Category Archives: Uncategorized

Authors: Hosea, J. O., Ogbonna, B. O.

Abstract: One of the problems associated with wind energy is fluctuations in wind speed, which cause unstable and inefficient operation of variable-speed wind turbines, especially above the rated operating range where blade pitch control is necessary to adjust aerodynamic power and maintain generator speed within the rated range. This study investigates the effect of a Fuzzy Logic Controller (FLC) on pitch-angle regulation compared with the dynamic performance of a proportional-integral (PI) controller. The same wind turbine model, generator reference speed, wind-speed profile, and operating conditions were used for both controllers. The proposed Mamdani-type FLC is based on generator-speed error and its rate of change, using scaling, saturation, fuzzification, rule-based inference, and defuzzification to derive an appropriate pitch-angle command. The FLC adjusts the pitch command according to changing turbine operating conditions, enabling control of the aerodynamic power coefficient, aerodynamic torque, generator speed, and output power under wind-speed disturbances. Controller performance was evaluated using rise time, settling time, percentage overshoot, steady-state error, RMSE, IAE, ISE, ITAE, and ITSE. The FLC increased rise time from 1.7677 s to 3.2920 s, while reducing settling time from 17.2793 s to 5.5018 s and overshoot from 7.662% to 0.880%. Furthermore, the FLC reduced steady-state error, IAE, ITAE, and ITSE by 19.20%, 20.76%, 71.56%, and 61.97%, respectively. The PI controller produced lower RMSE and ISE. Overall, the results showed that FLC-based pitch-angle control provides better damping, disturbance rejection, steady-state accuracy, and long-term tracking performance under variable wind operation, although its initial response is slower than conventional PI control.

Published by: vikaspatanker

Authors: Mr. Murari Kumar, Dr. Deepti shinghal

Abstract: In modern electronic systems, analog-to-digital converters (ADCs) play a critical role in bridging the analog and digital domains. At the heart of ADC architecture lies the comparator, a fundamental circuit responsible for precise and fast voltage comparison. With the rapid growth of portable and battery-powered devices, there is an increasing need for high-speed and low-power CMOS comparators to meet performance and energy efficiency demands. This review paper presents a comprehensive analysis of various design techniques and topologies used to achieve high speed and low power consumption in CMOS comparators. Key performance parameters such as propagation delay, power consumption, input offset voltage, and resolution are discussed in detail. Additionally, the paper highlights the trade-offs involved in comparator design and explores recent advancements, including dynamic comparators, adaptive biasing, and low-voltage operation techniques. The role of high-performance comparators in different ADC architectures—such as flash, SAR, and pipeline ADCs—is also reviewed, providing insights into future research directions in this critical field of analog circuit design.

DOI: http://doi.org/10.5281/zenodo.22935146

Published by: vikaspatanker

Authors: Professor Vikram Singh, Research Scholar Naresh Kumar

Abstract: The rapid generation of deepfake videos poses significant challenges to real-time detection systems, which often encounter a wide range of quality variations and novel manipulation techniques. In order to deal with the challenges faced we propose a hybrid InceptionV3-LSTM network that encapsulates spatial feature extraction and action sequence over time. This architecture employs InceptionV3 pretrained on ImageNet for modeling frame-level artifacts, then stacks a bidirectional LSTM to jointly learn the temporal problem from multiple sequences. The model displays robustness under high compression and when tested on low compression, it shows a decline of just 2.5% in accuracy which implies that the model is sensitive. According to temporal analysis, LSTM proves capable of detecting manipulations as illustrated due to reconstruction loss variance being higher by 3.2× of low-fidelity deepfakes. The deepfake inference data under tool-based evaluation shows this detection. The InceptionV3-LSTM’s full-fledged version achieves 150 ms latency and 96% accuracy while real-time performance granted 20 ms/frame latency using MobileNet-LSTM with no accuracy loss (96%). The solution presented in this study can be deployed, balancing detection accuracy and computational efficiency. However, limitations still exist for novel manipulation techniques and multi-modal integration could be an avenue for future work. The objective of this experimental study is to achieve the enhanced detection accuracy using separate training and testing datasets of deepfake videos which can help get a better security performance and robustness. By integrating these advances, the application allows users to check the authenticity of people and deep fake videos. A framework for real-time detection of visual deepfake videos analyzes pixel-level artifacts including blending boundaries, double edges, eyes, ears, nose, head pose inconsistencies and other possible parameters sub-pixel noise patterns unique to manipulation videos.

DOI: http://doi.org/10.5281/zenodo.22934344

Published by: vikaspatanker

Authors: Assistant Professor Hakam Chand

Abstract: The paper analyses infrastructure and market connectivity as joint determinants of climate-resilient horticulture in Kangra district, Himachal Pradesh. It uses a secondary-data case-study approach and the latest consistent state horticulture series available in the supplied publications, covering 2011-12 to 2024-25. Although horticulture output expanded during the period, climate variability, fragmented production, seasonal irrigation, post-harvest losses and costly transport threaten the stability of smallholder returns. Kangra’s traditional kuhl systems, road network, urban centres and crop diversity provide a strong base, but infrastructure must function as an integrated service chain. The study develops an Infrastructure Readiness Matrix covering water, planting material, extension, protection, aggregation, grading, cold chain, processing, roads, digital information and finance. It also proposes indicators for reliability, utilisation and inclusion. The analysis suggests that water harvesting and micro-irrigation should be combined with crop planning; pack houses should be located only where verified throughput exists; and producer aggregation should precede capital-intensive cold-chain investment. Climate resilience requires diversified crops and seasons, quality planting material, local weather and pest advisories, and multiple market channels. A phased investment and monitoring framework is recommended for Kangra.

DOI: http://doi.org/10.5281/zenodo.22933192

Published by: vikaspatanker

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