Strode College

Railway networks are critical components of modern transportation infrastructure, yet they remain vulnerable to structural degradation, particularly surface and subsurface cracks in rails. Traditional manual inspections, though widely adopted, suffer from limited frequency, human error, and substantial operational costs. In recent years, sensing technologies, machine learning models, and autonomous monitoring systems have emerged as promising tools for reliable and real-time crack detection. This paper reviews existing crack detection techniques, highlights emerging innovations, and proposes a hybrid, sensor-fusion-based framework to enhance accuracy and deployment feasibility. The analysis suggests that integrating visual, vibration, and ultrasonic modalities with deep-learning algorithms can significantly improve detection performance while supporting predictive maintenance. .

Thiruvalluvar's Thirukkural is a masterpiece that serves as a guide for all aspects of human life. It explains the fundamental moral and ethical principles required for business and economic life. Specifically, the text provides authentic pathways for a businessman or leader to achieve success while upholding ethics in constantly changing circumstances. Business is not static; by its very nature, it involves change and challenges. Rather than merely imposing rigid rules, the Thirukkural emphasizes preserving ethics by accepting situational changes based on wisdom, honesty, and foresight. For instance, several couplets express ideas such as acting after understanding the nature of time (Kalam), analyzing the quality of goods and the significance of location (Idan), and employing strategic maneuvers when necessary. It also highlights that one must continuously prepare themselves to face such transformations. Therefore, business ethics in the Thirukkural is not a narrow list of regulations. It is a way of life that involves thinking deeply and acting with flexibility, adapting to changing times without compromising fundamental justice. This philosophy serves as a solid foundation for achieving lasting success and gaining respect in the business world.

Transformer models have greatly changed how deep learning works, particularly in natural language processing, computer vision, and multi-modal learning problems. While these models have done great empirically, the mathematical foundations and stability of these models remain poorly understood. In this paper, we perform a holistic analysis of the mathematical foundations of the Transformer architecture such as self-attention and positional encodings, and mathematical properties of layer normalization. Specifically, our work explores the stability conditions given different settings of training and inference, using principles from linear algebra, spectral analysis, and dynamical systems. In our analysis, we elaborate on how architectural parameters (e.g., depth, width, and initialization scheme) could influence the behaviour of gradients, convergence, and robustness. We also investigate the conditions by which we can assure stable propagation of information through layers and identified the conditions in which transformers exhibit vanishing or exploding gradients. We hope our work will offer reliable theoretical prospects of robustness for practical implementations of Transformer-based systems, and offer ways to practically choose a model that is stable and demonstrably efficient. The paper offers a connection for theoretical analysis and practical implementation, which are the foundations we believe will facilitate the next generation of theoretically-based grounded deep learning architectures.

The principal objectives of national park monitoring programs are the production of inventories and the examination of ecosystem development via systematic, long-term research. Although crucial for reconciling public access with conservation initiatives, rigorous monitoring of visitors and their recreational activities remains infrequent. To understand the impact of people on biodiversity and to manage it sustainably, reliable wildlife monitoring methods are essential. This research employs a cutting-edge deep learning technique to improve monitoring accuracy. In the pre-processing phase, video frames are refined via the FAPG filter. The photos are subsequently scaled, and texture features are retrieved with the Hermite filter. This ensures that animal photographs can be precisely segmented utilising the importance of Hermite polynomials. They incorporate 3DCNN and 2DCNN inside our CNLSTM architecture to enhance spatial and temporal analysis. LSTM documents the sequential development of typhoons, facilitating our comprehension of how environmental patterns influence the migration of fauna and tourists. The proposed CNLSTM model surpasses existing approaches in large-scale wildlife monitoring datasets, with an accuracy of 97.65%. The findings indicate that deep learning is effective for monitoring wildlife and tourists, hence aiding conservation initiatives.

Abstract Lithium‐ion batteries have a very wide application range. They can power up small electronic devices such as smart watches to larger electric vehicles. Due to its varied range of applications, they come in different packaging and in such battery packs, even when individual cell voltage exceeds by a few milli‐volts above 4.2 V, it may result in thermal runaway and explode the cell. During discharge cycle, cell imbalances hinder the use of battery to its full capacity. This in turn decreases the battery lifetime. The individual battery cells should be equalized on a regular basis to keep the imbalances to a minimum and to have a good battery life. The process of balancing the individual cell charges by measuring the cell state of charge (SoC) and its voltage in a battery pack is known as cell balancing. This paper details an active cell balancing technique that uses a buck converter for balancing a series connected battery pack of lithium‐ion cells. A buck converter along with a pair of MOSFET switches for each cell, one turned on for charging the cell and the other one turned on while discharging the cell is used in this experiment. An algorithmic model suitable for reconfigurable battery systems that measures the individual cell voltages and is developed for balancing a pack of series connected Li‐ion battery cells. The developed model is simulated using MATLAB for verifying its performance. A state of charge of 25% is maintained across the cells and when SoC value drops below this even a difference of 0.02% is sensed by the algorithm to initiate balancing function. This balancing is found to take 275 ms to balance three 3.7 V batteries and thus the model is found to respond faster. The results show that this method can self‐adaptively attain satisfactory performance within a limited equalizing period.

Due to great need of agricultural operations, there was a shortage of workers during the harvest period. This results in a bottleneck in the operations of agricultural organizations. One of the most common problems in these sectors is the hazardous practice of farming. This type of farming operation makes vegetables very costly and unattractive. The reduction of costs is controlled by the need for labour. This issue is solved by adapting to new technology and compensating the labour deficiency. The algorithms that are processed in this method are Convolutional Neural Network (CNN), Object Detection and Generative Adversarial Network (GAN). These algorithms will enable the model to recognise faulty tomatoes. This model accurately identifies raw, ripe and defective tomatoes using deep learning methods and it is highly helpful for the betterment of agriculture.

Oxidative stress plays an important role in pathogenesis of chronic disease especially CVS disease (CVD), diabetes, cancer, neurodegenerative disease hence, the growing interest in the use of natural antioxidants as therapeutic tools exists. The aim of this study was to explore herbal drug which may become useful in the prevention of diabetes and antioxidant potential from ethanol extracts of Maytenus heyneana (MH) root belonging to the family Celastraceae and their different fractions were studied. Ethanolic extract of Maytenus heyneana root was fractionated into chloroform, ethyl acetate, and methanol by liquid-liquid extraction. The preliminary phytochemical analysis was done by standard methods to identify the presence of important compounds. In vitro antioxidants activities were carried by 2,2-diphenyl- 2-picrylhydrazyl (DPPH) and 2,2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) cation radical scavenging assays. For antidiabetic potential, \(\alpha\)-amylase and \(\alpha\)-glucosidase enzyme inhibitory studies were carried on different fractions. The qualitative phytochemical study on the different solvent fractions of the roots of MH showed the presence of some secondary metabolites such as cardiac glycosides, phenols, flavonoids, and terpenoids in all the fractions. Alkaloid was detected only in chloroform and ethyl acetate fractions and saponins were present only in methanol fraction. The presence of tannins was detected in ethyl acetate and methanol fractions except in chloroform fraction. However, steroids and proteins were absent in all the fractions. From the results of two assays prominent that at high concentration, methanolic fraction showed superior activity than the other two fractions, although among the different fractions, the methanol fraction showed significant increase in \(\alpha\)-amylase activity (3.14 \(\mu\)g/ml) when compared to the standard acarbose (4.67 \(\mu\)g/ml) (p<0.05). it can be concluded that the methanol fraction of MH root could be a good candidature for further studies and the isolation of active phytoconstituents which is responsible in the management of diabetic and as antioxidant.