Kahneman's two-systems model helps understand a student's cognitive processes while solving C programming concepts. System 1 is fast and unconscious, which may cause cognitive bias, leading to a wrong answer. System 2 is slow and conscious, which can help overcome the bias situation, but is lazy. Understanding any concepts or program must start with attempts to believe it, which is the nature of System 1, but System 2 checks those concepts are true first, then believing it or not believing it, which helps in finding errors, the correct output of a program, etc. The activation of System 2 guides the efforts of students for complex problem-solving. This paper shows the various natures of System 1 and System 2, which guide a student's cognitive processes while solving C-programming concepts-based quizzes. The Two-System model can help find a student's performance using System 1 and System 2 in a Computer Programming Course.

Hoop stresses are produced due to the electromagnetic forces in the winding. Free buckling of winding turns can lead to failure of the transformer. Design issues are a major reason for the catastrophic failure of 11kV/433kV distribution transformers. In a distribution transformer (DT), the LV winding is generally of helical type and is asymmetrical. Asymmetry in the transformer design causes change in the direction and magnitude of the forces. In this work, a comprehensive analysis of the short-circuit forces has been done for a DT with a double-layer helical winding with all three of the limbs modelled and phases energized. The forces are computed using magnetostatic analysis of a 315kVA DT. The transformer has been modelled in 3D CAD software and forces are computed using 3D finite element method (FEM) and the results obtained are compared to that obtained from first principals. The windings are assessed for the presence of hoop stresses in its turns, and along with it the safety factor is computed for free-bucking using von-Mises equivalent. The intent is to find the weak section of the double layer helical winding from short circuit forces a transformer is designed to withstand. Such simulations of energy conversion create a robust and sustainable design, which can prevent catastrophic failure and reduce the environmental impact due to major repairs or replacement of equipment.

Wireless Sensor Network presumed great global wide interest from organizations and researchers mainly due to their significant importance in the wireless transmission of huge information. Despite their adorable performance in obtaining effective QoS parameters, they are highly susceptible to security attacks that strongly affect the network performance and user privacy. Several security mechanisms for solving these security issues were proposed in the existing works. In recent years, emerging trends in the WSN tends to work on very complex sensor arrangement scenarios. Various limitations prevail during data collection like data aggregation, node election, load balancing etc. As a result, it is mandatory to provide strong attention for the feasibility and applicability of WSN features in accordance with the perspectives of IoT. This paper explored and depicted the security and various other problems of optimization algorithms, particularly swarm intelligence algorithm in WSN. These algorithms possess strong applicability and obtained better experimental results in resolving complex practical issues. Desirable properties of intelligence algorithms like adaptability, scalability, and robustness were discussed in this review. Highly preferred optimization algorithms like Particle swarm optimization (PSO), Ant colony optimization (ACO) and Artificial Bee colony algorithms (ABC) are deeply analyzed to provide insights on their effectiveness in WSN security. Finally, after analyzing open research and challenges, special attention would be paid in the optimization algorithm to put forward the development of security trends.

Scouring at a bridge is a problem that has always been a concern to engineers. The bed scours in alluvial rivers pose a threat to the stability and safety of foundations. Therefore, calculating the depth of scour around a bridge pier for different opening ratios is an important parameter in the design foundations of the bridge pier. In laboratory studies and field investigations, many investigators have suggested conditions for analyzing scour depth surrounding bridge piers in alluvial soils. Many studies have considered normal flow conditions around the bridge. In the present study, cylindrical piers of diameters 25mm, 40mm, 60mm, 75mm, and 90 mm are investigated for the experimental evaluation of scouring around piers. In the present experimental work, the depth of scouring is measured around the piers. It found that scour depth goes on increasing with an increase in pier diameter. As pier diameter increases, there is a decrease in the opening ratio and non-dimensional scour depth.

MRI is one of the most preferred imaging modality used by healthcare professionals for diagnosing brain tumors. Despite of aiding in diagnosis, the quality of MRI scans is degraded due to the distortion of visual signals, termed as ‘Speckle Noise'. Although other noises like Gaussian, Rician, Salt and pepper also degrade MRI images, speckle noise creates the most hindering effect as it diminishes the details of the image like edge details, contrast reduction, quality of fringe patterns, etc. As presence of noise in the medical images leads to ambiguity in the diagnosis being done, elimination of undesirable noise is an important pre-processing task. This paper evaluates different filters for their effectiveness in removing speckle noise and providing improved image quality perception for MRI scans. In this paper, an experimental dataset of T1 weighted brain MRI scans was used to evaluate the filters.

In the area of education, the evaluation process and degree of difficulty play a significant role. To produce the desired outcomes, the complexity level of the questions posed in the examination must be preserved. It is a subjective technique that should consider multiple indicators to determine the difficulty level of the paper in question. Physiological tests and cognitive processes are critical in assessing the difficulty of the questions posed in the trial. The question's readability is crucial to its comprehension. Remember, Understand, Analyze, Apply, Evaluate, and Create are the six forms of cognitive processes defined by the updated Bloom's Taxonomy (BT). Along with this, physiological factors such as stress affect how questions are classified into various difficulty levels. The questions have been divided into three categories of Difficulty: High, Medium, and Low. For experimentation and study, "C programming" course questions have been considered. The prediction method uses both an unsupervised and supervised learning approach. By considering unlabeled groups, the K Means clustering algorithm was applied. It correctly classifies three clusters with three different levels of Difficulty. Four supervised machine learning classifiers were used to estimate the difficulty level of the Questions: Decision Tree (DT), Naive Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Compared to NB, ANN, and SVM classifiers, the DT model produces better performance. This research aims to look at the impact of cognitive processes, stress, and readability during reading questions of different difficulty levels.

Computational Fluid Dynamics (CFD) is used to forecast and assess the impact of temperature, operating pressure, and inlet velocity on the overall performance of gas cyclones. The numerical answers were completed using spreadsheets and the commercial CFD language FLUENT. In addition, two models for predicting cyclone collecting performance are examined in this work. All of the forecasts have shown to be appropriate when compared to subsequent results. The CFD model is the most effective technique of simulating cyclone collecting efficiency, according to the findings of the computer modeling experiment. Cyclones are particularly well adapted to high temperature and pressure conditions because of their strong structure and lightweight component materials. Cyclone collection is effective for particles bigger than 5µm; however, we can deal with smaller particles of dust loadings of 5µm here. Refer to our forecast with the aid of 3 and 5 m/s speed. The difference between the results of pollutants removed by cyclones for air quality control and process applications were examined.

Machine learning (ML) is a powerful tool which empowers the practitioners for predictions upon any existing or real- time data. Here, the Machine first understands the valuable patterns from the dataset and then uses that information to make predictions on the unknown data. Further, classification is the commonly used machine learning approach (ML- Approach) to make such predictions. The objective of this work aims to design and development of an ensemble classifier for prognosing cardiovascular disease (heart disease). The developed classifier integrates Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Weighted K-NN. The applicability of ensemble classifier is evaluated on the Cleveland Heart disease dataset. Some other classifiers, such as Logistic Regression (LR), Sequential Minimal Optimization (SMO), K-NN+Weighted K-NN are all also implemented on the same dataset to make the performance analysis. The results of this study depict the significant improvement in the Sensitivity and Specificity parameter.