Silapathar College, established in 1979, is a major and general degree college situated in Silapathar, Assam. This college is affiliated with the Dibrugarh University.
We studied texture zero in the lepton mass matrices in the framework of minimal left-right symmetric model using the dihedral D_4 symmetry and Z_2 symmetry. This leads to interesting correlations between the neutrino parameters. We studied the observables like neutrinoless double beta decay (NDBD), charged lepton flavor violation (LFV) and baryogenesis (BAU) within this framework. The study is carried out for both normal and inverted ordering keeping in mind the recent global fit neutrino data. We have varied the mass of the new scale within the accessible collider limits and see its phenomenological implications.
The observed pattern for neutrino masses and mixing provides compelling evidence for Beyond Standard Model physics which further motivates the search for predictive frameworks that can simultaneously address flavor structure and its phenomenological consequences. This work particularly investigates the realization of all possible seven two-zero neutrino mass textures within the generic left-right symmetric model with A_4 modular symmetry. By considering modular weights 4,8 and 10, we systematically construct all the possible classes of 2-0 textures without the introduction of any flavon fields which enhances the predictive power of the framework. In this work, we also identify the texture classes capable of simultaneously accommodating current neutrino data, reproducing the observed baryon asymmetry and also yielding experimentally testable results for the effective Majorana neutrino mass for new physics contributions of neutrinoless double beta decay.
Noncommunicable diseases (NCDs), together with cancer, cardiovascular illnesses, diabetes, and chronic respiratory diseases, continue to be the biggest contributors to morbidity and mortality worldwide. The drawbacks of conventional medicines, such as low bioavailability, systemic toxicity, and ineffective targeted administration, have paved the path for the blooming of bioactive nanomaterials. Nanomaterials are now broadly utilized in the medical and health industries as an innovative treatment for various diseases, primarily NCDs, caused by rapid advancements in nanotechnology. These advanced materials offer distinct advantages, including precise targeting, controlled dispensing, and enhanced therapeutic efficacy. Bioactive nanomaterials (BNMs) use chemical and mechanical characteristics such as crystal structure, charge on the surface, functional groups on the surface, arrangement, and size to generate biological activity and treat illnesses. Unlike traditional nanometer pharmaceutical composing, BNMs do not rely on drug delivery and are anticipated to offer improved therapeutic outcomes. This study reviews the recent advancements, mechanisms, a thorough introduction to the usual biomedical applications involving bioactive nanoparticles and therapeutic prospects of futuristic bioactive nanoparticles in the treatment of major NCDs, as well as the accompanying challenges, technical hurdles and significant scientific issues confronting bioactive nanoparticles in disease diagnosis and therapy and forthcoming developments.
The construction industries are growing green to create a more sustainable society with eco-friendly materials. The building materials are expected to possess not only the attributes of reliability, consistency, and durability but also green characteristics. The materials are labeled as green based on their environmental impacts and lifecycle assessments and the eco components are considered to be an integral component of these materials. The choice making of these building materials as green materials depends both on their material properties and environmental performances. However, the decision-making on green material selection is an intricate process and this research work employs deep learning networks in formulating a choice-making decision model. The deep learning model is trained with different sets of structured data encompassing different input features. The resultants of the decision model assist the decision-makers in making optimal choices of materials possessing low carbon impacts, minimal waste generation, and building sustainability. This deep learning-based model is highly potent in contributing to the goal of attaining a greener and more sustainable society.
In fuzzy heuristic search algorithms embedded with reinforcement learning have been proven to be a highly capable technique for the degree of tuning as well. The performance of traditional heuristic search algorithms and single-reinforcement learning models is limited by inefficiency in dynamic environments. We propose a new combined fuzzy logic and reinforcement learning model for decision enhancement and system optimization. Online parameter adaptation through fuzzy heuristic search combined with reinforcement learning enhances scalability in both variations of the algorithm. Fuzzy logic uses linguistic variables and membership functions to account for uncertainty or imprecision, while reinforcement learning learns optimal policies through interaction with the environment. Following the above objective, the model tries to increase the convergence rate by 25
