Methyl 2-amino-5-iso­propyl-1,3-thia­zole-4-carboxyl­ate

The title compound, C8H12N2O2S, forms a supramolecular network based on N-HN hydrogen-bonded centrosymmetric dimers that are linked in turn by N-HO contacts

Entropy, longevity and the cost of annuities

This paper presents an extension of the application of the concept of entropy to annuity costs. Keyfitz (1985) introduced the concept of entropy, and analysed this in the context of continuous changes in life expectancy. He showed that a higher level of entropy indicates that the life expectancy has a greater propensity to respond to a change in the force of mortality than a lower level of entropy. In other words, a high level of entropy means that further reductions in mortality rates would have an impact on measures like life expectancy. In this paper, we apply this to the cost of annuities and show how it allows the sensitivity of the cost of a life annuity contract to changes in longevity to be summarized in a single figure index

Predicting Students Performance in Online Education through Deep Learning Model

This epidemic has prompted the development of Education 4.0, virtual learning, and the demand to adapt educational practices to meet the needs of younger demographics. A rising epidemic has necessitated the shutdown of campuses where education programs are now being carried out online in educational institutions all over the globe. The report includes a study on the effectiveness and perceptions of students toward digital learning during the pandemic. A Convolutional Neural Network (CNN) and Particle swarm optimization model, which forecasts the student’s learning rates, are used to tackle this issue. This study will categorize student performance into low, medium, and high grades to forecast student achievement. The Kaggle student’s performance assessment database is utilized to gather the student information logs, which are then pre-processed to eliminate noise and redundant data. The CNN derives features based on the student’s attention and arbitrary patterns sequencing by examining the pre-processed information. Then, utilizing the Minimum Redundancy Maximum Relevance (mRMR) approach, the retrieved characteristics are evaluated. The lowest one that treats each characteristic individually is chosen as the greatest feature by mRMR. CNN uses stochastic Gradient Descent (SGD) to calculate the characteristic weights, which are then modified for improved extracting features. Finally, the CNN-WOA method forecasts the final academic achievement forecast outcome. Studies revealed that the suggested approach outperforms existing ones in terms of accuracy, precision, recall, and F-score while requiring less computing time

Cauchy Problem for Fractional Ricatti‎ Differential Equations ‎Type with Alpha Order Caputo Fractional Derivatives

In this paper, we investigate solution of the fractional Ricatti differential equations (FRDEs) with alpha order Caputo fractional derivatives. In fact, FRDEs are analogous of the Ricatti‎ ordinary differential equations. The multi power series method is used to obtain a useful formula that is implemented to find an explicit solution of Cauchy problem for FRDEs without solving any integral. This formula is explicit and easy to compute by using Maple software to get explicit solution. Also, it is shown that the proposed formula can be used to solve the Cauchy problem for Ricatti‎ ordinary differential equations

The Effect of using Augmented Reality Technology on the Cognitive Holding Power and the Attitude Towards it Among Middle School Students in Al-Qurayyat Governorate, Saudi Arabia

The current study was to use augmented reality technology (ART) in the science course (SC) at the middle school level in Al-Qurayyat Governorate, Saudi Arabia, and to assess how it affected the students attitudes toward AR (ATAR) and cognitive holding power (CHP). The ART is utilized to enhance learning results, particularly when generating challenging, novel, and abstract scientific theories. The CHP measure, and the ATAR measure were developed for this research. 58 school students took part in this study. They have been split into two categories: the experimental group was in group one, and the control group was in group two. In each group, there were 29 students. Whereas the second group learned the SC through the conventional approach, the first group did it using ART. The outcomes demonstrated the first group (Experimental group) superiority. The study suggested that in order to improve students understanding of scientific topics, it is essential to increase knowledge of the value of ART

2,2,2-Trifluoro-N-(isoquinolin-5-ylmeth­yl)acetamide

The mol­ecular structure of the title compound at 123 K, C12H9F3N2O, presents a rotationally disordered CF3 group. Hydrogen bonds between the amide NH group and the N atom of the isoquinoline form a chain in the b-axis direction. The packed structure forms alternate layers of isoquinoline and amide groups parallel to the ab plane

Analysing an Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques

A stroke is a medical condition characterized by the rupture of blood vessels within the brain which can lead to brain damage. Various symptoms may be exhibited when the brain's supply of blood and essential nutrients is disrupted. To forecast the possibility of brain stroke occurring at an early stage using Machine Learning (ML) and Deep Learning (DL) is the main objective of this study. Timely detection of the various warning signs of a stroke can significantly reduce its severity. This paper performed a comprehensive analysis of features to enhance stroke prediction effectiveness. A reliable dataset for stroke prediction is taken from the Kaggle website to gauge the effectiveness of the proposed algorithm. The dataset has a class imbalance problem which means the total number of negative samples is higher than the total number of positive samples. The results are reported based on a balanced dataset created using oversampling techniques. The proposed work used Smote and Adasyn to handle imbalanced problem for better evaluation metrics. Additionally, the hybrid Neural Network and Random Forest (NN-RF) utilizing the balanced dataset by Adasyn oversampling achieves the highest F1-score of 75% compared to the original unbalanced dataset and other benchmarking algorithms. The proposed algorithm with balanced data utilizing hybrid NN-RF achieves an accuracy of 84%. Advanced ML techniques coupled with thorough data analysis enhance stroke prediction. This study underscores the significance of data-driven methodologies, resulting in improved accuracy and comprehension of stroke risk factors. Applying these methodologies to medical fields can enhance patient care and public health outcomes. By integrating our discoveries, we can enhance the efficiency and effectiveness of the public health system

Crystal structure of N,N-dimethyl-2-[(4-methylbenzyl)sulfonyl]ethanamine

In the crystal, the title compound, C12H19NO2S, has a disordered structure with two equally populated conformations of the amine fragment. A pair of weak C—HO intermolecular interactions between the CH2 and SO2 groups gives a one-dimensional supramolecular structure that propagates through translation along the a-axis direction

Artificial Intelligence for Detecting Preterm Uterine Activity in Gynacology and Obstertric Care

Preterm birth brings considerable emotional and economic costs to families and society. However, despite extensive research into understanding the risk factors, the prediction of patient mechanisms and improvements to obstetrical practice, the UK National Health Service still annually spends more than £2.95 billion on this issue. Diagnosis of labour in normal pregnancies is important for minimizing unnecessary hospitalisations, interventions and expenses. Moreover, accurate identification of spontaneous preterm labour would also allow clinicians to start necessary treatments early in women with true labour and avert unnecessary treatment and hospitalisation for women who are simply having preterm contractions, but who are not in true labour. In this research, the Electrohysterography signals have been used to detect preterm births, because Electrohysterography signals provide a strong basis for objective prediction and diagnosis of preterm birth. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Three different machine learning algorithm were used to identify these records. The results illustrate that the Random Forest performed the best of sensitivity 97%, specificity of 85%, Area under the Receiver Operator curve (AUROC) of 94% and mean square error rate of 14%