Daily Archives: February 11, 2026

Authors: Anand Kumar Gupta, Arshita Kumari, Swarangi Karangale, Shalni Kumar, Paramanand Kumar Bharti

Abstract: Objective: To systematically review and synthesize recent evidence (2020–2025) on the role of clinical pharmacists in type 2 diabetes management, focusing on clinical outcomes, patient education, adherence, and cost-effectiveness. Methods: Literature from PubMed, Scopus, and other databases (2020–2025) was reviewed, including randomized controlled trials, cohort studies, and systematic reviews examining pharmacist interventions in diabetes care. Results: Pharmacist-led interventions achieved significant reductions in HbA₁c (0.52 to 3.59%), improved patient adherence, and enhanced cost-effectiveness. Structured clinics such as DMTAC demonstrated consistent improvements in glycemic control and cardiovascular risk parameters. Conclusion: Clinical pharmacists enhance diabetes management through collaborative care, education, and therapy optimization, resulting in improved patient outcomes and reduced complications.

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Published by: vikaspatanker

Authors: Showkat Hussain Bhat

Abstract: Nowadays, the teaching and learning landscape is embracing a number of new pedagogical innovations and some of these involve the use of e-learning through Blended Learning (BL). This study attempts to assess the need of blended learning as an instructional paradigm to rejuvenate teaching. In this connection, it is substantial that innovative pedagogical approach must be embraced in the classrooms. Teaching classes could be completely combined together by using numerous synchronous and asynchronous gadgets. The way of fully integrating technologies could be helpful to increase styles of communication, mentor-learner engagement, learner satisfaction, academic motivation and performance of students. This study suggests that instructors could use blended learning pedagogy because students shifted to e-learning as an alternate to in-person classroom because of rising usage of smart phones because of anytime and anywhere class.

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Published by: vikaspatanker

Authors: K. Perarasu

Abstract: The stock market is a crucial component of the financial system, playing a significant role in economic development and wealth creation. Traditional financial theories assume that investors behave rationally and make decisions based on complete information. However, practical market observations reveal that investor behavior is often influenced by psychological, emotional, and social factors. This study aims to examine stock market trends and analyze how investor behavior impacts market movements. The research adopts an empirical approach using both primary and secondary data. Primary data is collected through structured questionnaires administered to individual investors, while secondary data is obtained from stock market indices, financial reports, and published research studies. Statistical tools such as percentage analysis, correlation, and graphical interpretation are employed to analyze the data. The findings reveal that investors frequently exhibit behavioral biases such as herd behavior, overconfidence, loss aversion, and risk aversion. Market trends show significant volatility during periods of economic uncertainty, indicating emotionally driven investment decisions. The study concludes that investor behavior plays a vital role in shaping stock market trends and that incorporating behavioral finance concepts can enhance investment decision-making and market stability.

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Published by: vikaspatanker

Authors: Chinmay Chouhan, Srashti Thakur

Abstract: Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

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Published by: vikaspatanker

Authors: Kartik Dadore, Jitendra Chouhan

 

Abstract: In India, the road traffic volume has increased manifolds during the post-independence period. The traffic axle loading may also in many cases be much heavier than the specified limit. As a result of which, the existing road network has been subjected to severe deterioration leading to premature failure of the pavements. In such a scenario, development of the effective pavement management strategies would furnish useful information to ensure the compatible and cost- effective decisions so as to keep the existing road network intact. The pavement deterioration models can prove to be an effective tool which can assist highway agencies to forecast economic and technical outcome of possible investment decisions regarding maintenance management of pavements. The optimum maintenance and rehabilitation strategies developed in this study would be useful in planning pavement maintenance strategies in a scientific manner and ensuring rational utilization of limited maintenance funds. Once this strategy for urban road network is implemented and made operational; this would serve as window to the other urban road network of different regions.

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Published by: vikaspatanker

Authors: Ms. Jyoti Ahlawat, Research Scholar, Dr. Banita, Associate Professor

Abstract: Leukemia is a life-threatening hematological malignancy that requires early and accurate diagnosis to improve patient outcomes. Manual examination of microscopic blood smear images is time-consuming, subjective, and highly dependent on expert pathologists. With recent advances in artificial intelligence, deep learning has emerged as a powerful tool for automated medical image analysis. The goal of this research paper is to develop a deep learning-based model that can accurately detect leukaemia from medical images, with a focus on optimizing the model’s performance using advanced techniques such as transfer learning, hyper parameter tuning, and regularization methods. Evaluation metrics such as accuracy, precision, recall, F1 score, and the ROC-AUC curve will be used to assess the model’s diagnostic ability. By building a robust and scalable deep learning model for leukaemia detection, this study aims to contribute to the growing body of research on AI-driven medical diagnostics and provide a practical tool to assist healthcare professionals in early leukaemia diagnosis.

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Published by: vikaspatanker

Authors: A.Swarna

Abstract: Self-reinforced composites (SRCs), also termed single-polymer composites, are engineered so that the reinforcing phase and matrix belong to the same polymer family. By eliminating chemical mismatch at the interface, SRCs typically show improved interfacial integrity, low density, and high impact tolerance while remaining compatible with single-stream recycling. Recent work (2020–2025) has emphasized processing control, bio-based SRC platforms, and microstructure-driven property tailoring.”This review provides a comprehensive discussion of SRC fundamentals, fabrication strategies, structure–property relationships, environmental advantages, application sectors, and future research directions.

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Published by: vikaspatanker

Authors: Annu Singh, Basant Kumar Mishra

Abstract: Blood flow in human arteries is inherently pulsatile and exhibits non-Newtonian behavior, driven by the rhythmic cardiac cycle and influenced by shear-dependent viscosity arising from plasma and cellular interactions. This study investigates the magnetohydrodynamic (MHD) effects of a transverse magnetic field on pulsatile non-Newtonian blood flow, with particular emphasis on velocity distribution, wall shear stress (WSS), flow resistance, and hemodynamic responses in stenosed arteries. Blood is modeled as a Casson fluid, capturing shear-thinning and yield stress characteristics, while the transverse magnetic field generates a Lorentz force opposing flow. Governing momentum equations are formulated in cylindrical coordinates and solved using analytical techniques (Finite Hankel transforms) complemented by numerical simulations for pathological and pulsatile conditions. The analysis reveals that increasing the Hartmann number (Ha) significantly reduces centerline velocity, flattens velocity profiles, and decreases WSS, whereas higher Casson parameters (β) produce blunter, plug-like profiles with higher central velocity and lower boundary shear. Pulsatility, represented by the Womersley number (α), introduces phase-lagged oscillations, and stenosis severity amplifies local velocities and WSS, increasing flow resistance. Additionally, Joule heating due to induced currents modestly raises blood temperature, relevant for hyperthermia therapy. These findings have significant implications for MRI safety, magnetic drug targeting, and vascular disease management, providing quantitative insight into the interplay of magnetic fields, non-Newtonian rheology, and pulsatile hemodynamics in arteries.

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Published by: vikaspatanker