Search Results (4,930)

The growing global demand for computational resources, particularly in Artificial Intelligence (AI) applications, raises increasing concerns about energy consumption and its environmental impact. This study introduces a newly developed energy consumption index that evaluates the energy efficiency of Deep Learning (DL) models, providing a standardized and adaptable approach for various models. Convolutional neural networks, including both classical and modern architectures, serve as the primary case study to demonstrate the applicability of the index. Furthermore, the inclusion of the Swin Transformer, a state-of-the-art and modern non-convolutional model, highlights the adaptability of the framework to diverse architectural paradigms. This study analyzes the energy consumption during both training and inference of representative DL architectures, including AlexNet, ResNet18, VGG16, EfficientNet-B3, ConvNeXt-T, and Swin Transformer, trained on the Imagenette dataset using TITAN XP and GTX 1080 GPUs. Energy measurements are obtained using sensor-based tools, including OpenZmeter (v2) with integrated electrical sensors. Additionally, software-based tools such as CarbonTracker (v1.2.5) and CodeCarbon (v2.4.1) retrieve energy consumption data from computational component sensors. The results reveal significant differences in energy efficiency across architectures and GPUs, providing insights into the trade-offs between model performance and energy use. By offering a flexible framework for comparing energy efficiency across DL models, this study advances sustainability in AI systems, supporting accurate and standardized energy evaluations applicable to various computational settings. Full article

Thermal coating paints offer a passive strategy to reduce heat gain in buildings, improve ventilation, and lower energy consumption. This study investigates the effectiveness of these technologies by comparing different housing structures and environmental conditions. Specifically, it examines thermal envelope solutions for cool roofs in homes along the Colombian Caribbean Coast. We quantify the thermal impacts using experimental data collected from 120 houses across eight municipalities in the Magdalena Department, Colombia. The research details the technology and analytical methods employed, focusing on thermal reductions achieved through thermal coatings to potentially reduce energy demand. A comprehensive measurement system, incorporating temperature and humidity sensors, is developed to assess the impact of the coatings. Thermal comfort is evaluated according to the ASHRAE 55 standard, with temperature reductions calculated for each house treated with thermal coatings. A methodology is applied to evaluate the thermal reduction between a house with a coating solution versus a house without it. The results show a temperature reduction on a house-by-house basis, from 1.5% to 16%. On average, the results yield a significant 7% reduction in thermal load. Additionally, a mobile application is developed to disseminate the results of this research, promoting the social appropriation of science among the involved communities. Full article

Wireless Sensor Networks (WSNs) connected to the Internet of Things (IoT) are increasingly employed in commercial and industrial applications to accomplish various tasks at a low cost. WSNs are essential for gathering diverse types of data within physical environments. A key design objective for WSNs is to balance energy consumption and increase the network’s operating lifetime. Recent studies have shown that mobile base stations (BSs) can significantly extend the lifetime of such networks, especially when their location is optimized using specific criteria. In this study, we propose an algorithm for selecting the optimal BS location in a large network. The algorithm computes a distance metric between sensor nodes (SNs) and potential BS locations on a virtual grid within the WSN. The selection process is repeated periodically to account for dead SNs, allowing the BS to relocate to a new optimal position based on the remaining active nodes after each iteration. Additionally, the inclusion of a relay node (RN) in large networks is explored to improve scalability. The impact of path loss within WSNs is also discussed. The proposed algorithms are applied to the well-known Stable Election Protocol (SEP). Simulation results demonstrate that, compared to other algorithms in the literature, the proposed approaches significantly enhance the lifetime of WSNs. Full article

Optical sensors are a promising technology in structural and health monitoring due to their high sensitivity and immunity to electromagnetic interference. Because of their high sensitivity, they can register the responses of buildings to a wide range of motions, including those induced by ambient noise, or detect small structural changes caused by aging or environmental factors. In previous work, an FBG-based accelerometer was introduced that is suitable for use as an autonomous unit since it does not make use of any interrogator equipment. In this paper, we present the results of the characterization of this device, which yielded the best precision and accuracy. The results show the following: (i) improvements in the orthogonality of the sensor axes, which impact their cross-axis sensitivity; (ii) reductions in the electronic noise, which increase the signal-to-noise ratio. The results of our static characterization show that, in the worst case, we can obtain a correlation coefficient R² of 0.9999 when comparing the output voltage with the input acceleration for the X- and Y-axes of the sensor. We developed an analytical, non-iterative, 12-parameter matrix calibration approach based on the least-squares method, which allows compensation for different gains in its axes, offset, and cross-axis. To improve the accuracy of our sensor, we propose a table with correction terms that can be subtracted from the estimated acceleration. The mean error of each estimated acceleration component of the sensor is zero, with a maximum standard deviation of 0.018 m/s². The maximum RMSE for all tested positions is 6.7 × 10⁻³ m/s². Full article

Urban afforestation is increasingly regarded as a key strategy for fostering biodiversity to restore and enhance the ecosystem services needed to counteract the effects of climate change in built-up areas. In Italy, several experimental afforestation projects have been launched as part of the National Recovery and Resilience Plan (NRRP), focusing on cities or metropolitan areas such as Milan, Rome, Pistoia and Campobasso. These projects follow a multidisciplinary approach, integrating botanists, foresters, urban planners, landscape architects and remote sensing specialists. The goal is to address the challenging complexity of urban forest restoration through reforestation and afforestation actions. Key innovations include the integration of transdisciplinary methodologies (landscape analysis, landscape design, forest and plant ecology) with the application of advanced remote sensing technologies and participatory community engagement frameworks to address ecological and social challenges. Experimental plots have been set up across various urban areas, testing a range of planting schemes to maximise climate change resilience and ensure long-term ecological sustainability. Emphasis has been placed on selecting drought-tolerant and thermophilic species that are better adapted to widespread warming and local urban heat islands. Biodiversity strips with perennial flowers for insects, shrubs with berries for birds and nests for wild bees and vertebrates have been set up to enhance biodiversity in new afforestation areas. Advanced monitoring tools, such as Light Detection and Ranging (LiDAR) and multi-sensor drones, have been employed alongside field observations to assess forest growth, species survival, structural complexity and biodiversity enhancement over time. Historical analyses of landscape patterns and ecological connectivity over the past 200 years, along with evaluations of afforestation projects from the last 70 years, have provided critical insights into the successes and challenges of previous interventions, serving as a guide for future efforts. By focusing on ecological connectivity, the integration of afforested areas into the urban matrix, and citizen engagement, the current project aims to align urban forestry efforts with sustainable development goals. This comprehensive project framework addresses environmental restoration and the social and aesthetic impacts on local communities, contributing to the overall resilience and well-being of urban and peri-urban ecosystems. Full article

The influence of processing procedures and microstructural features on the functional properties of relaxor ferroelectric ceramics are of fundamental interest and directly relevant to their applications in dielectric capacitors and electromechanical sensors/actuators. In the present work, solid solutions of 0.65(K_0.5Bi_0.5)TiO₃-0.35(Ba_0.94Ca_0.06)(Ti_0.93Zr_0.07)O₃ (0.65KBT-0.35BCZT) were processed by solid-state reaction using two different procedures, distinguished in terms of mixed or separate calcination of the KBT and BCZT components and leading to homogeneous or core-shell-type relaxor ferroelectric ceramics, respectively. Systematic research was conducted on the impact of the processing techniques and air-quenching procedures on the structure and ferroelectric and electromechanical properties. Higher remanent polarization of the separately calcined materials was ascribed to the ferroelectric nature of the core regions, along with the non-ergodic relaxor ferroelectric response in the shell, which was enhanced by the quenching process. It was also demonstrated that the thermal depolarization temperature increased significantly after quenching, from ~100 to ~160 °C for the separately calcined ceramic, and from ~50 to ~130 °C for the mixed material; moreover, these effects are linked to notable improvements in the ferroelectric tetragonal phase content by air-quenching. Full article

Monitoring animal welfare on farms and in research settings is attracting increasing interest, both for ethical reasons and for improving productivity through the early detection of stress or diseases. In contrast to video-based monitoring, which requires good light conditions and has difficulty tracking specific animals, recent advances in the miniaturization of wearable devices allow for the collection of acceleration and location data to track individual animal behavior. However, for broilers, there are several challenges to address when using wearables, such as coping with (i) the large numbers of chickens in commercial farms,(ii)the impact of their rapid growth, and (iii) the small weights that the devices must have to be carried by the chickens without any impact on their health or behavior. To this end, this paper describes a pilot study in which chickens were fitted with devices containing an Inertial Measurement Unit (IMU) and an Ultra-Wideband (UWB) sensor. To establish guidelines for practitioners who want to monitor broiler welfare and activity at different scales, we first compare the attachment methods of the wearables to the broiler chickens, taking into account their effectiveness (in terms of retention time) and their impact on the broiler’s welfare. Then, we establish the technical requirements to carry out such a study, and the challenges that may arise. This analysis involves aspects such as noise estimation, synergy between UWB and IMU, and the measurement of activity levels based on the monitoring of chicken activity. We show that IMU data can be used for detecting activity level differences between individual animals and environmental conditions. UWB data can be used to monitor the positions and movement patterns of up to 200 animals simultaneously with an accuracy of less than 20 cm. We also show that the accuracy depends on installation aspects and that errors are larger at the borders of the monitored area. Attachment with sutures had the longest mean retention of 19.5 days, whereas eyelash glue had the shortest mean retention of 3 days. To conclude the paper, we identify current challenges and future research lines in the field. Full article

Built indoor IoT-based urban farms successfully combine the cultivation of fresh vegetables with attractive architectural designs. Moreover, implementing IoT-driven urban agriculture requires installing multiple IoT devices containing sensors, controllers, transceivers, and antennas for real-time data transmission. In this context, several factors, including the height of the IoT device above the soil level and the water content in the soil, can affect antenna performance and, consequently, the propagation of radio waves. This paper presents the results from numerical and experimental studies that evaluate the impact of soil on the performance of a monopole antenna for three different antenna positions relative to the soil in a pot and two soil water contents, presented by twelve scenarios. The results show that the antenna has a stable performance in six of the twelve scenarios, with a minimal shift in the resonant frequency of 3% and a narrowing of the frequency bandwidth by 2% compared to the antenna in free space. In the worst-case scenario, the antennas demonstrate a reduction in radiation efficiency of 44%, with the frequency bandwidth narrowing by up to 14% for the antenna fabricated on a PLA substrate and up to 17% for the one built on a foam board substrate. Full article

This study evaluates the potential impact of the Copernicus Imaging Microwave Radiometer (CIMR) mission on the sea surface temperature (SST) products of the Mediterranean Sea. Currently, infrared (IR) radiometers provide accurate, high-resolution SST measurements, but they are limited by their inability to see through clouds. Passive microwave (PMW) radiometers, on the other hand, offer monitoring capabilities in almost all weather conditions but typically at lower spatial resolutions. The CIMR mission represents a notable advance in microwave remote sensing of SSTs, as it will ensure a ≤15 km spatial resolution in the recovered SST field. Using an observing system simulation experiment (OSSE), this study evaluates the effect of inserting synthetic CIMR observations into the Copernicus Mediterranean SST analysis system, which is based on an optimal interpolation (OI) algorithm. The OSSE was conducted using data for the year 2017, including daily SST and salinity outputs from a Mediterranean Sea model, hourly precipitation rates from the IMERG, and wind and cloud cover data from ERA5. The results suggest that the improved spatial resolution and accuracy of the CIMR could potentially improve SST retrievals in the Mediterranean Sea, offering better insights for climate and environmental monitoring in semi-closed basins. Including CIMR data in the OI algorithm reduced the mean error and root mean square error (RMSE) of the SST analysis, especially under conditions of low IR coverage. The greatest improvements were found to occur in July, corresponding to coastal upwelling and Atlantic inflow into the Alboran Sea. Improvements ranged from 16% to 29%, with an overall improvement of 26% for the full year of 2017. In conclusion, this preliminary study indicates that Copernicus Mediterranean Sea HR SST products could benefit from the inclusion of the CIMR in the current IR sensor constellation. Full article

Sharding is an emerging blockchain technology that is used extensively in several fields such as finance, reputation systems, the IoT, and others because of its ability to secure and increase the number of transactions every second. In sharding-based technology, the blockchain is divided into several sub-chains, also known as shards, that enhance the network throughput. This paper aims to examine the impact of integrating sharding-based blockchain network technology in securing IoT sensors, which is further used for environmental monitoring. In this paper, the idea of integrating sharding-based blockchain technology is proposed, along with its advantages and disadvantages, by conducting a systematic literature review of studies based on sharding-based blockchain technology in recent years. Based on the research findings, sharding-based technology is beneficial in securing IoT systems by improving security, access, and transaction rates. The findings also suggest several issues, such as cross-shard transactions, synchronization issues, and the concentration of stakes. With an increased focus on showcasing the important trade-offs, this paper also offers several recommendations for further research on the implementation of blockchain network technology for securing IoT sensors with applications in environment monitoring. These valuable insights are further effective in facilitating informed decisions while integrating sharding-based technology in developing more secure and efficient decentralized networks for internet data centers (IDCs), and monitoring the environment by picking out key points of the data. Full article

The exchange of water, sediment, and nutrients in wetlands occurs through a complex network of channels and overbank flow. Although optical sensors can map channels at high resolution, they fail to identify narrow intermittent channels colonized by vegetation. Here we demonstrate an innovative application of rapid-repeat interferometric synthetic aperture radar (InSAR) to study hydrologic connectivity and tidal influences in Louisiana’s coastal wetlands, which can provide valuable insights into water flow dynamics, particularly in vegetation-covered and narrow channels where traditional optical methods struggle. Data used were from the airborne UAVSAR L-band sensor acquired for the Delta-X mission. We applied interferometric techniques to rapid-repeat (~30 min) SAR imagery of the southern Atchafalaya basin acquired during two flights encompassing rising-to-high tides and ebbing-to-low tides. InSAR coherence is used to identify and differentiate permanent open water channels from intermittent channels in which flow occurs underneath the vegetation canopy. The channel networks at rising and ebbing tides show significant differences in the extent of flow, with vegetation-filled small channels more clearly identified at rising-to-high tide. The InSAR phase change is used to identify locations on channel banks where overbank flow occurs, which is a critical component for modeling wetland hydrodynamics. This is the first study to use rapid-repeat InSAR to monitor tidal impacts on water flow dynamics in wetlands. The results show that the InSAR method outperforms traditional optical remote sensing methods in monitoring water flow in vegetation-covered wetlands, providing high-resolution data to support hydrodynamic models and critical support for wetland protection and management. Full article

A new piezoelectric sensor with a triangular shear structure was designed to conduct the deformation monitoring of geotechnical bodies in mining airspace. Firstly, a three-dimensional sensor model was developed to analyze the impact of structural parameters on resonant frequency and voltage, utilizing both finite element and experimental methods. Secondly, the NSGA-II genetic algorithm was employed to optimize the sensor’s structural parameters, focusing on resonant frequency and voltage, resulting in a Pareto optimal solution set. For the first time, the optimal parameter combination was selected by minimizing the difference method (the height of the mass block was 10.6 mm, the thickness of the piezoelectric plate was 3.29 mm, the height of the piezoelectric plate was 8.1 mm, and the height of the central column was 19 mm). The optimized sensor exhibited a 4.14% increase in resonant frequency and a 9.11% increase in voltage. Finally, the prototype was fabricated, and the effectiveness and feasibility of the design were verified through experiments. The findings indicate the sensor’s promising potential for monitoring geotechnical deformation in mining airspace regions. Full article

This study investigates the relationship between lameness, milk composition, and rumination behaviour in dairy cows by leveraging sensor-based data for automated monitoring. Lameness was found to significantly impact both rumination and milk production. Lameness was assessed in 24 multiparous Holstein dairy cows throughout early lactation (up to 100 days postpartum), utilising a 1-to-5 scale. Lameness was found to significantly impact both rumination and milk production. On the day of diagnosis, rumination time decreased by 26.64% compared to the pre-diagnosis period (p < 0.01) and by 26.06% compared to healthy cows, indicating the potential of rumination as an early health indicator. The milk yield on the day of diagnosis was 28.10% lower compared to pre-diagnosis levels (p < 0.01) and 40.46% lower than healthy cows (p < 0.05). These findings suggest that lameness manifests prior to clinical signs, affecting productivity and welfare. Milk composition was also influenced, with lame cows exhibiting altered fat (+0.68%, p < 0.05) and lactose (−2.15%, p < 0.05) content compared to healthy cows. Positive correlations were identified between rumination time and milk yield (r = 0.491, p < 0.001), while negative correlations were observed between milk yield and milk fat, protein, and the fat-to-protein ratio (p < 0.001). Additionally, lameness was associated with elevated somatic cell counts in the milk, although sample size limitations necessitate further validation. This study highlights the critical role of rumination and milk performance metrics in identifying subclinical lameness, emphasising the utility of automated systems in advancing dairy cow welfare and productivity. The findings underscore the importance of early detection and management strategies to mitigate the economic and welfare impacts of lameness in dairy farming. Full article

Background/Objectives: Rock climbing is a multifaceted athletic activity that requires both psychological and physiological resilience. This study aimed to examine the differences in psychological factors and fatigue predictors between novice and advanced climbers, with a focus on the interplay between experience and performance. Methods: The study included 60 participants categorized based on climbing experience (novice or advanced). Psychological and physiological assessments were conducted, including heart rate variability (HRV), grip strength, rate of force development (RFD), subjective perceived stress (SPS), and anxiety levels using validated questionnaires. Results: Advanced climbers exhibited lower anxiety levels and better sympathetic modulation compared to novices. Significant differences in HRV parameters, grip strength, and RFD were observed, reflecting the impact of experience on physiological responses. Advanced climbers demonstrated notable strength decreases post-climbing, supporting the utility of a force sensor on a 20 mm edge for assessing forearm fatigue. Correlations between cortisol levels, anxiety, and self-confidence during climbing were also identified. Conclusions: The findings highlight the importance of psychological and physiological factors in climbing performance. Forearm fatigue emerged as a critical predictor, suggesting that portable force sensors can optimize training and injury prevention. Insights from this study may enhance training protocols and improve real-time performance monitoring in climbers. Full article

The leaching process for copper extraction has garnered significant attention due to its critical role in meeting the rising demand for copper, driven by global trends towards decarbonization and electrification. The accurate measurement of variables is essential for process control, prompting the development of advanced sensor technologies. This paper reviews the applications of dielectric permittivity measurements in the mining industry, focusing on their potential to enhance the monitoring and optimization of copper leaching processes. It evaluates the suitability of permittivity-based sensors, analyzing their advantages and limitations, and discusses the implications for process control and economic optimization. The study highlights the integration of permittivity measurements into existing monitoring systems, aiming to improve efficiency, reduce environmental impact, and increase ore recovery rates. This comprehensive review provides insights into the current state of permittivity measurement technologies and their future prospects in the context of copper leaching. Full article