Will it be duststorm or thunderstorm today ?

ABSTRACT. Attempt to develop a distinct technique for the prediction of duststorm or duststorm followed by thundershower during pre-monsoon season over Gwalior, has been made, Two mean 0000 UTC tephigrams have been produced for the days when the duststorms and thunderstorms occurred. Difference is highlighted in the 0000 UTC surface and TEMP data on the days of duststorm/duststorm followed by thundershower with those on the days of thunderstorm. Statistical analysis of the duststorm data over Gwalior has also been carried out with respect to direction, time, fortnight and month of occurrence of the event. For prediction of peak gust speed (PGS) of squall due to duststorm a single regression equation has been developed.

An experimental feasibility study evaluating the adequacy of sportswear-type wearables for recording physical exercise intensity

Sportswear-type wearables with integrated inertial sensors and electrocardiogram (ECG) electrodes, have been developed. We examined the feasibility of using sportswear-type wearables to evaluate exercise intensity within a controlled laboratory setting. Six male college athletes were asked to don a sportswear-type wearable while performing a treadmill test that reached up to 20 km/h. The magnitude of the filtered tri-axial acceleration signal, recorded by the inertial sensor, was used to calculate the acceleration index. The R-R intervals of ECG were used to determine heart rate; the external validity of the heart rate was then evaluated according to oxygen uptake, which is the gold standard physiological exercise intensity. Single regression analyses between treadmill speed and the acceleration index in each participant showed that the slope of the regression line was significantly greater than zero with a high coefficient of determination (walking, 0.95; jogging, 0.96; running, 0.90). Another single regression analyses between heart rate and oxygen uptake showed that the slope of the regression line was significantly greater than zero, with a high coefficient of determination (0.96). Together, these results indicate that sportswear-type wearables are a feasible technology for evaluating physical and physiological exercise intensity across a wide range of physical activities and sport performances.

Ismail Marzuki Siregar

This research is performed using census research method. In this research , the population is all employees of 32 people in the Regional Inspectorate of Padangsidimpuan. The data analysis technique used is descriptive and quantitative analysis. The results show that employees in the Office of the Regional Inspectorate of Padangsidimpuan have direct monitoring by a category quite good. It can also be seen from the quality, quantity, use of worktime and employee cooperationFrom the research that has been conducted at the Regional Inspectorate of Padangsidimpuan, there is a positive relationship between direct monitoring with the employee 's performance. The results of single regression analysis also shows a positive influence of the variable direct monitoring to the employee performance . If an increase in direct monitoring, the employee 's performance will increase as well. It can be concluded that the direct monitoring contributes to employee performance variables in the Regional Inspectorate Padangsidimpuan of 99.36 %.

Prediction of Best Corrected Visual Acuity with Hard Contact Lenses for Keratoconus Patients

Background: To develop a prediction formula for best corrected visual acuity with hard contact lenses (HCL-BCVA) and to identify clinical factors linearly related to HCL-BCVA in keratoconus patients. Methods: This retrospective study examined clinical data derived from 198 eyes of 131 keratoconus patients. The subjects were divided into a development group (102 eyes of 68 subjects) and a validation group (96 eyes of 63 subjects) on the basis of the date of their examination. HCL-BCVA measurement and anterior segment–optical coherence tomography (AS-OCT) were performed. A prediction formula for HCL-BCVA based on AS-OCT measurements was then developed. Single regression analysis was performed to identify clinical factors linearly related to HCL-BCVA. Results: Stepwise multiple regression analysis yielded a prediction formula for HCL-BCVA in keratoconus patients, with the correlation coefficient of the multiple regression equation being 0.728 (R2 = 0.530) for the development group. Application of the prediction formula to the validation group yielded a correlation coefficient for the multiple regression equation of 0.641 (R2 = 0.411). Single regression analysis identified anterior corneal refractive power, posterior corneal refractive power, and high-order aberrations as factors that are linearly correlated with HCL-BCVA, with R values of 0.606, -0.617, and 0.506, respectively. Conclusion: HCL-BCVA in keratoconus patients was predictable on the basis of AS-OCT measurements. Cutoff values for clinical factors found to correlate with HCL-BCVA may prove informative with regard to treatment options to maintain a favorable visual acuity in keratoconus patients.

Membangun Nilai Behavioral Intention melalui Tourist Experience

Floating Market Lembang is a destination in Lembang, the boundary of West Bandung. Visitors visit in Floating Market Lembang always increasing in years. But that fact is not the same with the data which say tourist’s loyality haven’t maximum because majority of the tourist whose come to Floating Market lembang are new visitors. Decision of this research is to know influence of tourist experience to behavioral intention of Floating Market Lembang. The type of research are descriptive and verification. The survey method used is incidental sampling, which a sample size of 120 respondents. Data analysis technique using single regression techniques with coefficient determinant (R2) and partial (T) techniques. These results shows that there is a significant partial influence tourist experience to behavioral intention of Floating Market Lembang. The higher the ability of destination management in building positive experience on the tourists, so that it will increase positive image of the tourists in their visits. In this condition, directly or indirectly it will be able to build tourist’s willing in re-visiting to the destination. The correlation of the two variables can be simplisized in a sentence that in order to be able to build value of behavioral intention is how to be able to build tourist experience.

From microstructural images to properties - an interpretable deep learning approach to predict elastic-plastic properties of fiber composites

The application of machine learning in the field of materials engineering can facilitate materials design and enable faster discovery of novel materials. This paper presents a deep learning approach for the prediction of the transverse elastic and plastic properties of unidirectional fibre reinforced composites directly from images of their microstructures. The training dataset consists of finite element predictions of the elastic-plastic properties of a set of 2D representative volume elements of unidirectional composites with different volume fraction and fiber diameter. Single-regression, Fully-connected Neural Networks (FcNNs) and a multi-regression Convolutional Neural Network (CNN) are designed and trained to predict 5 mechanical properties, using as input the images of the material’s microstructure. The performance of the FcNNs and of the CNN are compared; the CNN is shown to perform the most effective regression, predicting the properties of the composites with an accuracy of 99%. A key value addition in this research lies in the explainability of the otherwise ‘blackbox’ CNN model, guiding the reader through the model’s predictive process. We test the CNN on real, relatively low-resolution micrographs of composite microstructures and we find that this provides accurate predictions.

Localization of Immersed Sources by Modified Convolutional Neural Network: Application to a Deep-Sea Experiment

A modified convolutional neural network (CNN) is proposed to enhance the reliability of source ranging based on acoustic field data received by a vertical array. Compared to the traditional method, the output layer is modified by outputting Gauss regression sequences, expressed using a Gaussian probability distribution form centered on the actual distance. The processed results of deep-sea experimental data confirmed that the ranging performance of the CNN with a Gauss regression output was better than that using single regression and classification outputs. The mean relative error between the predicted distance and the actual value was ~2.77%, and the positioning accuracy with 10% and 5% error was 99.56% and 90.14%, respectively.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1,000 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a six-month time period that overlapped with fall 2020 and winter 2021 COVID-19 outbreaks in each municipality. We fit a single regression model to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. Estimation of case rates from wastewater data can be useful in locations with limited testing availability or testing disparities, or delays in individual COVID-19 testing programs.

KORELASI SPASIAL FREKUENSI KEBAKARAN HUTAN DAN LAHAN DENGAN AKTIVITAS MANUSIA: STUDI KASUS DI SUB SUB DAS RIAM KANAN PROVINSI KALIMANTAN SELATAN

Forest and land fires are a common phenomenon in several regions of Indonesia. It is assumed that most of the forest and land fires originate from human activities. This study aims to statistically test the spatial correlation between the number of hotspots or the frequency of forest and land fires, to the distance from various types of landuse in the Riam Kanan sub-watershed. The data used in this study are landuse and hotspot data. The spatial correlation analysis in this study was conducted using Euclidean Distance and single regression. Euclidean Distance is used to measure the flat distance between the fire location and the location of human activities. Meanwhile, single regression is used to measure the correlation between the number of fire occurrence points and the flat distance from the location of human activities. The single regression models used are linear, power, exponential, logarithmic, and polynomial. The results showed that the frequency of forest and land fires had a very strong spatial correlation with human activities, especially in the sub-watershed area of Riam Kanan. So it can be stated that the frequency of forest and land fires does have a strong correlation with human activities. The lowest spatial correlation is the distance from the rice fields, and the highest spatial correlation is the distance from the river. However, the number of hotspots increases drastically the more distance it is from the road, and almost approaches zero the farther the road is. Thus, although the spatial correlation with roads is not as high as other land uses, this drastic increase in the number of hotspots indicates that road accessibility has a strong contribution to forest and land fires.

Influence of xanthine oxidoreductase inhibitor, topiroxostat, on body weight of diabetic obese mice

Plasma xanthine oxidoreductase (XOR) activity is high in metabolic disorders such as diabetic mellitus, obesity, or overweight. Thus, this study investigated whether the XOR inhibitor, topiroxostat, affected body weight. Male db/db mice were fed standard diets with or without topiroxostat for 4 weeks. Body weight and food intake were constantly monitored, along with monitoring plasma biochemical markers, including insulin and XOR activity. Additionally, hepatic hypoxanthine and XOR activity were also documented. Single regression analysis was performed to determine the mechanism. Topiroxostat treatment suppressed weight gain relative to the vehicle without any impact on food intake. However, the weight of fat pads and hepatic and muscle triglyceride content did not change. Topiroxostat decreased the plasma uric acid and increased hepatic hypoxanthine in response to the inhibition of XOR activity. Plasma ketone body and free fatty acid were also increased. Moreover, fat weight was weakly associated with plasma XOR activity in the diabetic state and was negatively associated with ketone body by topiroxostat. These results suggested that topiroxostat amplified the burning of lipids and the salvage pathway, resulting in predisposing the body toward catabolism. The inhibition of plasma XOR activity may contribute to weight loss.