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DESIGN AND EVALUATION OF GUARGUM - BASED TIMOLOL MALEATE OCULAR INSERT Kumar, Sunil; Issarani, Roshan; Nagori, B P
INDONESIAN JOURNAL OF PHARMACY Vol 26 No 4, 2015
Publisher : Faculty of Pharmacy Universitas Gadjah Mada, Yogyakarta, Skip Utara, 55281, Indonesia

The objective of this study was to prepare ocular inserts of timolol maleate using guar gum as a polymer. Timolol maleate ocular inserts were prepared by solvent casting method using guar gum in different proportions (0.25% w/v, 0.50% w/v , 75 % w/v and 1.0% w/v). The prepared formulations were evaluated for thickness, weight variation, percentage drug content, surface pH, folding endurance, percentage moisture absorption and loss, percentage swelling, mechanical strength and in vitro transcorneal permeation. In-vitro transcorneal permeation study was performed on goat cornea using modified Franz diffusion cell. The inserts were found to be of uniform thickness (ranging from 41.12Â±0.04Âµm to 79.90Â±0.03Âµm) and weight (0.84Â±0.07 mg to 2.11Â±0.09 mg). The % drug content in the inserts was found to be varied between 98.69Â±0.58to 96.37Â±0.58. The cumulative % drug releases from the formulation ranged from 50.22Â±1.41 to 97.72Â±0.67over a period of 24 h. In-vitro transcorneal study revealed that an increase in concentration of polymer slows down the release of timolol maleate from the formulation. Ocular inserts using guar gum as a polymer were successfully prepared and can be effectively used for sustain ocular delivery over a period of 24 h.Key words: Guar gum, in vitro transcorneal permeation study, ocular insert, timolol maleate, sustained release

Analysis of Nature and Extent of Nutrition Education Imparted to the Rural School Children Pal, Ankit; Dixit, Awadhesh; Kumar, Sunil; Alok Dube; Ashish Singh; Akanksha Singh
International Journal of Social Sciences Review Vol. 5 No. 1: April, 2024
Publisher : Epistemik Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57266/ijssr.v5i1.270

With an emphasis on the responsibilities played by teachers, parents, and Anganwadi workers, this study intends to analyse the existing practises of nutrition teaching in rural schools in Bihar, India. An ex-post facto research design was used for the study, which was carried out in the Bihar district of Arwal. Teachers, parents, and Anganwadi staff made up the 240 participants who were drawn at random. Data collection was done using questionnaires, which were pretested for validity and reliability. As well as identifying any gaps or areas for improvement, the obtained data were analysed to ascertain the common tactics and methodology utilised by educators, parents, and Anganwadi staff in delivering nutrition education. According to the study, parents used a variety of tactics to promote healthy eating habits in their kids, including giving them choices, giving nutritious foods catchy names, and making food appealing to the eye. For nutrition teaching, teachers mostly used lectures, discussions, and visual aids like nutrition charts and posters.

Is depreciation fraud detectable using ADTFA and DAAT financial models? A case study Kumar, Sunil
International Journal of Financial, Accounting, and Management Vol. 5 No. 4 (2024): March
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ijfam.v5i4.1624

Purpose: Financial statement fraud, which is usually committed by insiders, aims to present a company positively and benefit fraudsters. Insiders commit fraud to deceive investors or hide their mistakes. This occurs in companies with weak control and unethical leaders. Prevention is important; however, early detection is crucial. Depreciation fraud manipulates the depreciation schedule to make financial statements look better. This involves inflating asset values and reducing expenses. Detecting depreciation fraud is difficult, and has severe consequences. Such activities can lead to penalties for both individuals and companies. Companies require accurate records, and auditors must review statements thoroughly to prevent and uncover fraud. New models were used to identify depreciation fraud in defaulting companies. Research methodology: Forensic accountants may analyze depreciation fraud. We use Depreciation Accumulated after Tax (DAAT) to accurately find depreciation fraud by the company. A comparatively low or negative impact indicates depreciation fraud. The ADTFA and DAAT financial models can be used to trace depreciation fraud. Results: The results are remarkable and should be tested in further depreciated fraud companies to detect their financial health position early. Limitations: Detecting depreciation fraud is difficult because of various factors, including complex accounting methods, subjective estimates, and lack of external verification. Contribution: This helps to account for users and investors, researchers detect depreciation fraud earliest, and present its financial accounting report. Novelty: The researcher may adopt and push validated reliability through ADTFA and DAAT tests to detect depreciation fraud.

Analysis, design, and control of standalone PV based boost DC-AC converter Nayak, Jnanaranjan; Kumar, Sunil; Sahu, Pradeep Kumar; Jena, Satyaranjan
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i2.pp294-302

This paper presents a new control scheme for a boost DC–AC converter which is used for solar power applications. The proposed DC-AC converter configuration can produce an AC voltage level across the output or load side greater than input DC voltage based on the operating duty cycle. Generally, the conventional DC-AC converter or voltage source inverter (VSI) generates AC voltage which is less than input DC voltage. Maintaining a constant voltage across the load with improved dynamic performance is challenging for anyone for the solar photovoltaic (PV) system. A dual-loop sliding mode control is proposed for the boost VSI to address the above issues. The proposed controller has robust in nature against the wide fluctuation in the plant or load parameters. The design, analysis and control of the boost DC-AC converter are briefly discussed in this paper. This topology can be broadly used in solar powered uninterruptible power supply (UPS) where boosting operation is essential for low voltage solar PV system. This topology eliminates the DC boosting power processing stage which leads an improved efficiency of the overall system. The MATLAB/Simulink results are presented to highlight the above issues.

Pyramid Quantum Neural Network Based Resource Allocation with IoT: A Deep Learning Method Singh, Khushwant; Yadav, Mohit; Kirti; Kumar, Sunil; Sobirov, Bobur
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1578

As more smart devices are connected and collecting massive quantities of data, the Internet of Things is growing rapidly. Resource management is another crucial issue since IoT networks are very diverse and often built and rebuilt dynamically. This study introduces a new kind of deep learning model known as the Pyramid Quantum Neural Network (PY-QNN) to solve the problem of resource allocation in Internet of Things systems. PY-QNN builds on quantum computing to improve the accuracy, scalability, and computation performance of Deep Learning. Because of superposition and entanglement, which increase generalization and provide faster convergence, QNNs enhance learning capabilities. The pyramid structure also helps manage the hierarchy of IoT networks. In order to forecast efficient resource assignment and implement this as soon as feasible to lower latency and boost efficiency, PY-QNN uses simulated resource and network requirements. Experimental findings demonstrate that PY-QNN outperforms baseline common deep learning techniques by reducing resource waste and offering online solutions, especially in large and complex IoT networks.

Role of fintech in financial inclusion: A quantitative review Sharma, Mona; Bahl, Jyotika; Gothwal, Pushpender; Kumar, Dev; Atree, Luxmi; Kumar, Sunil
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1133

This study presents a comprehensive bibliometric analysis of the scholarly literature on fintech and financial inclusion. Using a structured search strategy, 611 articles published in Scopus-indexed journals were analysed to uncover key trends, contributors, and research themes within this rapidly evolving field. The analysis identifies “sustainable development” and “China” as well-developed and central motor themes, while “fintech,” “financial inclusion,” and “electronic money” remain central yet still developing areas of inquiry. Notably, Ozili P.K. emerged as the most influential author, with a high citation impact and H-index, followed by Banna H and Mhlanga D. The Journal of Risk and Financial Management, Sustainability (Switzerland), and Finance Research Letters were the leading publication outlets. Geographically, India led in terms of publication volume, reflecting its dynamic fintech ecosystem, whereas the UK and US showed strong international research collaborations. Despite a solid foundation, the literature reveals underdeveloped focus areas, particularly regarding the traditional financial system and the integration of emerging technologies. These points point to meaningful gaps for future exploration, including the application of blockchain, artificial intelligence, and digital identity frameworks to promote inclusive finance. Additionally, socio-cultural factors influencing fintech adoption remain insufficiently explored, especially in underserved communities. Cross-country comparative research and long-term studies are also needed to deepen our understanding of fintech’s role in achieving inclusive and sustainable economic growth.

COV-TViT: An Improved Diagnostic System for COVID Pneumonitis Utilizing Transfer Learning and Vision Transformer on X-Ray Images Kumar, Sunil; Yadav, Amar Pal; Nandal, Neha; Awasthi, Vishal; Sapra, Luxmi; Chhabra, Prachi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1037

COVID is a contagious lung ailment that continues to be a world curse, and it remains a highly infectious respiratory disease with global health implications. Traditional diagnostic methods, such as RT-PCR, though widely used, are often constrained by high costs, limited accessibility, and delayed results. In contrast, radiology for lung disease detection has been proven advantageous for identifying deformities, and chest X-rays are the most preferred radiological method due to their non-invasive nature. To address these limitations, this study aims to develop an efficient, automated diagnostic system leveraging radiological imaging, specifically X-rays, which are cost-effective and widely available. The primary contribution of this research is the introduction of COV-TViT, a novel deep learning framework that integrates transfer learning with Vision Transformer (ViT) architecture for the accurate detection of COVID pneumonitis. The proposed method is evaluated using the COVID-QU-Ex dataset, which comprises a balanced set of X-ray images from COVID positive and healthy individuals. Methodologically, the system employs pre-trained convolutional neural networks (CNNs), specifically VGG16 and VGG19 (Visual Geometry Group), for transfer learning, followed by fine tuning to enhance feature extraction. The ViT model, known for its self-attention mechanism, is then applied to capture complex spatial dependencies in the X-ray images, enabling robust classification. Experimental results demonstrate that COV-TViT achieves a classification accuracy of 98.96% and an F1 score of 96.21%, outperforming traditional CNN based transfer learning models in several scenarios. These findings underscore the model’s potential for high-precision COVID pneumonitis detection. The proposed approach significantly transforms classification tasks using self-attention mechanisms to extract features and learn representations. Overall, the proposed diagnostic system COV-TViT can be advantageous in the fundamental identification of COVID pneumonitis.

Is depreciation fraud detectable using ADTFA and DAAT financial models? A case study Kumar, Sunil
International Journal of Financial, Accounting, and Management Vol. 5 No. 4 (2024): March
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ijfam.v5i4.1624

Purpose: Financial statement fraud, which is usually committed by insiders, aims to present a company positively and benefit fraudsters. Insiders commit fraud to deceive investors or hide their mistakes. This occurs in companies with weak control and unethical leaders. Prevention is important; however, early detection is crucial. Depreciation fraud manipulates the depreciation schedule to make financial statements look better. This involves inflating asset values and reducing expenses. Detecting depreciation fraud is difficult, and has severe consequences. Such activities can lead to penalties for both individuals and companies. Companies require accurate records, and auditors must review statements thoroughly to prevent and uncover fraud. New models were used to identify depreciation fraud in defaulting companies. Research methodology: Forensic accountants may analyze depreciation fraud. We use Depreciation Accumulated after Tax (DAAT) to accurately find depreciation fraud by the company. A comparatively low or negative impact indicates depreciation fraud. The ADTFA and DAAT financial models can be used to trace depreciation fraud. Results: The results are remarkable and should be tested in further depreciated fraud companies to detect their financial health position early. Limitations: Detecting depreciation fraud is difficult because of various factors, including complex accounting methods, subjective estimates, and lack of external verification. Contribution: This helps to account for users and investors, researchers detect depreciation fraud earliest, and present its financial accounting report. Novelty: The researcher may adopt and push validated reliability through ADTFA and DAAT tests to detect depreciation fraud.

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