Search Results (12,412)

In the context of the large-scale integration of high-proportion renewable energy into power grids, Power Conversion Systems (PCS) and Static Var Generator (SVG), as the core power electronic devices for ensuring grid frequency stability and power quality, demand high-level safety and reliability. To tackle challenges such as the wide range of defect sizes in PCS and SVG, the low recognition accuracy for microscopic fuzzy defects, and complex background interference, this paper presents a lightweight and high-precision defect recognition model (RD-YOLO) based on the latest YOLOv11 benchmark. First, an improved Mosaic algorithm is introduced. This algorithm utilizes conflict relationship tables to preserve physical context semantics during traditional non-discriminative data augmentation. Second, to surmount the limitations of scale-aware feature extraction, the YOLOv11 is re-engineered within the backbone network by integrating a Res2Net multi-scale cascaded mechanism. This enhances the network’s capacity to capture both fine-grained defect features and large-scale defect boundaries. Third, Focal Loss is employed for difficult sample detection. Nonlinear gradient modulation is utilized to guide the model to focus on ambiguous defect edges. Finally, the Soft-NMS post-processing strategy significantly enhances the regression accuracy in densely corroded regions. Experimental validation on a self-developed dataset consisting of 8500 high-resolution PCS and SVG defect images reveals that the enhanced RD-YOLO attains an average precision of 89.6% and a frame inference rate of 98 FPS (in RTX 3090), offering robust technical support for intelligent visual maintenance in renewable energy facilities. Full article

Quantitative operational data mining and control strategies are essential for energy-saving regulation in intelligent District Heating Systems (DHSs). However, due to complex industrial environments and heterogeneous sensor networks, operational heating data often face a “zero ground-truth labels” bottleneck, rendering conventional residual-based unsupervised quality control algorithms ineffective without calibration baselines. To resolve this, this paper proposes an unsupervised multivariate data quality assessment framework constrained jointly by physical and statistical topologies. The framework evaluates time-series data streams across five dimensions: completeness, typicality, consistency, uniqueness, and timeliness. At the statistical topology level, the Mahalanobis distance identifies the spatiotemporal distribution center of multivariate variables, replacing traditional accuracy metrics with statistical typicality to enable self-consistent quantification without ground truth. At the physical topology level, coupled logical relations between primary and secondary heating networks are extracted as rigid first-principles constraints. An information entropy weight method then adaptively determines indicator weights to eliminate subjective biases. Full-sample validation was conducted using real-world SCADA data across a complete heating season from a regional network zone (46 heat exchange stations). The network-wide average data quality score reached 0.905, confirming overall control-loop input readiness, though specific stations exhibited cascading degradation from localized physical faults. This framework systematically reveals data quality heterogeneity in complex DHS and provides a generalizable theoretical baseline for Industrial Internet of Things (IIoT) data cleansing under zero-ground-truth conditions. Full article

To address the limitations of traditional Voltage-Current (VI) trajectory features in appliance load identification—such as the difficulty in distinguishing similar appliances, weakened amplitude information, and the absence of dynamic characteristics—this paper proposes a dual-stage cyclic training method for load identification that integrates multi-feature reconstruction with Particle Swarm Optimization (PSO). First, to overcome the high similarity of original VI trajectories, a PSO-based threshold optimization algorithm is designed to reconstruct VI trajectories through reflection operations and normalization, thereby enhancing the geometric morphological differences among similar appliances. Second, to supplement dynamic impedance information and energy level features, conductance-time trajectories and mean-square current color-block maps are introduced to characterize dynamic impedance variations and energy level differences, respectively. Finally, a three-channel classification network based on ResNet18 is constructed, where the reconstructed VI trajectories, conductance-time trajectories, and mean-square current color-block maps are fused via RGB channels as inputs, forming a closed-loop “threshold optimization—feature reconstruction—cyclic training” framework. Experimental results on the PLAID dataset demonstrate that the proposed method achieves an identification accuracy of 98.29% and a macro-averaged F1-score of 97.93%. Comparative experiments verify the effectiveness of the reconstructed VI trajectories, the complementarity of multi-feature fusion, and the superiority of the combinatorial optimization network, significantly improving the identification of multi-state and similar-condition appliances. Full article

Carbonic anhydrases (CAs) are a class of zinc-containing metalloenzymes widely present in the biological world, catalyzing the reversible hydration of CO₂ to form HCO₃⁻ and H⁺. These enzymes play essential roles in pH homeostasis, gas exchange, metabolic regulation, and virulence expression in pathogens. In fungi, CAs mainly belong to the α- and β-classes and have undergone extensive diversification during evolution. In plant pathogenic fungi, the functions of CAs have extended beyond traditional metabolic roles, evolving into key “environmental adaptation and virulence regulatory factors.” This review takes Magnaporthe oryzae as a model organism and integrates recent advances in CA research across various microorganisms. It systematically summarizes the classification diversity, structural features, subcellular localization, and biological functions of fungal CAs. Particular emphasis is placed on the molecular profile, mitochondrial localization, physical interaction network, and multiple functional roles of the MoCA family members in conidial development, appressorium formation, oxidative stress response, HCO₃⁻ homeostasis, nitrogen metabolism, and mitochondrial energy metabolism. Based on these findings, this study proposes a multi-layered analytical framework integrating CA molecular characteristics, mitochondrial functional regulation, and fungal pathogenicity. It explores the potential of targeting fungal CAs for the development of novel selective fungicides and highlights key research directions, aiming to provide theoretical insights into plant-fungal interactions and innovative strategies for disease control. Full article

Large Language Models (LLMs) are emerging as key enablers of Future Internet ecosystems, supporting intelligent, adaptive, and context-aware services across distributed cyber-physical environments. Beyond traditional natural language processing, LLMs are increasingly integrated into Smart Internet of Things (SIoT) systems to enable semantic interoperability, edge intelligence, multimodal interaction, and autonomous service orchestration. This paper presents a taxonomy-based review of LLM architectures, training paradigms, deployment strategies, and emerging applications within Future Internet infrastructures. The review classifies existing studies by deployment environment, architecture, training strategy, accessibility, and application scope, and analyses the role of LLMs in intelligent IoT environments, with emphasis on edge-based reasoning, agentic AI, human-centric automation, and context-aware decision-making. Key challenges are examined, including scalability, inference latency, privacy, trustworthiness, security, hallucination, and energy efficiency in resource-constrained environments. A comparative analysis of representative LLMs is presented, based on deployment feasibility, multimodal capability, accessibility, and suitability for distributed intelligent services. The originality of the review lies in conceptualizing LLMs as cognitive middleware that provides semantic, reasoning, and coordination capabilities across Smart IoT infrastructures. Finally, future research directions are highlighted, including decentralized AI architectures, digital twins, the Model Context Protocol, retrieval-augmented generation, multimodal sensing, and autonomous agent-based ecosystems. Full article

Lead (Pb²⁺) pollution poses a severe threat to the ecological environment and human health due to its high toxicity, bioaccumulation, and refractory nature. Traditional treatment technologies for lead-contaminated wastewater, such as chemical precipitation, ion exchange, and membrane separation, often face limitations, including secondary pollution, high costs, and high energy consumption. In contrast, adsorption has emerged as a promising alternative technology with advantages such as a simple process, high efficiency at low concentrations, and renewability. Biomass-based hydrogels and their composite systems, as novel green adsorbent materials, combine the abundant functional groups of natural biomass with the structural stability, high porosity, and recoverability of hydrogels through a three-dimensional cross-linked network, offering unique advantages for lead ion adsorption. Depending on their composition, these systems range from fully biodegradable pure biopolymer networks to partly biodegradable or biomass-containing composites incorporating inorganic, carbon-based, or metal–organic framework (MOF) materials. This paper systematically reviews the latest research progress on cellulose, lignin, sodium alginate, chitosan, starch-based hydrogels, and their composite systems for lead (Pb²⁺) adsorption. First, the structural characteristics, cross-linking mechanisms, and functional modification strategies of various biomass hydrogels are introduced. Then, the adsorption mechanisms of Pb²⁺, including multiple modes of action such as coordination complexation, ion exchange, electrostatic interaction, and physical adsorption, are systematically analyzed. The adsorption performance of different material systems is compared in detail. The regeneration and recycling performance, as well as the potential practical applications, of the materials are evaluated. On this basis, the main challenges in current research are summarised: balancing adsorption capacity and mechanical strength, achieving selective adsorption in actual wastewater, improving regeneration efficiency, and optimizing costs. In addition, future development directions for biomass hydrogel adsorbent materials are discussed, including the design of multi-functional composite materials, the development of intelligent, responsive hydrogels, engineering-scale-up, and life-cycle assessment. This review aims to provide a theoretical framework and technical roadmap for the rational design of high-performance, sustainable hydrogel adsorbents and to promote their engineering application for the treatment of lead-contaminated wastewater. Full article

Sustainable Internet of Things (IoT) deployments require network planning strategies that explicitly account for environmental impact and not only traditional performance and energy metrics. This work analyzes how input representations affect a learning-based framework for carbon footprint (CF)-aware gateway placement in LoRa multi-hop IoT networks. Building on a previous CF model and an integer linear programming dataset, a multilayer perceptron is retrained using different input encodings: end-device coordinates, traffic-based weights, spatial sampling regions (SSRs), and a global CF estimate. Their contributions are evaluated through Shapley additive explanations (SHAP)-based explainability analysis, ablation studies, and sensitivity analysis. Results show that the CF estimate is the most influential input, acting as a global guidance signal that drives large gateway relocations. The combination of raw coordinates and SSR-based spatial summaries achieves the best performance by capturing both fine spatial detail and collective relay opportunities, while traffic-based weights mainly contribute through aggregate effects. These findings provide practical guidelines for designing CF-aware learning pipelines and offer insights to support future research on environmentally aware artificial intelligence for IoT network planning. Full article

To address the severe coverage blind spots and concurrent collision bottlenecks faced by LoRa networks in dense deployments and complex three-dimensional (3D) occlusion environments, this paper proposes a distributed cross-layer protocol framework for LoRa ad hoc networks supporting heterogeneous traffic. To break through the limitations of a single star architecture, this framework constructs a 3D penetration loss model at the physical layer and designs a distributed relay deployment algorithm based on hybrid simulated annealing, achieving blind-spot-free connectivity in complex spaces. At the MAC layer, a non-preemptive priority access mechanism based on symbol energy detection is introduced. Through differentiated backoff windows with time-domain isolation, it precisely guarantees the quality of service (QoS) requirements of heterogeneous traffic and significantly suppresses concurrent collisions. At the network layer, the CAM-AODV routing algorithm is proposed, which integrates hop count, link quality, MAC queue congestion, and nodal residual energy to achieve dynamic traffic diversion and network-wide energy balancing under bursty high loads. Simulation results demonstrate that this cross-layer framework effectively breaks the traditional network capacity bottlenecks. In a large-scale, high-density scenario with 300 nodes, CAM-AODV reduces the average end-to-end delay by 19.46% compared to the traditional AODV. Under high-concurrent loads, the packet delivery ratio (PDR) of the proposed framework improves by 16.32% over the traditional protocol, while the system delay is reduced by 13.66%. Furthermore, under the two aforementioned evaluation scenarios, the Energy Balancing Index (EBI) is significantly improved by 11.13% and 10.57%, respectively, compared to the traditional protocol. This study provides an efficient joint optimization scheme for building high-capacity, wide-coverage, and long-lifespan complex Internet of Things (IoT) networks. Full article

Environmental issues such as climate change and cumulative emissions have intensified debate on the effectiveness of public environmental protection expenditure. Traditional Pigouvian taxes and subsidies mainly target instantaneous flow externalities. However, it is difficult to resolve dynamic stock externalities that accumulate over many years, such as climate change and cumulative greenhouse gas emissions, through taxation policies alone. This situation requires the government to make direct environmental protection expenditure to support the ecosystem’s natural assimilation capacity and reduce the rate at which pollution accumulates. This study specifically examines the relationship between stock externalities and environmental protection expenditure within the context of the Turkish economy, which is highly industrialised and under pressure to comply with international environmental commitments such as the European Green Deal and the Paris Climate Agreement. In the study’s empirical analysis phase, long-term relationships between macroeconomic variables and pollution stocks were tested using Fourier cointegration methods and econometric time series analyses. The analysis revealed a long-term co-movement (cointegration) relationship between the series, indicating that public environmental protection expenditures and industrial emissions move in the same direction over the long term. FMOLS and DOLS estimates indicate that environmental protection expenditures are positively associated with industrial emissions in the long run (FMOLS coefficient = 2.1125; DOLS coefficient = 2.0375), whereas renewable energy consumption exerts a negative effect on emissions (FMOLS coefficient = −1.4391; DOLS coefficient = −1.4967). These results suggest that environmental protection expenditure in Türkiye is not independent of current production and industrialisation dynamics, and that its emission-reducing effects are influenced by technological transformation processes. Full article

H9N2 avian influenza virus (AIV) continues to mutate, leading to immunosuppression and secondary infections in poultry. Traditional inactivated vaccines mainly induce humoral immunity and have limited cross-protection efficacy against various subtypes of virus strains. In this study, we targeted the HA2 and M1 proteins of H9N2 as antigens and used immunoinformatics methods to design a broad-spectrum multi-epitope vaccine (MEV) that can simultaneously activate humoral and cellular immunity. Firstly, through systematic evolutionary analysis and sequence comparison, highly conserved amino acid sequence regions were selected from HA2 and M1 proteins. B-cell epitopes were predicted in the HA2 conserved sequence, and cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) epitopes were predicted in the M1 conserved sequence. Three candidate vaccines containing different epitope combinations were constructed. After secondary structure and physicochemical property comparisons, HM1 was determined as the optimal scheme. HM1 contains three B cell epitopes, two CTL epitopes, and three HTL epitopes, and was connected to chicken β-defensin at the N-terminus as a molecular adjuvant; a dendritic cell-targeting peptide was added at the C-terminus. The HM1 tertiary structure optimized by GalaxyRefine met the standards of a reliable model. The molecular docking results indicated that HM1 can form stable binding with chicken TLR2, TLR4, MHC I, and MHC II molecules, with binding free energies of −7.1 kcal/mol and −6.1 kcal/mol, respectively, and can form multiple hydrogen bonds and salt bridges. Normal mode analyses revealed that the HM1–TLR complex exhibits favorable dynamic properties at the computational level. The immune simulation prediction results showed that after vaccination with HM1, specific antibodies can be induced, B cells, helper T cells, and cytotoxic T cells can be activated, and IFN-γ and IL-2 can be secreted. In summary, the HM1 designed based on the conserved regions of HA2 and M1 proteins has good physicochemical stability and immunogenicity, providing a theoretical basis for the development of broad-spectrum and highly effective H9N2 vaccines. Full article

Efficient water vapor removal is important for both building humidity control and industrial gas dehydration, where operating conditions may span broader temperature and pressure ranges. Driven by a pressure gradient, membrane-based dehumidification has emerged as an energy-efficient alternative, employing polymeric composite membrane materials to achieve effective moisture separation. However, traditional development of such membranes remains heavily reliant on inefficient trial-and-error approaches. To overcome this limitation, this study employs machine learning to directly predict the relationships between physicochemical structure, operational conditions, and water vapor permeation performance of composite membrane materials. A dataset comprising 138 experimental samples from 26 published studies was compiled, featuring five input features: selective layer thickness, operating temperature, feed pressure, relative humidity, and a newly proposed hydrophilicity score based on functional group composition. Among six machine learning models evaluated, the Gradient Boosting Decision Tree (GBDT) achieved superior predictive performance, yielding a test R² of 0.912. SHAP analysis identified selective layer thickness as the dominant descriptor, followed by feed pressure, hydrophilicity score, operating temperature, and relative humidity, contributing 34.4%, 26.5%, 15.8%, 11.9%, and 11.5% to the model predictions, respectively. Within the investigated parameter space, a genetic algorithm integrated with the GBDT model identified a permeability-oriented parameter combination (18.25 μm thickness, 111.43 °C, 0.94 bar, 52.02%RH, and a hydrophilicity score of 5), achieving a predicted permeability of 136,418 Barrer. The framework offers a transferable strategy for accelerating the rational design of advanced membrane materials, significantly reducing the need for exhaustive experimental screening. Full article

African swine fever (ASF), caused by African swine fever virus (ASFV), is a highly contagious disease that has brought severe economic losses to the global swine industry. ASFV dUTPase is a key enzyme modulating viral nucleotide metabolism and genome stability, serving as a specific structural target for anti-ASF lead discovery. Based on the crystal structure of ASFV dUTPase (Georgia 2008/1 strain), we established a molecular docking model to virtually screen a Traditional Chinese Medicine (TCM) compound library following ADME-based filtration. We further conducted 100 ns molecular dynamics (MD) simulations via GROMACS and calculated binding free energies using the MM/PBSA method, followed by in vitro enzymatic inhibition assays for verification. Four candidate compounds, namely Salvianolic acid B, Kukoamine B, Ligustroflavone and 9‴-Methyl salvianolic acid B, showed favorable structural accommodation and binding potential. In vitro tests indicated that Ligustroflavone and 9‴-Methyl salvianolic acid B exhibited preliminary inhibitory activity against ASFV dUTPase, with IC₅₀ values of 1.87 mM and 0.92 mM, respectively, while Kukoamine B showed no inhibitory effect. This study identified potential TCM-derived lead molecules targeting ASFV dUTPase, providing new references and candidate compounds for the development of anti-ASF therapeutics. Full article

Distributed Acoustic Sensing (DAS) provides dense spatial measurements of the dynamic strain along fiber optic cables, offering high-resolution wave sensing for ground motion monitoring and subsurface imaging applications. However, DAS records the axial strain or strain rate, whereas traditional seismic and engineering ground motion equipment and derived metrics are based on particle displacement, velocity, or acceleration, necessitating reliable strain-to-velocity conversion methods. This study evaluates three widely used conversion approaches: the fk-rescaling, curvelet-based conversion, and slant-stack methods. These approaches are applied to a unique high-energy, near-field, active-source dataset collected at the Birds Landing Site in Sherman Island, California. The dataset includes wavefields generated by a large transmission tower collapse and sledgehammer impacts used for subsurface imaging. The wavefields were recorded simultaneously by a 1.4 km DAS array and 71 collocated nodal stations (NSs). Using 63 DAS–NS pairs, we quantify the strain-to-velocity conversion method performance using amplitude and phase transfer functions (TFs) between DAS-derived and NS particle velocity records, with the root-mean-square error (RMSE) evaluated across three frequency bands: 0.5–100 Hz, 1–10 Hz, and 10–100 Hz. The results show that fk-rescaling provides the most stable amplitude response across both source types, while both the fk-rescaling and slant-stack methods generally yield the best phase agreement. Curvelet-based conversion shows a greater variability and larger RMSE values. All methods yield a poorer amplitude reconstruction at higher frequencies, while the phase content is generally preserved more reliably than amplitudes. Differences between the tower collapse and sledgehammer sources demonstrate the influence of the source characteristics and spatial processing window length on the conversion performance. The findings provide practical guidance for selecting suitable strain-to-velocity conversion methods for active-source DAS applications, particularly where collocated reference sensors are unavailable. Full article

Gas separation is a critical industrial process, including natural gas processing, petrochemical production, carbon capture and storage, hydrogen separation, and air separation. Traditional separation methods usually involve high-energy-consuming processes, with inherent limitations in separation efficiency and molecular selectivity. In recent years, zeolite membranes have become an attractive platform for applications such as gas separation, owing to their outstanding selectivity, gas permeability, and energy efficiency. This review systematically summarizes the structural features, representative synthesis strategies, formation pathways, and typical gas separation performances of zeolite membranes. Moreover, existing bottlenecks related to membrane assembly techniques are summarized, and forward-looking perspectives on their further development are put forward. We present several strategic insights, which can provide theoretical guidance for the research and development of zeolite membranes. By providing a comprehensive and timely review, this article aims to promote the application of zeolite membranes in gas separation. Full article

As a paradigm-shifting hypervelocity propulsion technology, electromagnetic rail launch (EMRL) is fundamentally constrained by armature/rail (A/R) interface degradation, which directly erodes its service longevity and operational reliability. Small-caliber circular bore electromagnetic launchers (SCCB-EMRL) offer superior structural integration and ballistic stability over traditional rectangular bores. Their inherently lower self-centering capability imposes strict requirements on interfacial contact stability. This study investigates the physicochemical evolution of the A/R interface at extreme loading. Consecutive repetitive launch experiments were conducted, and samples were prepared by typical areas of rail according to the current curve. Characterization was performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Raman spectroscopy. Results revealed a bimodal non-uniform thickness distribution of the deposition layer along the launch direction. The maximum deposition thickness reached 60.6 μm within the acceleration-startup zone. The deposited material comprises transferred Al, oxidized phases (Al₂O₃), Al-Cu intermetallic, and a mixed carbonaceous system containing amorphous and graphitized carbon. Initial launches triggered rapid material accumulation and increased start-up times, after which the interface reached a dynamic equilibrium. This work reveals the evolution patterns of elemental composition and thickness distribution of the deposition layer at the armature/rail interface in small-caliber circular-bore electromagnetic launching and provides experimental reference for the design of anti-deposition coatings to extend the service lifespan of SCCB-EMRL systems. Full article