Search Results (711)

With the increasing penetration of distributed generation (DG), integrated main-distribution networks (IMDNs) face challenges in rapidly and effectively performing comprehensive operational risk assessments under multiple uncertainties. Thereby, using the traditional hierarchical economic scheduling method makes it difficult to accurately find the optimal scheduling operating point. To address this problem, this paper proposes a multi-objective dispatch decision-making optimization model for the IMDN with static security region (SSR) constraints. Firstly, the non-sequential Monte Carlo sampling is employed to generate diverse operational scenarios, and then the key risk characteristics are extracted to construct the risk assessment index system for the transmission and distribution grid, respectively. Secondly, a hyperplane model of the SSR is developed for the IMDN based on alternating current power flow equations and line current constraints. Thirdly, a risk assessment matrix is constructed through optimal power flow calculations across multiple load levels, with the index weights determined via principal component analysis (PCA). Subsequently, a scheduling optimization model is formulated to minimize both the system generation costs and the comprehensive risk, where the adaptive grid density-improved multi-objective particle swarm optimization (AG-MOPSO) algorithm is employed to efficiently generate Pareto-optimal operating point solutions. A membership matrix of the solution set is then established using fuzzy comprehensive evaluation to identify the optimal compromised operating point for dispatch decision support. Finally, the effectiveness and superiority of the proposed method are validated using an integrated IEEE 9-bus and IEEE 33-bus test system. Full article

The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, are introduced. As a result, conventional power sources require an advanced management system, for instance, a virtual power plant (VPP), capable of accurately monitoring power supply and demand. This study thoroughly explores the dispatch of battery energy storage systems (BESSs) and diesel generators (DGs) through a distributionally robust joint chance-constrained optimization (DR-JCCO) framework utilizing the conditional value at risk (CVaR) and heuristic-X (H-X) algorithm, structured as a bilevel optimization problem. Furthermore, Binomial expansion (BE) is employed to linearize the model, enabling the assessment of BESS dispatch through a mathematical program with equilibrium constraints (MPECs). The findings confirm the effectiveness of the DRO-CVaR and H-X methods in dispatching grid network resources and BE under the MPEC framework. Full article

Petal-shaped distribution networks are receiving increasing attention due to their enhanced reliability. However, the integration of distributed generators (DGs) significantly alters the fault characteristics during single-phase to ground faults. Traditional short-circuit calculation methods become inadequate due to the unmodeled effects of negative sequence current control in DGs. To address this challenge, this study establishes, for the first time, a mathematical model for single-phase to ground faults in a petal-shaped network with DG integration under both positive and negative sequence control. It explicitly derives the DGs’ output current under three control goals: maintaining constant active power, maintaining constant reactive power, and injecting a symmetric three-phase current. Utilizing the symmetrical component method, a composite sequence network incorporating the DGs’ negative sequence current output is developed. Based on the node–voltage relationships, an analytical short-circuit current calculation method suitable for multiple control goals is proposed. Validation via MATLAB R2022a simulations demonstrates high-fidelity accuracy: in Case 1 with different fault locations, the maximum relative error is 0.31%, while in Case 2, it is 2.04%. These results quantify the critical impact of the negative sequence current—reaching up to 14.78% of the DG output during severe voltage sags—providing theoretical support for the protection design of a petal-shaped distribution network with high DG integration. Full article

Renewable energy sources are being progressively incorporated into modern power grids to increase sustainability, stability, and resilience. To ensure that residential, commercial, and industrial customers have a dependable and efficient power supply, the transmission system must deliver electricity to end-users via the distribution network. To improve the performance of the distribution system, this study employs distributed generator (DG) units and focuses on determining their optimal placement, sizing, and power factor. A novel metaheuristic algorithm, referred to as Rüppell’s fox optimizer (RFO), is proposed to address this optimization problem under various scenarios. In the first scenario, where the DG operates at unity power factor, it is modeled as a photovoltaic system. In the second and third scenarios, the DG is modeled as a wind turbine system with fixed and optimal power factors, respectively. The performance of the proposed RFO algorithm is benchmarked against five well-known metaheuristic techniques to validate its effectiveness and competitiveness. Simulations are conducted on the IEEE 33-bus and IEEE 69-bus radial distribution test systems to demonstrate the applicability and robustness of the proposed approach. Full article

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility. Full article

Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations (EVCSs), RESs, and capacitors. The goal is to minimize both Energy Not Supplied (ENS) and operational costs, particularly under varying demand conditions caused by EV charging in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. To improve optimization accuracy and avoid local optima, an improved Whale Optimization Algorithm (IWOA) is employed, featuring a mutation mechanism based on Lévy flight. The model also incorporates uncertainties in electricity prices and consumer demand, as well as a demand response (DR) program, to enhance practical applicability. Simulation studies on a 95-bus test system show that the proposed approach reduces ENS by 16% and 20% in the absence and presence of distributed generation (DG) and EVCSs, respectively. Additionally, the operational cost is significantly reduced compared to existing methods. Overall, the proposed framework offers a scalable and intelligent solution for smart grid integration and distribution network modernization. Full article

Abnormalities in plasma lipoproteins observed in patients with diabetes promote atherosclerosis. However, the association between various lipid species and classes and atherosclerosis remains unclear. Here, we aimed to identify the plasma lipid characteristics associated with atherosclerosis progression in patients with diabetes. We performed semi-targeted lipidomic analysis of fasting plasma samples using supercritical fluid chromatography coupled with mass spectrometry in two independent patient groups with type 2 diabetes (n = 223 and 31) and evaluated cross-sectional associations between plasma lipids and carotid intima-media thickness (CIMT). Ten plasma lipid species, including eight diacylglycerols (DGs), and total DG levels were significantly associated with CIMT in both groups. Patients of the former group were partly observed for 5 years, and we investigated associations between DGs and CIMT progression in these patients (n = 101). As a result, 22 DGs among the 26 identified DGs and total DG (β = 0.398, p < 0.001) were significantly associated with the annual change in CIMT. Furthermore, plasma DG levels improved the predictive ability for CIMT progression, with an adjusted R-squared increase of 0.105 [95% confidence interval: 0.010, 0.232] in the models. Plasma DGs are associated with CIMT progression in patients with type 2 diabetes. Measurement of total plasma DG levels may be beneficial in assessing the risk of atherosclerosis progression. Full article

An efficient optimization method for designing defected ground structure (DGS)-based common-mode filters (CMFs) is proposed, utilizing equation-based transmission line models integrated with a genetic algorithm (GA). Designing an optimal DGS-based CMF using full-wave simulation tools is time-consuming due to its process-intensive nature. The proposed optimization method implements transmission line theory to allow for direct S-parameter calculation, enabling integration with an optimization algorithm to identify optimal parameters within a confined 5 mm × 10 mm design space. This work demonstrates a compact asymmetric DGS design to illustrate the method’s capability. The resulting compact asymmetric DGS-based CMF achieves wideband common-mode suppression with a –10 dB bandwidth from 3.18 GHz to 12.89 GHz. The optimization method significantly reduces design time by minimizing the need for lengthy and repetitive full-wave simulations. The measured S-parameters of the fabricated CMF closely match the simulated results, validating the model’s accuracy. Compared with traditional methods for designing DGS-based CMFs, the proposed method utilizes transmission line theory to optimize the design efficiently, providing a practical and efficient solution. Full article

This research investigates the metric accuracy of 3D transparent object reconstruction, a task where conventional photogrammetry often fails. The topic is especially relevant in cultural heritage (CH), where accurate digital documentation of glass and transparent artifacts is important. The work proposes a practical methodology using existing tools to verify metric accuracy standards. The study compares three methods, conventional photogrammetry, 3D Gaussian splatting (3DGS), and 2D Gaussian splatting (2DGS), to assess their ability to produce complete and metrically reliable 3D models suitable for measurement and geometric analysis. A transparent glass artifact serves as the case study. Results show that 2DGS captures fine surface and internal details with better geometric consistency than 3DGS and photogrammetry. Although 3DGS offers high visual quality, it introduces surface artifacts that affect metric reliability. Photogrammetry fails to reconstruct the object entirely. The study highlights that visual quality does not ensure geometric accuracy, which is critical for measurement applications. In this work, ground truth comparisons confirm that 2DGS offers the best trade-off between accuracy and appearance, despite higher computational demands. These findings suggest extending the experimentation to other sets of images featuring transparent objects, and possibly also reflective ones. Full article

When planning distributed intelligent power distribution networks, it is necessary to take into account the interests of various distributed generation (DG) operators and power supply enterprises, thereby diversifying and complicating planning models. Additionally, the integration of a high proportion of distributed resources has triggered a transformation in the power flow pattern of active distribution networks, shifting from the traditional unidirectional flow mode to a bidirectional interactive mode. The intermittent and fluctuating operation modes of distributed photovoltaic and wind power generation have also increased the difficulty of distribution network planning. To address the aforementioned challenges, this paper proposes an active distribution network planning strategy that considers the allocation of diverse flexible resources, exploring scheduling flexibility from both the power supply side and the load side. Firstly, a bi-level optimization model integrating planning and operation is constructed, where the upper-level model determines the optimal capacity of investment and construction equipment, and the lower-level model formulates an economic dispatching scheme. Through iterative solving of the upper and lower levels, the final planning strategy is determined. Meanwhile, to reduce the complexity of problem-solving, this paper employs an improved PSO-CS hybrid algorithm for iterative optimization. Finally, the effectiveness and feasibility of the proposed algorithm are demonstrated through validation using an improved IEEE-33-bus test system. Compared with conventional algorithms, the convergence speed of the method proposed in this paper can be improved by up to 21.4%, and the total investment cost can be reduced by up to 3.26%. Full article

In recent years, global energy transition has pushed distributed generation (DG) to the forefront in relation to new energy development. Most existing studies focus on DG or energy storage planning but lack co-optimization and power tracking analysis. To address this problem, a multi-objective genetic algorithm-based collaborative planning method for photovoltaic (PV) and energy storage is proposed. On this basis, power flow tracking technology is further introduced to conduct a detailed analysis of distributed energy power allocation, providing support for system operation optimization and responsibility sharing. To verify the validity of the model, a 14-node distribution network is used as an example. Voltage stability, PV consumption rate, and economy are taken as objective functions. By solving the three scenarios, it is determined that the introduction of energy storage increases the PV consumption rate from 85.6% to 96.3%; the average network loss for the whole day increases from 1.81 MW to 2.40 MW. Utilizing power tracking techniques, various causes were analyzed; it was found that the placement of energy storage leads to a multidirectional and repetitive flow of power. Full article

Three-dimensional (3D) reconstruction has become a fundamental technology in applications ranging from cultural heritage preservation and robotics to forensics and virtual reality. As these applications grow in complexity and realism, the quality of the reconstructed models becomes increasingly critical. Among the many factors that influence reconstruction accuracy, the lighting conditions at capture time remain one of the most influential, yet widely neglected, variables. This review provides a comprehensive survey of classical and modern 3D reconstruction techniques, including Structure from Motion (SfM), Multi-View Stereo (MVS), Photometric Stereo, and recent neural rendering approaches such as Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS), while critically evaluating their performance under varying illumination conditions. We describe how lighting-induced artifacts such as shadows, reflections, and exposure imbalances compromise the reconstruction quality and how different approaches attempt to mitigate these effects. Furthermore, we uncover fundamental gaps in current research, including the lack of standardized lighting-aware benchmarks and the limited robustness of state-of-the-art algorithms in uncontrolled environments. By synthesizing knowledge across fields, this review aims to gain a deeper understanding of the interplay between lighting and reconstruction and provides research directions for the future that emphasize the need for adaptive, lighting-robust solutions in 3D vision systems. Full article

Accurately reconstructing tree skeletons from multi-view images is challenging. While most existing works use skeletonization from 3D point clouds, thin branches with low-texture contrast often involve multi-view stereo (MVS) to produce noisy and fragmented point clouds, which break branch connectivity. Leveraging the recent development in accurate mask extraction from images, we introduce a mask-guided graph optimization framework that estimates a 3D skeleton directly from multi-view segmentation masks, bypassing the reliance on point cloud quality. In our method, a skeleton is modeled as a graph whose nodes store positions and radii while its adjacency matrix encodes branch connectivity. We use 3D Gaussian splatting (3DGS) to render silhouettes of the graph and directly optimize the nodes and the adjacency matrix to fit given multi-view silhouettes in a differentiable manner. Furthermore, we use a minimum spanning tree (MST) algorithm during the optimization loop to regularize the graph to a tree structure. Experiments on synthetic and real-world plants show consistent improvements in completeness and structural accuracy over existing point-cloud-based and heuristic baseline methods. Full article

Background: Endometrial cancer (EC) is the sixth most common cancer among women globally, with an estimated 420,000 new cases diagnosed annually. Methods: This study comprised patients with endometrial cancer (EC) (n = 17), hyperplasia (HY) (n = 17), and controls (CO) (n = 20). Tissue was collected from the endometrium of all 54 patients, including patients with HY, EC, and CO, who underwent total hysterectomy. EC and HY diagnoses were confirmed based on histological examination. Untargeted metabolomics profiling was conducted using LC-HRMS. The partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were used for univariate and multivariate statistical analysis. The fitness of the model (R2Y) and predictive ability (Q2) were used to create OPLS-DA models. ROC analysis was carried out, followed by network analysis using Ingenuity Pathway Analysis. Results: The top metabolites that can discriminate EC and HY from CO were identified. This revealed a decrease in the levels of the lipid species, specifically phosphatidic acid (PA) (PA (14:1/14:0), PA(10:0/17:0), PA(18:1-O(12,13)/12:0)), PG(a-13:0/a-13:0), ganglioside GA1 (d18:1/18:1), PS(14:1/14:0), TG(20:0/18:4/14:1), and CDP-DG(PGF2alpha/18:2), while the levels of 3-Dehydro-L-gulonate, Uridine diphosphate-N-acetylglucosamine, ganglioside GT2 (d18:1/14:0), gamma-glutamyl glutamic acid and oxidized glutathione were increased in cases of EC and HY as compared to CO. Bioinformatics analysis, specifically using Ingenuity Pathway Analysis (IPA), revealed distinct pathway enrichments for EC and HY. For EC, the most highly scored pathways were associated with cell-to-cell signaling and interaction, skeletal and muscular system development and function, and small-molecule biochemistry. In contrast, HY cases showed the highest scoring pathways related to inflammatory disease, inflammatory response, and organismal injury and abnormalities. Conclusions: Developing sensitive biomarkers could improve diagnosis and guide treatment decisions, particularly in identifying which patients with HY may safely avoid hysterectomy and be managed with hormonal therapy. Full article

Glycyrrhizic acid (GA) and its derivatives have been reported to have potent pharmacological effects against viral infections, including SARS-CoV and SARS-CoV-2. However, their antiviral mechanisms against coronaviruses are not fully understood. In this study, we found that diammonium glycyrrhizinate (DG) can effectively reduce infections of several human coronaviruses, including HCoV-OC43, HCoV-229E, and SARS-CoV-2, as well as newly emerged variants, with EC₅₀ values ranging from 115 to 391 μg/mL being recorded. Time-of-addition and pseudotype virus infection studies indicated that DG treatment dramatically inhibits the process of virus entry into cells. Furthermore, we demonstrated that DG broadly binds to the RBD of human coronaviruses, thereby blocking spike-mediated cellular entry, by using TR-FRET-based receptor-binding domain (RBD)-ACE2 interaction assay, capillary electrophoresis (CE), and surface plasmon resonance (SPR) assay. In support of this notion, studies of molecular docking and amino acid mutation showed that DG may directly bind to a conserved hydrophobic pocket of the RBD of coronaviruses. Importantly, intranasal administration of DG had a significant protective effect against viral infection in a HCoV-OC43 mouse model. Finally, we found that combinations of DG and other coronavirus inhibitors exhibited antiviral synergy. In summary, our studies strongly reveal that DG exerts broad-spectrum antiviral activity against human coronaviruses by interrupting spike-mediated cellular entry, demonstrating the pharmacological feasibility of using DG as a candidate for alternative treatment and prevention of coronavirus infection. Full article