Search Results (119)

The development of anode-free sodium metal batteries (AFSMBs) offers a promising pathway to achieve ultrahigh energy density and cost efficiency inherent to conventional sodium ion/metal batteries. However, irreversible Na plating/stripping and dendritic growth remain critical barriers. Herein, we demonstrate that separator engineering is a pivotal strategy for stabilizing AFSMBs. Through systematic evaluation of four separators—2500 separator (PP), 2325 separator (PP/PE/PP), glass fiber (GF), and an Al₂O₃-coated PE membrane, we reveal that the Al₂O₃-coated separator uniquely enables exceptional interfacial kinetics and morphological control. Na||Na symmetric cells with Al₂O₃ coated separator exhibit ultralow polarization (4.5 mV) and the highest exchange current density (1.77 × 10⁻² mA cm⁻²), while the anode-free AlC-NFPP full cells retain 91.6% capacity after 150 cycles at 2C. Specifically, the Al₂O₃ coating homogenizes Na⁺ flux, promotes dense and planar Na deposition, and facilitates near-complete stripping with minimal “dead Na”. This work establishes ceramic-functionalized separators as essential enablers of practical high-energy AFSMBs. Full article

Watershed ecosystem resilience (RES) plays a vital role in supporting ecosystem sustainability. However, comprehensive assessments and investigations into the complex mechanisms driving RES remain limited, particularly in ecologically sensitive basins. To address this gap, this study proposes a multidimensional RES evaluation framework tailored to watershed-specific natural characteristics. The framework integrates five core dimensions: ecosystem resistance, ecosystem recovery capacity, ecosystem adaptability, ecosystem services, and ecosystem vitality. RES patterns under 2030 different future scenarios were simulated using the PLUS model combined with CMIP6 climate projections. Spatial and temporal dynamics of RES from 2010 to 2020 were quantified using Geodetector and Partial Least Squares Path Modeling, offering insights into the interactions among natural and anthropogenic drivers. The results reveal that RES in the Upper Yangtze River Basin exhibits a spatial gradient of “high in the east and west, low in the middle” with an overall 2.80% decline during the study period. Vegetation coverage and temperature emerged as dominant natural drivers, while land use change exerted significant indirect effects by altering ecological processes. This study emphasizes the importance of integrated land-climate strategies and offers valuable guidance for enhancing RES and supporting sustainable watershed management in the context of global environmental change. Full article

To address the growing demand for environmentally friendly flexible sensors, here, a composite hydrogel of nanocellulose (NC) and polyvinyl alcohol (PVA) was designed and fabricated using Camellia oleifera shells as a sustainable alternative to petroleum-based raw materials. Firstly, NC was extracted from Camellia oleifera shells and modified with 2-chloropropyl chloride to obtain a nanocellulose-based initiator (Init-NC) for atomic transfer radical polymerization (ATRP). Subsequently, sulfonyl betaine methacrylate (SBMA) was polymerized by Init-NC initiating to yield zwitterion-functionalized nanocellulose (NC-PSBMA). Finally, the NC-PSBMA/PVA hydrogel was fabricated by blending NC-PSBMA with PVA. A Fourier transform infrared spectrometer (FT-IR), proton nuclear magnetic resonance spectrometer (¹H-NMR), X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), universal mechanical testing machine, and digital source-meter were used to characterize the chemical structure, surface microstructure, and sensing performance. The results indicated that: (1) FT-IR and ¹H NMR confirmed the successful synthesis of NC-PSBMA; (2) SEM, TEM, and alternating current (AC) impedance spectroscopy verified that the NC-PSBMA/PVA hydrogel exhibits a uniform porous structure (pore diameter was 1.1737 μm), resulting in significantly better porosity (15.75%) and ionic conductivity (2.652 S·m⁻¹) compared to the pure PVA hydrogel; and (3) mechanical testing combined with source meter testing showed that the tensile strength of the composite hydrogel increased by 6.4 times compared to the pure PVA hydrogel; meanwhile, it showed a high sensitivity (GF = 1.40, strain range 0–5%; GF = 1.67, strain range 5–20%) and rapid response time (<0.05 s). This study presents a novel approach to developing bio-based, flexible sensing materials. Full article

For large oil-immersed transformers, their metal-enclosed structure poses significant challenges for direct visual inspection of internal defects. To ensure the effective detection of internal insulation defects, this study employs a self-developed micro-robot for internal visual inspection. Given the substantial morphological and dimensional variations of target defects (e.g., carbon traces produced by surface discharge inside the transformer), the intelligent and efficient extraction of carbon trace features from complex backgrounds becomes critical for robotic inspection. To address these challenges, we propose the DCMC-UNet, a semantic segmentation model for carbon traces containing adaptive illumination enhancement and dynamic feature fusion. For blurred carbon trace images caused by unstable light reflection and illumination in transformer oil, an improved CLAHE algorithm is developed, incorporating learnable parameters to balance luminance and contrast while enhancing edge features of carbon traces. To handle the morphological diversity and edge complexity of carbon traces, a dynamic deformable encoder (DDE) was integrated into the encoder, leveraging deformable convolutional kernels to improve carbon trace feature extraction. An edge-aware decoder (EAD) was integrated into the decoder, which extracts edge details from predicted segmentation maps and fuses them with encoded features to enrich edge features. To mitigate the semantic gap between the encoder and the decoder, we replace the standard skip connection with a cross-level attention connection fusion layer (CLFC), enhancing the multi-scale fusion of morphological and edge features. Furthermore, a multi-scale atrous feature aggregation module (MAFA) is designed in the neck to enhance the integration of deep semantic and shallow visual features, improving multi-dimensional feature fusion. Experimental results demonstrate that DCMC-UNet outperforms U-Net, U-Net++, and other benchmarks in carbon trace segmentation. For the transformer carbon trace dataset, it achieves better segmentation than the baseline U-Net, with an improved mIoU of 14.04%, Dice of 10.87%, pixel accuracy (P) of 10.97%, and overall accuracy (Acc) of 5.77%. The proposed model provides reliable technical support for surface discharge intensity assessment and insulation condition evaluation in oil-immersed transformers. Full article

Mangrove forests are vital ecosystems along tropical coasts, playing crucial roles in water purification and biodiversity conservation. Diatoms, as sensitive ecological indicators, were employed in this study to evaluate the health of the mangrove forests along the Sanya River. The research involved analyzing the community structure of planktonic diatoms and water physicochemical properties during spring and winter, as well as carrying out a comprehensive assessment of the ecological health of the region in terms of four seasonal–spatial–environmental–biological indices. A total of 22 genera of planktonic diatoms were identified. In winter, Melosira sp. (34.94%), Skeletonema sp. (25.50%), and Chaetoceros sp. (15%) were dominant, with relative abundances of 34.94%, 25.50%, and 15.00%. In spring, Melosira sp. became the absolutely dominant species, averaging 70.16%. Diatom cell abundance shows both significant seasonal and spatial variation. In winter, it ranged from 0.53 to 17.4 × 10⁹ cells-L⁻¹, peaking in the midstream region, whereas in spring, it ranged from 2.48 to 21.0 × 10⁹ cells-L⁻¹, peaking at the mouth of the estuary. A higher abundance of diatoms in spring strengthens primary productivity and supports the subsequent functioning of the food chain. Diatom indices (Shannon–Wiener index H’, Pielou evenness index J, and Margalef richness index D) indicated an intermediate ecological health level for the Sanya River mangrove forests. Diversity was higher in winter than in spring, with the lowest values recorded in the midstream region. Redundancy analysis (RDA) indicated that T, pH, and PO₄³⁻ were the primary environmental drivers of diatom community succession. In spring, pH was positively correlated with T and PO₄³⁻, respectively. They drove the succession of diatom communities from diverse assemblages in winter to a single dominant species. Full article

To investigate the suppression method for vortex-induced vibrations (VIV) of two-degree-of-freedom (2-DOF) rotating cylinders with dual splitter plates, numerical simulations are conducted at a Reynolds number of 200, a mass ratio of 2.6, and rotation ratio of 2. The effects of the gap distance and the width of splitter plates on the vibration response, hydrodynamic coefficients, and flow wakes of rotating cylinders are examined. The numerical results show the existence of distinct suppression mechanisms between low gap distances (G/D = 0.25–0.5) and high gap distances (G/D = 0.75–2.0). Furthermore, the width (W/D) is considered as a critical factor in suppression effectiveness. The distributions of wake patterns under different gap distance and width are analyzed, and six wake patterns are observed. Finally, lift and drag coefficients are examined, revealing their distinct sensitivities to G/D and W/D. The optimal gap distance and width parameters of dual splitter plates for rotating cylinders suppression are determined. Marine drilling is persistently subjected to VIV, which critically compromise structural stability. The findings of this study deliver engineering value for marine riser VIV suppression. Full article

This paper focuses on the application of digital twins in the field of electric motor fault diagnosis. Firstly, it explains the origin, concept, key technology and application areas of digital twins, compares and analyzes the advantages and disadvantages of digital twin technology and traditional methods in the application of electric motor fault diagnosis, discusses in depth the key technology of digital twins in electric motor fault diagnosis, including data acquisition and processing, digital modeling, data analysis and mining, visualization technology, etc., and enumerates digital twin application examples in the fields of induction motors, permanent magnet synchronous motors, wind turbines and other motor fields. A concept of multi-phase synchronous generator fault diagnosis based on digital twins is given, and challenges and future development directions are discussed. Full article

This paper presents a comprehensive time-series analysis framework leveraging the Temporal Fusion Transformer (TFT) architecture to address the challenge of multi-horizon forecasting in complex ecological systems, specifically focusing on global fishery resources. Using global fishery data spanning 70 years (1950–2020), enhanced with key climate indicators, we develop a methodology for predicting time-dependent patterns across three-year, five-year, and extended seven-year horizons. Our approach integrates static metadata with temporal features, including historical catch and climate data, through a specialized architecture incorporating variable selection networks, multi-head attention mechanisms, and bidirectional encoding layers. A comparative analysis demonstrates the TFT model’s robust performance against traditional methods (ARIMA), standard deep learning models (MLP, LSTM), and contemporary architectures (TCN, XGBoost). While competitive across different horizons, TFT excels in the 7-year forecast, achieving a mean absolute percentage error (MAPE) of 13.7%, outperforming the next best model (LSTM, 15.1%). Through a sensitivity analysis, we identify the optimal temporal granularity and historical context length for maximizing prediction accuracy. The variable selection component reveals differential weighting, with recent market observations (past 1-year catch: 31%) and climate signals (ONI index: 15%, SST anomaly: 10%) playing significant roles. A species-specific analysis uncovers variations in predictability patterns. Ablation experiments quantify the contributions of the architectural components. The proposed methodology offers practical applications for resource management and theoretical insights into modeling temporal dependencies in complex ecological data. Full article

Methanol/diesel hybrid−powered vessels represent a significant advancement in green and low−carbon innovation in the maritime transportation sector and have been widely adopted across various shipping markets. However, the dual−fuel power system modifies the fire load within the engine room compared to traditional vessels, thereby significantly influencing the fire safety of methanol/diesel−powered ships. In this study, anhydrous methanol and light−duty diesel (with 0 °C pour point) were used as fuels to investigate the mixed combustion characteristics of these immiscible fuels in circular pools with diameters of 6, 10, 14, and 20 cm at various mixing ratios. By analyzing the fuel mass loss rate, flame morphology, and heat transfer characteristics, it was determined that methanol and diesel exhibited distinct stratification during combustion, with the process comprising three phases: pure methanol combustion phase, transitional combustion phase, and pure diesel combustion phase. Slopover occurred during the transitional combustion phase, and its intensity decreased as the pool diameter or methanol fuel quantity increased. Based on this conclusion, a quantitative relationship was established between slopover intensity, pool diameter, and the methanol/diesel volume ratio. Additionally, during the transitional combustion phase, the average flame height exhibited an exponential coupling relationship with the pool diameter and the methanol/diesel volume ratio. Therefore, a modification was made to the classical flame height model to account for these effects. Moreover, a prediction model for the burning rate of methanol/diesel pool fires was established based on transient temperature variations within the fuel layer. This model incorporated a correction factor related to pool diameter and fuel mixture ratio. Additionally, the causes of slopover were analyzed from the perspectives of heat transfer and fire dynamics, further refining the physical interpretation of the correction factor. Full article

Six-dimensional (6D) pose estimation remains a significant challenge in computer vision, particularly for objects in complex environments. To overcome the limitations of existing methods in occluded and low-texture scenarios, a lightweight, multiscale feature fusion network was proposed. In the network, a self-attention mechanism is integrated with a multiscale point cloud feature extraction module, enhancing the representation of local features and mitigating information loss caused by occlusion. A lightweight image feature extraction module was also introduced to reduce the computational complexity while maintaining high precision in pose estimation. Ablation experiments on the LineMOD dataset validated the effectiveness of the two modules. The proposed network achieved 98.5% accuracy, contained 19.49 million parameters, and exhibited a processing speed of 31.8 frames per second (FPS). Comparative experiments on the LineMOD, Yale-CMU-Berkeley (YCB)-Video, and Occlusion LineMOD datasets demonstrated the superior performance of the proposed method. Specifically, the average nearest point distance (ADD-S) metric was improved by 4.2 percentage points over DenseFusion for LineMOD and by 0.6 percentage points for YCB-Video, with it reaching 63.4% on the Occlusion LineMOD dataset. In addition, inference speed comparisons showed that the proposed method outperforms most RGB-D-based methods. The results confirmed that the proposed method is both robust and efficient in handling occlusions and low-texture objects while also featuring a lightweight network design. Full article

In recent years, the disposal of agricultural lignocellulosic residues has been accompanied by problems such as resource waste and environmental pollution. Therefore, the development of valorization technologies has emerged as a strategic priority in sustainable materials science. This study pioneered the use of corncob cellulose as the substrate (a representative agricultural lignocellulosic residue) and transformed it into ionized cellulose by grafting methacryloxyethyl trimethyl ammonium chloride (DMC) via atom transfer radical polymerization (ATRP) and UV-initiated polymerization. Characterizations demonstrated exceptional properties: robust mechanical strength (1.28 MPa tensile strength with 573% elongation); outstanding thermal stability (stable to 278 °C); cryogenic tolerance (retaining flexibility at −25 °C); and universal adhesion capability (4.23 MPa to glass substrates, with adequate interfacial bonding across diverse surfaces). Meanwhile, the ionogel exhibited exceptional sensing sensitivity (gauge factor, GF = 1.23–2.08), demonstrating versatile application potential in wearable electronics. It achieved the precise detection of subtle strains (1–5% strain range) and the high-fidelity acquisition of electrocardiogram (ECG) signals. This study broadens the design paradigm of agricultural lignocellulosic residue-based functional materials. It also provides a novel technical pathway to develop eco-friendly intelligent sensors. Full article

Wound healing is a complex biological process involving inflammation, proliferation, and remodeling phases. Effective healing is essential for maintaining skin integrity, driving the need for advanced materials like hydrogels, known for their high water retention and tunable mechanical properties. In this study, we synthesized a biocompatible composite hydrogel composed of γ-polyglutamic acid (γ-PGA) and ε-polylysine (ε-PL) through a Schiff base reaction, forming a stable crosslinked network. Its physicochemical properties, including rheological behavior and swelling capacity, were systematically evaluated. Biocompatibility was assessed via in vitro hemolysis and cytotoxicity assays, and in vivo testing was performed using a full-thickness skin defect model in Sprague Dawley (SD) rats to evaluate wound-healing efficacy. The PGA-PL hydrogel demonstrated excellent physicochemical properties, with a maximum swelling ratio of 65.6%, and biocompatibility as evidenced by low hemolysis rates (<5%) and high cell viability (>80%). It promoted wound healing by inhibiting the inflammatory response, reducing levels of the inflammatory cytokine IL-6, enhancing angiogenesis, and accelerating collagen deposition. The hydrogel showed complete biodegradation within 21 days in vivo without inducing a significant inflammatory response and significantly accelerated wound healing, achieving an 86% healing rate within 7 days compared to 67% in the control group. The PGA-PL composite hydrogel exhibits excellent mechanical strength and biocompatibility, and its effective wound-healing capabilities lay the groundwork for future development and optimization in various tissue engineering applications. Full article

The preference and demand for low-fat diets have increased due to their health benefits. This study aimed to develop a thermally stable water-in-oil (W/O) emulsion. The addition of 3.75 wt% of polysaccharide colloids, including curdlan gum (CG), kappa-carrageenan (kC), gellan gum (GEG), guar gum (GUG), high-ester pectin (HEP), and carboxymethyl cellulose (CMC), to the aqueous phase resulted in the formation of a gel structure within it. Furthermore, these polysaccharide colloids reduced the excessive mobility of water droplets under high-temperature conditions. The oil phase consisted of anhydrous butter and a lipophilic nonionic surfactant. The emulsion was subjected to a heat treatment at 95 °C for 30 min, and the emulsions before and after the heat treatment were characterized. The results showed that among the above colloidal emulsions, the 3.75 wt% CG emulsion did not show significant changes in viscosity, stability index, mean particle size, friction coefficient, and encapsulation efficiency before and after heat treatment. The 3.75 wt% CG colloid showed the most significant enhancement in the thermal stability of W/O emulsions. This study proposes a novel fat-replacement strategy for products requiring high-temperature processing, such as processed cheese. Full article

Exposure to low temperatures during honeybee development has been shown to impede brain development and affect cognitive function in adult bees. On the other hand, neuronal damage due to oxidative stress has been reported in many cases. Hence, biochemical parameters related to oxidative stress in honeybee pupae brain were determined. The levels of GSH in the pupal brain decreased after 24 h and 48 h of exposure to low temperatures; there were also reduced activities of SOD and CAT enzymes following 48 h of low-temperature treatment compared to the control group. Furthermore, analysis of transcriptome data post-24 h and -48 h low-temperature stress revealed the suppression of the glutathione metabolism and peroxisome pathways in pupal brains. Additionally, expression pattern clustering analysis and KEGG enrichment showed that 10 differentially expressed genes with down-regulated expression trends post-low-temperature treatment were significantly enriched in the peroxisome pathway, including PEX10, highlighting their connection to peroxisome function. RT-qPCR validation was conducted on 11 core enriched genes in pathways identified via GSEA, and all these genes exhibited a downregulated expression pattern, confirming the inhibition of glutathione metabolism and peroxisome function under low-temperature stress. The present study showed that exposing honeybee pupae to low temperatures suppressed both the glutathione metabolism and peroxisome pathways, resulting in increased oxidative stress. This research enhances our understanding of how the pupal brain reacts to cold stress and illuminates the neural damage associated with low temperatures during honeybee capped brood development. Full article

To address the issues of energy wastage and uncertainty impacts associated with high levels of renewable energy integration, a multi-objective distributed robust low-carbon optimization scheduling strategy for hydrogen-integrated Integrated Energy Systems (IES) is proposed. This strategy incorporates a green hydrogen trading mechanism and low-carbon demand response. Firstly, to leverage the low-carbon and clean characteristics of hydrogen energy, an efficient hydrogen utilization model was constructed, consisting of electricity-based hydrogen production, waste heat recovery, multi-stage hydrogen use, hydrogen blending in gas, and hydrogen storage. This significantly enhanced the system’s renewable energy consumption and carbon reduction. Secondly, to improve the consumption of green hydrogen, a novel reward–punishment green hydrogen certificate trading mechanism was proposed. The impact of green hydrogen trading prices on system operation was discussed, promoting the synergistic operation of green hydrogen and green electricity. Based on the traditional demand-response model, a novel low-carbon demand-response strategy is proposed, with carbon emission factors serving as guiding signals. Finally, considering the uncertainty of renewable energy, an innovative optimal trade-off multi-objective distributed robust model was proposed, which simultaneously considered low-carbon, economic, and robustness aspects. The model was solved using an improved adaptive particle swarm optimization algorithm. Case study results show that, after introducing the reward–punishment green hydrogen trading mechanism and low-carbon demand response, the system’s total cost was reduced by approximately 5.16% and 4.37%, and carbon emissions were reduced by approximately 7.84% and 6.72%, respectively. Moreover, the proposed multi-objective distributed robust model not only considers the system’s economy, low-carbon, and robustness but also offers higher solving efficiency and optimization performance compared to multi-objective optimization methods. Full article