Extraction, Validation, And Evaluation Of Motivational Tactics Rules In A Web-Based Intelligent Tutoring System (WITS)

Kajian ini memberi tumpuan terhadap cara menlestarikan serta meningkatkan motivasi pelajar semasa proses pembelajaran dalam persekitaran Sistem Pentutoran Cerdas Berasaskan Web (Web-Based Intelligent Tutoring System, WITS) The current study focuses on finding a way to sustain or enhance the learners’ motivation during the learning process within a Web-Based Intelligent Tutoring System (WITS) environmen

Using microwave energy for the removal of hardness from groundwater: Continuous flow lab-scale system

The present study investigates the characteristics of calcium hardness removal from water using a continuous flow microwave (MW) radiation system with heat exchange. The effects of initial calcium concentration, detention time, and initial temperature were investigated by the study. About 97% of calcium hardness removal was achieved at a detention time of 12.5 minutes. It was concluded from the experiments performed that the optimum conditions for this system use a detention time of 12.5 minutes. This leads to an initial temperature of 70°C when using the heat exchanger. These conditions are valid for the range of Ca initial concentrations between 92 and 204 mg/L as CaCO3. The residual concentrations under optimum conditions were 2.4, 2.5, 2.6, and 3 mg/L as CaCO3 for initial concentrations of 92, 141, 172, and 204 mg/L as CaCO3, respectively. The developed system proved to be practical in the continuous flow mode that simulates the actual operations in water treatment plants. It was concluded that MW energy could be one of the most effective methods for large scale removal of hardness from water

Brain Pathology Classification of MR Images Using Machine Learning Techniques

A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method’s efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%

Polymerization of human angiotensinogen: insights into its structural mechanism and functional significance

In the present study, we have investigated the in vitro polymerization of human plasma AGT (angiotensinogen), a non-inhibitory member of the serpin (SERine Protease INhibitor) family. Polymerization of AGT is thought to contribute to a high molecular mass form of the protein in plasma that is increased in pregnancy and pregnancy-associated hypertension. The results of the present study demonstrate that the polymerization of AGT occurs through a novel mechanism which is primarily dependent on non-covalent linkages, while additional disulfide linkages formed after prolonged incubation are not essential for either formation or stability of polymers. We present the first analyses of AGT polymers by electron microscopy, CD spectroscopy, stability assays and sensitivity to proteinases and we conclude that their structure differs from the `loop-sheet¿ polymers typical of inhibitory serpins. Histidine residues within the unique N-terminal extension of AGT appear to influence polymer formation, although polymer formation can still take place after their removal by renin. At a functional level, we show that AGT polymers are not substrates for renin, so polymerization of AGT in plasma would predictably lead to decreased formation of AngI (angiotensin I) with blood pressure lowering. Polymerization may therefore be an appropriate response to hypertension. The ability of AGT to protect its renin cleavage site through polymerization may explain why the AngI decapeptide has remained linked to the large and apparently inactive serpin body throughout evolution