Category Archives: Uncategorized

Authors: Usha Rani

Abstract: – In today’s dynamic enterprise IT landscape, system patching is a critical operation that ensures security, compliance, and performance. Manual patching processes are often fraught with delays, configuration drift, and inconsistencies, leading to potential security breaches and downtime. Automating this process using integrated tools like Red Hat Satellite and Puppet significantly enhances lifecycle management by aligning system states with organizational policies. Red Hat Satellite offers a centralized platform for managing Linux content, lifecycle environments, and host registration, while Puppet provides robust configuration management capabilities for enforcing desired system states. Together, they enable enterprises to deploy, audit, and maintain patches consistently across vast infrastructure landscapes. This review explores the symbiotic relationship between Satellite and Puppet, focusing on how their integration delivers operational efficiency and compliance. It discusses the underlying architecture of each tool, the mechanics of their integration, and the workflow that governs automated patching. The study highlights key functionalities such as content views, CVE mapping, node classification, and patch window orchestration. Additionally, the review presents real-world case studies from financial services, healthcare, and telecom sectors that have adopted this integration for scalable and secure patch management. The article also identifies challenges in implementation, including integration complexity, legacy system compatibility, and potential risks from misclassification or dependency conflicts. Future trends are examined, including the use of AI/ML for predictive patching, ChatOps for collaborative operations, and declarative frameworks for Patch as Code strategies. In conclusion, the integrated use of Satellite and Puppet forms a cornerstone for secure, compliant, and cost-effective system maintenance, empowering IT organizations to proactively manage vulnerabilities while reducing operational overhead.

DOI: https://doi.org/10.5281/zenodo.15798673

 

Published by: vikaspatanker

Authors: Hamid Ansari

Abstract: In today’s digital landscape, enterprise IT environments demand resilient and scalable disaster recovery (DR) solutions, especially in hybrid UNIX systems where Solaris, AIX, HP-UX, and Linux coexist. These systems often run critical workloads in sectors like finance, healthcare, telecommunications, and government, necessitating DR architectures that ensure high availability, data integrity, and business continuity across heterogeneous platforms. This review provides a comprehensive analysis of scalable DR architectures tailored for hybrid UNIX environments, addressing the complex interplay between storage replication, backup strategies, orchestration tools, and operating system-level recovery mechanisms. Key architectural patterns such as active-active and multi-site replication models are examined alongside file system-level and block-level replication technologies including ZFS send/receive, Veritas Volume Replicator, and SAN mirroring solutions. The paper compares OS-specific recovery tools like Ignite-UX, mksysb, and Solaris Unified Archives, and assesses their interoperability in multi-vendor environments. Further, the study explores the orchestration layer of disaster recovery, highlighting the role of configuration management and automation tools like Ansible, Puppet, and scripting frameworks. Monitoring, testing, and policy-driven recovery are addressed as essential pillars of a sustainable DR strategy. Real-world case studies are analyzed to illustrate practical implementations, performance outcomes, and lessons learned in deploying scalable DR across diverse UNIX infrastructures. Challenges such as format incompatibility, network reconfiguration, and security hardening are critically discussed. Finally, the review anticipates emerging trends, including the use of AI/ML for proactive fault prediction and the integration of DR into continuous compliance and observability pipelines. This article serves as a reference for system architects, disaster recovery planners, and enterprise IT professionals seeking to build resilient, automated, and cross-platform DR frameworks for UNIX-centric infrastructures.

DOI: http://doi.org/10.5281/zenodo.15798539

Published by: vikaspatanker

Authors: Albert Joshep

Abstract: Predictive maintenance is an emerging discipline that combines system telemetry, machine learning, and automation to preemptively identify and resolve failures in complex computing environments. This review explores the implementation of predictive maintenance in Solaris and Red Hat Enterprise Linux (RHEL) platforms two prominent Unix-based systems widely deployed across enterprise IT landscapes. By comparing architectural features, telemetry sources, and modeling techniques, the study highlights both the unique capabilities and challenges presented by each operating system. Solaris benefits from a robust fault management architecture (FMA), advanced diagnostics like DTrace, and SPARC hardware optimization, making it well-suited for hardware-level monitoring. Red Hat, on the other hand, excels in automation, scalability, and hybrid cloud compatibility through tools such as Red Hat Insights, Ansible, and Performance Co-Pilot. The article delves into key predictive modeling strategies including time-series forecasting, anomaly detection, and classification, utilizing methods ranging from ARIMA and Isolation Forests to neural networks. Integration and automation workflows are examined, showcasing how Unix-native tools and open-source frameworks are used to train, deploy, and act upon model predictions. Through case studies, the review quantifies the benefits of predictive maintenance, including reduced mean time to recovery (MTTR), enhanced SLA adherence, and cost savings. Finally, it discusses limitations such as data inconsistency, model drift, and cross-platform transferability, while outlining future directions including AI co-pilots, self-learning systems, and Predictive Maintenance-as-a-Service (PMaaS). By offering a detailed comparative analysis and strategic recommendations, this review serves as a practical guide for enterprises aiming to implement or enhance predictive maintenance in mixed Unix environments.

DOI: https://doi.org/10.5281/zenodo.15798515

Published by: vikaspatanker

Authors: Prachi Babasaheb Desai

Abstract: Mental health is an essential aspect of human well- being, yet millions remain undiagnosed or untreated due to stigma and lack of access to care. This research presents an AI-Based Mental Health Detection and Therapy Recommendation System designed to identify early signs of stress, anxiety, and depression using natural language processing (NLP), voice tone analysis, and user responses to validated questionnaires. The system recommends tailored therapeutic interventions such as mindfulness techniques, journaling, and referrals to professionals. This scalable, explainable, and user- friendly solution aims to democratize access to mental health support.

DOI: https://doi.org/10.5281/zenodo.15813444

 

Published by: vikaspatanker

Authors: Jupaka Mukesh Kumar, Kasaboina Mahesh, Thulugu Dileep, Dr. Yagya Dutta Dwivedi

Abstract: This project presents an overall aerodynamic analysis of an Audi R8 using computational fluid dynamics (CFD) for performance enhancement in terms of decreasing drag and increasing downforce. The study proposes to investigate the effect of a Selig S1223 (s1223-il) rear spoiler at varying angles of attack 0°, 3°, and 5° at varying inlet speeds of 20 m/s, 30 m/s, and 40 m/s. The analysis was conducted by simulating the model in SolidWorks for geometry and ANSYS Fluent for the flow study. The car was first analyzed in the base state without a spoiler, exhibiting growth in Coefficient of lift (CL) and Coefficient of drag (CD) coefficients as speed increases. After installing the spoiler, the lift decreased by a remarkable margin (with creation of downforce) while the drag increased. The study presents that the higher the angle of attack, the higher the downforce, thus improving the vehicle's stability but at greater drag forces. Using a experimental analysis of the result from the two cases involving a spoiler and no spoiler, this project proves optimal aerodynamic design changes that minimize drag and increase the aerodynamic efficiency of the vehicle as a whole. Such results are useful in designing performance vehicles with greater handling and lesser aerodynamic drag.

DOI: http://doi.org/

 

Published by: vikaspatanker

Authors: Nithin Nanchari

Abstract: The Internet of Things in healthcare provides healthcare with its delivery of patient care from real-time data monitoring, remote diagnostics, and personalized treatment. However, due to this advancement, there are data privacy and security issues like data breaches, cyber threats, and unauthorized access. The paper contributes by identifying the potential key security issues and vulnerabilities in IoT healthcare and how data has been routed through vulnerabilities, ensuring the security of the healthcare system.

DOI: http://doi.org/10.5281/zenodo.15796381

 

Published by: vikaspatanker

Authors: Nithin Nanchari

Abstract: With the rise of the web of things in the health sector, the personalized treatment of people in real-time using real data has evolved into shape. Through IoT and personalized healthcare, individual medical interventions are delivered so that patients can monitor themselves and gain better treatment effectiveness. The contribution of this paper consists of how IoT enables custom healthcare solutions through AI-driven health assistants, real-time data analytics, a patient-centric approach, and wearable technology. Also, the study highlights the utility of IoT in improving the accuracy of precision medicine and improving healthcare services. Such a personalized healthcare solution could progress by integrating IoT and AI.

DOI: http://doi.org/10.5281/zenodo.15796148

 

Published by: vikaspatanker

Authors: Nithin Nanchari

Abstract: The Internet of Things (IoT) is revolutionizing the healthcare industry by enabling unprecedented levels of connectivity, operational efficiency, and patient-centered care. With the help of smart medical devices and real-time data analytics, healthcare providers can now predict, monitor, and automate various clinical and administrative functions more effectively than ever before. This paper introduces the concept of IoT in healthcare, explores its primary applications such as remote patient monitoring, smart hospitals, and medication management, and outlines the benefits it delivers to patients and providers. While challenges such as cybersecurity threats and lack of standardization persist, the overall impact of IoT in healthcare continues to grow, driving improvements in outcomes, access, and efficiency.

DOI: http://doi.org/10.5281/zenodo.15795982

 

Published by: vikaspatanker

Authors: Mrs. Penki Tulasi Bai, Mrs. P. Manasa

Abstract: This study suggests employing predictive maintenance to enhance the operational efficiency and prolong the lifespan of industrial machinery and equipment through machine-learning techniques. As producers prioritize reducing downtime and cutting expenses, proactive maintenance strategies are becoming increasingly vital for ensuring operational reliability. The research aims to gather historical data to train machine-learning models that can predict equipment failures and develop an algorithmic framework for scheduling preventive maintenance. The primary objective is to assist in forming an effective anticipatory maintenance strategy, which can lower industrial maintenance costs and improve product prices. Various machine-learning techniques, along with extensive data preprocessing and feature engineering methods, will be utilized in this research. Data preprocessing will involve tasks such as cleaning, dataset conversion, and normalization prior to model training. Feature engineering will focus on identifying the most important characteristics for accurate prediction of machine failures. Numerous machine-learning methods, including Random Forest (RF), Long Short-Term Memory (LSTM), and Support Vector Machines (SVM), will be evaluated to determine the most effective model for precise forecasting. The performance of these models will be compared using metrics such as Root Mean Square Error (RMSE), R-squared (R²), and Mean Absolute Error (MAE) as indicators. Ultimately, the top-performing machine-learning models will be integrated into real industrial settings, with the optimal model expected to achieve a 5-10% increase in operational efficiency.

DOI: http://doi.org/10.5281/zenodo.15790457

 

Published by: vikaspatanker

Authors: Ms. Bobbili Bhargavi, Mrs. K. Sowjanya

Abstract: The complex evolution of railway cars influences transit routes. Many mistakes arise from the utilization of train lines. Both Manufacturing mistakes and improper rail usage are responsible. For these deficiencies. There are numerous techniques for detection. Errors must be recognized promptly and rectified. The camera-based technique is One of these methods. By utilizing cameras affixed to the railway vehicle, images of the rail components are examined. Flaws are identified in the rail components. A method for detecting and analysing defects in rail tracks. Surfaces are proposed in this document. The recommended method employs image processing to identify the rail surface. High resolution images captured by specialized cameras mounted on the proposed system encompasses railway inspection cars. A Variety of track issues, including cracks, weld defects, and track Misalignment and ballast degradation are detected. These images were utilized to perform an analysis. Pre-processing and feature extraction. Image processing entails the application of segmentation techniques. Procedures to isolate the track area and emphasize any Potential defects. Fuzzy logic is employed to prioritize maintenance tasks. Based on urgency once issues have been identified and their Severity has been evaluated. Fuzzy logic is particularly adept at capturing the subjective assessments involved in evaluating. track conditions as it offers a flexible framework. Processing ambiguous and imprecise data. To assign appropriate severity ratings for the identified features of each issue. Type, fuzzy rules, and membership functions are developed.

DOI: http://doi.org/10.5281/zenodo.15790262

 

Published by: vikaspatanker