Search Results (15,260)

This study explores the use of prompt engineering for big data analysis with large language models (LLMs) in the context of smart maintenance within Industry 4.0 environments. Industrial systems generate large volumes of heterogeneous data, which are often underutilized due to the complexity of traditional analytical approaches. This work employs a systematic literature review based on PRISMA to identify the current state of the art and existing gaps in the integration of big data and LLMs. Based on this analysis, an approach is proposed that uses prompt engineering as a core mechanism to transform industrial data into actionable information through language models. The proposed approach is validated through a case study in an industrial company, where the traditional manual data analysis process is compared with the proposed approach. The results demonstrate a significant reduction in analytical effort and in the time required to generate relevant information, highlighting gains in operational efficiency for decision-making in smart maintenance. Full article

The agricultural industry is at a critical juncture, experiencing global pressures in the form of climate volatility, a shortage of labor, and an increase in production costs. Although artificial intelligence (AI) has the potential for revolution due to its predictive analytics and self-controlled machinery, it has not achieved widespread and even distribution for use, especially among small-to-medium-sized farms in the Midwestern United States. This study formulates and empirically examines a comprehensive socio-technical model to determine the drivers and barriers to the adoption of AI in this agricultural region. Based on a synthesized framework of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) and “Task–Technology Fit” (TTF), the study incorporates agriculture-specific contextual factors such as “environmental risk, access to broadband, economic constraints, and policy support”. The analyses of the 489 farmers in the U.S. Midwest were conducted through the “partial least squares structural equation modeling” (PLS-SEM) “SmartPLS v.3.9”. The findings provide full empirical evidence of the proposed model, which supports 11 hypothesized relationships. The key results show that the strongest positive predictors of adoption intention are “performance expectancy, effort expectancy, and trust”. On the other hand, data security concerns and financial restrictions are strong deterrents. The paper also outlines the significant facilitating functions of the broadband infrastructure and policy support in building farmer perceptions of technology’s ease-of-use and facilitating conditions. These lessons can provide policymakers, ag-tech developers, and extension agencies with a roadmap on how to create more equitable and contextual interventions that overcome the rural digital divide and create resilient data-driven farming systems. Full article

With the increasing demand for flexible electric vehicle charging and grid-interactive energy utilization, wireless power transfer (WPT) systems with high efficiency, bidirectional power flow capability, and controllable charging characteristics have attracted growing attention. However, existing WPT systems for electric vehicles still suffer from challenges including low adaptability to multiple operating modes, difficulty in achieving stable constant-current/constant-voltage output, and limited bidirectional power transfer capability under weak-coupling conditions. To address these issues, two relay-based four-coil WPT topologies, namely S-SS-LCC and LCC-SS-LCC, are proposed for electric vehicle charging and bidirectional energy transfer applications. Based on fundamental frequency analysis, frequency-domain models of the two topologies are established to reveal the relationships among resonant characteristics, output behavior, and power transfer direction. The results show that the S-SS-LCC topology can achieve constant-current and constant-voltage output in the forward grid-to-vehicle charging mode, as well as constant-voltage output in the reverse vehicle-to-grid mode. In contrast, the symmetrical LCC-SS-LCC topology can achieve bidirectional constant-current power transfer, making it suitable for vehicle-to-vehicle emergency charging scenarios. Under weak-coupling conditions (k = 0.1), the S-SS-LCC system delivers an output current of approximately 12 A at 85.2 kHz and an output voltage of about 612 V at 87.7 kHz, with a peak efficiency of 91.63%. The LCC-SS-LCC system achieves bidirectional constant-current output at 87.7 kHz with a maximum efficiency of 92.23%. Low-power experimental results further verify the predicted constant-current and constant-voltage characteristics. The proposed topologies provide a promising solution for efficient electric vehicle wireless charging and flexible bidirectional energy interaction in future smart charging systems. Full article

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article

Glucose-responsive smart insulin delivery systems that mimic the pancreatic insulin release system can improve the health and quality of life of patients with diabetes. In this study, a spherical drug delivery carrier encapsulating insulin was developed to achieve improved glucose accessibility and a rapid pH response using polyhedral oligomeric silsesquioxane (POSS) as a sterically stabilizing structure. Highly sensitive poly(acrylic acid) (PAA)-POSS-aminophenylboronic acid (APBA)@insulin (386 ± 69 nm), a spherical drug delivery carrier encapsulating insulin, was synthesized using POSS, a hydrophobic material, and PAA and APBA, which respond to pH and glucose, respectively. The drug carrier has dual reactivity with pH and glucose, and the synthesis of the carrier was confirmed through Fourier transform infrared (FT-IR) spectroscopy, which verified that the particles were stable at each pH through the zeta-potential data. In particular, PAA-POSS-APBA@insulin exhibited highly sensitive drug delivery characteristics, in which the backbone of PAA was expanded under acidic conditions (around pH 5.0) and insulin bound to the boronic acid inside could rapidly and selectively react with trace amounts of glucose. PAA-POSS-APBA@insulin nanoparticles exhibited no HeLa cell cytotoxicity up to a high concentration of 640 μg/mL, and the cell growth rate increased with the concentration, indicating biocompatibility. The average blood glucose level of diabetic mice treated with POSS-APBA@insulin (4.0 IU/kg) decreased for >6 h and remained stable. Thus, PAA-POSS-APBA@insulin can function as a stimulatory-responsive drug carrier targeting hyperglycemic environments. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

Efficient solubilization of nonpolar substances in polar solvents represents a fundamental challenge in environmental remediation, green chemistry, and separation processes. This limitation stems from the hydrophobic effect, which creates thermodynamic barriers, resulting in low intrinsic solubility and strong phase separation. This review examines the thermodynamic basis of solubilization, focusing on free-energy changes and molecular interaction mechanisms. It discusses various strategies, including surface and interface engineering, host–guest inclusion, solvent engineering, and nanostructure encapsulation, along with their practical applications. Future research directions include smart responsive materials, green solvent design theories, and precise construction of solubilization systems through multi-scale simulations. Full article

Cropping systems and agronomic practices play a critical role in regulating soil organic matter dynamics and carbon dioxide (CO₂) emissions, which are key components of the global carbon cycle and climate change mitigation. However, the combined effects of tillage practices and seasonal climatic variability on CO₂ fluxes in chernozem soils (chernozems, WRB classification; highly fertile, humus-rich soils typical of steppe regions) of Northern Kazakhstan remain insufficiently understood. The aim of this study was to quantify soil CO₂ emissions under conventional tillage, no-till, and bare fallow systems during spring wheat cultivation on ordinary chernozems. Field experiments were conducted between 2023 and 2025 in the Kostanay Region (Kazakhstan). Soil CO₂ fluxes were measured using a chamber-based method, while soil temperature, moisture, and microbial community structure were monitored simultaneously. The results revealed pronounced seasonal and interannual variability in CO₂ emissions, ranging from 2 to 27 g CO₂·m⁻²·day⁻¹. Conventional tillage resulted in higher peak emissions due to increased soil aeration and accelerated organic matter mineralization, whereas no-till systems exhibited a more stable seasonal pattern and lower temperature sensitivity of soil respiration (Q₁₀ = 2.40 for no-till and 3.25 for conventional tillage). The application of machine learning techniques (Random Forest) significantly improved the prediction accuracy of CO₂ fluxes (R² = 0.67; RMSE = 3.37 g CO₂·m⁻²·day⁻¹) compared to linear models. These findings provide a scientific basis for the development of climate-smart agricultural practices aimed at improving carbon management in semi-arid steppe agroecosystems. Full article

Floods are among the most frequent and destructive natural hazards, causing significant socio-economic losses worldwide. This paper presents a comprehensive review of floodwall technologies, focusing on the integration of ultra-high-performance fibre-reinforced concrete (UHPFRC) to enhance structural and hydraulic performance. Flood protection systems are categorized into permanent, demountable, and temporary, and are evaluated based on parameters such as activation time, seepage resistance, and lifecycle cost. This review examines key structural applications, including floodwall barriers, wave-energy floaters, and retaining walls, in which UHPFRC provides significant advantages such as reduced material consumption, improved impact resistance, and increased durability in harsh environmental conditions. Additionally, recent advancements in floodwall systems are critically assessed through experimental investigations, numerical modelling, and hydraulic performance under varied loading and flow conditions. The analysis reveals that while UHPFRC systems can reduce material volumes by up to 73% and carbon emissions by 49% compared to conventional reinforced concrete, their adoption is currently limited by a lack of dedicated design standards. Based on a synthesis of peer-reviewed studies (2010–2026), findings indicate that autonomous, buoyancy-driven UHPFRC barriers offer the highest reliability in high-risk zones, whereas manual modular systems remain limited by human-factor vulnerabilities during rapid deployment. Critical research gaps are identified—specifically the need for standardized constitutive models for UHPFRC in hydrostatic environments and extensive long-term field validation—to support the transition toward resilient, smart urban flood defence infrastructure. Full article

Electric power distribution systems have been undergoing a transformation that can be attributed to factors such as the deregulation of the electric power supply industry, growing public concern over energy security and the environmental impact of energy generation and utilization, and technological advancements that have given impetus to concerted efforts to modernize the power grid in the framework of smart grid initiatives. The traditionally passive distribution network is increasingly becoming active due to the steady increase in the amount of distributed energy resources being integrated into the network. This has, in turn, given rise to a higher need for flexibility resources that can be used to handle the increased uncertainty caused by stochastic and intermittent distributed resources, such as variable renewable power generation. The provision of demand-side flexibility has largely been the purview of large industrial and commercial energy consumers. This article discusses the role that the aggregator can play in facilitating the provision of flexibility resources by small-scale consumers and prosumers and presents a case study on small-scale renewable generation and residential demand forecasting, which form an integral part of demand flexibility aggregation. Full article

We present an offline-first edge telemedicine platform designed for clinics and outreach programs where internet access, power, and IT support are unreliable. The system runs local electronic health record (EHR) and clinical “plug-in” screening services on a single embedded device, accessed through a clinician-facing web app over local WiFi. Data are stored locally with role-based access control and record-level encryption, while interoperability is provided as a best-effort queued synchronization pathway to external systems using HL7 FHIR when connectivity is available. As a representative plug-in, we implement non-invasive anemia screening from fingernail photographs. Images are processed fully on-device: an INT8-quantized YOLOv8n detector extracts nail regions, lightweight color and summary-statistic features are computed per ROI and concatenated, and a supervised regressor estimates hemoglobin. On an NVIDIA Jetson Orin Nano, ROI extraction runs in 22 ms and hemoglobin inference in 34 ms. Across six training strategies (unbalanced, augmented, and KDE-balanced by remark or severity), test RMSE ranges from 2.05–3.13 g/dL; the strongest numeric performance is achieved by severity-balanced SVR (RMSE 2.048 g/dL) and remark-balanced Gradient Boosting (RMSE 2.091 g/dL). Raincloud analyses restricted to true-anemic test cases show that balancing primarily reduces systematic overestimation (which drives false negatives) while augmentation can widen error tails, highlighting the importance of selecting training strategy to match screening objectives rather than optimizing a single aggregate metric. Full article

This study aimed to evaluate the within-system agreement and interchangeability of real-time and post-processed external load metrics in elite football. Data were collected from 50 official Serie A matches using Dynamix (K-Sport World S.R.L., Pesaro, Italy), the platform for acquiring and standardizing tracking inputs. SmartLive, a real-time monitoring module embedded within Dynamix, was compared with post-processed data from the league-approved optical tracking provider (Hawk-Eye Innovations Limited, Basingstoke, UK) in Serie A. The external load metrics analyzed included total distance covered; distances at speeds exceeding 15, 20, and 25 km·h⁻¹; distances within the 15–20 km·h⁻¹ and 20–25 km·h⁻¹ ranges; distance covered during accelerations > 2 m·s⁻² and decelerations < −2 m·s⁻²; and peak speed. Intraclass correlation coefficients (ICCs) demonstrated excellent agreement across all metrics, with values ranging from 0.929 to 0.999. Bland–Altman analysis revealed small mean differences between systems, indicating strong agreement. Overall, the findings confirm that both real-time and post-processed data are in close agreement across a wide range of performance metrics. Minor discrepancies were observed in intermediate speed zones and acceleration/deceleration events. This study provides the first validation of SmartLive’s within-system agreement with post-processed data, supporting its use alongside post-processed data in elite football environments. Full article

This research paper examines the environmental impact of natural and synthetic fur coats, focusing exclusively on the processing and manufacturing stages. Using one coat weighing approximately 5 kg as the functional unit, a comparative Life Cycle Assessment (LCA) is conducted from raw material processing to final garment production, explicitly excluding animal farming. The analysis includes key processes such as cleaning, tanning, dyeing, and sewing for natural fur, and polymer production, fabric formation, dyeing, and finishing for synthetic fur. Data from international academic literature (Google Scholar and Scopus) are used to evaluate CO₂ emissions, energy and water consumption, chemical inputs, and waste generation. Results indicate that synthetic fur production is energy-intensive but requires relatively low water use, whereas natural fur processing involves high water consumption and chemical treatments, resulting in significantly higher emissions—often reaching hundreds to thousands of kg CO₂e per coat. The study further investigates the role of embedded IoT systems in improving efficiency within tanneries and textile manufacturing. Real-time monitoring and automated dosing systems can reduce emissions and chemical use by approximately 10–20%. Case studies of a smart tannery and an IoT-enabled synthetic fur production line illustrate potential implementation pathways. Although such optimizations can reduce environmental impacts, the findings clearly show that natural fur processing remains considerably more carbon-intensive than synthetic alternatives. This research highlights the importance of integrating digital technologies into industrial processes and suggests directions for future work based on real-world operational data. Full article

Urban community parks are important public open spaces (POSs) that support residents’ quality of life (QoL) by aiding recreation, social interaction, and physical activity. However, evidence on how to design and assess sustainable POS in Saudi Arabia remains limited, particularly in relation to the country’s hot–arid climate, socio-cultural context, and emerging urban development priorities. This study aims to develop a context-sensitive framework for the design and assessment of sustainable POSs (a scope of urban community parks) in Saudi Arabia using a mixed-methods approach. The study combined: (i) a structured review of the literature on POSs’ sustainability and QoL/subjective well-being (SWB); (ii) naturalistic field observations in two community parks in Al-Khobar (Shells Park and Prince Ibn-Jalawy Park); (iii) an on-site questionnaire survey of park users assessing satisfaction and self-reported well-being (n = 89); and (iv) structured expert interviews to refine and prioritize the framework elements (n = 15). The quantitative analysis included descriptive statistics, Pearson correlation analysis, and reliability testing using Cronbach’s alpha, the Mann–Whitney U test, and the Kruskal–Wallis test to explore the associations between perceived park attributes, user satisfaction, and self-reported well-being. The framework was iteratively refined through triangulation via the literature, field evidence, user feedback, and expert judgement, while expert responses were synthesized using weighted mean scores, simple ranking system, and the Relative Importance Index (RII). The findings indicate that shading and thermal comfort, safety, accessibility, maintenance, and cultural alignment are the most important design priorities in the Saudi Arabian context. The empirical assessment also highlights recurrent shortcomings in the selected parks, particularly inadequate heat mitigation measures, inconsistent maintenance, limited recreational infrastructure, and the weak integration of smart support features. Based on this triangulated evidence, the study proposes a framework comprising nine categories, 43 sub-categories, with 137 indicators organized across environmental, socio-cultural, economic, and smart-enabler considerations. The framework provides a practical and context-sensitive tool for evaluating existing parks, prioritizing interventions, and guiding future community park development in support the Quality-of-Life Programme of Saudi Vision 2030. Full article

Drying is a critical stage in the postharvest chain, shaping product stability, quality, and economic value. Freshly harvested beans contain high moisture, and inadequate drying can lead to microbial growth, physical deterioration, and loss of key sensory attributes. In recent decades, diverse technologies have been developed to enhance drying efficiency while preserving flavor, improving consistency, and reducing environmental impacts. This review adopts a systematic and comparative approach, synthesizing peer-reviewed literature on conventional practices, advanced solar dryers, mechanical systems, hybrid configurations, and emerging techniques such as microwave, infrared, and desiccant-assisted drying. Emphasis is placed on heat and mass transfer mechanisms, the influence of environmental and operational parameters, and the role of varietal and processing differences. Comparative analyses reveal trade-offs between energy consumption, drying kinetics, and impacts on physical and chemical quality. Sustainability aspects are also examined, including energy use, carbon footprint, water consumption, and scalability for smallholders. Finally, key research gaps are identified, particularly in multiscale modeling, real-time monitoring, and integration with renewable energy and smart control systems. The review highlights pathways for achieving greater consistency, lower environmental burdens, and stronger value chains in producing regions worldwide. Full article