Search Results (10,581)

Digital development is becoming increasingly embedded in agricultural production, agricultural product circulation, and market service systems. The relationship between digital resources and the stable operation, coordinated response, and recovery adjustment of agricultural product supply chains under external shocks has become an important issue in agricultural modernization and sustainable agricultural development. Using provincial-level data from Zhejiang, Fujian, and Guangdong covering the period from 2014 to 2023, this study applies the entropy weight method, necessary condition analysis (NCA), and fsQCA. Based on the resource-based view and dynamic capability theory, this study constructs an analytical framework linking resource configuration, scenario embeddedness, and capability transformation for agricultural product supply chain resilience, and examines multiple configurational paths through which different digital conditions are associated with agricultural product supply chain resilience. The results show that high agricultural product supply chain resilience is closely related to the coordinated matching of digital infrastructure, rural logistics networks, information and communication services, digital finance, and governance support. Zhejiang, Fujian, and Guangdong present three paths: information service and circulation synergy, logistics network support, and digital circulation synergy. The degree of fit between digital resources and specific supply chain scenarios, including agricultural production, agricultural product circulation, financial services, and governance support, helps explain differences in agricultural product supply chain resilience among the three provinces. Full article

This study presents a systematic literature-based discovery of the energy management process within household microgrids, combining the methodologies of Systematic Literature Review (SLR) and Literature-Based Discovery (LBD). The objective is to identify and structure key activities that ensure the efficient, scalable, and resilient operation of household microgrids. Drawing on an extensive analysis of the literature, the study proposes a conceptual, process-oriented framework that integrates technological and organizational perspectives into an eight-step roadmap for household energy management. These steps include data acquisition, local weather forecasting, energy production and consumption prediction, demand and supply management, energy generation and storage, power distribution, control of technological and organizational infrastructure, and compliance with safety and regulatory standards. The model supports the integration of predictive, self-learning control systems and highlights the importance of user competence development alongside automation. By mapping out a structured and replicable approach to household microgrid energy management, the study provides a foundation for improved energy independence, operational reliability, and effective integration into decentralized energy markets. The roadmap offers practical insights for both researchers and practitioners aiming to support the sustainable development and governance of household microgrids. Full article

Vehicle-to-everything (V2X) information can support energy-efficient vehicle operation in signalized traffic. This paper presents a sequential hierarchical eco-driving strategy for fuel-cell hybrid electric vehicles (FCHEVs) traveling through multiple signalized intersections under prescribed signal-phase and road-speed settings. The upper layer employs an ${\mathrm{A}}^{\ast }$-inspired adaptive heuristic graph search in a discretized time–distance graph. A dimensionless motor-power-demand-based numerical ranking score, evaluated at the average speed of each candidate edge under a zero-acceleration edge approximation, introduces approximate powertrain-load information into node ranking. This score follows the supplied numerical implementation and is neither edge-integrated energy nor equivalent hydrogen cost; therefore, the upper-layer procedure is not claimed to inherit the admissibility, optimality, or bounded-suboptimality guarantees of standard ${\mathrm{A}}^{\ast }$ or weighted ${\mathrm{A}}^{\ast }$. The lower layer uses a twin delayed deep deterministic policy gradient (TD3)-based energy management strategy that penalizes raw equivalent hydrogen cost, SOC deviation, fuel-cell degradation increments, and battery degradation increments. The reported simulations show a lower raw equivalent hydrogen cost than the baseline strategy in the tested scenarios, while terminal-SOC correction reveals scenario-dependent trade-offs among corrected equivalent hydrogen cost, SOC regulation, and model-based component degradation indicators. The results support a practical multi-objective balance under the reported deterministic simulation settings rather than uniform superiority in every individual indicator. Full article

With the increasing integration of wind and solar energy, power systems face growing challenges in supply–demand balancing, renewable energy accommodation, and reserve regulation, especially in future power systems with evolving generation mixes. To this end, a system adaptability evaluation method particularly considering planned future wind farms and photovoltaic (PV) stations is proposed. Available historical meteorological data are converted into wind and PV power output sequences through renewable power conversion models. Then, a generative adversarial network (GAN)-based model is built to learn the temporal fluctuation characteristics and inter-station correlations of renewable generation, thereby generating multiple representative wind and PV output scenarios. Finally, these scenarios are embedded into a security-constrained unit commitment (SCUC)-based model to evaluate system adaptability in terms of load shedding, renewable energy curtailment, accommodation rate, and reserve margin. The proposed method provides a forward-looking framework for assessing the operational adaptability of power systems under high renewable energy penetration. Full article

This paper proposes an operation-index-driven intelligent generation method for distribution network simulation models, integrating model library predefinition, multi-level equivalent modeling, and multi-agent collaboration into an automated pipeline. Three agents collaborate through a unified message bus, task queue, and device model library as follows: Monitor Agent performs time-series data acquisition, national-standard threshold evaluation, and topology verification to trigger modeling tasks; Energy-flow-Model Agent uses a hierarchical model library and LLM (Large Language Model) to automatically match standardized models, parses CIM topology, and applies Thevenin/Ward/three-phase admittance matrix multi-level equivalent modeling to construct Energy Flow Network (EFN) computation graphs; Device-Model-Data Agent supplies missing impedance parameters via a public device standard library. The pipeline automatically generates 15 min granularity current-state assessment models synchronized with operating conditions. Validation on eight consecutive days of 10 kV feeder field measurements (96-time sections/day, 91 transformer areas) shows three-phase voltage mean MAPE of 2.52% and 98.9% correct trigger rate with no false activations or missed detections, achieving end-to-end automation from archive parsing to model generation. Full article

To address the challenge that microelectromechanical system (MEMS) gyroscope electrostatic force-modulated closed-loop self-excited driving circuits experience significant dynamic variations in capacitive load and driving demand under different operating conditions, such as start-up, steady-state resonance maintenance, and environmental perturbations, making it difficult to simultaneously achieve strong driving capability, stable oscillation, and low power consumption, this paper proposes a high-energy-efficiency adaptive output operational amplifier architecture. Based on a dynamic load-sensing mechanism, the design introduces a three-threshold decision scheme combining a high threshold, a low threshold, and a mid-supply reference voltage. By coordinating a continuous-time voltage detection circuit with a bidirectional shift register, the proposed approach enables accurate identification of the output state and the load level. A time-division-multiplexed two-stage control strategy is adopted to rapidly compensate for the drive capability under abrupt load changes, while proactively disabling redundant output units under steady-state conditions, thereby achieving power delivery on demand. The output stage employs a Class-AB push–pull structure integrating an improved low-leakage single-pole double-throw (SPDT) switch, which hard shuts off the power transistors in the non-operating state to effectively eliminate the subthreshold leakage current. Circuit simulations in a 0.18 μm CMOS process demonstrate that the proposed operational amplifier can adaptively regulate its output current in real time according to variations in the gyroscope driving demand, ensuring sufficient an electrostatic driving force and oscillation stability during transient conditions while significantly reducing static power consumption during the resonance steady state. The proposed design provides an effective solution for high-performance and high-energy-efficiency interface circuit design in MEMS gyroscope electrostatic force-modulated closed-loop self-excited driving systems. Full article

Office buildings accumulate embodied carbon not only during construction but repeatedly throughout operation, driven by tenant improvements (TI). In contexts where regulatory and supply-chain constraints limit full design-for-disassembly, this paper explores partial decoupling of a dry slab system within the high-churn zone of a Seoul office. Two area-normalised indicators are introduced—cumulative intervention area ($A_{\mathrm{cum}}$) and Reconfiguration Carbon Intensity (RCI)—defined on the structural slab/panel boundary to complement the mass-weighted Circularity Index (CI). The indicators are demonstrated on a 17-storey Seoul office over 60 years across three scenarios: a wet composite baseline (S1), full-dry CLT (S2), and a hybrid placing CLT within the high-churn zone (S3). S3 reduces RCI by approximately 45% relative to the wet baseline, capturing 71.7% of the full-decoupling benefit while converting only 11.5% of the floor area to CLT—a benefit-to-conversion ratio of $6.2\times$. Both shares are fixed by the floor-plate geometry, so this ratio is a consequence of the high-churn zone’s concentration rather than an independent empirical finding. A joint-uncertainty stress test confirms that S3 outcomes lie entirely below S1 across plausible parameter ranges. A placement test compares three configurations of the same floor that differ only in CLT placement location relative to the high-churn zone. Mass-weighted CI cannot distinguish these configurations, whereas RCI ranges from no reduction to the full hybrid benefit depending on placement—isolating the diagnostic value of the use-phase indicator. Hybrid zone-scale decoupling offers a feasible pathway for use-phase decarbonisation without committing to full-floor dry construction. Full article

Automated guided vehicles (AGVs) are key transportation resources in automated container terminals, where operational scheduling and charging decisions exhibit strong spatiotemporal coupling characteristics. When AGVs are assigned to transport “reefer” containers, interruptions in external power supply during transit may lead to temperature fluctuations, posing potential risks to cargo quality and transportation safety. To address this issue, this paper proposes a two-stage coordinated optimization framework for AGV operations and charging, considering reefer container temperature constraints. Specifically, an AGV transportation scheduling model is first developed to characterize quay-crane operations, yard allocation, AGV travel processes, battery dynamics, and reefer container transit-time limitations associated with temperature maintenance requirements. Subsequently, a port microgrid scheduling model integrating charging stations, photovoltaic generation, wind power, and energy storage systems is established to coordinate AGV charging strategies with energy system operations. Based on these models, a two-stage optimization framework is constructed, in which AGV task assignment and yard allocation are optimized in the first stage to improve operational efficiency, while energy scheduling is optimized in the second stage to minimize system operating costs under the operational decisions obtained in the first stage. Numerical results demonstrate that the proposed method effectively reduces the transportation time of reefer containers, alleviates temperature-related transportation risks, enhances the coordination between logistics operations and energy management, and improves terminal operational efficiency while ensuring the safety and quality of reefer container transportation. The proposed framework provides an effective solution for the integrated optimization of logistics and energy systems in automated container terminals. Full article

The growing integration of variable renewable energy sources (VRES) challenges power system flexibility and may cause curtailment due to excess capacity, grid constraints, or operational and market factors. Power-to-Heat (PtH) technology can mitigate these issues by coupling electricity and district heating sectors, providing additional flexibility and supporting decarbonisation. This study develops a long-term generation capacity expansion model that integrates PtH and district heating system (DHS) operation to achieve balanced VRES penetration. The model includes DHS heat demand balances and links electricity and heat via thermal power plants, combined heat and power (CHP) plants, and PtH units. The methodology is applied to Ukraine’s Integrated Power System and district heating demand through 2040, employing typical daily load profiles discretised into six four-hour segments. Results demonstrate the feasibility of deploying PtH electric boilers during the non-heating season, when high RES and base load nuclear generation create surplus electricity. These boilers convert excess wind and solar power into thermal energy for district heating, displacing natural gas-fired technologies and simultaneously decarbonising electricity and heat supply. Full article

Operational risk assessment in complex industrial systems like mineral processing plants is hindered by inherent uncertainty and incomplete information. This study presents an integrated grey system theory-based framework to address this challenge. Combining Grey Multi-Criteria Decision-Making (GST-MCDM) and Grey Relational Analysis (GRA), the methodology enables a systemic analysis that prioritizes risks, quantifies interdependencies, and measures cumulative burden across four key objectives: time, cost, quality, and safety. Applied to a case study at the Gohar Zamin iron ore processing plant (Iran), the model analyzed 26 operational risks, classifying them into Critical (8 risks), Significant (9), and Controllable (9) tiers. Electrical power shortage (RPS: 0.19) and raw material supply delay (RPS: 0.21) were identified as the most critical risks. The analysis quantified that the safety objective bears the highest cumulative risk burden at 32%, primarily due to human factor vulnerabilities, while cyber-physical threats ranked among the top 8 critical risks. Strong interdependencies were revealed, notably a quality cascade (relational grade: 0.84) between poor consumable materials and final product failure. Sensitivity analysis confirmed high model robustness (Spearman’s p = 0.91). The framework provides managers with an actionable tool for strategic, cluster-based mitigation and evidence-based resource allocation, emphasizing investment in human capital as a core risk reduction strategy. This research contributes a replicable, quantitative methodology for enhancing operational resilience under uncertainty in capital-intensive industries. Full article

Backgroung: The confluence of the Internet of Things (IoT) and the Physical Internet (PI) is a major accelerant of Logistics 4.0, which can provide significant upsides to supply chain performance. While prior reviews mainly concentrated on technological advancement and applications, limited consideration has been given to the mechanisms through which the PI-IoT system integration generates benefits, namely operational, economic, and environmental benefits. Methods: To fill this gap, this study undertakes a systematic literature review of 43 peer-reviewed studies guided by PRISMA. This study uses the Context, Intervention, Mechanism, Outcome (CIMO) framework to automatically conduct a mechanism-based synthesis of PI-IoT integration, unlike previous reviews. Results: The results show that enhanced operational and economic efficiency, as well as environmentally sustainable performance, can be achieved by using real-time visibility, predictive decision-making, collaborative resource optimization, intelligent automation, and adaptive system responsiveness. Conclusion: This review also proposes a conceptual framework, linking contextual conditions, technological interventions, mechanisms, and outcomes, while also identifying important research gaps and future research directions. This study offers essential findings that offer practical insights for researchers, logistics managers, and policymakers intending to implement collaborative, data-driven, and sustainable PI-IoT-enabled logistics systems. Full article

Sustainable aviation fuel is among the most practical near-term routes for aviation decarbonization because it can be used in existing aircraft, engines, and airport fuel systems with limited infrastructure changes while minimizing disruption to the aviation fuel supply chain. This review examines SAF applications in aerospace propulsion systems, focusing on production pathways, aero-engine compatibility, property prediction, and fuel heat sink potential. It compares hydroprocessed esters and fatty acids (HEFA), Fischer–Tropsch (FT), alcohol-to-jet (ATJ), synthesized iso-paraffins (SIP), and power-to-liquid (PtL) fuels in terms of feedstock type, process complexity, product composition, and blending constraints. It also assesses how molecular composition governs density, cold-flow behavior, thermal stability, coking propensity, seal compatibility, and emissions. Recent advances in molecular dynamics, machine learning, spectroscopic analysis, and uncertainty quantification show a shift from empirical estimation toward composition-based prediction, prescreening, and fuel design. For high-thermal-load propulsion systems, SAF is further evaluated as a fuel heat sink in active regenerative cooling. Current evidence points to advantages in thermal stability and low coking tendency, but important gaps remain in transcritical and supercritical heat transfer, pyrolytic heat absorption, wall-material effects, coke deposition, and heat sink capacity modeling across wide operating ranges. Full article

To address the supply–security pressures and hosting capacity assessment challenges arising from the high penetration of renewable energy into power systems, this paper proposes a comprehensive evaluation method for grid renewable energy hosting capacity based on the entropy weight method, technique for order preference by similarity to ideal solution, and grey relational analysis (EWM-GRA-TOPSIS). First, a multi-dimensional comprehensive evaluation index system is established, encompassing security and stability, operational economy, flexible power supply security, and renewable energy penetration. Subsequently, a comprehensive hosting capacity evaluation model is formulated. Within the operational simulation layer, key evaluation metrics across diverse renewable energy deployment alternatives are quantified based on time-series production simulation and power flow calculations. In the data processing layer, the EWM is employed to determine index weights, while GRA measures the morphological similarity between alternatives and the ideal sequence. Concurrently, TOPSIS evaluates their geometric proximity, enabling a coordinated trade-off among multi-dimensional metrics and the final ranking of the alternatives. Finally, the result output layer yields the comprehensive assessment outcomes and the prioritized ranking of the proposed scenarios. Simulation results demonstrate that the proposed method effectively evaluates the hosting capacity of various development scenarios, thereby providing a robust decision-making basis for power system planning and operation that balances high grid capacity with optimal comprehensive benefits. Full article

Dielectric Barrier Discharge (DBD) plasma actuators have shown considerable potential for aerodynamic flow control over fixed wings and helicopter rotors. However, their application to small unmanned aerial vehicle (UAV) propellers operating at high rotational speeds remains largely unexplored. This study experimentally investigates the effectiveness of leading-edge AC-DBD plasma actuators in improving the aerodynamic performance of rotating UAV propellers under hovering conditions. A custom-built experimental test stand was developed to measure thrust, rotational speed, and motor power consumption while supplying high voltage to the rotating blades through high-speed slip rings. A series of 3D-printed propellers with different blade pitch angles was tested at rotational speeds up to 4000 rpm. The results showed negligible performance changes for low-pitch propellers, whereas significant improvements were observed under separated-flow conditions. At nearly constant rotational speed and thrust, plasma actuation reduced the propeller power coefficient by up to 7.66%, resulting in a maximum 9.42% increase in Figure of Merit (FoM). The greatest benefits were obtained for intermediate blade pitch angles, while no measurable improvement was observed under severe separation conditions. These findings demonstrate that plasma actuation is most effective within an intermediate separated-flow regime and highlight its potential as a lightweight active flow-control technology for electrically powered UAVs. Full article

Failures and leakages in water distribution pipelines affect the sustainable operation of water supply systems. While water losses caused by leaks are widely recognized, their impact on soil stability and internal erosion processes remains insufficiently investigated. This study examines water flow velocity distributions in soil around leaking water pipes regarding suffosion risk. Numerical simulations were performed using the FEFLOW software for four scenarios combining two pipe diameters and two internal pressure levels. Each scenario assumed circumferential leakage with continuous water outflow into the surrounding soil. The numerical model was validated through field experiments conducted on four experimental setups. The simulation results showed that flow velocities near the pipe exceeded critical values in all scenarios, indicating a risk of suffosion. Although hydraulic pressure and leakage area significantly affected local flow velocities, their influence on the extent of the potential suffosion zone was negligible. In all cases, the zone where critical velocities were exceeded extended more than 1.5 m from the leakage location. The results highlight the importance of considering suffosion risk in the operation and risk assessment of sustainable water supply systems. They also indicate that this hazard should be taken into account during the design stage, particularly when selecting pipeline routes and assessing ground conditions. Full article