Search Results (505)

High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization method for addressing such a challenge. Both static and scanning experiments were performed on a developed mobile and non-destructive microwave detection system to quantify the MC of wheat and then locate abnormal moisture regions. For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. Furthermore, both one- and two-dimensional localizing methods were proposed for localizing abnormal moisture regions. The one-dimensional method evaluated two approaches—attenuation value and absolute attenuation gradient—using computer simulation technology (CST) modeling and scanning experiments. The experimental results confirmed the superior performance of the absolute gradient method, with a center detection error of less than 12 mm in the anomalous wheat moisture region and a minimum width detection error of 1.4 mm. The study performed two-dimensional antenna scanning and effectively imaged the high-MC regions using phase delay analysis. The imaging results coincide with the actual locations of moisture anomaly regions. This study demonstrated a promising solution for accurately localizing the wheat’s abnormal/high-moisture regions with the use of an emerging microwave transmission method. Full article

Cultivated land quality is a key factor in ensuring sustainable agricultural development. Exploring differences in cultivated land quality under distinct cropping systems is essential for developing targeted improvement strategies. This study takes place in Shenyang City—located in the typical black soil region of Northeast China—as a case area to construct a cultivated land quality evaluation system comprising 13 indicators, including organic matter, effective soil layer thickness, and texture configuration. A minimum data set (MDS) was separately extracted for paddy and upland fields using principal component analysis (PCA) to conduct a comprehensive evaluation of cultivated land quality. Additionally, an obstacle degree model was employed to identify the limiting factors and quantify their impact. The results indicated the following. (1) Both MDSs consisted of seven indicators, among which five were common: ≥10 °C accumulated temperature, available phosphorus, arable layer thickness, irrigation capacity, and organic matter. Parent material and effective soil layer thickness were unique to paddy fields, while landform type and soil texture were unique to upland fields. (2) The cultivated land quality index (CQI) values at the sampling point level showed no significant difference between paddy (0.603) and upland (0.608) fields. However, their spatial distributions diverged significantly; paddy fields were dominated by high-grade land (Grades I and II) clustered in southern areas, whereas uplands were primarily of medium quality (Grades III and IV), with broader spatial coverage. (3) Major constraint factors for paddy fields were effective soil layer thickness (21.07%) and arable layer thickness (22.29%). For upland fields, the dominant constraints were arable layer thickness (27.57%), organic matter (25.40%), and ≥10 °C accumulated temperature (23.28%). Available phosphorus and ≥10 °C accumulated temperature were identified as shared constraint factors affecting quality classification in both systems. In summary, cultivated land quality under different cropping systems is influenced by distinct limiting factors. The construction of cropping-system-specific MDSs effectively improves the efficiency and accuracy of cultivated land quality assessment, offering theoretical and methodological support for land resource management in the black soil regions of China. Full article

The Copernicus European Regional Reanalysis (CERRA) was released in August 2022, providing a continental atmospheric reanalysis, and, in addition, the New European Wind Atlas (NEWA) is a recently released hindcast product that can be used to create a high temporal and spatial resolution wind resource atlas of Europe. In order to demonstrate the suitability of the NEWA and CERRA wind datasets for offshore wind energy applications, the accuracy of these datasets was assessed for the Mediterranean Sea, a basin with a high potential for the development of offshore wind projects. Long-term in situ measurements from 13 offshore locations along the basin were used in order to assess the performance of the CERRA and NEWA wind speed datasets in the hourly and seasonal time scales by using a variety of different evaluation tools. The results revealed that the CERRA dataset outperforms NEWA and is a reliable source for offshore wind energy assessment studies in the examined areas, although special attention should be paid to extreme value analysis of the wind speed. Full article

Introduction: Deep dry needling (DDN) is commonly applied in physiotherapy to treat musculoskeletal pain. The quadratus femoris (QF) muscle, located in the ischiofemoral space (IFS), represents a clinically relevant yet anatomically complex target. However, limited evidence exists on the safety, accuracy, and reliability of non-ultrasound-guided DDN in this region. Aims: To assess the safety and accuracy of a standardized, non-ultrasound-guided DDN approach to the QF muscle, and to evaluate the intra- and inter-rater reliability of key procedural outcomes. Additionally, to determine the agreement between ultrasound imaging and anatomical dissection as validation methods for needle placement. Methods: An experimental cross-sectional study was conducted on five fresh cadavers (n = 24 approaches) by two physiotherapists with different DN experience. A standardized dry needling protocol was executed without ultrasound guidance, and anatomical and procedural variables were documented. Reliability (intra/inter-rater) was assessed for needle size, sciatic nerve (SN) puncture, IFS targeting, and overall success. In a subset, needle placement was validated through ultrasound and subsequent dissection. Results: The IFS was reached in 70.8% of procedures, and the SN was punctured in 16.7%. Inter-rater reliability for needle size was poor (κ = 0.04). Agreement between ultrasound and dissection was excellent for the ischiofemoral location and success (100%) and moderate for non SN puncture (90%; κ = 0.62). Conclusions: The standardized protocol demonstrated moderate accuracy and revealed a relevant clinical risk when targeting the quadratus femoris muscle. While inter-rater reliability was limited, agreement between ultrasound and dissection methods was high, supporting their complementary use for validating needle placement in anatomically complex procedures. Full article

In response to the global imperative of reducing greenhouse gas emissions, the optimisation of renewable energy systems under regionally favourable conditions has become increasingly essential. Solar irradiance forecasting plays a pivotal role in enhancing energy planning, grid reliability, and long-term sustainability. However, in the context of Turkey, existing studies on solar radiation forecasting often rely on traditional statistical approaches and are limited to single-site analyses, with insufficient attention to regional diversity and deep learning-based modelling. To address this gap, the present study focuses on Turkey’s Mediterranean region, characterised by high solar potential and diverse climatic conditions and strategically relevant to national clean energy targets. Historical data from 2020 to 2023 were used to forecast solar irradiance patterns up to 2026. Five representative locations—Adana, Isparta, Fethiye, Ulukışla, and Yüreğir—were selected to capture spatial and temporal variability across inland, coastal, and high-altitude zones. Advanced deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), were developed and evaluated using standard performance metrics. Among these, BiLSTM achieved the highest accuracy, with a correlation coefficient of R = 0.95, RMSE = 0.22, and MAPE = 5.4% in Fethiye, followed by strong performance in Yüreğir (R = 0.90, RMSE = 0.12, MAPE = 7.2%). These results demonstrate BiLSTM’s superior capacity to model temporal dependencies and regional variability in solar radiation. The findings contribute to the development of location-specific forecasting frameworks and offer valuable insights for renewable energy planning and grid integration in solar-rich environments. Full article

Background/Objectives: The SENTIREC-endo study identified a safe sentinel lymph node mapping algorithm combined with PET-positive node dissection, matching radical pelvic and paraaortic lymphadenectomy in high-risk endometrial cancer. The present study evaluated the diagnostic accuracy of FDG-PET/CT for lymph node metastases in the same population based on location, size, and Standardised Uptake Value (SUV), in addition to assessing interrater agreement across three Danish centres. Methods: This prospective multicentre study included women with high-risk endometrial cancer from the Danish SENTIREC study database (2017–2023). All patients underwent preoperative FDG-PET/CT. Diagnostic accuracy was evaluated against a pathology-confirmed reference standard. Interrater agreement was evaluated between trained specialists in Nuclear Medicine. Results: Among 227 patients, 52 patients (23%) had lymph node metastases. FDG-PET/CT identified lymph node metastases with 56% sensitivity (95% CI: 42–68) and 91% specificity (95% CI: 86–94). Positive and negative predictive values were 64% and 87%, respectively. Specificity for paraaortic nodes was high (97%), though sensitivity remained limited (56%). Lymph node size and SUVmax had moderate diagnostic value (AUC-ROC ~0.7). Interrater proportion of agreement was 95% and Cohen’s Kappa κ = 0.84 (95% CI: 0.73–0.94), the latter of which was ‘almost perfect’. Conclusions: FDG-PET/CT had limited sensitivity in lymph node staging in high-risk EC, and the diagnostic accuracy of FDG-PET/CT remains complementary to the sentinel node procedure. Due to its high specificity and strong interrater reliability, FDG-PET/CT is recommended for clinical implementation in combination with the sensitive sentinel node biopsy for the targeted dissection of PET-positive lymph nodes, particularly in paraaortic regions. Full article

Accurate detection of fractures in X-ray images is important to initiate appropriate medical treatment in time—in this study, an advanced combined attention CNN model with multiple attention mechanisms was developed to improve fracture detection by deeply representing features. Specifically, our model incorporates squeeze blocks and convolutional block attention module (CBAM) blocks to improve the model’s ability to focus on relevant features in X-ray images. Using computed tomography X-ray images, this study assesses the diagnostic efficacy of the artificial intelligence (AI) model before and after optimization and compares its performance in detecting fractures or not. The training and evaluation dataset consists of fractured and non-fractured X-rays from various anatomical locations, including the hips, knees, lumbar region, lower limb, and upper limb. This gives an extremely high training accuracy of 99.98 and a validation accuracy 96.72. The attention-based CNN thus showcases its role in medical image analysis. This aspect further complements a point we highlighted through our research to establish the role of attention in CNN architecture-based models to achieve the desired score for fracture in a medical image, allowing the model to generalize. This study represents the first steps to improve fracture detection automatically. It also brings solid support to doctors addressing the continued time to examination, which also increases accuracy in diagnosing fractures, improving patients’ outcomes significantly. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

Multispectral images obtained from Unmanned Aerial Vehicles (UAVs) have become strategic tools in precision agriculture, particularly for analyzing spatial variability in soil attributes. This study aimed to evaluate the spatial distribution of soil electrical (EC) and total organic carbon (TOC) in irrigated forage cactus areas in the Brazilian semiarid region, using field measurements and UAV-based multispectral imagery. The study was conducted in a communal agricultural settlement located in the Mimoso Alluvial Valley (MAV), where EC and TOC were measured at 96 points, and seven biophysical indices were derived from UAV multispectral imagery. Geostatistical models, including cokriging with spectral indices (NDVI, EVI, GDVI, SAVI, and NDSI), were applied to map soil attributes at different spatial scales. Cokriging improved the spatial prediction of EC and TOC by reducing uncertainty and increasing mapping accuracy. The standard deviation of EC decreased from 1.39 (kriging) to 0.67 (cokriging with EVI), and for TOC from 15.55 to 8.78 (cokriging with NDVI and NDSI), reflecting a 43.5% reduction in uncertainty. The indices, EVI, NDVI, and NDSI, showed strong potential in representing and enhancing the spatial variability in soil attributes. NDVI and NDSI were particularly effective at finer grid resolutions, supporting more efficient irrigation strategies and sustainable agricultural practices. Full article

Objectives: This pre-clinical feasibility study evaluates the accuracy of a novel augmented reality-based (AR-based) guidance technology using head-mounted displays (HMDs) for the placement of patient-specific instruments (PSIs)—also referred to as surgical guides—and osteotomy performance in pelvic tumour resections. The goal is to improve PSI placement accuracy and osteotomy execution while assessing user perception and workflow efficiency. Methods: The study was conducted on ten 3D-printed pelvic phantoms derived from CT scans of cadaveric specimens. Custom PSIs were designed and printed to guide osteotomies at the supraacetabular, symphysial, and ischial regions. An AR application was developed for the HoloLens 2 HMD to display PSI location and cutting planes. The workflow included manual supraacetabular PSI placement, AR-guided placement of the other PSIs and osteotomy execution. Postoperative CT scans were analysed to measure angular and distance errors in PSI placement and osteotomies. Task times and user feedback were also recorded. Results: The mean angular deviation for PSI placement was 2.20°, with a mean distance error of 1.19 mm (95% CI: 0.86 to 1.52 mm). Osteotomies showed an overall mean angular deviation of 3.73° compared to planned cuts, all within the predefined threshold of less than 5°. AR-assisted guidance added less than two minutes per procedure. User feedback highlighted the intuitive interface and high usability, especially for visualising cutting planes. Conclusions: Integrating AR through HMDs is a feasible and accurate method for enhancing PSI placement and osteotomy performance in pelvic tumour resections. The system provides reliable guidance even in cases of PSI failure and adds minimal time to the surgical workflow while significantly improving accuracy. Further validation in cadaveric models is needed to ensure its clinical applicability. Full article

Change detection (CD) is detecting and evaluating surface changes by comparing Remote Sensing Images (RSIs) at different times, which is of great significance for environmental protection and urban planning. Due to the need for higher standards in complex scenes, attention-based CD methods have become predominant. These methods focus on regions of interest, improving detection accuracy and efficiency. However, external factors can introduce many pseudo-changes, presenting significant challenges for CD. To address this issue, we proposed a Multi-level Intertemporal Attention-guided Network (MIANet) for CD. Firstly, an Intertemporal Fusion Attention Unit (IFAU) is proposed to facilitate early feature interaction, which helps eliminate irrelevant changes. Secondly, the Change Location and Recognition Module (CLRM) is designed to explore change areas more deeply, effectively improving the representation of change features. Furthermore, we also employ a challenging landslide mapping dataset for the CD task. Through comprehensive testing on two datasets, the MIANet algorithm proves to be effective and robust, achieving detection results that are either better or at least comparable with current methods in terms of accuracy and reliability. Full article

Water, the lifeblood of our planet, sustains ecosystems, economies, and communities. However, climate change and increasing hydrological variability have exacerbated global water scarcity, threatening livelihoods and economic stability. According to the United Nations, over 2 billion people currently live in water-stressed regions, a figure expected to rise significantly by 2030. To address this urgent challenge, this study proposes an AI-driven anomaly detection framework for smart water metering networks (SWMNs) using machine learning (ML) techniques and data resampling methods to enhance water conservation efforts. This research utilizes 6 years of monthly water consumption data from 1375 households from Location A, Windhoek, Namibia, and applies support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (kNN) models within ensemble learning strategies. A significant challenge in real-world datasets is class imbalance, which can reduce model reliability in detecting abnormal patterns. To address this, we employed data resampling techniques including random undersampling (RUS), SMOTE, and SMOTEENN. Among these, SMOTEENN achieved the best overall performance for individual models, with the RF classifier reaching an accuracy of 99.5% and an AUC score of 0.998. Ensemble learning approaches also yielded strong results, with the stacking ensemble achieving 99.6% accuracy, followed by soft voting at 99.2% and hard voting at 98.1%. These results highlight the effectiveness of ensemble methods and advanced sampling techniques in improving anomaly detection under class-imbalanced conditions. To the best of our knowledge, this is the first study to explore and evaluate the combined use of ensemble learning and resampling techniques for ML-based anomaly detection in SWMNs. By integrating artificial intelligence into water systems, this work lays the foundation for scalable, secure, and efficient smart water management solutions, contributing to global efforts in sustainable water governance. Full article

Renewable energy sources, such as solar and wind power, are gaining increasing global attention. To facilitate their integration into transportation infrastructure, this paper proposes a multi-criteria assessment framework for identifying the most suitable renewable energy sources for street lighting at any given location. The framework evaluates three key metrics: cost–benefit, reliability, and power generation potential, using time-series weather data. To demonstrate its effectiveness, we apply the framework to data from Georgia, USA. The results show that the proposed approach effectively classifies locations into four categories: solar-recommended, wind-recommended, hybrid-recommended, and no recommendation. Specifically, wind energy is primarily recommended in the southeastern region near the coastline, while solar energy is favored in the northwestern region. A hybrid of both sources is mainly recommended along the coast and in transitional areas. In several isolated parts of the northwest, neither energy source is recommended due to unfavorable weather conditions influenced by the local terrain. Since processing long-term time-series data is computationally intensive and challenging during inference, we train machine learning models, including Multilayer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost), using temporally aggregated features for efficient and rapid decision-making. The MLP model achieves an overall accuracy of 92.4%, while XGBoost further improves accuracy to 94.3%. This study provides a practical reference for regional energy infrastructure planning, promoting optimized renewable energy use in street lighting through a robust, data-driven evaluation framework. Full article