Search Results (22,797)

Many wind farms currently host turbines approaching their designed lifespan, which need to be repowered. Using historical operational data for wind resource evaluation can not only reduce costs but also improve efficiency. However, nacelle wind speed deviates from actual inflow wind speed due to rotor disturbance, thus demanding correction prior to use. This paper innovatively proposes a piecewise inflow wind speed calculation (PMCP) method based on pitch angle and power fusion. This method divides the full wind speed range into low and high regions by taking the rated wind speed as the boundary. Inflow wind speed in the low region is calculated via the turbine’s theoretical power curve, while that in the high region is derived from the pitch angle curve, with statistical methods establishing the mathematical relationship between inflow and nacelle wind speeds. Two wind farms are selected as cases to verify the method’s applicability across different topographies. Results show that the PMCP method exhibits varying performance in different terrains. In flat terrain, the time-series wind speed RMSE is 15.7% lower than that of direct nacelle wind speed, with accuracy comparable to the IEC nacelle transfer function (NTF) method. Moreover, the Weibull distribution curve of the calculated wind speed agrees significantly better with the measured one. In complex terrain, while its error is slightly higher than the NTF method, the accuracy is still markedly improved compared to direct use of nacelle wind speed. The PMCP method can accurately calculate full-range time-series inflow wind speed and improve the accuracy of wind resource assessment at turbine sites, while boasting the prominent advantage of relying solely on historical turbine operation data with no need for measured inflow wind speed. Full article

Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), is rapidly changing how higher education approaches teaching, learning, and assessment. In cybersecurity education, professional certification exams are key for measuring competence and helping professionals find better job offers, but there is little research on how GenAI systems perform in these exam settings. This study looks at how three popular LLMs, ChatGPT-5, Gemini-2.5 Pro, and Copilot-2.5 Pro, handle 183 practice questions from the CompTIA Security+ certification. The study used a two-phase evaluation: a domain-based assessment and a full-length practice exam that mirrors real certification tests. The researchers measured model performance with accuracy scores, chi-square tests for statistical differences, and an error taxonomy to spot patterns of mistakes important for education. All three GenAI systems scored above the passing mark, and there were no significant differences between them. Still, the error analysis showed ongoing conceptual and classification mistakes that did not show up in the overall accuracy scores. Our results show that GenAI systems can pass structured certification tests, but accuracy by itself does not fully measure professional skills. The study points out important issues for the reliability and validity of AI-based assessments in higher education and stresses the need for more realistic, concept-focused ways to evaluate GenAI in cybersecurity education. Full article

With the development of various emerging structures, concrete-filled steel tubular (CFST) columns have become critical load-bearing components in key infrastructures such as subways and underground utility tunnels. Accurately predicting their ultimate bearing capacity (N_u) is essential for guaranteeing structural safety. To address the limitations of traditional empirical formulas and code-based calculation approaches, this paper proposes a prediction model for ultimate bearing capacity based on the CatBoost algorithm optimized by Random Search. Furthermore, the marginal contribution of each key feature to the prediction results is measured through interpretability analysis. First, a database containing 438 CFST column ultimate bearing capacity test cases was established, with key parameters such as geometric dimensions and material properties as input variables. Second, the predictive performance of six machine learning algorithms—CatBoost, LightGBM, Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and XGBoost—was compared. A five-fold cross-validation integrated with a Random Search strategy was employed for joint hyperparameter optimization. The results show that the optimized CatBoost model significantly outperforms other algorithms and conventional design codes, achieving a coefficient of determination (R²) as high as 0.99 and a root mean square error (RMSE) of 174.29 kN. Furthermore, the SHAP (Shapley Additive exPlanations) method was used to perform global and local interpretability analyses of the prediction model. This not only quantified the individual contribution and interaction effects of each feature parameter on the bearing capacity but also revealed that geometric parameters are the primary influencing factor. This finding confirms a high degree of consistency between the prediction mechanism of the data-driven model and classical mechanical theories, effectively validating the model’s reliability. This study provides an efficient and reliable tool for the optimal design and rapid evaluation of CFST columns and establishes a new data-driven paradigm for the design and reinforcement of key components in underground structures. Full article

As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses on low-permeability reservoirs in the Changqing Oilfield, evaluating three surfactant systems—YHS-Z1 (a 7:3 mass ratio blend of hydroxypropyl sulfobetaine and cocamide),YHS-Z2 (a polyether carboxylate, a nonionic-anionic composite) and a middle-phase microemulsion system (Heavy alkylbenzene sulfonate and hydroxysulfobetaine were combined with a mass ratio of 7:3)—through a series of experiments including interfacial tension measurement, contact angle analysis, static and dynamic oil displacement tests, as well as emulsion transport/retention index assessments, to comprehensively characterize their oil displacement properties. Based on the experimental data, this study constructed four classical regression models: Ridge Regression, Random Forest (RF), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR), and conducted a comparative analysis of their predictive performance. The results demonstrate that the Random Forest (RF) model achieved the optimal prediction performance, with a Mean Absolute Error (MAE) of 1.8245, a Mean Absolute Percentage Error (MAPE) of 4.78%, and a coefficient of determination (R²) of 0.9428 on the training set. Further analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the retention index is the primary global factor (accounting for 49.79% of the variance), while significant intergroup differences exist in the primary factors across different surfactant systems. Concurrently, single-factor and multi-factor sensitivity analyses were conducted to elucidate synergistic effects and threshold behaviors among parameters. The optimal parameter combination, identified via a random search method, achieved a predicted recovery factor of 45.61%, representing a 6.57% improvement over the highest experimental value. This study demonstrates that machine learning methods can effectively identify the dominant factors in oil displacement and enable synergistic parameter optimization, thereby providing a theoretical foundation for the efficient development of surfactant flooding in low-permeability reservoirs. Full article

Background and Objectives: To assess the reliability and construct validity of the Functional Rating Index (FRI) in Italian-speaking individuals with chronic non-specific low back pain (CLBP), in order to improve assessment and clinical management in this population. Materials and Methods: This cross-sectional study consecutively enrolled 75 individuals with CLBP (52 females; mean age 48.71 ± 19.18 years; mean pain duration 298.64 ± 427.52 weeks). Internal consistency and test–retest reliability were evaluated using Cronbach’s α and the intraclass correlation coefficient [ICC_2,1], respectively, while measurement error was estimated through the minimum detectable change (MDC). Construct validity was examined by testing a priori hypotheses through correlations (Pearson’s r) between the FRI and disability measures (Roland–Morris Disability Questionnaire, RMQ; Oswestry Disability Index, ODI), pain intensity (Numerical Rating Scale, NRS), and quality of life (Short-Form Health Survey, SF-36). Results: Cronbach’s α was 0.88, and test–retest reliability showed an ICC_2,1 of 0.86 (95%CI: 0.82–0.93). The MDC was 18.05, corresponding to approximately 20% of the total score. The Italian FRI demonstrated strong correlations with the RMQ (r = 0.70) and ODI (r = 0.77), and a moderate correlation with the NRS (r = 0.60). The physical and social domains of the SF-36 showed stronger negative correlations with the FRI than the mental and emotional domains. Conclusions: The Italian version of the FRI is a reliable and valid instrument for individuals with CLBP and is recommended for both clinical practice and research applications. Full article

Introduction: Optical coherence tomography (OCT) is the gold standard for retinal nerve fibre layer (RNFL) assessment; its high cost and limited accessibility hinder widespread use. This study aims to develop deep learning models that generate RNFL thickness maps from fundus images, providing a cost-effective alternative to OCT. Methods: A dataset of 5000 fundus-OCT image pairs from 5000 unique glaucoma patients was used to train and compare the following four U-Net-based deep learning models: ResU-Net, R2U-Net, Nested U-Net, and Dense U-Net. All models were trained for up to 1000 epochs with early stopping (patience = 50 epochs). Performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Fréchet Inception Distance (FID). Results: ResU-Net demonstrated the best performance, achieving MSE = 0.00061, MAE = 0.01877, SSIM = 0.9163, PSNR = 32.19 dB, and FID = 30.08. These results represent a 108% improvement in SSIM and a 67% improvement in PSNR compared to previously published benchmark for this task. Conclusions: This study demonstrates that deep learning models, particularly ResU-Net, can generate high-fidelity RNFL thickness maps from fundus photographs, substantially outperforming prior published benchmarks. This approach represents a potential contribution toward accessible glaucoma assessment, contingent upon prospective clinical validation and regulatory evaluation. Full article

Background: Postpartum depression (PPD) is a clinically significant condition shaped by emotional regulation processes and close relational contexts. Anxiety is often theorized as a mediating mechanism linking relational vulnerabilities to depressive symptoms, yet empirical findings remain mixed. Objectives: This study examined whether state anxiety mediates the association between insecure attachment styles and PPD symptoms or whether its effects depend on relational context, specifically perceived partner support. Methods: In this cross-sectional study, a sample of 249 women assessed within 12 months postpartum completed self-report measures of attachment styles in the intimate relationship, state and trait anxiety, perceived partner support, and PPD symptoms. Hypotheses were tested using multiple regression analyses with heteroskedasticity-consistent standard errors, including mediation and moderation models. Results: Both anxious–ambivalent and avoidant attachment styles were associated with greater PPD symptom severity. State anxiety was neither an independent predictor nor a mediator of the attachment–PPD relationship. Instead, its association with PPD symptoms was conditional: anxiety was positively related to depressive symptoms only when perceived partner support was insufficient. Conclusions: Anxiety may function as a context-sensitive amplifier rather than a universal mechanism of postpartum depressive risk. These findings highlight the potential importance of relational context in understanding emotional vulnerability and depressive symptoms during the postpartum period. Full article

Background/Objectives: Laser Doppler flowmetry enables rapid and simple measurement of microcirculation. However, variations in probe configuration can influence signal acquisition, making it essential to understand each probe’s characteristics when selecting equipment for specific physiological assessments. Therefore, this study aimed to compare perfusion measurements obtained with single-fiber (VP1T) and multi-fiber (VP1T/7) probes and to evaluate the effects of normalization strategies. Methods: Nine healthy female volunteers were recruited. Probes were positioned on the palmar aspects of the index and middle fingers of both hands while participants underwent a standardized brachial artery occlusion protocol. Data are presented as mean ± standard error of the mean. Correlations were assessed using Pearson’s correlation coefficient. Coefficients of variation (CV) and intraclass correlation coefficients were calculated. Baseline normalization was applied to measurements. Statistical analyses were performed using Student’s t-test, with significance set at p < 0.05. Results: Analysis of the full protocol revealed significant positive correlations between probes, indicating consistent temporal perfusion patterns. The VP1T/7 probe yielded significantly higher perfusion values than the VP1T probe, although both exhibited similar CVs. Inter-probe reliability was good, and intra-probe reproducibility ranged from good to excellent, particularly for the VP1T/7 probe. During the reperfusion phase, significant differences were observed only for ipsilateral measurements obtained with the VP1T probe. Normalization effectively reduced variability, and significant differences during reperfusion were detected with both probes. Conclusions: Although the multi-fiber probe consistently recorded higher perfusion values, normalization was essential to reduce variability and to enhance the detection of microvascular reactivity parameters. Full article

Runway incursions remain a major threat to airport surface safety, and effective prevention depends on the accurate identification of causal risk factors and their interaction mechanisms. However, existing studies have mainly focused on isolated risk factors or static causal relationships, offering limited insight into the hierarchical structure and dynamic propagation of runway incursion risk in complex operational environments. To address this gap, this study proposes a quantitative framework for runway incursion risk analysis by integrating grounded theory and complex network theory. Published runway incursion cases in the Chinese civil aviation system from 2022 to 2025 were systematically analyzed through open coding, axial coding, and selective coding, resulting in a hierarchical indicator system comprising five main categories, twelve subcategories, and 112 risk indicators. Based on this system, a runway incursion causal network was constructed to characterize the causal interdependencies among risk factors. Node importance was evaluated using a TOPSIS-based multi-criteria method integrating multiple network metrics, and a load-distribution-based propagation mechanism was introduced to quantify the risk propagation probability and risk propagation intensity of each node. The results indicate that insufficient night lighting (N99), taxi-route memory errors (N14), ambiguous controller instructions (N1), and excessive controller workload (N10) exhibit relatively high risk propagation probability and risk propagation intensity, indicating their critical roles in the evolution and cascading propagation of runway incursion risk. These findings demonstrate that the proposed framework can effectively capture both the structural importance and propagation characteristics of causal risk factors. Therefore, this study provides quantitative support for understanding runway incursion risk evolution and for developing targeted prevention strategies and post-incident response measures to improve runway safety management. Full article

High-precision Doppler measurement is essential for deep-space Unified X-band (UXB) tracking systems, yet digital implementations suffer from finite word-length quantization errors that degrade performance. This study analyzes frequency offset errors in UXB transponder systems, focusing on the phase-locked loop (PLL) and system-level digital processing. A digital system model is presented, featuring an FFT-based coarse acquisition and a digital Costas loop for carrier synchronization. The simulation results reveal that 32-bit quantization yields unacceptable frequency offset errors. By extending critical paths to 48 bits, the system reduces frequency offset error by approximately 2¹⁶ and achieves sub-0.01 mm/s velocity accuracy, significantly improving coherence and meeting deep-space measurement requirements. Full article

Hematocrit (HCT) fluctuations and ambient temperature variations are two critical interference factors limiting the accuracy of electrochemical glucose test strips in self-monitoring of blood glucose (SMBG). In this study, a high-precision screen-printed glucose sensor incorporating in situ impedance-based HCT correction and temperature compensation was developed. The system employs a time-division multiplexing strategy, integrating a normalized thermodynamic model and an in situ impedance-based HCT correction algorithm, to achieve synergistic decoupling and precise compensation of temperature and HCT interferences. Experimental results demonstrate that after multi-parameter synergistic correction, the system exhibits excellent stability across a wide temperature range (10–35 °C) and a broad HCT range (10–70%). The accuracy indicators significantly surpass ISO 15197:2013 standards. In contrast, uncorrected measurements showed deviations ranging from approximately −80% to +30% due to HCT fluctuations. This multiple correction strategy effectively resolves systematic errors in whole blood testing without increasing electrode complexity or requiring pretreatment steps, providing a robust technical solution for high-precision, low-cost personal glucose monitoring. Full article

Building-Integrated and Building-Attached Photovoltaic (BIPV/BAPV) systems are increasingly being adopted in metropolitan areas worldwide, driven by international commitments to reduce greenhouse gas emissions and the declining cost of PV technology. A promising application involves the vertical integration of PV panels into building facades, which offers architectural and energy benefits, particularly in urban environments with limited roof space. This study experimentally evaluates the energy behavior of 12 vertically mounted 5 W PV panels (model SP005P) installed on university buildings in Ibarra, Ecuador, across four azimuth orientations (−135° SE, −45° NE, 45° NW, 135° SW). A continuous 8-month monitoring campaign was conducted using a custom-designed Arduino-based data logger, validated with multimeter measurements (error < 5%). The dataset was used to develop MATLAB version 2025b forecasting models based on Sum-of-Sine functions, achieving R² values between 0.83 and 0.98 and RMSE values between 0.024 and 0.082 W. The 45° (NW) orientation achieved the highest annual energy yield of 48% STC, reaching up to ≈440 kWh/kWp in the best-performing facade, while 135° (SW) also exhibited favorable performance compared with the northeast and southeast orientations. These findings provide significant evidence for facade-integrated PV design in equatorial latitudes, offering performance benchmarks and validated forecasting tools that can support architectural planning, BIPV feasibility analysis, and urban solar-energy strategies in regions with similar conditions. Full article

The growing need for remote patient monitoring, accelerated by the global pandemic and an aging population, necessitates the development of advanced non-contact technologies for measuring vital signs. In this study, an integrated, non-contact system for accurately measuring heart rate (HR) and body temperature (BT) is developed and validated. The proposed system combines a 60 GHz radar sensor and infrared (IR) sensor for HR and BT measurements, respectively, enhanced with advanced signal processing and an AI-based computer vision algorithm. A Window Filter and a Peak Uniformity algorithm were applied to the raw radar signal to mitigate noise and motion artifacts. For Temp measurement, an IR sensor with a narrow five-degree field of view (FOV) was integrated with a YOLO Pose-based tracking system using a camera and servo motors to automatically orient the sensor towards the user’s face. The system was validated with 30 healthy adult participants, benchmarked against a MAX30102 PPG sensor and Braun ThermoScan 7 for BT and BT measurements, respectively. The advanced signal processing reduced the HR Mean Absolute Error from 13.73 BPM to 5.28 BPM (p = 0.002), while the AI-guided IR sensor reduced the BT MAE from 4.10 °C to 1.64 °C (p < 0.001). These findings demonstrate that integrating 60 GHz radar with AI-driven tracking provides a promising approach for home-based trend monitoring. Full article

(1) Background: Idiopathic scoliosis is a three-dimensional deformity, yet clinical and research decision-making still relies largely on radiographic Cobb angle measurements. As a radiation-free alternative, clinical assessment of transverse and sagittal plane deformities has gained importance. This study evaluated the concurrent validity and intra- and interrater reproducibility of continuous measurements of rib hump, thoracic kyphosis, and lumbar lordosis obtained using a smartphone application in adolescents with spinal deformities. (2) Methods: Adolescents aged 10–17 years with scoliosis (>10° Cobb) or hyperkyphosis (>50° Cobb) were recruited. Continuous measurements of angle of trunk rotation (ATR) during the Adams forward bend test and in standing position, as well as sagittal profile, were collected using the ISICO app mounted on a standardized plastic tool. Concurrent validity was assessed against a scoliometer using Spearman correlation, root mean square error, and Bland–Altman analysis, while reproducibility was evaluated using intraclass correlation coefficients, standard error of measurement, and minimal detectable change. (3) Results: Thirty-two adolescents were included for validation and intrarater analyses and 34 for interrater analyses. ATR measured during the Adams test showed very high correlation with the scoliometer and minimal bias, while standing ATR showed moderate correlation. Reliability was excellent for rib hump during forward bending and moderate for sagittal parameters, with the lowest values observed for lumbar lordosis. (4) Conclusions: These findings support the clinical use of continuous app-based ATR assessment and suggest that sagittal measurements may be useful with appropriate examiner training. Full article

To address the limited in situ stress data and poor nonlinear fitting of existing methods, a Particle Swarm Optimization (PSO)–XGBoost inversion approach is proposed. XGBoost effectively models complex relationships between finite element results and measured stresses, leveraging its strong nonlinear mapping and suitability for small samples. PSO globally optimizes XGBoost hyperparameters, utilizing its fast convergence and global search capability. Combined with 5-fold cross-validation, this avoids empirical tuning errors and enhances generalization. The model uses finite-element-based stress-response values as inputs and calculates in situ stress data derived from hydraulic fracturing interpretations as targets. Engineering applications show that the PSO-XGBoost model outperforms common methods, achieving superior prediction accuracy and generalization with fast convergence. This offers a high-precision inversion approach for small-sample conditions, supporting engineering design and safety assessment. Full article