Volume 14 | Issue 6 |

 Abstract

The exponential surge in digital data across distributed systems has intensified the demand for compression mechanisms that are both lossless and computationally intelligent. Traditional algorithms such as Huffman coding, Lempel-Ziv variants, and arithmetic coding have served the field well for decades, yet exhibit fundamental limitations when confronted with contextually rich or heterogeneous data streams. This paper presents an intelligent file compression and decompression system that harnesses DeepSeek-R1:1.5B -- a lightweight yet powerful large language model -- deployed locally via Ollama to drive context-aware probability estimation and entropy encoding. The proposed system integrates LLM-generated probability distributions into an arithmetic coding backend, enabling significantly improved compression ratios for text-dominant file types compared to classical baselines. Experimental evaluation across diverse datasets demonstrates measurable gains in compression efficiency while maintaining perfect lossless reconstruction. The system addresses practical deployment concerns through local inference via Ollama, mitigating privacy and latency overheads associated with cloud-based LLMs. This work bridges the theoretical potential of neural language models with tangible, deployable compression infrastructure and charts a course for hybrid AI-classical compression frameworks.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Lossless Compression, Large Language Models, DeepSeek-R1, Ollama, Entropy Coding, Arithmetic Coding, Intelligent Compression, Sequence Modeling, Probability Estimation, AI-driven Data Management, etc.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Objectives: To evaluate the Current trends of molar mesialization Materials and Methods: This Review article includes articles (2019-2025) . Studies included orthodontic patients in the permanent dentition with Angles Class II division1 or 2, Class II subdivision, class I, mild or no skeletal discrepancies, 2-6 mm crowding, treated with CA. Results: Both Aligner and the Temporary Anchorage Devices (TADs) are effective in the molar mesialization. TADs (or a Hybrid TAD-Aligner system) are vastly superior and essential for predictable molar mesialization. Conclusions: The integration of Temporary Anchorage Devices (TADs) has effectively addressed the historical challenge of anchorage loss, while the advent of shape memory aligners and specialized molar protraction devices has refined the precision of tooth movement.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

molar mesialization, molar protraction, space closure, mandibular molar movement. TADs, mini-implants, transpalatal arch (TPA), skeletal anchorage

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The cloud-based systems and digital communication is increasing rapidly day by day and to ensure confidentiality, authenticity of information and integrity strong cryptographic mechanisms are needed. At present time in cyber security infrastructures, encryption key management and secure key exchange protocols plays a vital role. Various encryption key management techniques, public-key encryption methods, password-based key exchange protocols, chaos-based encryption systems, proxy re-encryption mechanisms, and homomorphic encryption approaches are inspected in this review paper. Twelve published papers are analysed to check the developments in encryption technologies and key distribution mechanisms. This review paper mainly focused on the transformation of secure key methodologies and their advantages, disadvantages, practical applications. Besides that, after analysis the different techniques revels the rising trends like, how to secure cloud-based communication? How to resist from cryptographic attacks? How to stronger the privacy preservation? In this review paper findings give right direction about traditional encryption system and advanced approaches indicate for future research. Different research gaps, future gaps also included in this study which helps for next generation applications cyber security.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cryptography, Encryption Keys, Key Exchange Protocols, Public-Key Encryption, Homomorphic Encryption

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Rapid urbanization and population growth have led to an increasing demand for high-rise buildings in urban areas where land availability is limited. As the height of buildings increases, their vulnerability to lateral loads such as earthquakes and wind also increases. High-rise reinforced concrete (RC) buildings are particularly susceptible to excessive lateral displacement, story drift, and torsional effects during seismic events. Therefore, it becomes essential to incorporate efficient lateral load-resisting systems to ensure the safety, stability, and serviceability of such structures. Among the various structural systems available, reinforced concrete shear walls and steel bracing systems have proven to be effective in enhancing the seismic performance of multi-storey buildings. Shear walls provide significant lateral stiffness and strength, while steel bracing systems improve the load-resisting capacity and energy dissipation of the structural frame. The combined use of these systems can further enhance the overall performance of high-rise buildings subjected to seismic forces. The present study focuses on the analysis and comparison of the structural behaviour of a high-rise RC building of G+40 storeys using different lateral load resisting systems. The main objective of this research is to evaluate the effectiveness of RC shear walls and steel bracing systems in controlling lateral displacement, story drift, and overall structural response under seismic loading conditions. A three-dimensional model of the building is developed and analysed using ETABS software. Different structural configurations are considered, including a bare frame model, a model with RC shear walls, a model with various steel bracing arrangements and a model with both the systems RC shear wall and steel bracing. These models are analysed to study their dynamic behaviour and seismic performance. The seismic analysis of the building is carried out using appropriate methods in accordance with relevant design codes. Important structural response parameters such as base shear, story displacement, story drift, fundamental time period, and story stiffness are evaluated and compared for different structural systems. The analysis aims to determine how the introduction of shear walls and bracing systems influences the lateral stiffness and overall stability of the building. The results obtained from the analysis indicate that the inclusion of RC shear walls and steel bracing significantly increases the stiffness of the building and reduces lateral displacement and story drift enhancing lateral load resistance and reducing the fundamental time period of the structure. The study also highlights that different bracing configurations have varying levels of effectiveness in controlling seismic response. In many cases, the combination of shear walls and steel bracing systems provides better performance compared to using either system alone. Overall, this study demonstrates that the proper selection and placement of shear walls and steel bracing systems play a crucial role in improving the seismic performance of high-rise buildings. The findings of this research provide useful insights for structural engineers in designing safer and more efficient high-rise RC buildings in earthquake-prone regions. The results can contribute to optimizing lateral load resisting systems for tall buildings and improving their resilience against seismic forces.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Shear wall, Steel bracing, Base Shear, Story Displacements, Story Drift, Fundamental time period, Story stiffness.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Micro enterprises operate with less capital, traditional machinery and few employees. They need finance for their day-to-day working and overall growth. Financial Institutions (FIs) play a crucial role in providing financial assistance and support at a reasonable rate and for the survival, growth and long-run development of micro enterprises. The researcher conducted the study on the impact of institutional financial support on growth and sustainability of micro enterprises in Ranchi district of Jharkhand. This study used primary data for testing the hypothesis and observed the response of entrepreneurs to know the opinions of respondents on the impact of FI support on growth and sustainability of ME. In this study, the survey method was used to collect data from approx 100 respondents. The researcher used the convenience sampling method for collecting data. After testing the hypothesis from the data collected by the researcher, it is evident that FIs' support had a very crucial impact on effective management and smooth working of micro enterprises. There is a positive relationship between credit support from FIs and growth and longevity of micro enterprises. While conducting this survey, the researcher evaluated that respondents agreed with the fact that financial support, like skill development, advising and guidance programs by the government and FIs, is necessary for the growth and survival of ME. The researcher also found that there are some major challenges which create a hindrance in easy accessibility of loan facilities by micro enterprises from FIs. It is concluded that institutional financial support has a strong, effective, and positive effect on overall growth and development of Micro enterprises.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Microenterprise, Financial Institutions, Sustainability, self sufficient

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Mizoram has one of the highest HIV prevalence rates in India, with female sex workers (FSWs) remaining one of the most vulnerable populations due to social stigma, economic insecurity, limited healthcare access, and marginalization. In this context, both faith-based organizations (FBOs) and non-governmental organizations (NGOs) have emerged as important actors in HIV prevention and support services. This study examines the role of FBOs and NGOs in reducing HIV transmission among female sex workers in Mizoram, with particular focus on Aizawl and Lunglei districts. Using a qualitative research design, the study draws on in-depth interviews with 25 female sex workers, interviews with NGO workers and faith leaders, participant observation, and document analysis. The findings reveal that NGOs primarily adopt a rights-based and harm-reduction approach by providing HIV testing, counselling, condom distribution, peer education, and linkage to antiretroviral therapy (ART) services. In contrast, faith-based organizations focus more on spiritual healing, behavioural reform, emotional support, rehabilitation, and social reintegration. Despite ideological differences, both sectors contribute significantly to HIV prevention and care among marginalized women. Many participants viewed NGOs as more approachable for immediate healthcare needs, while FBOs were valued for emotional healing, moral support, and community acceptance. The study also identifies emerging collaboration between NGOs and FBOs, particularly in awareness programmes and counselling initiatives. The paper argues that effective HIV intervention in Mizoram requires a culturally sensitive and collaborative approach that integrates biomedical support with emotional, spiritual, and social care. It recommends stronger partnerships between state agencies, NGOs, and FBOs, increased trauma-informed mental health services, and community-based anti-stigma interventions. The study contributes to broader discussions on religion, public health, gendered stigma, and community-led HIV interventions in Northeast India.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

HIV/AIDS, female sex workers, Mizoram, faith-based organizations, NGOs, stigma, harm reduction, public health, community support

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study evaluates the effectiveness of a yoga intervention in reducing insomnia symptoms, improving sleep quality, and lowering anxiety among middle-aged adults. Insomnia is a common condition in this population, often associated with poor sleep quality and heightened anxiety, which can significantly impact overall well-being. The present study compares an experimental group undergoing a structured yoga program with a control group that did not receive the intervention.The findings indicate a significant improvement in insomnia severity in the experimental group, with a mean difference of 15.92 (SD = 2.55, p < 0.05), demonstrating the positive impact of yoga in reducing insomnia symptoms. In contrast, the control group showed a lower mean difference of 13.82 (SD = 2.52), which was not statistically significant (p > 0.05), indicating minimal improvement without intervention. Similarly, sleep quality improved significantly in the experimental group, with a mean difference of 14.63 (SD = 2.41, p < 0.05). The control group, however, showed negligible change (mean = -0.63, SD = 1.56, p > 0.05), suggesting that yoga plays a crucial role in enhancing sleep patterns.In terms of anxiety, the experimental group demonstrated a significant reduction (mean difference = 15.22, SD = 2.28, p < 0.05), whereas the control group showed less improvement (mean = 13.7, SD = 2.46, p > 0.05).

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Yoga, Insomnia, sleep quality, anxiety, and middle-aged adults

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Dengue fever is a major vector-borne viral disease in India, with rising morbidity and mortality during seasonal outbreaks. Caregivers of admitted patients play a critical role in monitoring warning signs, ensuring hydration, and adopting preventive measures. Assessing their knowledge helps identify gaps and guide targeted education. To assess the knowledge regarding dengue fever among caregivers of dengue patients admitted at PBM Hospital, Bikaner, and examine its association with selected demographic variables.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Dengue fever; caregivers; knowledge assessment; hospital-based study; vector-borne disease prevention

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Instagram Reels content creators often struggle to predict how their videos will perform before publishing. "InstaViral AI" is an intelligent video analysis and optimization system that uses deep learning and machine learning to analyze video, audio, captions, and user data. It automatically identifies highlights, selects effective thumbnails, and suggests improvements such as trending hashtags, optimized captions, upload timing, and ideal video length. The system aims to increase content reach and reduce the trial-and-error process in social media content creation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, content optimization, Deep Learning, popularity prediction, Social Media Analysis, video analysis

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Blood donation prediction has emerged as a critical application of machine learning in healthcare analytics due to the continuous global demand for a safe and sufficient blood supply. This research proposes a Blood Donor Likelihood Evaluation System using Machine Learning Algorithms to predict whether an individual is likely to donate blood in the future. The system analyzes multiple demographic, medical, and behavioral attributes such as age, gender, blood group, hemoglobin level, weight, number of previous donations, and time since last donation. Advanced data preprocessing techniques including missing value handling, categorical encoding, feature scaling, and feature selection are applied to improve prediction accuracy. Two classification algorithms--Logistic Regression and Random Forest--are implemented and evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC score. Experimental results indicate that Logistic Regression provides high interpretability and stable performance. The trained model is deployed using a Flask-based web application that enables real-time donor likelihood prediction through a user-friendly interface. The proposed system assists blood banks and healthcare organizations in improving donor retention strategies, optimizing resource allocation, and ensuring consistent blood availability.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blood Donation Prediction, Machine Learning, Logistic Regression, Random Forest, Healthcare Analytics, Donor Retention, Flask Deployment, Predictive Modeling

  License

Creative Commons Attribution 4.0 and The Open Definition