Sarsuna College, established in 1999, is an undergraduate college in Kolkata, West Bengal, India, affiliated with the University of Calcutta. Sarsuna College is a relatively newer institution of higher learning under the University of Calcutta. It started to function as an evening College in the building of Sarsuna High School..
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
This study aims to understand whether any relationship exists between green revenue and reputation of India’s listed firms. It considers the sixty top performing companies in terms of their percentage of green revenue as extracted from LSEG database. The study period of nine years ranges from 2015-16 to 2023-24. Econometric tools such as correlation analysis and dynamic panel data regression analysis are applied for generating empirical outcomes. The regression result confirms that green revenue has significant positive influence on the corporate reputation of select companies in India.
For the design of the public-key exchange protocol, a chaos-based Triple-Layer Tree Parity Machine (TLTPM) directed group neural synchronization is presented in this study. For neural synchronization, a particular neural network structure called Tree Parity Machine (TPM) is utilized. Two TPMs take the same input but have distinct weight vectors, and update the weights using the neural learning algorithm by swapping output. It leads to perfect synchronization in certain phases, with the weights of the two TPMs becoming identical. The secret key is made up of these similar weights. However, there has not been much research on the randomness of the common input vector utilized in the synchronization process. A logistic chaotic system based on TLTPM is proposed in this paper. This proposed TLTPM model has several advantages like (1) leverages logistic chaos produced random common input vector for quicker synchronization. (2) With the same input, output, and hidden neurons, the suggested TLTPM model is quicker and more secure than TPM. (3) A binary tree framework considers each neural network as a node. (4) When the binary tree’s i -th and j -th nodes are synchronized, one of these two nodes is chosen as the leader. (5) This leader node will now coordinate with the other branch’s leader. After this procedure is completed, the coordinated weight has now become the cluster’s session key. (6) And no need to synchronize each neural network in the group; rather, by coordinating between the elected leader nodes with just logarithmic synchronization steps, the whole group may share the same secret key. A variety of parametric tests have been performed on the suggested approach. The results were compared to some more contemporary methods. The suggested technique’s findings have proven that it has both effective and resilient potential.
A two-dimensional warranty coverage is available for those items for which age and usage jointly fall below the respective warranty limits, thus making the warranty field data incomplete. In this paper, the joint reliability function of age and usage from the observed incomplete two-dimensional warranty data has been estimated in terms of reliability function and density function of age and usage rate respectively by using non-parametric method under the assumption of independence between age and usage rate. Bivariate mean residual life for age and usage has also been determined using the bivariate reliability function. From the reliability engineering perspective, mean residual life provides valuable information for various decision making problems, such as optimizing burn-in test, extending warranty policy, and making maintenance decision. In this paper, the mean residual life has been predicted for formulating extended warranty policy. Pro-rata warranty policy has also been considered here using linear and non-linear pro-rata functions. The utilities of this research work have been demonstrated with the help of a real-life warranty data set.
Early detection of reliability related problems through the use of sensitive statistical methods, allowing early actions to mitigate potential reliability problems could save a considerable amount of money and product goodwill for large manufacturing companies. The items which are covered under two-dimensional warranty, both age and usage are important and apart from the number of failures, the cost of repair needs to be considered to adequately tackle the problem. In the present paper, we have addressed the above issues and developed an index, known as Labour Code Priority Index (LCPI), based on number of failure at different age-usage windows as well as the cost of repair of the failed items. We have explored various desirable and interesting properties of the index and borrowed the strength of the CUSUM technique applied on the sequentially generated index values to identify early reliability related issues both for a simulated as well as, a real-life synthetic data.
