Maulana Abul Kalam Azad Institute of Asian Studies

The transformation of a normal cell state to malignant cell is a multifactorial process, as cancer is a complex network of diseases. There are various types of cancer, each with its own underlying mechanisms and symptoms. The rise in modernization has significantly limited the mobility and has also altered the diets, leading to an increased rate of cancer mortality worldwide. The treatment options in cancer therapy, such as chemotherapy and radiotherapy, result in compromising the well-being of patients with cancer. Nanoformulation has emerged as convincing tools in cancer drug delivery. In order to gather pertinent information on Nanoformulations in Cancer Patents and Clinical Trials from the year 2000 to 2026, the majority of the electronic databases including Google Scholar, Embase, Web of Science, Scopus, and PubMed/MEDLINE were searched. The key benefits of nanoformulation lie in their ability to enhance the water solubility of drugs, thereby overcoming the limitations of traditional drug formulations. The nanoformulation facilitates efficiency and enhances therapeutic efficacy. The perfection portrayed by nanoformulation in carrying the therapeutics with better performance. This article provides comprehensive details on nanoformulation-based patents and clinical trials of various cancer conditions and draws attention to novel formulation of drugs that has been designed to target specific diseases. The article will likely discuss the outcomes of the clinical trials that evaluated the effectiveness of the formulations. The current nanoformulation-based therapies can be a potential source to address the unmet medical needs that advance the patient outcomes.

The digital transformation has come to be at the forefront of modern-day education reform, but its capacity to bring truly inclusive and accessible learning remains unreliable across the settings. Current studies mostly consider inclusion, accessibility, pedagogy, and governance as parallel or sequential issues and have created disjointed frameworks that cannot explain why differences in inclusive outcomes persist. The innovation of the approach is the inclusion as a structural attribute of the digital learning ecosystem and not as a supplementary feature that is supported by formalized indices and multi-criteria approach to evaluation. Findings indicate that the suggested framework has a Pedagogical Accessibility Index (PAI) of 0.87, which is in contrast to 0.56 in the case of blended and 0.32 in the case of traditional digital instruction.

Polymer concrete, also known as mineral casting, is a composite material renowned for its exceptional mechanical properties and durability, and has garnered significant interest in various engineering applications. However, achieving optimal machinability—particularly in terms of thrust forces and tool wear—remains a critical challenge. This study investigates the relationship between processing parameters and machinability characteristics of polymer concrete, with a particular focus on thrust forces, chip formation, and tool wear. Hole drilling tests were carried out using a Quaser-MV 154C CNC 3-axis vertical machining center, and thrust forces were measured online using a Kistler 9257B force dynamometer. HSS and carbide drills were employed in the experiments. The results show that the use of a carbide drill resulted in lower thrust forces, reduced entry and exit damage, and minimal tool wear compared to the HSS drill. Higher thrust forces were observed at low spindle speeds when using the HSS drill. Overall, the findings indicate that drilling polymer concrete requires the use of a carbide drill, high spindle speeds, and a medium feed rate to minimize hole damage and tool wear.

The climbing in population displacement in urban sectors has accelerated the adaptation of smart city policies across developing regions. Utilization of clean and sustainable energy is of utmost importance such as the demands of the entities can be met without any resource constraints. The management of energy in terms of electricity is therefore crucial to human livelihood. However, the present management process has flaws, leads to inefficiencies, and fraudulent activities, which further result in economic loss and resource wastage. To address such issues, this chapter proposes an intelligent Internet of Things (IoT) framework that uses tools like microcontrollers and sensors to minimize the need for human involvement, enhancing the system’s transparency and reliability. With such a consumer-friendly and secure system, live meter readings, automatic power cuts, and the detection of power theft, accidental situations can be performed effectively. The absence of wiring not only reduces installation costs but also minimizes the likelihood of faults. The integration of technologies such as IoT and artificial intelligence (AI) further improves reliability and consumer service. This proposed system can be a real game-changer that minimizes the involvement of intermediaries, promoting transparency and streamlining electricity management.

Therapeutic drug monitoring (TDM) is a precision medicine tool that measures drug concentrations in biological fluids to guide individualized dosing. TDM has evolved into a multidisciplinary approach used across various therapeutic areas. In India, despite a growing clinical need, TDM remains underutilized due to systemic and infrastructural barriers. This narrative review aims to summarize the evolution, current status, analytical approaches, and future prospects of TDM in India and is based on published literature retrieved from databases, including PubMed, Scopus, and Google Scholar, and outlines TDM's utility in optimizing pharmacotherapy for drugs with narrow therapeutic windows or nonlinear pharmacokinetics. It compares analytical methods used for TDM and discusses challenges in the workflow of TDM. Special emphasis is placed on its relevance in children, pregnant women, the elderly, and critically ill patients. It also highlights barriers, including a lack of infrastructure, trained personnel, and standardized guidelines. TDM in India requires policy-level support, integration into national health programs, clinician education, and adoption of point-of-care technologies. Strengthening research in pharmacogenetics can help establish TDM as a cornerstone of individualized therapy.

In deep learning methods to estimate system parameters and evaluate the global Lipschitz condition with respect to input/output data, it is possible for an unknown-dynamic iterative learning control system. Neuron models: M-P including habituation network neurons can enhance convolutional network performance via nonassociative training. A novel solution to this problem is a deep layer-by-layer supervised pretraining framework utilizing stacked supervised encoder–decoder, which jointly prelearns the feature extraction and also soft sensor modeling in industrial processes. The BLEU helps the architect to unroll higher computationally layers and connection between them in feed forward as well as recurrent neural network architectures. Leveraging offline data mining with hardware design results in intelligent memory systems, which balances energy efficiency and cost against classification accuracy during execution. The mixture of synaptic- and SRAM-type computation greatly improves power efficiency at only minor accuracy degradation for state-of-the-art deep learning models. Combining theoretical research and practical applications, this interdisciplinary effort ultimately leads to technological advances, driving technological advancements across various domains. Transfer learning, leveraging pretrained models, enhances learning efficiency and performance across tasks. Academics or educational field and industry together highlight the dynamic nature of technological progress and its societal impact.