Technology Usage and Academic Performance in the Pisa 2018 Report

In the last two decades, the great technological advances sweeping society have made inroads into the educational sphere. The use of information and communication technology and social networks has opened up new possibilities for student learning, which require appropriate treatment by family and teachers. This quantitative study takes a new approach to investigating the relationship between Spanish teenage students’ academic success and their use of technology and social networks. It analyses data published in the 2018 PISA report to assess whether the use of these resources is appropriate, and to determine their impact on students’ learning and performance in reading, mathematics and science. The study takes a new approach in terms of the variables selected and the analysis of the data through two statistical measures. The results suggest that excessive use of technology and social networks, both during the week and at weekends, impairs performance. This finding is more acute in the case of male students, as the data indicates that they start at an earlier age and are more likely to use social media for the detrimental activity of online gaming.

Sector Rotation with Leading Macroeconomic Indicators

In this study, an attempt has been made to examine whether the theory of sector rotation has been empirically valid in the Indian equity market, during the period April, 2000 to March, 2020. The time period has been divided into many sub-periods according to the real GDP growth rate and the annualized returns of eleven stock market indices have been analyzed in different periods. Going forward, leading macroeconomic indicators, which coincide with overall economy, have been taken and their association with stock market indices have been analyzed through statistical measures to assess any possible forecasting. In the first part of the study, cyclical and non-cyclical sectors have been found to beat the benchmark index during periods of growth and stagnancy, respectively, but no particular ordinality was observed. Amongst the leading economic variables, M3 Money Supply was found to have high degree of association with some indices, namely Sensex, Healthcare, CDGS, Consumer Durables and IT, but no linear relation was observed.

Levy Flight and Chaos Theory Based Gravitational Search Algorithm for Global Optimization

The Gravitational Search Algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on well-known 23 classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has also been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.

Academic Stress among Rural Adolescents due to COVID -19 : A Comparative Analysis

Adolescence is a period during which individuals’ transit from puberty to adulthood. Children go through many changes throughout this time, including biological, cognitive, and emotional changes. Excessive stress caused by studies, high expectations, and lack of capacity to maintain studies is referred to as academic stress. The study’s major goal was to assess the academic stress among rural adolescents owing to COVID- 19. The study was conducted in Udaipur district of Rajasthan. For the sample selection from four villages having Sr. Sec, schools were randomly selected. From selected schools, 180 students of age group of 16-18 years were selected randomly. The sample consisted equal no. of adolescent boys and adolescent girls. Slightly modified Academic stress scale developed by Rao (2012) was used to assess academic stress in adolescent boys and girls. Collected data was further classified, in tabulated form and analyzed through using suitable statistical measures. Results indicated that adolescents’ boys and girls faced moderate to high academic stress. Furthermore, girls were facing high academic stress as compared to boys.

Intraday patterns of price clustering in Bitcoin

In this study, an investigation is conducted into the phenomenon of price clustering in Bitcoin (BTC) denominated in the Japanese yen (JPY). It answers two questions using tick-by-tick data. The first is whether price clustering exists in BTC/JPY transactions, and the other is how the scale of price clustering varies throughout a trading day. With the assistance of statistical measures, the last two digits of BTC price were discovered to cluster at the numbers that end with ’00’. In addition, the scales of BTC/JPY clustering at ’00’ tended to decline at the specific hour intervals. This study contributes to the emerging literature on price clustering and investor behavior.

Application of Data-Driven Techniques For Landslide Susceptibility Prediction Along An Earthquake Affected Road Section in Kashmir Himalaya

The interaction of seismic events with geo-environmental conditions and anthropogenic activities may exacerbate the risk of landslide hazard in a mountainous region. As an example of this, 2005 Kashmir earthquake triggered a large number of shallow to deep slope failures, which was further intensified in following years by human activities notably along road networks, posing a long-term hazard. Hence, this study was planned to evaluate the effectiveness of landslide susceptibility prediction along earthquake affected road-section of Neelum Highway using six different data-driven models. We applied analytical hierarchy process as heuristic approach, weight of evidence and index of entropy as statistical models and multi-layer perceptron, support vector machine and binary logistic regression (BLR) as machine learning models. Initially, 224 landslides locations were marked through field surveys to prepare landslide inventory which was further randomly divided into training (70%) and testing (30%) datasets. Then, 13 landslide causative factors (LCFs) were extracted from geo-spatial database and analysed by measuring collinearity among factors and assessing their contribution in landslide occurrence using different feature selection methods for inclusion in susceptibility modelling. Thereafter, six employed models were trained to produced landslide susceptibility maps of investigated road-section. Finally, the area under receiver operating characteristics (AU-ROC) curve and various statistical measures were applied to validate and compare the performance of modeled landslide susceptibility. The results revealed that no collinearity issue exists among all 13 LCFs, and all six models exhibited satisfying performance in predicting landslide susceptibility of study area. However, BLR model have produced most promising and optimum results as compared to other models with AU-ROC (0.881), Matthew’s correlation coefficient (0.609), Kappa coefficient (0.604), accuracy (0.797) and F-score (0.787). The outcomes of this study can be used as pertinent guide for preventing and managing the landslide disaster risk along Neelum Highway and beyond.

Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos

In medical imaging, the detection and classification of stomach diseases are challenging due to the resemblance of different symptoms, image contrast, and complex background. Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid method to detect and classify stomach diseases using endoscopy videos. The proposed methodology comprises seven significant steps: data acquisition, preprocessing of data, transfer learning of deep models, feature extraction, feature selection, hybridization, and classification. We selected two different CNN models (VGG19 and Alexnet) to extract features. We applied transfer learning techniques before using them as feature extractors. We used a genetic algorithm (GA) in feature selection, due to its adaptive nature. We fused selected features of both models using a serial-based approach. Finally, the best features were provided to multiple machine learning classifiers for detection and classification. The proposed approach was evaluated on a personally collected dataset of five classes, including gastritis, ulcer, esophagitis, bleeding, and healthy. We observed that the proposed technique performed superbly on Cubic SVM with 99.8% accuracy. For the authenticity of the proposed technique, we considered these statistical measures: classification accuracy, recall, precision, False Negative Rate (FNR), Area Under the Curve (AUC), and time. In addition, we provided a fair state-of-the-art comparison of our proposed technique with existing techniques that proves its worthiness.

Derivation of the Gaussian plume model in three dimensions

Gaussian plume model is a common model to study advection diffusion equation which is solved in three dimensions by using Laplace transformation considering constant eddy diffusivity and wind speed power law. Different schemes such as Irwin, Power Law, Briggs and Standard methods are used to obtain crosswind integrated concentration. Statistical measures are used in this paper to know which is the best scheme which agrees with the observed concentration data obtained from Copenhagen, Denmark. The results of model are compared with observed data.

Crosswind integrated concentration for various dispersion parameter systems

LFkkuh; Lrj izdh.kZu ds fy, xkSlh;u fiPNd ekWMy ¼Gaussian Plume Model½ dk O;kid :i ls iz;ksx fd;k tkrk gSA vuqizLFk iou dh dqy lkanzrk Kkr djus ds fy, xkSlh;u lw= ¼QkWewyk½ dks laxfBr fd;k gSA vuqizLFk iou dh dqy lkanzrk dh x.kuk djus ds fy, izdh.kZu izkpyksa dh fHkUu&fHkUu iz.kkfy;ksa dk mi;ksx fd;k x;k gSA lrg Lrj esa Å¡pkbZ ds vuqlkj iou xfr dh fHkUurk dk o.kZu djus ds fy, ykxfjFehd foaM izksQkby dk mi;ksx fd;k x;k gSA blesa NksM+h tkus okyh izHkkoh Å¡pkbZ dks /;ku esa j[kk x;k gSA fHkUu fHkUu izdh.kZu izkpy iz.kkfy;ksa ds fy, iwokZuqekfur lkanzrkvksa vkSj dksisugsxu ds folj.k iz;ksx ls izkIr fd, x, izsf{kr vk¡dM+ksa dh rqyuk djus ds fy, lkaf[;dh; ifjekiksa dk mi;ksx fd;k x;k gSA The Gaussian plume model is the most widely used model for local scale dispersion. The Gaussian formula has been integrated to obtain the crosswind-integrated concentration. Different systems of dispersion parameters are used to calculate the crosswind integrated concentration. A logarithmic wind profile is used to describe the variation of wind speed with height in the surface layer. The effective release height was taken into consideration. Statistical measures are utilized in the comparison between the predicted concentrations for different dispersion parameter systems and the observed concentrations data obtained from Copenhagen diffusion experiment.

The use and applicability of machine learning algorithms in predicting the surgical outcome for patients with benign prostatic enlargement. Which model to use?

Objectives: Artificial intelligence (AI) is increasingly used in medicine, but data on benign prostatic enlargement (BPE) management are lacking. This study aims to test the performance of several machine learning algorithms, in predicting clinical outcomes during BPE surgical management. Methods: Clinical data were extracted from a prospectively collected database for 153 men with BPE, treated with transurethral resection (monopolar or bipolar) or vaporization of the prostate. Due to small sample size, we applied a method for increasing our dataset, Synthetic Minority Oversampling Technique (SMOTE). The new dataset created with SMOTE has been expanded by 453 synthetic instances, in addition to the original 153. The WEKA Data Mining Software was used for constructing predictive models, while several appropriate statistical measures, like Correlation coefficient (R), Mean Absolute Error (MAE), Root Mean-Squared Error (RMSE), were calculated with several supervised regression algorithms - techniques (Linear Regression, Multilayer Perceptron, SMOreg, k-Nearest Neighbors, Bagging, M5Rules, M5P - Pruned Model Tree, and Random forest). Results: The baseline characteristics of patients were extracted, with age, prostate volume, method of operation, baseline Qmax and baseline IPSS being used as independent variables. Using the Random Forest algorithm resulted in values of R, MAE, RMSE that indicate the ability of these models to better predict % Qmax increase. The Random Forest model also demonstrated the best results in R, MAE, RMSE for predicting % IPSS reduction.Conclusions: Machine Learning techniques can be used for making predictions regarding clinical outcomes of surgical BPRE management. Wider-scale validation studies are necessary to strengthen our results in choosing the best model.