Search Results (12,583)

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

Atmospheric laser beam propagation is typically perturbed by the dual influences of aerosol particle systems and atmospheric turbulence. This joint perturbation induces intensity fluctuations in the transmitted optical field, which significantly degrades the performance of laser-based systems. This study integrates and improves upon existing simulation algorithms, establishing a coupled model that combines the Monte Carlo method and multi-phase screens. The model accurately characterizes optical field evolution and reveals that the impacts of scattering and turbulence on the scintillation index (SI) are not simply additive: turbulence perturbation enhances intensity fluctuations, leading to an increase in SI; however, as the energy proportion of scattered light rises, its statistical stationarity begins to dominate the optical field characteristics, stabilizing SI. Based on radiative transfer and Mie scattering theories, an analytical formula for single-scattering SI is derived, enabling direct calculation from fundamental parameters. Furthermore, a composite SI expression is established using the scattered-to-transmitted light intensity ratio. To address model deviations along the dimensions of visibility and turbulence strength, a sinusoidal compensation model and a logarithmic compensation model are proposed, respectively. Validation results verify the complementary and competitive mechanisms of scattering and turbulence in modulating intensity fluctuations. This research provides efficient theoretical tools and practical references for simulating and optimizing laser transmission in complex atmospheric environments. Full article

Triphenyl phosphate (TPHP), a typical organophosphate flame retardant, has been listed as an emerging pollutant, yet its biodegradation remains poorly studied. Herein, an efficient TPHP-degrading marine bacterium, Pseudomonas abyssi RL-WG04, was isolated from mangrove sediments, which could degrade 95.22% of 100 mg/L TPHP within 120 h. RL-WG04 exhibited good tolerance to varied environmental conditions, maintaining over 70% TPHP degradation percentages (100 mg/L, 7 d) across 20–50 °C, pH 7.0–9.0, and salinity 2.0–4.0% (NaCl, w/v). Organic solvents (p-xylene, biphenyl, toluene and ethyl acetate, 0.5% v/v) had a negligible impact, whereas metal ions (Mn²⁺, Fe³⁺, Ca²⁺, Cu²⁺, Mg²⁺, Zn²⁺, and Co²⁺) strongly inhibited degradation, especially at 1 mM. Under optimized conditions, TPHP degradation by RL-WG04 followed the improved Gompertz model (R² = 0.99927). Metabolite identification indicated that RL-WG04 transformed TPHP into phenol but failed to utilize phenol for growth because of the phenol 2-monooxygenase deficiency. Nevertheless, the constructed consortia of RL-WG04 and Pseudomonas sp. RL-LY03 (phenol-degrading bacterium) achieved complete TPHP degradation and cell proliferation. Additionally, RL-WG04 could efficiently remove TPHP (25 mg/kg) from clay and sandy mangrove sediments with 100% and 90.04% removal percentages, respectively. Overall, this work provides novel insights into the fate of TPHP and a potential approach for its remediation. Full article

Trichothecene mycotoxins, especially T-2 toxin, represent a significant threat to food safety and public health. Although the enzymatic degradation of deoxynivalenol has been extensively investigated, there are few reports of enzymes capable of efficiently degrading T-2 toxin. This study identified that the aldo-keto reductase AKR13B3 from Devosia A6-243 exhibits 3-keto-DON-degrading and a little T-2 toxin-degrading activity. To address this limitation, a rational design strategy targeting the substrate-binding pocket was employed to enhance its activity. Utilizing site-directed and combinatorial mutagenesis, a double mutant R134F/D217A was successfully screened. R134F/D217A retains catalytic activity towards 3-keto-DON while significantly enhancing its catalytic capacity for T-2. Specifically, the R134F/D217A variant exhibited a 2.88-fold increase in catalytic activity and a 3.15-fold enhancement in catalytic efficiency (k_cat/K_m) relative to the wild type enzyme. Notably, a substantial improvement in thermal stability was also observed. After incubation at 55 °C, the residual activity of the R134F/D217A mutant was 2.63 times that of the wild type. Molecular dynamics (MD) simulations and three-dimensional structural modeling suggested the mechanistic basis for the enhanced performance of the R134F/D217A double mutant. Catalytic enhancement stems from a shortened nucleophilic attack distance, a positively biased electrostatic environment, combined with an enlarged pocket and reduced binding free energy. Concurrently, the increased thermal stability results from decreased flexibility and a more rigid structural architecture. This work presents the first report of AKR13B3 as an effective enzyme for T-2 toxin transformation, and its catalytic activity was significantly enhanced through rational design. Thus, a novel enzymatic strategy was proposed, and could inform future approaches to study issues related to T-2 toxin contamination. Full article

Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but large-scale deployment depends on users’ willingness to participate, particularly in public charging environments. While prior research has examined V2G from technical feasibility and system-level perspectives, everyday public settings remain unexplored. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and how participation aligns with existing routines and parking conditions. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and perceived control. Home charging practices emerged as a key reference point shaping expectations of public V2G services. Across both methods, simple and transparent incentives, such as reduced charging or parking costs, were consistently preferred over more complex reward models, including point-based systems or dynamic energy trading. Concerns related to control over trips, battery degradation, trust in service providers, and added complexity remain important barriers to participation. The findings highlight the need for user-centered and socio-technical design of public V2G services that align with users’ everyday routines, parking conditions, and expectations to support broader adoption beyond the home context. Full article

This paper first identifies and analyzes the phenomenon of output-voltage collapse under step load perturbations in dual-bridge converters where an extremum seeking control (ESC) optimization algorithm is employed. Although ESC is an effective online duty-cycle optimization method under steady-state power transfer conditions, it can result in severe output-voltage degradation during large-signal transients. This degradation is primarily caused by the following two factors: the reduced power transfer capability associated with the optimized duty cycles, and the limited dynamic capability of the ESC structure to rapidly adjust the duty cycles. To overcome this limitation, an enhanced extremum seeking control (EESC) structure is proposed for the first time, which enables fast output-voltage reference tracking under dynamic operating conditions, while preserving ESC’s capability for online duty-cycle optimization to minimize losses and improve efficiency. The proposed method extends the applicability of ESC from steady-state optimization to large-signal dynamic scenarios. Comparative experimental results on a dual active half-bridge (DAHB) converter reveal that the conventional ESC structure can cause dynamic collapse, corresponding to a 100% output voltage and current drop under a sudden increase in reference power from 25% to 50% of the rated power with resistive loads. In contrast, the proposed EESC structure not only maintains the same efficiency optimization as the conventional ESC but also exhibits only a brief 5% drop in output voltage and current under the same dynamic conditions, immediately recovering and thus avoiding dynamic collapse. Full article

The widespread utilization of synthetic dyes within the textile industry, driven by their chemical recalcitrance and diverse chromatic spectra, constitutes a significant global environmental challenge. Improper discharge of these highly stable effluents into natural water bodies leads to severe ecological imbalances, affecting aquatic life and soil integrity while posing indirect risks to human health due to their mutagenic potential. Conventional physicochemical treatment methods are often hindered by prohibitive operational costs and the frequent generation of hazardous secondary pollutants. Consequently, there is an urgent demand for sustainable biotechnological alternatives to mitigate these industrial impacts. Bioremediation, specifically using white-rot fungi, represents a robust and eco-friendly strategy for the degradation of complex aromatic structures. Species such as Trametes versicolor, Pleurotus ostreatus, and Phanerochaete chrysosporium utilize a specialized extracellular enzymatic complex to mineralize toxic compounds effectively. Here we review the ligninolytic capacity of white-rot fungi and their specialized enzymatic systems for environmental sustainability. The primary points are: (i) the biochemical mechanisms of the ligninolytic system of laccases and peroxidases during dye degradation; (ii) the influence of operational parameters such as pH, temperature, and nutrient availability on fungal metabolic efficiency; (iii) the diverse environmental applications of these microorganisms in treating real textile effluents; (iv) the current biotechnological challenges, including maintaining enzymatic stability in non-sterile industrial environments; and (v) the future perspectives for scaling up fungal treatment systems from laboratory research to large-scale industrial implementation. Full article

Hyaluronic acid (HA) is widely used in medical, cosmetic, and nutraceutical applications, yet the systemic fate of orally administered HA, particularly non-animal forms, remains poorly characterised. This study investigates the stability, absorption, metabolism, and biological effects of a novel fungal-derived HA extracted from Tremella fuciformis using a sequential in vitro multi-barrier model simulating human physiological compartments, including gastric, intestinal, hepatic, renal, chondrocyte, and keratinocyte environments. Across the gastrointestinal stages, fungal-derived HA demonstrated high structural stability, maintained molecular weight, and exerted superior antioxidant and anti-inflammatory activity compared with sodium hyaluronate. It efficiently crossed the intestinal barrier without increasing hyaluronidase activity, indicating protection from premature enzymatic degradation. In hepatic cells, fungal-derived HA exhibited reduced intracellular uptake and greater extracellular persistence, suggesting lower first-pass metabolism and suggesting improved persistence under in vitro conditions. At peripheral targets, it increased the cluster of differentiation 44 (CD44) expression and HA internalisation in chondrocytes and keratinocytes, supporting anti-inflammatory and pro-regenerative effects. Renal assessments revealed minimal excretion and no cytotoxicity, supporting potential systemic availability. Overall, these results provide the first integrated in vitro evidence describing the absorption, distribution, metabolism, and excretion process of fungal-derived HA. This supports the conclusion that this form of HA is stable, biocompatible, and bioactive with therapeutic potential for joint and skin health, as suggested by the in vitro models. Full article

Parabolic trough collectors (PTCs) are highly sensitive to receiver positioning accuracy; however, most existing studies report optical efficiency degradation without formally defining alignment tolerance limits. This study proposes a tolerance-based thermo-optical risk framework to quantify allowable receiver misalignment envelopes for reliable PTC operation. A Monte Carlo Ray Tracing (MCRT) methodology is employed to evaluate the impact of angular receiver misalignment on optical efficiency and circumferential heat flux redistribution. Beyond conventional efficiency metrics, normalized flux-based thermal non-uniformity indicators are introduced to assess thermo-mechanical risk without requiring full thermo-fluid modeling. The results reveal a nonlinear decoupling between optical acceptability and thermal safety. While optical efficiency remains above 0.80 up to approximately ±6°, pronounced flux localization and rapid growth of thermal stress indicators occur beyond ±4°, marking the onset of thermally critical behavior. The identified ±4° threshold corresponds to approximately twice the collector half-acceptance angle (θ_₍crit₎/δ ≈ 2), demonstrating geometry-dependent scaling characteristics. The proposed framework formalizes the optical–thermal decoupling phenomenon and transforms conventional efficiency-based evaluation into a reliability-informed alignment tolerance assessment tool applicable to manufacturing precision, installation control, and operational quality management in CSP systems. Full article

The transition from a linear to a circular, resource-efficient economy is crucial in order to address the growing scarcity of resources, environmental degradation and the rapid increase in electronic waste and end-of-life products. Artificial Intelligence (AI) has emerged as a key enabling technology, capable of enhancing decision making, automation and optimization across Circular Economy (CE) pathways, including reuse, remanufacturing and recycling. This perspective paper presents a comprehensive and critical overview of AI’s role in supporting the transition to a circular, resource-efficient economy, introducing the Digital CE Architecture (DCEA-4) as a novel framework for integrating AI across the circular value chain. Recent advances in machine learning, deep learning and data-driven optimization are analyzed in the context of electronic waste and used battery management. This highlights how AI-based solutions can improve material recovery rates, reduce environmental impact and enhance system-level efficiency. Additionally, we examine major challenges concerning data availability, model generalization, industrial deployment, and explainability, together with relevant industrial case studies. Although AI offers substantial potential for optimizing circular resource systems, its environmental benefits must be balanced against the computational energy demands of large-scale AI models. This perspective discusses the potential rebound effects associated with AI deployment and emphasizes the importance of energy-efficient algorithms and sustainable digital infrastructures. By bringing together current developments and highlighting future opportunities, this paper aims to help researchers, practitioners and policymakers leverage AI to speed up the transition to sustainable, circular and resource-efficient systems. Full article

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has emerged as a promising technique to enhance spectral efficiency and coverage in fifth- and sixth-generation wireless networks. However, asymmetric indoor propagation conditions characterized by heterogeneous line-of-sight (LoS) and non-line-of-sight (NLoS) links often degrade user fairness. This paper investigates a downlink RIS-assisted NOMA system under the standardized 3GPP indoor office (InH) channel model to address fairness-oriented design under realistic link-budget constraints. We formulate an optimization problem for max–min fairness that jointly considers discrete RIS element partitioning and NOMA power allocation to achieve a symmetrical allocation of quality of service (QoS). To enable efficient computation, the non-convex problem is transformed into an epigraph form and solved using a low-complexity, bisection-based quasi-convex optimization framework combined with enumeration over RIS partitions. Numerical results demonstrate significant fairness gains; for instance, doubling the RIS array size yields a substantial improvement in the ergodic max–min rate, corresponding to approximately a 66% gain at moderate transmit power levels. Furthermore, by accounting for practical impairments such as imperfect successive interference cancellation (iSIC), imperfect channel state information (iCSI), and RIS implementation losses, the results reveal that fairness-optimal operation consistently prioritizes the far user to overcome severe indoor NLoS attenuation. The proposed framework is also compared with alternating optimization (AO)-based RIS-NOMA, conventional RIS beamforming without partition and RIS-assisted orthogonal multiple access (OMA) schemes. Simulation results confirm that the proposed framework achieves low computational complexity, making it suitable for practical indoor wireless environments. Full article

Underwater images often suffer from color distortion and low contrast, severely limiting the reliability of visual perception systems. Existing methods struggle to balance enhancement quality and computational efficiency. To address this issue, we propose PCF-Net (Perception-driven Color Fusion Network), a lightweight dual-branch network for underwater image enhancement based on a stable HSV-CS (Hue-Saturation-Value with sine–cosine transformation) color-space representation. Specifically, a sine–cosine transformation is introduced to construct a stable HSV-CS color space, effectively avoiding hue discontinuities at boundary regions in conventional HSV representations. To compensate for underwater degradation, a Color-Bias-Aware module and a Value-Confidence module are designed to adaptively correct color distortion and luminance degradation. Furthermore, a lightweight Channel-Spatial Adaptive Gated Fusion module dynamically aggregates features from the RGB and HSV-CS branches in a perception-driven manner. The overall architecture incorporates multi-branch re-parameterizable convolutions, significantly reducing computational cost while preserving strong representational capacity. Extensive experiments on underwater image enhancement benchmarks, including UIEB and RUIE, demonstrate that PCF-Net achieves state-of-the-art performance in terms of PSNR, SSIM, and UIQM, along with visually superior color correction and contrast enhancement. With only 0.17 M parameters, the proposed model runs at 118.6 FPS on an RTX 3090 and 35.3 FPS on a Jetson Orin Nano at a resolution of 512 × 512, making it well suited for resource-constrained real-time underwater vision applications. Full article

Synthetic dyes discharged from the textile and dyeing industry present a significant environmental and health hazard due to their inherent toxicity, environmental persistence, and potential carcinogenicity. Microbial degradation has garnered significant interest as a cost-effective and eco-friendly strategy for dye wastewater treatment in recent years. The study systematically evaluated the decolorization performance, degradation pathways, and detoxification effects of three bacterial strains, including Rhodopseudomonas palustris gh32, Bacillus cereus HL7, and Bacillus safensis X64, on the dye indigo carmine (IC) and three azo dyes: reactive black 5 (RB5), direct black G (DBG), and direct blue 15 (DB15). The degradation mechanisms were elucidated through UV-Vis spectroscopy, UPLC-Orbitrap-HRMS analysis, and enzyme activity assays. Molecular docking simulations were employed to investigate the interactions between key redox enzymes (such as laccase, tyrosinase, and azoreductase) and the dye molecules. The results demonstrated that the strain-specific enzymatic systems effectively disrupted the dye structures. Significant detoxification effects were further confirmed through a series of bio toxicity assays involving Escherichia coli, Bacillus subtilis, plant seeds, and erythrocytes. The addition of Fe³⁺, sodium citrate, or yeast extract significantly enhanced both the decolorization efficiency and enzyme activity. This study provides an in-depth understanding of the bacterial dye degradation process at the mechanistic level, highlighting the potential of customized bacterial systems for eco-friendly dye wastewater treatment. It offers theoretical support for elucidating the mechanisms of bacterial dye degradation and advancing bioremediation technologies. Full article

Natural fibre hybrid composites have gained attention as cleaner alternatives to synthetic glass fibre systems due to their renewable feedstocks and inherent density advantage. However, moisture ingress degrades fibre–matrix integrity and mechanical performance, making durability a critical design constraint. This study systematically investigates the water absorption kinetics and post-immersion mechanical property retention in ramie–flax/epoxy hybrid composites across four fibre loadings (10–40 wt.%), with the ramie-to-flax weight ratio fixed at 1:1 in all formulations. Tensile, flexural, and impact properties were evaluated under dry and saturated conditions; Fickian diffusion kinetics were analysed to quantify moisture transport parameters; and fracture surfaces were examined by SEM. A density-based material efficiency analysis quantified the lightweighting benefit relative to equivalent synthetic glass/epoxy composites. Water absorption increased monotonically with fibre content; all formulations reached equilibrium after approximately 120 h. The 30 wt.% composite achieved dry-state tensile, flexural, and impact strengths of ca.49 MPa, ca.58 MPa, and 2.82 kJ/m² respectively, retaining ca.78%, ca.69%, and ca.82% after full saturation, superior to all other loadings. These results establish 30 wt.% as the optimal fibre loading for moisture-exposed semi-structural applications, supporting the adoption of ramie–flax composites within a cleaner manufacturing framework. Full article

Enhancing urban ecological resilience (UER) is crucial for mitigating soil erosion, improving land use efficiency, and preventing ecological degradation. The digital–real economy integration (DRI) plays a pivotal role in strengthening UER, offering a vital pathway for modernizing ecological governance systems and capabilities in the Yellow River Basin (YRB). Based on ecological resilience theory, this study establishes a three-dimensional evaluation framework centered on “resistance–recovery–adaptation”. Using panel data from 78 cities in the YRB from 2011 to 2023, we empirically examine the impact of DRI on UER. The results indicate that DRI significantly improves UER in the YRB, with notably strong positive effects on recovery and adaptation capacities, although there is no significant effect on resistance capacity. Mechanism analysis reveals that DRI promotes UER primarily through three channels: upgrading the industrial structure, strengthening government governance, and spurring green technological innovation. Heterogeneity analysis further shows that the positive impact of DRI on UER is more pronounced in downstream cities, urban agglomerations, non-resource-based cities, key environmental protection cities, green data center pilot cities, and informatization–industrialization integration pilot cities. Spatial analysis confirms DRI generating positive spatial spillover effects on the UER of neighboring cities. This study provides a theoretical basis for understanding the ecological governance potential of DRI and offers policy insights to support coordinated digital and green transformation in the YRB. Full article