Search Results (1,522)

This article presents an empirical evaluation of the technical and economic performance of a building-integrated photovoltaic (PV) system implemented at the Faculty of Architecture and Urbanism of the University of Cuenca, Ecuador. This study explores both stages of deployment, beginning with a 7.7 kWp pilot system and later scaling to a full 75.6 kWp configuration. This hourly monitoring of power exchanges with utility was conducted over several months using high-resolution instrumentation and cloud-based analytics platforms. A detailed comparison between projected energy output, recorded production, and real energy consumption was carried out, revealing how seasonal variability, cloud cover, and academic schedules influence system behavior. The findings also include a comparison between billed and actual electricity prices, as well as an analysis of the system’s payback period under different cost scenarios, including state-subsidized and real-cost frameworks. The results confirm that energy exports are frequent during weekends and that daily generation often exceeds on-site demand on non-working days. Although the university benefits from low electricity tariffs, the system demonstrates financial feasibility when broader public cost structures are considered. This study highlights operational outcomes under real-use conditions and provides insights for scaling distributed generation in institutional settings, with particular relevance for Andean urban contexts with similar solar profiles and tariff structures. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

Background/Objectives: Repetitive behaviors such as hand flapping, body rocking, and head shaking characterize Autism Spectrum Disorder (ASD) while functioning as early signs of neurodevelopmental variations. Traditional diagnostic procedures require extensive manual observation, which takes significant time, produces subjective results, and remains unavailable to many regions. The research introduces a real-time system for the detection of ASD-typical behaviors by analyzing body movements through the You Only Look Once (YOLOv11) deep learning model. Methods: The system’s multi-layered design integrates monitoring, network, cloud, and typical ASD behavior detection layers to facilitate real-time video acquisition, wireless data transfer, and cloud analysis along with ASD-typical behavior classification. We gathered and annotated our own dataset comprising 72 videos, yielding a total of 13,640 images representing four behavior classes that include hand flapping, body rocking, head shaking, and non_autistic. Results: YOLOv11 demonstrates superior performance compared to baseline models like the sub-sampling (CNN) (MobileNet-SSD) and Long Short-Term Memory (LSTM) by achieving 99% accuracy along with 96% precision and 97% in recall and the F1-score. Conclusions: The results indicate that our system provides a scalable solution for real-time ASD screening, which might help clinicians, educators, and caregivers with early intervention, as well as ongoing behavioral monitoring. Full article

To address the issue of structural tilting caused by uneven foundation settlement in soft soil areas, this study combined a specific engineering case to conduct numerical simulations of the rectification process for an inclined reinforced concrete building using ABAQUS finite element software. Micropile-raft combined jacking technology was employed, applying staged jacking forces (2400 kN for Axis A, 2200 kN for Axis B, and 1700 kN for Axis C) with precise control through 20 incremental steps. The results demonstrate that this technology effectively halted structural tilting, reducing the maximum inclination rate from 0.51% to 0.05%, significantly below the standard limit. Post-rectification, the peak structural stress decreased by 42%, and displacements were markedly reduced. However, the jacking process led to a notable increase in the column axial forces and directional changes in beam bending moments, reflecting the dynamic redistribution of internal forces. The study confirms that micropile-raft combined jacking technology offers both controllability and safety, while optimized counterforce pile layouts enhance the long-term stability of the rectification system. Based on stress and displacement cloud analysis, a monitoring scheme is proposed, forming an integrated “rectification-monitoring-reinforcement” solution, which provides a technical framework for building rectification in soft soil regions. Full article

The accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persistent cloud cover make consistent monitoring challenging. We integrated multi-temporal Sentinel-2 and Landsat-8 imagery from 2017 to 2021 on the Google Earth Engine platform and applied a sample migration strategy to construct multi-year training data. A random forest classifier was used to identify nine major planting patterns at a 10 m resolution. The classification achieved an average overall accuracy of 88.3%, with annual Kappa coefficients ranging from 0.81 to 0.88. A spatial analysis revealed that single rice was the dominant pattern, covering more than 60% of the area. Temporal variations in cropping patterns were categorized into four frequency levels (0, 1, 2, and 3 changes), with more dynamic transitions concentrated in the central-western and northern subregions. A multiscale geographically weighted regression (MGWR) model revealed that economic and production-related factors had strong positive associations with crop planting patterns, while natural factors showed relatively weaker explanatory power. This research presents a scalable method for mapping fine-resolution crop patterns in complex agroecosystems, providing quantitative support for regional land-use optimization and the development of agricultural policies. Full article

The quality of machined marine propellers plays a critical role in underwater propulsion performance. Precision casting is the predominant manufacturing technique; however, deformation of wax models and rough blanks during manufacturing frequently cause deviations in the dimensions of final products and, thus, affect propellers’ performance and service life. Current inspection methods primarily involve using coordinate measuring machines and sampling. This approach is time-consuming, has high labor costs, and cannot monitor manufacturing quality in real-time. This study developed a real-time online automated measurement system containing a high-resolution CITIZEN displacement sensor, a four-degree-of-freedom measurement platform, and programmable logic controller-based motion control technology to enable rapid, automated measurement of blade deformation across the wax model, rough blank, and final product processing stages. The measurement data are transmitted in real time to a cloud database. Tests conducted on a standardized platform and real propeller blades confirmed that the system consistently achieved measurement accuracy to the second decimal place under the continual measurement mode. The system also demonstrated excellent repeatability and stability. Furthermore, the continuous measurement mode outperformed the single-point measurement mode. Overall, the developed system effectively reduces labor requirements, shortens measurement times, and enables real-time monitoring of process variation. These capabilities underscore its strong potential for application in the smart manufacturing and quality control of marine propellers. Full article

Air pollution poses a growing threat to public health and the environment, highlighting the need for continuous and precise urban air quality monitoring. The aim of this study was to implement and evaluate an autonomous air quality monitoring platform developed by the University of Ruse, “Angel Kanchev”, under Bulgaria’s National Recovery and Resilience Plan (project BG-RRP-2.013-0001), co-financed by the European Union through the NextGenerationEU initiative. The system, based on Libelium’s mobile sensor technology, was installed at a height of two meters on the university campus near Rodina Boulevard and operated continuously from 1 March 2024 to 30 March 2025. Every 15 min, it recorded concentrations of CO, CO₂, NO₂, SO₂, PM₁, PM_2.5, and PM₁₀, along with meteorological parameters (temperature, humidity, and pressure), transmitting the data via GSM to a cloud-based database. Analyses included a distributional assessment, Spearman rank correlations, Kruskal–Wallis tests with Dunn–Sidak post hoc comparisons, and k-means clustering to identify temporal and meteorological patterns in pollutant levels. The results indicate the high operational stability of the system and reveal characteristic pollution profiles associated with time of day, weather conditions, and seasonal variation. The findings confirm the value of combining calibrated IoT systems with advanced statistical methods to support data-driven air quality management and the development of predictive environmental models. Full article

Intelligent mining technology, as the core driving force for the digital transformation of the mining industry, integrates cyber-physical systems, artificial intelligence, and industrial internet technologies to establish a “cloud–edge–end” collaborative system. In this paper, the development trajectory of intelligent mining technology has been systematically reviewed, which has gone through four stages: stand-alone automation, integrated automation and informatization, digital and intelligent initial, and comprehensive intelligence. And the current development status of “cloud–edge–end” technologies has been reviewed: (i) The end layer achieves environmental state monitoring and precise control through a multi-source sensing network and intelligent equipment. (ii) The edge layer leverages 5G and edge computing to accomplish real-time data processing, 3D dynamic modeling, and safety early warning. (iii) The cloud layer realizes digital planning and intelligent decision-making, based on the industrial Internet platform. The three-layer collaboration forms a “perception–analysis–decision–execution” closed loop. Currently, there are still many challenges in the development of the technology, including the lack of a standardization system, the bottleneck of multi-source heterogeneous data fusion, the lack of a cross-process coordination of the equipment, and the shortage of interdisciplinary talents. Accordingly, this paper focuses on future development trends from four aspects, providing systematic solutions for a safe, efficient, and sustainable mining operation. Technological evolution will accelerate the formation of an intelligent ecosystem characterized by “standard-driven, data-empowered, equipment-autonomous, and human–machine collaboration”. Full article

As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5–70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R²) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51–56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques’ data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

Swarm-based unmanned aerial vehicle (UAV) systems offer enhanced spatial coverage, collaborative intelligence, and mission scalability for various applications, including environmental monitoring and emergency response. However, their onboard processing is limited by stringent size, weight, and power constraints, posing challenges for real-time computation and autonomous control. This paper presents an integrated communication and computation framework that combines cloud–edge–end collaboration with cell-free massive multiple-input multiple-output (CF-mMIMO) to enable scalable and efficient task offloading in UAV swarms. Furthermore, we implement a prototype system testbed with nine UAVs and validate the proposed framework through real-time object detection tasks. Results demonstrate over $30\%$ reduction in onboard computation and significant improvements in communication reliability, highlighting the framework’s potential for enabling intelligent, cooperative aerial systems. Full article

To better understand the temperature changes in face slab concrete and address challenges such as delayed curing and outdated methods in complex and variable environments, this study investigates the use of visualization and real-time feedback control in concrete construction. The conducted study systematically develops an intelligent curing control system for face slab concrete based on multi-source measured data. A tailored multi-source data acquisition scheme was proposed, supported by an IoT-based transmission framework. Cloud-based data analysis and feedback control mechanisms were implemented, along with a decoupled front-end and back-end system platform. This platform integrates essential functions such as two-way communication with gateway devices, data processing and analysis, system visualization, and intelligent curing control. In conjunction with the ongoing Maerdang concrete face rockfill dam (CFRD) project, located in a high-altitude, cold-climate region, an intelligent curing system platform for face slab concrete was developed. The platform enables three core visualization functions: (1) monitoring the pouring progress of face slab concrete, (2) the early warning and prediction of temperature exceedance, and (3) dynamic feedback and adjustment of curing measures. The research outcomes were successfully applied to the intelligent curing of the Maerdang face slab concrete, providing both theoretical insight and practical support for achieving scientific and precise curing control. Full article

This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies. Full article

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

Monitoring forest structure, diversity, and biomass in restoration areas is both expensive and time-consuming. Metrics derived from digital aerial photogrammetry (DAP) may offer a cost-effective and efficient alternative for monitoring forest restoration. The main objective of this study was to use metrics derived from digital aerial photogrammetry (DAP) point clouds obtained by remotely piloted aircraft (RPA) to estimate aboveground biomass (AGB), species diversity, and structural variables for monitoring restored secondary tropical forest areas. The study was conducted in three active and one passive forest restoration systems located in a secondary forest in Sergipe state, Brazil. A total of 2507 tree individuals from 36 plots (0.0625 ha each) were identified, and their total height (ht) and diameter at breast height (dbh) were measured in the field. Concomitantly with the field inventory, the plots were mapped using an RPA, and traditional height-based point cloud metrics and Fourier transform-derived metrics were extracted for each plot. Regression models were developed to calculate AGB, Shannon diversity index (H′), ht, dbh, and basal area (ba). Furthermore, multivariate statistical analyses were used to characterize AGB and H′ in the different restoration systems. All fitted models selected Fourier transform-based metrics. The AGB estimates showed satisfactory accuracy (R² = 0.88; RMSE = 31.2%). The models for H′ and ba also performed well, with R² values of 0.90 and 0.67 and RMSEs of 24.8% and 20.1%, respectively. Estimates of structural variables (dbh and ht) showed high accuracy, with RMSE values close to 10%. Metrics derived from the Fourier transform were essential for estimating AGB, species diversity, and forest structure. The DAP-RPA-derived metrics used in this study demonstrate potential for monitoring and characterizing AGB and species richness in restored tropical forest systems. Full article