Authors: Basant Kumar Sahu, Lata Pradhan

Abstract: The increasing use of engineered nanoparticles (NPs) across consumer products, medicine, and industrial applications has led to their unintended release into natural ecosystems, sparking ecotoxicological concerns. Due to their small size, high surface area, and reactivity, nanoparticles interact uniquely with microorganisms in soil, water, and sediment ecosystems. These environmental microbiomes—complex networks of bacteria, archaea, fungi, and protozoa—play essential roles in nutrient cycling, decomposition, and pollutant degradation. However, exposure to nanoparticles often results in oxidative stress, disruption of cellular membranes, genotoxicity, and changes in metabolic functions. Such stress responses can reduce microbial diversity, impair ecosystem processes, and destabilize trophic networks. Despite these critical risks, traditional environmental risk assessments fail to incorporate microbial endpoints, focusing instead on higher organisms. This review explores the pathways through which nanoparticles induce stress in microbiomes, the ecological consequences of such interactions, and the current limitations in detection and regulation. Emphasis is placed on using omics tools and community-level bioindicators to assess sub-lethal effects. Addressing nanoparticle impacts at the microbial level is vital for maintaining ecological balance and sustainability. The paper concludes by recommending policy frameworks and green nanotechnologies that prioritize microbiome integrity in environmental safety assessments.

DOI: https://doi.org/10.5281/zenodo.16835383

 

Published by: vikaspatanker

Authors: Satish Kumar Lodhi, Shikha Gupta

Abstract: Microbial nanowires represent a transformative advancement in bioenergy science, offering a novel mechanism for extracellular electron transport (EET) that can be harnessed for sustainable energy generation. These protein-based conductive filaments, produced by certain electroactive bacteria such as Geobacter and Shewanella species, enable microbes to transfer electrons across cell membranes to external electron acceptors such as metal oxides or electrodes. This unique capability has immense implications for microbial fuel cells (MFCs), bioremediation, and electro-fermentation. Unlike traditional conductive materials, microbial nanowires are biodegradable, self-assembling, and functionally dynamic under ambient environmental conditions. Recent discoveries have revealed the complex structure of nanowires, often comprising multi-heme cytochromes or type IV pili modified with aromatic amino acids, which contribute to long-range electron conductivity. Their integration into bioelectrochemical systems significantly enhances current output and efficiency. This review synthesizes current knowledge of microbial nanowire biology, electrochemical behavior, and engineering strategies to optimize their conductive properties. It also highlights future directions in synthetic biology and materials science for scalable bioenergy solutions. As global energy demands grow, microbial nanowires stand at the forefront of next-generation, eco-friendly energy technologies, bridging the gap between living systems and electrical networks.

DOI: https://doi.org/10.5281/zenodo.16835489

 

Published by: vikaspatanker

Authors: Nitish Sharma, Dr Komal Garg

Abstract: The rising demand for food worldwide and declining freshwater resources have intensified the need for efficient agricultural practices. Smart irrigation, powered by the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), offers a transformative solution for precise and sustainable water management. This review examines current advancements in smart irrigation technologies, concentrating on IoT-enabled sensor networks, real-time environmental monitoring, and AI-powered judgment models such as machine learning and predictive analytics. It examines the evolution among these technologies, their application in farming with accuracy, and the synergetic benefits of combining IoT and AI. Moreover, the review highlights implementation challenges, including high costs, energy constraints, data security, and region-specific limitations. The paper concludes with future research directions aimed at enhancing system efficiency, adaptability, and accessibility, especially in water-scarce and developing regions.

DOI: http://doi.org/10.5281/zenodo.16810519

 

 

Published by: vikaspatanker

Authors: Mr. Kartik, Dr. Bijendra Singh, Dr. Kavita

Abstract: This research aims to analyze and design an effective ransomware detection technique using machine learning algorithms. The study explores various ML approaches—such as classification, anomaly detection, and clustering—and evaluates their performance in identifying ransomware from normal and benign system behavior. Key features, such as file access patterns, process activities, and network communication, are extracted and analyzed to train and test ML models capable of early detection with high accuracy and low false positives. The primary aims of this study are to understand the behavioral characteristics of ransomware attacks; Identify and select relevant features for effective detection; Evaluate different machine learning models based on precision, recall, F1-score, and accuracy; and Propose a novel or improved ML-based detection framework tailored for real-time ransomware threat identification. This research contributes to the ongoing efforts to fortify cybersecurity by presenting a data-driven, machine learning-powered methodology that enhances early detection capabilities, thereby reducing potential damage and enabling quicker incident response.

DOI:

 

Published by: vikaspatanker

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