Search Results (11,768)

As the automation and intelligence of low-voltage distribution networks continue to advance, the inter-layer coupling between medium- and low-voltage distribution networks is increasingly strengthened, making traditional fixed-point iteration methods inadequate for distributed power flow calculation in such a collaborative framework. To address this issue, this paper proposes a distributed power flow calculation method for medium- and low-voltage distribution networks based on edge intelligence. First, a cooperative operational framework for medium- and low-voltage distribution networks is designed by integrating edge intelligence technology. Then, a distributed power flow calculation model is established, and its fixed-point iterative characteristics are analyzed. A convergence index calculation method based on small perturbations is proposed, followed by an iterative algorithm based on continuous intersection estimation. Finally, simulation case studies validate the proposed method in terms of accuracy, convergence, and computational efficiency, demonstrating its capability to meet the modeling and analytical needs of power flow calculation in medium- and low-voltage distribution networks, providing methodological support for the development of distributed intelligent power grids. Full article

Utilizing coordinated UAV formations for emergency disaster relief is a key future trend, but traditional evaluation methods have three major drawbacks: high computational complexity, heavy reliance on expert experience, and poor generalization in multi-scenario small-sample settings. To address these issues, this paper first designs a four-level evaluation index system that covers 5 core capabilities and targets 4 typical disaster relief scenarios. Next, it establishes an AHP model that quantifies the performance of 406 UAV formations, thereby providing high-quality labeled data for subsequent research. Furthermore, the paper constructs a ResNet + Atten deep learning network with a hybrid architecture, which improves both the self-learning ability of expert knowledge and the efficiency of multi-scenario evaluation. To solve small-sample overfitting and expert bias, the paper proposes a physically meaningful controllable perturbation data augmentation method: one that works by perturbing 23 UAV performance metrics within a 5–15% range to expand the sample size. Comparative experiments are conducted using three methods, BP neural networks, ResNet, and LSTM, and results show that ResNet + Atten achieves superior performance. Additionally, the data augmentation method effectively enhances the generalization ability of the model. The proposed method provides a reliable method for evaluating the performance of UAV multi-scenario collaborative disaster relief operations. Full article

Blockchain technology has attracted considerable attention in the supply chain literature for its potential to enhance operational traceability, transparency, and trust, as well as to advance greening initiatives. Given current supply chain configurations, exploring barriers to implementation is a consequential agenda, and current studies have devoted substantial effort to identifying and offering guidance to address them. Despite recent findings, insights into how blockchain technology adoption can support green supply chain management are missing, particularly in the maritime sector, which receives limited attention. Thus, this work outlines a methodological approach to examine the suitability of maritime routes for addressing barriers to implementing blockchain technology in green supply chain management. Viewing the evaluation as a multi-criteria decision-making (MCDM) problem, the proposed approach performs the following actions on a case study evaluating four maritime lines. Firstly, from the 13 identified barriers in the literature review and expert interviews, nine relevant barriers were determined after one round of a Delphi process. These barriers eventually comprise the set of evaluation criteria. Secondly, to satisfy the assumption of criterion independence in most MCDM methods, this work proposes a novel extended grey relational analysis (GRA) that allows for the measurement of criterion independence based on the concept of grey relational space. Proposed here for the first time, the extended GRA offers a distribution-free overall independence index for each criterion based on pattern similarity. Finally, an integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and ARAS (Additive Ratio Assessment) methods under Z-information is developed to address the evaluation problem involving expert judgments in a highly uncertain decision-making context. Results show that transaction-level uncertainty is the most critical barrier to blockchain adoption, followed by technology risks and higher sustainability costs. Among the four maritime lines, Line 3 is best prepared for a blockchain-enabled green supply chain. The agreement between these results and those of other MCDM methods is shown in the comparative analysis. Also, ranking remains unchanged even when the criteria weights are adjusted. The proposed approach provides a computationally efficient and tractable framework for maritime managers to make informed decisions about blockchain adoption to promote green supply chains. Full article

Background: This study examines whether participation in STEAM-based educational activities is associated with improvements in executive functions (EFs) and cognitive skills in children with Attention Deficit Hyperactivity Disorder (ADHD). Methods: A total of 60 children diagnosed with ADHD (mean age = 8 years) participated, with 30 following a traditional educational approach and 30 engaged in STEAM-based activities. Executive functions and cognitive abilities were assessed using standardized instruments (BRIEF, WISC-V, CARAS-R), and data were analyzed with IBM SPSS Statistics 25. Results: Children in the STEAM group outperformed the control group across several domains, showing statistically significant gains in inhibition, planning and organization, verbal comprehension, visuospatial skills, processing speed, total IQ, efficiency, and the Impulsivity Control Index (ICI). Conclusions: These findings suggest that STEAM-based educational experiences may support neurodevelopmental growth and enhance cognitive and executive functioning in children with ADHD, although causal inferences cannot be drawn due to the cross-sectional design. Full article

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. Full article

This study provides a comprehensive analysis of the impact of different marine fuels such as heavy fuel oil (HFO), methane, methanol, ammonia, or hydrogen, on energy efficiency and pollutant emissions in maritime transport, using a combined application of the Energy Efficiency Design Index (EEDI), Energy Efficiency Operational Indicator (EEOI), and Carbon Intensity Indicator (CII). The results show that methane offers the most balanced alternative, reducing CO₂ by more than 30% and improving energy efficiency, while methanol provides an intermediate performance, eliminating sulfur and partially reducing emissions. Ammonia and hydrogen eliminate CO₂ but generate NO_x (nitrogen oxides) emissions that require mitigation, demonstrating that their environmental impact is not negligible. Unlike previous studies that focus on a single fuel or only on CO₂, this work considers multiple pollutants, including SO_x (sulfur oxides), H₂O, and N₂, and evaluates the economic cost of emissions under the European Union Emissions Trading System (EU ETS). Using a representative model ship, the study highlights regulatory gaps and limitations within current standards, emphasizing the need for a global system for monitoring and enforcing emissions rules to ensure a truly sustainable and decarbonized maritime sector. This integrated approach, combining energy efficiency, emissions, and economic evaluation, provides novel insights for the scientific community, regulators, and maritime operators, distinguishing itself from previous multicriteria studies by simultaneously addressing operational performance, environmental impact, and regulatory gaps such as unaccounted NO_x emissions. Full article

Because wildfire spread is strongly influenced by instantaneous gusts, models that use only mean wind speed typically underestimate spread. In contrast, incorporating suppression effects often leads to overestimation. To reduce these errors, this paper newly proposes the concepts of an effective wind speed (EWS) and an EWS coefficient that jointly account for short-range forecast mean wind speed and the maximum gust from numerical weather prediction. The EWS is defined as an EWS coefficient-weighted average of the mean wind speed and maximum gust, so that the simulated perimeter matches the observed wildfire perimeter as closely as possible. Here, EWS refers exclusively to near-surface horizontal wind speed; vertical wind components are not considered. The EWS coefficient is modeled as a function of elapsed time since ignition, thereby implicitly reflecting the level of suppression resource deployment. The proposed frameworks are described in detail using time-stamped perimeters from multiple large-scale wildfires that occurred concurrently in South Korea during a specific period. On this basis, an EWS coefficient suitable for operational use in South Korea is derived. Using the derived EWS for spread prediction, the Sørensen index increased by up to 0.4 compared with predictions based on maximum gust alone. Incorporating the proposed EWS and coefficient into Korean wildfire spread simulators can improve the accuracy and robustness of predictions under extreme weather conditions, supporting safer and more efficient wildfire response. Full article

Electrically conductive cement-based composites exhibit significant potential for a range of multifunctional applications. Nonetheless, the electrical and mechanical performance of ductile cement-based composites incorporating compound conductive additives has not been sufficiently explored. This study examines the effects of two distinct carbon-based fillers, namely carbon black and chopped carbon fibers, on strain-hardening cementitious composites (SHCC), and elucidates the synergistic mechanism of hybrid conductive fibers and fillers within SHCC. The findings indicate that a sufficiently high electrical conductivity can be achieved by incorporating 5 wt.% carbon black and 0.2–0.4 vol.% carbon fibers. The introduction of hybrid carbon-based fillers reduces the resistivity of SHCC by three orders of magnitude to less than 150 Ω∙cm, surpassing the performance of composites with a single carbon-based filler. Furthermore, the incorporation of hybrid carbon-based fillers and fibers enhances the compressive and flexural strength of cementitious composites. Compared to the referenced PE-SHCC, the tensile strength and strain of SHCC with 5 wt.% carbon black and 0.4 vol.% carbon fibers increased by 37.3% and 82.6%, respectively. A hybrid efficiency index (HEI) is proposed to assess both electrical conductivity and mechanical properties of SHCC incorporating with carbon-based fillers. The study’s findings offer an effective approach for utilizing hybrid carbon-based conductive fillers in the multifunctional applications of SHCC. Full article

Ghost imaging (GI) offers a robust framework for remote sensing under degraded visibility conditions. However, atmospheric scattering in phenomena such as fog introduces significant noise and signal attenuation, thereby limiting its efficacy. Inspired by the selective attention mechanisms of biological visual systems, this study introduces a novel deep learning (DL) architecture that embeds a self-attention mechanism to enhance GI reconstruction in foggy environments. The proposed approach mimics neural processes by modeling both local and global dependencies within one-dimensional bucket measurements, enabling superior recovery of image details and structural coherence even at reduced sampling rates. Extensive simulations on the Modified National Institute of Standards and Technology (MNIST) and a custom Human-Horse dataset demonstrate that our bio-inspired model outperforms conventional GI and convolutional neural network-based methods. Specifically, it achieves Peak Signal-to-Noise Ratio (PSNR) values between 24.5–25.5$\mathrm{dB}/\mathrm{m}$ and Structural Similarity Index Measure (SSIM) values of approximately 0.8 under high scattering conditions ($\beta \ge 3.0 \mathrm{dB}/\mathrm{m}$) and moderate sampling ratios ($N \ge 50\%$). A comparative analysis confirms the critical role of the self-attention module, providing high-quality image reconstruction over baseline techniques. The model also maintains computational efficiency, with inference times under $0.12 \mathrm{s}$, supporting real-time applications. This work establishes a new benchmark for bio-inspired computational imaging, with significant potential for environmental monitoring, autonomous navigation and defense systems operating in adverse weather. Full article

In precision agriculture, the delineation of Management Zones (MZs) is essential for optimizing input use efficiency and site-specific nutrient management. MZs are established based on spatial variability derived from remote sensing data—such as Normalized Difference Vegetation Index (NDVI) from satellite or UAV-based imagery—and yield maps collected during harvest. However, the microbial community composition of the soil is often overlooked in MZ delineation. To address this gap, we investigated the soil bacterial community structure across different MZs in an arable field. The zones were delineated using NDVI data, soil profiles were described, and bulk soil samples were collected. Soil physicochemical parameters were analyzed in parallel with 16S rRNA gene amplicon sequencing to characterize bacterial community composition and diversity. The results demonstrated that soil texture and soil organic matter content were the primary drivers influencing bacterial community structure across the field. Moreover, patterns in microbial composition aligned closely with MZ delineations, indicating that microbial profiles could aid in better understanding and supporting the nutrient management practices. Our findings suggest that soil microbiological data can enhance the stability and biological relevance of MZ definitions, thereby improving resource allocation, soil health management, and overall sustainability in precision farming systems. Full article

With the increasing penetration of the distributed generation and the growing variability of loads, flexible microgrids (FMGs) require operational strategies that can adapt to seasonal changes while maintaining reliable performance. To overcome the limitations of fixed-interval partition updates, this paper proposes a threshold-triggered dynamic operation strategy for FMGs. A composite partition-updating index is formulated by integrating an operation optimization index, which reflects network loss and hybrid energy storage (HES) cost, with a seasonal load uniformity index, so that partition reconfiguration is triggered only when scenario transitions significantly deteriorate operating performance. By enhancing seasonal load uniformity across partitions, the proposed framework reflects a symmetry-oriented operation philosophy for FMGs. An HES model is further established to coordinate short-term energy storage (STES) and long-term energy storage (LTES) across multiple timescales. In conjunction with remotely controlled switches (RCSs), the proposed framework enables adaptive adjustment of FMG boundaries and source scheduling under diverse seasonal conditions. A case study on the IEEE 123-bus distribution system demonstrates that the proposed strategy effectively reduces power fluctuations and redundant switching operations, improves seasonal load uniformity, and enhances both the operational flexibility and economic efficiency of FMGs. Full article

In recent years, the increasing frequency of extreme weather events, the large-scale integration of distributed generation into distribution networks, and the widespread application of new power electronic devices have posed severe challenges to the security of power supply in distribution networks. To enhance the power supply reliability of the distribution network while considering its economic efficiency, this paper proposes a collaborative planning method for a flexible distribution network focused on power supply restoration and resilience enhancement In this method, a planning model for flexible distribution networks is established by optimally determining the siting and sizing of soft open point (SOP), with the objective of minimizing the annual comprehensive cost of the distribution network under multiple operational and planning constraints. Second-order cone programming (SOCP) relaxation and polyhedral approximation-based linearization techniques are employed to reformulate and solve the model, thereby obtaining the optimal siting and sizing Case for SOPs. Finally, simulations are conducted on a modified IEEE 33-bus test system to verify the effectiveness of the proposed method. The results show that, through appropriate siting and sizing of SOPs, outage loss costs can be significantly reduced, nodal voltage profiles can be improved, and load support can be provided to de-energized areas, leading to a reduction of more than 70% in the annual comprehensive cost of the distribution network and an improvement in the system reliability index from 99% to 99.999%, thus effectively enhancing both the economic efficiency and reliability of the distribution system. Full article

Creating an optimal light environment for different crops is crucial for achieving high yields under controlled environment agriculture conditions. Currently, there are no optimal technologies, including lighting technologies, for growing root crops (in particular radish) in CEA (Controlled Environment Agriculture). This study examined the effects of HPS (High-pressure sodium vapor lamps) and three original sunlike full-spectrum LED lamps on the morphophysiological characteristics and the diffuse reflectance indices of the leaves of two contrast radish cultivars. It was found that a higher blue light content (24%) in the spectrum of the LED 3 lamp contributed to the formation of radish plants with a more compact leaf rosette and maximum yield of roots (up to 19%) compared to the other two types of LED lamps. When treated with LED 3, photosynthesis efficiency was probably higher compared to LED 1 and LED 2, which led to a significant decrease in reflected radiation, especially in the blue and red ranges (by 5–143% and 32–86%, respectively). It was found that the genotype had a significant effect on all morphophysiological parameters of radish, while lighting treatment only affected the integral parameters (Pr—proportion of root crop, and Ai—attraction index) and leaf thickness. However, lighting treatment exhibited a greater impact on leaf reflection indices compared to the genotype, especially those related to chlorophyll content. The results of the study indicate that LED 3 lamps, simulating natural light at midday, are suitable for the production of radish root crops under CEA conditions. Full article

Amid fossil-fuel depletion and worsening environmental impacts, battery electric vehicles (BEVs) are pivotal to the energy transition. Energy management in BEVs relies on accurate motor efficiency maps, yet real-time onboard control demands models that balance fidelity with computational cost. To address map inaccuracy under real driving and the high runtime cost of 2-D interpolation, we propose a driving-cycle-aware, physically interpretable quadratic polynomial-surface framework. We extract priority operating regions on the speed–torque plane from typical driving cycles and model electrical power $P_e$ as a function of motor speed $n$ and mechanical power $P_m$. A nested model family (M3–M6) and three fitting strategies—global, local, and region-weighted—are assessed using $R^2$, RMSE, a computational complexity index (CCI), and an Integrated Criterion for accuracy–complexity and stability (ICS). Simulations on the Worldwide Harmonized Light Vehicles Test Cycle, the China Light-Duty Vehicle Test Cycle, and the Urban Dynamometer Driving Schedule show that region-weighted fitting consistently achieves the best or near-best ICS; relative to Global fitting, mean ICS decreases by 49.0%, 46.4%, and 90.6%, with the smallest variance. Regarding model order, the four-term M4 $\left(+P_m^2\right)$ offers the best accuracy–complexity trade-off. Finally, the region-weighted fitting M4 $\left(+P_m^2\right)$ polynomial model was integrated into the vehicle-level economic speed planning model based on the dynamic programming algorithm. In simulations covering a 27 km driving distance, this model reduced computational time by approximately 87% compared to a linear interpolation method based on a two-dimensional lookup table, while achieving an energy consumption deviation of about 0.01% relative to the lookup table approach. Results demonstrate that the proposed model significantly alleviates computational burden while maintaining high energy consumption prediction accuracy, thereby providing robust support for real-time in-vehicle applications in whole-vehicle energy management. Full article

Efficient cutting transport is crucial in challenging drilling environments such as ultra-short-radius horizontal wells. Flexible drill pipes, designed for complex wellbore geometries, offer a potential solution. However, the cutting transport behavior within them remains poorly understood. To improve wellbore cleaning and drilling efficiency, this study investigates the underlying transport mechanisms. The investigation employs a coupled CFD-DEM approach to model cutting transport in flexible drill pipes. This method combines fluid dynamics and particle motion simulations to analyze the interaction between drilling fluid and cuttings, evaluating the impact of factors such as rotational speed, flow rate, and fluid properties on cleaning efficiency. The results indicate that increasing the flow rate at a constant rotational speed significantly reduces the cutting concentration. Nevertheless, beyond a critical flow rate of 1.5 m/s, further increases yield diminishing returns in cleaning efficiency due to transport capacity saturation. In contrast, increasing the rotational speed at a fixed flow rate of 1.42 m/s has a less pronounced effect on cutting transport and increases frictional torque, thereby reducing energy efficiency. Higher rotational speeds primarily enhance the suspension of fine cuttings, with minimal impact on larger particles. Additionally, the rheological properties of the drilling fluid play a key role. A higher flow behavior index increases viscosity near the wellbore, improving transport performance. Conversely, a higher consistency index enhances the fluid’s carrying capacity but increases annular pressure drop, which imposes greater demands on pump capacity. Thus, optimal drilling performance requires balancing pressure losses and cleaning efficiency through comprehensive parameter optimization. Full article