Search Results (822)

The high spatial complexities of soil temperature modeling over semiarid land have challenged the calibration–forecast framework, whose composited objective lacks comprehensive evaluation. Therefore, this study, based on the Noah land surface model and its full parameter table, utilizes two global searching algorithms and eight kinds of objectives with dimensional-varied metrics, combined with dense site soil moisture and temperature observations of central Tibet, to explore different metrics’ performances on the spatial heterogeneity and uncertainty of regional land surface parameters, calibration efficiency and effectiveness, and spatiotemporal complexities in surface forecasting. Results have shown that metrics’ diversity has shown greater influence on the calibration—predication framework than the global searching algorithm’s differences. The enhanced multi-objective metric (EMO) and the enhanced Kling–Gupta efficiency (EKGE) have their own advantages and disadvantages in simulations and parameters, respectively. In particular, the EMO composited with the four metrics of correlated coefficient, root mean square error, mean absolute error, and Nash–Sutcliffe efficiency has shown relatively balanced performance in surface soil temperature forecasting when compared to other metrics. In addition, the calibration–forecast framework that benefited from the EMO could greatly reduce the spatial complexities in surface soil modeling of semiarid land. In general, these findings could enhance the knowledge of metrics’ advantages in solving the complexities of the LSM’s parameters and simulations and promote the application of the calibration–forecast framework, thereby potentially improving regional surface forecasting over semiarid regions. Full article

Inertial measurement units (IMU) are increasingly utilized to capture biomechanical measures such as joint kinematics outside traditional biomechanics laboratories. These wearable sensors have been proven to help clinicians and engineers monitor rehabilitation progress, improve prosthesis development, and record human performance in a variety of settings. The Valor IMU aims to offer a portable motion capture alternative to provide reliable and accurate joint kinematics when compared to industry gold standard optical motion capture cameras. However, IMUs can have disturbances in their measurements caused by magnetic fields, drift, and inappropriate calibration routines. Therefore, the purpose of this investigation is to validate the joint angles captured by the Valor IMU in comparison to an optical motion capture system across a variety of movements. Our findings showed mean absolute differences between Valor IMU and Vicon motion capture across all subjects’ joint angles. The tasks ranged from 1.81 degrees to 17.46 degrees, the root mean squared errors ranged from 1.89 degrees to 16.62 degrees, and interclass correlation coefficient agreements ranged from 0.57 to 0.99. The results in the current paper further promote the usage of the IMU system outside traditional biomechanical laboratories. Future examinations of this IMU should include smaller, modular IMUs with non-slip Velcro bands and further validation regarding transverse plane joint kinematics such as joint internal/external rotations. Full article

Soil electrical conductivity has an important influence on the growth and development of plants. The existing real-time soil electrical conductivity detection device is affected by temperature, inconvenient to use, expensive, etc.; therefore, based on the classical four-terminal method of soil electrical conductivity detection principle, in this study, we aim to improve the limitations of the constant current source, selecting the high-performance integrated chip AD5941, optimizing the detection circuit and probe structure, improving the achievability of the detection circuit, and designing a type of in situ on-line real-time access to a soil electrical conductivity detection device, and improve the detection accuracy by temperature compensation. In this paper, dynamic performance, steady state performance, radial sensitivity range, and calibration test are carried out for the soil electrical conductivity detection prototype. The test results show that the dynamic response speed of the prototype is less than 50 ms, the steady state error is not more than ±2%, and the radial measurement sensitivity range is 8~10 cm. A comparison with the commercial sensor shows that the linear fit of the two measurements reaches 0.9995, and the absolute error ranges from −61.40 µS/cm to 23.90 µS/cm, with a relative error range of −1.94~1.86%. It shows that the performance of the two sensors is comparable, but the quality/price ratio of the prototype is much higher than that of the commercialized product. In this study, it is demonstrated that a high-precision, low-cost, and easy-to-use in situ online soil electrical conductivity detection device can be provided for agricultural and forestry production. Full article

The isle of Gavdos, and its wider area, is one of the few places worldwide where the calibration and validation of altimetric satellites has been carried out during the last, more than, two decades using dedicated techniques at sea and on land. The sea-surface calibration employed for the determination of the bias in the satellite altimeter’s sea-surface height relies on the use of a gravimetric geoid in collocation with data from tide gauges, permanent global navigation satellite system (GNSS) receivers, as well as meteorological and oceanographic sensors. Hence, a high-accuracy and high-resolution gravimetric geoid model in the vicinity of Gavdos and its surrounding area is of vital importance. The existence of such a geoid model resides in the availability of reliable, in terms of accuracy, and dense, in terms of spatial resolution, gravity data. The isle of Gavdos presents varying topographic characteristics with heights larger than 400 m within small spatial distances of ~7 km. The small size of the island and the significant bathymetric variations in its surrounding marine regions make the determination of the gravity field and the geoid a challenging task. Given the above, the objective of the present work was two-fold. First, to collect new land gravity data over the isle of Gavdos in order to complete the existing database and cover parts of the island where voids existed. Relative gravity campaigns have been designed to cover as homogenously as possible the entire island of Gavdos and especially areas where the topographic gradient is large. The second focus was on the determination of a high-resolution, 1′×1′, and high-accuracy gravimetric geoid for the wider Gavdos area, which will support activities on the determination of the absolute altimetric bias. The relative gravity campaigns have been designed and carried out employing a CG5 relative gravity meter along with geodetic grade GNSS receivers to determine the geodetic position of the acquired observations. Geoid determination has been based on the newly acquired and historical gravity data, GNSS/Leveling observations, and topography and bathymetry databases for the region. The modeling was based on the well-known remove–compute–restore (RCR) method, employing least-squares collocation (LSC) and fast Fourier transform (FFT) methods for the evaluation of the Stokes’ integral. Modeling of the long wavelength contribution has been based on EIGEN6c4 and XGM2019e global geopotential models (GGMs), while for the contribution of the topography, the residual terrain model correction has been employed using both the classical, space domain, and spectral approaches. From the results achieved, the final geoid model accuracy reached the ±1–3 cm level, while in terms of the absolute differences to the GNSS/Leveling data per baseline length, 28.4% of the differences were below the 1cmSij [km] level and 55.2% below the 2cmSij [km]. The latter improved drastically to 52.8% and 81.1%, respectively, after deterministic fit to GNSS/Leveling data, while in terms of the relative differences, the final geoid reaches relative uncertainties of 11.58 ppm (±1.2 cm) for baselines as short as 0–10 km, which improves to 10.63 ppm (±1.1 cm) after the fit. Full article

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT–BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis. Full article

Accurate prediction of the friction factor is fundamental for designing and calibrating fluid transport systems. While the Colebrook–White equation is the benchmark for precision due to its physical basis, its implicit nature hinders practical applications. Explicit correlations like Churchill’s equation are commonly used but often sacrifice accuracy. This study introduces two novel modifications to Churchill’s equation to enhance predictive capabilities. Developed through a rigorous analysis of 240 test cases and validated against a dataset of 21,000 experiments, the proposed Churchill B(Re) and Churchill B(V,ε) models demonstrate significantly improved accuracy compared to the original Churchill equation. The development of these functions was achieved through generalized reduced gradient (GRG) nonlinear optimization. This optimized equation offers a practical and precise alternative to the Colebrook–White equation. The mean relative errors (MRE) for the modified models, Churchill B(Re) and Churchill B(V,ε), are 0.025% and 0.807%, respectively, indicating a significant improvement over the original equation introduced by Churchill in 1973, which exhibits an MRE of 0.580%. Similarly, the mean absolute errors (MAE) are 0.0008% and 0.0154%, respectively, compared to 0.0291% for the original equation. Beyond practical applications, this research contributes to a deeper understanding of friction factor phenomena and establishes a framework for refining other empirical correlations in the field. Full article

An Ultraviolet-Visible Hyperspectral Sensors (UVS) instrument is an ultraviolet-visible imaging spectrograph equipped with two-dimensional charge-coupled device detectors. It records both the spectrum and the swath perpendicular to the flight direction, offering a wide 112° swath. This configuration enables global daily ground coverage with high spatial resolution. The absolute values of in-orbit solar irradiance can be evaluated using the bidirectional reflectance distribution function (BRDF), with the measurement accuracy directly affecting the accuracy of constituent inversion. This paper outlines the calibration process for the BRDF of the UVS, detailing the calibration methods and equipment used. It also proposes a BRDF model and discusses key coefficients. The accuracy levels of the UVS in the UV1, UV2, and VIS channels were 2.162%, 2.162%, and 2.173%, respectively. Full article

Car-following models are crucial in adaptive cruise control systems, making them essential for developing intelligent transportation systems. This study investigates the characteristics of high-speed traffic flow by analyzing the relationship between headway distance and dynamic desired distance. Building upon the optimal velocity model theory, this paper proposes a novel traffic car-following computing system in the time domain by incorporating an absolutely safe time headway strategy and a relatively safe time headway strategy to adapt to the dynamic changes in high-speed traffic flow. The interpretable physical law of motion is used to compute and analyze the car-following behavior of the vehicle. Three different types of car-following behaviors are modeled, and the calculation relationship is optimized to reduce the number of parameters required in the model’s adjustment. Furthermore, we improved the calculation of dynamic expected distance in the Intelligent Driver Model (IDM) to better suit actual road traffic conditions. The improved model was then calibrated through simulations that replicated changes in traffic flow. The calibration results demonstrate significant advantages of our new model in improving average traffic flow speed and vehicle speed stability. Compared to the classic car-following model IDM, our proposed model increases road capacity by 8.9%. These findings highlight its potential for widespread application within future intelligent transportation systems. This study optimizes the theoretical framework of car-following models and provides robust technical support for enhancing efficiency within high-speed transportation systems. Full article

The potential of generalized deep learning models developed for crop water estimation was examined in the current study. This study was conducted in a semiarid region of India, i.e., Karnataka, with daily climatic data (maximum and minimum air temperatures, maximum and minimum relative humidity, wind speed, sunshine hours, and rainfall) of 44 years (1976–2020) for twelve locations. The Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), and Random Forest (RF) are three ensemble deep learning models that were developed using all of the climatic data from a single location (Bengaluru) from January 1976 to December 2017 and then immediately applied at eleven different locations (Ballari, Chikmaglur, Chitradurga, Devnagiri, Dharwad, Gadag, Haveri, Koppal, Mandya, Shivmoga, and Tumkuru) without the need for any local calibration. For the test period of January 2018–June 2020, the model’s capacity to estimate the numerical values of crop water requirement (Penman-Monteith (P-M) ETo values) was assessed. The developed ensemble deep learning models were evaluated using the performance criteria of mean absolute error (MAE), average absolute relative error (AARE), coefficient of correlation (r), noise to signal ratio (NS), Nash–Sutcliffe efficiency (ɳ), and weighted standard error of estimate (WSEE). The results indicated that the WSEE values of RF, GB, and XGBoost models for each location were smaller than 1 mm per day, and the model’s effectiveness varied from 96% to 99% across various locations. While all of the deep learning models performed better with respect to the P-M ETo approach, the XGBoost model was able to estimate ETo with greater accuracy than the GB and RF models. The XGBoost model’s strong performance was also indicated by the decreased noise-to-signal ratio. Thus, in this study, a generalized mathematical model for short-term ETo estimates is developed using ensemble deep learning techniques. Because of this type of model’s accuracy in calculating crop water requirements and its ability for generalization, it can be effortlessly integrated with a real-time water management system or an autonomous weather station at the regional level. Full article

Estimating live fuel moisture content (LFMC) is critical for assessing vegetation flammability and predicting potential fire behaviour, thus providing relevant information for wildfire prevention and management. Previous research has demonstrated that empirical modelling based on spectral data derived from remote sensing is useful for retrieving LFMC. However, these types of models are often very site-specific and generally considered difficult to extrapolate. In the present study, we analysed the performance of empirical models based on Sentinel-2 spectral data for estimating LFMC in fire-prone shrubland dominated by Cistus ladanifer. We used LFMC data collected in the field between June 2021 and September 2022 in 27 plots in the region of Andalusia (southern Spain). The specific objectives of the study included (i) to test previous existing models fitted for the same shrubland species in a different study area in the region of Madrid (central Spain); (ii) to calibrate empirical models with the field data from the region of Andalusia, comparing the model performance with that of existing models; and (iii) to test the capacity of the best empirical models to predict decreases in LFMC to critical threshold values in historical wildfire events. The results showed that the empirical models derived from Sentinel-2 data provided accurate LFMC monitoring, with a mean absolute error (MAE) of 15% in the estimation of LFMC variability throughout the year and with the MAE decreasing to 10% for the critical lower LFMC values (<100%). They also showed that previous models could be easily recalibrated for extrapolation to different geographical areas, yielding similar errors to the specific empirical models fitted in the study area in an independent validation. Finally, the results showed that decreases in LFMC in historical wildfire events were accurately predicted by the empirical models, with LFMC <80% in this fire-prone shrubland species. Full article

Background and purpose: Clinically, the ability to identify individuals at risk of ischemic stroke remains limited. This study aimed to develop a nomogram model for predicting the risk of acute ischemic stroke. Methods: In this study, we conducted a retrospective analysis on patients who visited the Department of Neurology, collecting important information including clinical records, demographic characteristics, and complete hematological tests. Participants were randomly divided into training and internal validation sets in a 7:3 ratio. Based on their diagnosis, patients were categorized as having or not having ischemic stroke (ischemic and non-ischemic stroke groups). Subsequently, in the training set, key predictive variables were identified through multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods, and a nomogram model was constructed accordingly. The model was then evaluated on the internal validation set and an independent external validation set through area under the receiver operating characteristic curve (AUC-ROC) analysis, a Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) to verify its predictive efficacy and clinical applicability. Results: Eight predictors were identified: age, smoking status, hypertension, diabetes, atrial fibrillation, stroke history, white blood cell count, and vitamin B12 levels. Based on these factors, a nomogram with high predictive accuracy was constructed. The model demonstrated good predictive performance, with an AUC-ROC of 0.760 (95% confidence interval [CI]: 0.736–0.784). The AUC-ROC values for internal and external validation were 0.768 (95% CI: 0.732–0.804) and 0.732 (95% CI: 0.688–0.777), respectively, proving the model’s capability to predict the risk of ischemic stroke effectively. Calibration and DCA confirmed its clinical value. Conclusions: We constructed a nomogram based on eight variables, effectively quantifying the risk of ischemic stroke. Full article

Hydrological modeling plays a vital role in water-resource management and climate-change studies in hyper-arid regions. In the present investigation, surface runoff was estimated by a Soil and Water Assessment Tool (SWAT) model for Wadi Al-Aqul, Saudi Arabia. The Sequential Uncertainty Fitting version 2 (SUFI-2) technique in SWAT-CUP was adopted for the sensitivity analysis, calibration, and validation of the SWAT model’s components. The observational runoff data were scarce and only available from 1979 to 1984; such data scarcity is a common problem in hyper-arid regions. The results show good agreement with the observed daily runoff, as indicated by a Pearson Correlation Coefficient (r) of 0.86, a regression (R²) of 0.76, and a Nash–Sutcliffe coefficient (NSE) of 0.61. Error metrics, including the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), were notably low at 0.05 and 0.58, respectively. In the daily validation, the model continued to perform well, with a correlation of 0.76 and regression of 0.58. As a new approach, fitted parameters of daily calibration were incorporated into the monthly simulation, and they demonstrated an even better performance. The correlation coefficient (regression) and Nash–Sutcliffe were found to be extremely high during the calibration period of the monthly simulation, reaching 0.97 (0.95) and 0.73, respectively; meanwhile, they reached 0.99 (0.98) and 0.63 in the validation period, respectively. The sensitivity analysis using the SUFI-2 algorithm highlighted that, in the streamflow estimation, the Curve Number (CN) was found to be the most responsive parameter, followed by Soil Bulk Density (SOL_BD). Notably, the monthly results showed a higher performance than the daily results, indicating the inherent capability of the model in regard to data aggregation and reducing the impact of random fluctuations. These findings highlight the applicability of the SWAT model in predicting runoff and its implication for climate-change studies in hyper-arid regions. Full article

In synchrotrons, accurate knowledge of the magnetic field generated by bending dipole magnets is essential to ensure beam stability. Measurement campaigns are necessary to characterize the field. The choice of the measurement method for such campaigns is determined by the combination of magnet dimensions and operating conditions and typically require a trade-off between accuracy and versatility. The single stretched wire (SSW) is a well-known, polyvalent method to measure the integral field of magnets having a wide range of geometries. It, however, requires steady-state excitation. This work presents a novel implementation of this method called pulsed SSW, which allows the system to measure rapidly time-varying magnetic fields, as is often needed, to save power or gain beam time. We first introduce the measurement principle of the pulsed SSW, followed by a combined strategy to calculate the absolute magnetic field by incorporating the classic DC SSW method. Using a bending magnet from the Proton Synchrotron Booster located at the European Organization for Nuclear Research as a case study, we validate the pulsed SSW method and compare its dynamic measurement capabilities to a fixed induction coil, showing thereby how the coil calibration must be adjusted according to the field level. Finally, we assess the method’s measurement accuracy using the standard SSW as a reference and present an analysis of the primary noise contributors. Full article

The quinoline alkaloid 2-(quinoline-8-carboxamido)benzoic acid (2-QBA), which is isolated from Aspergillus sp. SCSIO06786, a deep sea-derived fungus, has been suggested as a therapeutic candidate for the treatment of Parkinson’s disease. We developed an analytical method for 2-QBA using a liquid chromatography–tandem mass spectrometry (LC-MS/MS) in mouse plasma, in which a protein precipitation method for the sample preparation of 2-QBA in mouse plasma was used due to its simplicity and good extraction recovery rates (80.49–97.56%). The linearity of the calibration standard sample, inter- and intraday precision and accuracy, and stability of three quality control samples were suitable based on the assessment criteria and the lower limit of quantification (LLOQ) of the 2-QBA was 1 ng/mL. A pharmacokinetic study of 2-QBA was performed in mice divided into oral (2.0, 5.0, and 15 mg/kg) and intravenous (0.5 and 1.0 mg/kg) administration groups. The absolute oral bioavailability (BA) range of 2-QBA was calculated as 68.3–83.7%. Secondary peaks were observed at approximately 4–8 h after the oral administration of 2-QBA at all doses. The elimination half-life of the orally administered 2-QBA was significantly longer than that of the intravenous 2-QBA. In addition, glucuronide metabolites of 2-QBA were identified. They were transformed into 2-QBA using the β-glucuronidase treatment. Furthermore, the 2-QBA was readily absorbed from the jejunum to lower ileum. Taken together, the secondary peaks could be explained by the enterohepatic circulation of 2-QBA. In conclusion, the reabsorption of orally administered 2-QBA could contribute to the high oral BA of 2-QBA and could be beneficial for the efficacy of 2-QBA. Moreover, the simple and validated analytical method for 2-QBA using LC-MS/MS was applied to the pharmacokinetic study and BA assessments of 2-QBA in mice and would be helpful for subsequent pharmacokinetic studies, as well as for evaluations of the toxicokinetics and pharmacokinetic–pharmacodynamic correlation of 2-QBA to assess its potential as a drug. Full article

Oil peony is an important oil crop that is primarily sown by using artificial methods at present. Its seeder encounters the problems of low efficiency of seeding that significantly limits the highly efficient mechanized production of high-quality peony oil. In this study, Fengdan white oil peony seeds were used as the research object and repose angle as the response value to establish a discrete element model (DEM) and parameter calibration. The range of parameters of oil peony seeds was first obtained through an experiment, and their repose angle was obtained by an inclinometer. A three-dimensional DEM of oil peony seeds was then established. The Plackett–Burman (PB) test was utilized to screen the parameters that had a significant influence on the repose angle, and the steepest ascent (SA) test was applied to determine their optimum range of testing. Following this, based on Box–Behnken (BBD) test results, a second-order regression model between the important parameters and the repose angle was constructed. Finally, the absolute minimum difference between simulated and measured repose angles was utilized as the objective of optimization to obtain the following optimum combination of parameters: The values of the seed–steel collision recovery coefficient (CRC), seed–seed static friction coefficient (SFC), seed–steel SFC, and seed–seed rolling friction coefficient (RFC) were 0.704, 0.324, 0.335, and 0.045, respectively. This optimal combination of parameters was confirmed through simulations, and the error between simulated and measured repose angles was only 0.67%, indicating that the calibrated DEM of oil peony seeds was reliable. Full article