Authors: Mr. Prashant Patel, Mr. Pankaj Agrawal

Abstract: Concrete is the most widely used construction material, yet its production consumes large quantities of natural sand, while end-of-life vehicle tyres remain a difficult waste stream. Partial replacement of fine aggregate with crumb rubber from waste tyres addresses both problems, but untreated rubber lowers strength, stiffness and workability and increases water absorption, largely because of a weak bond between the hydrophobic rubber surface and the cement paste. This paper gives a simple review of published work on rubberised concrete, with emphasis on pre-treatment of crumb rubber with sodium hydroxide (NaOH). It covers the properties of waste tyre rubber, the reported effects of rubber on the fresh, mechanical, physical and durability properties of concrete, the nature of the rubber–cement interface, alternative methods of improving that interface, and the mechanisms and parameters of NaOH treatment. The literature agrees on the broad effects of rubber. NaOH treatment is the most widely studied and practical chemical treatment and generally reduces, but does not remove, the strength penalty. Gaps remain in controlled comparison of treated and untreated rubber, reporting of treatment conditions, physical-property data and statistical analysis.

DOI: https://doi.org/10.5281/zenodo.23810899

Published by: vikaspatanker

Authors: Bolarinwa,Sehilat Abike, Adu, Cecilia, Ejiks, Oluwakemi Latifat

Abstract: This study empirically examines how institutional quality moderates the relationship between fiscal policy and fiscal effectiveness in Nigeria from 1996 to 2024. The study employs annual time series data sourced from the Central Bank of Nigeria Statistical Bulletin and World Bank World Governance Indicators. Data were analyzed using Augmented Dickey-Fuller and Phillips-Perron unit root tests, Johansen cointegration, Vector Error Correction Model, Impulse Response Function, Variance Decomposition, Granger Causality, and an interaction regression model. Findings: The results reveal that all variables are integrated of order one I(1) and cointegrated, indicating a long-run relationship. The VECM shows a negative and significant error correction term. The interaction term between fiscal policy and institutional quality is positive and statistically significant, confirming that strong institutional quality amplifies the effect of fiscal policy on economic growth. Impulse response shows that shocks to institutional quality have persistent positive effects on growth. Variance decomposition reveals that institutional quality and fiscal policy jointly explain over 35% of growth variation. Practical Implications: Fiscal reforms in Nigeria will be ineffective without corresponding governance reforms. Policy should focus on strengthening budget credibility, tax administration, and accountability institutions. This study provides direct empirical evidence of governance as a transmission mechanism in Nigeria, moving beyond treating it as a mere control variable.

DOI: https://doi.org/10.5281/zenodo.23805565

Published by: vikaspatanker

Authors: Mr. Prashant Patel, Mr. Pankaj Agrawal Assistant Professor

Abstract: Replacing natural sand with crumb rubber from waste tyres can reduce the demand for river sand and the burden of waste tyres, but untreated rubber weakens concrete because it bonds poorly with cement paste. This paper reports an experimental study of M25 concrete in which 5, 10, 15 and 20% of the fine aggregate by volume was replaced with untreated or sodium hydroxide (NaOH)-treated crumb rubber, compared with a conventional control mix. Workability, fresh density, compressive strength at 7 and 28 days, splitting tensile strength, flexural strength, hardened density and water absorption were measured. All properties deteriorated as rubber content increased. At 20% replacement the 28-day compressive strength fell from 33.4 MPa for the control to 18.9 MPa with untreated rubber and 22.0 MPa with treated rubber. Relative to untreated mixes, treatment raised compressive, splitting tensile and flexural strength by up to 16.4%, 12.9% and 10.8% and reduced water absorption by up to 12.9%, but no rubberised mix matched the control. Two-factor analysis of variance showed significant effects of replacement level and rubber condition (p < 0.001) and no significant interaction. Using a minimum of 25 MPa at 28 days, treatment raised the acceptable replacement level from 10% to 15%. The results suggest that treated rubber concrete is better suited to non-structural and low-to-moderate strength applications.

DOI: https://doi.org/10.5281/zenodo.23804716

Published by: vikaspatanker

Authors: Dr. Pankaj Malik,, Hamid Ansari, Rajveer Raghuvanshi, Shahid Ali,, Ahmad Raza Qureshi

Abstract: Modern law enforcement agencies face the dual challenge of managing complex public safety threats while maintaining strict institutional accountability. This paper explores the integration of artificial intelligence (AI), automation, and advanced digital tools within modern policing frameworks. By analyzing current implementations—including predictive policing algorithms, automated facial recognition, automated license plate readers (ALPRs), and AI-driven evidence management systems—this study assesses how these technologies enhance operational efficiency, accelerate response times, and optimize resource allocation. Furthermore, the paper addresses critical systemic challenges, specifically targeting algorithmic bias, citizen privacy concerns, data security vulnerabilities, and the regulatory frameworks required to govern automated decision-making. Ultimately, this research provides a balanced framework for law enforcement executives and policymakers, demonstrating how agencies can leverage cutting-edge technical innovation while upholding the core principles of ethical, transparent, and community-oriented justice.

DOI: https://doi.org/10.5281/zenodo.23793526

Published by: vikaspatanker

Authors: Deepak Singh

 

 

Abstract:

DOI: https://doi.org/10.5281/zenodo.23763322

 

 

Published by: vikaspatanker

Authors: Research Scholar S.Arockia Vinotha, Madhura B S

Abstract: The rising rate and speed of spread of infectious disease outbreaks require surveillance systems that precede traditional reporting flows. In this paper, we conduct a systematic exploration of the design, development, and assessment of a predictive surveillance framework based on artificial intelligence technologies for outbreak detection and short-term forecasting. The suggested approach entails the integration of multiple types of data sources – open source epidemic intelligence, syndromic data, mobility data, and climatic variables – into a unified pipeline encompassing natural language processing for event extraction, machine learning ensembles for risk classification, and temporal deep learning for trajectory forecasting. An AI framework involving Bidirectional Encoder Representations from Transformers (BERT) technology for signal classification and Gated Recurrent Unit (GRU) network for case forecasting is described in algorithmic pseudocode and illustrated via system diagrams. Retrospective analysis in multiple outbreak cases indicates that the ensembling technique yields better discrimination than single-model approaches, whereas the GRU model decreases forecast error in early outbreak growth stages. Results are summarized in a comparative table of different models' configurations. We conclude the paper with an identification of practical hurdles in the deployment of an AI-based surveillance system and the consideration of governance strategies for such systems.

DOI: https://doi.org/10.5281/zenodo.23730612

Published by: vikaspatanker

Authors: Assistant Professor N.Navaneetha, Assistant Professor L Bhavani

Abstract: The increasing demands for energy by cloud data centers have made it even more urgent to devise scheduling techniques that address the competing issues of achieving performance goals and maintaining sustainable practices. The proposed approach in this paper is a Quantum-Inspired Energy-Aware Scheduler (QIEAS) that utilizes concepts of quantum superposition along with a rotation gate-based local search strategy to schedule tasks on virtual machines. Contrary to hardware-assisted quantum-inspired techniques, QIEAS runs on purely classical systems but employs quantum state modeling with probabilistic amplitude coding technique. A multi-objective function consisting of energy consumption, makespan and load balance is employed by the proposed scheduler and a quantum measurement strategy is used to collapse possible solutions. Comparison of QIEAS against three meta-heuristic-based algorithms shows a reduction in energy consumption and scheduling delay in all scenarios.

DOI: https://doi.org/10.5281/zenodo.23730456

Published by: vikaspatanker

Authors: Assistant Professor Mahesh S, Dr. Jayasree P

Abstract: Digital health literacy (DHL) has been recognized as a significant determinant of telemedicine engagement, however, the pathways that link DHL with telemedicine engagement have not been sufficiently quantified. This cross-sectional study aims to establish the relationship between digital health literacy and patient engagement among 412 adults utilizing telemedicine services at an urban academic health system during the period from January to December 2025. DHL has been assessed by means of eHealth Literacy Scale (eHEALS), whereas patient engagement was measured via Patient Health Engagement Scale (PHE-s) and behavioral measures of telemedicine use. Logistic regression and explainable machine learning (CatBoost with SHAP) have been used to determine DHL engagement associations and the most important predictors. Adequate DHL levels were detected in 58.7% of subjects. Increased eHEALS scores were positively correlated with increased PHE-s positions (adjusted OR = 3.42; 95% CI: 2.18-5.37) and completed telemedicine sessions (adjusted OR = 2.89; 95% CI: 1.76-4.74) when adjusted for age, education, income, and chronic conditions presence. SHAP analysis showed self-care confidence, health information searching behavior and number of digital devices as the most influential predictors of high patient engagement level.

DOI: https://doi.org/10.5281/zenodo.23730316

Published by: vikaspatanker

Authors: Pawar Pritesh Sandip, Mrs. Khushbu Trivedi

Abstract: This paper presents a design in which every decision produced by a cloud-hosted AI model is examined by a dedicated Responsible AI layer before anyone acts on it. Cloud platforms now run models that classify, predict and recommend in settings where errors are costly, yet an accuracy score alone cannot show whether a particular decision was fair, private, secure or adequately justified. The proposed framework adds an assessment stage downstream of the model. This stage tests each output against fairness, privacy, security and reliability criteria, attaches a feature-level explanation generated with SHAP or LIME, merges the findings into a single risk label, and passes high-risk or uncertain outputs to a human reviewer. Every final decision is written to an audit log, and monitoring follows model behaviour, input drift and responsible-AI risks over time. The paper lays out the architecture, a thirteen-step decision procedure, a compact prototype plan built on Python, Scikit-learn, SHAP, LIME, Fairlearn and a public cloud, and an evaluation plan that contrasts the framework with an unmodified baseline. Because the prototype has not yet been built or tested, no experimental results are reported. The contribution is the integrated design together with a measurable plan for validating it.

DOI: https://doi.org/10.5281/zenodo.23726342

Published by: vikaspatanker

Authors: Pranav Sajeev, Dilraj N V, Dr. Saravanakumar R

Abstract: This paper describes a low-cost closed-loop speed controller for a 12 V brushed DC motor, written so that the build can be repeated. An ESP32 times the pulses of an optical slot sensor on an 8-slot disc, filters the speed and sends it over USB serial every 20 ms. A LabVIEW VI runs the PID controller and returns a PWM command (0–255) that the ESP32 applies to an L298N driver. All hardware, firmware, VI and test settings are listed, together with the tuning procedure. Mechanical load is applied by hand with a friction pad. We analyse the delay in the loop and show, from the firmware parameters, that the speed filter (about 47 ms of mean lag) dominates the estimated 63 ms loop dead time, while USB-serial timing jitter is only a few milliseconds. In a demonstration run, the PID output stayed between 231 and 248 counts and the mean speed was within about 4 % of the setpoint, but the speed estimate was very noisy because of the hand-cut disc.

DOI: https://doi.org/10.5281/zenodo.23723648

Published by: vikaspatanker