Search Results (17,577)

Large language models (LLMs) offer new possibilities for natural-language interaction with geospatial analysis systems, but their use in remote sensing instrument data analysis remains limited by weak execution control, poor reproducibility, and limited integration with domain-specific computation. The paper presents an agent for Incoherent Doppler wind LiDAR (ICDL) data analysis, named ICDL-Agent, a tool-augmented LLM framework for remote sensing instrument workflows. The system maps conversational user requests to executable analysis pipelines for wind retrieval, uncertainty estimation, visualization, and higher-level diagnostics through structured planning over a registry of domain-specific tools. To improve execution reliability, the system combines schema-constrained workflow generation, shared-state reuse of intermediate scientific products, and validation with bounded repair. In addition to supporting routine LiDAR processing, the framework can generate new tools when required and adapt to related analytical tasks through domain-aware guidance and procedural documentation. We evaluate the system on multiple atmospheric wind-observation datasets in China and show that it faithfully reproduces the refined Doppler wind-retrieval pipeline, achieving representative R²/MAE values of 0.52/3.73 m/s against ERA5 and 0.80/2.31 m/s against radiosonde observations, while supporting downstream analyses such as profile comparison, climatological interpretation, and gravity-wave diagnostics. More broadly, this study demonstrates how constrained LLM orchestration can support LiDAR researchers, remote-sensing instrument teams, and geospatial analysts seeking transparent, reproducible, and automated scientific data-processing workflows. Full article

Diabetic peripheral neuropathy (DPN) remains a leading cause of disability in diabetes, yet current care is largely symptomatic and does not directly address early neurovascular-immune pathology. This narrative review synthesizes clinical, redox, vascular, and immunological evidence into a peripheral nerve neurovascular unit (PNVU)/blood–nerve barrier (BNB)-centered framework for DPN. First, the review outlines the diagnostic and translational endpoint landscape of DPN, emphasizing that commonly used clinical, neurophysiological, small-fiber, and imaging-based tools capture important disease domains but do not directly assess early BNB dysfunction. It then reviews the anatomical and functional basis of the PNVU and BNB, including endoneurial microvascular endothelial cells, pericytes, basement membrane components, immune cells, and tight-junction proteins. Next, it discusses how chronic hyperglycemia and dyslipidemia drive metabolic-to-vascular coupling, redox imbalance, antioxidant defense failure, advanced glycation end products (AGEs), receptor for AGEs (RAGE), and nuclear factor-κB (NF-κB) signaling, endothelial activation, leukocyte recruitment, macrophage polarization, and junctional disassembly, culminating in increased BNB permeability and exposure of peripheral nerves to pro-inflammatory and neurotoxic mediators. Finally, it evaluates incretin-based therapies—including glucagon-like peptide-1 receptor agonists (GLP-1RAs), dipeptidyl peptidase-4 inhibitors (DPP-4 inhibitors, DPP-4is), and emerging multi-agonists—as potential modulators of oxidative and inflammatory stress within this framework. Although semaglutide and related agents show mechanistic plausibility and preclinical promise, direct evidence for incretin-mediated BNB stabilization in human DPN remains limited. By reframing DPN as a redox-driven neurovascular-immune disorder, this review highlights barrier-focused biomarkers, translational endpoints, and hypothesis-generating therapeutic opportunities that require clinical validation. Full article

Rising ambient temperatures and the increasing frequency of training and competition in hot climates have made heat stress a major challenge in football. Under such conditions, players experience greater cardiovascular and thermoregulatory strain, faster glycogen use, higher perceived exertion, and progressive impairment in repeated high-intensity actions and decision-making. These responses have intensified interest in nutritional strategies that might complement heat acclimation, hydration/electrolyte planning, cooling practices, and recovery management. This narrative review critically synthesizes current evidence on nutritional interventions that may be relevant to football performed in the heat, with emphasis on hydration and electrolyte replacement, carbohydrate–protein strategies, taurine, branched-chain amino acids (BCAAs), creatine, menthol, antioxidant- and nitrate-related approaches, and selected multi-ingredient products. Across the available literature, hydration/electrolyte planning and carbohydrate–protein feeding remain the practical foundation, menthol appears most consistently useful for perceptual cooling, creatine seems safe and potentially helpful for repeated-sprint support, and taurine is promising but still supported by relatively few trials. By contrast, evidence for BCAAs, antioxidants, nitrates, and caffeine as stand-alone heat strategies, as well as for many compound supplements, remains inconsistent, context-specific, or too indirect for strong football-specific endorsement. Overall, the evidence base remains heterogeneous in study quality, protocol design, exercise mode, and sport specificity. A substantial proportion of the available data is derived from cycling, endurance, or laboratory heat-exercise models rather than football-specific trials. Accordingly, any practical recommendation should be interpreted cautiously and embedded within broader heat-management strategies. Future work should prioritize ecologically valid randomized controlled trials in football or football-like intermittent protocols, with transparent reporting of dose, timing, perceptual outcomes, and match-relevant performance measures. Full article

The complex fracture networks, multiphase flow behavior, and nonlinear flow mechanisms induced by hydraulic fracturing in horizontal wells of shale oil reservoirs pose significant challenges to production evaluation. In this study, a semi-empirical productivity evaluation method for multiphase shale oil systems is developed by integrating production dynamics with flow mechanisms. Three-phase productivity equations for oil, gas, and water are established, explicitly incorporating the underlying flow mechanisms. A nonlinear flow index is introduced to characterize both the stress sensitivity of fractures and the threshold pressure gradient in the matrix. Key unknown parameters, including oil saturation, water cut, stimulated reservoir volume, and nonlinear coefficients, are determined through history matching of production data. The impacts of geological properties, fracturing parameters, operating conditions, and nonlinear flow parameters on oil–gas productivity are systematically investigated using the proposed multiphase semi-empirical model. The model is validated against production data from fractured horizontal wells in a field case, demonstrating its accuracy and applicability. Furthermore, the model enables reliable production forecasting based on the derived productivity relationships. The proposed approach provides a practical and efficient tool for rapid post-fracturing productivity evaluation in shale oil reservoirs. Full article

Land surface temperature (LST) is a core physical parameter that characterizes land surface processes and surface-atmosphere energy exchange. As the demand for high-accuracy LST products intensifies across diverse research domains—including climate science, hydrology, and ecosystem modeling—the systematic quantification of pixel-level retrieval uncertainties has become essential for generating long-term, consistent Climate Data Records (CDRs). However, existing studies predominantly emphasize algorithmic development or localized validation, with limited attention to systematic cross-site and long-term uncertainty assessments. This gap impedes a comprehensive understanding of the compositional structure and spatiotemporal variability of LST retrieval uncertainties under heterogeneous surface and atmospheric conditions. In this study, based on the improved generalized split-window (GSW) algorithm and error propagation theory, the total uncertainty (U_total) and its four primary components—algorithm uncertainty (U_a), land surface emissivity uncertainty (U_e), noise equivalent delta temperature uncertainty (U_n), and atmospheric water vapor uncertainty (U_w)—at the pixel level over long time series and across multiple sites are quantified. Our analysis spans a 21-year period (2003–2023) and encompasses multiple geographically distributed sites, utilizing high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data—specifically MYD11_L2 and MOD11_L2 products—collocated at the locations of 15 globally distributed ground-based reference sites. These sites are used to represent diverse climatic regimes and land-cover conditions, rather than to provide point-scale “true” LST values for residual-based validation. Results show that the interquartile range (IQR) of U_total is consistently concentrated between 1.0 and 1.2 K, demonstrating long-term stability. Systematic differences in U_total are identified across sensor platforms and diurnal cycles: U_total for Aqua/MYD data (1.13–1.25 K) is marginally higher than that for Terra/MOD data (1.05–1.17 K); similarly, daytime U_total (1.08–1.23 K) is generally slightly elevated relative to nighttime U_total (1.05–1.18 K). The contributions of individual uncertainty components to U_total exhibit substantial variation, with mean relative contributions of 81.97%, 11.32%, 4.46%, and 2.25% for U_e, U_a, U_n, and U_w, respectively. The dominant drivers of U_total differ markedly across climatic regions: in arid regions, U_total is predominantly governed by U_e, termed “emissivity-dominated,” accounting for over 85% of the total; conversely, humid tropical regions exhibit a “surface-atmosphere co-influenced” regime, characterized by a reduced contribution from U_e and correspondingly enhanced contributions from U_a and U_w. Furthermore, U_total decreases with increasing total column water vapor (TCWV) (Pearson correlation coefficient r = −0.498; linear slope k = −0.0425 K/(g/cm²)), and increases with increasing viewing zenith angle (VZA) (r = 0.208; k = 0.0022 K/degree). While U_a, U_n, and U_w all increase with TCWV, U_e decreases. Full article

Over the last two decades, the automotive industry has spearheaded a shift toward data-centric manufacturing, where Real-Time Ethernet (RTE) networks defined in IEC61784-2 serve as critical components for ensuring deterministic communication at the Operation Technology level. Although AI-based systems offer significant potential for predictive maintenance and cybersecurity, their effectiveness is currently limited by a lack of structured datasets from real-world industrial environments. Most existing research relies on small-scale simulations or laboratory setups that fail to capture the scale and complexity of actual production. To address this gap, this paper introduces a novel methodology for repurposing network data collected throughout a plant’s lifecycle, specifically during the commissioning and validation phases of RTE networks according to IEC61918. An additional important contribution is the creation of the first multi-plant dataset for real RTE (PROFINET) traffic in the automotive sector, aggregating 300 GB of data from 54,000+ devices across nearly 700 production lines in 17 industrial sites. The work defines standardized methodologies and replicable processes for systematic data acquisition, validation, and labeling to ensure long-term usability for training AI models. Finally, four case studies (focused on performance, maintenance, security, and machine learning) show how this dataset can be used to enhance the reliability of modern smart manufacturing. Full article

Digital Product Passports (DPPs) are being introduced in the European Union to support circular economy strategies, improve product transparency, and enable lifecycle-based compliance and decision-making. Despite growing interest, research on DPPs remains fragmented, and there is limited consensus on how to design and validate DPP frameworks in real-world contexts. This paper presents a systematic literature review of peer-reviewed studies that explicitly define, structure, or assess DPP-related frameworks. Using a transparent search strategy based on Scopus and IEEE Xplore, combined with structured screening, the review assesses framework design elaboration and validation maturity across included studies and interprets recurring framework archetypes across application sectors. The results show that most studies emphasise conceptual or architectural designs. These commonly adopt data-centric, layered, technology-anchored, or ecosystem-oriented structures and frequently refer to enabling technologies such as digital twins, blockchain, data spaces, and knowledge graphs. However, explicit validation remains limited and is primarily restricted to illustrative case studies, stakeholder-informed assessments, or prototypes, with few studies evaluating scalability, interoperability, or lifecycle-spanning operation in real-world contexts. By consolidating design principles and validation practices across sectors in this targeted corpus, the review clarifies the current state of the art and highlights critical research gaps. The findings indicate that DPP research is characterised by a strong emphasis on framework design, with comparatively limited empirical validation. Furthermore, critical research gaps include the lack of rigorous empirical validation, cross-organisational testing, lifecycle-spanning evaluation, clearly defined data governance responsibilities, convergence towards shared reference architectures, and sector-specific adaptation. Full article

The ceramic tile industry is increasingly required to reduce its environmental impact while maintaining high technological and aesthetic standards. In this context, the use of secondary raw materials (SRMs) represents a promising strategy to decrease the consumption of virgin resources and the energy demand associated with conventional frit production. At the same time, solar-reflective engobes can contribute to passive cooling by limiting solar heat absorption and mitigating the urban heat island effect. In this study, white solar-reflective engobes were developed by incorporating at least 8 wt.% of SRMs, including various recycled glass streams, ceramic wastes, and yttria-stabilized zirconia residues. The results demonstrate that optimized formulations achieve high solar reflectance values (up to 0.79) while maintaining the technological and aesthetic requirements of industrial ceramic tiles. Recycled glasses act as effective fluxing agents, whereas waste zirconia enhances optical performance due to its strong light-scattering capability. The most promising formulations were validated at the industrial scale, confirming their applicability under real production conditions. Overall, the developed engobes represent a scalable alternative to traditional frit-based systems, enabling reduced resource consumption and supporting the development of energy-efficient ceramic surfaces. Full article

Ilex guayusa Loes., an Amazonian holly cultivated by indigenous Kichwa communities, is valued for its caffeine-rich leaves (2.0–3.5% dry weight). However, industrial processing generates substantial by-products that remain undercharacterized and underutilized. This study provides baseline quantitative assessment and physicochemical characterization of guayusa processing residues from the Kallari cooperative (Napo, Ecuador) to explore their potential within a circular bioeconomy framework. Granulometric analysis showed that processing produces predominantly coarse material (>425 μm, 67.5%), while intermediate and fine fractions (<425 μm) account for 32.5% of total biomass. Comparative analysis of pooled fractions (n = 10 subsamples per fraction) did not show clear compositional differences across twelve physicochemical parameters (p > 0.05), suggesting relatively comparable compositional profiles within the analyzed material. Residues contained relevant bioactive compounds, including total phenolics (15.7–16.0 mg GAE g⁻¹ DW) and condensed tannins (9.4–10.0 mg GAE g⁻¹ DW). Preliminary caffeine analysis (n = 2 composite samples) indicated values of 1.89–2.09% DW. Correlation analysis showed a negative association between protein and tannins (r = −0.785, p = 0.007) and a positive relationship between fiber and tannins (r = 0.660, p = 0.038). Exploratory principal component analysis suggested structural–phenolic patterns, although results should be interpreted cautiously due to the limited sample size. At the cooperative scale (18–25 t yr⁻¹), these residues represent 5.8–8.1 t yr⁻¹ of underutilized biomass. While the findings suggest potential suitability for applications such as functional ingredients, bioactive extraction, and cosmetic formulations, further validation including independent biological replication, compound-specific profiling, and techno-economic assessment is required. This study establishes a baseline dataset to support future valorization strategies within Amazonian indigenous bioeconomy contexts. Full article

Cold stress poses a major threat to global agricultural productivity. As a tropical woody oil crop, oil palm is highly susceptible to chilling damage; however, the molecular mechanisms underlying its cold response remain largely unknown. In this study, we profiled spear leaves of oil palm seedlings exposed to 8 °C for 0, 0.5, 1, 2, 4 and 8 h, using transcriptomic analysis across the full time course, complemented by metabolomic profiling at the 2 h time point. Physiological measurements showed cold stress-associated changes in chlorophyll and malondialdehyde (MDA) levels, as well as in the activities of antioxidant enzymes (SOD, POD, and CAT). Transcriptome analysis identified 31,576 expressed genes, including 9042 differentially expressed genes (DEGs). The highest number of specific DEGs was observed at the 2 h time point. Weighted gene co-expression network analysis (WGCNA) revealed nine co-expression modules with distinct temporal patterns. A total of 46 hub genes were identified, including WRKY, ERF, and seven genes encoding key enzymes involved in the biosynthesis of phenylalanine, tyrosine, and tryptophan (LOC105041937, LOC105056784, LOC105048637, LOC105055093, LOC105038203, LOC105033050, and LOC105037948). Metabolomic analysis detected 98 differentially accumulated metabolites, which were enriched in the phenylalanine, tyrosine, and tryptophan pathway. qRT-PCR analysis showed that WRKY and ERF expression peaked at 2 h, coinciding with phenylalanine accumulation. In summary, this study describes the temporal dynamics of the cold stress response in oil palm, identifies the 2 h time point as a transition period, and provides a set of prioritized hub genes for further functional validation. These findings may support future breeding efforts aimed at improving cold tolerance in oil palm. Full article

Seawater and sea sand as constituents in concrete are valuable alternatives to freshwater and river sand. Further, the use of seawater and sea sand in projects located in the proximity of a sea/ocean can reduce the overall project cost and lower the carbon footprint. Nevertheless, seawater contains high concentrations of chloride (Cl⁻), sulphate (SO₄²⁻) and magnesium (Mg²⁺), which can react with tricalcium aluminate (C₃A) in cement and the byproduct calcium hydroxide (Ca(OH)₂), and form Friedel’s salt, delayed ettringite and brucite, respectively. These chemical compounds are aggressive and can degrade the strength and durability of the concrete. Differences in the physical properties of sea sand compared to river sand can also lead to weak and porous concrete. In reinforced concrete, steel bars are susceptible to corrosion due to the formation of corrosion products as a result of high concentrations of Cl⁻. Whilst mitigation strategies such as the use of supplementary cementitious materials (SCMs) and fibre-reinforced polymer (FRP) reinforcements have been investigated in the literature, no validated method that enables the use of concrete with seawater and sea sand has been established. Based on research reported in the literature, the present study investigates the chemistry, strength and microstructure of concrete mixed with seawater and sea sand as a means of establishing their use in concrete without compromising the properties of the concrete. The study shows that the compressive strength of seawater–sea sand mixed concrete (SWSSC) is increased in the short term (up to 28 days) due to the formation of additional chemical compounds in the former. However, the long-term (i.e., beyond 28 days) compressive strength of concrete reduces by up to 20% after one year due to the weakening of the microstructure (more flaws/expansions), which further reduces the durability of the reinforced concrete. Although the long-term degradation of SWSSC has been noticed, the underlying causes are not fully understood. The present critical review study provides chemical and microstructural insight into the degradation of concrete with seawater and sea sand, and the current developing understanding is used to develop a mitigation strategy towards the use of seawater and sea sand in real-world concrete applications. Full article

Tool wear monitoring is essential for ensuring machining efficiency and product quality, particularly for difficult-to-machine materials such as Inconel 718 (IN718). Traditional deep learning models, such as Conventional Convolutional Neural Networks (CNNs), often struggle to capture complex wear patterns and lack accuracy across varying machining conditions while developing image-based tool wear identification systems. To address these limitations, this paper presents a Vision Transformer (ViT) model for identifying tool-wear categories during end-milling of IN718. The performance of the ViT-based model is systematically compared with a CNN-based EfficientNet-b0 model. The robustness and generalization of the ViT-based model are validated on two previously unseen image datasets: one with conditions similar to those of the training data and another acquired under varying lighting conditions. The results indicate that the ViT model outperforms the EfficientNet-b0 model in terms of classification accuracy and computational efficiency. The ViT model achieves higher accuracy with fewer training epochs and faster convergence. Furthermore, it exhibits strong generalization across different lighting conditions, demonstrating robustness to variations in the machining environment. The findings presented in this work clearly demonstrate ViT’s effectiveness in tool wear classification and its potential as a reliable, efficient algorithm for developing tool wear monitoring systems for practical machining applications. Full article

Cage-based tilapia farming is highly vulnerable to rapid variations in water-quality parameters, particularly dissolved oxygen (DO) fluctuations, which can cause fish stress, fish mortality, and economic losses. In this study, we developed and field-evaluated an Internet of Things (IoT)- and LoRa-based water-quality-monitoring and aeration-actuation system for open-water tilapia cage farming. The system consists of distributed control nodes, a main node, a cloud database, and a mobile application for real-time monitoring of DO, pH, and water temperature, as well as remote and automatic oxygen-pump actuation. An automatic probe-lifting mechanism is integrated into the control node to reduce probe-submersion duration and mitigate the risk of sensor fouling during field operation. Field validation showed that the node equipped with the probe-lifting mechanism achieved better agreement with the reference instruments than the continuously submerged node, particularly for DO measurement, with RMSE values of 0.186 mg/L and 0.683 mg/L, respectively. A communication-performance evaluation showed 100% packet reception up to 1640 m, whereas packet reception was reduced at the longest tested distance of 2290 m, indicating that the field-deployment range should be interpreted cautiously under the tested LoRa configuration. Detection-latency experiments showed sub-second responsiveness, with average delays of 208.6–289.7 ms for single-hop communication and 438.9–529.4 ms for two-hop communication. Expert evaluation and farmer satisfaction assessment indicated positive perceptions of the system’s usability and practical relevance. However, the study has several limitations, including the short field-validation period, limited sensor replication, and a lack of direct fish production outcome measurements, which should be considered when interpreting the findings. Overall, the proposed system provides a practical platform for water-quality monitoring and aeration actuation in cage-based tilapia farming. Full article

Centralised architectures in contemporary manufacturing systems impose structural constraints on resilience, scalability, and operational transparency that existing approaches have failed to resolve. This work reports the development and empirical validation of AgentBlock, a framework integrating blockchain technology with multi-agent robotic systems to enable decentralised autonomous manufacturing. The architecture operates across three functionally decoupled layers: a React-based decentralised application interface, an Ethereum Sepolia blockchain interaction layer with Solidity 0.8.18 smart contracts following an upgradeable proxy architecture (EIP–1967) coordinated through an Industrial PoA consensus mechanism, and a physical execution layer comprising two heterogeneous robotic agents (KUKA youBot and UFactory Lite 6) and one edge validation agent on an NVIDIA Orin platform that also hosts Q-Learning optimisation, with inter-agent coordination provided by ROS Noetic Ninjemys under Ubuntu 20.04 LTS. Experimental validation conducted over 15 days across 1500 training episodes in a controlled 5 m × 3 m industrial laboratory reveals a task success rate of 95.58%, sustained throughput of 49.0 tasks per hour, average cycle time of 1.224 min, blockchain transaction latency below 15 s (mean: 11.4 s), and gas costs averaging US $0.000669 per operation. These findings establish that blockchain-enabled autonomous manufacturing is not merely theoretically sound but operationally viable, delivering immutable traceability, decentralised coordination, and transparent verification at performance levels compatible with Industry 4.0 and 5.0 production demands. Full article

As global industrialization and energy demands rise, excessive reliance on fossil fuels escalates carbon emissions, making clean energy alternatives an urgent priority for sustainable development. As a key transition pathway, wind and solar power can be converted into hydrogen via electrolyzers for electricity generation, thermal supply, or natural gas synthesis. This enables flexible multi-energy coordination and improves overall renewable energy utilization efficiency. However, conventional electrolyzer scheduling approaches typically assume fixed hydrogen production efficiency, failing to account for dynamic variations in operating conditions, efficiency attenuation, and lifetime degradation under fluctuating renewable inputs. This inadequacy compromises the long-term sustainability of green hydrogen systems. To address these challenges, this paper proposes a hybrid AEL-PEM electrolyzer power allocation and operating condition array rotation strategy. Piecewise linear models are established to characterize the efficiency and full life cycle degradation of both electrolyzer types across normal operation, overload, and start–stop transitions. A mixed-integer linear programming (MILP) model is formulated with an objective function incorporating energy purchase costs, start–stop penalty costs, and electrolyzer lifetime degradation costs, and is solved using the Gurobi solver. Simulation validation is conducted using a 24 h typical summer day dataset with a 15 min resolution. Three comparative schemes are evaluated to verify the strategy’s effectiveness in minimizing total system operation costs and enhancing renewable energy utilization efficiency through optimized operating condition management. Results demonstrate that the proposed strategy reduces total system costs by 23%, entirely eliminates renewable energy curtailment, and balances electrolyzer lifespan degradation across all units, collectively advancing the economic efficiency, asset sustainability, and long-term operational reliability of green hydrogen systems. Full article