Daily Archives: June 15, 2026

Authors: Prerna Charis J

Abstract: Sign Language Recognition (SLR) has emerged as an important research area at the intersection of computer vision and deep learning, and human and machine interaction with an objective of enabling effective communication between deaf and hearing communities. Recent advances in deep learning have improved the performance of vision-based Sign Language Recognition systems, particularly by using hybrid architectures that combine spatial features extraction and temporal sequence modelling. The goal of this review is to provide a overview of the recent developments in hybrid Vision-based Sign Language Recognition and to examine the advantages, limitation and practical deployment challenges of the current approaches. This paper provides a systematic review of the literature, the surveyed methods broadly classified into CNN-LSTM architectures, Transformer-based models and multimodal integrated frameworks which integrates visual and skeletal information. This review further investigates critical challenges affecting the deployment in real-world scenarios which includes domain shift, data scarcity, co-articulation, sign ambiguity and computational constrain. We will also discuss about emerging research direction such as self-supervised learning, cross-linguistic transfer learning, generative domain adaptation, multimodal bio signal integration, and community-centered dataset development. This survey also highlights the significant progress achieved in continuous sign language recognition while identifying the remaining technical and practical barriers that must be removed to develop robust, scalable, and user-independent SLR systems capable of operating in real-world environments.

Published by: vikaspatanker

Authors: Rushikesh Pawar, Vijaykumar Kale, Sakshi Mane, Mahesh Thakare, Vaibhav Narwade

Abstract: Traditional herbal medicines serve as a primary healthcare pillar for approximately 80% of the population in various Asian and African nations. Despite their extensive experiential evidence and therapeutic benefits, conventional herbal formulations face significant pharmacokinetic limitations. These include poor aqueous solubility, unstable gastrointestinal pH degradation, high presystemic metabolism, and an inability to cross lipid biomembranes effectively, often resulting in sub-therapeutic blood levels.[1] Modern quality control has transitioned from single-marker assays to comprehensive metabolic profiling using High-Performance Liquid Chromatography coupled with Mass Spectrometry (HPLC-MS) and genomic DNA barcoding for precise species identification. Concurrently, international bodies (including the WHO, ASEAN, EU, and FDA) are collaborating to harmonize regulatory frameworks. To enhance therapeutic efficacy, nanotechnology is being deployed to engineer nano-phytomedicines. Various carrier systems including polymeric nanoparticles, solid lipid nanoparticles, liposomes, nanoemulsions, and phytosomes are evaluated. Notably, while liposomes encapsulate extracts within an aqueous core or lipid bilayer, phytosomes chemically anchor phytochemicals directly to phospholipid head groups, drastically improving lipophilicity and membrane permeation.[2] Incorporating plant actives into nanostructured matrices significantly optimizes their hydrophilic-lipophilic balance. This structural modification provides sustained release, shields molecules from chemical degradation, minimizes off-target toxicity (e.g., localized accumulation of chemotherapeutics in healthy tissues), and increases bioavailability. However, transitioning these formulations from bench to industrial scale introduces complex challenges, including maintaining uniform encapsulation efficiency within multi-component plant extracts, preventing nanoparticle aggregation driven by high surface energy, and satisfying stringent regulatory safety assays regarding tissue accumulation.[3].

Published by: vikaspatanker

Authors: Ar. Suman Sharma, Muskan Gour

Abstract: Vastu Shastra is an ancient Indian architectural science that establishes harmony between buildings, nature, and human activities through orientation and spatial planning. Traditional Indian architecture evolved according to climatic conditions, environmental understanding, and sustainable planning principles. The orientation principles of Vastu are closely related to thermal comfort, daylight performance, natural ventilation, and passive cooling strategies. This research paper studies the climatic relevance of Vastu orientation and analyses how traditional Indian architecture responded effectively to environmental conditions using climate responsive architectural techniques. The study follows a qualitative and analytical research methodology based on literature review, comparative analysis, and case studies of traditional Indian houses. The paper examines the relationship between orientation, sunlight, wind movement, thermal comfort, and passive environmental control. Traditional architectural elements such as courtyards, verandahs, jaalis, shaded openings, and thick walls are analysed in relation to Vastu principles. The study also compares these traditional concepts with modern sustainable architectural practices. The findings indicate that many Vastu principles are scientifically relevant and environmentally responsive. Proper orientation improves daylight quality and ventilation while reducing heat gain and energy consumption. East-facing openings provide healthy morning sunlight, while reduced western exposure minimizes thermal discomfort. Courtyard planning enhances air circulation and creates thermal balance within buildings. The research concludes that Vastu orientation is not merely a cultural or spiritual concept but also a climate responsive architectural strategy based on environmental understanding and passive design principles. Many concepts of Vastu remain relevant in contemporary sustainable architecture and energy-efficient building design.

Published by: vikaspatanker

Authors: Kovuru. Rasi

Abstract: Artificial Intelligence (AI) is transforming the pharmaceutical industry by introducing advanced computational approaches for formulation design, drug delivery optimization, and personalized medicine. Conventional pharmaceutical development methods are often time-consuming, expensive, and dependent on repeated experimental trials. AI-based technologies such as machine learning, deep learning, artificial neural networks, and predictive analytics provide innovative solutions by analyzing large datasets, predicting formulation behavior, and optimizing drug delivery systems with improved precision and efficiency. AI assists researchers in identifying critical formulation variables, predicting drug–excipient interactions, enhancing stability, and improving bioavailability while reducing development time and manufacturing costs. In drug delivery optimization, AI supports the development of targeted, controlled, and patient-specific delivery systems including nanoparticles, liposomes, transdermal systems, and smart drug carriers. Furthermore, AI-driven models facilitate quality-by-design approaches, real-time monitoring, and automated decision-making during pharmaceutical manufacturing. The integration of AI with pharmaceutical sciences also promotes personalized therapeutics by enabling dose optimization according to patient-specific factors such as genetics, age, disease condition, and metabolic profile. Despite its significant advantages, challenges including data reliability, regulatory concerns, ethical issues, and the need for interdisciplinary expertise remain barriers to widespread implementation. This review highlights recent advancements, applications, benefits, challenges, and future prospects of AI in pharmaceutical formulation development and drug delivery optimization, emphasizing its potential to revolutionize modern pharmaceutics and improve healthcare outcomes.

DOI: http://doi.org/10.5281/zenodo.20696741

Published by: vikaspatanker