Absolute Coefficients Research Articles

Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria

Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).

Enhanced performance of Mn-Co-Ni-O thermosensitive material by Cu and Sc co-dopants for uncooled infrared detector

We prepare undoped Mn1.5Co1Ni0.5O4 and Cu and Sc co-doped Mn1.5Co1Ni0.25CuxSc0.25−xO4 (x = 0.05, 0.1, 0.15) films on Al2O3 substrate by chemical solution deposition method. The crystallinity, surface morphologies, electrical transport property, optical property, and 1/f noise property of these films are characterized. It shows that the Mn1.5Co1Ni0.25Cu0.15Sc0.1O4 film possesses excellent electrical transport, optical and 1/f noise properties compared with the other films. The absolute temperature coefficient of resistance and characteristic temperature of the Mn1.5Co1Ni0.25Cu0.15Sc0.1O4 film are higher than that of Mn1.5Co1Ni0.5O4 film, while its resistivity of 250 Ω·cm at 295 K is half of that Mn1.5Co1Ni0.5O4 film. The transmittance and reflectance spectra of the films are measured and analyzed in the range of 0.55–2.0 µm, all the films show similar absorption structures in the measured spectrum. The 1/f noise of the co-doped Mn1.5Co1Ni0.5O4 films decreases significantly. The (γ/n)0.5/|TCR| value of the Mn1.5Co1Ni0.25Cu0.15Sc0.1O4 reaches 2.3 × 10−12 cm1.5 K/% at 295 K, which is two orders lower than that of Mn1.5Co1Ni0.5O4 film. Our results discover a thermosensitive material with excellent performance, which is expected to be applied to high performance uncooled infrared detectors.

Modelling dry-weather temperature profiles in urban stormwater management ponds

One of the unintended consequences of urban stormwater quality management wet ponds is the thermal enrichment of the shallow pond water during the summer months. The outflow from thermally enriched ponds can degrade the cold and cool water aquatic habitat in urban streams. An accurate model of the pond temperature profile is needed to assess the pond effluent thermal load. However, most models developed for this purpose are process-based and not simple to use, requiring a lengthy monitoring dataset to calibrate. This paper introduces a new design/assessment tool to predict the hourly water temperatures in these ponds at different depths during the summer season dry-weather warming periods and the diurnal cycles. We compiled monitoring data from five ponds in the Greater Toronto Area from 2013 − 2016 to evaluate the ponds' thermal impact on the receiving cold water streams. Using these datasets, we developed a new equation that allows for separating the active upper portion and stable lower portion of the pond. This ensures the focus is on the active upper part, where the majority of the heat transfer within the pond occurs and exhibits diurnal temperature fluctuations. We also used machine-learning tools to develop accurate equations for the bottom temperature and surface temperature, as well as, key equation parameters that characterize the thermal profiles. The developed thermal profile equation allows for the simple determination of other key thermal metrics such as average temperature, energy stored, thermocline location, which may be used as inputs to model wet weather flow conditions and pond outflow temperature. The results show that the proposed machine-learning model is acceptably accurate and capable, according to the low mean absolute percentage error (MAPE) and coefficient of determination (R2) values of 5% and 0.965, respectively, for temperature prediction for all depths. This work provides a building block for the overall objective to develop a new, easy to use, wet weather explicit equation for predicting the outlet temperature from stormwater management wet ponds since it establishes the initial thermal condition before the storm hits.

Air temperature prediction using different machine learning models

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R<sup>2</sup>).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.

Region- and degeneration dependent stiffness distribution in intervertebral discs derived by shear wave elastography

Information on the local stiffness characteristics of the intervertebral disc (IVD) is crucial for the understanding of its structure-function properties in health and disease and may improve numerical modeling. Previous studies have attempted to map local tissue stiffness by sectioning the disc and performing mechanical testing on these discrete tissue units, which is technically challenging and may bias the results. Shear wave elastography (SWE) represents a nondestructive alternative that can provide spatially continuous elasticity estimates. We investigated the feasibility of SWE for human intervertebral disc elasticity mapping in a laboratory setting.To this end, global spinal segment mechanical behavior was determined in 6 loading directions and served as ground truth data for the validation of the approach. Subsequently, the cranial spinal vertebra was removed and shear wave elastographic scans of the IVD were acquired. SWE-measurements were reconstructed into three-dimensional elastographic maps, discretized into distinct IVD regions and correlated with global segment mechanical parameters. SWE-derived Young’s modulus estimates were compared among different regions and as a function of their state of degeneration.We found annulus shear wave speed to be moderately correlated with segment mechanical behavior irrespective of the loading direction whereas shear wave speed in the nucleus pulposus showed a very weak association (mean (SD) absolute Pearson correlation coefficients: 0.51 (0.14) and 0.17 (0.12), respectively). Young’s modulus mapping of the intervertebral disc revealed stiffness to be highest in the ventral annulus with a stiffness decrease both circumferentially towards the dorsal aspect as well as towards the center of the disc. SWE hence provides a valid alternative to disc sectioning and piecewise mechanical testing.

Tissue Sodium Concentration within White Matter Correlates with the Extent of Small Vessel Disease

Introduction: Sodium MRI (²³Na MRI) derived biomarkers such as tissue sodium concentration (TSC) provide valuable information on cell function and brain tissue viability and has become a reliable tool for the assessment of brain tumors and ischemic stroke beyond pathoanatomical morphology. Patients with major stroke often suffer from different degrees of underlying white matter lesions (WMLs) attributed to chronic small vessel disease. This study aimed to evaluate the WM TSC in patients with an acute ischemic stroke and to correlate the TSC with the extent of small vessel disease. Furthermore, the reliability of relative TSC (rTSC) compared to absolute TSC in these patients was analyzed. Methodology: We prospectively examined 62 patients with acute ischemic stroke (73 ± 13 years) between November 2016 and August 2019 from which 18 patients were excluded and thus 44 patients were evaluated. A 3D ²³Na MRI was acquired in addition to a T2-TIRM and a diffusion-weighted image. Coregistration and segmentation were performed with SPM 12 based on the T2-TIRM image. The extension of WM T2 hyperintense lesions in each patient was classified using the Fazekas scale of WMLs. The absolute TSC in the WM region was correlated to the Fazekas grades. The stroke region was manually segmented on the coregistered absolute diffusion coefficient image and absolute, and rTSC was calculated in the stroke region and compared to nonischemic WM region. Statistical significance was evaluated using the Student t-test. Results: For patients with Fazekas grade I (n = 25, age: 68.5 ± 15.1 years), mean TSC in WM was 55.57 ± 7.43 mM, and it was not statistically significant different from patients with Fazekas grade II (n = 7, age: 77.9 ± 6.4 years) with a mean TSC in WM of 53.9 ± 6.4 mM, p = 0.58. For patients with Fazekas grade III (n = 9, age: 81.4 ± 7.9 years), mean TSC in WM was 68.7 ± 10.5 mM, which is statistically significantly higher than the TSC in patients with Fazekas grade I and II (p < 0.001 and p = 0.05, respectively). There was a positive correlation between the TSC in WM and the Fazekas grade with r = 0.48 p < 0.001. The rTSC in the stroke region was statistically significant difference between low (0 and I) and high (2 and 3) Fazekas grades (p = 0.0353) whereas there was no statistically significant difference in absolute TSC in the stroke region between low (0 and I) and high (2 and 3) Fazekas grades. Conclusion: The significant difference in absolute TSC in WM in patients with severe small vessel disease; Fazekas grade 3 can lead to inaccuracies using rTSC quantification for evaluation of acute ischemic stroke using ²³ Na MRI. The study, therefore, emphasizes the importance of absolute tissue sodium quantification.

Detection of multiple sclerosis from photic stimulation EEG signals

BackgroundMultiple Sclerosis (MS) is characterized as a chronic, autoimmune and inflammatory disease of the central nervous system. Early diagnosis of MS is of great importance for the treatment and course of the disease. In addition to the many methods, cost-effective and non-invasive electroencephalogram signals may contribute to the pre-diagnosis of MS. ObjectivesThe aim of this paper is to classify male subjects who have MS and who are healthy control using photic stimulation electroencephalogram signals. MethodsFirstly the continuous wavelet transformation (CWT) method was applied to electroencephalogram signals under photic stimulation with 5Hz, 10Hz, 15Hz, 20Hz, and 25Hz frequencies. The sum, maximum, minimum and standard deviation values of absolute CWT coefficients, corresponding to “1–4 Hz” and “4–13 Hz” frequency ranges, were extracted in each stimulation frequency region. The ratios of these values obtained from the frequency ranges “1–4Hz” and “4–13Hz” was decided as features. Finally, various machine learning classifiers were evaluated to test the effectivity of determined features. ResultsConsequently, the overall accuracy, sensitivity, specificity and positive predictive value of the proposed algorithm were 80 %, 72.7 %, 88.9 %, and 88.9 %, respectively by using the Ensemble Subspace k-NN classifier algorithm. ConclusionsThe results showed how photic stimulation electroencephalogram signals can contribute to the pre-diagnosis of MS.

An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study.

Background:During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most.Methods:We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country.Results:The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61–0.86), 0.58 (0.31–0.84), and 0.54 (0.44–0.64), respectively. An online time–event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents.Conclusion:An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.

Utility of absolute apparent diffusion coefficient and chemical-shift imaging versus CT attenuation for predicting malignancy from percutaneous bone biopsies.

IntroductionBone lesions are sometimes detected on computed tomography studies, and biopsies are performed to evaluate whether these are malignant. The aim of the study is to evaluate whether chemical‐shift imaging (CSI) and diffusion‐weighted imaging (DWI) magnetic resonance imaging (MRI) are more informative than the CT attenuation for predicting malignancy.MethodsRetrospective analysis of 86 patients who underwent both diagnostic CT, CSI MRI and DWI MRI within 6 weeks prior to bone biopsy at a tertiary care academic institution between 01/01/2010 and 03/01/2020. The CT attenuation, signal intensity on in‐phase sequences (SIIP), signal intensity on out‐of‐phase sequences (SIOP), signal intensity ratio (SIR = SIOP/SIIP) and the apparent diffusion coefficient (ADC) of the lesions over the region of the biopsy tract were measured.ResultsA threshold CT attenuation of 157 Hounsfield Units (HU) had a sensitivity of 47.7%, specificity of 83.3% and area under the curve (AUC) of 0.59. A threshold ADC of 793 × 10−6 mm2/s had a sensitivity of 75.8%, specificity of 85.7% and AUC of 0.83 to predict whether a bone biopsy would detect malignancy. A threshold SIR of 0.949 had a sensitivity of 77.8%, specificity of 77.8% and AUC of 0.81 to predict whether a bone biopsy would detect malignancy. ADC (P = 0.029) and SIR (P = 0.009) were significantly better than CT attenuation. There was no predictive difference between SIR and ADC (P = 0.742).ConclusionsThe CT attenuation of a lesion is a poor predictor of malignancy in bone lesions. CSI and DWI are significantly better for predicting malignancy.

Modelling of the adsorption of urea herbicides by tropical soils with an Adaptive‐Neural‐based Fuzzy Inference System

AbstractSorption of pesticides by soils holds a major consequence for their fate in the environment. As such, sorption coefficient (Kd/Koc), which is derived from laboratory or field experiments is a fundamental parameter used in almost all screening tools to evaluate the fate or mobility of these compounds. The value of this coefficient is controlled by many soil and solute specific properties, as well as environmental variables. Soft computing techniques such as Adaptive Neuro‐Fuzzy Inference System (ANFIS) have been successfully used to predict the equilibrium partitioning of many compounds in various engineered systems. Application of these techniques to natural systems such as soils is however lacking. Here, we present the use of ANFIS in predicting the sorption per unit mass of soil, Qe, used in the calculation of Kd/Koc of compounds in soils. In a previous study, we collected data associated to the adsorption of five phenylurea herbicides in 18 tropical soils. Here, we analysed such data and based on established correlations, nine variables were selected as potential input vectors (i.e., six soil properties, two herbicides molecular descriptors and initial solute concentrations). A total of 255 ANFIS models of one to eight input vectors were elaborated under 10‐fold cross‐validation. Multiple linear regression (MLR) models were similarly developed, and compared with the ANFIS in terms of mean absolute error (MAE), root‐mean‐square error (RMSE) and coefficient of determination (R2). The best ANFIS model (M94) has an MAEtest, RMSEtest and R2test of 3.43 ± 0.43, 4.94 ± 0.80 and 0.95 ± 0.01, respectively, whereas the best MLR model (M13) returned an MAE, RMSE and R2 of 7.71 ± 0.13, 10.11 ± 1.21 and 0.81 ± 0.01, respectively. We observed that generally ANFIS performed better than MLR regarding both accuracy and interpretability. Accordingly, we recommend the use of ANFIS for predicting the sorption coefficients of phenylurea herbicides (PUHs) in soils.

Comparison of RSM and ANN Optimization Techniques and Modeling of Ultrasonic Energy Assisted Trans-esterification of Salviniaceae Filiculoides Oil Blended to Biodiesel

For the transesterification of biodiesel from Azolla oil, the safe and successful use of feed stocks is a very significant prerequisite. It is of high importance to determine the optimal reaction parameters to maximize the yield of low-cost biodiesel generated from Azolla oil. Ultrasonic energy was used in this work for the development of biodiesel from Azolla oil catalyzed by the KOH catalyst under different conditions. The effect on the transesterification of Azolla Oil to biodiesel of four reaction parameters, namely the methanol/Azolla oil molar ratio (A), KOH catalyst concentration (B), reaction time (C) and reaction temperature (D) were considered. In order to optimize the effects of reaction parameters for the transesterification of Azolla oil to biodiesel, response surface methodology (RSM) based on central composite rotatable design (CCRD) is applied. To obtain a good correlation between the input reaction parameters and the output response parameter (FAME yield) from Azolla oil to biodiesel, an artificial neural network (ANN) model with two feed-forward back-propagation neural-network architecture Multilayer Perceptron Network (MLP) and Radial Basis Function Network (RBFN) was developed. With the experimental information obtained from the RSM model, the built ANN models were trained and evaluated. Absolute Average Deviation (AAD), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination were statistically compared with the predictive capacity of both RSM and ANN models (R2). The statistical analysis showed that the measured FAME yield from both the RSM and ANN models was able to predict the FAME yield, and the findings limited the ANN model to the much more reliable FAME yield prediction compared to the RSM model.

High-performance electrical properties of La-based perovskite ceramics for the functional phase of thick film resistors

In order to explore an economical functional phase alternative material for thick film resistors, the crystal structure, microstructure, and electrical properties of (1-x)La0.5Sr0.5Co0.96Ni0.04O3-δ + xLaCo0.4Ni0.58Zn0.02O3 (x = 0.0–1.0) composite ceramics were studied through solid-state reaction experiments. The composite ceramics were characterized by x–ray diffraction, scanning electron microscopy, energy dispersive x–ray spectroscopy, X-ray photoelectron spectroscopy, and DC four–probe method. Results suggested that the main phases of La0.5Sr0.5Co0.96Ni0.04O3-δ and LaCo0.4Ni0.58Zn0.02O3 were formed, along with a small part of impurity phases. The addition of LaCo0.4Ni0.58Zn0.02O3 to La0.5Sr0.5Co0.96Ni0.04O3-δ decreased the electrical conductivity and changed the temperature coefficient of resistance from positive to negative. Zero temperature coefficient of resistance could be achieved around 0.6 < x < 0.8 and relatively low absolute temperature coefficient of resistance values could be obtained for the samples of 0.4 ≤ x ≤ 0.8. The ceramic of 0.6La0.5Sr0.5Co0.96Ni0.04O3-δ + 0.4LaCo0.4Ni0.58Zn0.02O3 showed the optimal performances of conductivity = 1923 S/cm, temperature coefficient of resistance = 379.54 ppm/°C, and relative density = 95.05%.

Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study.

Coronavirus disease 2019 (COVID-19) occurred in Wuhan and rapidly spread around the world. Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and the corresponding cumulative number of infected cases (CNICs) are the two viewpoints that should be jointly considered to differentiate the impact of struggling to fight against COVID-19 (SACOVID). The CNIC data were downloaded from the GitHub website on 23 November 2020. The item response theory model (IRT) was proposed to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was determined by multiplying the IP days with the corresponding CNICs. The IRT model was parameterized, and the IP days were determined using the absolute advantage coefficient (AAC). The difference in SACOVID was compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were Hong Kong, Shanghai, and Hubei, with IPcase indices of 1744, 723, and 698, respectively, and the top three areas with the most aberrant patterns were Yunnan, Sichuan, and Tianjin, with IP days of 5, 51, and 119, respectively. The difference in IP days was determined (χ2 = 5065666, df = 32, p < 0.001) among areas in China. The IRT model with the AAC is recommended to determine the IP days during the COVID-19 pandemic.

The effectiveness of feeding artemia enriched with vitamin c on the growth performance and survival of Lemeduk fish larvae (Barbonymus schwanenfeldii)

The culture of lemeduk fish (Barbonymus schwanenfeldii) still have several obstacles including lack of nutrients in fish diet which causes slow growth rate and low survival. The objective of this study was to analyze the effectiveness of feeding Artemia enriched with vitamin C at different doses on the growth performance and survival of lemeduk fish (Barbonymus schwanenfeldii). This research used completely randomized design method consisting 5 treatments and 3 replications. The treatments tested were Artemia enriched with vitamin C at different doses, namely A (Artemia without vitamin C enrichment), B (Artemia enriched with vitamin C 100 mg/l), C (Artemia enriched with vitamin C 200 mg/l), D (Artemia enriched with vitamin C 300 mg/l) and E (Artemia enriched with vitamin C 400 mg/l). The results showed that the addition of vitamin C in the Artemia had a significant effect (P<0.05) on absolute length growth, daily growth rate and survival of lemeduk fish larvae, but there were no significant effect (P>0.05) on the absolute weight and coefficient of variation. The research showed that the highest value of absolute length growth and daily growth rate were found in treatment D (Artemia enriched with vitamin C 300 mg/l) with an absolute length growth of 6.6±0.48 mm and daily growth rate of 0.330±0.026 mm/day, and the highest survival value was found in treatment C (Artemia enriched with vitamin C 200 mg/l) of 91.7±7.6%.

Development of Inhomogeneity Measurement System for Type T Thermocouple with Local Heating Method

The voltage of thermocouple during process contribution is determined by the absolute thermal electromotive force coefficient in each wire and the temperature difference across the section. This paper described the development of a system as a local heating method for measurements of inhomogeneities in type T thermocouples with various temperature scanning using hot air. The system could control automatically measurement voltage, temperature, distance, and speed of the stepper motor for moving the hot air scanning along with the thermocouple and control with temperature monitoring. Both the measuring and reference junctions were using an ice bath kept at 0 °C. In this paper calculation of interpretation of the measurement results of inhomogeneity using movable heating zone system of type T thermocouples and the system could detected inhomogeneity of thermocouple and there have different characteristics of voltage and seebeck effect.

STUDY ON A SPECIFIC TEST METHOD FOR DOSIMETRIC CHARACTERIZATION OF ULTRA-WIDE DETECTOR COMPUTED TOMOGRAPHY

The purpose of this study was to establish a specific test method for dosimetric characterization of wide-beam computed tomography (CT). For a wide beam, the dose distribution curve and the area of the curve were obtained by using pencil-like ionization chamber, a long CT dose profiler probe, a head phantom and a body phantom. The absolute dose conversion coefficient was multiplied to obtain the total integration integral of the absolute dose distribution, and then the computed tomography dose index (CTDI) value under any wide beam condition was obtained by dividing the collimation width. It was calculated that the absolute dose conversion coefficient was 1.135 under the narrow beam of 8mm. To a 160mm-wide beam, the value of CTDI was 7.57mGy/100mAs after normalized in the head 80kV CT scanning, and it was 9.80mGy/100mAs after normalized in the body 120kV CT scanning. The specific test method solves the problem that the previous measurement method underestimates the CTDI value.

The influence of language knowledge and test components on reading comprehension scores.

This study investigated the dependability of reading comprehension scores across different text genres and response formats for readers with varied language knowledge. Participants included 78 fourth-graders in an urban elementary school. A randomized and counterbalanced 3 × 2 study design investigated three response formats (open-ended, multiple-choice, retell) and two text genres (narrative, expository) from the Qualitative Reading Inventory (QRI-5) reading comprehension test. Standardized language knowledge measures from the Woodcock Johnson III Tests of Achievement (Academic Knowledge, Oral Comprehension, Picture Vocabulary) defined three reader profiles: (a) < 90 as emerging, (b) 90-100 as basic, and (c) >100 as proficient. Generalizability studies partitioned variance in scores for reader, text genre, and response format for all three groups. Response format accounted for 42.8 to 62.4% of variance in reading comprehension scores across groups, whereas text genre accounted for very little variance (1.2-4.1%). Single scores were well below a 0.80 dependability threshold (absolute phi coefficients = 0.06-0.14). Decision studies projecting dependability achieved with additional scores varied by response format for each language knowledge group, with very low projected dependability on open-ended and multiple-choice scores for readers with basic language knowledge. Multiple-choice scores had similarly low projected dependability levels for readers with emerging language knowledge. Findings evidence interactions between reader language knowledge and response format in reading comprehension assessment practices. Implications underscore the limitations of using a single score to classify readers with and without proficiency in foundational skills.

Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO56 Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month−1 and 22–28.3 mm month−1, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month−1 had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month−1. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.

Validation of the influence of CT slice thickness on the quantitative accuracy and image quality of single photon emission computed tomography.

Computed tomography (CT) images are used for precise anatomical location of lesions and for accurate attenuation correction in single-photon emission computed tomography (SPECT) image reconstruction in SPECT/CT examination. The aim of this study was to verify the effects of varying CT collimation width and slice thickness on CT images and on CT attenuation corrected SPECT images. We acquired SPECT/CT images of a micro-coin phantom and the National Electrical Manufacturers Association body phantom filled with 99mTc-pertechnetate while varying the abovementioned CT parameters. The full width at half maximum of the slice sensitivity profile, the standard deviation of CT image background noise, and the radiation dose from CT scans were evaluated. Subsequently, the percentage contrast, background variability, and absolute recovery coefficient of the SPECT image were measured. Furthermore, we retrospectively reviewed the clinical bone SPECT images of 23 patients, and statistical testing of differences was performed. As the collimation width and reconstruction slice thickness of the CT image increased, z-axis resolution deteriorated, and background noise decreased. In addition, CT radiation dose decreased with increasing collimation width. Meanwhile, SPECT image quality and quantitative accuracy were unchanged with varying CT collimation width and slice thickness. There were no notable variations in clinical SPECT images and no statistically significant differences. When high-resolution CT slices on the z-axis are not required for clinical diagnosis, increasing collimation width or slice thickness can reduce the radiation dose and image noise with no influence on the quality of SPECT images .

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

This paper presents a prediction error-based power forecasting (PEBF) method for a Photovoltaic (PV) system, using Photovoltaics for Utility Scale Applications (PVUSA) model based grey box neural network (GBNN). First, the differential equation based PVUSA model is transformed into a neural network. In the proposed PEBF scheme, the neural network is set to train whenever the difference between predicted and output powers increases from a certain threshold defined based on system dynamics and requirements. The unique design of the PVUSA model based grey box neural network takes far less training time than usual black-box neural network based models. This gives the proposed prediction scheme an advantage of updating the prediction model parameters from frequent training of neural networks with the change in metrological variables. The effectiveness of the proposed prediction scheme is demonstrated by a real case study regarding a 20MW grid-connected PV system located in Dongying city of Shandong province China. To evaluate the efficiency of the developed scheme, different assessment metrics, mean absolute error (MAE), root mean square error (RMSE), weighted mean absolute error (WMAE) and coefficient of determination ( R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) are applied. The average values of MAE, RMSE, and WMAE were 0.12 %, 0.20% and 0.23% respectively for all cases. The results demonstrate that the proposed scheme predicts the PV power efficiently within the defined error tolerance level, which shows the effectiveness and feasibility of the proposed prediction scheme. The prediction accuracy of the proposed scheme has been compared with the conventional black box neural network models and reveals outperformed performance with respect to prediction accuracy improvement. The proposed prediction scheme will help to balance power production and demands across integrated networks through economic dispatch decisions between the power sources.