Search Results (542)

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

An important aspect of geoscience and energy research is the analysis of microscopic images, where the assessment of rock properties combines imaging methods with numerical analysis. Given the significant advancements in generative artificial intelligence technologies in recent years, which have enabled the creation of realistic images, a need arises to assess the authenticity of synthetic visual data compared to authentic geological data images. This article evaluates the potential for identifying artificially generated microscopic rock images. Synthetic images were generated using a widely accessible diffusion model, based on real training data. Expert evaluation noted high realism, though some structural and rock-type differences remained detectable. In the study, image descriptors were analyzed to assess their usefulness in distinguishing synthetic data from real data. Discriminative feature selection was conducted, and the effectiveness of various classification models based on the selected parameter sets was compared. The study also proposes a heuristic coefficient demonstrating discriminative potential for the analyzed images. The results confirm the feasibility of building classifiers for synthetic images that could aid in detecting generated visual data in geological and petrographic research. They also serve as a foundation for further exploration of the importance of individual features in such applications. Full article

Enhanced Geothermal Systems (EGS) require thermochemically stable proppant materials capable of sustaining fracture conductivity under harsh subsurface conditions. This study systematically investigates the response of commercial proppants to coupled thermo-hydro-chemical (THC) effects, focusing on chemical stability and microstructural evolution. Four proppant types were evaluated: an ultra-low-density ceramic (ULD), a resin-coated sand (RCS), and two quartz-based silica sands. Experiments were conducted under simulated EGS conditions at 130 °C with daily thermal cycling over a 25-day period, using diluted site-specific Utah FORGE geothermal fluids. Static batch reactions were followed by comprehensive multi-modal characterization, including scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), and micro-computed tomography (micro-CT). Proppants were tested in both granular and powdered forms to evaluate surface area effects and potential long-term reactivity. Results indicate that ULD proppants experienced notable resin degradation and secondary mineral precipitation within internal pore networks, evidenced by a 30.4% reduction in intragranular porosity (from CT analysis) and diminished amorphous peaks in the XRD spectra. RCS proppants exhibited a significant loss of surface carbon content from 72.98% to 53.05%, consistent with resin breakdown observed via SEM imaging. While the quartz-based sand proppants remained morphologically intact at the macro-scale, SEM-EDS revealed localized surface alteration and mineral precipitation. The brown sand proppant, in particular, showed the most extensive surface precipitation, with a 15.2% increase in newly detected mineral phases. These findings advance understanding of proppant–fluid interactions under low-temperature EGS conditions and underscore the importance of selecting proppants based on thermo-chemical compatibility. The results also highlight the need for continued development of chemically resilient proppant formulations tailored for long-term geothermal applications. Full article

In this paper, we presents a case study involving the implementation experience and a methodological framework through a comprehensive comparative analysis of the YOLOX and YOLOv12 object detection models for agricultural automation systems deployed in the Jetson AGX Orin edge computing platform. We examined the architectural differences between the models and their impact on detection capabilities in data-imbalanced potato-harvesting environments. Both models were trained on identical datasets with images capturing potatoes, soil clods, and stones, and their performances were evaluated through 30 independent trials under controlled conditions. Statistical analysis confirmed that YOLOX achieved a significantly higher throughput (107 vs. 45 FPS, p < 0.01) and superior energy efficiency (0.58 vs. 0.75 J/frame) than YOLOv12, meeting real-time processing requirements for agricultural automation. Although both models achieved an equivalent overall detection accuracy (F1-score, 0.97), YOLOv12 demonstrated specialized capabilities for challenging classes, achieving 42% higher recall for underrepresented soil clod objects (0.725 vs. 0.512, p < 0.01) and superior precision for small objects (0–3000 pixels). Architectural analysis identified a YOLOv12 residual efficient layer aggregation network backbone and area attention mechanism as key enablers of balanced precision–recall characteristics, which were particularly valuable for addressing agricultural data imbalance. However, NVIDIA Nsight profiling revealed implementation inefficiencies in the YOLOv12 multiprocess architecture, which prevented the theoretical advantages from being fully realized in edge computing environments. These findings provide empirically grounded guidelines for model selection in agricultural automation systems, highlighting the critical interplay between architectural design, implementation efficiency, and application-specific requirements. Full article

This study presents a methodology for developing and validating digital models of tooth-inlay systems, aiming to trace the complete workflow from clinical procedures to simulation by involving dental professionals—dentists for manual cavity preparation and dental technicians for restoration modelling—while integrating micro-computed tomography (micro-CT) imaging with finite element analysis (FEA). The proposed workflow includes (1) the acquisition of high-resolution 3D micro-CT scans of a non-restored tooth, (2) image segmentation and reconstruction to create anatomically accurate digital twins and mesh generation, (3) the selection of proper resin and the 3D printing of four typodonts, (4) the manual preparation of cavities on the typodonts, (5) the acquisition of high-resolution 3D micro-CT scans of the typodonts, (6) mesh generation, digital inlay and onlay modelling and material property assignment, and (7) nonlinear FEA simulations under representative masticatory loading. The approach enables the visualisation of stress and deformation patterns, with preliminary results indicating stress concentrations at the tooth-restoration interface integrating different cavity alternatives and restorations on the same tooth. Quantitative outputs include von Mises stress, strain energy density, and displacement distribution. This study demonstrates the feasibility of using image-based, tooth-specific digital twins for biomechanical modelling in dentistry. The developed framework lays the groundwork for future investigations into the optimisation of restoration design and material selection in clinical applications. Full article

Laser energy is widely used as a selective photothermal heating agent in cancer treatment, standing out for not relying on ionizing radiation. However, in vivo tests have highlighted the need to develop irradiation techniques that allow precise control over the illuminated area, adapting it to the tumor size to further minimize damage to surrounding healthy tissue. To address this challenge, a proof of concept based on a laser irradiation system has been designed, enabling control over energy, exposure time, and irradiated area, using galvanometric mirrors. The control software, implemented in Python, employs a set of cameras (visible and infrared) to detect and monitor real-time thermal distributions in the region of interest, transmitting this information to a microcontroller responsible for adjusting the laser power and controlling the scanning process. Image alignment procedures, tunning of the controller’s gain parameters and the impact of the different engineering parameters are illustrated on a dedicated setup. As proof of concept, this approach has demonstrated the ability to irradiate a phantom of black modeling clay within an area of up to 5 cm × 5 cm, from 15 cm away, as well as to monitor and regulate the temperature over time (5 min). Full article

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article

For the first time, a mixture of PbTe and Pb- and Te-oxides coated with carbon, under electron beam irradiation (EBI), was transformed into quantum dots, nanocrystals, nanoparticles and grains of PbTe with a sintered appearance. A small portion of non-stoichiometric phases was also obtained. By selecting conditions that favor the instantaneous transformation, the Gibbs free energy barrier is lowered for obtaining different PbTe structures. The driving force associated with the high-energy milling requires 4 h of processing time to reach a complete transformation, while a high-energy source kinetically affects precursor surfaces to cause an abrupt global chemical transformation instantly. Importantly, the size of the PbTe structures increases as they approach the irradiation point, implying a growth process that is affected by the local temperature reached during the EBI. Imaging after the EBI process revealed morphological variations in PbTe, which can be attractive for use in thermoelectric materials. The results of this study provide the first insights into electron-beam-induced reactions using a multiphase mixture of precursors. Therefore, it is believed that this proposal can also be applied to obtain other binary semiconductor structures, even ternary ones. Full article

Green roofs are increasingly being adopted as sustainable, nature-based solutions for managing urban stormwater, mitigating the urban heat island effect, and saving energy in buildings. However, the long-term performance of their individual components—particularly filter geotextiles—remains understudied, despite their critical role in maintaining system functionality. The filter layer, responsible for preventing clogging of the drainage layer with fine substrate particles, directly affects the hydrological performance and service life of green roofs. While most existing studies focus on the initial material properties, there is a clear gap in understanding how geotextile filters behave after prolonged exposure to real-world environmental conditions. This study addresses this gap by assessing the mechanical and structural integrity of geotextile filters after five years of use in both extensive and intensive green roof systems. By analyzing changes in surface morphology, microstructure, and porosity through tensile strength tests, digital imaging, and scanning electron microscopy, this research offers new insights into the long-term performance of geotextiles. Results showed significant retention of tensile strength, particularly in the machine direction (MD), and a 56% reduction in porosity, which may affect filtration efficiency. Although material degradation occurs, some geotextiles retain their structural integrity over time, highlighting their potential for long-term use in green infrastructure applications. This research emphasizes the importance of material selection, long-term monitoring, and standardized evaluation techniques to ensure the ecological and functional resilience of green roofs. Furthermore, the findings contribute to advancing knowledge on the durability and life-cycle performance of filter materials, promoting sustainability and longevity in urban green infrastructure. Full article

Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents a non-iterative real-time Feature Sub-image Based Stripmap Phase Gradient Autofocus (FSI-SPGA) algorithm. The FSI-SPGA algorithm combines 2D Constant False Alarm Rate (CFAR) for coarse point selection and spatial decorrelation for refined point selection. This approach enables the accurate extraction of high-quality scattering points. Using these points, the algorithm constructs a feature sub-image containing comprehensive phase error information and performs a non-iterative phase error estimation based on this sub-image. To address the multifunctional, low-power, and real-time requirements of small UAV SAR, we designed a highly efficient hybrid architecture. This architecture integrates dataflow reconfigurability and dynamic partial reconfiguration and is based on an ARM + FPGA platform. It is specifically tailored to the computational characteristics of the FSI-SPGA algorithm. The proposed scheme was assessed using data from a 6 kg small SAR system equipped with centimeter-level INS/GPS. For SAR images of size 4096 × 12,288, the FSI-SPGA algorithm demonstrated a 6 times improvement in processing efficiency compared to traditional methods while maintaining the same level of precision. The high-efficiency reconfigurable ARM + FPGA architecture processed the algorithm in 6.02 s, achieving 12 times the processing speed and three times the energy efficiency of a single low-power ARM platform. These results confirm the effectiveness of the proposed solution for enabling high-quality real-time SAR imaging under stringent SwaP constraints. Full article

Solar energy has become a vital renewable energy source (RES), and photovoltaic (PV) systems play a key role in its utilization. However, the performance of these systems can be compromised by faulty panels. This paper proposes an innovative framework that combines the deep neural network VGG19 with the Jellyfish Optimization Search Algorithm (JFOSA) for efficient fault detection using aerial images. VGG19 excels in automatic feature extraction, while JFOSA optimizes feature selection and significantly improves classification performance. The new framework achieves impressive results, including 98.34% accuracy, 98.71% sensitivity, 98.69% specificity, and 94.03% AUC. These results outperform baseline models and various optimization techniques, including ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO). The system demonstrated superior performance in detecting solar panel defects such as cracks, hot spots, and shadow defects, providing a robust, scalable, and automated solution for PV monitoring. This approach provides an efficient and reliable way to maintain energy efficiency and system reliability in solar energy applications. Full article

Many research studies have looked at process characteristics to improve color choices and create more simulation-accurate models. This research evaluated the processing factors speed (Sp), temperature (T), and feed rate (FRate) and employed two response surface techniques, the three-level full-factorial design (3LFFD) and Box–Behnken design (BBD), to optimize uniform processing settings. An experimental approach was employed to optimize process parameters while holding all other variables constant. The Design Expert software enabled the creation of statistical and numerical optimization models, as well as simulated regression models, to find the optimal tristimulus color values with minimal color variance (dE*). The three examined parameters significantly affected the color parameters dL*, da*, and db*, and specific mechanical energy (SME) based on the analysis of variance (ANOVA). In addition, SME was calculated for the experimental trials. A decrease in SME was found as the FRate increased. The collected data were analyzed to determine pigment dispersion using scanning electron microscopy (SEM) as well as micro-CT (MCT) scanner images. Regarding the BBD, the processing conditions revealed a minimum deviation of 0.26 but a maximum design desirability appeal of 87%. The three-level full-factorial design (3LFFD) revealed a maximum desirability of 77% and a minimum acceptable color variation (dE*) of 0.25. Therefore, BBD had a marginally superior performance. These results demonstrate that the processing parameters have a significant impact on the output quality, including reducing variation, improving color consistency, minimizing waste, and promoting sustainable production. This study found that both sets of process parameters were statistically significant after comparing the two designs. However, BBD is the preferred design for the selection needed and offers better outcomes in future experiments. Full article

Aza-crown ether structures have been proven to be effective in constructing fluorescent biosensors for selectively detecting and imaging alkali metal ions in biological environments. However, choosing the right aza-crown ether for a specific alkali metal ion remains challenging for synthetic chemists because theoretical guidance on the chelating activities between aza-crown ethers and alkali metal ions has not been available up to now. Predicting the physical properties of the chelator–metal complexations poses a greater challenge due to the numerous quantum mechanical functionals and basis sets to be used in any theoretical investigation. In this study, we report a theoretical investigation of different aza-crown ether structures and their selectivities to alkali metal ions via a novel relationship between the binding energy and charge transfer calculated using twelve different quantum mechanical methods, using a myriad of bases, within the Jacob’s Ladder of Chemical Accuracies. Furthermore, this report represents a guide for the synthetic chemist in the selection of aza-crown ethers in the capturing of specific alkali metal ions, primary objectives, while benchmarking different quantum mechanical calculations, as a secondary objective. Full article

Background/Objectives: Focal therapy (FT) for prostate cancer (PCa) is an alternative to radical treatments that aims to balance cancer control and quality of life preservation in well-selected patients. Understanding its general principles and outcomes is key for its widespread adoption and proper implementation. Methods: The International Consultation on Urological Diseases nominated a committee to review the literature on FT for PCa. A comprehensive PubMed search was conducted to identify articles focused on the different aspects of FT, including patient selection, imaging techniques, treatment modalities, cancer control and safety outcomes, integration with other approaches and future perspectives. Results: FT for PCa was introduced in the 1990s with cryotherapy and high-intensity focused ultrasound (HIFU) as pioneering modalities. Though initially guided by transrectal ultrasound (TRUS) and large biopsy templates, FT implementation expanded significantly with the advent of multiparametric magnetic resonance imaging (MRI) and the validation of the index lesion concept. Appropriate patient selection is key for FT and relies on prostate-specific antigen (PSA) metrics, MRI findings and targeted biopsy information. Multiple energy sources are now available, each with specific technical characteristics. Cancer control rates vary by energy modality, tumor characteristics, and institutional experience, demonstrating comparable outcomes to radical treatments in well-selected patients. The safety profile is excellent, with high rates of urinary continence and sexual function preservation. Post-treatment surveillance integrates PSA measurements, imaging, and histological assessment. Future directions for further FT adoption include the availability of long-term data, protocol standardization and technological improvements to enhance patient selection and treatment planning and delivery. Conclusions: FT is a valuable therapeutic option for selected patients with localized PCa, demonstrating promising oncological outcomes and better functional preservation compared to radical treatments. Understanding its principles and technical aspects is essential for offering comprehensive PCa care. Full article

As a key parameter in the sintering process, the ferrous oxide content of sinter can reflect the working condition, energy consumption level, and quality level of the final sintered products in the sintering process. It has become a key problem to realize the prediction of ferrous oxide content in sinter and feedback control of sinter quality accordingly. The two commonly used methods for detecting ferrous oxide content in industrial production currently do not meet real-time requirements and cannot provide timely feedback for production regulation. Therefore, research on real-time prediction technology of ferrous oxide content in sinter was carried out, and an optimized back propagation neural network model was established to realize the mapping between characteristic parameters and the FeO content in sinter. The characteristic parameters include image parameters and process parameters. Through the research on the brightness change trend of the machine tail cross-section image, the best cross-section image acquisition method based on brightness difference is realized, and image parameters are obtained by image processing technology. The process parameters were selected using correlation analysis. Through data processing techniques such as data cleaning, normalization, and feature fusion, feature parameters were obtained as input vectors for the neural network. To improve prediction accuracy and system stability, an adaptive learning rate and genetic algorithm were used to optimize the traditional BP neural network. The average test error of the optimized prediction model was 0.32%. Taking actual data production as an example, test data on the FeO content of sinter were extracted from the laboratory. Compared with the FeO content predicted by the system, the prediction time of the system was about 2 h earlier than the test time. In terms of prediction accuracy, the average absolute error was 0.25%, and the absolute prediction error was not more than ±1%. Full article