Search Results (3,933)

Accurate multi-leaf collimator (MLC) motion prediction is a prerequisite for precise dose delivery in advanced techniques such as IMRT and VMAT. Traditional patient-specific quality assurance (QA) methods remain resource-intensive and prone to physical measurement uncertainties. This study aimed to develop machine learning (ML) models to predict delivered MLC positions using kinematic parameters extracted from DICOM-RT plans for the Elekta Versa HD system. A dataset comprising 200 patient plans was constructed by pairing planned MLC positions, velocities, and accelerations with corresponding delivered values parsed from unstructured trajectory logs. Four regression models, including linear regression (LR), were trained to evaluate the deterministic nature of the Elekta servo-mechanism. LR demonstrated superior prediction accuracy, achieving the lowest mean absolute error (MAE) of 0.145 mm, empirically confirming the fundamentally linear relationship between planned and delivered trajectories. Subsequent dosimetric validation using ArcCHECK measurements on 17 clinical plans revealed that LR-corrected plans achieved statistically significant improvements in gamma passing rates, with a mean increase of 2.24% under the stringent 1%/1 mm criterion (p < 0.001). These results indicate that the LR model successfully captures systematic mechanical signatures, such as inertial effects. This study demonstrates that a computationally efficient LR model can accurately predict Elekta MLC performance, providing a robust foundation for implementing ML-based virtual QA. This approach is particularly valuable for time-sensitive workflows like adaptive radiotherapy (ART), as it significantly reduces reliance on physical QA resources. Full article

Hydrological droughts represent a growing challenge for northern watersheds in Thailand, where climate change is projected to intensify seasonal water stress and destabilize agricultural productivity and water resource management. This study employed the Composite Hydrological Drought Index (CHDI) to evaluate the spatiotemporal characteristics of future droughts under representative concentration pathway (RCP) scenarios. The findings revealed a pronounced seasonal contrast: under RCP8.5, the CHDI values indicated more severe drought conditions during the dry season and greater flood potential during the wet season. Consequently, the region faces dual hydrological threats: prolonged water deficits and increased flood exposure within the same annual cycle. Drought persistence is expected to intensify, with maximum consecutive drought runs extending up to 10–11 months in future projections. The underlying mechanisms include increased actual evapotranspiration, which accelerates soil moisture depletion, enhanced rainfall variability, which drives the sequencing of floods and droughts, and catchment storage properties, which govern hydrological resilience. These interconnected processes alter the timing and clustering of drought events, concentrating hydrological stress during periods that are sensitive to agriculture. Overall, drought behavior in northern Thailand is projected to intensify in a spatially heterogeneous pattern, emphasizing the need for localized, integrated adaptation measures and flexible water management strategies to mitigate future risks of drought. Full article

The Tensift River Basin, part of the Mediterranean region, faces significant agricultural losses due to increasing drought frequency and severity, impacting up to 15% of the national GDP. The increasing climate crisis demands our immediate attention and proactive adaptation measures, including the enhancement of early-warning tools to support timely and informed responses. To this end, our study aims to achieve the following goals: (1) evaluate satellite and reanalysis products against in situ observations using statistical metrics; (2) identify the best probability distribution for calculating drought indices using goodness-of-fit testing; (3) compare the performances of the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) at different aggregation timescales by comparing index-based and reported (i.e., impact-based) drought events using receiver operating characteristic (ROC) analysis. Our findings indicate that CHIRPS and ERA5-Land datasets perform well compared to in situ measurements for drought monitoring in the Tensift River Basin. Pearson Type 3 was identified as the optimal distribution for SPI calculation, while log-logistic was confirmed for SPEI. We also explored the effect of using the Thornthwaite method and the Hargreaves method when computing the SPEI. These results can serve as a basis for drought monitoring, modeling, and forecasting, to support decision-makers in the sustainable management of water resources. Full article

Background: CrossFit could be an innovative alternative for older adults. Traditional strength training is well-established for safety and progressive overload, while concerns exist about overexertion or poor technique in modified CrossFit, especially for those with musculoskeletal or cardiovascular conditions. However, scaled and supervised CrossFit sessions have shown low injury rates and high satisfaction among older adults. Objective: to evaluate the effects of a CrossFit-adapted program on balance and muscular power. Methods: 60 older adults participated in the study. Participants were randomized into two groups: CrossFit-adapted and control. Functional capacity, balance and strength variables were analyzed. The sample size was calculated a priori using G*Power 3.1 software, considering an effect size of 0.25 [medium], α = 0.05, and a power [1–β] of 0.80 for a repeated-measures ANOVA with two groups and three measurement points. Data were analyzed using SPSS Statistics version 25. Results: Significant improvements in balance scores were observed in the CrossFit group compared to the control group. In the Timed Up and Go test, the CrossFit group improved from 9.83 ± 1.3 s to 8.74 ± 1.1 s, [p = 0.002]. Lower limb muscle power increased significantly in CrossFit group across all tests: chair stand test, the stair ascent and stair descent [p < 0.001]. Conclusions: A CrossFit-adapted program can significantly improve functional capacity, balance, and strength in older adults. Full article

Accurate state estimation is essential for the real-time operation and control of modern distribution systems characterized by high renewable energy penetration, bidirectional power flows, and volatile loads. Conventional model-driven approaches such as the Weighted Least Squares (WLS) exhibit limited robustness under noisy and sparse measurements, while existing data-driven methods often neglect critical physical constraints inherent to power systems. To address these limitations, this paper proposes a physics-constrained Graph Attention Network (GAT) framework for distribution system state estimation (DSSE) that synergistically integrates data-driven learning with physical domain knowledge. The proposed method comprises three key components: (1) a Gaussian Mixture Model (GMM)-based data augmentation strategy that captures the stochastic characteristics of loads and distributed generation to generate synthetic samples consistent with actual operating distributions; (2) a GAT-based feature extractor with topology-aware admittance matrix embedding that effectively learns spatial dependencies and structural relationships among network nodes; and (3) a physics-constrained loss function that incorporates nodal power and voltage limit penalties to enforce operational feasibility. Comprehensive evaluations on the real-world 141-bus test system demonstrate that the proposed method achieves mean absolute error (MAE) reductions of 52.4% and 45.5% for voltage magnitude and angle estimation, respectively, compared to conventional Graph Convolutional Network (GCN)-based approaches. These results validate the superior accuracy, robustness, and adaptability of the proposed framework under challenging measurement conditions. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) enables the large-scale collection of ecological data, yet translating this dynamic sensor data into engaging, personalized public experiences remains a significant challenge. Existing solutions fall short: static exhibitions lack adaptability, while general-purpose LLM agents struggle with real-time responsiveness and reliability. To address this, we introduce CurationAgent, a novel intelligent agent built upon the State-Gated Agent Architecture (SGAA). Its core innovation is an advanced hybrid curation pipeline that synergizes Retrieval-Augmented Generation (RAG) for broad semantic recall with an Intent-Driven Curation (IDC) Funnel for precise intent formalization and narrative synthesis. This hybrid model robustly translates user intent into a curated, multi-modal narrative. We validate this framework in a proof-of-concept virtual exhibition of the Lalu Wetland’s biodiversity. Our comprehensive evaluation demonstrates that CurationAgent is significantly more responsive (1512 ms vs. 4301 ms), reliable (95% vs. 57% task success), and precise (85.5% vs. 52.7% query precision) than standard agent architectures. Furthermore, a user study with 27 participants confirmed our system leads to measurably higher user engagement. This work contributes a robust and responsive agent architecture that validates a new paradigm for interactive systems, shifting from passive information retrieval to active, partnered experience curation. Full article

Ensuring high-quality forage under Mediterranean conditions requires careful evaluation of genetic resources. Two perennial forage species, cocksfoot (Dactylis glomerata L.) and tall fescue (Festuca arundinacea Schreb.), were evaluated to determine the stability and broad-sense heritability of major forage quality traits across Greek environments. The objective was to identify stable, heritable traits contributing to consistent forage quality under climatic variability. Measured traits included crude protein (CP%), crude fiber (CF%), ash, acid detergent fiber (ADF), neutral detergent fiber (NDF), cellulose, hemicellulose, acid detergent lignin (ADL), digestible dry matter (DDM%), dry matter intake (DMI%), and relative feed value (RFV). Significant genotype × environment (G × E) interactions were observed for most traits, highlighting the importance of multi-environment testing, except for RFV in cocksfoot, which was non-significant. Principal Component Analysis (PCA) helped clarify how these traits covary across environments. The traits Crude Protein, Ash Content, and ADL (on PC1) are largely independent of the traits Cellulose and Hemicellulose (on PC2) in the case of cocksfoot. The pattern of loadings in the case of Tall fescue revealed that hemicellulose represents a completely separate dimension of variation, which is uncorrelated to the rest of the traits that form a unified, highly correlated group. In both cases, the first two PCs explained over 82% of the total variance, separating genotypes and environments. By integrating stability (SI) and heritability (H²) results, Cock2D and T2fes were identified as the most stable and high-performing genotypes across environments. These findings could support breeding strategies for developing resilient forage cultivars with consistent quality and adaptability to Mediterranean environments, thereby enhancing sustainable livestock production. Full article

Background/Objectives: Acute stress is known to influence food-related motivation and decision-making, often promoting a preference for energy-dense, palatable foods. However, traditional laboratory paradigms have limited ecological validity. This study examined the relationship between stress-induced physiological changes, eating behavior traits, and food cravings using a virtual reality (VR) adaptation of the Trier Social Stress Test (VR-TSST) followed by a VR supermarket task in adolescents. Methods: Thirty-eight adolescents (mean age 15.8 ± 0.6 years) participated in the study. Physiological parameters (HR, QT, PQ intervals) were recorded pre- and post-stress using a portable ECG device (WIWE). Perceived stress and eating behavior traits were evaluated with the Perceived Stress Scale (PSS) and the Three-Factor Eating Questionnaire (TFEQ-R21C), respectively. Immediately after the VR-TSST, participants performed a VR supermarket task in which they rated cravings for sweet, fatty, and healthy foods using visual analog scales (VAS). Paired-samples t-tests examined pre–post changes in physiological parameters, partial correlations explored associations between ECG responses and eating traits, and a 2 × 3 mixed-model Repeated Measures ANOVA assessed the effects of food type (sweet, fatty, healthy) and uncontrolled eating (UE) group (low vs. high) on post-stress cravings. Results: Acute stress induced significant increases in HR and QTc intervals (p < 0.01), confirming a robust physiological stress response. The ANOVA revealed a strong main effect of food type (F(1.93, 435.41) = 168.98, p < 0.001, η²p = 0.43), indicating that stress-induced cravings differed across food categories, with sweet foods rated highest. A significant food type × UE group interaction (F(1.93, 435.41) = 16.49, p < 0.001, η²p = 0.07) showed that adolescents with high UE exhibited greater cravings for sweet and fatty foods than those with low UE. Overall, craving levels did not differ significantly between groups. Conclusions: The findings demonstrate that acute stress selectively enhances cravings for high-reward foods, and that this effect is modulated by baseline uncontrolled eating tendencies. The combined use of VR-based stress induction and VR supermarket simulation offers an innovative, ecologically valid framework for studying stress-related eating behavior in adolescents, with potential implications for personalized nutrition and the prevention of stress-induced overeating. Full article

Purposes: Beam hardening artifacts caused by dental implants remain one of the most significant limitations of cone-beam computed tomography (CBCT), often compromising the evaluation of peri-implant bone and potentially masking critical diagnostic findings. Although metal artifact reduction (MAR) and noise-optimization filters such as the Adaptive Image Noise Optimizer (AINO) are widely available in commercial CBCT systems, their effectiveness varies depending on implant configuration and scanning parameters. A clearer understanding of how implant positioning influences artifact severity—together with how MAR and AINO perform under different conditions—is essential for improving diagnostic reliability. Materials and Methods: A fresh frozen cadaver head, with dental implants inserted using two configurations (C1 and C2), was scanned using different scan parameters, with and without metal artifact reduction and image optimization filters. The percentages of gray value alteration due to artifacts were evaluated, using registered pre-implant scans as a control. Regions of interest were defined by an experienced researcher. For the two implant conditions, ROIs were placed as follows: C1—lingual, buccal and mesial to the mesial implant; lingual, buccal and distal to the distal implant; and an additional ROI between the implants (n = 7); C2—lingual, buccal, mesial and distal to each implant (n = 8). For each ROI, the mean gray value was measured in five consecutive axial slices, and rescaled according to calibration points in air and soft tissue. Results: Significant differences were found in gray values across configurations and scan modes. In the C2 configuration, combined MAR and AINO restored gray values in certain ROIs from 1.227 (OFF) to 1.223 (MAR+AINO), closely matching the control (1.227). In contrast, C1 showed limited improvement; for example, buccal ROI gray values decreased from 3.978 (OFF) to 3.323 (AINO) compared to the control (3.273), with no significant benefit from additional MAR. Conclusion: Artifacts from implants can be significantly affected by their (relative) position and the use of MAR and AINO. Full article

Elucidating shrub ecohydrological adaptation is critical for optimizing vegetation-restoration strategies in arid regions and maintaining regional ecological stability. This study examined typical desert shrubs at the northern edge of the Mu Us Sand Land. During the growth peak season (July–September), we measured understory-soil δ¹⁸O, soil water content (SWC), leaf δ¹³C_p, stem δ¹⁸O, and gas-exchange rates, and evaluated shrub drought resistance and water-use efficiency using Mantel tests and principal component analysis (PCA). Based on the VPDB standard, the δ13Cp values of leaves ranked as follows: Caragana microphylla (−27.21‰) > Salix psammophila (−27.80‰) > Artemisia ordosica (−28.48‰). The results indicate that leaf δ¹³C_p and water δ¹⁸O are effective indicators of shrub water-use efficiency, reflecting Cᵢ/Cₐ dynamics and water-transport pathways, respectively. The three shrubs exhibit distinct water-use strategies: Caragana microphylla follows a conservative strategy that relies on deep-water sources and tight stomatal regulation; Salix psammophila shows an opportunistic strategy, responding to precipitation pulses and drawing from multiple soil layers; Artemisia ordosica displays a vulnerable, shallow-water-dependent strategy with high drought susceptibility. SWC was the primary driver of higher Long Water Use Efficiency (WUE), whereas Mean Air Temperature (MMAT) and Mean Relative Humidity (MMRH) exerted short-term regulation by modulating the vapor-pressure deficit (VPD). We conclude that desert-shrub water-use strategies form a complementary functional portfolio at the community scale. Vegetation restoration should prioritize high-WUE conservative species, complement them with opportunistic species, and use vulnerable species cautiously to optimize community water-use efficiency and ecosystem stability. Full article

To ensure reliable, efficient and sustainable operation of modern power networks, accurate load forecasting is an important task in system planning and control. It is also a crucial task for the efficient operation of smart grids to maintain a balance between load shifting, load management and power dispatch. In this regard, this research study aims to investigate the efficiency of various machine learning models for whole-house energy consumption prediction and appliance-level load disaggregation using Non-Intrusive Load Monitoring (NILM). The primary objective is to determine which model offers the most accurate forecasts for both individual appliance consumption patterns and the total amount of energy used by the household. The empirical study presents comparative performance analysis of machine learning models, i.e., Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting and Support Vector Regressor (SVR) for load forecasting and load disaggregation. This research is conducted on PRECON: Pakistan Residential Electricity Dataset consisting of 42 Pakistani households. The dataset was recorded originally as one minute per sample, but the proposed study aggregated it to hourly samples to evaluate models’ alignment with the typical sampling rate of smart meters in Pakistan. It enables the models to more accurately depict implementation scenarios in real-world settings. The statistical measures MAE, MSE, RMSE and R² have been employed for performance evaluation. The proposed Random Forest algorithm out-performs all other employed models, with the lowest error values (MAE: 0.1316, MSE: 0.0367, RMSE: 0.1916) and the highest R² score of 0.9865. Furthermore, for detecting appliance events from aggregate power data, ensemble models such as Random Forest performed better than other models for ON/OFF prediction. To evaluate the suitability of machine learning models for real-time, appliance-level energy forecasting using Non-Intrusive Load Monitoring (NILM), this study presents a novel evaluation framework that combines learning speed and edge adaptability with conventional performance metrics (e.g., R², MAE). This paper introduces a NILM-based approach for load forecasting and appliance-level ON/OFF prediction, representing its capacity to improve residential energy efficiency and encourage sustainable energy consumption, while emphasizing operational metrics for implementation in embedded smart grid systems—an area mainly neglected in prior NILM-based research articles. The results provide useful information for improving demand-side energy management, facilitating more effective load disaggregation, and maximizing the energy efficiency and responsiveness of smart grids. Full article

This study aims to identify optimal parameters for the clinical implementation of magnetic fields in therapeutic contexts, with a particular focus on in vitro magneto-mechanical actuation in biological systems. This approach relies on the transduction of magnetic energy into mechanical stress at low frequencies (<<100 Hz). Accordingly, the investigation centers on evaluating the magnetic field gradients responsible for initiating the motion of intracellular magnetic nanoparticles and the resulting mechanical forces acting upon them. To achieve this, a novel, custom-built, and highly adaptable three-dimensional turntable system was designed, calibrated, and implemented. This apparatus allows the generation of magnetic fields with precisely tunable amplitude and frequency, enabling controlled activation of magneto-mechanical mechanisms. In vitro experiments using this device facilitated the exposure of cancer cells to well-characterized magnetic fields, thereby inducing mechanical stimulation in the presence of nanoparticles distributed within intracellular or extracellular environments. Quantitative measurements of magnetic field intensities were performed, providing estimations of the forces exerted by magnetic nanoparticles with diverse physical characteristics (phase, size, and shape) under varying magnetic field gradients. Full article

This work presents the development and evaluation of a low-cost and low-power IoT system for monitoring slope instabilities, rockfalls, and landslides using LoRa communication. The prototype integrates commercial ESP32-based hardware with an SX1276 transceiver, a triaxial MEMS accelerometer, and a GPS module for real-time tilt and location measurements. A tilt-estimation expression was derived from accelerometer data, enabling adaptation to different terrain inclinations. Laboratory tests were performed to validate the stability and accuracy of the inclination measurement, followed by outdoor LoRa range tests under mixed line-of-sight conditions. A lightweight dashboard was implemented for real-time visualization of GPS position, signal quality, and tilt data. The results show reliable tilt detection, consistent long-range communication, and low power consumption, highlighting the potential of the proposed prototype as a scalable and energy-efficient tool for geotechnical monitoring. Full article

Climate change and water scarcity are major threats to the sustainability of wheat production in Mediterranean regions. Thus, timely and reliable water demand assessments are crucial to drive decisions on crop management strategies that are useful for agricultural adaptation to climate change challenges. Although the AquaCrop model is widely used to infer crop yields, it requires continuous field-based observations (mainly soil water content and crop coverage). Often, these areas suffer from a scarcity of in situ data, suggesting the need for remote sensing and model-based decision support. In this framework, this research intends to compare the performance of the AquaCrop model using four different input combinations, with one employing ERA5-Land and crop cover retrieved by satellite images exclusively. A field experiment was conducted on durum wheat (highly sensitive to water stress and playing a strategic role in national food security) in northwest Tunisia during the growing season of 2024–2025, where meteorological variables, green Canopy Cover (gCC), Soil Water Content (SWC), and final yields (biological and grain) were monitored. The AquaCrop model was applied. Four model input combinations were evaluated. In situ meteorological data or ERA5-Land (E5L) reanalysis were combined with either measured-gCC (measured-gCC) or Sentinel-2 NDVI-derived gCC (NDVI-gCC). The results showed that E5L reproduced temperature with RMSE < 2.4 °C (NSE > 0.72) and ETo with RMSE equal to 0.57 mm d⁻¹ (NSE = 0.79), while precipitation presented larger discrepancies (RMSE = 4.14 mm d⁻¹, NSE = 0.58). Sentinel-2 effectively captured gCC dynamics (RMSE = 15.65%, NSE = 0.73) and improved AquaCrop perfomance (RMSE = 5.29%, NSE = 0.93). Across all combinations, AquaCrop reproduced yields within acceptable deviations. The simulated biological yield ranged from 9.7 to 11.0 t ha⁻¹ compared to the observed 10.3 t ha⁻¹, while grain yield ranged from 3.0 to 3.5 t ha⁻¹ against the observed 3.3 t ha⁻¹. As expected, the best agreement with measured yield data was obtained using in situ meteorological data and measured-gCC, even if the use of in situ meteorological data coupled with NDVI-gCC, or E5L-based meteorological data coupled with NDVI-gCC, produced realistic estimates. These results highlight that the application of AquaCrop employing E5L and Sentinel-2 inputs is a feasible alternative for crop monitoring in data-scarce environments. Full article

Objective: Body awareness is the conscious, subjective multimodal integration of body-related sensitivity from bodily signals—detecting states and subtle reactions to internal and environmental conditions—modifiable by attention, interpretation, appraisal, beliefs, memories, conditioning, attitudes, and affect. The aim of our study is to identify patient-reported outcome measures (PROMs) of BA and evaluate their psychometric properties and cross-cultural adaptation processes. Literature Survey: We searched PubMed, Scopus, and PsycINFO; the last search was conducted on 1 July 2025. Methodology: We included studies that psychometrically evaluated PROMs regarding BA in the general adult population and their translations into other languages, with no time-range restrictions. Study selection was performed independently by two reviewers in a blind manner. Evaluation followed COSMIN guidance for systematic reviews of PROMs: (1) risk of bias assessment, (2) application of quality criteria for measurement properties, and (3) GRADE rating of the certainty of evidence. Synthesis: We identified 12 BA questionnaires and more than 30 cross-cultural adaptations, from a total of 50 studies. In summary, the Revised Body Awareness Rating Questionnaire and the Multidimensional Assessment of Interoceptive Awareness (MAIA 1 and 2) showed good results for structural validity and internal consistency, which were the most frequently assessed psychometric properties. In contrast, construct validity was highly variable, and the findings on reliability were far from optimal. MAIA-2 was one of the most studied and showed stronger evidence and better pooled results (4 out of 5 properties) than other instruments. Conclusions: The psychometric quality of BA PROMs varies widely, reflecting challenges in operationalizing the construct of body awareness and related domains. While MAIA-2 currently presents the most acceptable—though still imperfect—evidence, further high-quality studies are needed to strengthen their measurement properties and clarify construct coverage. Full article