Search Results (670)

To address the bottlenecks of traditional jaw crusher liner wear detection—high safety risks, insufficient precision, and limited full-range analysis—this paper proposes a non-contact, high-precision wear analysis method based on binocular vision and deep learning. At its core is the integration of the state-of-the-art FoundationStereo zero-shot stereo matching algorithm, following scenario-specific adaptations, into the 3D reconstruction of industrial liners for wear analysis. A novel wear quantification methodology and corresponding indicator system are also proposed. After calibrating the ZED2 binocular camera and fine-tuning the algorithm, FoundationStereo achieves an Endpoint Error (EPE) of 0.09, significantly outperforming traditional algorithms. To meet on-site efficiency requirements, a “single-view rapid acquisition + CUDA engineering acceleration” strategy is implemented, reducing point cloud generation latency from 165 ms to 120 ms by rewriting kernel functions and optimizing memory access patterns. Geometric accuracy verification shows a Mean Absolute Error (MAE) ≤ 0.128 mm, fully meeting industrial measurement standards. A complete process of “3D reconstruction–model registration–quantitative analysis” is constructed, utilizing three core indicators (maximum wear depth, average wear depth, and wear area ratio) to characterize liner wear. Statistical results—such as an average maximum wear depth of 55.05 mm—are highly consistent with manual inspection data, providing a safe, efficient, and precise digital solution for the predictive maintenance and intelligent operation and maintenance (O&M) of liners. Full article

The Lyot filter, a fundamental element of the Yunnan Observatories Coronagraph Green-line Imaging System (YOGIS) at Lijiang Observatory, utilizes a Liquid Crystal Variable Retarder (LCVR) for swift electrical modulation. This filter allows for precise observations of the coronal green line (Fe XIV, central wavelength 5303 Å) with a narrow full-width at half-maximum (FWHM) of 1 Å and enables rapid adjustment of the transmission band wavelength. This feature aids in capturing the sky background intensity around the green line and images of two line wings (offset by $\pm 0.45$ Å from the central wavelength), crucial for determining the green line’s Doppler shift. By employing sky background subtraction and processing line wing images, an improved signal-to-noise ratio (SNR) in coronal green line images is achieved. The YOGIS Lyot filter, an enhancement of the NOrikura Green-line Imaging System (NOGIS) filter, operates at a wavelength of 5303 Å, offers a wavelength tuning range of $\pm 2$ Å, and tunes within <60 ms. This study elucidates the filter’s design principles, outlines essential calibration procedures, and validates its performance through on-site observations using the YOGIS. Full article

Hydrophobic nanofiber membranes derived from the biopolymer alginate were fabricated by electrospinning followed by metal ion crosslinking, and their potential as oil-water separation membranes was primarily investigated. Sodium alginate (SA) was co-electrospun with polyethylene glycol and subsequently crosslinked using calcium chloride and group IV metal ions (zirconium or titanium). Metal ion crosslinking changed the surface wettability of the nanofiber membranes, as confirmed by water contact angle measurements. Both zirconium- and titanium-crosslinked SA nanofiber membranes exhibited effective gravity-driven oil–water separation with complete water blocking. Although hydrophobic modification reduced direct water affinity, the resulting membranes retained residual adsorption capability toward methylene blue, indicating the presence of accessible internal polar sites. The adsorption behavior varied depending on the crosslinking ion. In addition, titanium-crosslinked membranes showed an auxiliary UV-assisted dye removal contribution under irradiation, arising from photoactive Ti species. These findings demonstrate that metal ion crosslinking provides a practical route for tuning the functional properties of alginate nanofiber membranes, with oil-water separation as the primary application and dye adsorption/photocatalysis as secondary functionalities. Full article

Protecting aquatic biodiversity while ensuring reliable hydropower production and water supply remains a core challenge for both water security and biosecurity. In this PRISMA-based systematic review, we synthesize evidence from 96 studies on fish guidance and deterrence at hazardous water intakes. We examine non-physical barriers, including acoustic and light cues, electric fields, bubble curtains, and chemical stimuli, as well as physical barriers such as racks, guidance structures, and nets or screens that aim to divert fish away from intakes and toward selective passage routes. Overall, guidance and deterrence performance is strongly species- and site-specific. Multimodal systems that combine multiple cues show the highest mean guidance efficiency (~80%), followed by light-based deterrents (~77%). Acoustic, electric, and bubble barriers generally achieve intermediate efficiencies (~55–58%), whereas structural devices alone exhibit lower mean performance (~46%), with substantial variability among sites and designs. Physical screens remain effective for larger size classes but can increase head loss and debris accumulation. By contrast, non-physical systems offer more flexible, low-footprint options whose success depends critically on local hydraulics, the sensory ecology of target species, and ambient environmental conditions. We identify major knowledge gaps relating to underlying sensory and behavioral mechanisms, hydraulics-based design rules, and standardized performance metrics. We also highlight opportunities to integrate advanced monitoring and AI-based analytics into adaptive, site-specific guidance systems. Taken together, our findings show that carefully selected and tuned barrier technologies can provide practical pathways to enhance water security and biosecurity, while supporting sustainable fish passage, improving invasive-species control, and reducing ecological impacts at water infrastructure. Full article

Background/Objectives: In 2023, the United States Congress amended Section 13 of the National School Lunch Act to allow non-congregate meal service as an option within the Summer Food Service Program in rural areas, creating “SUN Meals To-Go.” The purpose of this qualitative study was to explore caregivers’ perceptions of USDA rural non-congregate summer meal programs in California during the summer of 2024. Methods: This was a cross-sectional, qualitative study using an electronic 20-item survey instrument that was available in English and Spanish. Five school foodservice directors in California shared and/or posted at meal pick-up sites a flyer with a QR code leading caregivers to the survey instrument. A conventional content analysis was conducted with the open-ended responses and descriptive statistics were calculated for close-ended items. Results: Caregivers (n = 827) were primarily married (70.5%) and Hispanic/Latino (54.3%) women (85.5%). They (55%) reported using the 2024 summer meal program “most times” or “every time” it was available. Three themes were constructed through qualitative content analysis: (1) Family support and resource relief, (2) Navigating program accessibility and logistics, and (3) Nourishment and practicality: Reflections on food quality, nutrition, and sustainability. Conclusions: Caregivers highlighted that the program supported their families and provided resource relief. They indicated that accessibility and logistics were effective, provided ideas for fine-tuning the delivery of the program, described this program as supporting their children’s nutrition. Full article

Fruit development and ripening in peach (Prunus persica) involve complex transcriptional and post-transcriptional regulation. While short-read sequencing has advanced transcriptome studies, it often fails to accurately resolve complex transcript isoforms. This study employed Oxford Nanopore Technologies’ (ONT) full-length RNA-Seq to comprehensively characterize the transcriptomic landscape of peach fruits across three key developmental stages: the first exponential stage, the second exponential stage, and the ripening stage. Our analysis identified 44,042 non-redundant isoforms, including 1109 novel genes and 32,289 novel isoforms, significantly expanding the peach genome annotation. We further investigated alternative splicing (AS) events, revealing that intron retention (IR) and alternative 3′ splice site (A3′S) were the most prevalent types, with AS abundance peaking at the S1 stage. A total of 10,236 differentially expressed transcripts (DETs) were identified, highlighting dynamic expression patterns during fruit development. Functional characterization focused on a MADS-box gene, PpMADS6, which produced two isoforms via alternative splicing. Dual luciferase assays in tobacco leaves demonstrated that the full-length isoform, PpMADS6a, specifically activated the promoter of the fruit-softening gene PpPG1, while the truncated isoform, PpMADS6b, lost this transactivation ability. This study provides a valuable resource of full-length transcriptomes for peach and underscores the critical role of alternative splicing in generating functional diversity to fine-tune fruit development and ripening processes. Full article

Electrochemical CO₂ reduction (CO₂RR) is a promising route to transform a major greenhouse gas into value-added fuels and chemicals. However, its deployment is still hindered by the sluggish activation of CO₂, poor selectivity toward multielectron products, and competition with the hydrogen evolution reaction (HER). Single-atom catalysts (SACs) have emerged as powerful materials to address these challenges because they combine maximal metal utilization with well-defined coordination environments whose electronic structure can be precisely tuned through metal–support interactions. This minireview summarizes current understanding of how structural, electronic, and chemical features of SAC supports (e.g., porosity, heteroatom doping, vacancies, and surface functionalization) govern the adsorption and conversion of key CO₂RR intermediates and thus control product distributions from CO to CH₄, CH₃OH and C₂⁺ species. Particular emphasis is placed on selectivity descriptors (e.g., coordination number, d-band position, binding energies of *COOH and *OCHO) and on rational design strategies that exploit curvature, microenvironment engineering, and electronic metal–support interactions to direct the reaction along desired pathways. Representative SAC systems based primarily on N-doped carbons, complemented by selected examples on oxides and MXenes are discussed in terms of Faradaic efficiency (FE), current density and operational stability under practically relevant conditions. Finally, the review highlights remaining bottlenecks and outlines future directions, including operando spectroscopy and data-driven analysis of dynamic single-site ensembles, machine-learning-assisted DFT screening, scalable mechanochemical synthesis, and integration of SACs into industrially viable electrolyzers for carbon-neutral chemical production. Full article

In this study, a ternary Ni/Mg/g-C₃N₄ composite was synthesized via a controlled precipitation–calcination route and evaluated for its visible-light-assisted degradation of methylene blue (MB). The structural, morphological, and optical characteristics of the composites were systematically investigated using XRD, FT-IR, FESEM, BET, and UV–Vis analyses. The results confirmed the successful construction of Ni/Mg/g-C₃N₄ heterojunctions with strong interfacial coupling and enhanced surface porosity. Among all samples, the Ni/Mg/CN20 composite exhibited the highest activity, achieving 66% MB degradation within 180 min under visible light. This superior performance was attributed to synergistic effects arising from efficient interfacial charge transfer, broadened light absorption, and abundant active sites. The composite also displayed excellent thermal stability. This work demonstrates that the rational control of g-C₃N₄ loading plays a decisive role in tuning the physicochemical and catalytic properties of Ni/Mg/g-C₃N₄ composites. The findings provide new insights into the design of cost-effective, thermally stable, and high-performance photocatalysts for visible-light-driven wastewater treatment. 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

This study focuses on the calibration and verification of a large-scale coupled numerical model to simulate the complex hydrodynamic–wave–sediment transport processes in the coastal and estuarine regions of the Mekong River Delta (MRD), Vietnam. Using the MIKE 21/3 modeling system, the research integrates Hydrodynamics (HD), Spectral Wave (SW), and Mud Transport (MT) modules across a computational domain of 270 × 300 km. The models were rigorously tested using field measurement data from three distinct periods: May 2004 (dry season calibration), September 2017 (first verification), and June 2024 (second verification). The results from the hydrodynamic model demonstrated high accuracy in predicting water levels, with the average Root Mean Square Error (RMSE) values ranging between 4.4% and 5.8%. The wave spectral model showed reliable performance, with the average RMSE values for wave height ranging from 15.1% to 18.0%. Furthermore, the Mud Transport module successfully captured suspended sediment concentrations (SSC), yielding average RMSE values between 26.0% and 32.1% after the fine-tuning of site-specific parameters such as critical shear stress for erosion and deposition. The study highlights the critical importance of utilizing site-specific sedimentological parameters to accurately predict morphological changes in highly dynamic estuarine environments. This validated model provides a robust tool for assessing coastal erosion and developing protection measures in regions that are increasingly vulnerable to climate change and human activities. Full article

Safety on construction sites is an essential yet challenging issue due to the inherently hazardous nature of these sites. Workers are expected to wear Personal Protective Equipment (PPE), such as helmets, vests, and safety glasses, to prevent or minimize their exposure to injuries. However, ensuring compliance remains difficult, particularly in large or complex sites, which require a time-consuming and usually error-prone manual inspection process. The research proposes an automated PPE detection system utilizing the deep learning model YOLO11, which is trained on the CHVG dataset, to identify in real-time whether workers are adequately equipped with the necessary gear. The proposed PPE-EYE method, using YOLO11x, achieved a mAP50 of 96.9% and an inference time of 7.3 ms, which is sufficient for real-time PPE detection systems, in contrast to previous approaches involving the same dataset, which required 170 ms. The model achieved these results by employing data augmentation and fine-tuning. The proposed solution provides continuous monitoring with reduced human oversight and ensures timely alerts if non-compliance is detected, allowing the site manager to act promptly. It further enhances the effectiveness and reliability of safety inspections, overall site safety, and reduces accidents, ensuring consistency in follow-through of safety procedures to create a safer and more productive working environment for all involved in construction activities. Full article

Keeping the levels of soil major nutrients (total nitrogen, TN; available phosphorous, AP; and available potassium, AK) in optimum condition is important to achieve the goals of precision agriculture systems. To address the issues of slow speed and low accuracy in soil nutrient detection, this study developed a prediction model for soil major nutrients content based on an improved Extreme Learning Machine (ELM) algorithm. This model utilizes a soil major nutrients detection system integrating pyrolysis and artificial olfaction. First, the Bootstrap Aggregating (Bagging) ensemble strategy was introduced during the model integration phase to effectively reduce prediction variance through multi-submodel fusion. Second, Generative Adversarial Networks (GAN) were employed for sample augmentation, enhancing the diversity and representativeness of the dataset. Subsequently, a multi-scale convolutional and Efficient Lightweight Attention Network (ELA-Net) was embedded in the feature mapping layer to strengthen the representation capability of soil gas features. Finally, adaptive hyperparameter tuning was achieved using the Adaptive Chaotic Bald Eagle Optimization Algorithm (ACBOA) to enhance the model’s generalization capability. Results demonstrate that this model achieves varying degrees of performance improvement in predicting total nitrogen (R² = 0.894), available phosphorus (R² = 0.728), and available potassium (R² = 0.706). Overall prediction accuracy surpasses traditional models by 8–12%, with significant reductions in both RMSE and MAE. These results demonstrate that the method can rapidly, accurately, and non-destructively estimate key soil nutrients, providing theoretical guidance and practical support for field fertilization, soil fertility assessment, and on-site decision-making in precision agriculture. Full article

Background/Objectives: NANOBODY^® molecules (VHHs) are attractive vectors for radiopharmaceuticals due to their small size and high target affinity, but rapid clearance and pronounced kidney retention limit their therapeutic applicability. Binding to serum albumin is a widely used strategy to prolong circulation, yet the respective contributions of albumin-binding affinity and molecular format remain insufficiently defined. This study aimed to systematically evaluate how affinity and valency modulate VHH pharmacokinetics. Methods: Four monovalent albumin-binding VHHs spanning nanomolar to micromolar affinities and two bivalent constructs were engineered, generated by fusing an albumin-binding VHH to an irrelevant non-binding VHH. All constructs incorporated a site-specific cysteine for DFO* conjugation, enabling uniform zirconium-89 labeling with high radiochemical purity. Pharmacokinetics were assessed in healthy mice using serial blood sampling and positron emission tomography. Blood and kidney exposure were quantified by non-compartmental analysis. Results: All albumin-binding constructs showed increased systemic exposure and reduced kidney uptake relative to a non-binding control. Nanomolar-affinity binders reached maximal exposure, and further affinity increases (K_D < ~100 nM) did not improve pharmacokinetics, suggesting a threshold. The micromolar binder showed intermediate exposure but still reduced renal retention compared with control. Valency effects were affinity-dependent. They were negligible at high affinity but pronounced at low affinity, where bivalency reduced systemic exposure and increased kidney uptake toward control levels. Conclusions: Albumin binding enables tuning of VHH pharmacokinetics in an affinity-dependent manner. Above an apparent affinity threshold, pharmacokinetics become format independent, whereas below this threshold, molecular format substantially influences systemic and renal disposition. Full article

Photovoltaic generation systems (PVGSs) face significant efficiency challenges under partial shading conditions and rapidly changing irradiance due to the limitations of conventional maximum power point tracking (MPPT) methods. To address these challenges, this paper proposes a Transfer Learning-based Fractional-Order Recurrent Neural Network (TL-FRNN) for robust global maximum power point (GMPP) tracking across diverse operating conditions. The incorporation of fractional-order dynamics introduces long-term memory and non-local behavior, enabling smoother state evolution and improved discrimination between local and global maxima, particularly under weak and partially shaded conditions. The proposed approach leverages Caputo fractional derivatives with Grünwald–Letnikov approximation to capture the history-dependent behavior of PVGSs while implementing a parameter-partitioning strategy that separates shared features from task-specific parameters. The architecture employs a multi-head design with GMPP regression and partial shading classification capabilities, trained through a two-stage process of pretraining on general PV data followed by efficient fine-tuning on target systems with limited site-specific data. The TL-FRNN achieved 99.2% tracking efficiency with 98.7% GMPP detection accuracy, reducing convergence time by 53% compared to state-of-the-art alternatives while requiring 72% less retraining time through transfer learning. This approach represents a significant advancement in adaptive, intelligent MPPT control for real-world photovoltaic energy-harvesting systems. Full article

Efficient conversion of biomass-based platform molecules into high-value derivatives is recognized as one formidable challenge in biomass upgrading. In this work, a one-pot deep eutectic solvents-assisted solvothermal method was developed for the modification of the commercial Pt/C catalysts by introducing a secondary metal (M = Sn, Bi, Ge, Sb, Pb). The structural and electronic properties of the catalysts were precisely tuned. Among the screened metals, the addition of Sn yielded the most significant improvement in catalytic activity. The optimized PtSn_0.5/C-140 catalyst achieved superior furfural (FAL) conversion and furfuryl alcohol (FOL) selectivity under mild conditions (20 °C, 2 MPa H₂). Comprehensive characterizations, including XRD, HRTEM, XPS, and H₂-TPD, confirmed the formation of Pt-Sn solid-solution phase. Furthermore, Characterization and reaction results revealed that the electronic and geometric effects induced by Sn modulated Pt active sites, significantly enhancing the adsorption of the active H species. Additionally, the SnO_x species adjacent to the Pt-Sn sites served as hydrogen spillover acceptors, further accelerating the hydrogenation process. The synergy between the Pt-Sn solid-solution phase and SnO_x species is identified as the origin of the superior performance at room temperature. These findings provide a new strategy for the design of high-performance biomass conversion catalysts by upgrading commercial noble metal catalysts. Full article