Authors: Parav Sharma, Rajesh Chauhan, Akshay Bhardwaj

Abstract: In today's complex cloud-native microservice architectures, the traditional rule-based monitoring approaches are insufficient for system reliability. The solution to this is the integration of Artificial Intelligence (AI) into DevOps, often known as AIOps. This paper introduces an AI-powered observability framework that combines the LSTM (Long Short-Term Memory) autoencoder with the Prometheus-Grafana observability stack for anomaly detection in Kubernetes-based microservice environments. The system collects real-time CPU utilization metrics from different microservices, trains an LSTM model on metric records, and detects anomalies using reconstruction error thresholding. Under controlled CPU stress injection, the characteristic scale of reconstruction error, measured as the mean plus two standard deviations of each condition's own reconstruction errors, is approximately 27 times higher than under baseline conditions, indicating that the model's reconstruction error responds strongly to abnormal workload patterns. Because a single training run may not be representative, the model is retrained ten times on a 6,407-timestep dataset; the reconstruction-error threshold is 0.000676 +/- 0.000038 and the anomaly rate 4.38% +/- 0.26% (mean +/- SD), a coefficient of variation of 5.6%, indicating that the result is reproducible rather than an artifact of one random initialization. The trained model is then deployed as a continuous detector that scores live metrics every 60 seconds; in a controlled test it flagged all 17 readings taken while an injected workload was active and returned to normal after its removal. The LSTM-based model detects anomalies without relying on fixed alert boundaries, offering a more adaptive alternative to static threshold approaches and enabling proactive detection of system abnormalities in cloud-native DevOps environments.

DOI: http://doi.org/10.5281/zenodo.22932622

Published by: vikaspatanker

Authors: Khasanova Shakhnoza

Abstract: This article investigates the lexical-semantic organization of French diplomatic discourse and the principal methods used to translate it into English. Diplomatic documents combine terminological precision with political caution; consequently, their meaning is produced not only by dictionary definitions but also by institutional convention, modality, collocation, politeness, and controlled ambiguity. The study applies descriptive, contextual, contrastive, and functional analysis to representative units drawn from recurrent diplomatic genres, including notes verbales, communiqués, declarations, agreements, and official statements. The analysis identifies eight central features: standardized terminology, formulaic expressions, polysemy, nominalization, modality, euphemistic mitigation, constructive ambiguity, and institutional collocation. It demonstrates that established equivalence is the safest method for conventional legal-diplomatic terms, whereas transposition, modulation, functional equivalence, explicitation, borrowing, calque, and cautious adaptation are required when structural or pragmatic asymmetry occurs. Particular attention is paid to the preservation of legal force and degrees of commitment. The article proposes a controlled translation workflow that integrates genre identification, terminology verification, pragmatic analysis, drafting, and bilingual quality assurance. It concludes that high-quality diplomatic translation is a form of disciplined intercultural mediation in which semantic fidelity, institutional consistency, and pragmatic equivalence must be evaluated together.

DOI: http://doi.org/10.5281/zenodo.22913354

Published by: vikaspatanker

Authors: Aarya Moossaddee

Abstract: The swift adoption of artificial intelligence (AI) tools in higher education has greatly impacted the academic activities and study approaches of a large number of students, as well as their thinking styles. This research is concerned with the investigation of the parties’ AI employment extent in multiple academic work, as well as exploring how patterns of AI use relate to level of critical thinking. Based on primary data, the study makes use of a structured questionnaire to assess the frequency and purpose of AI-aided learning, as well as self-reported measures of critical thinking, among undergraduate and graduate students. This descriptive–correlational study can reveal patterns of use and it can explore potential relationships. Descriptive statistics, reliability tests as well as correlation analyses will be used to analyse the data to investigate the relationship between the degree of AI implementation in academic work and students’ cognitive engagement. The results will likely add substance to continuing conversations surrounding the potential educational fallout from AI and also offer actionable recommendations for educators attempting to right the scale between tech adoption and essential skills development.

DOI: http://doi.org/10.5281/zenodo.22912929

Published by: vikaspatanker

Authors: Mataprasad Chaurasia

Abstract: The past five years have produced one of the fastest demographic shifts in the history of retail investing: tens of millions of people under 30 have opened their first brokerage or demat account, most of them through a mobile app rather than a bank branch or broker's office. This paper examines that shift at the intersection of three forces — investor behaviour, financial technology, and financial mathematics — drawing on primary market data (NSE, RBI, SEBI), international survey research (the FINRA Investor Education Foundation, CFA Institute, TIAA Institute-GFLEC, Gemini, YouGov, IPX1031), and peer-reviewed academic literature. Using secondary-data analysis, the study documents how young investors are entering markets earlier than any prior generation, disproportionately through low-cost, app-based fintech platforms, while simultaneously scoring lowest of any generation on standardised tests of financial literacy. A worked compound-interest illustration then quantifies what this “literacy-behaviour gap” is worth in practice. The paper concludes that fintech has solved the problem of market access far faster than anyone — fintech included — has solved the problem of financial education, and it proposes concrete, evidence-based measures for investors, platforms, and policymakers to close that gap.

DOI: http://doi.org/10.5281/zenodo.22895860

Published by: vikaspatanker

Authors: Mataprasad Chaurasia

Abstract: The past five years have produced one of the fastest demographic shifts in the history of retail investing: tens of millions of people under 30 have opened their first brokerage or demat account, most of them through a mobile app rather than a bank branch or broker's office. This paper examines that shift at the intersection of three forces — investor behaviour, financial technology, and financial mathematics — drawing on primary market data (NSE, RBI, SEBI), international survey research (the FINRA Investor Education Foundation, CFA Institute, TIAA Institute-GFLEC, Gemini, YouGov, IPX1031), and peer-reviewed academic literature. Using secondary-data analysis, the study documents how young investors are entering markets earlier than any prior generation, disproportionately through low-cost, app-based fintech platforms, while simultaneously scoring lowest of any generation on standardised tests of financial literacy. A worked compound-interest illustration then quantifies what this “literacy-behaviour gap” is worth in practice. The paper concludes that fintech has solved the problem of market access far faster than anyone — fintech included — has solved the problem of financial education, and it proposes concrete, evidence-based measures for investors, platforms, and policymakers to close that gap.

DOI: http://doi.org/10.5281/zenodo.22895860

Published by: vikaspatanker

Authors: Mansi Mishra

Abstract: Employee retention has become an important concern for organisations because employee turnover can increase recruitment costs, disrupt organisational continuity, and lead to the loss of knowledge and experience. Training and development can influence employees’ perceptions of organisational support by providing opportunities to acquire skills, improve performance, and develop their careers. This research paper examines the role of training and development in employee retention, with particular attention to the mediating role of employee satisfaction. The proposed study will examine whether access to relevant training, career development opportunities, learning support, and development programmes is associated with employee satisfaction and employees’ intention to remain with their organisations. A quantitative research design is proposed, using a structured questionnaire administered to employees from different organisations. The data may be analysed using descriptive statistics, reliability analysis, correlation, regression, and mediation analysis. The study is expected to contribute to human resource management literature by clarifying the relationship between development opportunities, employee satisfaction, and retention. It may also provide practical guidance to organisations seeking to design training and development practices that support employee retention.

DOI: https://doi.org/10.5281/zenodo.22895793

Published by: vikaspatanker

Authors: Vishal Sahu, Sandeep Choudhary

Abstract: Reinforced Cement Concrete (RCC) buildings with floating columns are increasingly adopted in modern urban construction to achieve architectural flexibility, open parking spaces, and large column-free areas. However, the discontinuity introduced by floating columns significantly alters the load transfer mechanism and may adversely affect the structural durability and long-term performance of buildings, particularly under seismic and environmental loading conditions. Recent advances in Artificial Intelligence (AI) provide an opportunity to predict the durability and service life of such complex structural systems more accurately than conventional empirical methods. This review paper presents a comprehensive analysis of AI-based durability prediction techniques for RCC buildings incorporating floating columns, with structural behavior evaluated using STAAD.Pro. The review summarizes the influence of floating column configurations on stress distribution, deflection, drift, and load-carrying capacity, while examining AI approaches such as Artificial Neural Networks (ANN), Machine Learning (ML), Deep Learning (DL), Support Vector Machines (SVM), Random Forest (RF), and ensemble models for predicting durability indicators including crack development, corrosion potential, service life, and structural degradation. The paper also discusses the integration of finite element analysis results obtained from STAAD.Pro with AI algorithms to improve prediction accuracy and facilitate intelligent structural health assessment. Furthermore, existing research gaps, challenges, and future opportunities in AI-assisted durability evaluation are highlighted. The review concludes that the combination of STAAD.Pro-based structural analysis and AI-driven predictive models offers a reliable and efficient framework for enhancing the safety, durability, maintenance planning, and sustainable design of RCC buildings with floating columns. This integrated approach supports data-driven decision-making and contributes to the development of resilient and smart infrastructure.

Published by: vikaspatanker

Authors: Girish Prasad, Professor Vinay Deulkar, Assistant Professor Piyush Mahajan

Abstract: Ultra-High-Performance Concrete (UHPC) is increasingly recognized as an advanced construction material for high-traffic highway pavements because of its outstanding mechanical performance, durability, and extended service life. This review provides a comprehensive assessment of UHPC, covering its development, constituent materials, mix-design principles, mechanical characteristics, durability performance, and recent developments in pavement applications. Particular attention is given to its superior compressive and flexural strength, very low permeability, high abrasion resistance, and enhanced resistance to freeze–thaw cycles and chemical deterioration. These properties make UHPC particularly suitable for pavements subjected to heavy traffic and demanding environmental conditions. Despite these benefits, its wider adoption is constrained by factors such as relatively high initial cost, limited availability of specialized materials, and challenges associated with large-scale construction and implementation. Overall, the reviewed research indicates that UHPC offers considerable potential for improving pavement performance, extending service life, reducing maintenance requirements, and supporting the development of more durable and sustainable highway infrastructure.

Published by: vikaspatanker

Authors: Krishna Kumar Singh, Professor Vinay W. Deulkar, Professor Piyush Mahajan

Abstract: The rapid growth of freight transportation has increased the need for efficient traffic load scheduling in integrated rail-road freight corridors. Conventional scheduling methods often suffer from poor coordination, traffic congestion, delivery delays, and inefficient resource utilization. This research proposes an Artificial Intelligence (AI)-based Coordinated Traffic Load Scheduling Framework using an Artificial Neural Network (ANN) to predict traffic conditions, freight demand, travel time, and scheduling performance. Historical and simulated transportation data are used to train and validate the ANN model, and the predicted outputs are integrated into a traffic scheduling simulation for intelligent freight allocation and route optimization. The performance of the proposed framework is evaluated using key indicators such as transportation cost, travel time, delivery delay, vehicle utilization, rail utilization, fuel consumption, carbon emissions, and scheduling efficiency. The results demonstrate that the ANN-based approach improves prediction accuracy, optimizes multimodal freight scheduling, reduces operational costs and congestion, and enhances the overall efficiency of rail-road freight transportation. The proposed framework provides an effective and intelligent solution for sustainable freight corridor management and future smart logistics systems.

Published by: vikaspatanker

Authors: Sambhaji Borgude, Vinayak Kanchan, Pranav Ghumare, Sohel Shaikh, Tanmay Gangawane, Bhagyshree Gaikwad

Abstract: The rising worldwide need for safe, cost-effective, and reversible contraception has sparked new interest in using medicinal plants as alternative fertility-controlling solutions. Conventional medical systems have utilized various plants for contraception by impacting ovulation, spermatogenesis, fertilization, implantation, and uterine functions. Recent pharmacological studies have discovered various phytochemicals such as alkaloids, flavonoids, saponins, terpenoids, coumarins, lignans, and phytoestrogens that influence reproductive endocrine pathways and gamete activity. This review thoroughly outlines existing evidence related to phytochemistry, action mechanisms, pharmacological effects, and safety profiles of herbal contraceptives. Key medicinal plants like Azadirachta indica, Carica papaya, Ruta graveolens, Juglans regia, Ricinus communis, Daucus carota, and other commonly utilized species are thoroughly examined with a focus on their antifertility properties, active compounds, and empirical support. Despite considerable in vitro and animal research supplementing contraceptive effectiveness via anti-ovulatory, spermicidal, anti-implantation, endocrine-modulating, and uterotonic processes, strong clinical evidence continues to be scarce. Moreover, issues related to toxicity, reproductive safety, teratogenic effects, dose consistency, and long-term reversibility still impede clinical translation. Future studies must focus on standardized phytochemical profiling, mechanistic molecular investigations, GLP-compliant toxicity assessments, and rigorously structured randomized clinical trials. Herbal contraceptives thus serve as encouraging options for creating new contraceptive methods; however, significant scientific confirmation is necessary before their regular clinical use.

DOI: https://doi.org/10.5281/zenodo.22875171

Published by: vikaspatanker