Search Results (29,170)

Background: Continuous monitoring of hemoglobin (HB) and hematocrit (HCT) during hemodialysis could improve fluid management and patient safety. The Fresenius 5008 dialysis machine includes an ultrasound-based sensor that estimates HB and HCT values, though its accuracy compared to standard laboratory measurements remains unclear. Methods: This exploratory observational study assessed the agreement between sensor-derived and laboratory-derived HB and HCT values in 20 patients at the start of hemodiafiltration. A total of 177 paired blood samples were collected. Results: Sensor values significantly underestimated laboratory HB (9.61 vs. 11.31 g/dL) and HCT (27% vs. 34%) (p < 8 × 10⁻²⁵). Correlations were strong for both parameters (HB: r = 0.788; HCT: r = 0.876). Regression analyses revealed consistent proportional bias. Applying a fixed correction of +1.69 g/dL for HB and +7.55% for HCT eliminated the statistical differences and reduced intercepts in regression models. Bland–Altman plots confirmed improved agreement post-correction. Albumin levels correlated modestly with error magnitude. Conclusions: HB and HCT values from the Fresenius 5008 sensor are strongly correlated with laboratory data but are systematically underestimated at treatment start, likely due to hemodilution. Applying fixed correction factors improves accuracy and supports the sensor’s use for real-time monitoring. Full article

Chinese Optical Character Recognition (OCR) technology is essential for digital transformation in Chinese regions, enabling automated document processing across various applications. However, Chinese OCR systems struggle with visually similar characters, where subtle stroke differences lead to systematic recognition errors that limit practical deployment accuracy. This study develops FLIP (Feedback Learning-based Intelligent Plugin), a lightweight post-processing plugin designed to improve Chinese OCR accuracy across different systems without external dependencies. The plugin operates through three core components as follows: UTF-8 encoding-based output parsing that converts OCR results into mathematical representations, error correction using information entropy and weighted similarity measures to identify and fix character-level errors, and adaptive feedback learning that optimizes parameters through user interactions. The approach functions entirely through mathematical calculations at the character encoding level, ensuring universal compatibility with existing OCR systems while effectively handling complex Chinese character similarities. The plugin’s modular design enables seamless integration without requiring modifications to existing OCR algorithms, while its feedback mechanism adapts to domain-specific terminology and user preferences. Experimental evaluation on 10,000 Chinese document images using four state-of-the-art OCR models demonstrates consistent improvements across all tested systems, with precision gains ranging from 1.17% to 10.37% and overall Chinese character recognition accuracy exceeding 98%. The best performing model achieved 99.42% precision, with ablation studies confirming that feedback learning contributes additional improvements from 0.45% to 4.66% across different OCR architectures. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article

The safety and longevity of engineering structures depend on precise and timely monitoring, especially during load testing inspections. Conventional displacement measurement methods—such as LVDT sensors, GNSS, RTS, and levels—each present benefits and limitations in terms of accuracy, applicability, and practicality. Photogrammetry has emerged as a promising alternative, offering non-contact measurement, cost-effectiveness, and adaptability in challenging environments. This study investigates the potential of photogrammetric methods for determining structural displacements during load testing in real-world conditions where such approaches remain underutilized. Two photogrammetric techniques were tested: (1) a single-image homography-based approach, and (2) a multi-image bundle block adjustment (BBA) approach using both UAV and tripod-mounted imaging platforms. Displacement results from both methods were compared against reference measurements obtained by traditional LVDT sensors and robotic total station. The study evaluates the influence of different camera systems, image acquisition techniques, and processing methods on the overall measurement accuracy. The findings suggest that the photogrammetric method, especially when optimized, can provide reliable displacement data with sub-millimeter accuracy, highlighting their potential as a viable alternative or complement to established geodetic and sensor-based approaches in structural testing. Full article

The Vesical Imaging Reporting and Data System (VI-RADS) is used to detect muscle-invasive bladder cancer, with emerging prognostic implications. Integrating imaging parameters with molecular biomarkers may improve risk stratification in bladder cancer. This study evaluated whether combining VI-RADS scores with serum cytokeratin fragment 19 (CYFRA 21-1) levels—a clinically relevant biomarker for bladder cancer—could improve overall survival (OS) prediction. We retrospectively analyzed 134 patients who underwent transurethral resection of bladder tumors, magnetic resonance imaging, and postoperative serum CYFRA 21-1 measurements. In total, 15 cancer-specific deaths were observed during follow-up. Receiver operating characteristic curve analysis identified optimal prognostic cut-off values: VI-RADS score ≥ 4 and CYFRA 21-1 level ≥ 1.8 ng/mL. The 1-, 2-, and 3-year OS in patients with both high VI-RADS scores and CYFRA 21-1 levels were 42.9%, 16.7%, and 8.3%, respectively, significantly lower than those in other groups (p < 0.001, 0.002, and 0.003, respectively). Multivariate Cox proportional hazards analysis demonstrated that such patients had the poorest OS (hazard ratio: 7.51; p = 0.002). This suggests that combining VI-RADS and CYFRA 21-1 improves prognostic accuracy in bladder cancer, demonstrating potential clinical utility by informing individualized treatment strategies; however, limitations include the retrospective study design and absence of external validation. Full article

Near-field acoustic holography (NAH) provides an effective way to achieve wide-band, high-resolution visualization measurement of the sound insulation performance of building components. However, based on Green’s function, the microphone array’s inherent amplitude and phase mismatch errors will exponentially amplify the sound field inversion process, significantly reducing the measurement accuracy. To systematically evaluate this problem, this study combines numerical simulation with actual measurements in a soundproof room that complies with the ISO 10140 standard, quantitatively analyzes the influence of array system errors on NAH reconstructed sound insulation and acoustic images, and proposes an error correction strategy based on channel transfer function normalization. The research results show that when the array amplitude and phase mismatch mean values are controlled within 5% and 5°, respectively, the deviation of the weighted sound insulation measured by NAH can be controlled within 1 dB, and the error in the key frequency band of building sound insulation (200–1.6k Hz) does not exceed 1.5 dB; when the mismatch mean value increases to 10% and 10°, the deviation of the weighted sound insulation can reach 2 dB, and the error in the high-frequency band (≥1.6k Hz) significantly increases to more than 2.0 dB. The sound image shows noticeable spatial distortion in the frequency band above 250 Hz. After applying the proposed correction method, the NAH measurement results of the domestic microphone array are highly consistent with the weighted sound insulation measured by the standard method, and the measurement difference in the key frequency band is less than 1.0 dB, which significantly improves the reliability and applicability of low-cost equipment in engineering applications. In addition, the study reveals the inherent mechanism of differential amplification of system errors in the propagating wave and evanescent wave channels. It provides quantitative thresholds and operational guidance for instrument selection, array calibration, and error compensation of NAH technology in building sound insulation detection. Full article

Various nature-based solutions (NbS)—such as constructed wetlands, drainage ditches, and vegetated buffer strips—have recently demonstrated strong potential for mitigating pollutant transport in open channels and river systems. Numerical modeling is a widely adopted and effective approach for assessing the performance of these interventions. This study presents the first development of a two-dimensional (2D) meshless advection–diffusion model based on an Eulerian smoothed particle hydrodynamics (SPH) framework, specifically designed to simulate passive pollutant transport in open channel flows. The proposed model marks a pioneering application of the ESPH technique to environmental pollutant transport problems. It couples the 2D depth-averaged shallow water equations with an advection–diffusion equation to represent both fluid motion and pollutant concentration dynamics. A uniform particle arrangement ensures that each fluid particle interacts symmetrically with eight neighboring particles for flux computation. To represent the pollutant transport process, the dispersion coefficient is defined as the sum of molecular and turbulent diffusion components. The turbulent diffusion coefficient is calculated using a prescribed turbulent Schmidt number and the eddy viscosity obtained from a Smagorinsky-type mixing-length turbulence model. Three analytical case studies, including one-dimensional transcritical open channel flow, 2D isotropic and anisotropic diffusion in still water, and advection–diffusion in a 2D uniform flow, are employed to verify the model’s accuracy and convergence. The model demonstrates first-order convergence, with relative root mean square errors (RRMSEs) of approximately 0.2% for water depth and velocity, and 0.1–0.5% for concentration. Additionally, the model is applied to a laboratory experiment involving 2D pollutant dispersion in a 90° junction channel. The simulated results show good agreement with measured velocity and concentration distributions. These findings indicate that the developed model is a reliable and effective tool for evaluating the performance of NbS in mitigating pollutant transport in open channels and river systems. Full article

Objectives: The aim of this study was to evaluate the effectiveness of spectrophotometers for objective tooth color measurement, particularly in bleaching procedures enhanced by digital positioning templates. Methods: Tooth color registration was conducted using both subjective methods with shade guides and objective methods with spectrophotometers. Spectrophotometers were chosen for their ability to provide objective, quantifiable, and reproducible results, crucial for monitoring color modifications accurately. Digital workflows were implemented to enhance the registration process further. These workflows included providing a precise positioning matrix for spectrophotometer sensors and optimizing working models to ensure high-quality therapeutic splints. Results: The use of spectrophotometers demonstrated superior performance in registering tooth color objectively compared to subjective shade guides. Digital workflows significantly improved the precision and efficiency of spectrophotometer measurements through a digital matrix, enhancing the quality of therapeutic splints obtained. Conclusions: Spectrophotometers are recommended for objective and precise tooth color registration, particularly in bleaching procedures. Integrating a digital positioning matrix enhances measurement accuracy and reliability, supporting effective monitoring and treatment outcomes. Full article

Fractional Vegetation Cover (FVC) is an important parameter to reflect vegetation growth and describe plant canopy structure. This study integrates both active and passive remote sensing, capitalizing on the complementary strengths of optical and radar data, and applies various machine learning algorithms to retrieve FVC. The results demonstrate that, for FVC retrieval, the optimal combination of optical remote sensing bands includes B2 (490 nm), B5 (705 nm), B8 (833 nm), B8A (865 nm), and B12 (2190 nm) from Sentinel-2, achieving an Optimal Index Factor (OIF) of 522.50. The LiDAR data of ICESat-2 imagery is more suitable for extracting FVC than that of GEDI imagery, especially at a height of 1.5 m, and the correlation coefficient with the measured FVC is 0.763. The optimal feature variable combinations for FVC retrieval vary among different vegetation types, including synthetic aperture radar, optical remote sensing, and terrain data. Among the three models tested—multiple linear regression, random forest, and support vector machine—the random forest model outperformed the others, with fitting correlation coefficients all exceeding 0.974 and root mean square errors below 0.084. Adding LiDAR data on the basis of optical remote sensing combined with machine learning can effectively improve the accuracy of remote sensing retrieval of vegetation coverage. Full article

Isobaric vapor-liquid equilibrium (VLE) data for binary mixtures of biomass–derived ethyl levulinate and ethanol were measured using an apparatus comprising a modified Rose-Williams still and a condensation system. Measurements were taken at temperatures ranging from 329.58 K to 470.00 K and pressures of 40.0, 60.0 and 80.0 kPa. The thermodynamic consistency of the VLE data was evaluated using the Redlich-Kister area test, the Fredenslund test and the Van Ness point-to-point test. The data was correlated using three activity coefficient models: Wilson, NRTL and UNIQUAC. The Gibbs energy of mixing of the VLE data was analyzed to verify the suitability of the binary interaction parameters of these models. The activity coefficients and excess Gibbs free energy, calculated from the VLE experimental data and model correlation results, were analyzed to evaluate the models’ fit and the non–ideality of the binary system. The accuracy of the regression results was also assessed based on the root mean square deviation (RMSD) and average absolute deviation (AAD) for both temperature and the vapor phase mole fraction of ethyl levulinate. The results indicate that the NRTL model provided the best fit to the experimental data. Notably, the experimental data showed strong correlation with the predictions of all three models, suggesting their reliability for practical application. Full article

Given the importance of turbidity as a key indicator of water quality, this study investigates the use of a convolutional neural network (CNN) to classify water samples into five turbidity-based categories. These classes were defined using ranges inspired by Mexican environmental regulations and generated from 33 laboratory-prepared mixtures with varying concentrations of suspended clay particles. Red, green, and blue (RGB) images of each sample were captured under controlled optical conditions, and turbidity was measured using a calibrated turbidimeter. A transfer learning (TL) approach was applied using EfficientNet-B0, a deep yet computationally efficient CNN architecture. The model achieved an average accuracy of 99% across ten independent training runs, with minimal misclassifications. The use of a lightweight deep learning model, combined with a standardized image acquisition protocol, represents a novel and scalable alternative for rapid, low-cost water quality assessment in future environmental monitoring systems. Full article

The identification of structural damage types remains a key challenge in electromechanical impedance/admittance (EMI/EMA)-based structural health monitoring realm. This paper proposed a damage classification approach for concrete structures by using integrating discrete wavelet transform (DWT) decomposition of EMA signatures with supervised machine learning. In this approach, the EMA signals of arranged piezoelectric ceramic (PZT) patches were successively measured at initial undamaged and post-damaged states, and the signals were decomposed and processed using the DWT technique to derive indicators including the wavelet energy, the variance, the mean, and the entropy. Then these indicators, incorporated with traditional ones including root mean square deviation (RMSD), baseline-changeable RMSD named RMSDk, correlation coefficient (CC), and mean absolute percentage deviation (MAPD), were processed by a support vector machine (SVM) model, and finally damage type could be automatically classified and identified. To validate the approach, experiments on a full-scale reinforced concrete (RC) slab and application to a practical tunnel segment RC slab structure instrumented with multiple PZT patches were conducted to classify severe transverse cracking and minor crack/impact damages. Experimental and application results cogently demonstrated that the proposed DWT-based approach can precisely classify different types of damage on concrete structures with higher accuracy than traditional ones, highlighting the potential of the DWT-decomposed EMA signatures for damage characterization in concrete infrastructure. Full article

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research. Full article

(1) On-board accelerometers are increasingly employed in real-world biomechanics to monitor vibrations and shocks. This study assesses the accuracy, repeatability, and variability of three commercially available inertial measurement units (IMUs)—Xsens, Blue Trident, and Shimmer 3—in measuring vibration and shock parameters relevant to human motion analysis. (2) A controlled laboratory setup utilizing an electrodynamic shaker was employed to generate sine waves at varying frequencies and amplitudes, as well as shock profiles with defined peak accelerations and durations. (3) The results showed that Blue Trident demonstrated the highest accuracy in shock amplitude and timing, with relative errors below 6%, while Xsens provided stable measurements for low-frequency vibrations. In contrast, Shimmer 3 exhibited considerable variability in signal quality. (4) These findings offer critical insights into sensor selection based on specific application needs, ensuring optimal accuracy and reliability in dynamic measurement environments. This study lays the groundwork for improved IMU application in biomechanical research and practical deployments. Future research should continue to investigate sensor performance, particularly in angular motion contexts, to further enhance motion analysis capabilities. Full article

This study explores the challenges hindering the government’s adoption of cloud computing despite its benefits in improving services, reducing costs, and enhancing collaboration. Key barriers include information security, privacy, compliance, and perceived risks. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study conceptualises a model incorporating privacy, governance framework, performance expectancy, and information security as independent variables, with perceived risk as a moderator and government intention as the dependent variable. The study employs exploratory factor analysis (EFA) based on survey data from 71 participants in Nigerian government organisations to validate the measurement scale for these factors. The analysis evaluates variable validity, factor relationships, and measurement reliability. Cronbach’s alpha values range from 0.807 to 0.950, confirming high reliability. Measurement items with a common variance above 0.40 were retained, explaining 70.079% of the total variance on the measurement items, demonstrating reliability and accuracy in evaluating the challenging factors. These findings establish a validated scale for assessing government cloud adoption challenges and highlight complex relationships among influencing factors. This study provides a reliable measurement scale and model for future research and policymakers on the government’s intention to adopt cloud services. Full article