Search Results (4,390)

Marine oil spill detection using Synthetic Aperture Radar (SAR) is crucial but challenged by dynamic marine conditions, diverse spill scales, and limitations in existing algorithms regarding model size and real-time performance. To address these challenges, we propose LSFE-YOLO, a YOLOv8s-optimized (You Only Look Once version 8) lightweight model with an original, domain-tailored synergistic integration of FasterNet, GN-LSC Head (GroupNorm Lightweight Shared Convolution Head), and C2f_MBE (C2f Mobile Bottleneck Enhanced). FasterNet serves as the backbone (25% neck width reduction), leveraging partial convolution (PConv) to minimize memory access and redundant computations—overcoming traditional lightweight backbones’ high memory overhead—laying the foundation for real-time deployment while preserving feature extraction. The proposed GN-LSC Head replaces YOLOv8’s decoupled head: its shared convolutions reduce parameter redundancy by approximately 40%, and GroupNorm (Group Normalization) ensures stable accuracy under edge computing’s small-batch constraints, outperforming BatchNorm (Batch Normalization) in resource-limited scenarios. The C2f_MBE module integrates EffectiveSE (Effective Squeeze and Excitation)-optimized MBConv (Mobile Inverted Bottleneck Convolution) into C2f: MBConv’s inverted-residual design enhances multi-scale feature capture, while lightweight EffectiveSE strengthens discriminative oil spill features without extra computation, addressing the original C2f’s scale variability insufficiency. Additionally, an SE (Squeeze and Excitation) attention mechanism embedded upstream of SPPF (Spatial Pyramid Pooling Fast) suppresses background interference (e.g., waves, biological oil films), synergizing with FasterNet and C2f_MBE to form a cascaded feature optimization pipeline that refines representations throughout the model. Experimental results show that LSFE-YOLO improves mAP (mean Average Precision) by 1.3% and F1 score by 1.7% over YOLOv8s, while achieving substantial reductions in model size (81.9%), parameter count (82.9%), and computational cost (84.2%), alongside a 20 FPS (Frames Per Second) increase in detection speed. LSFE-YOLO offers an efficient and effective solution for real-time marine oil spill detection. 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

Water exchange capacity is critical for maintaining marine environmental quality and supporting the sustainable development of aquaculture. This study applies a high-resolution three-dimensional FVCOM hydrodynamic model coupled with the DYE-RELEASE module. The model was validated against tidal, current, and thermohaline observations. Water residence time (Tre) was used as the primary evaluation metric, supplemented by analyses of residual circulation, material diffusion, and regional variability, to systematically quantify the water exchange mechanisms and seasonal variations in the coastal waters of Changhai County under the combined influence of tides, wind forcing, and thermohaline conditions. Results show that overall residual currents in Changhai County are weak (average velocity: 0.032 m s⁻¹). However, local circulations and stagnation zones frequently develop near islands and channels, strongly influencing material diffusion. In summer, water exchange is primarily controlled by thermohaline effects, which strengthen density stratification, suppress vertical mixing, and modify circulation patterns, thereby reducing the efficiency of tide-driven exchange. Water exchange is weakest near Guanglu Island (46.6–48.6 d) and strongest near Haiyang Island (13–14 d). In winter, wind forcing dominates, enhancing vertical mixing and accelerating water renewal. Residence time in the Changshan Archipelago–Guanglu Island region decreases by 30–50% compared with summer. Overall, winter water renewal is 15–25% more efficient than in summer. This study demonstrates that water exchange in Changhai County is regulated by the combined effects of tides, wind forcing, and thermohaline dynamics. The identified spatial heterogeneity and seasonal characteristics provide a scientific basis for optimizing aquaculture planning and mitigating marine environmental risks. Full article

Grounding insulation is a key component of electric vehicle drive motors, and cracks may appear during the manufacturing process and assembly. In this paper, the novel method of coupling phase field, mechanic field and electric field is proposed to investigate the coupled propagation characteristics of electromechanical damage in stator end-wingding insulation. The crack propagation model is derived by using the phase field method, where the maximum historical variable is introduced to ensure the forward propagation of the crack damage in insulation. According to the crack evolution states, the electric potential distributions in the insulation domain are determined and the electrical damage variable is defined to quantitatively describe the dynamical evolution mechanism of electric damage with the variation in mechanical damage. The results in this research will contribute to understanding the electrical performance degradation and electromechanical failure of the end-winding insulation in electric vehicle drive motors, which also provides the basis for the mechanism of insulation damage, insulation fault diagnosis and residual life prediction of electrical machines. Full article

Chemokines play a central role in orchestrating neutrophil recruitment from the bloodstream and determining their effector functions at sites of infection. Chemokine activity is determined by three key properties: reversible monomer–dimer equilibrium, binding to glycosaminoglycans (GAGs), and signaling through the GPCR class of receptors CXCR1 and CXCR2. In this study, we investigated the structural basis of CXCL8 monomer and dimer binding to GAG chondroitin sulfate (CS) using nuclear magnetic resonance (NMR) spectroscopy, docking, and molecular dynamics (MD) measurements. Our studies reveal that both the monomer and dimer use essentially the same set of basic residues for binding, that the interface is extensive, that the dimer is the high-affinity CS ligand, and that the CS-binding residues form a contiguous surface within a monomer. Several of these residues also participate in receptor interactions, suggesting that CS-bound CXCL8 is likely impaired in its ability to bind receptors. Notably, we observe that the same basic residues are involved in binding CS and heparin/heparan sulfate, even though these GAGs differ in backbone structures and sulfation patterns. We conclude that the strategic distribution and topology of basic residues on the CXCL8 scaffold enable engagement with diverse GAG structures, which likely allows fine-tuning receptor signaling to regulate neutrophil trafficking and effector functions. Full article

Per- and polyfluoroalkyl substances (PFASs), as a class of “permanent chemicals” with high environmental persistence and bioaccumulation, have attracted much attention. In this study, we focused on the molecular mechanism of the interaction between perfluoroalkyl acids (PFAAs) and peroxisome proliferator-activated receptor δ (PPARδ). Using molecular docking, binding free energy calculation, and structural analysis, we systematically investigated the binding modes, key amino acid residues, and binding energies of 20 structurally diverse PFAAs with PPARδ. The results showed that the binding energies of PFAAs with PPARδ were significantly affected by the molecular weight, the number of hydrogen bond donors, and the melting point of PFAAs. PFAAs with smaller molecular weights and fewer hydrogen bond donors showed stronger binding affinity. The binding sites were concentrated in high-frequency amino acid residues such as TRP-256, ASN-269, and GLY-270, and the interaction forces were dominated by hydrogen and halogen bonds. PFAAs with branched structure of larger molecular weight (e.g., 3m-PFOA, binding energy of −2.92 kcal·mol⁻¹; 3,3m₂-PFOA, binding energy of −2.45 kcal·mol⁻¹) had weaker binding energies than their straight-chain counterparts due to spatial site-blocking effect. In addition, validation group experiments further confirmed the regulation law of binding strength by physicochemical properties. In order to verify the binding stability of the key complexes predicted by molecular docking, and to investigate the dynamic behavior under the conditions of solvation and protein flexibility, molecular dynamics simulations were conducted on PFBA, PFOA, 3,3m₂-PFOA, and PFHxA. The results confirmed the dynamic stability of the binding of the high-affinity ligands selected through docking to PPARδ. Moreover, the influence of molecular weight and branched structure on the binding strength was quantitatively verified from the perspectives of energy and RMSD trajectories. The present study revealed the molecular mechanism of PFAAs interfering with metabolic homeostasis through the PPARδ pathway, providing a theoretical basis for assessing its ecological and health risks. Full article

Neonicotinoids are synthetic nicotine-like compounds extensively used globally as insecticides for agricultural and urban purposes. Neonicotinoid-contaminated produce is a major public health concern worldwide. Limited epidemiological studies have shown an association of neonicotinoid exposure with abnormal semen analysis. This study aimed to elucidate the potential disruption of the androgen receptor (AR) by eight common neonicotinoids, including imidacloprid (IMI), acetamiprid, clothianidin, thiamethoxam, dinotefuran, thiacloprid (THI), nitenpyram, and nithiazine using docking and molecular dynamics (MD) simulation. The results showed good binding strength of all compounds (except THI) with AR, as indicated by high binding energy, high binding affinity, and number of bonding interactions. The results of MD simulation supported the conformational stability and structural dynamic behavior of the AR-IMI (receptor-neonicotinoid) complex upon binding. This was indicated by root mean square deviation showing stability of the complex; the root mean square fluctuation showing minimized residual fluctuations upon binding; the radius of gyration showing greater compactness of the protein structure; the solvent-accessible surface area showing no changes upon binding; and the Gibbs funnel energy of the landscape showing a stable conformation state with minimum energy and slight change in size and position of the sampled energy basin of the AR, with a stable equilibrium. Taken together, the structural dynamics results showed that neonicotinoids are bound stably in the same ligand-binding domain of the AR as the native ligand testosterone. This may perturb the natural binding of testosterone with the AR and potentially disrupt downstream signaling and biological pathways, leading to male reproductive dysfunction. Full article

Non-repetitive scanning LiDARs provide high coverage yet exhibit irregular sampling patterns, which destabilize local features and correspondences. To address this, we propose a novel spatiotemporal unified energy framework that integrates fractional calculus into rigid pose estimation. Spatially, we introduce a Riesz fractional regularization term to impose non-local smoothness constraints on the residual field, mitigating structural inconsistencies. Temporally, we design a Grünwald–Letnikov fractional dynamics solver that leverages long-memory effects of historical gradients to reduce the risk of being trapped in local minima. Comparative experiments on the Stanford 3D, MVTec ITODD, and HomebrewedDB (HB) datasets demonstrate that our method significantly outperforms state-of-the-art geometric and learning-based approaches. Specifically, it maintains a success rate exceeding 90% even under severe sampling perturbations where traditional methods fail. Ablation studies further validate that the introduction of non-local spatial constraints and historical gradient memory significantly reshapes the energy landscape, ensuring robust convergence. This work provides a rigorous theoretical foundation for applying fractional operators to point cloud processing. Full article

Fermentation is a complex biochemical process that transforms brewer’s wort into beer. Beer fermentation is driven by yeast and is influenced by process parameters such as the content of fermentable sugars in wort, temperature, and pH. Traditional methods of modelling this process rely heavily on empirically tuned kinetic models. However, these models tend to be recipe-specific and often require retuning when processes change. This paper proposes a data-driven approach using a Long Short-Term Memory (LSTM) network, a type of recurrent neural network, to model beer fermentation dynamics. By training the LSTM model on real-world fermentation data (1305 fermentations across ales, IPAs, lagers, and mixed-culture beers), including variables such as apparent extract (derived from specific gravity), temperature, and pH, we demonstrate that this technique can accurately predict key fermentation trajectories and support process monitoring and optimisation. When evaluated on representative medoid fermentations as one-step-ahead roll-outs over 0–300 h, the model produces accurate predictions with low errors and minimal residuals. These results show that the LSTM-based model provides accurate and robust predictions across beer styles and operating conditions, offering a practical alternative to traditional mechanistic kinetic models. This work highlights the potential of LSTM networks to enhance our understanding, monitoring, and control of fermentation processes, providing a scalable and efficient tool for both research and industrial applications. The findings suggest that LSTM models can be effectively adapted to model other fermentation processes in beverage production, opening new possibilities for advancing food science and engineering. Full article

Cholinergic dysfunction is a hallmark of Alzheimer’s disease (AD), driven by elevated acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) activity that depletes acetylcholine and contributes to amyloid pathology. Current AD treatments face major challenges, including poor brain penetration, short effect duration and safety concerns, highlighting the need for compounds suitable for preventive or earlier-stage intervention. This study investigated marine phytohormones as modulators of cholinergic imbalance, using an integrative strategy encompassing enzymatic assays, QSAR and DFT calculations, molecular docking, molecular dynamics (MD) simulations, and ADMET profiling. Among them, isopentenyl adenine (IPA) and abscisic acid (ABA) showed inhibitory activity against cholinesterases. IPA inhibited both AChE and BChE through distinct mechanisms with noncompetitive inhibition of AChE and competitive inhibition of BChE, while ABA showed selective noncompetitive inhibition of AChE. DFT-based analysis revealed distinct electronic properties supporting differential reactivity. Moreover, IPA interacted with both catalytic and peripheral residues in AChE, and aligned with BChE’s active site, while ABA was bound more peripherally. MD simulations confirmed complex-specific conformational stability based on RMSD, RMSF, Rg, and hydrogen bonding analysis. Both compounds showed low off-target potential against serine proteases and favorable predicted ADMET profiles. These results support the potential of marine phytohormones as preventive modulators of cholinergic dysfunction in AD. Full article

In the medical industry, strong disinfectants are used to limit bacterial proliferation on the surface of polymer-based materials; however, they may leave hazardous residues. To prevent potential harm to human health, safer disinfection substitutes are continuously searched. This study evaluates the effect of a natural biocidal modifier, geraniol (GR), on the properties of flax-reinforced biocomposites. Biocomposites containing 80 wt% polylactide (PLA) and 20 wt% flax fibres were prepared, and fibres were modified with 1%, 5%, 10%, or 20% GR. The materials were examined using tensile tests, dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC), thermogravimetry (TG), contact angle measurements, scanning electron microscopy (SEM), and antibacterial activity tests. The incorporation of flax fibres increased the storage modulus from 2730 MPa (PLA) to 3447 MPa, while GR-modified fibres further enhanced stiffness up to 3769 MPa for the 20% GR sample. Strong antibacterial activity against Escherichia coli and Staphylococcus aureus was achieved in biocomposites containing ≥10% GR, with R = 5 and R ≥ 6, respectively. Surface hydrophobicity also improved progressively, and a water contact angle of 92° was obtained at 20% GR. These results demonstrate that geraniol-modified flax fibres effectively impart antibacterial activity and hydrophobicity to PLA biocomposites, indicating their potential for use in sustainable packaging applications and materials for the medical sector. Full article

Surface charge accumulation and trap distribution are the core factors affecting the surface flashover characteristics of insulating materials. Considering the low-temperature gradient environment of superconducting energy pipeline terminations, this paper systematically studies the surface charge dynamic characteristics and trap distribution law of epoxy glass fiber (GFRP) by using the isothermal surface potential decay (ISPD) method combined with finite element simulation. A temperature-controlled ISPD test platform of −30~20 °C (193~293 K) was built to measure the surface potential decay curves at different temperatures and calculate the trap energy level and density; a charge migration model considering temperature gradient was established to analyze the influence of trapped charges on surface potential and electric field distribution. The results show that low temperature significantly reduces the surface potential decay rate (the residual potential after 5000 s is 92.91% of the initial value at 193 K, and only 3.51% at 293 K); the traps of GFRP at 193 K are dominated by deep traps (central energy level 0.68 eV, density 1.63 × 10²⁰ m⁻³·eV), while there is a bimodal distribution of shallow traps (0.92 eV) and deep traps (0.98 eV) at 293 K; under temperature gradient, the accumulation of deep trap charges in the low-temperature region leads to a surface electric field distortion rate of 12.60, which is the key microscopic mechanism for the decrease of flashover voltage. Full article

Extensive coal mining activities can exert substantial negative impacts on surface ecosystems. Vegetation indices are widely recognized as effective indicators of land ecological conditions and provide valuable insights into long-term ecological changes in mining areas. In this study, the Zhaogu mining area of the Jiaozuo Coalfield was selected as the study site. Using the Google Earth Engine (GEE) platform, the Kernel Normalized Difference Vegetation Index (kNDVI) was constructed to generate a vegetation dataset covering the period from 2010 to 2024. The temporal dynamics and future trends of vegetation coverage were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, the Hurst index, and residual analysis. Furthermore, the relative contributions of climatic factors and human activities to vegetation changes were quantitatively assessed. The results indicate that: (1) vegetation coverage in the Zhaogu mining area exhibits an overall improving trend, affecting approximately 77.1% of the study area, while slight degradation is mainly concentrated in the southeastern region, accounting for about 15.2%; (2) vegetation dynamics are predominantly characterized by low and relatively low fluctuations, covering approximately 78.5% of the region, whereas areas with high fluctuations are limited and mainly distributed in zones with intensive mining activities; although the current vegetation trend is generally increasing, future projections suggest a potential decline in approximately 55.8% of the area; and (3) vegetation changes in the Zhaogu mining area are jointly influenced by climatic factors and human activities, with climatic factors promoting vegetation growth in approximately 70.6% of the study area, while human activities exert inhibitory effects in about 24.2%, particularly in regions affected by mining operations and urban expansion. Full article

Background: Melanin protects skin and hair from the effects of ultraviolet (UV) radiation damage, which contributes to all forms of skin cancer, including melanoma. Human melanocytes produce two main types of melanin: eumelanin provides effective photoprotection, and pheomelanin offers less protection against UV-induced skin damage. The agouti signaling protein (ASIP) antagonizes the melanocortin-1 receptor (MC1R), hinders melanocyte signaling, and shifts pigmentation toward pheomelanin, promoting UV vulnerability. In this study, we aim to discover compounds that inhibit ASIP–MC1R interaction and effectively preserve eumelanogenic signaling. Methods: The ASIP–MC1R interface-based pharmacophore model from ASIP is implicated in MC1R receptor protein engagement. We performed virtual screening with a validated pharmacophore model for ~4000 compounds curated from ZINCPharmer and applied drug-likeness filters, viz. ADMET and toxicity profiling tests. Further, the screened candidates were targeted for docking to the ASIP C-terminal domain corresponding to the MC1R-binding moiety. Top compounds underwent a 100-nanosecond (ns) run of molecular dynamics (MD) simulations to assess complex stability and persistence of key contacted residues. Results: Sequential triage, including pharmacophore, ADME–toxicity (ADMET), and docking/ΔG, yielded a focused group of candidates against ASIP antagonists with a favorable fit value. The MD run for 100 ns supported pose stability at the targeted pocket. Based on these predictions and analyses, compound ZINC14539068 was screened as a new potent inhibitor of ASIP to preserve α-MSH-mediated signaling of MC1R. Conclusions: Our in silico pipeline identifies ZINC14539068 as a potent inhibitor of ASIP at its C-terminal interface. This compound is predicted to disrupt ASIP–MC1R binding, thereby maintaining eumelanin-biased signaling. These findings motivate experimental validation in melanocytic models and in vivo studies to confirm pathway modulation and anti-melanoma potential. Full article