Search Results (74,056)

The study aims to assess the performance of European sustainable agriculture through a new model of agricultural sustainability, addressing a significant gap identified in the literature: the lack of a systematic framework integrating the economic, environmental, and resource efficiency dimensions of agricultural resource use in the context of the EU Common Agricultural Policy and the Green Deal. The research develops four synthetic indicators: ISPAS (Index of Sustainable Agricultural Productivity), IREA (Index of Reduced Emissions from Agriculture), ISAC (Index of Combined Agricultural Sustainability), and IESA (Index of Agricultural Land Area Efficiency), each reflecting complementary aspects of sustainable agricultural performance. The methodology is based on an econometric linear model and a dynamic Arellano–Bond model, which allows the analysis of the temporal relationships between synthetic indicators and agricultural sustainability performance, capturing the inertia effects and structural dynamics of the European agricultural sector. The modeling provides a robust approach to capture the interdependencies between agricultural emission reductions, sustainability mainstreaming, and land use efficiency. The results of the study indicate a superior quality of measurement by applying this integrated framework, highlighting significant relationships between emission reductions, the integration of economic and environmental dimensions, and the optimization of agricultural land use. The analysis also provides valuable policy implications, suggesting concrete directions for adapting European agricultural policies to the structural particularities of Member States. By integrating a dynamic methodological framework and innovative synthetic indicators, this study contributes to a thorough understanding of agricultural sustainability performance and provides a practical tool for underpinning sustainable agricultural policies in the European Union. Full article

Crop damage caused by wild animals, particularly wild boars (Sus scrofa), significantly impacts agricultural yields, especially in maize fields. This study evaluates two methods for assessing maize crop damage using UAV-acquired data: (1) a deep learning-based approach employing the Deepness plugin in QGIS, utilizing high-resolution RGB imagery; and (2) a method based on digital surface models (DSMs) derived from LiDAR data. Manual visual assessment, supported by ground-truthing, served as the reference for validating these methods. This study was conducted in 2023 in a maize field in Central Poland, where UAV flights captured high-resolution RGB imagery and LiDAR data. Results indicated that the DSM-based method achieved higher accuracy (94.7%) and sensitivity (69.9%) compared to the deep learning method (accuracy: 92.9%, sensitivity: 35.3%), which exhibited higher precision (92.2%) and specificity (99.7%). The DSM-based method provided a closer estimation of the total damaged area (9.45% of the field) compared to the reference (10.50%), while the deep learning method underestimated damage (4.01%). Discrepancies arose from differences in how partially damaged areas were classified; the deep learning approach excluded these zones, focusing on fully damaged areas. The findings suggest that while DSM-based methods are well-suited for quantifying extensive damage, deep learning techniques detect only completely damaged crop areas. Combining these methods could enhance the accuracy and efficiency of crop damage assessments. Future studies should explore integrated approaches across diverse crop types and damage patterns to optimize wild animal damage evaluation. Full article

Regional development represents a pivotal component in advancing the Chinese path to modernization. The pace of modernization is intrinsically linked to the effectiveness of regional development strategies. In this context, the innovative evaluation of regional development assumes critical importance. Understanding the status and dynamics of regional development offers valuable insights into the progress of Chinese-style modernization. This study, grounded in a scientific interpretation of the concept and distinctive characteristics of Chinese-style modernization, proposes an enhanced regional development evaluation indicator system within this conceptual framework. By employing dynamic comprehensive evaluation and gravitational models, this study examines the regional modernization process in China from 2012 to 2021, exploring development patterns and inter-regional relationships. The findings reveal that regional modernization has evolved into four distinct types of agglomerated areas, with a strong correlation observed between regional development patterns and geographic location. Based on these findings, targeted recommendations for optimizing regional development are provided. Full article

As a result of global climate change and human production activities, algal blooms are occurring in aquatic environments. The problem of eutrophication in water bodies is becoming increasingly severe, affecting the safety of drinking water sources. In this study, an algal bloom risk index model combining the Improved Fuzzy Analytic Hierarchy Process (IFAHP), Entropy Weight Method (EWM), and Game Theory (GT) was proposed for the Shanxi Reservoir based on the TOPSIS method. After the seasonal and spatial variability in algal bloom risk from 2022 to 2023 was analyzed, an adaptive simplification of the algal bloom risk index calculation was proposed to optimize the model. To enhance its practical applicability, this study proposed an adaptive simplification of the algal bloom risk index calculation based on an improved TOPSIS approach. The error indexes R² for the four seasons and the annual analysis were 0.9884, 0.9968, 0.9906, 0.9946, and 0.9972, respectively. Additionally, the RMSE, MAE, and MRE values were all below 0.035, indicating the method’s high accuracy. Using the adaptively simplified risk index, a risk grading and a spatial delineation of risk areas in Shanxi Reservoir were conducted. A comparison with traditional risk classification methods showed that the error in the risk levels did not exceed one grade, demonstrating the effectiveness of the proposed calculation model and risk grading approach. This study provides valuable guidance for the prevention and control of algal blooms in reservoir-type drinking water sources, contributing to the protection of drinking water sources and public health. Full article

Abstract: Implant-supported prosthetic rehabilitation for patients with severely atrophic jaws is challenging due to complex anatomical considerations and the limitations of conventional augmentation techniques. This study explores the potential of subperiosteal (juxta-osseous) implants as an alternative solution, using finite element analysis (FEA) to evaluate mechanical performance. Realistic jaw models, developed from radiographic data, are utilized to simulate various implant configurations and load scenarios. Results indicate that different screw placements, implant designs, and structural modifications can significantly influence stress distribution and biomechanical behavior. Upper and lower jaw models were assessed under multiple load conditions to determine optimal configurations. Findings suggest that strategic adjustments, such as adding posterior screws or altering implant connections, can enhance load distribution and reduce stress concentration, particularly in critical areas. Tensile loads in critical bone areas near cortical fixing screws exceeded 50 MPa under anterior loading, while configurations with larger load distributions reduced stress on both implant and bone. The study provides evidence-based insights into optimizing subperiosteal implant design to improve stability, longevity, and patient outcomes. Full article

In modern ocean exploration, forward-looking sonar (FLS) provides real-time 2D imaging of the seabed ahead, but its detection range is relatively limited. Conversely, side-scan sonar (SSS) enables large-scale imaging of the seabed during movement but struggles to effectively image areas directly beneath the sensor. Integrating FLS and SSS offers a promising solution by leveraging their complementary strengths to achieve comprehensive seabed mapping. However, no prior research has explored this fusion approach. This paper presents a novel method for FLS and SSS fusion mapping. Firstly, a novel sonar image enhancement method based on equalization is proposed, enabling simultaneous enhancement and grayscale unification of two sonar images. Additionally, an effective area extraction approach for FLS images, grounded on the approximate erosion method, is introduced to produce high-quality FLS mapping. Furthermore, by examining the data distribution in FLS and SSS mappings, the standard deviation of these datasets is utilized to refine the grayscale distribution of FLS mapping, thereby enhancing the grayscale distribution similarity between the two mapping results. Finally, FLS map data are seamlessly integrated into the gaps of the SSS map, resulting in a fused, comprehensive seabed representation. Large-scale experiments demonstrate that the proposed method effectively combines the strengths of FLS and SSS, producing complete and detailed seabed topography maps. Simultaneously, numerous ablation experiments are conducted to evaluate the impact of various parameters on fusion mapping, providing guidelines for selecting the optimal parameters. This fusion approach, thus, holds significant practical value for ocean exploration and seabed mapping applications. Full article

Tea tree oil (TTO), acquired from Melaleuca alternifolia (Maiden & Betche) Cheel, Myrtaceae, is a widely utilized essential oil (EO) due to its bioactive properties. The identification and quantification of TTO ingredients is generally performed by GC-MS, which provides the most accurate results. However, in some instances, the cost and time of the analysis may pose a challenge. Thin-layer chromatography (TLC) and high-performance thin-layer chromatography (HPTLC) offer a simpler, faster, cost-effective alternative capable of simultaneously analyzing and quantifying multiple samples. In addition, for more complex oils, two-dimensional (2D) or multigradient development (MGD) TLC provide better separation. Nevertheless, further development is sometimes necessary for the isolation of comigrating components. This study showcases a combined 2D-MGD TLC/HPTLC method for the successful separation of TTO components of interest. While human error, limited separation, and the partial evaporation of volatile components may still present a challenge during the process, considerable recovery of mono- and sesquiterpenes was achieved. This protocol also resulted in the successful isolation of target oxygenated monoterpenes (OMs) producing highly pure terpinen-4-ol (100%) and α-terpineol (≥94%), confirmed by GC-MS. The accurate enantiomeric distribution of these major OMs was verified by GC-FID through the use of a chiral cyclodextrin-based stationary phase. The observed positive enantiomer range (area percent) as well as (+)/(−) ratio for each terpinen-4-ol and α-terpineol were within acceptable ISO criteria. Full article

This study explores the impact of geographical origin, harvest time, and cooking on the volatile organic compound (VOC) profiles of wild and reared seabream from the Adriatic and Tyrrhenian Seas. A Proton Transfer Reaction–Time of Flight–Mass Spectrometry (PTR-ToF-MS) allowed for VOC profiling with high sensitivity and high throughput. A total of 227 mass peaks were identified. Principal component analysis (PCA) showed a clear separation between cooked and raw samples, with cooking causing a significant increase in 64% of VOCs, especially hydrogen sulphide, methanethiol, and butanal. A two-way ANOVA revealed significant effects of origin, time, and their interaction on VOC concentration, with 102 mass peaks varying significantly based on all three factors. Seasonal effects were also notable, particularly in reared fish from the Adriatic Sea, where compounds like monoterpenes and aromatics were higher during non-breeding months, likely due to environmental factors unique to that area. Differences between wild and reared fish were influenced by lipid content and seasonal changes, impacting the VOC profile of seabream. These findings provide valuable insights into how cooking, geographical origin, and seasonality interact to define the flavour profile of seabream, with potential applications in improving quality control and product differentiation in seafood production. Full article

Background: A machine learning prognostic mortality scoring system was developed to address challenges in patient selection for clinical trials within the Intensive Care Unit (ICU) environment. The algorithm incorporates Red blood cell Distribution Width (RDW) data and other demographic characteristics to predict ICU mortality alongside existing ICU mortality scoring systems like Simplified Acute Physiology Score (SAPS). Methods: The developed algorithm, defined as a Mixed-effects logistic Random Forest for binary data (MixRFb), integrates a Random Forest (RF) classification with a mixed-effects model for binary outcomes, accounting for repeated measurement data. Performance comparisons were conducted with RF and the proposed MixRFb algorithms based solely on SAPS scoring, with additional evaluation using a descriptive receiver operating characteristic curve incorporating RDW’s predictive mortality ability. Results: MixRFb, incorporating RDW and other covariates, outperforms the SAPS-based variant, achieving an area under the curve of 0.882 compared to 0.814. Age and RDW were identified as the most significant predictors of ICU mortality, as reported by the variable importance plot analysis. Conclusions: The MixRFb algorithm demonstrates superior efficacy in predicting in-hospital mortality and identifies age and RDW as primary predictors. Implementation of this algorithm could facilitate patient selection for clinical trials, thereby improving trial outcomes and strengthening ethical standards. Future research should focus on enriching algorithm robustness, expanding its applicability across diverse clinical settings and patient demographics, and integrating additional predictive markers to improve patient selection capabilities. Full article

Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference data needed for model training and validation. However, traditional ground truthing methods are labor-intensive, time-consuming and restricted in spatial coverage, posing challenges for large-scale or complex landscapes. The advent of drone technology offers an efficient and cost-effective solution to these limitations, enabling the rapid collection of high-resolution imagery even in remote or inaccessible areas. This study proposes an approach to enhance the efficiency of supervised vegetation mapping in complex landscapes, integrating Multivariate Functional Principal Component Analysis (MFPCA) applied to the Sentinel-2 time series with drone-based ground truthing. Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. The proposed methodology achieves a classification accuracy of 92.59%, significantly exceeding the commonly reported thresholds for habitat mapping. This approach, characterized by its efficiency, repeatability and adaptability, aligns seamlessly with key environmental monitoring and conservation policies, such as the Habitats Directive. By integrating advanced remote sensing with drone-based technologies, it offers a scalable and cost-effective solution to the challenges of biodiversity monitoring, enabling timely updates and supporting effective habitat management in diverse and complex environments. Full article

This paper proposes mathematical optimization models for solving the network planning problem using millimeter wave technology for 5G wireless communications networks. To this end, it is assumed that a set of users, $M=\{1,\dots ,m\}$, and a set of base stations, $N=\{1,\dots ,n\}$, are deployed randomly in a square area. In particular, the base stations should be connected, forming a star backbone so that users can connect to their nearest active base stations forming the backbone where the connections are symmetric. In particular, the first two models maximize the number of users connected to the backbone and minimize the distance costs of connecting users to the base stations, and distances of connecting the base stations themselves. Similarly, the last two models maximize and minimize the same objectives and the number of base stations to be activated to form the star backbone. Each user is allowed to connect to a unique active base station. In general, the millimeter wave technology presents a high path loss. Consequently, the transmission distances should be no larger than 300 m at most for different radial transmissions. Thus, a direct line of sight between users and base stations is assumed. Finally, we propose local search-based algorithms that allow finding near-optimal solutions for all our tested instances. Our numerical results indicate that we can solve network instances optimally with up to $k=100$, $n=200$, and $m=5000$ users. Full article

Vegetation coverage in the upper reaches of the Yangtze River is very important to the ecological balance in this area, and it also has an impact on the inflow runoff and sediment transport processes of the Three Gorges Reservoir. Based on the normalized vegetation index data (NDVI) with 250 m resolution in the upper reaches of the Yangtze River, annual runoff, sediment transport, land use, meteorology, and other data—and by using the methods of Sen + Mann–Kendall trend analysis, partial correlation analysis, and Hurst index—this paper analyzes the temporal and spatial variation characteristics, driving factors, and the influence on the water and sediment inflow processes of the Three Gorges Reservoir in each sub-basin in the upper reaches of the Yangtze River. The results show that (1) NDVI in the upper Yangtze River showed a fluctuating upward trend from 2001 to 2022, and the overall vegetation cover continued to increase, showing a spatial pattern of low in the west and high in the east. At the same time, the runoff volume of the upper reaches of the Yangtze River did not show a significant upward trend from 2006 to 2022, while the sand transport decreased significantly; (2) Among the NDVI-influencing factors in the upper reaches of the Yangtze River, the area driven by the land use factor accounts for about 43% of the whole study area, followed by precipitation; (3) Precipitation significantly affected runoff, and NDVI was negatively correlated with sand transport in most of the watersheds, suggesting that improved vegetation could help reduce sediment loss. In addition, the future trend of vegetation change was predicted to be dominated by improvement (Hurst > 0.5) based on the Hurst index, which will provide a reference for the NDVI change in the upper Yangtze River and the prediction of sediment inflow to the Three Gorges Reservoir. Full article

Bisphenol A (BPA) is a typical environmental estrogen that is distributed worldwide and has the potential to pose a hazard to the ecological environment and human health. The development of an efficient and sensitive sensing strategy for the monitoring of BPA residues is of paramount importance. A novel electrochemical sensor based on carbon black and carbon nanofibers composite (CB/f-CNF)-assisted signal amplification has been successfully constructed for the amperometric detection of BPA in foods. Herein, the hybrid CB/f-CNF was prepared using a simple one-step ultrasonication method, and exhibited good electron transfer capability and excellent catalytic properties, which can be attributed to the large surface area of carbon black and the strong enhancement of the conductivity and porosity of carbon nanofibers, which promote a faster electron transfer process on the electrode surface. Under the optimized conditions, the proposed CB/f-CNF/GCE sensor exhibited a wide linear response range (0.4–50.0 × 10⁻⁶ mol/L) with a low limit of detection of 5.9 × 10⁻⁸ mol/L for BPA quantification. Recovery tests were conducted on canned peaches and boxed milk, yielding satisfactory recoveries of 86.0–102.6%. Furthermore, the developed method was employed for the rapid and sensitive detection of BPA in canned meat and packaged milk, demonstrating comparable accuracy to the HPLC method. This work presents an efficient signal amplification strategy through the utilization of carbon/carbon nanocomposite sensitization technology. Full article

This study explores advanced methodologies for estimating subcanopy solar radiation using LiDAR (Light Detection and Ranging)-derived point clouds and GIS (Geographic Information System)-based models, with a focus on evaluating the impact of different LiDAR data types on model performance. The research compares the performance of two modeling approaches—r.sun and the Point Cloud Solar Radiation Tool (PCSRT)—in capturing solar radiation dynamics beneath tree canopies. The models were applied to two contrasting environments: a forested area and a built-up area. The r.sun model, based on raster data, and the PCSRT model, which uses voxelized point clouds, were evaluated for their accuracy and efficiency in simulating solar radiation. Data were collected using terrestrial laser scanning (TLS), unmanned laser scanning (ULS), and aerial laser scanning (ALS) to capture the structural complexity of canopies. Results indicate that the choice of LiDAR data significantly affects model outputs. PCSRT, with its voxel-based approach, provides higher precision in heterogeneous forest environments. Among the LiDAR types, ULS data provided the most accurate solar radiation estimates, closely matching in situ pyranometer measurements, due to its high-resolution coverage of canopy structures. TLS offered detailed local data but was limited in spatial extent, while ALS, despite its broader coverage, showed lower precision due to insufficient point density under dense canopies. These findings underscore the importance of selecting appropriate LiDAR data for modeling solar radiation, particularly in complex environments. Full article

The Xiangjiaba and Xiluodu reservoirs, as important components of the large cascade reservoirs in the lower Jinsha River, and the interactive changes in sediment trapping amounts, the differences in sedimentation dynamics, and the potential mutual influence mechanisms among them are scientific issues worthy of attention. Based on the multiple observed data of thalweg elevation before and after the completion of the dam construction, this study calculated the average sedimentation rates of all 20 km segments of the above-mentioned reservoirs in different periods. Meanwhile, the local mean gradients between adjacent segments and the regional mean gradients from the segments to the dam in the corresponding periods were calculated. The results show that the maximum and average sedimentation rates of the Xiangjiaba Reservoir, which was built earliest and is located downstream, were as high as 19.62 m yr⁻¹ and 8.88 m yr⁻¹, respectively, in the first half year after the dam closure. After the completion of the Xiluodu Reservoir, an adjacent cascade reservoir upstream, the average sedimentation rate of the Xiangjiaba Reservoir in the following seven years dropped to 0.67 m yr⁻¹. The maximum and average sedimentation rates of the Xiluodu Reservoir were 9.07 m yr⁻¹ and 4.15 m yr⁻¹, respectively, within one year after the dam closure, and its average sedimentation rate in the following six years was 2.51 m yr⁻¹. The spatial variations of sedimentation rates in these two reservoirs follow different changing patterns. There is an obvious correlation between the change in mean gradient and the change in sedimentation rate. The sequence of dam construction, the relative positions of the reservoirs, the differences in sediment trapping amounts, and operation modes are the key factors controlling the changes in sedimentation rate and gradient in the reservoir area. This study reveals the interactive changes in sedimentation rates among cascade reservoirs and the response mechanism of river channel morphology, and has a guiding role for the formulation of effective measures for the sustainable utilization of cascade reservoirs. Full article