Search Results (617)

With the rapid development of renewable energy, the proportion of small hydropower as a clean energy in the distribution network (DN) is increasing. However, the randomness and intermittence of small hydropower has brought new challenges to the operation of DN; especially, the problems of increasing network loss and reactive voltage exceeding the limit have become increasingly prominent. Aiming at the above problems, this paper proposes a reactive power optimization control method for DN with hydropower based on an improved discrete particle swarm optimization (PSO) algorithm. Firstly, this paper analyzes the specific characteristics of small hydropower and establishes its mathematical model. Secondly, considering the constraints of bus voltage and generator RP output, an extended minimum objective function for system power loss is established, with bus voltage violation serving as the penalty function. Then, in order to solve the following problems: that the traditional discrete PSO algorithm is easy to fall into local optimization and slow convergence, this paper proposes an improved discrete PSO algorithm, which improves the global search ability and convergence speed by introducing adaptive inertia weight. Finally, based on the IEEE-33 buses distribution system as an example, the simulation analysis shows that compared with GA optimization, the line loss can be reduced by 3.4% in the wet season and 13.6% in the dry season. Therefore, the proposed method can effectively reduce the network loss and improve the voltage quality, which verifies the effectiveness and superiority of the proposed method. Full article

To support carbon peaking and neutrality goals in the city transportation sector, this paper accounts for CO₂ emissions from the transport sector in Xuzhou City, North Jiangsu Province, from 1995 to 2023. This study explores the relationship between transport-related carbon emissions and economic growth, using the TAPIO decoupling index. Meanwhile, a carbon emission prediction model based on the STIRPAT framework is constructed, with scenario analysis applied to forecast future emissions. Results show three decoupling stages: the first, dominated by weak and expansive negative decoupling, reflects extensive economic growth; the second features weak decoupling with expansive coupling, indicating a more environmentally coordinated phase; the third transitions from expansive negative decoupling and weak decoupling to strong decoupling and expansive coupling, suggesting a shift in development patterns. Under the baseline, low-carbon, and enhanced low-carbon scenarios, by 2030, the CO₂ emissions of the transportation industry in Xuzhou will be 10,154,700 tons, 9,072,500 tons, and 8,835,000 tons, respectively, and the CO₂ emissions under the low-carbon scenario and the enhanced low-carbon scenario will be reduced by 10.66% and 13.00%, respectively. Based on these findings, the study proposes carbon reduction strategies for Xuzhou’s transport sector, focusing on policy regulation, energy use, and structural adjustments. Full article

Accurate and efficient cloud detection is critical for maintaining the usability of optical remote sensing imagery, particularly in large-scale Earth observation systems. In this study, we propose FEMNet, a lightweight dual-branch network that combines state space modeling with convolutional encoding for multi-class cloud segmentation. The Mamba-based encoder captures long-range semantic dependencies with linear complexity, while a parallel CNN path preserves spatial detail. To address the semantic inconsistency across feature hierarchies and limited context perception in decoding, we introduce the following two targeted modules: a cross-stage semantic enhancement (CSSE) block that adaptively aligns low- and high-level features, and a multi-scale context aggregation (MSCA) block that integrates contextual cues at multiple resolutions. Extensive experiments on five benchmark datasets demonstrate that FEMNet achieves state-of-the-art performance across both binary and multi-class settings, while requiring only 4.4M parameters and 1.3G multiply–accumulate operations. These results highlight FEMNet’s suitability for resource-efficient deployment in real-world remote sensing applications. Full article

This study investigated the gel performance of Spanish mackerel surimi gels (SMSGs) modified by three anionic polysaccharides: κ-carrageenan (KC), ι-carrageenan (IC), and gellan gum (GG). By incorporating polysaccharides, SMSGs showed a 24.9–103.4% improvement in gel and textural properties, in which KC and IC had more improvement effects than GG. Moreover, polysaccharides led to a 10.7–13.1% increment in WHC, a shortened water migration from 61.34 to 52.43–55.93 ms in T₂₂, and enhanced thermal stability of SMSGs. The content of α-helix in SMSGs reduced markedly accompanied by a concurrent enhancement of β-sheet and β-turn by adding polysaccharides, where β-sheet and β-turn are positively correlated with hardness being favorable for gelling. The microstructure of SMSGs/polysaccharides showed a homogeneous network mainly due to hydrophobic interactions and disulfide bonds in SMSG-based gels. This study will demonstrate the effectiveness of KC, IC, and GG in improving the texture and functionality as well as expanding the application of surimi products. Full article

In this paper, we calibrate the luminosity relation of gamma−ray bursts (GRBs) by employing artificial neural networks (ANNs) to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset is used to calibrate the Amati relation ($E_{\mathrm{p}}$-$E_{\mathrm{iso}}$) at low redshift with the ANN framework, facilitating the construction of the Hubble diagram at higher redshifts. Cosmological models are constrained with GRBs at high redshift and the latest observational Hubble data (OHD) via the Markov chain Monte Carlo numerical approach. For the Chevallier−Polarski−Linder (CPL) model within a flat universe, we obtain ${\Omega }_{\mathrm{m}}={0.321}_{-0.069}^{+0.078}$, $h={0.654}_{-0.071}^{+0.053}$, $w_0={-1.02}_{-0.50}^{+0.67}$, and $w_a={-0.98}_{-0.58}^{+0.58}$ at the 1 −$\sigma$ confidence level, which indicates a preference for dark energy with potential redshift evolution ($w_a\ne 0$). These findings using ANNs align closely with those derived from GRBs calibrated using Gaussian processes (GPs). Full article

At emergency sites, bacteria in the environment can cause secondary wound infections. Timely treatment of infected wounds can improve the prognosis. In this study, we designed a closed-loop system for real-time wound infection monitoring and electronically controlled drug release, enabling rapid and stable deployment at disaster sites. Multilayer screen-printed electrodes were developed to detect uric acid (UA), pH, and temperature biomarkers. The electrode’s outermost layer was shielded by a zwitterionic conductive hydrogel (Gel) to prevent environmental interference and achieve systematic antibacterial protection through in situ reduction of silver nanoparticles (AgNPs) on its surface. For rapid and efficient drug delivery, amikacin (Ami) loaded cationic liposomes (Lipo) embedded in the zwitterionic conductive hydrogel (Gel-Lipo@Ami) were integrated as the core therapeutic carrier. This closed-loop system provides timely infection detection and enables in situ treatment during emergency rescues. Full article

The increasing deployment of turbines installed offshore is critical for sustainable energy development, yet accurate performance assessment remains challenging due to complex environmental influences, diverse turbine control strategies, and issues with data quality. Traditional performance metrics and power curve models often fail to provide reliable cross-turbine comparisons because they neglect multivariate environmental factors and turbine-specific biases. To address these limitations, this study develops a novel multivariate environmental factor-driven power assessment framework employing segmented long short-term memory (LSTM) models. A hybrid data cleaning method, combining bidirectional quartile analysis with the power curtailment detection, is proposed to effectively identify outliers, including subtle anomalies within typical data ranges. Samples are segmented based on rated wind speed to reflect differences in control strategies, and turbine-specific operational parameters are excluded to ensure unbiased comparisons among turbines. The proposed method achieves substantial improvements in predictive accuracy, with decreases of 9.39% in mean absolute error (MAE) and 11.75% in root mean square error (RMSE), compared to conventional binning approaches. When applied to three 5.5 MW offshore wind turbines, the proposed method reveals significant differences among the units. Turbine A demonstrates the highest performance, while turbines B and C exhibit reductions of 14.35% and 8.29%, respectively. Operational state analysis shows that turbine B experiences substantially longer maintenance durations, indicating severe faults that adversely affect its operational reliability and power output. These findings provide valuable insights for maintenance prioritization and performance benchmarking among wind turbines. Full article

With the accelerated development of urban underground spaces, prefabricated tunnel linings have become a research focus due to their advantages in construction efficiency and cost effectiveness. However, issues such as stress concentration at joints and insufficient overall stability hinder their broader application. This study investigates a cut-and-cover prefabricated tunnel project in the Chongqing High-Tech Zone through scale model tests and numerical simulations to systematically compare the mechanical behaviors of cast-in situ linings and three-segment prefabricated linings under surrounding rock loads. The experimental results show that the ultimate bearing capacity of the prefabricated lining is 15.3% lower than that of the cast-in situ lining, with asymmetric failure modes and cracks concentrated near joint regions. Numerical simulations further reveal the influence of joint stiffness on structural performance: when the joint stiffness is 30 MN·m/rad, the bending moment of the segmented lining decreases by 37.7% compared to the cast-in situ lining, while displacement increments remain controllable. By optimising joint pre-tightening forces and stiffness parameters, prefabricated linings can achieve stability comparable to cast-in situ structures while retaining construction efficiency. This research provides theoretical and technical references for the design and construction of open-cut prefabricated tunnel linings. Full article

Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper proposes an end-to-end polygon dynamic adjustment algorithm (PDAA) to improve the accuracy and geometric consistency of building contour extraction by dynamically generating and optimizing polygon vertices. The method first locates building instances through the region of interest (RoI) to generate initial polygons, and then uses four core modules for collaborative optimization: (1) the feature enhancement module captures local detail features to improve the robustness of vertex positioning; (2) the contour vertex tuning module fine-tunes vertex coordinates through displacement prediction to enhance geometric accuracy; (3) the learnable redundant vertex removal module screens key vertices based on a classification mechanism to eliminate redundancy; and (4) the missing vertex completion module iteratively restores missed vertices to ensure the integrity of complex contours. PDAA dynamically adjusts the number of vertices to adapt to the geometric characteristics of different buildings, while simplifying the prediction process and reducing computational complexity. Experiments on public datasets such as WHU, Vaihingen, and Inria show that PDAA significantly outperforms existing methods in terms of average precision (AP) and polygon similarity (PolySim). It is at least 2% higher than existing methods in terms of average precision (AP), and the generated polygonal contours are closer to the real building geometry. Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response. Full article

Drought stress poses a major constraint on global crop productivity. Although aspartic proteases (APs) are primarily characterized in plant disease resistance, their roles in abiotic stress adaptation remain largely unexplored. Here, we demonstrate that rice (Oryza sativa) OsAP4 critically regulates drought stress tolerance at the seedling stage. Genetic manipulation through overexpression (OsAP4-OE) or CRISPR knockout (OsAP4-KO) resulted in significantly reduced or enhanced stress tolerance compared to wild-type plants, respectively. Through integrated approaches including yeast two-hybrid, bimolecular fluorescence complementation, pull-down, co-immunoprecipitation, and protein degradation assays, we established that OsAP4 physically interacts with and destabilizes OsCATA/OsCATC, two catalase enzymes responsible for reactive oxygen species (ROS) scavenging. Importantly, OsAP4 modulates ROS production under drought stress treatment conditions. Together, these findings reveal a novel OsAP4-OsCATA/OsCATC regulatory module governing rice drought stress responses. Full article

In China, Anostostomatidae is represented by a single tribe, Anabropsini; two genera; and 33 species. Although extensive research has been conducted on Anabropsini, the monophyly of this tribe within Anostostomatidae remains unverified. Furthermore, the phylogenetic relationships within Anabropsis remain under debate. To address these gaps, we sequenced and annotated the mitochondrial genomes of five Anabropsini species to investigate their mitochondrial characteristics and phylogenetic positions and clarify the relationships among Anabropsis subgenera. The total mitochondrial length of the five species ranged from 15,985 bp to 16,423 bp and contained 13 protein-coding genes, 22 tRNAs, 2 rRNAs, and 1 control region. A grouped analysis of selection pressure on Anabropsis revealed that the Ka/Ks values for alate and apterous forms are not significantly different, suggesting that using wing length alone as the basis for dividing subgenera within Anabropsis may be unreliable. Tertiary structure modeling of proteins showed that the variable sites were concentrated in α-helix regions. Phylogenetic trees were reconstructed using the Bayesian inference and maximum likelihood methods and were based on two better datasets, namely, PCG123 (all codon positions of the PCGs) and PCG123 + 2R (all codon positions of PCGs, 12SrRNA, and 16SrRNA). The results indicate that the Chinese Anabropsini is paraphyletic, whereas Anabropsis is monophyletic, with a stable subgeneric topology. Full article

This paper focuses on the use of advanced optimization design strategies to improve the performance and service life of engine pistons, with emphasis on enhancing their stiffness, strength, and dynamic characteristics. As a core component of the engine, the structural design and optimization of the piston are of great significance to its efficiency and reliability. First, a three-dimensional (3D) model of the piston was constructed and imported into ANSYS Workbench for finite element modeling and high-quality meshing. Based on the empirical formula, the actual working environment temperature and heat transfer coefficient of the piston were accurately determined and used as boundary conditions for thermomechanical coupling analysis to accurately simulate the thermal and deformation state under complex working conditions. Dynamic characteristic analysis was used to obtain the displacement–frequency curve, providing key data support for predicting resonance behavior, evaluating structural strength, and optimizing the design. In the optimization stage, five geometric dimensions are selected as design variables. The deformation, mass, temperature, and the first to third natural frequencies are considered as optimization goals. The response surface model is constructed by means of the design of the experiments method, and the fitted model is evaluated in detail. The results show that the models are all significant. The adequacy of the model fitting is verified by the “Residuals vs. Run” plot, and potential data problems are identified. The “Predicted vs. Actual” plot is used to evaluate the fitting accuracy and prediction ability of the model for the experimental data, avoiding over-fitting or under-fitting problems, and guiding the optimization direction. Subsequently, the sensitivity analysis was carried out to reveal the variables that have a significant impact on the objective function, and in-depth analysis was conducted in combination with the response surface. The multi-objective genetic algorithm (MOGA), screening, and response surface methodology (RSM) were, respectively, used to comprehensively optimize the objective function. Through experiments and analysis, the optimal solution of the MOGA algorithm was selected for implementation. After optimization, the piston mass and deformation remained relatively stable, and the working temperature dropped from 312.75 °C to 308.07 °C, which is conducive to extending the component life and improving the thermal efficiency. The first to third natural frequencies increased from 1651.60 Hz to 1671.80 Hz, 1656.70 Hz to 1665.70 Hz, and 1752.90 Hz to 1776.50 Hz, respectively, significantly enhancing the dynamic stability and vibration resistance. This study integrates sensitivity analysis, response surface models, and genetic algorithms to solve multi-objective optimization problems, successfully improving piston performance. Full article

Epoxy resin paper-impregnated bushings, as critical insulating components in power transformers, are subjected to complex electric fields, thermal fields, and mechanical stresses over extended periods. Their performance stability is directly linked to the safe operation of transformers. Given the significant costs associated with their production, reliability verification is a crucial aspect of their design and manufacturing process. This study employs the finite element simulation technology to systematically investigate the electric field distribution characteristics, thermal field distribution characteristics, and seismic performance reliability verification methods of epoxy resin paper-impregnated bushings. The simulation and calculation results indicate that for bushings with rated voltages of 40.5 kV, 72.5 kV, and 126 kV, the maximum radial electric field strengths are 1.38 kV/mm, 2.74 kV/mm, and 3.0 kV/mm, respectively, with axial electric field strengths all below allowable values. The insulation margin meets the 1.5 standard requirements. Under short-circuit conditions, the thermal stability analysis of the bushings reveals that the final conductor temperatures are all below 180 °C, indicating sufficient safety margins. All three types of bushings comply with the design requirements for an 8-degree earthquake intensity and are capable of effectively withstanding seismic loads. This research provides a theoretical foundation for the development and application of epoxy resin paper-impregnated bushings, offering a significant engineering application value in enhancing the safety and stability of transformers and power systems. Full article

Apparent power and power factor are crucial metrics for evaluating the energy transmission efficiency and reactive power management in power systems. The increasing complexity of power load structures, driven by evolving energy production and consumption models, has intensified the nonlinear and unbalanced characteristics of circuits, presenting significant challenges to accurate apparent power measurement. The IEEE 1459-2010 standard introduces the concept of effective apparent power to enhance the assessment of energy transmission efficiency under non-sinusoidal and unbalanced conditions. However, the absence of a physical standard and a standardized traceability method for effective apparent power results in inconsistent measurement outcomes across instruments. This study proposes a novel method to trace effective apparent power measurements to the International System of Units (SI) benchmarks, based on the loss characteristics of transmission lines. The method includes a comprehensive analysis of measurement uncertainty. Simulation and experimental validation confirm that the proposed traceability circuit can achieve a measurement uncertainty of 0.0110% (coverage factor k = 2), satisfying the engineering requirement of expanded uncertainty U approximately 0.02% (k = 2). These results demonstrate the method’s practical suitability for engineering applications. Full article

Induction in pineapples requires the targeted delivery of specific chemical solutions into the plant’s central core to enable batch management, a task currently reliant on manual operation. This study addressed this challenge by analyzing the physical characteristics of pineapple plants and establishing a perception-based mathematical model for core position localization. An integrated hardware–software system was developed, complemented by a human–machine interface for real-time operational monitoring. Comprehensive experiments were conducted to evaluate the spraying accuracy, nozzle response time, and prototype performance. The results demonstrate that the actuation system—comprising solenoid valves, pumps, and flowmeters—achieved an average spraying error of 2.72%. The average nozzle opening/closing time was 0.111 s; with a standard operating speed of 0.5 m/s, a delay compensation distance of 55.5 mm was implemented. In human–machine comparative trials, the automated system outperformed manual spraying in both efficiency and stability, with average errors of 7.1% and 6.4%, respectively. The system reduced chemical usage by over 67,500 mL per hectare while maintaining a miss-spray rate of 5–6%. Both two-tailed tests revealed extremely significant differences (p < 0.001). These findings confirm that the developed solution meets the operational requirements for pineapple floral induction, offering significant improvements in precision and resource efficiency. Full article