Mainul Islam Mahi

This research proposes an optimized set of parameters for a design of the 6-T (Six Transistor CMOS SRAM) SRAM cell using CNTFET technology. The CNTFET utilized a planar gate structure with multiple cylindrical conducting channels and high k (high dielectric constant) gate dielectric material on a substrate with a different dielectric. In addition, the Stanford CNTFET model has studied, which is organized hierarchically in three main levels to simulate a MOSFET like CNFET device. The performance of the CNTFET-based (Carbon Nano Tube Field-Effect Transistor) SRAM (Static Random Access Memory) cell has evaluated using three main criteria: Static Noise Margin (SNM), Critical Write Time, and Standby Power. The optimization is conducted for two different voltage levels: 0.9V and 0.7V. The SNM is calculated using HSPICE simulation and the obtained data has utilized to optimize the CNTFET SRAM cell by varying chirality and channel length. Furthermore, a comparison of the three matrices for CNTFET, CMOS, and FinFET processes is conducted. The simulation results confirm that the CNTFET SRAM design is a significant improvement in Read SNM with superior writability and lower power consumption compared to CMOS and FinFET processes. Additionally, the simulation of SNM for a wide temperature range demonstrates that the CNTFET SRAM has a relatively stable response to temperature variations.

Cognitive Load Theory (CLT) and Human-Computer Interaction (HCI) concepts need to be combined to understand better how students learn in more complex environments. For this reason, this paper combines Cognitive Load Theory (CLT) and Human-Computer Interaction (HCI). The primary purpose of this paper was to reduce the student’s cognitive load through a lecture note. With the help of HCI design-centered principles on a lecture note, we have implemented the lecture note to lessen students’ cognitive load. The lecture note could be an example of how HCI can be designed in a lecture note. This paper has discussed the research questions with several learning theories that measured students’ cognitive load. Based on learning theories and HCI design principles, we have developed two lecture notes; the first is HCI design-based, and the second is in the absence of HCI design principles. The reason for making two lecture notes is to observe which lecture note is reducing or increasing students’ cognitive load when we may conduct the survey1. The survey was performed among some undergraduate students. When studied, the cognitive load was lowered among the student in lecture one, where the HCI design concepts were applied, and the average cognitive efficiency was 1.52 from 143 participants. And the lecture note two, where HCI design principles did not apply, has increased students’ cognitive load. The cognitive efficiency of lecture note two was 0.60 from 112 participants, which illustrates the importance of HCI in lecture notes.

Employee churn is a notable nuisance for organizations to maintain a cost-effective position and brand strategy. This research has proposed an integrated approach to overcome this issue systematically. All employees in this study were divided into three categories using a combination of machine learning algorithms and Multi-Criteria Decision Making (MCDM) techniques. The techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytical Hierarchy Process (AHP) have been consolidated. The dataset's feature importance was retrieved, and AHP was used to determine the criteria that caused the most turnover. TOPSIS was given the derived criterion weights from AHP to rank every personnel according to their propensity to depart the organization. To estimate staff turnover, seven machine learning algorithms were applied. After comparing the results, the Random Forest algorithm produces the best accuracy for assessing employee churn.