Meghnad Saha Institute of Technology is private college located in West Bengal, India. The college is located in eastern suburb of the city at Nazirabad, Rajpur Sonarpur. The college is approved by the AICTE and the Directorate of Technical Education, and affiliated with Maulana Abul Kalam Azad University of Technology.
This study primarily aims at investigating the performance of large-area multi-crystalline silicon (mc-Si) solar cells through the optimization of zinc sulfide (ZnS), a cost-effective thin film antireflection coating (ARC) deposited using chemical bath deposition (CBD) technique. The films are deposited on NaOH-NaOCl polished mc-Si solar cells using a complexing agent, tri-sodium citrate that is non-toxic in nature. The antireflection properties of the films are optimized by varying the molar concentration of tri-sodium citrate, the deposition time, and the angle of substrate tilt during deposition. The molar concentration of tri-sodium citrate is varied from 0.10 to 0.40 M. The atomic force microscopy (AFM) analysis reveals that the film deposited at 0.30M tri-sodium citrate for one hour exhibits a uniform surface morphology with a root mean square (RMS) roughness of 2.97 nm. The optimized film demonstrates good uniformity (standard deviation <1), a high deposition rate, a refractive index of 2.35 and a minimum reflectance of 4
Due to high operating voltage, recent power devices are prone to performance degradation related to aging and electrical stress. Under electrical stress, devices manifest different reliability issues such as bias temperature instability (BTI), time-dependent dielectric breakdown, etc. These issues typically arise from the pre-existing defects within the oxide. In this paper, we implement the four-state non-radiative multi-phonon (NMP) model to investigate the impact of negative bias temperature instability (NBTI) on threshold voltage shift of the device transfer characteristics as well as the ON resistance of power transistors such as P- type laterally diffused metal-oxide semiconductor field-effect transistors (P-LDMOSFET). The dynamics of charge trapping and de-trapping during electrical stress at the gate terminal is inquired over various drift doping levels and temperature in terms of the donor-like trap occupational probabilities at various stress and relax times, the fundamental origin of threshold voltage shifts. Furthermore, we demonstrated the theoretical impact of NBTI on the ON resistance of the device under test along with a novel sensitivity metric that helps unveiling the in-depth long-term instabilities.
Generative Artificial Intelligence (AI) has become a magical tool in the linguistic field for producing textual contents, news and even the source along with date and time. With the enhancement of Generative AI on one hand, fake news creation has become evident but on the other side, it’s detection is constantly facing new challenges day by day. ChatGPT-4, DALL-E2, etc. are easily available tools for building up linguistic and pictorial data using Generative AI. Fake news can easily be generated by Generative AI, misleading people and society to a great extent. In this paper, a comprehensive survey on fake news creation through Generative AI and some of the fake news detection methods through Generative AI has been discussed.
Soil strength improvement is essential for weak soil to enhance the bearing capacity of the soil. Compaction is a widely used method to achieve maximum dry density (MDD) at optimum moisture content (OMC). Standard Proctor test is used worldwide for determining the compaction characteristics of soil. However, Modified Proctor test is required for important structures. The use of jute fiber in soil may improve the strength where soil quality is not very good and compaction is not enough for construction works. The use of jute fiber may also reduce construction costs substantially. In this study, the fiber was cut into two lengths (20 and 25 mm) and mixed with soil samples at varying percentages from 2 to 6
This study investigates sentiment analysis on code-mixed Bengali, English, and Hindi text, evaluating both transformer-based architectures and traditional machinelearning models. Experimental results show that multilingual transformers significantly outperform classical approaches in capturing complex multilingual and contextual patterns. The mBERT model achieved the highest weighted F1 score of 0.75, followed by XLM RoBERTa with 0.73, highlighting the effectiveness of fine-tuned multilingual Pre trained Language Models (PLMs) for code-mixed data. Among classical baselines, Support Vector Machine performed best with an F1 score of 0.677, demonstrating that TF IDF and n-gram features remain competitive for mixed-language tasks. These findings emphasize that multilingual pre training and cross lingual alignment are essential for robust sentiment analysis in code-mixed environments.
