Search Results (191)

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

Background/Objectives: Global warming and rising CO₂ concentrations pose significant challenges to plant systems. Amid these pressures, this study contributes to understanding how tropical species respond by simultaneously evaluating reproductive and genetic traits. It specifically investigates the effects of maternal exposure to warming and elevated CO₂ on progeny physiology, genetic diversity, and population structure in Stylosanthes capitata, a resilient forage legume native to Brazil. Methods: Maternal plants were cultivated under controlled treatments, including ambient conditions (control), elevated CO₂ at 600 ppm (eCO₂), elevated temperature at +2 °C (eTE), and their combined exposure (eTEeCO₂), within a Trop-T-FACE field facility (Temperature Free-Air Controlled Enhancement and Free-Air Carbon Dioxide Enrichment). Seed traits (seeds per inflorescence, hundred-seed mass, abortion, non-viable seeds, coat color, germination at 32, 40, 71 weeks) and abnormal seedling rates were quantified. Genetic diversity metrics included the average (A) and effective (Ae) number of alleles, observed (Ho) and expected (He) heterozygosity, and inbreeding coefficient (Fis). Population structure was assessed using Principal Coordinates Analysis (PCoA), Analysis of Molecular Variance (AMOVA), number of migrants per generation (Nm), and genetic differentiation index (Fst). Two- and three-way Analysis of Variance (ANOVA) were used to evaluate factor effects. Results: Compared to control conditions, warming increased seeds per inflorescence (+46%), reduced abortion (−42.9%), non-viable seeds (−57%), and altered coat color. The germination speed index (GSI +23.5%) and germination rate (Gr +11%) improved with warming; combined treatments decreased germination time (GT −9.6%). Storage preserved germination traits, with warming enhancing performance over time and reducing abnormal seedlings (−54.5%). Conversely, elevated CO₂ shortened GSI in late stages, impairing germination efficiency. Warming reduced Ae (−35%), He (−20%), and raised Fis (maternal 0.50, progeny 0.58), consistent with the species’ mixed mating system; A and Ho were unaffected. Allele frequency shifts suggested selective pressure under eTE. Warming induced slight structure in PCoA, and AMOVA detected 1% (maternal) and 9% (progeny) variation. Fst = 0.06 and Nm = 3.8 imply environmental influence without isolation. Conclusions: Warming significantly shapes seed quality, reproductive success, and genetic diversity in S. capitata. Improved reproduction and germination suggest adaptive advantages, but higher inbreeding and reduced diversity may constrain long-term resilience. The findings underscore the need for genetic monitoring and broader genetic bases in cultivars confronting environmental stressors. Full article

There is a lack of an effective approach to measure vacuum conditions inside sealed vacuum electronic devices (VEDs) and other small-space vacuum instruments. In this study, the application performance of an innovative low-pressure gas sensor based on the emission enhancements of multi-walled carbon nanotube (MWCNT) field emitters was investigated, and the in situ vacuum performance of X-ray tubes was studied for the advantages of miniature dimension and having low power consumption, extremely low outgassing, and low thermal disturbance compared to conventional ionization gauges. The MWCNT emitters with high crystallinity presented good pressure sensing performance for nitrogen, hydrogen, and an air mixture in the range of 10⁻⁷ to 10⁻³ Pa. The miniature MWCNT sensor is able to work and remain stable with high-temperature baking, important for VED applications. The sensor monitored the in situ pressures of the sealed X-ray tubes successfully with high-power operations and a long-term storage of over two years. The investigation showed that the vacuum of the sealed X-ray tube is typical at a low 10⁻⁴ Pa level, and pre-sealing degassing treatments are able to make the X-ray tube work under high vacuum levels with less outgassing and keep a stable high vacuum for a long period of time. Full article

Predictive maintenance (PdM) is a proactive strategy that enhances safety, minimizes unplanned downtime, and optimizes operational costs by forecasting equipment failures before they occur. This study presents a novel Field Programmable Gate Array (FPGA)-accelerated predictive maintenance framework for UAV engines using a Singular Value Decomposition (SVD)-optimized Long Short-Term Memory (LSTM) model. The model performs binary classification to predict the likelihood of imminent engine failure by processing normalized multi-sensor data, including temperature, pressure, and vibration measurements. To enable real-time deployment on resource-constrained UAV platforms, the LSTM’s weight matrices are compressed using Singular Value Decomposition (SVD), significantly reducing computational complexity while preserving predictive accuracy. The compressed model is executed on a Xilinx ZCU-104 FPGA and uses a pipelined, AXI-based hardware accelerator with efficient memory mapping and parallelized gate calculations tailored for low-power onboard systems. Unlike prior works, this study uniquely integrates a tailored SVD compression strategy with a custom hardware accelerator co-designed for real-time, flight-safe inference in UAV systems. Experimental results demonstrate a 98% classification accuracy, a 24% reduction in latency, and substantial FPGA resource savings—specifically, a 26% decrease in BRAM usage and a 37% reduction in DSP consumption—compared to the 32-bit floating-point SVD-compressed FPGA implementation, not CPU or GPU. These findings confirm the proposed system as an efficient and scalable solution for real-time UAV engine health monitoring, thereby enhancing in-flight safety through timely fault prediction and enabling autonomous engine monitoring without reliance on ground communication. Full article

Cable-stayed bridge cables experience significant tension over time, making the bridge cables prone to corrosion and fatigue. The direct measurement of cable length is not a standard capability in most current structural health monitoring systems, nor is long-term monitoring of cable changes. Bridge displacements are caused by both dynamic loads (wind and traffic) and quasi-static factors, primarily temperature. This study filtered out dynamic responses by the three-sigma rule, multiple linear regression, interpolation method, and not-a-number calibration. Monitoring data were used to analyze the bridge’s thermal field distribution and the time-dependent variation of tower displacements. Correlation analysis revealed a strong linear correlation between air temperature and quasi-static tower-girder displacements. This research proposes to use the tower-girder distance (effective cable length) to represent the length of the cable, take the thermal expansion coefficient of the effective length of the cable as the quantitative index for long-term monitoring, and take its error as the performance early warning indicator. This method effectively monitors cable health and provides damage warnings. Full article

Investigating soil temperature and the heat transfer process is essential for understanding water–heat changes and energy balance in farmland. The conversion from upland fields (UFs) to paddy fields (PFs) alters the land cover, irrigation regimes, and soil properties, leading to differences in soil temperature, thermal properties, and heat fluxes. Our study aimed to quantify the effects of converting UFs to PFs on soil temperature and heat transfer processes, and to elucidate its underlying mechanisms. A long-term cultivated UF and a newly developed PF (converted from a UF in May 2015) were selected for this study. Soil water content (SWC) and temperature were monitored hourly over two years (June 2017 to June 2019) in five soil horizons (i.e., 10, 20, 40, 60, and 90 cm) at both fields. The mean soil temperature differences between the UF and PF at each depth on the annual scale varied from −0.1 to 0.4 °C, while they fluctuated more significantly on the seasonal (−0.9~1.8 °C), monthly (−1.5~2.5 °C), daily (−5.6~4.9 °C), and hourly (−7.3~11.3 °C) scales. The SWC in the PF was significantly higher than that in the UF, primarily due to differences in tillage practices, which resulted in a narrower range of soil temperature variation in the PF. Additionally, the SWC and soil physicochemical properties significantly altered the soil’s thermal properties. Compared with the UF, the volumetric heat capacity (C_s) at the depths of 10, 20, 40, 60, and 90 cm in the PF changed by 8.6%, 19.0%, 5.5%, −4.3%, and −2.9%, respectively. Meanwhile, the thermal conductivity (λ_θ) increased by 1.5%, 18.3%, 19.0%, 9.0%, and 25.6%, respectively. Moreover, after conversion from the UF to the PF, the heat transfer direction changed from downward to upward in the 10–20 cm soil layer, resulting in a 42.9% reduction in the annual average soil heat flux (G). Furthermore, the differences in G between the UF and PF were most significant in the summer (101.9%) and most minor in the winter (12.2%), respectively. The conversion of the UF to the PF increased the C_s and λ_θ, ultimately reducing the range of soil temperature variation and changing the direction of heat transfer, which led to more heat release from the soil. This study reveals the effects of farmland use type conversion on regional land surface energy balance, providing theoretical underpinnings for optimizing agricultural ecosystem management. Full article

Ocean ecosystem services provisioning is driven by phytoplankton, which form the base of the ocean food chain in aquatic ecosystems and play a critical role as the Earth‘s carbon sink. Phytoplankton is highly sensitive to temperature, making it vulnerable to the effects of temperature variations. The aim of this research was to develop and test a workflow analysis to monitor the impact of sea surface temperature (SST) on phytoplankton biomass and primary production by combining field and remote sensing data of Chl-a and net primary production (NPP) (as proxies of phytoplankton biomass). The tropical zone was used as a case study to test the procedure. Firstly, machine learning algorithms were applied to the field data of SST, Chl-a and NPP, showing that the Random Forest was the most effective in capturing the dataset’s patterns. Secondly, the Random Forest algorithm was applied to MODIS SST images to build Chl-a and NPP time series. The time series analysis showed a significant increase in SST which corresponded to a significant negative trend in Chl-a concentrations and NPP variation. The recurrence plot of the time series revealed significant disruptions in Chl-a and NPP evolutions, potentially linked to El Niño–Southern Oscillation (ENSO) events. Therefore, the analysis can help to highlight the effects of temperature variation on Chl-a and NPP, such as the long-term evolution of the trend and short perturbation events. The methodology, starting from local studies, can support broader spatial–temporal-scale studies and provide insights into future scenarios. Full article

Distributed optical fiber sensing (DOFS) is an advanced tool for structural health monitoring (SHM), offering high precision, wide measurement range, and real-time as well as long-term monitoring capabilities. It enables real-time monitoring of both temperature and strain information along the entire optical fiber line, providing a novel approach for safety monitoring and structural health assessment in transportation engineering. This paper first introduces the fundamental principles and classifications of DOFS technology and then systematically reviews the current research progress on Brillouin scattering-based DOFS. By analyzing the monitoring requirements of various types of transportation infrastructure, this paper discusses the applications and challenges of this technology in SHM and damage detection for roads, bridges, tunnels, and other infrastructure, particularly in identifying and tracking cracks, deformations, and localized damage. This review highlights the significant potential and promising prospects of Brillouin scattering technology in transportation engineering. Nevertheless, further research is needed to optimize sensing system performance and promote its widespread application in this field. These findings provide valuable references for future research and technological development. Full article

This article presents the development of a compact, high-precision, and energy-efficient temperature monitoring system designed for tracking applications where continuous and accurate thermal monitoring is essential. Built around the HY0020 System-on-Chip (SoC), the system integrates two bandgap-based temperature sensors—one internal to the SoC and one external (Si7020-A20)—mounted on a custom PCB and powered by a coin cell battery. A distinctive feature of the system is its support for real-time parameterization of the internal sensor, which enables advanced capabilities such as thermal profiling, cross-validation, and onboard diagnostics. The system was evaluated under both room temperature and refrigeration conditions, demonstrating high accuracy with the internal sensor showing an average error of 0.041 °C and −0.36 °C, respectively, and absolute errors below ±0.5 °C. With an average current draw of just 0.01727 mA, the system achieves an estimated autonomy of 6.6 years on a 1000 mAh battery. Data are transmitted via Bluetooth Low Energy (BLE) to a Raspberry Pi 4 gateway and forwarded to an IoT cloud platform for remote access and analysis. With a total cost of approximately EUR 20 and built entirely from commercially available components, this system offers a scalable and cost-effective solution for a wide range of temperature-sensitive applications. Its combination of precision, long-term autonomy, and advanced diagnostic capabilities make it suitable for deployment in diverse fields such as supply chain monitoring, environmental sensing, biomedical storage, and smart infrastructure—where reliable, low-maintenance thermal tracking is essential. Full article

Understanding the complex phenomena of interactions between the elements of a combined piled raft foundation (CPRF) is essential for the proper design of such foundations. To evaluate the effects of mutual influence among the CPRF’s elements, a series of long-term measurements of selected physical quantities related to the performance of the foundation were conducted on a building with a frame structure, stiffening walls, and monolithic technology, consisting of seven aboveground stories and one underground story. The analysis distinguishes the real deformations resulting from temperature changes and from stress strains resulting from load changes. The two types of deformations were subjected to further interpretation of only changes in the stress and strain over time. Changes in stress values in the subsoil, as well as strain measurements in the vertical direction of concrete columns, were recorded to assess the load distribution between the CPRF’s components. The numerical analysis results obtained for a fragment of the monitored foundation were compared with actual measurement results to verify the numerical model of interaction between the structure and the soil. Field monitoring and FEA methods were used to compare the long-term deformation analysis, and they helped to minimize the monitoring time. This comparison also served to supplement and simultaneously expand the dataset of test results on a real-world scale. Full article

CO₂-enhanced coalbed methane recovery (CO₂-ECBM) represents a promising pathway within carbon capture, utilization, and storage (CCUS) technologies, offering dual benefits of methane production and long-term CO₂ sequestration. This review provides a comprehensive analysis of CO₂-ECBM from a full-chain perspective (Mechanism, Practices, and Outlook), covering fundamental mechanisms and key engineering practices. It highlights the complex multi-physics processes involved, including competitive adsorption–desorption, diffusion and seepage, thermal effects, stress responses, and geochemical interactions. Recent progress in laboratory experiments, capacity assessments, site evaluations, monitoring techniques, and numerical simulations are systematically reviewed. Field studies indicate that CO₂-ECBM performance is strongly influenced by reservoir pressure, temperature, injection rate, and coal seam properties. Structural conditions and multi-field coupling further affect storage efficiency and long-term security. This work also addresses major technical challenges such as real-time monitoring limitations, environmental risks, injection-induced seismicity, and economic constraints. Future research directions emphasize the need to deepen understanding of coupling mechanisms, improve monitoring frameworks, and advance integrated engineering optimization. By synthesizing recent advances and identifying research priorities, this review aims to provide theoretical support and practical guidance for the scalable deployment of CO₂-ECBM, contributing to global energy transition and carbon neutrality goals. Full article

Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of Things (IoT)-based air quality monitoring system was developed and tested using the most commonly preferred sensor types for air quality measurement: fine particulate matter (PM_2.5), carbon dioxide (CO₂), temperature, and humidity sensors. To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). Sensor performance was evaluated by comparing measurements with a reference device, and the best-performing ML model was determined for each sensor. The results indicate that GB and kNN achieved the highest accuracy. For CO₂ sensor calibration, GB achieved R² = 0.970, RMSE = 0.442, and MAE = 0.282, providing the lowest error rates. For the PM_2.5 sensor, kNN delivered the most successful results, with R² = 0.970, RMSE = 2.123, and MAE = 0.842. Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R² = 0.976, RMSE = 2.284). These findings demonstrate that, by identifying suitable ML methods, ML-based calibration techniques can significantly enhance the accuracy of LCSs. Consequently, they offer a viable and cost-effective alternative to traditional high-cost air quality monitoring systems. Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field. Full article

Wind turbines’ volume and power generation capacity have increased worldwide. Consequently, their inspection, maintenance, and repair are garnering increasing attention. Structural defects are common in turbine blades, but their detection is difficult due to the relatively large size of the blades. Therefore, engineers often use nondestructive testing. This study employed an unmanned aerial vehicle (UAV) to simultaneously capture visible-light and infrared thermal images of wind power blades. Subsequently, instant neural graphic primitives and neural radiance fields were used to reconstruct the visible-light image in three dimensions (3D) and generate a 3D mesh model. Experiments determined that after converting parts of the orthographic-view images to elevation- and depression-angle images, the success rate of camera attitude calculation increased from 85.6% to 97.4%. For defect measurement, the system first filters out the perspective images that account for 6–12% of the thermal image foreground area, thereby excluding most perspective images that are difficult to analyze. Based on the thermal image data of wind power generation blades, the blade was considered to be in a normal state when the full range, average value, and standard deviation of the relative temperature grayscale value in the foreground area were within their normal ranges. Otherwise, it was classified as abnormal. A heat accumulation percentage map was established from the perspective image of the abnormal state, and defect detection was based on the occurrence of local minima. When a defect was observed in the thermal image, the previously reconstructed 3D image was switched to the corresponding viewing angle to confirm the actual location of the defect on the blade. Thus, the proposed 3D image reconstruction process and thermal image quality analysis method are effective for the long-term monitoring of wind turbine blade quality. Full article

Accurate prediction of greenhouse temperatures is essential for developing effective environmental control strategies, as the precision of minimum temperature data acquisition significantly impacts the reliability of predictive models. Traditional monitoring methods face inherent challenges due to the conflicting demands of temperature-field uniformity assumptions and the costs associated with sensor deployment. This study introduces an ARIMA-Kriging spatiotemporal coupling model, which combines temperature time-series data with sensor spatial coordinates to accurately determine minimum temperatures in greenhouses while reducing hardware costs. Utilizing the high-quality data processed by this model, this study proposes and constructs a novel Grey Wolf Optimizer and Bidirectional Long Short-Term Memory (GWO-BiLSTM) temperature prediction framework, which combines a Grey Wolf Optimizer (GWO)-enhanced algorithm with a Bidirectional Long Short-Term Memory (BiLSTM) network. Across different prediction horizons (10 min and 30 min intervals), the GWO-BiLSTM model demonstrated superior performance with key metrics reaching a coefficient of determination (R²) of 0.97, root mean square error (RMSE) of 0.79–0.89 °C (41.7% reduction compared to the PSO-BP model), mean absolute percentage error (MAPE) of 4.94–8.5%, mean squared error (MSE) of 0.63–0.68 °C, and mean absolute error (MAE) of 0.62–0.65 °C, significantly outperforming the BiLSTM, LSTM, and PSO-BP models. Multi-weather validation confirmed the model’s robustness under rainy, snowy, and overcast conditions, maintaining R² ≥ 0.95. Optimal prediction accuracy was observed in clear weather (RMSE = 0.71 °C), whereas rainy/snowy conditions showed a 42.9% improvement in MAPE compared to the PSO-BP model. This study provides reliable decision-making support for precise environmental regulation in facility greenhouse environments, effectively advancing the intelligent development of agricultural environmental control systems. Full article

This research makes a strong focus on improving fluid dynamics inside the reservoir after stimulation for enhancing oil and gas well performance, particularly in terms of increasing the Gas–oil ratio (GOR) and injectivity leading to a better productivity index (PI). Advanced stimulation operation using new formulated emulsified acid treatment greatly improves the reservoir permeability, allowing for better fluid movement and less formation damage. This, in turn, results in injectivity increases of at least 2.5 times and, in some situations, up to five times the original rate, which is critical for sustaining reservoir pressure and ensuring effective hydrocarbon recovery. The emulsified acid outperforms typical 15% HCl treatments in terms of dissolving and corrosion rates, as it is tuned for the reservoir’s pressure, temperature, permeability, and porosity. This dual-phase technology increases injectivity by five times while limiting the environmental and material consequences associated with spent and waste acid quantities. Field trials reveal significant improvements in injection pressure and a marked reduction in circulation pressure during stimulation, underscoring the treatment’s efficient penetration within the rock pores to enhance oil flow and sweep. This increase in performance is linked to the creation of the wormholing impact of the emulsified acid, resulting in improved fluid dynamics and optimized reservoir efficiency, as shown by the enhanced gas–oil ratio (GOR) in the four mentioned cases. A critical component of attaining such improvements is the capacity to effectively analyze and forecast reservoir behavior prior to executing the stimulation in real life. Engineers can accurately forecast injectivity gains and improve fluid injection tactics by constructing an advanced predictive model with low error margins, decreasing the need for time-consuming and costly trial-and-error approaches. Importantly, the research utilizes sophisticated neural network modeling to forecast stimulation results with minimal inaccuracies. This predictive ability not only diminishes the dependence on expensive and prolonged trial-and-error methods but also enables the proactive enhancement of treatment designs, thereby increasing efficiency and cost-effectiveness. This modeling approach based on several operational and reservoir factors, combines real-time field data, historical well performance records, and fluid flow simulations to verify that the expected results closely match the actual field outcomes. A well-calibrated prediction model not only reduces uncertainty but also improves decision making, allowing operators to create stimulation treatments based on unique reservoir features while minimizing unnecessary costs. Furthermore, enhancing fluid dynamics through precise modeling helps to improve GOR management by keeping gas output within appropriate limits while optimizing liquid hydrocarbon recovery. Finally, by employing data-driven modeling tools, oil and gas operators can considerably improve reservoir performance, streamline operational efficiency, and achieve long-term production growth through optimal resource usage. This paper highlights a new approach to optimizing reservoir productivity, aligning with global efforts to minimize environmental impacts in oil recovery processes. The use of real-time monitoring has boosted the study by enabling for exact measurement of post-injectivity performance and oil flow rates, hence proving the efficacy of these advanced stimulation approaches. The study offers unique insights into unconventional reservoir growth by combining numerical modeling, real-world data, and novel treatment methodologies. The aim is to investigate novel simulation methodology, advanced computational tools, and data-driven strategies for improving the predictability, reservoir performance, fluid behavior, and sustainability of heavy oil recovery operations. Full article