Search Results (25,904)

Efficient crop management requires intelligent control strategies capable of handling uncertainty, nonlinear environmental interactions and dynamic crop growth conditions. This study presents a multisensor data fusion-based intelligent crop management framework for Capsicum cultivation using both a Mamdani fuzzy inference system (MFIS) and an adaptive Mamdani fuzzy inference system (AMFIS). The Capsicum dataset from the SmartFasal platform includes temperature, humidity and soil moisture at three depths, recorded over a four-month period (March–June 2020) with a total of 7188 samples. The proposed MFIS and AMFIS models are implemented and evaluated in the simulation environment. A Capsicum yield of 60–63 t/ha (3.6–3.8 kg/plant) is predicted via a regression model built on raw sensor inputs under conventional environmental management. An expert-rule MFIS with triangular memberships improves the regulation of agricultural parameters, increasing yield to 70–73 t/ha (4.2–4.4 kg/plant), a 15–18% increase. To improve adaptability, the AMFIS model incorporates fuzzy C-means (FCM) clustering for the automatic tuning of Gaussian membership functions and enables the controller to adjust dynamically to sensor data distributions. The adaptive system achieves a predicted productivity range of 82–87 t/ha (4.9–5.2 kg/plant), a 30–35% increase over the baseline. The regression model validation metrics R² = 0.86, RMSE = 2.1 t/ha, and MAE = 1.7 t/ha confirm the reliability of the yield estimation within the simulation framework rather than experimentally measuring crop performance. A correlation analysis, histograms, scatter plots, and Bland–Altman assessments reveal that compared with the MFIS, the AMFIS results in smoother control transitions, lower variability, and higher resource-use efficiency. This study represents a data-driven simulation framework, and future work will focus on real-time implementation and experimental validation under actual greenhouse conditions. Full article

This study proposes a stress field data-driven prediction method that combines a finite element thermo-mechanical coupling model with a multi-machine learning framework. This method takes the inversion of stress based on the shrinkage behavior of deposition layers as the core logic, extracts the node displacement shrinkage during the cooling to solidification process of the melt pool in the thermal coupling simulation as the key feature input, and constructs extreme gradient boosting (XGBoost), Gaussian process regression (GPR), and deep convolutional neural network (DCNN) models, respectively, to achieve accurate prediction of nodal effect stress and triaxial stress in the laser directed energy deposition (L-DED) node process. The experimental results show that the XGBoost algorithm performs the best in various stress prediction indicators, and its generated stress distribution cloud map is highly consistent with the thermal coupling simulation results, suggesting a strong correlation between deposition layer shrinkage behavior and the stress field under the investigated conditions. In addition, compared to traditional finite element simulations, this method significantly improves computational efficiency while ensuring prediction accuracy, providing a new approach for rapid assessment of residual stresses. Full article

Background: Intensive care units (ICUs) provide management of critically ill patients requiring continuous monitoring and complex therapeutic interventions. The aim of this study was to analyze the clinical and biological characteristics associated with mortality in patients admitted to the intensive care unit. Methods: This retrospective observational study included 108 adult patients admitted to the Anesthesia and Intensive Care Unit of the “Sf. Apostol Andrei” Emergency County Clinical Hospital in Galați, who were in a coma at the time of admission. Demographic data, comorbidities, clinical parameters and biological biomarkers determined at admission were analyzed. Statistical analysis was performed using the SPSS program and included non-parametric tests (Mann–Whitney U), Spearman correlation analysis, multivariate logistic regression and ROC curve analysis to evaluate the predictive performance of biomarkers. Results: Hypertension (60.2%) and diabetes mellitus (35.2%) were the most common comorbidities. Comparative analysis revealed significant differences between deceased and surviving patients for several biological parameters, including leukocytes, C-reactive protein, LDH, D-dimers, INR and APTT. In multivariate analysis, LDH (OR = 0.998; p < 0.001) and APTT (OR = 0.951; p = 0.033) remained independently associated with mortality. ROC analysis revealed good discrimination capacity for LDH (AUC ≈ 0.805) and moderate performance for APTT. Conclusions: Determination of LDH and APTT at the time of admission to the ICU may provide useful information for assessing the prognosis of critically ill patients and for early stratification of mortality risk. Full article

Modern agriculture must balance productivity with sustainability. In this context, unmanned aerial vehicles (UAVs) offer flexible, cost-effective tools for crop and soil monitoring in precision agriculture. This study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation. A factorial field experiment with four varieties, two planting densities, and two tillage systems was monitored using high-resolution RGB orthomosaics acquired at key phenological stages. From these images, 17 RGB-based indices were computed and related to soil variables and crop traits using Spearman correlation and two regression algorithms: Random Forest (RF) and k-Nearest Neighbors (KNN). RF models outperformed KNN, with the Red Chromatic Coordinate (RCC) index achieving an R² of 0.87 for predicting soil organic matter content. Indices such as visible NDVI and the Green Vegetation Index also provided robust estimates of canopy condition and leaf chlorophyll. Overall, the results demonstrate that UAV RGB imagery, processed through simple vegetation indices and RF models, constitutes an effective, low-cost approach for monitoring key agronomic parameters in peanut farming. Full article

Reliable characterization of the wellbore temperature field is essential for ensuring drilling safety and optimizing operational parameters in shale oil horizontal wells. To address the limitations of conventional models that assume constant thermophysical properties and neglect interactions among multiple heat sources, a transient heat transfer model featuring one-dimensional heat transfer in the wellbore and two-dimensional heat transfer in the formation is developed. The model uniquely accounts for variable thermophysical properties along with three internal heat sources: bit–rock interaction heat (BRIH), viscous dissipation heat (VDH), and drillpipe–formation friction heat (DFFH). The governing equations are implemented numerically using a fully implicit finite-difference approach and verified against field measurements from 10 wells in the Shengli Oilfield. The model demonstrates high predictive accuracy, with an average relative error of 1.58%. VDH contributes significantly to wellbore temperature elevation (≈3.33 °C), whereas BRIH and DFFH exert comparatively minor effects (≈0.34 °C). Sensitivity analysis shows that geothermal gradient is the dominant factor controlling BHCT (correlation coefficients: 0.74 for OBDF; 0.65 for WBDF), followed by drilling fluid density, with all parameters exhibiting weak intercorrelations. Furthermore, a PSO-RBF optimization framework is developed, reducing computation time from 48.34 min per evaluation to an average of 9.0 min per well (81.4% efficiency improvement) while maintaining high prediction accuracy. Overall, this study contributes theoretical understanding and practical value to temperature prediction and parameter optimization in shale oil horizontal well drilling. Full article

It is widely acknowledged that academic writing constitutes a challenge for EFL learners. This is why areas like the use of writing strategies, writing motivation and writing anxiety have attracted extensive attention in the literature. However, the interplay between these three areas has not been researched sufficiently, particularly among Arab learners. The present study bridges this gap by examining the use of writing strategies by Saudi EFL learners and the relationship between the learners’ use of these strategies and their writing motivation and writing anxiety. A total of 206 Saudi EFL learners responded to a questionnaire that assessed how they used writing strategies, as well as their writing motivation and anxiety. Using descriptive statistics, the results showed that the Saudi EFL learners use most writing strategies moderately or to a high extent. It was also found that students generally experience a high degree of motivation and a moderate degree of anxiety while writing. The Pearson Correlation analysis indicated that students’ use of writing strategies is somewhat positively correlated with their writing motivation and negatively correlated with their writing anxiety. Additionally, multiple regressions revealed that the use of writing strategies was predicted by both writing motivation and writing anxiety, although prediction by the writing motivation was more prominent. The results are discussed in reference to the relevant theories and pedagogical implications and future research directions are proposed. Full article

There are many factors that affect the shear capacity of FRP (fiber-reinforced polymer)-strengthened reinforced concrete (RC) beams, and traditional capacity models based on empirical or semi-empirical formulas often suffer from insufficient accuracy. To enhance the predictive accuracy and generalization ability of the shear capacity of FRP-strengthened RC beams, this study proposes an interpretable machine learning model based on the Jaya-CNN-LSTM model. A comprehensive database consisting of 315 test data on shear capacity of FRP-strengthened RC beams, encompassing various FRP reinforcement modes, has been established. Key feature parameters for predicting the shear capacity of FRP-strengthened RC beams are selected through Pearson correlation coefficient analysis. Based on the Jaya algorithm, the hyperparameters of the ensemble CNN-LSTM prediction model are adaptively optimized. A comparative analysis is conducted between the proposed method, other machine learning models, and existing empirical formulas to evaluate the proposed model’s efficacy. The results demonstrate that the proposed model outperforms other machine learning models and empirical formulas in terms of prediction accuracy and stability. Furthermore, the machine learning-based predictions align more closely with experimental values than those derived from empirical formulas. Additionally, the SHAP method is utilized to quantify the critical parameters’ impact on predicting the shear capacity of FRP-strengthened RC beams. The results reveal that there is an explicit mapping relationship between key features such as shear-span ratio, concrete strength, and yield strength of stirrups and the shear capacity of FRP-strengthened RC beams, providing technical support for practical applications. Full article

Skid resistance is a critical functional property of asphalt pavements and is strongly influenced by surface topography. However, existing studies often rely on limited texture indicators, making it difficult to comprehensively characterize pavement surface morphology and directly relate it to braking performance. In this study, the surface topography of eight asphalt mixtures, including six porous asphalt concrete (PAC-13) mixtures with different air-void contents, one stone mastic asphalt (SMA-13) mixture, and one asphalt concrete (AC-13) mixture, was characterized using a high-precision three-dimensional laser scanner. The acquired point-cloud data were analyzed using one-dimensional, two-dimensional, three-dimensional, and ISO 25178 surface parameters. Correlation analysis was first used to remove redundant indicators, and principal component analysis was then performed to reduce dimensionality. Three principal components explaining 67.45%, 9.94%, and 6.42% of the total variance, respectively, were extracted and combined into a comprehensive surface topography index (F). The results showed that F effectively distinguished different mixture types and PAC surfaces with different air-void levels. Field validation was further conducted on PAC, SMA, and AC pavements in Xi’an, China, and a regression model relating F to the braking distance from 60 km/h to 0 km/h (D60) was established, with an R² of 0.8864. The proposed index provides a multidimensional and practical approach for asphalt pavement surface characterization and offers a useful basis for skid-resistance evaluation and braking distance prediction. Full article

Background/Objectives: Preservation of critical white matter (WM) pathways is essential for maximizing surgical safety in neuro-oncology and functional neurosurgery. Constrained spherical deconvolution (CSD) offers superior modeling of complex fiber architecture compared to diffusion tensor imaging (DTI). This case series evaluates the clinical utility of CSD in surgical planning and intraoperative navigation. Methods: A retrospective review of 20 patients (15 brain tumors, 5 functional disorders) treated between September 2022, and September 2024 was performed. All patients underwent preoperative MRI with CSD-based reconstruction of eloquent WM tracts. Clinical presentation, tract involvement, surgical strategy, and postoperative outcomes were analyzed. Results: CSD reliably reconstructed CST, AF, IFOF, OT, and DRTT depending on tumor location or DBS target. Compared with standard DTI, CSD provided improved delineation of tract extent and tumor–tract interfaces. Gross total resection (GTR) was achieved in all tumor patients without new neurological deficits. DBS cases showed precise correlation between stimulation thresholds, side effects, and CSD-predicted distances to critical WM tracts. DRTT targeting resulted in marked clinical improvement in Holmes tremor. Conclusions: CSD enhances anatomical accuracy in WM tract visualization, supporting safer resections in eloquent areas and improving DBS targeting. Its integration into routine workflow may optimize neurosurgical outcomes. Full article

This study addresses the critical need for accurate sea wind speed predictions to support ocean wind farm operations, equipment maintenance, and maritime navigation safety. To enhance prediction accuracy for any location within target sea areas, we propose a short-to-medium-term wind speed prediction method that effectively explores spatiotemporal correlations in ocean reanalysis grid data. The method involves collecting and reanalyzing data, as well as spatial processing, to reconstruct the historical wind speed sequence at the target point. Finally, a future wind speed time series is generated using an LSTM network and a Transformer encoder. Test results validated against NOAA buoy data demonstrate the effectiveness of our spatiotemporal prediction model, achieving RMSE values of 1.161 m/s, 1.500 m/s, and 1.854 m/s for 1 h, 6 h, and 12 h predictions, respectively, outperforming comparative methods. The conclusions are threefold: (1) The proposed hybrid model effectively captures spatiotemporal dependencies and achieves more accurate spatiotemporal predictions compared to the benchmark model; (2) taking into account seasonal factors and forecasting time periods, the method proposed in this paper maintains good stability; (3) this framework provides a reliable technical approach for generating operational references in maritime navigation and wind power maintenance, with potential applications in wind farm siting and resource assessment. Full article

Reservoir fracability evaluation is critical for tight reservoir hydraulic fracturing optimization. This study introduces a novel physics-based fracability evaluation framework integrating stacking ensemble learning (SEL) and the marginal effect of the conditional expectation (MECE). First, a multidimensional indicator system was established, covering characteristics such as reservoir geomechanics, rock mechanics, and the development of natural fractures. Second, SEL models were developed to predict open flow capacity, and four performance metrics were compared to select the optimal model from 26 SEL candidates. Finally, to quantify the individual contribution of each fracability indicator while eliminating interference from treatment and petrophysical parameters, the MECE approach was adopted, thereby developing a new fracability model that quantitatively describes the reservoir’s ability to achieve greater stimulated reservoir volume (SRV) under similar hydraulic fracturing parameters. The experimental results indicate that the RF+KNN model demonstrates optimal performance in both prediction accuracy and model stability. Comparing the fracability index with microseismic monitoring data, the linear correlation coefficient between the fracability index and SRV reached 92%, validating the reliability of the fracability evaluation model. This framework provides a transferable interpretable tool for selecting reservoir sweet spots and fracturing parameter optimization. Full article

Cervical cancer is associated with persistent human papillomavirus (HPV) infections. The early detection of HPV is one of the key strategies for the effective treatment of cervical cancer. Current HPV molecular detection methods use enzyme-based nucleic acid amplification strategies that, although specific and sensitive, involve extensive workflows. Enzyme-free isothermal amplification detection strategies with the potential to adapt to low-resource settings for HPV oncogenic transcripts remain limited. This study aimed to validate a fluorescence-based branched hybridization chain reaction (bHCR) assay for the targeted detection of HPV 16/35 E6 oncogenic transcripts. Analytical performance was evaluated using a synthetic target and a negative clinical matrix, whereas the diagnostic performance of the bHCR assay was evaluated using clinically characterized samples (n = 67). The study demonstrated assay linearity over an analyte concentration range of 0.625–40 µM, with a statistically significant correlation between the fluorescence signal and target concentration (r² = 0.928, p < 0.0001). Analytical accuracy was assessed by pre-extraction spike recovery; achieved recoveries ranged from 70% to 86%, indicating potential RNA loss during the assay workflow. Analytical sensitivity determined the background signal threshold limit of blank (LoB) as 16,251.6 RFU, with detection and quantification at concentrations of 0.0625 µM (≈2.6 × 10¹¹ copies per reaction, limit of detection (LoD) and 0.125 µM (≈5.3 × 10¹¹ copies per reaction, limit of quantification (LoQ). The assay exhibited high diagnostic performance, with a diagnostic cut-off of 16,481 RFU and an area under the curve (AUC) of 0.9194. Specificity and sensitivity of the assay were 94% and 86%, respectively, with a Negative Predictive Value (NPV) of 85% and a Positive Predictive Value (PPV) of 94%. These findings demonstrate a reliable analytical assay with excellent diagnostic discrimination and warrant further optimization and expanded clinical validation. Full article

This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity. Full article

Electrotactile feedback is a compact approach for providing tactile cues in robotic teleoperation, but personalized calibration remains time-consuming because tactile perception varies across users. To address this problem, this study develops a subject-informed multi-layer finite element model of fingertip electric-field distribution coupled with a neural-response model and proposes a simulation-derived configuration-ranking method termed the Perceived Correctness Score (PCS). A gradient boosting regression model is then used to recommend among 36 candidate electrode diameter–spacing combinations. Validation was conducted using a custom-developed $3\times 2$ multi-channel fingertip electrotactile stimulation system in a shape/area recognition task involving six healthy subjects. The predicted PCS showed a moderate positive correlation with the measured mean recognition accuracy across configurations (Pearson $r=0.48$, $p<0.05$). The model achieved Top-1 exact matching for three of six subjects and Top-5 coverage for five of six subjects. Compared with conventional exhaustive psychophysical calibration, the proposed method reduced the average configuration time from 122.7 min to 16.0 min, corresponding to an efficiency improvement of 87.0%. These results show that model-guided ranking can substantially reduce the burden of individualized electrotactile configuration. Full article

Limited data are available regarding the physiological profile of youth triathletes. The aim of this study was to characterize the physiological and body composition profile of Hungarian youth triathletes and to examine the relationships between anthropometric characteristics and aerobic performance indicators. Forty-one youth triathletes (20 females and 21 males; age: 15.8 ± 1.7 years), members of the Hungarian national development squad, participated in the study. Anthropometric and body composition parameters were assessed using standardized procedures and multi-frequency bioelectrical impedance analysis. Aerobic performance was evaluated using a graded cardiopulmonary exercise test on a treadmill with breath-by-breath gas analysis. Male athletes demonstrated higher body height, body mass, fat-free mass, and skeletal muscle mass compared with females (p < 0.05). Cardiopulmonary exercise testing revealed high aerobic capacity, with mean VO₂max values of 73.2 ± 5.4 mL·kg⁻¹·min⁻¹ in males and 63.1 ± 5.0 mL·kg⁻¹·min⁻¹ in females. The second ventilatory threshold occurred at approximately 82–86% of VO₂max. Strong positive correlations were observed between anthropometric parameters and absolute oxygen uptake (mL·min⁻¹), particularly for fat-free mass, skeletal muscle mass, and body surface area (r = 0.83–0.95). However, these relationships are influenced by body size and were weaker or inverse when relative oxygen uptake (mL·kg⁻¹·min⁻¹) was considered. Regression analyses further indicated that body composition variables, especially fat-free mass and skeletal muscle mass, were positively associated with aerobic performance, while body fat percentage was not a significant predictor when body size and sex were controlled. These findings are based on cross-sectional associations and should be interpreted as descriptive reference data for this population rather than predictive criteria. The results contribute to the characterization of physiological and anthropometric profiles in youth triathletes and may support future research and athlete monitoring. Full article