Guru Ghasidas Vishwavidyalaya

Handwritten digit recognition is an important problem in the field of machine learning and computer vision, which is used in many practical applications such as document digitization, banking systems, postal automation, and academic assessment. Automatic identification of handwritten digits becomes a challenging task due to their variety in writing style, shape and thickness. The paper designed, implemented and evaluated a deep learning model based on the Convolutional Neural Network (CNN) using TensorFlow and Keras to accurately classify handwritten scores.The study used the MNIST dataset, which includes 60,000 training samples and 10,000 test samples in the form of grayscale images of 28 × 28 size. Under data pre-processing, techniques like normalization, re-reasoning and label encoding were adopted. The proposed CNN model incorporated convolution layers, max-pooling layers, fully-connected layers, and Softmax output layers, which enabled effective acquisition of spatial characteristics of handwritten digits.It was evident from the experimental results that the model achieved high classification accuracy, the difference between training and validation accuracy was minimal and the reliability of the classification was confirmed by confusion matrix analysis. The findings of the study indicate that the CNN-based deep learning model provides an accurate, reliable and practical solution for identifying handwritten marks.

This article aims to investigate the moderating role of digitalization of trade (DT) on the association among foreign direct investment (FDI) and sustainable economic growth in BRICS+ nations over the period 1996–2023. This research employed a panel quantile regression approach for an empirical analysis and the two-stage least squares model for robustness. The findings depict the direct influence of both FDI and DT, which substantially and favourably enhance sustainable economic growth. Together, they stimulate innovation, enhance infrastructure and foster equitable and sustainable economic development for long-term prosperity. The moderating influence of DT on the FDI–growth relationship is found to be significant at all quantiles (except the 10th and 90th). The study recommended that policymakers should lower trade barriers, reduce tariff rates and form investor-friendly policies to attract FDI and promote DT by fostering digital literacy and investment in digital infrastructure for the economic advancement of BRICS+ nations.

Abstract: Skin aging is a multifactorial biological process influenced by both intrinsic factors, such as genetics and hormonal changes, and extrinsic factors, including UV radiation, pollution, and lifestyle. These contribute to oxidative stress, leading to the degradation of structural proteins like collagen and elastin, resulting in wrinkles, hyperpigmentation, and reduced skin elasticity. Conventional anti-aging skincare products often contain synthetic ingredients such as retinoids, peptides, and hyaluronic acid. However, concerns over adverse effects have driven interest toward natural, plant-based alternatives. Herbal and ayurvedic formulations, rich in bioactive compounds like polyphenols, flavonoids, carotenoids, and essential oils, offer a promising and safer approach to skin rejuvenation. Phytochemicals such as curcumin, resveratrol, quercetin, and betacarotene exhibit potent antioxidant, anti-inflammatory, and collagen-boosting properties, making them suitable candidates for anti-aging interventions. Despite their potential, issues like low stability, poor solubility, and limited bioavailability restrict their efficacy in topical applications. Advances in nanotechnology, particularly the use of liposomes, transfersomes, and nanoemulsions, have significantly enhanced the delivery, stability, and skin penetration of these herbal actives, enabling sustained release and improved therapeutic outcomes. This review explores the underlying mechanisms of skin aging, the roles of collagen and elastin in maintaining dermal structure, and the therapeutic promise of herbal compounds in combating signs of aging. Additionally, it discusses challenges in formulation, regulatory considerations, and future perspectives in the development of natural, nanotechnology-enabled skincare products.

Abstract The rapid expansion of digital technologies within educational systems has generated renewed attention to issues of accessibility and inclusion. While technology is frequently promoted as a solution to educational inequities, its actual impact depends on how effectively accessibility and inclusive design principles are embedded into digital learning environments. This paper critically examines the role of technology for accessibility with a particular focus on digital tools and inclusive design frameworks such as Universal Design for Learning (UDL). Drawing on empirical and theoretical literature from Scopus- and SSCI-indexed journals, the paper analyses how assistive technologies, digital learning platforms, and inclusive design strategies address physical, sensory, cognitive, linguistic, and socio-economic barriers to education. The discussion highlights both the transformative potential of technology and the structural challenges that limit its effectiveness, including the digital divide, uneven institutional capacity, and insufficient teacher preparedness. The paper argues that accessibility must be understood as a systemic and ethical commitment rather than a technical add-on. It concludes by emphasizing the need for policy alignment, inclusive pedagogical practices, and sustained investment in accessibility-oriented technology design to ensure that digital innovations meaningfully contribute to equitable and inclusive education. Keywords: Technology for accessibility, inclusive design, Universal Design for Learning, assistive technology, inclusive education

Scientific literacy has become a central goal of contemporary education systems, particularly in the context of rapid scientific and technological advancements. At the secondary school level, students develop the cognitive and analytical abilities necessary to understand and apply scientific knowledge in real-life situations. This theoretical paper examines the concept, relevance, and challenges of scientific literacy among secondary school students and proposes effective strategies for its enhancement. Drawing upon established literature, the paper highlights the importance of inquiry-based learning, integration of technology, contextual teaching, and teacher professional development. The study concludes that fostering scientific literacy is essential not only for academic success but also for preparing responsible and informed citizens in a knowledge-driven society.

В данной работе рассматривается двухшаговый метод решения нелинейных уравнений, содержащих дифференцируемый и недифференцируемый операторы. Наш подход основан на комбинации трех различных методов. Мы проанализировали локальную сходимость предлагаемого метода, учитывая как условия Липшица, так и условия L-среднего, и установили сверхквадратичный (≈ 2.414) порядок сходимости. В заключение мы выполнили сравнение численных результатов с результатами нескольких существующих методов. In this paper, we explore a double-step method for solving nonlinear equations containing a differentiable and non-differentiable operator. Our approach is built upon a combination of three different methods. We have analyzed the local convergence of the suggested method, considering both Lipschitz and L-average conditions & established the superquadratic (≈ 2.414) order of convergence. Finally we have pictured numerical results in comparison with several existing methods.

Precision agriculture requires accurate discrimination between winter crops and weeds, but there is a lack of annotated image data for winter cropping systems. This paper investigates a two-stage deep learning approach that integrates self-supervised feature learning with supervised fine-tuning for winter crop and weed image classification. A new winter crop and weed image dataset, WinterCropWeedDB, is proposed and used in this paper, which contains 1,136 high-resolution images of six winter crop species and four weed species collected from agricultural fields in central India. In the first stage of self-supervised learning, an EfficientNet-B3 model is pre-trained using a SimCLR-style self-supervised learning approach with an InfoNCE loss function (temperature τ = 0.5) on the images. The average contrastive loss value reduces from 2.0712 in the first iteration to 1.6835 at the end of pretraining. In the second stage of supervised fine-tuning, the pre-trained EfficientNet-B3 model is fine-tuned with a supervised classifier head on the images and tested on a single internal validation split (30%) of the dataset. The fine-tuned model reaches a maximum validation accuracy of 98.27%, with a macro-averaged F1-score of 0.98. Gradient-weighted class activation mapping (Grad-CAM) and Grad-CAM++ are used on the fine-tuned model to provide a qualitative visualization of the image regions that contribute to class predictions. The experiment outcomes demonstrate the viability of using self-supervised pretraining and supervised fine-tuning for the classification of winter crop and weed images on a region-specific dataset, while also emphasizing the importance of additional testing on independent test sets.

Vigna mungo (L.) Hepper, commonly called as black gram is one of the major pulse crop belongs to fabaceae family. Plant annexins are a well-known protein family involved in crucial biological processes such as membrane binding, calcium signalling, and stress responses through Ca2+ transport. In rice and Arabidopsis, annexins have been already identified and characterized. But information about annexins in Vigna mungo is limited. Based on the availability of the Vigna mungo genome sequence, we have identified eleven annexin gene members and performed the comprehensive analysis. Four conserved domains were found in all members except VmAnn10 and VmAnn11, which contained three domains in each. The motifs analysis showed the presence of 20 motifs in eleven VmAnn protein sequences. Gene structure analysis showed the presence of 3–5 introns in VmAnn gene members. Chromosomal distribution suggested that VmAnn genes might have undergone gene expansion through both segmental and tandem duplication events. For both paralogous and orthologous genes, the Ka/Ks ratios were found to be less than 1, suggesting that VmAnn genes have experienced purifying selection. Based on phylogenetic analysis of VmAnn proteins with OsAnn and AtAnn, all the members were clustered into six groups and shared a close evolutionary relationship. Moreover, the upstream promoter sequence analysis showed the presence of core cis-elements such as; TATA box, CAAT box and others elements related to growth and development, phytohormones and abiotic stress. It indicates the regulatory role of annexins in Vigna mungo in different processes. These results provide important information for further research on VmAnn genes and their characterization.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge, exacerbated by the rapid emergence of drug-resistant strains. In this study, a series of thiadiazoleazetidinone hybrid molecules was designed and synthesized by integrating two pharmacophores with known relevance in antimycobacterial drug discovery. The hybrid framework was conceived to explore the structural compatibility of thiadiazole-2-sulfonamide and azetidinone motifs within a single molecular architecture targeting two essential mycobacterial enzymes, decaprenylphosphoryl-β-Dribose 2′-oxidase (DprE1) and dihydrofolate reductase (DHFR), involved in cell wall biosynthesis and folate metabolism, respectively. The synthesized compounds displayed in vitro antimycobacterial activity against M. tuberculosis H37Rv and were further analyzed through molecular docking and molecular dynamics simulations (200 ns) to rationalize their interactions with both targets under dynamic conditions. These computational studies provided mechanistic insights into the binding modes, stability, and key interactions governing enzyme recognition within this hybrid series. In silico ADMET analysis indicated acceptable drug-like profiles across the scaffold. Rather than defining a clinically optimized candidate, this work establishes a structure-activity and structure-interaction framework that supports the thiadiazole-azetidinone hybrid concept and guides future chemical optimization toward antitubercular agents.