Daily Archives: May 18, 2026

Authors: Anshu Kumar Mishra, Sohail Verma

Abstract: This paper investigates the relationship between leadership styles and team performance in startup organisations, using survey-based data collected from 120 respondents comprising founders, co-founders, team leads and early-stage employees across multiple sectors. The study identifies transformational leadership as the dominant style in the sample and finds strong positive associations between vision-driven leadership, team trust, communication frequency and performance outcomes. Transactional leadership shows moderate relevance in goal-setting and accountability, while laissez-faire approaches correlate with lower performance consistency. Exploratory chi-square testing reveals significant concentration in leadership style distribution, a meaningful link between startup stage and performance rating, and a strong association between trust levels and team output. The paper concludes that startup performance is not driven by a single leadership template but by the leader's ability to adapt style to team maturity, organisational stage and the demands of rapid growth. A hybrid leadership model combining transformational inspiration with transactional clarity emerges as the most effective pattern for high-performing startup teams.

Published by: vikaspatanker

Authors: Shah Md. Tanzimul Kabir, Zahid Hassan Ome

Abstract: This paper provides a comprehensive analysis of scalable database systems, specifically designed to support big data analytics, and examines their evolution, challenges, and emerging technologies in the exascale data processing era. By examining recent research studies from 2021 to 2026, the current paper seeks to investigate how distributed database architectures, including NewSQL, cloud-native, and data lakehouse, address the fundamental scalability challenge known as the "scalability trilemma" consisting of consistency, availability, and partition tolerance. The current research introduces the Adaptive Scalability Evaluation Framework (ASEF), which integrates horizontal scaling, elastic resources, query optimization, and storage efficiency. The analysis shows that recent scalable database architectures are based on disaggregated storage and compute architectures, enabling near-linear scaling to thousands of nodes with query latencies under 100ms for petabyte-scale data sets. Cloud-native database architectures are shown to be highly elastic, with variations in query latency at the 95th percentile below 15% during scaling events. Newly emerging architectures for lakehouses, which bring the flexibility of data lakes and the performance of data warehouses, provide query performance that is 3 to 5 times better than traditional data lakes and reduce the total cost of ownership by 30 to 50 percent. Evaluation in five dimensions for analytical workloads, such as scaling behavior, consistency model, query performance, storage efficiency, and operational complexity, shows that systems with workload awareness and adaptivity perform much better than static configurations. Continuous optimization provides an improvement in throughput performance that is between 2 to 4 times.

DOI: https://doi.org/10.5281/zenodo.20270564

Published by: vikaspatanker

Authors: Sk Samsul Hoda, Dr. Vipin kumar

Abstract: Remote sensing techniques used for measurement of atmospheric cloud properties operate under the notion that light extinction caused by scattering and absorption is exponential due to Beer-Lambert law. This is expected to be valid for a uni-form medium with no spatial correlations between particle position. The aim of this research was to show that under turbulent conditions, cloud droplets cannot be inter-preted as non-correlated, and in turn will exhibit a lower than exponential light decay from scattering. The research took place at the MTU π-Chamber laboratory. A tem-perature difference between the floor and ceiling of the chamber was applied to create convection- driven turbulence. When turbulent cloud conditions were achieved, it’s optical depth properties was analyzed. This was done by deriving the optical depth by computational means through the acquisition of its droplet size distribution, and processing it through Mie scattering theory, while simultaneously acquiring direct measurement of optical depth using a Laser-Hygrometer. Results showed that there is a trend where larger temperature differences inside the chamber caused the direct extinction of light to deviate more strongly from the computed extinction. This less then exponential extinction parameter allows us to understand the significant effect that a turbulent cloud cover has on radar and satellite signals.

DOI: http://doi.org/10.5281/zenodo.20269814

Published by: vikaspatanker

Authors: Abdullahi Idris, Aminu A. Abdullahi, Jamilu Awwalu, Abdullahi Uwaisu Muhammad

Abstract: The increasing availability of Electronic Health Records (EHRs), ICU monitoring systems and clinical sensor technologies has generated large volumes of temporal healthcare data that require advanced analytical approaches for effective interpretation and prediction. Traditional machine learning and statistical models often face challenges in handling complex temporal dependencies, irregular sampling, missing values and censored survival outcomes in clinical time-series data. This study employed a Hybrid Transformer Framework for Temporal Representation and Longitudinal Risk Prediction in Clinical Time Series synthesizing the relevant studies and clinical decision-making. The framework integrates the Transformer-LSTM architecture with Cox Proportional Hazards (Cox PH), Survival Random Forest (SRF) and XGBoost algorithms. The Transformer component captures long-range temporal dependencies using self-attention mechanisms, while the LSTM network models short-term sequential clinical patterns. Cox PH is applied for interpretable survival analysis, SRF for nonlinear ensemble survival prediction and XGBoost for high-performance risk classification and prediction. The review study utilizes healthcare datasets such as MIMIC-III, MIMIC-IV, elCU and PhysioNet as well as providing suitable comparative approaches against baseline models.

DOI: https://doi.org/10.5281/zenodo.20269570

Published by: vikaspatanker

Authors: Shivanshi Verma

Abstract: This study evaluates leachate quality from a municipal solid waste disposal site in Lucknow and examines its impact on nearby groundwater. The analytical framework, sampling design and index-based interpretation were prepared in line with the uploaded thesis and sample journal paper, while the numerical results were derived from the uploaded laboratory workbook. One leachate sample and seven groundwater samples were assessed for physicochemical and heavy metal parameters using APHA-based methods. The leachate showed acidic to near-neutral reaction (pH 6.1), very high electrical conductivity (83,892 µS/cm), total dissolved solids (38,180 mg/L), chemical oxygen demand (16,800 mg/L), biochemical oxygen demand (2,000 mg/L), hardness (1,620 mg/L), chloride (980 mg/L), sulphate (678.5 mg/L), nitrate (103.44 mg/L), fluoride (8.8 mg/L) and substantial heavy metal burden, indicating strong contaminant potential. The Leachate Pollution Index was 25, confirming significant pollution load. Groundwater quality varied spatially: Sample-7 recorded a WQI of 75.13 and fell in the good category, whereas Samples 2–4 were poor and Samples 1, 5 and 6 were very poor. Elevated TDS, alkalinity, hardness, iron, manganese, nickel, copper and zinc were the major causes of groundwater deterioration. The data indicate that leachate migration has affected groundwater quality in the vicinity of the disposal site, although the effect is not controlled by distance alone. The study recommends leachate containment, regular groundwater surveillance, and priority treatment for metal and salinity-related contamination.

DOI: https://doi.org/10.5281/zenodo.20269275

Published by: vikaspatanker

Authors: Zaira Siddiqui

Abstract: Groundwater is an essential source of drinking water in urban regions; however, rapid urbanization, industrial growth, and anthropogenic activities have significantly deteriorated groundwater quality in many Indian cities, including Lucknow. The present study aims to evaluate the spatial variability of groundwater quality in Lucknow district using Geographic Information System (GIS)-based techniques and Water Quality Index (WQI) approaches. Major physicochemical parameters including pH, electrical conductivity (EC), total hardness, calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), fluoride (F⁻), nitrate (NO₃⁻), and sulphate (SO₄²⁻) were analyzed for groundwater quality assessment. Spatial interpolation of groundwater parameters was performed using the Inverse Distance Weighting (IDW) method in GIS to generate thematic distribution maps and identify contamination hotspots. Two groundwater quality assessment approaches, namely Arithmetic Water Quality Index (AWQI) and Weighted Water Quality Index (WWQI), were applied to evaluate overall groundwater suitability for drinking purposes. The results revealed significant spatial variability in groundwater quality across Lucknow district. Elevated concentrations of hardness, EC, nitrate, chloride, and sulphate were observed in several urbanized and densely populated regions, indicating strong anthropogenic influence on groundwater systems. The AWQI and WWQI hotspot maps indicated that eastern and southeastern parts of Lucknow district exhibited comparatively poor groundwater quality, while northern and western regions showed relatively better water quality conditions. Comparative analysis demonstrated that WWQI provided a more realistic and reliable assessment because parameter-specific weighting improved sensitivity toward critical contaminants. GIS-based hotspot mapping successfully delineated vulnerable groundwater zones and highlighted areas requiring immediate monitoring and management intervention. The study demonstrates that integration of GIS and WQI techniques is highly effective for groundwater quality assessment, contamination hotspot identification, and sustainable groundwater resource management. The findings of this study can support policymakers and environmental planners in developing targeted groundwater protection and remediation strategies for rapidly urbanizing regions.

DOI: http://doi.org/10.5281/zenodo.20268893

Published by: vikaspatanker

Authors: Nisha Gautam

Abstract: Heavy metal contamination in river sediments is a major environmental concern due to rapid urbanization and industrialization. The present study assessed heavy metal contamination in sediment samples collected from selected sites of the Gomti River in Lucknow, Uttar Pradesh, India. Sediment samples were collected from Gaughat, Kudiya Ghat, Daliganj Bridge, and Hanuman Setu during November 2025 and analysed for chromium (Cr), nickel (Ni), arsenic (As), cadmium (Cd), and iron (Fe). Pollution assessment indices including Enrichment Factor (EF), Contamination Factor (CF), and Pollution Load Index (PLI) were used to evaluate contamination levels and anthropogenic influence. The results revealed significant spatial variation in heavy metal concentrations. Iron showed the highest concentration, while cadmium exhibited extremely high concentrations compared to its background value, indicating severe contamination. The concentration pattern followed the order: Fe > Cr > Ni > Cd > As. EF analysis indicated extremely severe enrichment of Cd, whereas Cr showed moderate enrichment. CF results also confirmed very high contamination by Cd. PLI values at all sampling sites were greater than 1, indicating polluted sediment conditions. The study concludes that anthropogenic activities such as sewage discharge, urban runoff, and industrial effluents are major contributors to heavy metal accumulation in the Gomti River sediments.

DOI: http://doi.org/10.5281/zenodo.20268759

Published by: vikaspatanker

Authors: Kamble Abhishek B, Nimbalkar Saurabh S, Patil Abhayrajsinh M.

Abstract: The increasing demand for eco-friendly and automated landscaping solutions has led to the development of a Solar-Powered Grass Cutter with Mobile Remote Control for medium outdoor spaces such as gardens, parks, and institutional campuses. The system harnesses solar energy via photovoltaic panels, which charge a 12V, 7Ah lead-acid battery through an MPPT charge controller. An ESP32 microcontroller receives Bluetooth commands from a mobile application to drive four Johnson DC gear motors (12V, 10 RPM) for mobility and a PMDC motor (12V, 5000 RPM) for blade actuation, interfaced via an L298N motor driver. Field testing demonstrated a cutting efficiency of 98.5% for grass heights of 15–25 mm and 78% for overgrown grass exceeding 60 mm, with operational runtime of 1.5–2 hours per full charge. The total fabrication cost is approximately Rs. 10,955, making it a cost-effective and environmentally sustainable alternative to conventional fuel-powered grass cutters.

Published by: vikaspatanker

Authors: Nitin Mishra

Abstract: Groundwater is the principal source of drinking and irrigation water in the Indo-Gangetic alluvial plains of Uttar Pradesh, India. Rapid urbanization, agricultural intensification, excessive groundwater abstraction, and geogenic contamination have significantly affected groundwater quality in the region. The present study evaluates groundwater quality in Central Uttar Pradesh using hydrogeochemical assessment, entropy-weighted water quality index (EWQI), and machine learning (ML) prediction techniques. A total of 178 groundwater samples were analyzed for major physicochemical parameters including pH, EC, TDS, TH, Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, NO3−, F−, SiO2, and CO32−. The entropy weight method was employed to minimize subjectivity in water quality assessment, while hydrogeochemical interpretations were carried out using Piper and Gibbs diagrams. Three machine learning models, namely Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were implemented to predict groundwater quality conditions. The results revealed that groundwater chemistry is predominantly controlled by rock–water interaction and ion exchange processes, with Ca–HCO3 and mixed hydrochemical facies dominating the study area. The EWQI values indicated that most groundwater samples fall within good to medium drinking water quality categories, although localized fluoride enrichment was observed in several locations. Among the applied models, XGBoost demonstrated superior predictive capability with R2 = 0.9597, RMSE = 2.2376, and MAE = 1.7690, outperforming RF and CART models. The findings highlight the effectiveness of integrating GIS-based hydrogeochemical analysis with machine learning approaches for groundwater quality prediction and sustainable groundwater management in Central Uttar Pradesh.

DOI: http://doi.org/10.5281/zenodo.20268302

Published by: vikaspatanker

Authors: Prof. Chetan Kumar V, Hardik Jain, Pranathi B H, Vikas R P, Srinidhi Prabhu M U

Abstract: The deployment of production-grade Multi-Agent Systems (MAS) from natural language specifications remains a significant challenge in software engineering, requiring so- phisticated role decomposition, reliable tool integration, exe- cutable code synthesis, and robust packaging with dependency management. This paper presents MindMeld, a novel tiered LLM orchestration framework that transforms natural language requirements into deployable, containerized multi-agent systems through a three-phase pipeline architecture. MindMeld introduces several key innovations: (1) a formal planning phase that generates machine-verifiable JSON agent specifications with explicit dependency graphs and interface con- tracts; (2) a closed-loop validation tier combining static analysis, dynamic runtime testing in isolated sandboxes, and iterative self-refinement based on structured error feedback; and (3) an automated integration phase that synthesizes orchestration logic, manages inter-agent communication, and produces containerized artifacts with complete dependency resolution. We conduct comprehensive evaluation on 47 diverse natural- language build requests spanning 8 task categories (data pro- cessing, API integration, document analysis, notification systems, workflow automation, content generation, monitoring, and multi- modal processing). Our results demonstrate that MindMeld achieves 78.7% end-to-end build success compared to 34.0% for single-pass generation baselines, with an average of 1.8 validation iterations per sub-agent. Ablation studies reveal that the planning phase contributes 23.4% improvement and the val- idation loop adds 21.3% improvement to overall success rates. A controlled user study with 24 participants shows 3.2× reduction in deployment time and 4.1/5.0 satisfaction scores. These results establish MindMeld as a practical framework for bridging the gap between natural language intent and production-ready multi- agent systems.

Published by: vikaspatanker