Category Archives: Uncategorized

Authors: Seema Kumari, Dr. Mukta Jain

Abstract: Biogeochemical cycles represent natural routes through which vital chemical elements circulate among the atmosphere, hydrosphere, lithosphere, and biosphere. These cycles are crucial for preserving ecological equilibrium and supporting life on our planet. Among the significant biogeochemical cycles, carbon, Sulphur, and oxygen cycles are essential in regulating environmental processes and aiding living organisms. The carbon cycle encompasses the transfer of carbon through photosynthesis, respiration, decomposition, and combustion, thus sustaining atmospheric carbon dioxide levels. The Sulphur cycle involves the transit of sulphur compounds through rocks, soil, water, atmosphere, and organisms via weathering, volcanic activities, microbial decomposition, and industrial emissions. The oxygen cycle is intricately linked to the carbon cycle, where oxygen is generated during photosynthesis and utilized in respiration, oxidation, and combustion processes. These interrelated cycles facilitate nutrient recycling, energy transfer, and the maintenance of ecosystem stability. Human activities such as deforestation, industrialization, mining, fossil fuel combustion, and environmental pollution have disrupted the natural equilibrium of these cycles, resulting in climate change, acid rain, global warming, ozone depletion, and ecological imbalance. Consequently, comprehending the operation and importance of carbon, Sulphur, and oxygen cycles is vital for environmental conservation, sustainable resource management, and safeguarding life on Earth.

DOI: https://doi.org/10.5281/zenodo.20157119

Published by: vikaspatanker

Authors: Dr. Kevin Brooks, Laura Mitchell, Dr. Daniel Foster, Christopher Evans, Dr. Olivia Bennett, Jeji Krishnan

Abstract: Large-scale messaging systems serve as the backbone of enterprise communication, supporting millions of users and high volumes of real-time interactions, but their growing complexity presents significant challenges for escalation support teams tasked with rapid issue diagnosis during outages and performance degradations. Traditional troubleshooting methods rely heavily on manual analysis of thread dumps, mailbox logs, and distributed system metrics, which is time-consuming, error-prone, and inefficient under critical conditions. This paper proposes a dashboard-driven operational intelligence framework that transforms escalation support by integrating diverse data sources into a unified, interactive platform offering real-time visibility into system behavior. By leveraging advanced analytics, automated event correlation, and visual representations such as graphs and heatmaps, the framework enables faster detection of anomalies, bottlenecks, and failure patterns. The system introduces intelligent data aggregation and one-click diagnostic capabilities that significantly reduce the effort required for root cause analysis while enhancing accuracy. Additionally, predictive insights derived from historical patterns support proactive issue resolution, minimizing system downtime. Experimental evaluation demonstrates substantial improvements in mean time to resolution (MTTR), diagnostic precision, and overall operational efficiency compared to conventional approaches. The results emphasize the effectiveness of combining operational intelligence with visual analytics to enhance the reliability, scalability, and performance of large-scale messaging systems, providing a practical foundation for next-generation escalation engineering and intelligent observability solutions.

DOI: http://doi.org/10.5281/zenodo.20156995

Published by: vikaspatanker

Authors: Ms. Arati H. Kunjir, Mr. S.P. Shinde

Abstract: The rapid growth of the Internet of Things (IoT) has significantly transformed the healthcare sector by enabling real-time monitoring, automation, and intelligent decision-making. Traditional hospital systems often rely on manual monitoring and limited automation, which can lead to delayed responses and inefficiencies in patient care. This paper presents an IoT-based hospital automation and patient monitoring system that continuously monitors vital health parameters such as temperature, heart rate, oxygen saturation (SpO₂), and environmental conditions. The system integrates smart sensors, microcontrollers, and cloud platforms to collect, process, and transmit data in real time. Medical staff can access patient data remotely through a web or mobile interface, enabling timely intervention and improved healthcare management. The proposed system enhances patient safety, reduces workload on medical staff, and improves the overall efficiency of hospital operations.

DOI: https://doi.org/10.5281/zenodo.20157156

Published by: vikaspatanker

Authors: Ms. Arati H. Kunjir, Mr. S.P. Shinde

Abstract: The rapid growth of the Internet of Things (IoT) has significantly transformed the healthcare sector by enabling real-time monitoring, automation, and intelligent decision-making. Traditional hospital systems often rely on manual monitoring and limited automation, which can lead to delayed responses and inefficiencies in patient care. This paper presents an IoT-based hospital automation and patient monitoring system that continuously monitors vital health parameters such as temperature, heart rate, oxygen saturation (SpO₂), and environmental conditions. The system integrates smart sensors, microcontrollers, and cloud platforms to collect, process, and transmit data in real time. Medical staff can access patient data remotely through a web or mobile interface, enabling timely intervention and improved healthcare management. The proposed system enhances patient safety, reduces workload on medical staff, and improves the overall efficiency of hospital operations.

DOI:

Published by: vikaspatanker

Authors: Ms. Arati H. Kunjir, Mr. S.P. Shinde

Abstract: The rapid growth of the Internet of Things (IoT) has significantly transformed the healthcare sector by enabling real-time monitoring, automation, and intelligent decision-making. Traditional hospital systems often rely on manual monitoring and limited automation, which can lead to delayed responses and inefficiencies in patient care. This paper presents an IoT-based hospital automation and patient monitoring system that continuously monitors vital health parameters such as temperature, heart rate, oxygen saturation (SpO₂), and environmental conditions. The system integrates smart sensors, microcontrollers, and cloud platforms to collect, process, and transmit data in real time. Medical staff can access patient data remotely through a web or mobile interface, enabling timely intervention and improved healthcare management. The proposed system enhances patient safety, reduces workload on medical staff, and improves the overall efficiency of hospital operations.

DOI:

Published by: vikaspatanker

Authors: Ms. Sanika Sachin Jadhav

Abstract: The growing use of algorithmic management systems in pharmaceutical organisations has changed how employees are supervised, evaluated, and directed. Instead of relying on human managers, these systems use automated data collection and continuous monitoring to govern how employees work. This study looks at both sides of this shift, the operational benefits it produces and the psychological harm it causes. A cross sectional survey was conducted with 250 pharmaceutical professionals, comprising 125 Sales Representatives and 125 Quality Control Analysts. The study measured technostress, psychological contract breach, and perceived algorithmic opacity. Results showed that AI supervision increased output by 18.4% but also led to a 22% rise in technostress scores and a 128% jump in turnover intention among algorithmically managed workers. A strong negative correlation of r = 0.74 (p < 0.01) between algorithmic opacity and organisational trust confirms that lack of transparency is a key mechanism through which algorithmic management damages the employee organisation relationship. Based on these findings, this paper proposes a Human Centric Algorithmic Framework that incorporates Human in the Loop design as a practical governance solution.

Published by: vikaspatanker

Authors: Nishant Singh, Sudhanshu Kumar, Shushant Mani Tripathi, Manisha Pundir

Abstract: With the rapid growth of digital streaming platforms, users are exposed to a vast amount of movie content, making it difficult to identify relevant choices. This paper presents a Content-Based Movie Recommendation System that suggests movies based on their inherent features such as genre, cast, and keywords. The proposed system utilizes Machine Learning techniques, including TF-IDF (Term Frequency–Inverse Document Frequency) or Count Vectorization for feature extraction and Cosine Similarity for measuring similarity between movies. Unlike collaborative filtering methods, the system does not rely on user interaction data, thereby effectively addressing the cold start problem for new users. The model processes a structured movie dataset, converts textual data into numerical vectors, and generates recommendations based on similarity scores. The system is implemented using Python and deployed using Streamlit, providing an interactive and user-friendly interface. Experimental results demonstrate that the proposed system can efficiently generate accurate and relevant movie recommendations in real time. This approach highlights the effectiveness of content-based filtering techniques in enhancing user experience and improving content discovery in modern digital platforms.

Published by: vikaspatanker

Authors: Gaikwad Komal Vitthal, Shaikh Javed Ahmad, Shaikh Aslam Amir

Abstract: The detection and Classification of military aircraft play a crucial role in modern defence and surveillance systems. Traditional radar based approaches are often limited by high cost, environmental constraint, and reduced effectiveness against stealth aircraft. This paper presents a deep learning based approach for automatic military aircraft detection using the YOLOv5 object detection framework. The model is trained on publicly available framework. Experimental results demonstrate that the proposed system successfully detects aircraft such as F-35 and F-16 with confidence score of 0.94 and 0.80, respectively, while achieving an inference speed of approximately 6ms per image. The system provides high accuracy,robustness, and real time capability, Making it suitable for defence surveillance applications.

DOI: https://doi.org/10.5281/zenodo.20155776

Published by: vikaspatanker

Authors: Pragnesh L Thakkar, H C Patel

Abstract: An endeavor has been made to check and investigate the impact of surface roughness on the characteristics of squeeze film between porous annular discs is bestowed in presence of magnetic fluid. The involved Reynolds equation is solved with suitable boundary conditions and expressions for pressure and load carrying capacity are obtained. The expressions are obtained numerically and results are bestowed graphically. It is found that the load carrying capacity increase with increasing magnetization. The impact becomes more sharp when mean (-ve) is involved. In addition, standard deviation and aspect ratio decrease the load carrying capacity this negative effect is going to be minimized by the magnetic fluid lubricant in the case of negatively skewed roughness. Moreover, the investigation makes it clear that a performance of a bearing system is going to be enhanced by choosing suitable values of magnetization parameter and aspect ratio.

DOI: http://doi.org/10.5281/zenodo.20153697

Published by: vikaspatanker

Authors: Harshith S, Nikhilesh G, Raghunandan S, Shashank D S, Mrs. Shwetha S K

Abstract: Crop illnesses remain one of the major sources of farm losses worldwide, and identifying them at an early stage can greatly improve yield protection. Many farmers still depend on manually walking across their fields and inspecting each plant, a process that consumes significant time and can be inconsistent. This study presents a low-cost detection system we developed using an ESP32-CAM to capture images of leaves and transmit them wirelessly to a cloud-based machine learning model. The system analyses each image to determine if the leaf is healthy or affected by a particular disease, and the outcome appears immediately on a web interface accessible to farmers via their phones. Our aim was to design an affordable and easy-to-use solution so that even smallholder farmers without technical expertise can operate it with ease and confidence in their daily farming activities without needing additional support.

DOI: http://doi.org/10.5281/zenodo.20153156

Published by: vikaspatanker