Search Results (186)

Geological modeling is generally based on deterministic models, which provide a single representation of reality. Probabilistic modeling is more appropriate when quantifying or understanding the uncertainty associated with a parameter of interest as it considers several equally probable geological scenarios. The object of this study is to quantify the uncertainty in the estimation of the minerals in the Punta Alegre gypsum deposit, by applying a new method based on the simple normal equation geostatistical simulation technique. The Punta Alegre gypsum deposit is a sedimentary deposit of clastic origin, formed by the complex redeposition of salts, gypsum and other sediments. To carry out this research, 50 equiprobable scenarios were simulated, reproducing overburden, gypsum series (different types of gypsum) and intercalated non-mineral lithologies (limestone and other rocks) in a network of nodes measuring 5 × 5 × 5 m, using a training image, composites and prior probability maps as input data. As a result of scaling the previously simulated geological units, three-dimensional models of volume proportions and estimation error for gypsum were obtained for panels measuring 10 × 10 × 5 m. The quantification of the uncertainty of the gypsum volume, determined by the root mean square error, established that the volume estimation error is small at a global scale (6.51%), given that there is no significant variation when comparing the deterministic model with the gypsum proportion model obtained from the 50 simulated scenarios. Conversely, at the local scale, there is a significant variation in gypsum volume of 42% in the 10 × 10 × 5 m panels with a future impact on recoverable mining resources, given the uncertainty at a local scale, which will cause an increase in mining dilution due to the inclusion of non-mineral lithologies within the extracted mineral that will be sent to the processing plant. On the other hand, it will cause changes in the mining company’s plan in areas where there are panels that were previously accounted for by the deterministic model as minerals and are not actually exploitable. Full article

Hydroelectricity, one of the oldest and most potent forms of renewable energy, not only provides low-cost electricity for the grid but also preserves nature through flood control and irrigation support. Forecasting hydroelectricity generation is vital for utilizing alleviating resources effectively, optimizing energy production, and ensuring sustainability. This paper provides an innovative approach to hydroelectricity generation forecasting (HGF) of a 138 MW hydroelectric power plant (HPP) in the Eastern Mediterranean by taking electricity productions from the remaining upstream HPPs on the Ceyhan River within the same basin into account, unlike prior research focusing on individual HPPs. In light of tuning hyperparameters such as number of trees and learning rates, this paper presents a thorough benchmark of the state-of-the-art tree-based machine learning models, namely categorical boosting (CatBoost), extreme gradient boosting (XGBoost), and light gradient boosting machines (LightGBM). The comprehensive data set includes historical hydroelectricity generation, meteorological conditions, market pricing, and calendar variables acquired from the transparency platform of the Energy Exchange Istanbul (EXIST) and MERRA-2 reanalysis of the NASA with hourly resolution. Although all three models demonstrated successful performances, LightGBM emerged as the most accurate and efficient model by outperforming the others with the highest coefficient of determination (R²) (97.07%), the lowest root mean squared scaled error (RMSSE) (0.1217), and the shortest computational time (1.24 s). Consequently, it is considered that the proposed methodology demonstrates significant potential for advancing the HGF and will contribute to the operation of existing HPPs and the improvement of power dispatch planning. Full article

Accurate runoff simulation is essential for water resources planning and development projects. At present, the commonly employed runoff simulation approaches are categorized into two types: process- and data-driven models. Process-driven models pertain to the enhancement of the structural framework in conceptual rainfall–runoff models using hydrological principles to estimate runoff but have low accuracy at the monthly scale. Unlike the process-driven models, data-driven models (DDMs) can simulate the relationship between input factors and output runoff data without regard to complex and unknown runoff production and acquire satisfactory simulation results. Here, we comparatively investigate the applicability of DDMs, including traditional shallow DDMs, deep learning-based (DL) models for monthly runoff simulation, and select the Autoregressive (AR) model as the baseline model for comparison. Moreover, four evaluation indicators, including mean absolute percentage error (MAPE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R²), are employed to evaluate the runoff simulation effects of the above methods. We systematically compare the AR model, and shallow and deep learning-based DDMs for runoff simulation at four hydrological stations in the Yalong River basin (YRB), respectively. The finding results reveal that the DDMs demand limited data and can offer satisfactory prediction effects. Also, the DL models outperform other shallow DDMs and the AR model in terms of the above evaluation criteria. Full article

This article presents the results of a performance comparison of four forecasting methods for prediction of electric power quality parameters (PQPs) in small-scale off-grid environments. Forecasting PQPs is crucial in supporting smart grid control and planning strategies by enabling better management, enhancing system reliability, and optimizing the integration of distributed energy resources. The following methods were compared: Bagging Decision Tree (BGDT), Boosting Decision Tree (BODT), and the K-Nearest Neighbor (KNN) algorithm with $k-5$ and $k-10$ nearest neighbors considered by the algorithm when making a prediction. The main goal of this study is to find a relation between the input variables (weather conditions, first and second back steps of PQPs, and consumed power of home appliances) and the power quality parameters as target outputs. The studied PQPs are the amplitude of power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), Power Factor ($PF$), and Power Load ($PL$). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for $TH{D}_u$, $TH{D}_i$, and $PF$. Only BODT obtained a good forecasting result for $PL$. The KNN (k = 5) algorithm obtained a good result for $PF$ prediction. The KNN (k = 10) algorithm predicted acceptable results for U and $PF$. The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees. Full article

This paper proposes a structured design framework and system architecture for a non-intrusive spatial system aimed at supporting child behavior in everyday domestic environments. Rooted in ethical considerations, our approach defines four core behavior-guided design strategies: routine recovery, emotion-responsive adjustment, behavioral transition induction, and external linkage. Each strategy is meticulously translated into a detailed system logic that outlines input conditions, trigger thresholds, and feedback outputs, designed for implementability with ambient sensing technologies. Through a comparative conceptual analysis of three sensing configurations—low-resolution LiDARs, mmWave radars, and environmental sensors—we evaluate their suitability based on technical feasibility, spatial integration, operationalized privacy metrics, and ethical alignment. Supported by preliminary technical observations from lab-based sensor tests, low-resolution LiDAR emerges as the most balanced option for its ability to offer sufficient behavioral insight while enabling edge-based local processing, robustly protecting privacy, and maintaining compatibility with compact residential settings. Based on this, we present a working three-layered system architecture emphasizing edge processing and minimal-intrusion feedback mechanisms. While this paper primarily focuses on the framework and design aspects, we also outline a concrete pilot implementation plan tailored for small-scale home environments, detailing future empirical validation steps for system effectiveness and user acceptance. This structured design logic and pilot framework lays a crucial foundation for future applications in diverse residential and care contexts, facilitating longitudinal observation of behavioral patterns and iterative refinement through lived feedback. Ultimately, this work contributes to the broader discourse on how technology can ethically and developmentally support children’s autonomy and well-being, moving beyond surveillance to enable subtle, ambient, and socially responsible spatial interactions attuned to children’s everyday lives. Full article

Background and Objectives: Periodontitis is a chronic inflammatory disease that leads to progressive destruction of periodontal tissues and remains a significant global health burden. While conventional therapies such as scaling and root planning offer short-term improvements, they often fall short in maintaining long-term microbial control, underscoring the need for adjunctive strategies. This study evaluated the clinical and microbiological effects of a novel essential oil (EO)-based gel—SmartGel OV—formulated with Origanum vulgare. Materials and Methods: Thirty adults with periodontitis were enrolled in a 4-month observational study, during which SmartGel OV was applied daily via gingival massage. Clinical outcomes and bacterial profiles were assessed through probing measurements and real-time PCR analysis. Additionally, a pilot AI-based tool was explored as a supplemental method to monitor inflammation progression through intraoral images. Results: Significant reductions were observed in Fusobacterium nucleatum and Capnocytophaga spp., accompanied by improvements in clinical markers, including probing depth, bleeding on probing, and plaque index. The AI framework successfully identified visual inflammation changes and supported early detection of non-responsiveness. Conclusions: SmartGel OV demonstrates promise as a natural adjunctive treatment for periodontitis and AI monitoring was included as an exploratory secondary tool to assess feasibility for future remote tracking. Full article

The rubber industry is facing major socio-economic and environmental constraints. Rubber-based agroforestry systems represent a more sustainable solution through the diversification of income and the provision of greater ecosystem services than monoculture plantations. Participative approaches are known for their ability to co-construct solutions with stakeholders and to promote a positive impact on smallholders. This study therefore implemented a participatory research process with stakeholders in the natural rubber sector for the purpose of improving inclusion, relevance and impact. Facilitation training sessions were first organised with academic actors to prepare participatory workshops. A working group of stakeholder representatives was set up and participated in these workshops to share a common representation of the value chain and to identify problems and solutions for the sector in Indonesia. By fostering collective intelligence and systems thinking, the process is aimed at enabling the development of adaptive technical solutions and building capacity across the sector for future government replanting programmes. The resulting adaptive technical packages were then detailed and objectified by the academic consortium and are part of a participatory plant breeding approach adapted to the natural rubber industry. On-station and on-farm experimental plans have been set up to facilitate the drafting of projects for setting up field trials based on these outcomes. Research played a dual role as both knowledge provider and facilitator, guiding a co-learning process rooted in social inclusion, equity and ecological resilience. The initiative highlighted the potential of rubber cultivation to contribute to climate change mitigation and food sovereignty, provided that it can adapt through sustainable practices like agroforestry. Continued political and financial support is essential to sustain and scale these innovations. Full article

Background/Objectives: Periodontitis is a chronic inflammation of the periodontium that induces damage in the periodontal ligaments and the surrounding alveolar bone. This study aimed to comparatively evaluate the clinical outcomes of two therapies used in the management of periodontal conditions, represented by scaling and root planing (SRP) alone and laser-assisted new attachment procedure (LANAP). Methods: Two quadrants of the oral cavity from each selected patient were randomly allocated to one of the treatment groups, SRP or LANAP. The periodontal status was documented in a periodontal chart at baseline, six weeks, and one year after treatment. SRP was performed in the first group of patients. The LANAP protocol was carried out on the patients belonging to the second group. Results: The outcomes of the study highlighted that LANAP leads to a reduction in periodontal disease signs (pocket depth, bleeding on probing, and gingival recession), contributing to the formation of new attachment tissues. LANAP shows more stability in maintaining the improvements achieved during six weeks, while SRP shows a slight deterioration in several parameters, particularly attachment loss, between six weeks and one year. The collected data at six-week and one-year follow-ups show improvements in periodontal health, thus improving oral health. Conclusions: Both minimally invasive periodontal procedures were effective, with LANAP demonstrating greater efficiency in patients with chronic periodontal disease, a greater reduction in pocket depth, and improved clinical outcomes compared to SRP alone. Full article

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

Carbon emissions from global warming pose significant threats to both regional ecology and sustainable development. Understanding the factors affecting emissions is critical to developing effective carbon neutral strategies. This study constructed a precise 1 km resolution net carbon emissions map of Hubei Province, China (2000–2020), and compared the ten distinct machine learning models to identify the most effective model for revealing the relationship between carbon emissions and their influencing factors. The random forest regressor (RFR) demonstrates optimal performance, achieving root mean square error (RMSE) and mean absolute error (MAE) values that are nearly 10 times lower on average than the other models. The results are interpreted using Shapley additive explanation (SHAP), revealing dynamic factor impacts. Our findings include the following. (1) Between 2000 and 2020, net carbon emissions in Hubei increased threefold, with emissions from construction land rising by approximately 7.5 times over the past two decades. Woodland, a major carbon sink, experienced a downward trend. (2) Six key factors are population, the normalized difference vegetation index (NDVI), road density, PM_2.5, the degree of urbanization, and the industrial scale, with only the NDVI reducing emissions. (3) Net carbon emissions displayed significant spatial differences and aggregation and are mainly concentrated in the central urban areas of Hubei Province. Overall, this study evaluates various regression models and identifies the primary factors influencing net carbon emissions. The net carbon emission map we have developed can visually identify and locate high-emission hotspots and vulnerable carbon sink areas, thereby providing a direct basis for provincial land use planning. Full article

The Huang-Huai-Hai Plain is one of China’s primary winter wheat production regions, making accurate yield estimation critical for agricultural decision-making and national food security. In this study, a yield estimation framework was developed by integrating Sentinel-2 and Landsat-8 satellite data with the WOFOST crop growth model and deep learning techniques. Initially, a multi-scenario sample dataset was constructed using historical meteorological and agronomic data through the WOFOST model. Leaf Area Index (LAI) values were then derived from Landsat-8 and Sentinel-2 imagery, and a GRU (Gated Recurrent Unit) neural network was trained on the simulation samples to establish a relationship between LAI and yield. This trained model was applied to the remote sensing-derived LAI to generate initial yield estimates. To enhance accuracy, the results were further corrected using county-level statistical data, producing a spatially explicit winter wheat yield dataset for the Huang-Huai-Hai Plain from 2014 to 2022. Validation against statistical yearbook data at the county level demonstrated a correlation coefficient (r) of 0.659, a root mean square error (RMSE) of 578.34 kg/ha, and a mean relative error (MRE) of 6.63%. These results indicate that the dataset provides reliable regional-scale yield estimates, offering valuable support for agricultural planning and policy development. Full article

The fundamental goal of endodontic surgery is to remove the infection cause and create an ideal environment for periapical tissue and bone recovery. This systematic review aims to present evidence-based findings regarding the healing ability of endodontic materials in retrograde treatment. The study evaluates the advantages and drawbacks of commonly utilized materials, empowering clinicians with valuable insights for preoperative planning in endodontic surgery. A comprehensive search was conducted across multiple databases, including MEDLINE, Scielo, Web of Science, Scopus, Embase, and Google Scholar, using the PIOT framework. A total of 3124 papers were identified, of which 2534 remained after removing duplicates. Following a stringent selection process, 35 clinical studies were included for qualitative assessment. The risk of bias was assessed using the Risk of Bias in Non-randomized Studies—of Interventions (ROBINS-I) tool for non-randomized trials, the Newcastle–Ottawa Scale for cohort studies, and the Joanna Briggs Institute (JBI) critical appraisal checklist for cross-sectional studies. Due to high heterogeneity in study designs and outcomes, a meta-analysis could not be performed. The review identified Super Ethoxybenzoic Acid (Super EBA), Mineral Trioxide Aggregate (MTA), and Intermediate Restorative Material (IRM^®), Retroplast, Endosequence^®, and gutta-percha as the primary retrograde root filling materials. Follow-up periods ranged from 6 months to 17.5 years. Although the materials showed varying degrees of success, the overall findings highlighted that no single material demonstrated universally superior healing ability. The review also emphasized the need for standardization in future clinical trials to facilitate better comparisons. The selection of retrograde filling materials plays a pivotal role in the success of endodontic surgery. New bioceramic materials like MTA and Biodentine offer improved sealing, biocompatibility, and tissue regeneration compared to traditional materials, leading to better clinical outcomes. Full article

Objective: The purpose of this study was to evaluate the accuracy and feasibility of magnetic resonance (MR)-guided periradicular nerve root injection therapy (PRT) using a 0.55 T magnetic resonance imaging (MRI) system with fast dynamic imaging in a phantom. Methods: Five radiologists with varying levels of experience in PRT performed nine randomly assigned PRT procedures: three under MR guidance, three under CT guidance using a fully integrated laser navigation system, and three under conventional CT guidance, all on a specialized phantom of the lumbar spine. The PRTs were assessed by two experienced neuroradiologists with expertise in interventions, using a scale of 1–5, as follows: 5 = excellent to very good, 4 = good, 3 = satisfactory 2 = bad, 1 = very bad. The puncture time and total intervention time were noted. Results: All procedures were technically successful. The subjective evaluation of the PRTs showed similar results with a median of 5 for all three guidance systems. Additionally, there was no significant difference with respect to pure puncture time (the period after needle path determination) among all PRTs (Mean ± SD): MR-guided 178 ± 117 s, CT-guided with laser system 186 ± 73 s, and the conventional CT-guided 218 ± 91 s (p = 0.482). However, the total procedure time including planning images was significantly higher for MR-guided PRT (700 ± 182 s) compared to CT guidance with laser system (366 ± 85 s) and conventional CT guidance (358 ± 150 s; p = 0.012). Conclusions: Real-time MRI-guided lumbosacral periradicular injection therapy utilizing a 0.55 T MRI system is feasible with similar puncture times to CT guidance but consumes more intervention time due to the duration of planning sequences. Limitation: The study utilized a stationary phantom made of homogeneous material, which provides an incomplete representation of real tissue properties and motion complexity applied to human beings. Full article

Aiming to address the problems of fewer related studies on autonomous navigation algorithms based on multi-sensor fusion in complex scenarios in pastures, lower degrees of fusion, and insufficient cruising accuracy of the operation path in complex outdoor environments, a multimodal autonomous navigation system is proposed based on a loosely coupled architecture of Cartographer–RTAB-Map (real-time appearance-based mapping). Through laser-vision inertial guidance multi-sensor data fusion, the system achieves high-precision mapping and robust path planning in complex scenes. First, comparing the mainstream laser SLAM algorithms (Hector/Gmapping/Cartographer) through simulation experiments, Cartographer is found to have a significant memory efficiency advantage in large-scale scenarios and is thus chosen as the front-end odometer. Secondly, a two-way position optimization mechanism is innovatively designed: (1) When building the map, Cartographer processes the laser with IMU and odometer data to generate mileage estimations, which provide positioning compensation for RTAB-Map. (2) RTAB-Map fuses the depth camera point cloud and laser data, corrects the global position through visual closed-loop detection, and then uses 2D localization to construct a bimodal environment representation containing a 2D raster map and a 3D point cloud, achieving a complete description of the simulated ranch environment and material morphology and constructing a framework for the navigation algorithm of the pushing robot based on the two types of fused data. During navigation, the combination of RTAB-Map’s global localization and AMCL’s local localization is used to generate a smoother and robust positional attitude by fusing IMU and odometer data through the EKF algorithm. Global path planning is performed using Dijkstra’s algorithm and combined with the TEB (Timed Elastic Band) algorithm for local path planning. Finally, experimental validation is performed in a laboratory-simulated pasture environment. The results indicate that when the RTAB-Map algorithm fuses with the multi-source odometry, its performance is significantly improved in the laboratory-simulated ranch scenario, the maximum absolute value of the error of the map measurement size is narrowed from 24.908 cm to 4.456 cm, the maximum absolute value of the relative error is reduced from 6.227% to 2.025%, and the absolute value of the error at each location is significantly reduced. At the same time, the introduction of multi-source mileage fusion can effectively avoid the phenomenon of large-scale offset or drift in the process of map construction. On this basis, the robot constructs a fusion map containing a simulated pasture environment and material patterns. In the navigation accuracy test experiments, our proposed method reduces the root mean square error (RMSE) coefficient by 1.7% and Std by 2.7% compared with that of RTAB-MAP. The RMSE is reduced by 26.7% and Std by 22.8% compared to that of the AMCL algorithm. On this basis, the robot successfully traverses the six preset points, and the measured X and Y directions and the overall position errors of the six points meet the requirements of the pasture-pushing task. The robot successfully returns to the starting point after completing the task of multi-point navigation, achieving autonomous navigation of the robot. Full article

Maintaining a balance between electricity generation and consumption is vital for ensuring grid stability and preventing disruptions. Spatial load forecasting (SLF) predicts geographical electricity demand, thereby aiding in power system planning. However, conventional methods like multiple linear regression and autoregressive integrated moving average struggle to capture the complex spatiotemporal patterns in historical data. Advanced methods like spatiotemporal graph transformers, graph convolutional networks, and improved scale-limited dynamic time warping better capture these dependencies, thereby enhancing prediction accuracy. Despite the advancements, challenges persist, particularly in developing economies with limited reliable data. This paper presents a novel SLF approach that divides the grid into predefined clusters based on regional characteristics and economic activity. For each cluster, a customized long short-term memory (LSTM) model captures unique spatiotemporal dependencies for more accurate predictions. The proposed method was tested across five load clusters using real-world data from Cameroon’s National Electricity Transmission Company and Energy Utilities. The results, compared against a linear regression model, demonstrated the superior performance of the LSTM approach across metrics like the mean absolute percentage error, root-mean-square error, mean absolute error, and R² score. This approach highlights the potential for enhanced, localized load forecasting in regions with data constraints. Full article