4–8% over two hydrological years. Multi-objective optimization further reveals large gains in water availability, confirming PSH’s dual role as energy storage and water management infrastructure. Different FPV configurations analyzed show a 2–3% increase in photovoltaic energy yield due to the water-cooling effect, while the overall hybrid PSH–FPV integration mainly reduces grid dependency and pumping-related emissions, with near-complete decarbonization achievable under optimized scheduling. Overall, PSH provides the primary economic and operational advantage, while FPV strengthens sustainability, enabling resilient hydro–solar operation and contributing to renewable integration and decarbonization in future energy systems. Full article

The process industry (PI) plays a vital role in the global economy and faces mounting pressure to enhance sustainability, operational agility, and resource efficiency amid tightening regulatory and market demands. Although artificial intelligence (AI) has been explored in this domain for decades, its adoption in industrial practice remains limited. Recently, machine learning (ML) has gained momentum, particularly when integrated with core PI systems such as process control, instrumentation, quality management, and enterprise platforms. Among ML techniques, reinforcement learning (RL) has emerged as a promising approach to tackle complex operational challenges. In contrast to conventional data-driven methods that focus on prediction or classification, RL directly addresses sequential decision making under uncertainty, a defining characteristic of dynamic process operations. Given RL’s growing relevance, this study conducts a systematic literature review to evaluate its current applications in the PI, assess methodological developments, and identify barriers to broader industrial adoption. The review follows the PRISMA methodology, a structured framework for identifying, screening, and selecting relevant publications. This approach ensures alignment with a clearly defined research question and minimizes bias, focusing on studies that demonstrate meaningful industrial applications of RL. The findings reveal that RL is transitioning from a theoretical construct to a practical tool, particularly in the chemical sector and for tasks such as process control and scheduling. Methodological maturity is improving, with algorithm selection increasingly tailored to problem-specific requirements and a trend toward hybrid models that integrate RL with established control strategies. However, most implementations remain confined to simulated environments, underscoring the need for real-world deployment, safety assurances, and improved interpretability. Overall, RL exhibits the potential to serve as a foundational component of next-generation smart manufacturing systems. Full article

Unmanned Aerial Vehicle (UAV)-aided two-way relaying systems have attracted widespread attention due to their ability to improve communication efficiency, reduce deployment costs, and enhance reliability. However, most existing systems employ the Time-Division Multiple Access (TDMA) protocol, which suffers from rigid resource allocation and fails to efficiently manage antenna resources within a time slot for multiple users. Furthermore, the reliance on simple Line-of-Sight (LoS) channel models in many studies is often inaccurate, leading to significant performance degradation. To address these issues, this paper investigates a UAV-assisted two-way relaying system based on the Probabilistic Line-of-Sight (PrLoS) model. We propose a novel two-way transmission protocol, termed the Dynamic Dual-Antenna Time-Slot Allocation Protocol (DDATSAP), to facilitate flexible antenna resource allocation for multiple user pairs. To maximize the minimum average message rate for ground users, we jointly optimize the Resource Scheduling Factor (RSF), transmit power, and UAV trajectory. Since the formulated problem is non-convex and challenging to solve directly, we propose an efficient iterative algorithm based on Successive Convex Approximation (SCA) and Block Coordinate Descent (BCD) techniques. Numerical simulation results demonstrate that the proposed scheme exhibits superior performance compared to benchmark systems. Full article

This study presents an integrated predictive maintenance framework for industrial machinery, designed through the combined use of digital twin technology, enterprise resource planning (ERP) systems, and machine learning algorithms. The proposed system focuses on enhancing machine reliability and operational automation by connecting physical assets with their virtual counterparts and management systems. The digital twin acts as a real-time virtual model of critical equipment—such as aeration motors, mixers, and reactors—enabling continuous monitoring, dynamic simulation, and predictive fault detection. Meanwhile, the ERP system provides an integrated environment for maintenance scheduling, data management, and resource allocation, ensuring that maintenance decisions are data-driven and synchronized with operational workflows. Machine learning algorithms, implemented using hybrid physical–data models, predict equipment degradation trends and optimize maintenance interventions. The proposed framework was validated in an industrial-scale composting facility, where results demonstrated a 40% increase in mean time to failure (MTTF), a 35% reduction in repair time, and a 30% decrease in maintenance costs, resulting in a return on investment of 42.5% within the first year. The system’s modular architecture and high adaptability to different machinery types confirm its potential applicability to broader machine design and automation contexts, supporting the transition toward intelligent, self-maintaining industrial systems. Full article

The penetration of distributed energy resources (DERs), such as photovoltaic (PV) generation and electric vehicles (EVs), in distribution systems has been increasing rapidly. At the same time, load demand is rising due to the proliferation of data centers and the growing use of artificial intelligence. These trends have introduced new operational challenges: reverse power flow from PV generation during the day and low-voltage conditions during periods of peak load or when PV output is unavailable. To address these issues, this paper proposes a two-stage adaptive rolling horizon (ARH)-based model predictive control (MPC) framework for coordinated voltage and power factor (PF) control in distribution systems. The proposed framework, designed from the perspective of a distributed energy resource management system (DERMS), integrates EV charging and discharging scheduling with PV- and EV-connected inverter control. In the first stage, the ARH method optimizes EV charging and discharging schedules to regulate voltage levels. In the second stage, optimal power flow analysis is employed to adjust the voltage of distribution lines and the power factor at the substation through reactive power compensation, using PV- and EV-connected inverters. The proposed algorithm aims to maintain stable operation of the distribution system while minimizing PV curtailment by computing optimal control commands based on predicted PV generation, load forecasts, and EV data provided by vehicle owners. Simulation results on the IEEE 37-bus test feeder demonstrate that, under predicted PV and load profiles, the system voltage can be maintained within the normal range of 0.95–1.05 per unit (p.u.), the power factor is improved, and the state-of-charge (SOC) requirements of EV owners are satisfied. These results confirm that the proposed framework enables stable and cooperative operation of the distribution system without the need for additional infrastructure expansion. Full article

Background/Objectives: The European Society for Emergency Medicine reports that emergency department visits have increased by nearly 30% over the past decade, yet resources have not kept pace with this growing demand. Lean Healthcare Management has emerged as a promising approach to optimizing emergency department operations. This study aims analyze the specific Lean Healthcare Management interventions implemented across three major Barcelona hospitals. Methods: Three Barcelona hospitals were analyzed. Revision of the Lean Healthcare Management tools, hospital staff observation and focus groups with nurses, physicians, and administrators were performed to evaluate impact of Lean Healthcare Management interventions. A cumulative SWOT analysis was performed as a synthesis of individual responses and focus groups for the three included hospitals separately. Results: The average adherence scores to implemented Lean Healthcare Management solutions were 87% at Vall d’Hebron, 85% at Sant Joan de Déu, and 89% at Hospital Clínic de Barcelona. Implementation of Lean Healthcare Management led to 20% fewer cancelations of scheduled surgical procedures, decreased patient hospitalization times for targeted pathways (from 8 h to 70 min) and significant increase in patient satisfaction. All centers shared a common foundation in Value Stream Mapping. Implemented Lean Healthcare Management solutions were personalized for each hospital. Conclusions: Lean Healthcare Management’s effectiveness is contingent on aligning the Lean approach with the hospital’s specific mission, constraints, and patient population. This contextual dependency explains the variation in the tools adopted and the outcomes prioritized across the three analyzed hospitals. Full article