Standard Deviation Error Research Articles

Modeling the electrical properties of heterojunctions using ANFIS, ANFIS-GA and ANFIS-PSO Models

The present research introduces a theoretical study that aims to utilize ANFIS in estimating and predicting the electrical behavior of heterojunctions. For this purpose, five different heterojunctions were chosen. The experimental datasets that represent the electrical behavior of the chosen heterojunctions were extracted and employed in ANFIS as targets. To enhance the ANFIS performance two hybrid heuristic algorithms, genetic algorithm (GAs) and particle swarm optimization (PSO) were combined with ANFIS. The major contribution of the current research is to predict the electric characteristics of heterojunctions using ANFIS and increase the modeling accuracy of ANFIS by optimizing the premise and consequent parameters using (GAs) and (PSO). Also, compare the proportion of enhancement produced by using ANFIS-GA and ANFIS-PSO to decide which of them is more powerful under the study conditions. However, to the author’s knowledge, the presented goals have not been investigated before for heterojunctions. The mean squared error (MSE), the correlation coefficient (R2), and the standard deviation error (Std. error) were calculated for all trained models. The modeling errors of ANFIS-GA and ANFIS-PSO were compared to the error values produced by ANFIS. According to modeling results, simulation ANFIS outputs follow the experimental data patterns in excellent response. Predictions of electrical characteristics for heterojunctions using the trained models provide acceptable results where the MSE values obtained by training ANFIS-PSO are lower than their values obtained by ANFIS and ANFIS-GA models. The improvements in average percentages in ANFIS performance when combined with GA and PSO are equal to 2.2% and 3%, respectively. Consequently, the proposed ANFIS-PSO model is more accurate in predicting the electrical behavior of heterojunctions under the study conditions.

Hybrid Deep Morpho-Temporal Framework for Oscillometric Blood Pressure Measurement.

Oscillometric blood pressure (BP) measurement devices are widely utilized as the primary automated BP measurement tools in non-specialist environments. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. An essential constraint of current oscillometric BP measurement devices is their analysis algorithms' incapacity to capture the BP information encoded in the pattern of recorded oscillometric pulses to its fullest extent. In this paper, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.

Improving the accuracy of identifying objects in digital frames using a procedure of full identification of measurements

The variability of shooting conditions affects the quality of images of Solar System objects in a series of frames. Identification of a frame with the corresponding part of the sky becomes difficult if the quality is poor. Because of this fact, the detection quality indicators and estimation of the position of Solar System objects are significantly reduced when using already known methods and international astronomical catalogs. To solve this problem, the procedure of full identification of measurements of objects on digital frames was devised. This procedure is based on the formation of triplets (triangles) of primary identification from the side of the digital frame and the astronomical catalog. Positional coordinates on the frame and ideal tangential coordinates from the catalog were used. Owing to this, a comparison of the primary identification triplets was carried out by comparing the calculated angles of the triangle vertices. The identity of the hypothesis was determined by comparison with the acceptable deviation. The use of the developed full identification procedure makes it possible to reduce the number of false detections and improve identification with reference astronomical objects. The study showed that when identifying frames, astrometry has better accuracy of reference to the starry sky. In addition, the standard deviation of frame identification errors in this case is 6–9 times less than without using the devised procedure. The procedure developed for complete identification was practically tested within the framework of the CoLiTec project. It was implemented in the Lemur software for automated detection of new and tracking of known objects. Owing to the use of Lemur software and the proposed computational procedure implemented in it, more than 700,000 measurements of various astronomical objects under study were successfully identified.

Light efficacy estimation for fruit trees based on LIDAR point clouds: A case study on pear trees

It is important to evaluate the effect of fruit-tree pruning based on canopy-structure characteristics so as to keep appropriate illumination for tree growth. However, current pruning and evaluation mainly rely on manual experience due to lacking a convenient and automatic approach to obtain such features. This paper proposes a method for estimating light efficacy based on layered canopy point-clouds, where leaf-area was selected as the canopy characteristic. LIDAR was utilised to scan pear trees in three dimensions to acquire initial point clouds, and preprocess was conducted to obtain the point-cloud images of different canopy layers. Then, an optimised algorithm based on Freeman Chain was developed to accurately draw the outlines of point-cloud clusters in the images, and pixel proportion was used to calculate the real leaf-area of each layer. Finally, based on Deep Neural Network, an estimation model was established between different layer leaf-areas and their average illumination acquired in tests. The proposed method was applied to estimate the illumination of a random pear tree to evaluate the variation of light intensity. The results showed that: 1) the average relative error of leaf-area calculation of the optimised Freeman Chain method was about 6.74 %, although the running duration was longer than the conventional one, 2) the overall correlation coefficient, R, of the model was more than 0.95, while that of validation and test sets were more than 0.94, and 3) the illumination estimation was accurate and precise with an average relative error of 1.42 % and a standard deviation of relative errors of 1.19. The validation indicated that this method could achieve the prediction of illumination based on different leaf-area characteristics. The study is expected to give a technical solution for the illumination evaluation before and after tree pruning.

Positive experience of introducing telemedicine technologies in the Ekaterinburg Clinical Perinatal Centre

Introduction A clinical and statistical analysis of the medical records of the first obstetric hospital of the Ekaterinburg Clinical Perinatal Centre (ECPC) was performed to assess the medical effectiveness of the integration of telemedicine technology (TMT) into the system of medical care in the case of threatened and preterm birth (PB).The aim of the work was to assess the medical effectiveness of integrating a range of telemedicine technologies into the system of medical care in the case of threatened and preterm birth as exemplified by the ECPC (First Obstetric Hospital).Materials and methods Preterm births between 22 and 36 weeks and 6 days of gestation were selected for the study. The number of PBs was 2,468 before the introduction of telemedicine technology and 2,112 after the introduction of telemedicine technology. The incidence of extremely low birth weight was analyzed to assess the impact of TMT implementation on neonatal outcomes. Criterion of Kolmogorov – Smirnov was applied to assess the normality of the distribution; to analyse the difference between groups – criterion of Kruskal – Wallis for K-independent samples. The arithmetic mean, standard error and standard deviation were calculated. Significance of differences between the averages was assessed using Student’s t-test.Results Before the introduction of TMT, no significant difference was found in the proportion of PB groups between different years and the structure of PP did not change according to gestational age. After the introduction of TMT there was also no significant difference in the pattern of PB. The proportion of extremely early PB before TMT implementation was significantly higher than after implementation. There was a significant decrease in the number of preterm infants with extremely low birth weight after implementation compared with the period before TMT.Discussion The use of automated systems implemented in the Sverdlovsk region, which are applications for mobile phones or personal computers, does not require the financial investment associated with the purchase of additional expensive equipment by pregnant women. Like the authors of earlier studies, we note a prolongation of gestational age and a reduction in the frequency of extremely low birth weight babies.Conclusion The incidence of extremely early PB decreased from 10.81 % to 7.13 % (p = 0.012) during 2016–2021, the incidence of late PB increased from 54.6 % to 60.1 % (p = 0.027) due to increasing gestational age. The number of babies born with extremely low birth weight decreased from 13.69 % to 8.36 % (p ≤ 0.001).

Nonlinear position control of electro-hydraulic servo system based on lyapunov robust integral backstepping controller

In electro-hydraulic servo systems (EHSS), high nonlinearity and time-varying features limit the hydraulic actuator performance. In feedback-based systems, linearization reduces nonlinearities. Their cancellation may produce instability when modeling uncertainty exists. This work uses a robust nonlinear controller based on Lyapunov theory to control the nonlinear position of the electro-hydraulic servo system (EHSS). The dynamics of the system were modeled as extensively as possible, and then a simplified mathematical model based on the EHSS dynamics was created for the controller design. Integral backstepping was used to design the nonlinear robust controller utilizing the Lyapunov theory of nonlinear systems. Lyapunov functions theoretically ensured closed-loop stability. Comparative simulation analysis and experimental investigation in real-time with the proportional integral derivative (PID) controller optimized offline for position tracking control were carried out. Besides tracking performance and steady-state error, the mean square error (MSE), the root mean square error (RMSE), the maximum absolute value of tracking errors and the standard deviation of tracking errors (σ) was utilized to evaluate the controllers’ performance. The simulation analysis and experimental verification were accomplished under various sinusoidal trajectories using different testing conditions (amplitudes, frequencies, and external disturbance), in all testing conditions the effectiveness of the robust nonlinear integral backstepping controller was demonstrated.

Evaluation of collum angle of maxillary incisors on impacted vs non impacted canine Side: A CBCT analysis

The angle which is formed between the crown axis and root axis of an upper central incisor is called collum angle and this angle serves as an important morphological feature for normal development of the dentition and plays a vital role during diagnosis and treatment planning and may vary in different malocclusions i.e; skeletal and dental malocclusions. To quantify and compare the collum angle (crown root angulation) of maxillary incisors (central and lateral incisor) in subjects with unilateral permanent maxillary canine impaction between the impacted and non impacted side using Cone Beam Computed Tomography (CBCT). Thirty subjects with unilateral permanent maxillary canine impaction who had to undergo fixed orthodontic treatment were selected and their CBCT images were analysed. The collum angle of maxillary central and lateral incisor on impaction and non impaction side was measured using CBCT imaging. Descriptive statistical analysis was carried out and the mean±standard deviation was calculated and comparison between two sides was made by using paired t-test and a p value of less <0.05 was considered as statistically significant. The collum angle of central and lateral incisor between impacted and non impacted side were averaged. Thus, the mean±standard deviation and standard error of the collum angle (n-30) were calculated for maxillary central incisor and lateral incisor on both sides. The mean±sd of the collum angle of maxillary central incisor on impacted and non impacted side was 1.39±7.16 and 2.33±5.86 respectively with a p value of 0.282, while as the mean±sd of the collum angle of maxillary lateral incisor on impacted and non impacted side was 3.09±6.26 and 5.24±5.28 respectively with a p value of 0.060 which was considered as statistically insignificant between the two sides. A decrease in collum angle was found on impaction side with a more decreased value for lateral incisor when comparison was made with non impaction side, but no statistically significant difference was found.

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest.

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound

Fetal weight is currently estimated from fetal biometry parameters using heuristic mathematical formulas. Fetal biometry requires measurements of the fetal head, abdomen, and femur. However, this examination is prone to inter- and intraobserver variability because of factors, such as the experience of the operator, image quality, maternal characteristics, or fetal movements. Our study tested the hypothesis that a deep learning method can estimate fetal weight based on a video scan of the fetal abdomen and gestational age with similar performance to the full biometry-based estimations provided by clinical experts. This study aimed to develop and test a deep learning method to automatically estimate fetal weight from fetal abdominal ultrasound video scans. A dataset of 900 routine fetal ultrasound examinations was used. Among those examinations, 800 retrospective ultrasound video scans of the fetal abdomen from 700 pregnant women between 15 6/7 and 41 0/7 weeks of gestation were used to train the deep learning model. After the training phase, the model was evaluated on an external prospectively acquired test set of 100 scans from 100 pregnant women between 16 2/7 and 38 0/7 weeks of gestation. The deep learning model was trained to directly estimate fetal weight from ultrasound video scans of the fetal abdomen. The deep learning estimations were compared with manual measurements on the test set made by 6 human readers with varying levels of expertise. Human readers used standard 3 measurements made on the standard planes of the head, abdomen, and femur and heuristic formula to estimate fetal weight. The Bland-Altman analysis, mean absolute percentage error, and intraclass correlation coefficient were used to evaluate the performance and robustness of the deep learning method and were compared with human readers. Bland-Altman analysis did not show systematic deviations between readers and deep learning. The mean and standard deviation of the mean absolute percentage error between 6 human readers and the deep learning approach was 3.75%±2.00%. Excluding junior readers (residents), the mean absolute percentage error between 4 experts and the deep learning approach was 2.59%±1.11%. The intraclass correlation coefficients reflected excellent reliability and varied between 0.9761 and 0.9865. This study reports the use of deep learning to estimate fetal weight using only ultrasound video of the fetal abdomen from fetal biometry scans. Our experiments demonstrated similar performance of human measurements and deep learning on prospectively acquired test data. Deep learning is a promising approach to directly estimate fetal weight using ultrasound video scans of the fetal abdomen.

A simplified mechanism of hydrogen addition to methane combustion for the pollutant emission characteristics of a gas-fired boiler

The reaction mechanism of hydrogen-enriched methane (HEM) combustion is significant in the combustion process. In this study, four widely used reaction mechanisms for methane combustion are simplified to investigate the pollutant emission characteristics of a gas-fired boiler used for HEM combustion. The laminar burn velocity (LBV) and ignition delay time (IDT) of the four detailed and four simplified mechanisms are compared, and the simplified San Diego mechanism is identified as the optimal mechanism using relative error and population standard deviation analysis. The temperatures and key components of the simplified San Diego mechanism are also compared with those of the detailed San Diego mechanism under various XH2. Based on the simplified San Diego mechanism, a chemical reactor network model for gas-fired boilers is further established to investigate the pollutant emissions of HEM combustion, in which the hydrogen doping ratio XH2 ranged from 0 to 80% and the inlet air and fuel temperature Tin ranged from 300 K to 600 K. Results showed that at Tin = 300 K with XH2 ranging from 0 to 80%, the mole fraction of NO increase by 1.94 times because of the increase in peak flame temperature; moreover, the mole fractions of N2O, CO, and CO2 decrease by approximately 25%, 50%, and 52%, respectively. With XH2 ranging from 0 to 40% and Tin ≥ 500 K, the mole fraction of NO decreases because the reduction reaction of NO + H = N + OH is promoted, whereas when XH2 is further increased to 80%, the increased H promotes the increase in NO again. CO emissions decreased as XH2 increased from 0 to 80%, whereas they increased by approximately 1.3 times as Tin increased from 300 K to 600 K because of the CO2 decomposition. N2O emissions decreased by more than 25% as XH2 increased from 0 to 80%. Additionally, CO2 emissions decrease by 52% with an increase in XH2 and by only 5% with an increase in Tin, indicating that XH2 has a larger impact on CO2 emissions. The proposed optimal HEM combustion mechanism is conducive to improving the efficiency of numerical calculations.

Development of Electrometer for Dose Area Product (DAP) Measurement to Monitor Patient Dose in Radiography Examinations

This study aimed to develop an electrometer for dose area product (DAP) measurement to monitor patient doses on X-ray radiography examinations. The electrometer was developed based on the Atmega8535 microcontroller as the processing unit. This electrometer was operated at 12 volts of direct current (DC) and controlled by buttons for navigation. The system (i.e. in-house electrometer and DAP meter) was carefully designed so that it can easily perform calibration and dose measurements. The system was evaluated at variations of tube voltage, tube current, and exposure time. The developed system results in high accuracy and precision as indicated by low percent error (PE) and relative standard deviation (RSD) compared to standard system. The highest PE and RSD are 1.69% and 1.42% for tube voltage variation, 3.85% and 3.58% for tube current variations, and 12.10% and 0.92% for exposure time variations. In conclusion, the developed system is feasible to be used as a patient dose measurement tool to meet the needs of radiation protection to achieve the principle of “as low as reasonably achievable (ALARA).

A new method to determine composition of sphalerite without secondary pollution based on CIELAB color space

AbstractCurrently, most of the methods for mineral materials analysis generate secondary pollution, which is detrimental to human health. For instance, traditional methods for sphalerite analysis in the zinc (Zn) smelting industry including chemical titration, atomic absorption spectrometry, and inductively coupled atomic emission spectroscopy. Colored indicators and toxic heavy metals are used in the analytical processes, causing severe pollution. For some methods, liquid is transformed into gaseous plasma, which is more dangerous to human health. Due to large quantities of sphalerite being used, secondary pollution cannot be ignored. This study proposes a green analysis method for the detection of sphalerite based on colorimetry, which does not generate secondary pollution. The results show that the strong substitution ability of iron (Fe) for Zn contributes to their inverse correlation in contents. The lattice parameters decrease with the increasing Fe content, resulting in a darker coloration. Here, key colorimetry parameters of L*, a*, and b* show clear linear correlations with the Zn and Fe contents. Compared with traditional approaches, this new method is environmental friendly with high sensitivity and accuracy. The relative error and relative standard deviation were less than 10% and 5%, respectively. This study provides a significant reference for nonpollution determination of other mineral materials.

Applicability of the equilibrium adsorption isotherms and the statistical tools on to them: a case study for the adsorption of fluoride onto Mg-Fe-CO3 LDH

The demand for a low-cost, easy-to-use, low-maintenance system of adsorbents for detoxifying wastewater effluents is growing. A proper understanding of adsorption mechanisms, their controls, and adsorbate-adsorbent behavior is essential. Thus, the current study deals with the required understanding of linear equilibrium adsorption isotherm models. The adsorption data of different concentrations of fluoride onto Mg-Fe-CO3 LDH is fitted to isotherm models. The two-parameter models discussed are Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich, whereas the three-parameter model cited is Redlich-Peterson. To identify the best-fitting model(s) in an equilibrium isotherm study to quantitatively represent the relevant sorption system, various error functions and statistical tools, such as average relative error deviation (ARED), sum of square errors (ERRSQ or SSE), hybrid relative error function (HYBRID), Marquardt’s percent standard error deviation (MPSD), sum of absolute errors (EABS), sum of normalized errors (SNE), correlation coefficient of Pearson (r), coefficient of determination (r2), chi-square test (χ2), student’s T-test and F-test were applied. It is found that the Temkin model best fits the isotherm data, and the sorption process occurs over multiple layers as per the Freundlich isotherm and was found to be more promising than Langmuir’s monolayer sorption process. The exothermic physisorption course for the adsorbate-absorbent interactions is deduced from the isotherm parameters.

Memory-efficient low-compute segmentation algorithms for bladder-monitoring smart ultrasound devices

Post-operative urinary retention is a medical condition where patients cannot urinate despite having a full bladder. Ultrasound imaging of the bladder is used to estimate urine volume for early diagnosis and management of urine retention. Moreover, the use of bladder ultrasound can reduce the need for an indwelling urinary catheter and the risk of catheter-associated urinary tract infection. Wearable ultrasound devices combined with machine-learning based bladder volume estimation algorithms reduce the burdens of nurses in hospital settings and improve outpatient care. However, existing algorithms are memory and computation intensive, thereby demanding the use of expensive GPUs. In this paper, we develop and validate a low-compute memory-efficient deep learning model for accurate bladder region segmentation and urine volume calculation. B-mode ultrasound bladder images of 360 patients were divided into training and validation sets; another 74 patients were used as the test dataset. Our 1-bit quantized models with 4-bits and 6-bits skip connections achieved an accuracy within 3.8% and 2.6%, respectively, of a full precision state-of-the-art neural network (NN) without any floating-point operations and with an 11.5times and 9.0times reduction in memory requirements to fit under 150 kB. The means and standard deviations of the volume estimation errors, relative to estimates from ground-truth clinician annotations, were 5.0pm 33 ml and 6.8pm 29 ml, respectively. This lightweight NN can be easily integrated on the wearable ultrasound device for automated and continuous monitoring of urine volume. Our approach can potentially be extended to other clinical applications, such as monitoring blood pressure and fetal heart rate.

Enhancing Accessibility: Assistive Devices Pairing Mobile Apps for Visually Impaired People

Vision impairment and blindness are significant health conditions that affect a considerable portion of the global population and have significant consequences, particularly in developed nations. Its harmful consequences can have a negative influence on a person's personal quality of life, as well as limit their ability to live independently. Navigation plays a crucial role in the activities that are impacted by vision loss. This is because it is almost impossible for the individual to move safely and independently. The goal of this project is to improve mobility for visually impaired people by employing Light Detection and Ranging (LiDAR) sensors to identify obstacles and alert users via haptic feedback if there are potential obstacles in their way. In addition, this study compares two sensors, LiDAR and ultrasonic sensors, besides the implementation of the Internet of Things (IoT), which was integrated into the small, portable robot by pairing a mobile phone. This is to ensure the user may be securely guided to their destination. I-Walk is a mobile app with a voice user interface (VUI) for GPS navigation systems and emergency calling. This app has been developed to allow users safe and independent travel. The simulation result shows that even though ultrasonic sensors are cheaper and serve the same function as LiDAR, their huge standard deviation and percentage error shown in this study cannot equal LiDAR's accuracy in which LiDAR has an average error of 0.63%. In contrast, ultrasonic sensors have an average error of 2.39%. Overall, this obstacle detection and navigation system comprises robust hardware and software to assist visually impaired people effectively.Keywords: Assistive technology, blind, mini guider robot, obstacle detection, navigation, IoT

Error homogenization in physics-informed neural networks for modeling in manufacturing

Physics-informed neural networks (PINNs) have demonstrated effectiveness in solving partial differential equations (PDEs) associated with manufacturing scenarios, due to their physically interpretable training logic. One limitation has been that PINNs often exhibit heterogenous error maps and high-error “hot spots” throughout the solution domain, which reduce not only the solutions’ accuracy but also their overall consistency with the physical laws. This study addresses this limitation by presenting an efficient and error-aware PINN ensembling technique for error homogenization in solving manufacturing problems. Specifically, a PINN is first established by constraining its training process through a manufacturing specific PDE and corresponding boundary conditions to ensure physical consistency. Next, the loss landscape in the neighborhood of three PINNs trained with varying network parameter initialization is sampled to generate a PINN ensemble. Finally, the outputs of the ensemble members are combined through an inverse error-weighted average to yield the prediction of the PDE solution. Evaluation using the Allen-Cahn PDE, which describes phase separation in the solidification of metallic alloys, shows that the developed method reduces the average prediction error by 63% and error standard deviation by 30% across the solution space, demonstrating its effectiveness for PINN error reduction and homogenization. Additionally, the method has also demonstrated 96% reduction in the computational time as compared to conventional ensembling methods.

Impact of SAR-based vegetation attributes on the SMAP high-resolution soil moisture product

NASA's Soil Moisture Active Passive (SMAP) mission employs L-band radiometer observations with a spatial resolution of ~33 km to produce global soil moisture products at various spatial resolutions and regular intervals. Among these products is the SMAP-Sentinel active-passive high-resolution soil moisture, available at spatial resolutions of 1 km and 3 km. The SMAP-Sentinel product combines data from the SMAP L-band radiometer and the Copernicus Sentinel-1A and Sentinel-1B C-band Synthetic Aperture Radar (SAR) observations. The SMAP-Sentinel high-resolution soil moisture product enables various hydrological and agricultural applications that were previously beyond the capability of coarser resolution radiometer-only products. However, the assessment of SMAP-Sentinel product over SMAP core validation sites (CVS) has revealed high errors and biases, particularly in agricultural regions. We suspect that the discrepancy may be related to the vegetation attribute parameters (e.g., vegetation optical depth, τ) used in the SMAP-Sentinel baseline Single-Channel Algorithm (SCA) microwave emission model (i.e., τ-ω model) for soil moisture retrievals, which might be out of sync due to reliance on NDVI climatology in the τ estimation. In this study, we leveraged and evaluated the advantage of using vegetation attribute information inherently available through the SMAP ratiometer and Sentinel- 1A/1B SAR backscatter as an alternative to NDVI climatology-derived τ in the SCA to enhance soil moisture retrievals. The SAR cross-polarization (vh) backscatter potentially contains vegetation attribute information that can be correlated with the coarse resolution τ derived from SMAP observations. Therefore, we hypothesized that including Sentinel- 1A/1B vh observations to derive high-resolution (1 km and 3 km) τ, and subsequently incorporating it into the SCA, can significantly improve the accuracy of the SMAP-Sentinel soil moisture product. We established a statistical relationship between Sentinel- 1A/1B SAR backscatter and the SMAP Dual-Channel Algorithm (DCA)-based τ retrieval at ~33 km, for a specific region and different landcover classes at a global extent, using seven years of observations (2015–2022). Further, we developed an algorithm to downscale the SMAP DCA τ of ~33 km to 1 km and 3 km resolutions based on the developed statistical relationships, enabling us to capture the actual temporal variability in vegetation attributes (i.e., τ) at higher resolutions. The downscaled τ values were then incorporated into the SCA to retrieve SMAP-Sentinel high-resolution (1 km and 3 km) soil moisture. The validation analysis of high-resolution soil moisture retrievals demonstrates that the inclusion of the downscaled τ approach significantly improved the performance of the SMAP-Sentinel product by reducing both bias and error standard deviation compared to the retrievals based on τ derived from the NDVI climatology.

Improved deep mixed kernel randomized network for wind speed prediction

Abstract Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions. Although there are several intelligent techniques in the literature for wind speed prediction, their accuracies are not yet very reliable. Therefore, in this paper, a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder (AE) is proposed for wind speed prediction. The proposed method eliminates manual tuning of hidden nodes with random weights and biases, providing prediction model generalization and representation learning. This reduces reconstruction error due to the exact inversion of the kernel matrix, unlike the pseudo-inverse in a random vector functional-link network, and shortens the execution time. Furthermore, the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction accuracy. The kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization technique. The lowest errors in terms of mean absolute error (0.4139), mean absolute percentage error (4.0081), root mean square error (0.4843), standard deviation error (1.1431) and index of agreement (0.9733) prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs, deep kernel extreme learning machine AEs, deep kernel random vector functional-link network AEs, benchmark models such as least square support vector machine, autoregressive integrated moving average, extreme learning machines and their hybrid models along with different state-of-the-art methods.

Design and Characterization of a single-axis solar tracker system for Small Scale Parabolic trough collectors

ABSTRACT This paper introduces a detailed design and development of a solar tracker (ST) prototype for small-sized parabolic trough collectors (PTCs) with one degree of freedom. The PTCs are intended to produce industrial heat at a moderate temperature. The contribution of this work lies in its introduction of a new and optimized solar tracker, which prioritizes both accuracy and cost-efficiency in its design. This aspect was emphasized throughout the experimental investigation, along with a comparison of findings from existing literature. Therefore, the open-loop strategy based on the solar position algorithm (SPA) is adopted here to determine the tracking-elevation angle. A software algorithm is proposed and implemented in a control unit managed by low-cost board. Then, the tracking angle is updated by controlling the electro-mechanical system using an on-off hysteresis approach. An experimental characterization of the tracker is carried out regarding the tracking precision by directly measuring the errors in positioning angle and their impact on the optical losses. The obtained results show good performance of the tracker with a standard deviation of tracking errors of 0.55°, which is adequate for such collectors with tolerance angles of more than 2°. This makes the 96th percentile of the optical loss efficiency almost zero for our PTC.

Experimental Study of Mechanical Properties of Rice Husk Ash - Cement Concrete Made from Magnetized and Normal Water

This work aims at comparing the mechanical properties of Rice-Husk-Ash-Cement (RHA) concrete produced from magnetized water and normal water. Cement was replaced with 25% RHA being the optimum value from previous research. Water was magnetized by a magnetic device fabricated by the research team and level of magnetization was determined using magnetic field sensor application. The chemical composition of RHA shows that it contained 89.42% of oxides to be used as a pozzolan. Magnetization reduced total dissolved solid, total soluble solid, chloride content and surface tension while pH increased. The workability of magnetized and normal water concrete samples was determined. It was magnetized water concrete samples that gave higher workability than normal water concrete. Compressive, Flexural and Split tensile strengths of magnetized and normal water concrete were determined at 7, 14, 28 and 56 days of curing by immersion in portable water. Magnetization shown higher influence on early strengths of concrete. Compressive, Flexural and Split tensile strengths of concrete improved between 13.25%-18.63%, 14.83%-18.02% and 9.80%-31.63%, respectively when magnetized water was introduced during concrete production. STATA package was used to analysis the data. The descriptive statistics show that mean, standard deviation error and standard deviation for concrete produced with magnetized water were all higher than that of concrete produced with normal water for all properties tested. However, inferential statistics show that there is no significant difference in the mean compressive, flexural and tensile strengths of concrete produced with magnetized water and normal water since P-value obtained was higher than the error margin of 0.05(P>0.05) for all the tested properties of concrete.