J.C. Bose University of Science & Technology, YMCA

ABSTRACT The usage of multimedia data has grown exponentially over the past few decades, with a significant portion of its content in the form of images and videos, which are transmitted across various public networks. As the prevalence of video content continues to rise, so does the critical need for enhanced data security. Recently, several techniques have been proposed to create efficient data security procedures, focusing on security, robustness and computational complexity. This paper proposes a secure and robust video encryption technique based on the concepts of confusion and diffusion. During the confusion phase, horizontal and vertical permutations of pixel values are performed using a permutation box and then pixel values are substituted based on a lookup table. In the diffusion phase, the pixel values of the first frame are XORed with a defined box, and then, sequentially, the pixel values in the remaining frames are XORed using the previous frame as a reference. Each stage requires keys for processing that are generated by three different chaotic logistic maps, which successfully passed the NIST test. These numbers are then XORed to implement a multi‐chaotic logistic map effectively. Simulation metrics, including entropy, correlation, NPCR, UACI, and so forth demonstrate the strength of the proposed method. The result analysis also states that the proposed methodology performs well in terms of Quantitative Security Analysis Correlation Analysis 0.0017, Entropy Analysis 7.99, Peak Signal to Noise Ratio 7.898, Mean Square Error 10549.37, Structural Similarity Index (0.0075), Key Space Analysis (), Differential Attack Analysis (Number of Pixel Change Rate 99.61 and Unified Average Change Intensity 33.46), Robustness Analysis (Salt and Paper Noise (pass), Gaussian Noise (pass), Occlusion Attack (pass) and Execution Time Analysis θ ( n )).

The high level of computerisation of modern production systems has led to the generation of large volumes of machining data, offering significant potential for using data-driven decision making (DDDM) for examining the fault analysis and incorporating efficient preventive maintenance measures. This research proposes a DDDM framework to incorporate data driven preventive maintenance and fault analysis in modern production systems. This framework uses IoT sensors, cloud computing, and machine learning for real-time fault analysis and preventive maintenance. This approach incorporates the analysis of failures using past machining data and suggests maintenance strategies based on fault analysis. The framework exhibits a continuous model improvement nature based on the system data by including continuous monitoring and model refinement loops. The failure mode effect analysis does systematic fault identification, predictive maintenance planning, and continuous improvement through feedback loops. The applicability of the framework is demonstrated through an experimental study on CNC turning of D2 alloy steel performed on a FANUC CNC lathe. The systematic fault analysis, visualisation of tool wear with machining parameters, capacity utilisation analysis, tool life prediction by multivariate linear regression machine learning model, failure analysis and FMEA were performed by the deep analysis of the recorded experimental data. A random forest model demonstrated an accuracy of 84.37% in tool failure prediction, effectively correlating predicted and actual tool life. Thus, by proposing framework and demonstrating its applicability in an industrial setup, this research establish theoretical as well as practical foundation for applying data driven preventive maintenance and fault analysis in modern production systems to enhance production quality and efficiency.

As the world faces the growing challenge of climate change, the transportation sector is at the forefront of reducing our carbon footprint. This chapter aims to explore the importance of low-carbon products in the evolution of the transportation environment, focusing on their applications in automobiles. It aims to demonstrate the intersection of data science and automotive engineering and presents new solutions that promise to reinvent the transportation industry for a long time to come. It focuses on a variety of low-carbon materials, from lightweight materials and composites to bio-based polymers and recycled materials. Each element discussed has been carefully examined for its unique features, benefits, and issues to fully understand its suitability for automotive use. It also explains the difficulty of integrating low-carbon information into vehicle design, production, and end-of-life decisions. Numerous challenges like cost implications, scalability, and performance optimization are also addressed, which offers insights into the practical considerations that automotive engineers and manufacturers confront in adopting sustainable materials. This contribution strives to inspire informed decision-making and catalyze the transition toward a more environmentally responsible transportation sector.

Thiazole and bi-thiazole-based compounds’ vital role in broad-spectrum biological activities attracts immeasurable research interest to investigate their molecular characteristics. This work aims to explore the molecular insights of bi-thiazole compound ethyl 2’-(4-methoxyphenyl)-4’-methyl-2-phenyl-[4,5’-bithizole]-5-carboxylate (EMPB) and its methyl, methoxy and chloro derivatives such as EMTB, EMB, ECMB. B3LYP hybrid functional at the 6-31+G(d,p) basis set level theory implemented to get molecular geometry features, charge density distribution and insights into physicochemical properties. IR spectral signatures obtained from optimised geometry through frequency calculation were mapped with their IR spectra from experiments. The key interaction sites were analysed through electrostatic potential maps, and reaction parameters were evaluated using thermochemical properties. Frontier molecular orbitals were assessed to get the reactivity descriptor of these bi-thiazole derivatives.

In a multi-microgrid (MMG) system, flexible loads (FLs) and hydrogen energy storage systems (HESS) are crucial for demand response, significantly improving system reliability. However, communication delays may hinder the timely scheduling of FLs, HESS, and generators, potentially reducing operational efficiency. To address this challenge, a multi-objective distributed economic dispatch (MODED) strategy is proposed, considering both communication delay and FLs. The strategy employs a unified framework that ensures comprehensive optimization by transforming the economic dispatch problem (EDP) of MMG into a multi-agent consensus problem. The MMG model is constructed with multiple microgrids (MGs), each comprising diesel generators (DGs), renewable energy (RE), and FLs. Meanwhile, it improves environmental sustainability that considering both the environmental pollution caused by fossil fuels and inclusion of HESS in economic scheduling improves environmental sustainability. Firstly, the incremental costs of generators (DGs and RE) and benefits of FLs are marginal costs by leveraging multi-agent consensus theory, ensuring optimal power generation and maintaining the supply-demand balance within each MG. Then the maximum allowable communication delay bound is determined by Z-transform and generalized Nyquist. Furthermore, an improved Metropolis method is proposed to solve the weight matrix in the consensus algorithm, facilitating accurate power flow interactions between the MGs and accelerating system convergence. Stability analysis of the proposed method is also conducted. Finally, the effectiveness and economic benefits of the proposed strategy are demonstrated through experiments.

BackgroundNeurodegenerative illness has become much more common in recent years, and this trend is predicted to continue as the world's population ages quickly. Protein aggregation, metabolic disorders, immunological dysregulation, and a gradual loss of neurons in the brain or peripheral nervous system are the hallmarks of these diseases.MethodThe nucleotide‐binding domain leucine‐rich repeat and pyrin domain containing receptor protein 3 (NLRP3) is a crucial pattern recognition receptor in human innate immunity. It is largely responsible for triggering elements such as reactive oxygen species, lysosomal damage, and mitochondrial DNA, ultimately leads to Alzheimer's disease (AD) ‐ like pathology. Furthermore, several reported studies from literature supported this phenomenon. Additionally, by generating cytokines and chemokines such as IL‐1β, IL‐18, and other subsequently leads to the deposition of tau and Aβ proteins (pathological hallmark of AD) and caused AD development. Further, certain mechanisms, including autophagy, receptor binding blocking, and aberrant cytokine release, have been found to have a detrimental impact on NLRP3 inflammasome activation.ResultFrom these insights we were able to summarize that the most recent advancements in AD prevention and treatment involve NLRP3 inflammasome as well as above mentioned possible mechanisms involved in AD pathogenesis.ConclusionBy targeting the activation or inactivation of the NLRP3 inflammasome, it could be possible to reveal the pathophysiology of AD from a new perspective and provide a novel strategy for its prevention and treatment.Keywords: AD(Alzheimer's disease), NLRP3 inflammasome, interleukins, Cytokines, Chemokines, Tau protein, leucine, neurodegenerative diseases.