Category Archives: Uncategorized

Authors: Moslem Mousavi khademi

Abstract: In this paper, the dissimilar welding microstructure of the duplex stainless steel SFA 2205 with the high strength low alloy A378 Gr.11 was studied.The microstructure investigations indicated that the weld obtained has a two-phase structure, including dendritic and interdendritic areas. A high hardness transition area was detected in the interface of the A378 low alloy steel and ER 309L metal filler. An unmixed area was observable at the melting boundary of SFA2205 duplex steel and both austenitic and duplex filler metals. The results showed that for joining the two-phase stainless steel SFA2205 with the high strength low alloy A378 Gr.11, using the metal filler ER2209 is more appropriate as a result of forming a more suitable properties microstructure.

Published by: vikaspatanker

Authors: Ms. Shruti Rawat, Manasvee Jain

Abstract: – In today's fast-paced and digitally driven work environment, organizations are increasingly relying on e-learning platforms for employee training and development. While digital training offers flexibility, scalability, and cost-efficiency, its actual impact on employee performance remains a critical area of inquiry. This study explores the relationship between e-learning effectiveness and employee performance metrics, aiming to bridge the gap between training delivery and measurable workplace outcomes. By analyzing data from employees across multiple departments in a mid-sized technology company, the research examines how engagement with online training modules correlates with key performance indicators such as task accuracy, productivity levels, customer satisfaction scores, and overall goal completion rates. A combination of pre-training and post-training performance data, user feedback, and system usage logs were used to assess the tangible benefits of e-learning initiatives. The findings suggest a positive link between well-structured e-learning programs and improved employee performance, particularly when courses are interactive, aligned with job roles, and supported by timely feedback. However, the study also highlights that the effectiveness of digital training is influenced by factors such as learner motivation, management support, and course design quality. This research underscores the importance of integrating performance metrics into e-learning evaluations to ensure learning investments translate into real-world results. The insights provided can help HR professionals, training managers, and organizational leaders refine their digital learning strategies for maximum impact.

DOI: http://doi.org/10.5281/zenodo.16792612

 

 

Published by: vikaspatanker

Authors: Avdesh Kumar Ahirwar, Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: Gravity dams are essential infrastructural elements used for water storage, flood control, and hydropower generation. Their structural safety and performance under varying loading and reservoir conditions are critical due to the potential risks associated with failure. This paper presents a comprehensive review of structural analysis techniques for gravity dams, with a specific focus on modeling through STAAD.Pro software. The study highlights the application of finite element methods (FEM) in simulating dam behavior under different forces, including hydrostatic pressure, uplift pressure, seismic effects, and self-weight. The role of STAAD.Pro as a versatile tool capable of handling complex geometries and material properties is explored in detail. Emphasis is placed on its ability to model solid elements and perform static and dynamic analyses in accordance with IS 6512:1984 and IS 875 standards. The review synthesizes results from multiple case studies and simulations, examining factors such as stress distribution, displacement, sliding resistance, overturning moments, and shear friction parameters. Findings indicate that STAAD.Pro provides accurate and reliable predictions for assessing dam safety under full and partial reservoir conditions. Furthermore, the study identifies common stress concentration zones—particularly at the heel—and discusses reinforcement strategies to mitigate structural vulnerability. It also evaluates the factor of safety under various loading combinations and confirms compliance with national safety codes. This paper contributes to the evolving methodology of dam design and evaluation, offering valuable insights for engineers, researchers, and policymakers aiming to enhance the resilience of gravity dams through advanced numerical modeling.

DOI: http://doi.org/10.5281/zenodo.16875326

Published by: vikaspatanker

Authors: Ahmed Abshir Ahmed

Abstract: This study investigates the impacts of climate change on water resources in arid and semi-arid regions, with a focused case study on Hargeisa, Somaliland. As climate change intensifies hydrological variability, regions already facing environmental and demographic pressures experience exacerbated water scarcity. Analyzing climatic data (1991-2020) from SWALIM and regional groundwater studies, we identify three critical trends: (1) decreasing rainfall reliability (annual averages of 250-400mm with high interannual variability), (2) increasing evapotranspiration rates (up to 2100mm annually), and (3) progressive groundwater quality deterioration (TDS >1g/L in 90% of sampled wells). Our findings demonstrate particular vulnerability in shallow aquifers – the primary water source for most communities – which face both seasonal depletion and contamination risks. The research reveals a dual stressor system where climate variability interacts with rapid population growth to threaten water security. We propose four key interventions: (i) enhanced hydro-climatic monitoring networks, (ii) integrated climate adaptation planning, (iii) targeted aquifer recharge strategies, and (iv) policy reforms for sustainable groundwater governance. These recommendations provide actionable pathways for building resilience in water-stressed regions facing escalating climate risks

DOI: https://doi.org/10.5281/zenodo.16791946

 

Published by: vikaspatanker

Authors: Avdesh Kumar Ahirwar, Assistant Professor Murlidhar Chourasia, Rahul Kumar Satbhaiya

Abstract: Gravity dams, typically constructed using concrete or masonry, are massive hydraulic structures designed to resist external loads primarily through their own weight. These dams usually exhibit a triangular cross-sectional profile, with a wide base and a narrow crest, ensuring inherent stability against hydrostatic and seismic forces. Accurate analysis of such structures is essential for ensuring their safety and performance under varying reservoir conditions.This study presents a detailed evaluation of gravity dam behavior using STAAD.Pro, widely adopted structural analysis software. While traditionally employed for designing framed structures such as buildings, STAAD.Pro is also capable of modeling complex elements including plates, shells, and solid components. This flexibility makes it suitable for simulating gravity dams under different loading scenarios.In this analysis, the dam is modeled using solid elements to accurately represent its mass and geometry. The effects of hydrostatic pressure, uplift pressure, and other reservoir-induced forces are incorporated to simulate real-world conditions. By leveraging the computational capabilities of STAAD.Pro, stress distribution, deformation profiles, and stability parameters of the dam are systematically investigated. This approach eliminates the limitations of manual analysis, offering a time-efficient and precise method to assess dam safety under diverse operational conditions.

DOI: https://doi.org/10.5281/zenodo.16759714

 

Published by: vikaspatanker

Authors: Lionel Seah

Abstract: Artificial Intelligence (AI) is the field of study and development of computer systems capable of performing tasks that would typically require human intelligence. These tasks include reasoning, problem-solving, learning, understanding natural language, and perceiving the environment. As defined by John McCarthy, one of the pioneers of AI, “Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs” (McCarthy, 2007). AI aims to replicate or simulate human cognitive functions in machines to enhance or automate decision-making, pattern recognition, and interactions. According to Stuart Russell and Peter Norvig, in their foundational textbook Artificial Intelligence: A Modern Approach, “AI is concerned with intelligent behaviour in artifacts” (Norvig, 2021). They categorise AI systems based on their capabilities to act and think rationally or humanly.

DOI: http://doi.org/10.5281/zenodo.16757750

Published by: vikaspatanker

Authors: Ishan Deshpande, Mohit Jagtap, Akash Satras, Mrs. Shreyasi Watve

Abstract: india’s vibrant cultural traditions involve many water-based religious rituals, which unintentionally contribute to environmental pollution. During festivals such as Ganesh Visarjan and Kumbh Mela, water bodies like the Godavari River in Nashik often become heavily contaminated due to the disposal of idols, flowers, and plastic items. These pollutants accumulate on the water surface, disturbing the aquatic balance. To address this issue, we propose an eco- conscious solution—an automated bot system designed for surface waste collection. This bot uses renewable energy to operate and effectively removes plastic, debris, and water hyacinth from still water bodies, supporting a cleaner and more sustainable environment.

DOI: https://zenodo.org/uploads/16755368

 

 

Published by: vikaspatanker

Authors: Dr Balaji Shivaji Pasare

Abstract: Background: Rural areas such as Osmanabad are plagued by sustained degradation of infrastructure driven by climate variation, material fatigue, and restricted maintenance capabilities. Structural concrete, while strong, is susceptible to microcracking that can expedite deterioration and impede long-term resilience. Fostered by the recent advent of self-healing technologies, namely bio-concrete and polymer-fused conduit systems, these represent adequate solutions for low or no-maintenance, climate-responsive, and adaptable buildings in impoverished areas. Objectives: The current study is set forth with the objectives of field performance, healing potential, and stakeholders’ acceptance for the bio-concrete and polymer-based self-healing concrete (SHC) under semi-arid conditions in Osmanabad. It aims at the transition from laboratory innovation to rural deployment conditions, highlighting humanised engineering and participatory validation. Methods: A mixed-method design of experimental trials in 3-gram panchayats was implemented with stakeholder involvement. Quantitative data were compressive strengths, crack closure rates, and environmental (humidity, temperature) correlation variables. Qualitative information was obtained through interviews, focus groups, and participatory observation. The analytic techniques used were ANOVA, regression modelling, and thematic coding of the data. Results: Bio-concrete exhibited enhanced crack healing (94% recovery) and strength increases (~20% compared to control mixes), especially in high-humidity zones. Positive correlations were found between healing rates and environmental humidity. The highest level of trust from the stakeholders was observed from the farmers (avg. rating: 9.1/10), noting that it helped minimize maintenance and seemed to heal. The polymer SHC had milder performances, though lower photo-hysteresis. Conclusion: Bio-concrete is found to be a socially and technically acceptable, climate change resilient alternative for rural infrastructure. The research confirms its relevance in Osmanabad and supports community-scale scaling. Through the convergence of high-performance materials and ethical, participatory adoption, this research configures a new model of resilient, humanised infrastructure for the disenfranchised.

DOI: https://doi.org/10.5281/zenodo.16753496

 

Published by: vikaspatanker

Authors: Rushil Vijay Salian

Abstract: The growing demand for accessible, scalable, and automated machine learning (ML) platforms has highlighted the need for intuitive tools that democratize model development. ModelLab is a proprietary AI/ML model training platform that enables users to upload datasets, train models, and generate predictions through an interactive web interface. Built with React, Supabase, and TensorFlow.js, it supports end-to-end workflows from data ingestion to model deployment. This paper presents the architecture, features, and performance of ModelLab and explores its potential to revolutionize AI development for non-experts and professionals alike. Future work includes model versioning, expanded algorithm support, and production-level deployments.

DOI:

 

Published by: vikaspatanker

Authors: Ashwin Sanghi

Abstract: Financial institutions operate in a dynamic and high-stakes environment where data integrity, system availability, and uninterrupted service are paramount. In recent years, the increasing complexity of IT infrastructures, along with the growing threat of cyberattacks and natural disasters, has prompted a strategic shift toward virtualized disaster recovery (VDR) models. VDR enables the replication and recovery of data and critical systems through virtual environments, offering increased flexibility, faster recovery times, and reduced reliance on physical infrastructure. This article presents a comprehensive review of the adoption and implementation of virtualized disaster recovery strategies in financial institutions. It evaluates technological architectures, regulatory requirements, integration challenges, and case studies to illustrate real-world applications. Furthermore, it delves into cost-benefit analyses, risk mitigation tactics, and the role of automation and orchestration in streamlining recovery processes. Through this analysis, we aim to demonstrate how VDR can enhance business continuity, improve compliance postures, and provide a robust response mechanism to both anticipated and unforeseen disruptions.

DOI: https://doi.org/10.5281/zenodo.16751967

 

Published by: vikaspatanker