Search Results (10,189)

Swarm coverage by unmanned underwater vehicles (UUVs) is essential for inspection, environmental monitoring, and search operations, but remains challenging in three-dimensional domains under limited sensing and communication. Pheromone-based stigmergic coordination provides a low-bandwidth alternative to explicit communication, yet conventional single-field models are susceptible to depth-dependent sensing inconsistencies and multi-source signal interference. This paper introduces a dual-trail stigmergic coordination framework in which a virtual pheromone field encodes short-term motion cues while an auxiliary coverage trail records the accumulated exploration effort. UUV motion is guided by the combined gradients of these two fields, enabling more consistent behavior across depth layers and mitigating ambiguities caused by overlapping pheromone sources. At the macroscopic level, swarm evolution is modeled by a coupled system of partial differential equations (PDEs) describing vehicle density, pheromone concentration, and coverage trail. A Lyapunov functional is constructed to derive sufficient conditions under which perturbations around the uniform coverage equilibrium decay exponentially. Numerical simulations in three-dimensional underwater domains demonstrate that the proposed framework reduces coverage holes, limits redundant overlap, and improves robustness with respect to a single-pheromone baseline and a potential-field-based controller. These results indicate that dual-field stigmergic control is a promising and scalable approach for UUV coverage in constrained underwater environments. Full article

As global agriculture seeks to reconcile the dual imperatives of food security and environmental sustainability, this study examines the role of Internet access in promoting green agricultural production, specifically by reducing fertilizer and pesticide use. Using a panel dataset from 16 rural fixed observation points in Henan Province from 2009 to 2022, we find that Internet access significantly lowers per-unit farmland expenditures on fertilizers and pesticides by 6.0% and 7.3%, respectively. Mechanism analysis reveals that these positive effects operate through three main channels: improved information accessibility delivers timely agricultural data and guides input decisions; enhanced technical learning efficiency reduces barriers to adopting green technologies; and stronger market connectivity via e-commerce platforms shortens supply chains and provides price incentives. Heterogeneity analysis further identifies more pronounced effects among farmers with higher human capital (higher education, better health, younger age), higher production capital (greater mechanization, larger farmland, stronger decision-making capacity), lower livelihood capital (lower income, lower consumption, less communication expenditure), and higher spatial capital (residing in urban suburbs, poverty registration villages, and traditional villages). This study provides micro evidence for digital technology to empower sustainable agricultural development and provides policy implications for building a sustainable agri-food system. Full article

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

The energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation. Full article

Nanoparticle–surfactant composite flooding systems significantly enhance oil recovery through synergistic effects. When the optimal ratio of SiO₂ nanoparticles to nonionic surfactant alkylphenol polyoxyethylene ether (OP-10) in the composite system is 3:2, the oil–water interfacial tension (IFT) decreases to 0.005 mN/m, and the contact angle changes from the original 128° to 42°, achieving effective wettability alteration. Core displacement experiments demonstrate that the recovery rate using nanoparticles alone is 46.8%, and using surfactant alone is 52.3%, while the composite system achieves 71.5%, representing a 39.2 percentage point improvement over water flooding. The composite system operates through multiple mechanisms including interfacial tension reduction, wettability alteration, stable emulsion formation, and enhanced sweep efficiency. The wedging effect of nanoparticles at pore throats and the interfacial activity of surfactants form significant synergistic enhancement, providing a new technical pathway for efficient development of low-permeability reservoirs. Full article

Background/Objectives: Rosacea increasingly appears to involve systemic immune and metabolic disturbances rather than isolated cutaneous inflammation. To evaluate inflammatory, platelet, and oxidative–metabolic biomarkers in rosacea and explore their interrelations. Methods: 90 patients with rosacea and 90 healthy controls were evaluated for hematologic inflammatory indices—neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune–inflammation index (SII), pan-immune–inflammation value (PIV), mean platelet volume (MPV), and C-reactive protein (CRP)—along with oxidative–metabolic regulators including sirtuin 1 (SIRT1), sirtuin 3 (SIRT3), visfatin, and irisin. Logistic regression and receiver operating characteristic (ROC) analyses were used to identify independent predictors of rosacea, while inter-marker associations were evaluated using Spearman’s rank correlation. Results: Rosacea patients showed higher NLR, PLR, SII, PIV, MPV, CRP, and LDL cholesterol (p < 0.05) and lower SIRT1, SIRT3, visfatin, and irisin (p < 0.01). MPV independently predicted rosacea (OR = 7.24; AUC = 0.827), whereas SIRT1 inversely correlated with disease risk. SIRT1, SIRT3, and visfatin showed inverse correlations with HbA1c and waist-to-height ratio, while fasting glucose and HOMA-IR remained within normal ranges. Conclusions: Rosacea exhibits dual systemic activation, an inflammatory–platelet and an oxidative–metabolic axis bridging immune dysregulation, mitochondrial stress, and vascular dysfunction. Recognition of these pathways highlights the potential of redox-targeted and metabolic interventions beyond symptomatic treatment. Full article

To prepare and characterize low-bitter angiotensin-converting enzyme (ACE)-inhibitory peptides from sesame protein, a triple-enzyme hydrolysis system was optimized using mixture design and response surface methodology. The resulting hydrolysate was separated by ultrafiltration and medium-pressure chromatography, followed by identification through nano-liquid chromatography–electrospray ionization-tandem mass spectrometry. Finally, the mechanism of typical low-bitter ACE-inhibitory peptides was elucidated by molecular docking and molecular dynamics simulation. Results showed that the optimal enzyme activity ratio of 1:0.94:1.07 for Alcalase, trypsin, and Flavourzyme, combined with optimized hydrolysis conditions (E/S ratio of 126,793.03 nkat/g, pH 8.40, 4.82 h hydrolysis time, and 45 °C), resulted in a peptide yield of 93.19 ± 0.14%, ACE-inhibitory rate of 95.92 ± 0.23%, and bitter value of 3.15 ± 0.09. APQLGR and APWLR exhibited high ACE-inhibitory activity and minimal bitterness among the seventeen identified peptides. Although both peptides bound to the S1 pocket and Zn²⁺ catalytic site of ACE, APWLR exhibited an additional interaction with the S2 pocket. Both peptides were predicted to antagonize the bitter taste receptor T2R14 by forming stable complexes with key residues, but two complexes exhibited distinct mechanisms of stabilization. This work demonstrates a method for producing dual-functional peptides from sesame protein, paving the way for their application in functional foods. Full article

Optical Fiber Composite Overhead Ground Wire (OPGW) cables serve dual functions in power systems, lightning protection and critical communication infrastructure for real-time grid monitoring. Accurate OPGW identification during UAV inspections is essential to prevent miscuts and maintain power-communication functionality. However, detecting small, twisted OPGW segments among visually similar ground wires is challenging, particularly given the computational and energy constraints of edge-based UAV platforms. We propose OPGW-DETR, a lightweight detector based on the D-FINE framework, optimized for low-power operation to enable reliable detection. The model incorporates two key innovations: multi-scale convolutional global average pooling (MC-GAP), which fuses spatial features across multiple receptive fields and integrates spectrally motivated features for enhanced fine-grained representation, and a hybrid gating mechanism that dynamically balances global and spatial features while preserving original information through residual connections. By enabling real-time inference with minimal energy consumption, OPGW-DETR addresses UAV battery and bandwidth limitations while ensuring continuous detection capability. Evaluated on a custom OPGW dataset, the S-scale model achieves 3.9% improvement in average precision (AP) and 2.5% improvement in AP${}_{50}$ over the baseline. By mitigating misidentification risks, these gains improve communication reliability. As a result, uninterrupted grid monitoring becomes feasible in low-power UAV inspection scenarios, where accurate detection is essential to ensure communication integrity and safeguard the power grid. Full article

Mozambique’s rapidly urbanizing landscape presents both opportunities and challenges for achieving Sustainable Development Goals (SDGs) 7 and 11, which aim to ensure access to clean energy and sustainable cities. This study employs the HESPECT analytical framework—emphasizing Historical, Economic, Social, Political, Ecological, Cultural, and Technological dimensions of the energy context—to examine the factors shaping renewable energy transitions in Mozambican cities. The analysis reveals a dual dynamic: facilitating factors such as abundant solar and wind potential, expanding urban energy demand, and growing policy support; and inhibiting factors including deforestation-driven ecological stress, poverty, infrastructural deficits, and uneven access to technology and education. By linking renewable energy development to urban planning, service delivery, and social inclusion, the study underscores how energy systems shape the sustainability and livability of Mozambique’s cities. The paper concludes that advancing Mozambique’s renewable energy agenda requires targeted interventions to mitigate constraints while leveraging enabling factors to strengthen institutional capacity, enhance social inclusion, and accelerate progress toward guaranteeing clean and affordable energy to all (SDG 7) and livable, sustainable cities (SDG 11). Full article

This study investigates the activation mechanism of boron-doped carbon (BMC) catalysts for the degradation of the antibiotic sulfamethoxazole (SMX) via persulfate (PMS) activation. The catalysts were synthesized using a sequential double-melting calcination method, resulting in mesoporous carbon nanosheets characterized by hierarchical macro-mesopores and atomically dispersed dual active sites. Comprehensive characterization was performed using BET, SEM, TEM, FT-IR, XPS, XRD, and Raman techniques. The optimized BMC catalyst demonstrated excellent performance, achieving complete removal of sulfamethoxazole (100%) and a high mineralization rate (~90%) within 45 min. Mechanistic analysis, including electron paramagnetic resonance (EPR), revealed that the degradation predominantly follows a singlet oxygen (1O2)-dominated pathway. The system exhibited broad applicability to various pollutants, along with notable operational stability and robust resistance to common environmental interferents. Persulfate activation was primarily attributed to boron-active sites, while the hierarchical mesoporous structure facilitated both pollutant enrichment and catalytic efficiency. Full article

Early detection of small-scale fires is crucial for minimizing damage and enabling rapid emergency response. While recent deep learning-based fire detection systems have achieved high accuracy, they still face three key challenges: (1) limited deployability in resource-constrained edge environments due to high computational costs, (2) performance degradation caused by feature interference when jointly learning flame and smoke features in a single backbone, and (3) low sensitivity to small flames and thin smoke in the initial stages. To address these issues, we propose a lightweight dual-stream fire detection architecture based on YOLOv5n, which learns flame and smoke features separately to improve both accuracy and efficiency under strict edge constraints. The proposed method integrates two specialized attention modules: ESCFM++, which enhances spatial and channel discrimination for sharp boundaries and local flame structures (flame), and ESCFM-RS, which captures low-contrast, diffuse smoke patterns through depthwise convolutions and residual scaling (smoke). On the D-Fire dataset, the flame detector achieved 74.5% mAP@50 with only 1.89 M parameters, while the smoke detector achieved 89.2% mAP@50. When deployed on an NVIDIA Jetson Xavier NX(NVIDIA Corporation, Santa Clara, CA, USA)., the system achieved 59.7 FPS (single-stream) and 28.3 FPS (dual-tream) with GPU utilization below 90% and power consumption under 17 W. Under identical on-device conditions, it outperforms YOLOv9t and YOLOv12n by 36–62% in FPS and 0.7–2.0% in detection accuracy. We further validate deployment via outdoor day/night long-range live-stream tests on Jetson using our flame detector , showing reliable capture of small, distant flames that appear as tiny cues on the screen, particularly in challenging daytime scenes. These results demonstrate overall that modality-specific stream specialization and ESCFM attention reduce feature interference while improving detection accuracy and computational efficiency for real-time edge-device fire monitoring. Full article

Digital technologies have transformed the spatial organization of finance. As a result, geographic and virtual agglomerations co-exist. In this paper, we model the synergistic integration of geographic and virtual agglomerations within China’s financial industry from a systems perspective. Using provincial panel data from 2011 to 2023, we develop an entropy-weighted coupling coordination model to measure the interaction between the two agglomerations. Furthermore, we employ spatial and convergence analyses to reveal their evolutionary characteristics. Our findings reveal three key results. First, financial geographic agglomeration shows an overall increasing trend, with regional levels ranked as follows: eastern region, northeastern region, western region, and central region. It exhibits significant positive spatial correlation and convergence characteristics. Second, financial virtual agglomeration also continues to strengthen, with regional levels ranked as eastern, central, western, and northeastern regions. Its convergence patterns display regional heterogeneity, and no significant spatial correlation is observed. Third, the coupling coordination degree between the two agglomerations has steadily improved nationwide and across all four major regions with convergent trends. By 2023, the eastern region has entered a stage of primary coordination, while the central, western, and northeastern regions remain in a near-dysfunctional state. In terms of driving patterns, most provinces are primarily driven by geographic agglomeration. Hunan, Hainan, and Guizhou are driven by virtual agglomeration, whereas Beijing, Anhui, Shandong, Guangdong, and Yunnan demonstrate a synchronized pattern driven by both agglomeration types. Overall, our findings highlight the systemic nature of financial agglomeration in the digital economy and enrich the theoretical understanding of financial dual-agglomeration synergy. They provide an analytical framework and empirical evidence for designing differentiated regional financial development policies. Full article

To address the dual demands of accuracy and real-time performance in power quality disturbance (PQD) recognition for new power system, this paper proposes a lightweight model named the Cross-Channel Attention Three-Layer Convolutional Model (1D-CCANet-3), specifically designed for edge deployment. Based on the one-dimensional convolutional neural network (1D-CNN), the model features an ultra-compact architecture with only three convolutional layers and one fully connected layer. By incorporating a set of cross-channel attention (CCA) mechanisms in the final convolutional layer, the model further enhances disturbance recognition accuracy. Compared to other deep learning models, 1D-CCANet-3 significantly reduces computational and storage requirements for edge devices while achieving accurate and efficient PQD recognition. The model demonstrates robust performance in recognizing 10 types of PQD under varying signal-to-noise ratio (SNR) conditions. Furthermore, the model has been successfully deployed on the FPGA platform and exhibits high recognition accuracy and efficiency in real-world data validation. This work provides a feasible and effective solution for accurate and real-time PQD monitoring on edge devices in new power systems. Full article

Background/objectives: Metabolomics has emerged as a powerful systems-biology tool for deciphering dynamic metabolic alterations occurring during infectious diseases and following vaccination. While genomics and proteomics provide extensive molecular and regulatory information, metabolomics uniquely reflects the biochemical phenotype associated with infection, immune activation, and immunometabolic reprogramming. The objective of this review is to provide an integrated analysis of metabolomics applications across both neglected tropical diseases (NTDs) and non-NTD pathogens, highlighting its dual role in biomarker discovery and vaccine response evaluation. Methods: A comprehensive literature-based synthesis was conducted to examine metabolomic studies in infectious diseases and vaccinology. Metabolic perturbations associated with specific pathogens, as well as vaccine-induced metabolic changes and correlates of immune responses, were systematically analyzed and compared across NTD and non-NTD contexts. Results: Distinct pathogen- and vaccine-associated metabolic signatures were identified, reflecting alterations in glycolysis, amino acid metabolism, lipid remodeling, and immunoregulatory pathways. Comparative analysis revealed both shared and disease-specific metabolic biomarkers across NTDs and non-NTD infections. Importantly, vaccine-related metabolic correlates were shown to mirror immune activation states and, in some cases, predict immunogenicity and response durability. Conclusions: This review bridges metabolomics research in infectious disease pathogenesis and vaccine immunology across the NTD and non-NTD spectrum. By integrating these domains, it introduces the concept of “metabolic immuno-signatures” as predictive and translational tools for evaluating vaccine efficacy and immune response outcomes. Full article

This numerical simulation study introduces a novel phase-shifted Gaussian and donut beam excitation strategy for frequency-domain photoacoustic microscopy, capable of achieving optical sub-diffraction-limited lateral resolution. We demonstrate that the spatial overlapping of Gaussian and donut beams with π-radian phase-shifted intensity modulation may confine the effective photoacoustic excitation region, substantially reducing the beam-waist-normalized full width at half maximum value from 1.177 to 0.828 units. This effect corresponds to a ~1.42-fold lateral resolution enhancement compared with conventional focused Gaussian beam excitation. Furthermore, the influence of the optical power ratio between the beams was systematically analyzed, revealing an optimal value of 1.16, balancing excitation confinement and side-lobe suppression. Within this framework, the presented simulation results establish a basis for the experimental realization of phase-shifted dual-beam excitation photoacoustic microscopy systems, with a potential impact on high-resolution biomedical imaging of subcellular and microvascular structures using low-cost continuous-wave optical sources such as laser diodes. Full article