Search Results (13,817)

The main purpose of electrical transmission and distribution networks is to carry electrical energy from the places where it is produced to the places of consumption, where the energy is used. Electrical energy is produced in power plants by generating units, which convert a form of primary energy into electrical energy. Primary energy comes from a number of sources, such as fossil fuels, nuclear energy, hydropower, wind, and solar. The carbon neutrality targets set by the European Union and several countries around the world have driven a transformation characterized by the gradual replacement of synchronous thermal generation based on fossil fuels with Renewable Energy Sources (RES), such as wind and solar. The energy transition, while necessary to achieve the established targets, introduces significant challenges to the stability of Electrical Power Systems (EPS) and electrical grids, since RES do not yet contribute to stability at levels comparable to the generating units of large thermal power plants, whether in terms of inertia, which has seen a notable reduction in recent years, or in voltage control or short-circuit power. This article presents and discusses solutions to mitigate the effect of this reduction in inertia in power plants using synchronous compensators and synthetic inertia emulation using battery storage. Full article

Spent coffee grounds (SCGs) are abundant byproducts generated during coffee processing that are unsuitable for storage and subsequent value-added utilization owing to their high moisture content and water activity (a_w). This study investigated the effects of different infrared power levels (800, 900, and 1000 W) on drying kinetics, product quality, and energy efficiency to determine the preferred drying parameters for SCGs. The initial moisture content and a_w of SCGs were 63.56% (wet basis) and 0.95, respectively. To enhance mechanistic understanding, the drying data were fitted to four mathematical models, with the Midilli and Page models providing the best fit (R² > 0.99). Drying experiments were conducted under a sample thickness of 0.7 cm and a loading of 500 g, with a final moisture content of <10% as the drying endpoint. The results showed that as infrared power increased, drying time decreased from 30 to 24 min and the drying rate significantly increased from 10.32 to 12.77 g H₂O/min (p < 0.05). The drying process was mainly characterized by a falling-rate period, with the effective moisture diffusivity ranging from 0.97 to 1.15 × 10⁻⁸ m²/s and （increasing with rising power, indicating that internal moisture diffusion was the dominant drying mechanism. The final a_w of each treatment group was ≤0.60, indicating good storage stability. Color analysis showed that the color differences in treatments at higher power levels (900 W and 1000 W) were significantly lower than those at lower ones (p < 0.05). While the specific energy consumption (SEC) showed a marginal decrease from 5.80 to 5.68 kWh/kg at higher power, a comprehensive evaluation of drying efficiency, quality characteristics, and energy consumption indicated that 1000 W was the preferred infrared drying power under the conditions employed in this study. These results confirm that infrared drying is an efficient stabilization method with strong potential for rapid stabilization of food processing byproducts. Full article

Publicly available electricity datasets from operational industrial facilities remain limited due to instrumentation cost, retrofit complexity, and data governance constraints. This paper presents an openly accessible dataset of asset-level electrical energy measurements collected from a medium-scale industrial manufacturing facility over an approximately one-year observation window, with staged commissioning resulting in heterogeneous temporal coverage. The dataset includes time-series measurements from production machinery, auxiliary systems, and distribution-level assets instrumented using a heterogeneous fleet of Ethernet and RS-485 energy meters integrated via industrial gateways and programmable logic controllers. Measurements were acquired via a SCADA-based logging infrastructure and exported from an operational SQL historian. The publicly released dataset comprises fixed 15 min aggregated energy and power metrics derived from high-frequency SCADA telemetry. In its released ALL-phase representation, the dataset comprises measurements from 43 monitored assets and 1,039,873 15 min windows, corresponding to 2.96 GWh of measured electrical energy. Mean window-level data coverage is 99.99%, and 97.72% of ALL-phase windows satisfy the dataset’s reliability criterion. Interval records include energy consumption, demand, data coverage metrics, and reliability indicators. The dataset reflects real-world industrial monitoring conditions, including mixed communication pathways and irregular sampling behaviour, and is intended to support research in industrial energy analytics, data quality assessment, load profiling, and operational energy modelling. Full article

Energy consumption is a critical concern for Internet of Things (IoT) platforms lacking abundant resources, particularly for swarm robotic systems that rely on numerous devices operating collaboratively over extended periods. This study presents a comprehensive design strategy for improving processing and communication to enhance system efficiency and reduce energy consumption. We incorporate energy harvesting (photovoltaic and RF), dynamic power management, and energy-efficient communication protocols (e.g., duty cycle, power control, data compression) into two complementary platforms built for swarm robotics: MCU-based nodes (TI MSP430 with LoRa transceiver), which serve as the experimental prototype for validating energy-aware communication, compression, and scheduling mechanisms; edge platforms (Jetson Nano and TX2), which are used for high-level power profiling and system-level evaluation, particularly for computation intensive workloads and comparative analysis. Our technique involves analyzing the device’s energy usage and harvesting processes, developing efficient communication protocols, and validating the system through simulations and hardware prototypes. Experimental results under outdoor and indoor conditions show that the device maintains an energy neutrality ratio well above unity, even with limited ambient energy. Key findings include significant reductions in energy per bit transmitted and reliable long-term operation. These insights pave the way for deploying swarms of autonomous IoT-based robots with minimal maintenance and maximal longevity. Full article

Electro-optic frequency combs (EOFCs), generated through the microwave-driven modulation of continuous-wave lasers, have emerged as a highly reconfigurable and system-compatible class of optical frequency combs with growing importance in microwave photonics, coherent communications, spectroscopy, and precision metrology. In contrast to mode-locked lasers and Kerr microresonator combs, EOFCs offer electrically programmable repetition rates, deterministic phase coherence, and intrinsic compatibility with radiofrequency electronic systems, making them particularly attractive for integrated and application-oriented implementations. As EOFCs evolve toward broader bandwidths, lower power consumption, and full on-chip integration, their achievable performance is increasingly constrained by the interplay between electro-optic physical mechanisms, modulator architectures, and material platform properties. This review establishes a unified analytical framework that systematically connects EOFC generation mechanisms, device configurations, key performance metrics, and platform-level limitations. We first summarize the fundamental electro-optic effects underpinning EOFC generation and analytically examine representative modulator architectures, including phase modulators, Mach–Zehnder modulators, and microresonator-based schemes, to clarify their respective comb-generation characteristics. Key performance determinants, such as modulation depth, bandwidth, electro-optic efficiency, and optical loss, are then discussed to elucidate their coupled influence on comb-line count, spectral flatness, output power, and phase noise. Subsequently, the performance of EOFCs implemented on major integrated platforms, including Silicon on Insulator (SOI), Indium Phosphide on Insulator (InPOI), Lithium Niobate on Insulator (LNOI), and Lithium Tantalate on Insulator (LTOI), is comparatively reviewed to highlight the material-dependent advantages and constraints. Finally, emerging directions based on heterogeneous integration and ferroelectric materials with ultrahigh electro-optic coefficients are discussed as promising pathways to overcome the current performance bottlenecks. This review provides clear physical insights and engineering guidance for the future development of high-performance, integrated EOFC systems. Full article

Smart care applications impose increasingly stringent requirements on low-latency execution, privacy preservation, and continuous monitoring. These requirements are driving intelligent services from cloud-centric architectures toward edge-side deployment. When multiple care-related workloads are deployed on resource-constrained edge devices, performance bottlenecks arise not only from model inference itself, but also from process scheduling, inter-process communication, and resource coordination overhead. To address this issue, this paper presents a task scheduling and management platform for multi-workload smart elderly care on a single pure-edge CPU–TPU heterogeneous node. The platform adopts a shared-memory and event-driven synchronization mechanism together with fine-grained process partitioning, thereby establishing a data-sharing and runtime-coordination framework for concurrent multi-workload execution. To evaluate the effectiveness of the proposed platform, experiments were conducted under single-workload, multi-workload, multi-resolution, and long-term runtime settings. The results show that, compared with two baseline schemes, the proposed platform improves the average frame rate by 66.7% and 71.1%, reduces net memory usage by 96.3% and 45.3%, and lowers net power consumption by 46.8% and 37.7%, respectively, under the single-workload setting. Under 10 concurrent workload instances, the system still maintains a stable frame rate of 42.03 ± 0.73 fps, demonstrating strong concurrency scalability. Multi-resolution experiments further indicate that the performance degradation at higher resolutions is mainly constrained by the front-end data supply stage. A continuous 10-day runtime experiment additionally verifies the sustained operating capability and resource stability of the platform under pure-edge deployment. These results demonstrate that node-level shared-memory and event-driven coordination can effectively improve the execution efficiency, scalability, and stability of real-time multi-workload analytics on such pure-edge heterogeneous nodes, providing a useful basis for future extensions to multi-node edge environments and edge–cloud collaborative task scheduling. Full article

In vehicle-mounted thermoelectric refrigerators, limited installation space and fluctuating ambient conditions make it difficult to achieve both sufficient cooling capacity and low power consumption. However, most previous studies have focused on thermoelectric materials or standalone devices rather than system-level optimization under realistic vehicle constraints. To address this issue, a three-dimensional multiphysics-coupled finite element model combined with a parametric optimization approach was developed for a vehicle-mounted thermoelectric refrigerator used in one of Dongfeng Motor’s new energy vehicle models. Based on this model, the effects of key geometric parameters, including thermoelectric leg height (l), leg width (w), and leg number (p_d), as well as operating conditions, namely input voltage (U) and ambient temperature (T_a), on the overall performance of the refrigerator, including cooling capacity (Q_c), coefficient of performance (COP), and interior center temperature (T), were systematically investigated. The results show that under nominal operating conditions (U = 13.5 V, T_a = 25 °C), increasing p_d from low to moderate values significantly improves cooling capacity, reduces the interior temperature, and decreases power consumption. However, further increases in p_d lead to diminishing improvements in cooling performance because of the heat dissipation limitation on the hot side. By comprehensively evaluating cooling performance and energy consumption, the optimal design was determined to have 322 legs, a leg width of 1.4 mm, and a leg height of 1.8 mm. Under these conditions, the refrigerator achieved a cooling capacity of 13.95 W, a power consumption of 38.4 W, a COP of 0.36, and a compartment center temperature of 10.71 °C. Compared with the conventional 254-leg module (w = 1.4 mm, l = 1.6 mm), the optimized design improved the COP by more than 45.1% and reduced power consumption by 28.8%. In addition, the results indicate that under high ambient temperature conditions, the overall system performance is mainly limited by the hot-side heat rejection capacity. Overall, this study provides an effective structural optimization approach for improving the energy efficiency of compact thermoelectric refrigerators in confined spaces and offers a useful reference for the low-power design of vehicle-mounted cooling devices. Full article

Climate change is increasingly recognized as a source of psychological distress, yet the prevalence, predictors, and behavioral implications of climate anxiety remain unevenly understood. This study examines climate anxiety, its key drivers, and associated behavioral responses in a multi-country sample of adults. A cross-sectional online survey was conducted across 21 countries using the Climate Change Anxiety Scale (CCAS), alongside measures of awareness, coping strategies, social support, and food-related behaviors, including food waste reduction, increased plant-based food consumption, and home or community gardening. Analyses included descriptive statistics, exploratory factor analysis (EFA), and multivariable regression. Given the uneven country-level representation, results are reported as pooled patterns with a few exploratory cross-country comparisons. Climate anxiety was widely reported, with over 60% of participants indicating that climate challenges were emotionally overwhelming. Regression analyses showed that climate awareness and frequency of climate-related thinking were positively associated with higher anxiety, although the effect sizes were small and explanatory power was limited (R² = 0.055). EFA identified two related dimensions: cognitive concern about future impacts and affective distress. Climate anxiety across countries showed modest variation (2.44–3.23) and no statistically significant differences, despite variation in awareness. A gap between concern and climate action was evident: only 39.1% reported environmentally motivated dietary changes. Cost, limited availability, and lack of information were the main barriers to climate action, and only 24.4% reported frequent social support. These findings indicate that climate anxiety is shaped by both psychological and structural factors, and that reducing it requires not only increasing awareness but also enabling conditions that support meaningful climate action. Full article

One-dimensional mass transfer resistance-in-series framework was developed theoretically and validated experimentally using a flat-plate polytetrafluoroethylene/polypropylene (PTFE/PP) membrane module to predict CO₂ absorption fluxes and concentration distributions. The decline in CO₂ absorption efficiency along the membrane module is primarily attributed to increased concentration polarization resistance and a reduced driving force concentration gradient. To alleviate these limitations, carbon fiber promoters were strategically embedded to suppress concentration polarization, reduce the mass transfer resistances, and enhance turbulence intensity. In the present study, device performance was further improved by implementing properly ascending or descending hydraulic equivalent widths along the absorbent feed channel. Under the descending configuration, an absorption flux enhancement of up to 44.94% was achieved relative to an empty-channel module (i.e., without S-ribbed carbon fiber inserts). Theoretical formulations were established to predict absorption fluxes under varying monoethanolamine (MEA) volumetric flow rates, CO₂/N₂ mixture flow rates, and inlet CO₂ feed concentrations. The model predictions showed good agreement with experimental results obtained using MEA solutions under both ascending and descending hydraulic width operations, demonstrating effective mitigation of polarization effects and enhanced absorption flux along the absorbent feed channel. An economic assessment of the S-ribbed carbon fiber module was conducted by evaluating the trade-off between absorption flux enhancement and incremental power consumption. The results indicate that the proposed design provides a practical and economically viable approach for improving the performance of membrane-based CO₂ capture technologies. In addition, an enhanced Sherwood number correlation, expressed in a simplified form, was developed and employed to estimate the mass transfer coefficients of CO₂ membrane absorption modules incorporating S-ribbed carbon fiber promoters. Full article

Maritime transport is under increasing pressure to cut greenhouse gas and pollutant emissions to meet global decarbonization goals and tighter environmental standards. Ship electric propulsion systems offer a promising solution for short-range maritime operations, particularly for small vessels and coastal activities. Full-electric vessels can significantly reduce operational emissions; however, a key challenge is the extensive charging time for onboard energy storage, which can affect operational continuity and logistical efficiency. This study examines mission planning and energy management for a hybrid multi-source electric mail boat operating in the Aeolian archipelago. It evaluates the viability and performance of a daily inter-island route powered by a high-temperature methanol fuel cell, batteries, and photovoltaic panels. A routing and simulation framework was developed to model the boat’s itinerary among seven islands, accounting for realistic navigation speeds, scheduled stops, solar energy availability, and battery state-of-charge constraints. The study analyzes distance, travel time, energy consumption, solar power generation, and fuel–electric usage with high temporal resolution, enabling detailed analysis of power flows during sailing and docking. Several operational strategies were assessed, including periods of increased speed supported by battery assistance and fuel–electric cell output, combined with coordinated energy management to keep battery levels above a lower acceptable threshold while completing the route in a single day. The methodology provides a practical tool for planning low-emission island networks and supports the integration of innovative energy systems into small electric workboats operating in specific maritime regions. Full article

Comminution is the most energy-intensive stage in mineral processing and a major source of indirect greenhouse gas (GHG) emissions in mining. This study evaluates the impact of Ultra-High-Intensity Blasting (UHIB) on downstream comminution energy demand and associated GHG emissions under conditions representative of large-scale Chilean mining. Fragmentation from conventional blasting and UHIB was simulated using JKSimBlast, and the resulting particle size distributions were used as input for four comminution circuit configurations modeled in JKSimMet. Two ore hardness scenarios were analyzed: hard ore (Bond Work Index, BWI = 19 kWh/t) and soft ore (BWI = 11 kWh/t). Power draw of crushers and mills was used to estimate specific energy consumption and GHG emissions based on the Chilean electrical system emission factor. Results show that UHIB enables significant reductions in comminution energy demand, reaching approximately 18% for hard ore and over 30% for soft ore. These reductions are primarily associated with circuit simplification, including the removal of energy-intensive stages such as primary crushing and SAG milling. The results demonstrate that improved fragmentation can reduce downstream energy demand and carbon intensity, highlighting UHIB as an effective mine-to-mill strategy for energy efficiency and emission reduction. Full article

This study aims to improve the accuracy of cruise-phase power consumption prediction for multirotor unmanned aerial vehicles operating under varying wind conditions. Existing parametric energy models typically retain the wind velocity vector in the ground or inertial reference frame, and this representation does not distinguish between axial drag contributions along the fuselage and lateral attitude-correction contributions perpendicular to it. The proposed framework addresses this limitation through a physics-informed coordinate transformation that projects the measured wind vector into the body frame of the aircraft using quaternion-derived heading angles, yielding separate axial and lateral wind components. These components enter the power model as two additional predictors that augment the induced-power baseline, with the axial term following a cubic airspeed–power relationship consistent with parasitic drag formulations and the lateral term following a quadratic relationship consistent with attitude-correction mechanics. The framework is validated on a publicly available flight dataset, which comprises 188 flights of a DJI Matrice 100 quadcopter across payloads of 0 to 0.75 kg, ground speeds of 4 to 12 m/s, and altitudes of 25 to 100 m. Compared with the induced-power baseline, the proposed model reduces the root mean square error by 15.9% and the mean squared error by 29.7% during the cruise phase. The improvement is larger when wind speeds exceed 6 m/s, a regime in which the baseline residuals increase while the proposed model retains a comparatively stable error profile. Residual analysis indicates that baseline errors follow an approximately quadratic trend relative to the axial and lateral wind components, consistent with established parasitic-power and attitude-correction formulations. The closed-form structure of the proposed model is compatible with onboard execution on flight controllers, which suggests a feasible pathway toward its use as the power-prediction module within downstream range-estimation and energy-reserve sizing routines. Full article

Accurate prediction of electricity demand during hot seasons is essential for maintaining power system reliability, particularly in humid subtropical regions such as Guangdong, China, where high temperatures strongly influence consumption. However, many models rely primarily on air temperature and may not fully capture combined atmospheric effects. This study evaluates the potential of biometeorological thermal indices for improving summer electricity load forecasting. Daily maximum load and meteorological data during May–September 2019–2021 were analyzed using Back-Propagation Neural Network (BP), Random Forest (RF), and a Stacking ensemble model. Three indices—Effective Temperature (ET), Physiological Equivalent Temperature (PET), and the Universal Thermal Climate Index (UTCI)—were introduced as predictors. The ensemble model achieved the best performance, with Ensemble–UTCI yielding the highest accuracy (R² = 0.559, RMSE = 60.96 × 10⁴ kW, MAE = 45.10 × 10⁴ kW). Compared with temperature-based models, biometeorological indices consistently improved predictions, with UTCI performing best (average RMSE = 62.81 × 10⁴ kW). Bayesian analysis shows strong evidence of improvement in RF and ensemble models, but not in BP or linear models, indicating model dependence. During the July 2021 heat event, RF showed greater robustness, with PET–RF achieving the lowest error (MAPE = 3.03%). These results demonstrate the value of biometeorological indices for load forecasting in humid subtropical regions. Full article

Underwater-transportation systems have significant potential for both military and commercial applications. Neural Network (NN)-based control offers enhanced robustness for actuators to manage the states of autonomous underwater-transportation systems which include Rigid-Connection Transportation Systems (RCTSs), Flexible-Connection Transportation Systems (FCTSs) and Leader–Follower-Formation Control Transportation Systems (LFFCTSs). In this study, NN-Augmented Control (NNAC) is applied to the aforementioned three transportation systems to enable accurate path tracking by the actuators installed onboard these systems under both ideal operating conditions and in the presence of sensor and process noise. The Extended Kalman Filter (EKF) is employed to estimate the system states under noisy conditions. The results demonstrate that NNAC provides robust and adaptive control of actuators, achieving efficient trajectory tracking via the transportation systems despite the influence of sensor and process noise disturbances. NNAC predominance was also observed in comparison with the conventional PID controller. Among the transportation configurations under the NNAC strategy, the RCTS exhibited the highest tracking accuracy with the lowest power consumption by the actuators. The power consumption of actuators installed on the LFFCTS was marginally higher than that of the RCTS. However, the translational motion accuracy of the follower vehicle in the LFFCTS was the lowest due to indirect actuation control through the formation controller. In contrast, actuators in the FCTS showed the highest power consumption while motion accuracy was comparatively lowest, attributed to the increased complexity of its dynamic positioning requirements. Full article

This study presents a headset-type biofluorometric gas sensor incorporating a CMOS camera for continuous, non-invasive monitoring of transcutaneous ethanol from the ear canal. The sensor employs alcohol dehydrogenase (ADH) to catalyze the NAD⁺-to-NADH conversion during ethanol oxidation, enabling quantitative measurement through NADH fluorescence detection (λ_ex = 340 nm, λ_em = 490 nm). The integrated system comprises a wireless CMOS camera, an ADH-immobilized cotton mesh enzyme membrane, UV-LED excitation source, optical bandpass filters, and a dual convex lens assembly housed in a 3D-printed headset powered by a lithium battery. Key improvements include a 3.5-fold enhancement in fluorescence collection efficiency achieved through optimized dual convex lens configuration. Systematic screening of seven cotton mesh materials identified Iwatsuki cotton mesh as the optimal enzyme immobilization substrate, exhibiting minimal autofluorescence and 14.2-fold higher water retention capacity compared to H-PTFE membranes. The glutaraldehyde-crosslinked ADH-immobilized cotton mesh maintained enzymatic activity for over 45 min with a 10-fold improvement in signal-to-noise ratio. The system demonstrated a dynamic detection range spanning 10 ppb to 10 ppm for gaseous ethanol and exhibited high selectivity against interfering volatile organic compounds in skin gas, including methanol, acetaldehyde, formaldehyde, and acetone. Human experiments validated the system’s practical performance. Following alcohol consumption, subjects wore the device for 50 min while real-time fluorescence monitoring captured dynamic ethanol concentration changes in the ear canal. The dose-dependent fluorescence response—approximately 2-fold higher at 0.4 g/kg versus 0.04 g/kg alcohol intake—correlated well with calibration data. This headset-type biofluorometric sensor enables unrestrained continuous monitoring of ear canal ethanol, providing a novel wearable platform for alcohol metabolism assessment with potential applications in health monitoring and clinical research. Full article