Search Results (85)

Optimal allocation of distributed generation (DG) and feeder reconfiguration are critical strategies for improving the operational efficiency and voltage stability of modern radial distribution networks under increasing penetration of renewable resources. However, the simultaneous optimization of DG placement, sizing, and network topology constitutes a highly nonlinear multi-objective problem subject to electrical, operational, and radiality constraints. Unlike existing studies that treat DG allocation and feeder reconfiguration as separate or weakly coupled problems, this work introduces a unified mixed-integer nonlinear optimization framework that captures their strong interdependency. In addition, a hybrid Big Bang–Big Crunch (HBB-BC) algorithm is proposed, combining stochastic contraction with adaptive learning mechanisms to improve convergence robustness in highly nonlinear search spaces. This contribution addresses the limitations of conventional metaheuristics in handling coupled topology–generation optimization problems and provides a scalable solution for modern active distribution networks. We propose a coordinated optimization framework for optimal DG placement and feeder reconfiguration aimed at minimizing real power losses while enhancing voltage stability and reducing both operational cost and environmental impact. The problem is formulated as a constrained multi-objective optimization model and solved using an improved hybrid Big Bang–Big Crunch metaheuristic algorithm which integrates exploration and exploitation mechanisms to achieve fast convergence and robust global search performance. The proposed method is validated on both IEEE 33-bus and IEEE 69-bus radial distribution systems under multiple operational scenarios. The results demonstrate that the coordinated optimization consistently achieves significant performance improvements across different network scales, confirming the robustness and scalability of the proposed framework. Full article

Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance. Full article

Background/Objectives: Seasonal waves of respiratory viruses—including SARS-CoV-2, influenza A virus (IAV), and respiratory syncytial virus (RSV)—continue to pose a global health burden and highlight the need for antiviral agents that are effective, safe, broadly active, affordable, and widely accessible. Current interventions are limited by the need for their early administration, the risk of resistance, their costs, and the restricted availability in large parts of the world. For certain natural products, such as European black elderberry (Sambucus nigra L.) fruit extract (ElderCraft^®; EC) and the seaweed-derived sulfated polymer iota-carrageenan (IC), antiviral activities against respiratory viruses, particularly IAV and SARS-CoV-2, have previously been shown. Here, we assessed the antiviral activity of IC and an anthocyanin-standardized EC extract against SARS-CoV-2, IAV, and RSV, either as monotherapy or in multiple-dose combinations. Methods: MDCKII cells were infected with IAV_PR8, human Calu-3 lung epithelial cells with the SARS-CoV-2 Omicron variant, and HEp-2 cells with RSV (A2 strain). Inhibitors were administered either by pre-incubation of cell-free virions prior to infection or, in separate time-of-addition experiments, during or post-infection. Viral replication was quantified by qRT-PCR or intracellular immunostaining. Cytotoxicity was evaluated using a neutral red uptake assay. Results: Most intriguingly, both EC and IC are able to neutralize virions derived from SARS-CoV-2, IAV, or RSV extracellularly in a dose-dependent manner. Notably, EC and IC alone exhibited strong anti-RSV activity, which was not reported previously. Most importantly, combined treatment with IC and EC caused a pronounced synergistic antiviral effect against the tested viruses, as confirmed by the Bliss independence model, without any detectable impact on cell viability. Finally, solutions prepared from matrix-standardized mono- or combi-lozenges, containing IC and/or EC in high or low doses, reproduced the antiviral and synergistic combination effects observed with the pure compounds. Conclusions: In summary, these findings support further development of EC and IC as a topically accessible, virion-neutralizing combination (e.g., lozenges) to provide additional protection against major respiratory viruses and potentially strengthen pandemic preparedness. Full article

Eddy-current inspection of anisotropic composites, such as aeronautical CFRP, demands models that ensure mathematical rigor, tensorial consistency, and clear energetic interpretation. This work presents a novel unified variational framework with a multiplicative tensor perturbation for the time-harmonic eddy-current problem in anisotropic media with defective regions. The formulation is posed in the natural spaces $H(\mathrm{curl};\Omega )\times H^1\left({\Omega }_c\right)$, and the well-posedness is established via the Lax–Milgram theorem under physically consistent assumptions on permeability and conductivity. The sesquilinear form admits a Hermitian decomposition that separates dissipative and reactive contributions, revealing the energetic structure of the weak formulation. Defects are modeled through multiplicative modifications of the baseline anisotropic conductivity tensor. This congruence-based approach preserves symmetry and positive definiteness, ensuring non-negative Joule losses and structural stability, allowing a modular representation of subsurface delamination, fiber breakage, conductive inclusions, and distributed porosity within a single tensorial framework. A central result of the present formulation is the reconstruction of the complex power functional from the evaluation of the weak form at the solution, showing that the active dissipated power and the magnetic reactive power arise directly from the same integral terms. Through the complex Poynting theorem, the quadratic form is linked to the internal complex power, establishing a direct connection between the variational formulation and measurable quantities such as probe impedance variations. Simulations of realistic layered CFRP configurations, including single- and multi-defect scenarios, confirm that, compared with additive perturbations, the multiplicative model provides enhanced energetic contrast, particularly in strongly anisotropic and interacting defect conditions. Agreement with experimental measurements, supported by a quantitative comparison of dissipated power variations obtained from controlled EC experiments, corroborates the physical relevance and robustness of the proposed complex power functional. Full article

Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the efficiency of the optimization algorithm used to solve their underlying dual problem, which is often complex and constrained. Classical solvers, such as Sequential Minimal Optimization (SMO) and Stochastic Gradient Descent (SGD), present inherent limitations: SMO ensures numerical stability but lacks scalability and is sensitive to heuristics, while SGD scales well but suffers from unstable convergence and limited suitability for nonlinear kernels. To address these challenges, this study proposes a novel hybrid optimization framework based on Open Competency Optimization and Particle Swarm Optimization (OCO–PSO) to enhance the training of SVMs. The proposed approach combines the global exploration capability of PSO with the adaptive competency-based learning mechanism of OCO, enabling efficient exploration of the solution space, avoidance of local minima, and strict enforcement of dual constraints on the Lagrange multipliers. Across multiple datasets spanning medical (diabetes), agricultural yield, signal processing (sonar and ionosphere), and imbalanced synthetic data, the proposed OCO-PSO–SVM consistently outperforms classical SVM solvers (SMO and SGD) as well as widely used classifiers, including decision trees and random forests, in terms of accuracy, macro-F1-score, Matthews correlation coefficient (MCC), and ROC-AUC. On the Ionosphere dataset, OCO-PSO achieves an accuracy of $95.71\%$, an F1-score of $0.954$, and an MCC of $0.908$, matching the accuracy of random forest while offering superior interpretability through its kernel-based structure. In addition, the proposed method yields a sparser model with only 66 support vectors compared to 71 for standard SVC (a reduction of approximately $7\%$), while strictly satisfying the dual constraints with a near-zero violation of $1.3\times {10}^{-3}$. Notably, the optimal hyperparameters identified by OCO-PSO ($\mathrm{C}=2$, $\gamma \approx 0.062$) differ substantially from those obtained via Bayesian optimization for SVC ($\mathrm{C}=10$, $\gamma \approx 0.012$), indicating that the proposed approach explores alternative yet equally effective regions of the hypothesis space. The statistical significance and robustness of these improvements are confirmed through extensive validation using 1000 bootstrap replications, paired Student’s t-tests, Wilcoxon signed-rank tests, and Holm–Bonferroni correction. These results demonstrate that the proposed metaheuristic hybrid optimization framework constitutes a reliable, interpretable, and scalable alternative for training SVMs in complex and high-dimensional classification tasks. Full article

Coastal wetland vegetation exhibits pronounced spectral mixing, complex mosaic spatial patterns, and small target sizes, posing considerable challenges for fine-grained classification in high-resolution UAV imagery. At present, remote sensing classification of ground objects based on deep learning mainly relies on spectral and structural features, while the frequency domain features of ground objects are not fully considered. To address these issues, this study proposes a vegetation classification model that integrates spatial-domain and frequency-domain features. The model enhances global contextual modeling through a large-kernel convolution branch, while a frequency-domain interaction branch separates and fuses low-frequency structural information with high-frequency details. In addition, a shallow auxiliary supervision module is introduced to improve local detail learning and stabilize training. With a compact parameter scale suitable for real-world deployment, the proposed framework effectively adapts to high-resolution remote sensing scenarios. Experiments on typical coastal wetland vegetation including Reeds, Spartina alterniflora, and Suaeda salsa demonstrate that the proposed method consistently outperforms representative segmentation models such as UNet, DeepLabV3, TransUNet, SegFormer, D-LinkNet, and MCCA across multiple metrics including Accuracy, Recall, F1 Score, and mIoU. Overall, the results show that the proposed model effectively addresses the challenges of subtle spectral differences, pervasive species mixture, and intricate structural details, offering a robust and efficient solution for UAV-based wetland vegetation mapping and ecological monitoring. Full article

Sustainable renovation is a critical aspect for designing energy-efficient buildings with reasonable cost and high indoor living standards. The objective of this paper is to investigate various renovation scenarios for an old, uninsulated building with a floor area of 100 m² located in Athens, aiming to determine the global optimal solution through a multi-criteria analysis. The multi-criteria analysis considers energy, economic, and thermal comfort criteria to perform a multi-lateral approach. Specifically, the criteria are: (i) maximization of the energy savings, (ii) minimization of the life cycle cost (LCC), and (iii) minimization of the mean annual predicted percentage of dissatisfied (PPD). These criteria are combined within a multi-criteria evaluation procedure that employs a global objective function for determining a global optimum solution. The examined retrofitting actions are the addition of external insulation, the replacement of the existing windows with triple-glazed windows, the addition of shading in the openings in the summer, the application of cool roof dyes, the use of a mechanical ventilation system with a heat recovery unit, and the installation of a highly efficient heat pump system. The interventions were examined separately, and the combined renovation scenarios were studied by including them in the external insulation because of their high importance. The present study encompassed the investigation of a baseline scenario and 26 different renovation scenarios, conducted through dynamic simulation on an annual basis. The results of the present analysis indicated that the global optimal renovation scenario, including the addition of external insulation, the installation of highly efficient heat pumps, and the use of shading in the openings in the summer, saved energy by 74% compared to the baseline scenario. The LCC was approximately EUR 33,000, the simple payback period of the renovation process was around 6 years, the annual CO₂ emissions avoidance reached 4.6 tn_CO2, and the PPD was at 9.7%. An additional sensitivity analysis for determining the optimal choice under varying weights assigned to the criteria revealed that this renovation design is the most favorable option in most cases. These results prove that the suggested renovation scenario is a feasible and viable solution that leads to a sustainable design from multiple perspectives. Full article

Mixed amine/sulfolane (TMS) biphasic solutions have gained attention for their adjustable structure–activity relationships and lower regeneration energy. In this study, monoethanolamine (MEA) is employed as the main absorbent and polyamine as the co-absorbent, which are subsequently mixed with the phase separation promoter sulfolane (TMS) to form ternary biphasic solvent systems. Polyamine co-absorbents include 3-Dimethylaminopropylamine (DMAPA), 3-Diethylaminopropylamine (DEAPA), and Diethylenetriamine (DETA). Phase separation, absorption, and desorption performances were systematically studied. Reaction and phase separation mechanisms were elucidated through ¹³C nuclear magnetic resonance (NMR) spectroscopy. The overall mass transfer coefficients (K_G) were measured using a wetted wall column (WWC). Variations in the amine-to-sulfolane concentration ratio showed minimal impact on phase volume, while temperature and solvent composition significantly influenced phase separation behavior. All three solvents exhibited superior CO₂ capture performance, with CO₂ loadings in the rich phases ranging from 4.09 to 4.71 mol/L and over 96.82% of CO₂ concentrated in them, cyclic capacities reached or exceeded 3 mol/L, and regeneration energy consumption was 29.63–55.51% lower than 5 M MEA. ¹³C NMR analysis indicated that multiple N atoms in polyamines promoted the formation of additional ionic species during CO₂ absorption, thereby enhancing phase separation completeness. Furthermore, K_G values for the ternary systems exceeded that of conventional MEA, with the MEA/DEAPA/TMS system exhibiting a 1.7-fold increase. These findings demonstrated the industrial potential of MEA/polyamine/TMS biphasic solvents for efficient CO₂ capture. Full article

Background: A key barrier to realizing the full potential and long-term collection of patient-reported outcomes (PROs) is the limited understanding of user experience (UX) factors that influence sustained patient engagement with digital PRO tools. Most existing research focuses on disease-specific or country-specific solutions, leaving a gap in identifying shared UX determinants that could inform scalable, cross-disease European digital health frameworks. This fragmentation hinders interoperability and increases development costs by requiring separate tools for each context. This case study aims to address this gap by identifying key UX features that optimize PRO collection across diverse chronic conditions in Europe within the Health Outcomes Observatory project, enhancing continuous (primary use) and large-scale (secondary use) data collection. Objective: This study aimed to identify and analyze key UX factors that support adoption and sustained use of PRO collection tools among patients with chronic diseases across multiple European countries. Methods: Patient focus groups were conducted in four chronic disease areas: cancer, inflammatory bowel disease (IBD), and diabetes (type I and II) across six European countries. Participants were recruited purposively through national patient advisory boards to ensure diversity in age, gender, and disease type. Sessions were moderated by trained qualitative researchers following a standardized guide, and discussions were transcribed verbatim and coded in researcher pairs to ensure intercoder reliability through iterative consensus. A modified thematic analysis, guided deductively by the UX Honeycomb model and inductively by emergent themes, was used to identify cross-disease UX determinants. Results: In total, 17 patients and patient representatives participated (76% female; 4 diabetes, 6 IBD and 7 cancer). We identified six core UX factors driving patient engagement for all disease groups: compatibility with other technologies, direct communication with the care team, personalization, ability to share data, the need for educational material and data protection were identified as key aspects of PRO technologies. However, the customizability of the app is crucial. Not all disease groups had the same needs, and participants specifically requested that the app provide information relevant to their own condition. Disease-specific needs, like T1D patients desiring glucose monitoring integration, were identified. IBD patients highlighted flare detection abilities and cancer patients especially sought side-effect comparisons. Conclusions: Our findings indicate that a unified yet customizable PRO platform can address shared UX needs across diseases, improving patient engagement and data quality. Incorporating features such as seamless data transfer, personalization, feedback, and strong privacy measures can foster trust and long-term adoption across European contexts. In addition to some disease-specific issues, most needs for the backbone of the app were shared among the disease areas. This shows that a shared app between diseases might be preferable and, in case of comorbidities, could ease self-management for patients. Last, to ensure full potential for every user and every disease, customization is crucial. Full article

Pharmaceutically active compounds (PhACs), pesticides, and poly- and perfluoroalkyl substances (PFAS) are increasingly detected in surface waters at trace concentrations, raising concerns for both aquatic systems and, consequently, human health. Conventional solutions are insufficient to achieve complete removal at trace compound concentrations, highlighting the need for advanced separation technologies. This study aims to comprehensively analyze rejection and removal mechanisms of selected PhACs, pesticides, and PFAS present in water solutions at reported environmentally relevant concentrations (300 ng L⁻¹), using two nanofiltration (NF) and one reverse osmosis (RO) polyamide membrane. PhACs, pesticides, and PFAS were selected to cover a broad range of physicochemical properties, specifically molecular mass (MM), dissociation constant (pKa), and octanol–water partition coefficient (logK_o/w). Rejection values ranged from 42.1% (acetaminophen) to apparent 100% (for multiple compounds), depending on water pH, solute properties, and membrane characteristics. Size exclusion and electrostatic interactions were identified as the primary removal mechanisms, with hydrophobic interactions having a lower impact, particularly for carbamazepine, bezafibrate, and perfluorooctane sulfonic acid (PFOS). Addition of sodium chloride (3 g L⁻¹) decreased rejection of most negatively charged compounds due to suppression of membrane surface charge, although clarithromycin and ofloxacin exhibited improved rejection. Presented results provide fundamental insight into compound-specific membrane rejection and highlight the importance of membrane–solute interactions under environmentally realistic conditions. The results support further optimization of NF and RO for targeted compound rejection and provide a baseline for data-driven membrane process modeling. Full article

We study stochastic variants of the Kairat-II and Kairat-X equations in (3 + 1) dimensions, two canonical models in soliton theory. Random fluctuations are incorporated through a Wiener process, yielding a multiplicative stochastic embedding of the wave fields. By combining the enhanced direct algebraic technique with the new projective Riccati equation approach, we obtain closed-form stochastic soliton solutions and analyze how noise modulates their amplitude and localization. The solutions are illustrated with consistent 3D surface plots (mean field vs. sample paths) and 2D time traces to highlight wave geometry and variability. In addition, we employ the energy balance approach to separate kinetic and potential contributions and to verify an energy balance relation for the derived solutions, thereby clarifying their physical plausibility and stability under noise. The results provide exact, easily verifiable benchmarks for stochastic nonlinear wave models and a practical template for incorporating randomness into nonlinear dispersive systems. Full article

Reliable fault diagnosis of milling machines is essential for maintaining operational stability and cost-effective maintenance; however, it remains challenging due to limited labeled data and the highly non-stationary nature of acoustic emission (AE) signals. This study introduces an optimized Few-Shot Learning framework (FSL) that integrates time–frequency analysis with attention-guided representation learning and distribution-aware classification for data-efficient fault detection. The framework converts AE signals into Continuous Wavelet Transform (CWT) scalograms, which are processed using a self-attention-enhanced ResNet-50 backbone to capture both local texture features and long-range dependencies in the signal. Adaptive prototype computation with learnable importance weighting refines class representations, while Mahalanobis distance-based matching ensures robust alignment between query and prototype embeddings under limited sample conditions. To further strengthen discriminability, contrastive loss with hard negative mining enforces compact intra-class clustering and clear inter-class separation. Comprehensive experiments under 7-way 5-shot settings and 5-fold stratified cross-validation demonstrate consistent and reliable performance, achieving a mean accuracy of 98.86% ± 0.97% (95% CI: [98.01%, 99.71%]). Additional evaluations across multiple spindle speeds (660 rpm and 1440 rpm) confirm that the model generalizes effectively under varying operating conditions. Grad-CAM++ activation maps further illustrate that the network focuses on physically meaningful fault-related regions, enhancing interpretability. The results verify that the proposed framework achieves robust, scalable, and interpretable fault diagnosis using minimal labeled data, offering a practical solution for predictive maintenance in modern intelligent manufacturing environments. Full article

In school settings, nomophobia—a newly identified form of problematic mobile phone use characterized by anxiety and discomfort experienced when an individual is unable to use or access their smartphone—poses significant challenges to students’ learning and daily life. Prior research on nomophobia has predominantly adopted a variable-centered perspective. However, if nomophobia is heterogeneous across subgroups, acknowledging this heterogeneity may inform the advancement of more tailored and productive therapeutic methods. Latent profile analysis (LPA) was conducted separately among high school students (N = 446) and college students (N = 667) to identify potential subgroup heterogeneity in nomophobia. To examine cross-group similarities in nomophobia profiles, a multi-group LPA was employed. Based on multiple model fit criteria, a three-profile solution—high nomophobia, moderate nomophobia, and low nomophobia—was identified for both groups. However, the multi-group LPA provided only partial support for the similarity of nomophobia profiles across educational stages, specifically in terms of configural and dispersion similarity. While similar nomophobia profiles emerged across groups, the partial equivalence suggests that intervention strategies for nomophobia may not be universally applicable across different educational levels. Additional studies should investigate the mechanisms underlying students’ nomophobia profiles and to inform differentiated interventions for educators, institutions, and policymakers. Full article

This study presents an improved variant of the greater cane rat algorithm (GCRA), called adaptive and global-guided greater cane rat algorithm (AGG-GCRA), which aims to alleviate some key limitations of the original GCRA regarding convergence speed, solution precision, and stability. GCRA simulates the foraging behavior of the greater cane rat during both mating and non-mating seasons, demonstrating intelligent exploration capabilities. However, the original algorithm still faces challenges such as premature convergence and inadequate local exploitation when applied to complex optimization problems. To address these issues, this paper introduces four key improvements to the GCRA: (1) a global optimum guidance term to enhance the convergence directionality; (2) a flexible parameter adjustment system designed to maintain a dynamic balance between exploration and exploitation; (3) a mechanism for retaining top-quality solutions to ensure the preservation of optimal results.; and (4) a local perturbation mechanism to help escape local optima. To comprehensively evaluate the optimization performance of AGG-GCRA, 20 separate experiments were carried out across 26 standard benchmark functions and six real-world engineering optimization problems, with comparisons made against 11 advanced metaheuristic optimization methods. The findings indicate that AGG-GCRA surpasses the competing algorithms in aspects of convergence rate, solution precision, and robustness. In the stability analysis, AGG-GCRA consistently obtained the global optimal solution in multiple runs for five engineering cases, achieving an average rank of first place and a standard deviation close to zero, highlighting its exceptional global search capabilities and excellent repeatability. Statistical tests, including the Friedman ranking and Wilcoxon signed-rank tests, provide additional validation for the effectiveness and importance of the proposed algorithm. In conclusion, AGG-GCRA provides an efficient and stable intelligent optimization tool for solving various optimization problems. Full article

The order picking process in Goods-to-Person (G2P) systems involves a set of interdependent yet often separately addressed decisions, such as order allocation, sequencing, and rack handling. This study focuses on the joint optimization of order allocation, order sequencing, rack selection, and rack sequencing in a G2P robotic mobile fulfillment system with multiple picking stations. To model this complex problem, we develop a mathematical formulation and propose a two-phase heuristic algorithm that combines simulated annealing, genetic algorithms, and beam search for efficient solution. In addition, we explore and compare two order allocation strategies—order similarity and order association—across a range of operational scenarios. Extensive computational experiments and sensitivity analyses demonstrate the effectiveness of the proposed approach and provide insights into how strategic order allocation can significantly improve picking efficiency. Computational experiments on small-scale instances show that our algorithm achieves near-optimal solutions with up to 93.3% reduction in computation time compared to exact optimization for small cases. In large-scale scenarios, the order similarity strategy reduces rack movements by up to 44.8% and the order association strategy by up to 33.5% relative to a first-come, first-served baseline. Sensitivity analysis reveals that the association strategy performs best with fewer picking stations and lower rack capacity, whereas the similarity strategy is superior in systems with more stations or higher rack capacity. The findings offer practical guidance for the design and operation of intelligent warehousing systems. Full article