Authors: Professor Dr. B.R. Shaalini, Raja Senthil

Abstract: Background- Essential hypertension is a major modifiable risk factor for cardiovascular disease and remains a leading cause of morbidity and mortality worldwide. Aerobic exercise is recommended as a first-line non-pharmacological intervention for blood pressure management. However, evidence comparing the effectiveness of low-intensity and high-intensity aerobic exercise on cardiovascular and functional outcomes remains limited, particularly in the Indian population. Objective- To compare the effectiveness of low-intensity and high-intensity aerobic exercise on blood pressure, functional capacity, cardiorespiratory fitness, resting heart rate, body mass index (BMI), and health-related quality of life in adults with essential hypertension. Methods- A prospective comparative pre-post study was conducted among 60 adults aged 40–65 years with essential hypertension. Participants were allocated into two groups (n = 30 each). Group A performed supervised low-intensity aerobic exercise (40–55% heart rate reserve), whereas Group B performed supervised high-intensity aerobic exercise (70–85% heart rate reserve) for 12 weeks. Outcome measures included systolic blood pressure (SBP), diastolic blood pressure (DBP), Six-Minute Walk Test (6MWT), estimated VO₂max, resting heart rate (RHR), BMI, and Short Form-36 (SF-36) quality-of-life scores. Assessments were performed at baseline and after completion of the intervention. Data were analysed using paired and independent Student's t-tests, with statistical significance set at p < 0.05. Results- Both exercise programmes produced significant improvements in all measured outcomes (p < 0.001). In the low-intensity group, mean SBP decreased from 146.8 ± 8.7 mmHg to 136.4 ± 8.1 mmHg, whereas the high-intensity group demonstrated a greater reduction from 147.3 ± 9.1 mmHg to 128.1 ± 8.8 mmHg. Functional capacity improved significantly, with the 6MWT increasing from 418.6 ± 52.3 m to 456.7 ± 52.1 m in Group A and from 421.7 ± 50.8 m to 490.8 ± 48.6 m in Group B. Significant improvements were also observed in VO₂max, resting heart rate, BMI, and all SF-36 domains, with superior outcomes in the high-intensity exercise group. Conclusion- Both low-intensity and high-intensity aerobic exercise are effective physiotherapy interventions for improving blood pressure control, functional capacity, cardiorespiratory fitness, body composition, and health-related quality of life in adults with essential hypertension. High-intensity aerobic exercise demonstrated significantly greater improvements across all outcome measures, supporting its use in appropriately screened and supervised patients.

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Authors: Utkarsh Singh

Abstract: Despite the widespread adoption of chatbots for customer service, small businesses continue to lack the resources required to develop and customize such solutions effectively. In this work, we propose an approach that enables small businesses to construct tailored customer service chatbots by fine-tuning an open-weight large language model (LLM) using QLoRA [1] on a domain-specific customer service dataset. The proposed method leverages parameter-efficient transfer learning to adapt a pre-trained LLM to the customer service domain, preserving its general natural language understanding and generation capabilities while specializing its behaviour. To extend the chatbot’s functional scope beyond the fine-tuning corpus, we integrate a Retrieval-Augmented Generation (RAG) [4] module that retrieves relevant product information, warranty details, and installation guides from PDF documentation at inference time. This allows the chatbot to ground its responses in accurate, business-specific information rather than relying solely on parametric knowledge. In addition, we log chatbot conversations and apply sentiment analysis to the resulting interaction data, forming a feedback loop that surfaces unsatisfactory exchanges for human follow-up and informs iterative improvement of the system. Taken together, these components constitute a cost-effective pipeline through which small businesses can deploy personalized customer service chatbots without incurring the cost of large-scale data collection or bespoke system engineering.

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Dr. Bibin Pappen Babu
Affiliation	Global Ambassador, Vice Patron, Advisor of International Organizations.
Email-Id:	bibinpababu@gmail.com
Publication:  Blockchain-Enabled Industrial Engineering Frameworks for Closed-Loop E – Waste Re manufacturing: Risk-Aware Optimization, Data Governance, and Social Sustainability. The Stewardship of Process, Operations, and Compliance: Integrating Industrial Engineering, Management, and Law within Christian Organizational Leadership. Stochastic Neuromorphic Fabric (SNF): Gracefully Degrading Computing via Approximate Runtimes on Legacy Nodes.

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Authors: Tanhaji Walunj, Madhukar Badgujar

Abstract: A new series of transition metal complexes derived from 2-fluorobenzylidene)-2-(2-(hydroxyimino)-1,2-diphenylethylidene)hydrazine-1-carbothiohydrazide (FBHT) was successfully synthesized and systematically characterized using a range of spectroscopic and analytical techniques. The complexes were obtained through the reaction of the FBHT ligand with copper(II), zinc(II), and nickel(II) salts in a 1:2 metal-to-ligand molar ratio. Comprehensive characterization was carried out by elemental analysis, UV–Visible spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and mass spectrometry to confirm the structures and coordination behavior of the synthesized compounds. The electronic absorption spectra of the metal complexes exhibited noticeable shifts relative to the free ligand, providing clear evidence of successful metal coordination. Infrared spectral analysis further supported complex formation by displaying new absorption bands in the low-frequency region, which were assigned to metal–ligand vibrations involving the hydroxyimino and thiohydrazide donor sites. Moreover, the 1H and 13C NMR spectra revealed significant changes in the chemical shifts of the ligand signals following complexation, indicating alterations in the electronic environment caused by coordination with the metal ions. The antimicrobial potential of the synthesized complexes was assessed against representative Gram-positive and Gram-negative bacterial strains, namely Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, as well as the fungal pathogen Candida albicans, using the disk diffusion assay. All of the metal complexes demonstrated enhanced antimicrobial activity compared with the uncoordinated ligand. Among them, the copper(II) complex exhibited the strongest inhibitory effect, particularly against S. aureus and P. aeruginosa. This enhanced performance was further confirmed by lower minimum inhibitory concentration (MIC) values compared with the corresponding zinc(II) and nickel(II) complexes. In contrast, the free FBHT ligand displayed only weak antimicrobial activity, highlighting the beneficial role of metal complexation in improving biological efficacy. Overall, these findings suggest that the synthesized FBHT transition metal complexes, especially the copper(II) derivative, represent promising candidates for the development of novel antimicrobial agents.

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Authors: Assistant Professor Dr. Ranjeet Kumar Ambast, Aditya Vikram

Abstract: The foreign exchange (Forex) market is characterised by high liquidity, pronounced volatility, and non-linear dynamics. This makes accurate prediction of currency fluctuation and investment risk exceptionally difficult for traditional econometric models (Bollerslev, 1986; Cont, 2001). This paper synthesises secondary evidence on the comparative predictive performance of two Artificial Intelligence (AI) approaches Long Short-Term Memory (LSTM) deep learning networks (Hochreiter & Schmidhuber, 1997) and Random Forest (RF) ensemble learning (Breiman, 2001) against the traditional Generalised Autoregressive Conditional Heteroskedasticity (GARCH) family of models (Bollerslev, 1986), drawing on published studies that use historical USD, EUR, GBP, JPY, BRL, and ZAR exchange-rate data spanning approximately 2012–2025. The review evaluates model performance across stable and volatile market regimes for directional accuracy through Value-at-Risk (VaR) estimation, and risk-adjusted investment signals. Findings from the reviewed literature indicate that AI-based LSTM models achieve superior performance for short-horizon volatility forecasts. It’s particularly in capturing sudden shifts in implied volatility (Kraus & Feuerriegel, 2024). Random Forest models tend to deliver the highest directional accuracy and the lowest point-prediction error across several currency pairs and cryptocurrency markets (Milionis & Konstantinou, 2024; Ndlovu, 2025). Hybrid GARCH-LSTM architectures further improve predictive accuracy, with an APARCH-LSTM specification reported to achieve a coefficient of determination (R²) of 95.53% for USD/BRL volatility forecasting (Hottz, 2025), while hybrid models improve Value-at-Risk estimation accuracy by up to 10% during periods of elevated volatility relative to standalone GARCH or LSTM specifications (Nsengiyumva et al., 2025). The synthesis further shows that risk-adjusted performance measured through the Sharpe ratio, Calmar ratio, and maximum drawdown favours hybrid deep-learning architectures for multi-asset portfolios and tree-based ensembles such as XGBoost for equity-index applications (Saly-Kaufmann et al., 2026; Singh & Praveen, 2025). The paper concludes that no single AI technique dominates across all currency pairs, forecast horizons, and objectives, and that model choice should be aligned with the specific trading, risk-management, or regulatory objective at hand.

DOI: https://doi.org/10.5281/zenodo.21641515

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Authors: Sweta Kumari

Abstract: Indian agriculture faces the dual challenge of enhancing soil health while mitigating climate change. This research paper presents a comprehensive agricultural soil carbon credit framework specifically designed for Indian smallholder farmers operating under semi-arid conditions. The framework integrates six core research objectives: (1) developing a reliable measurement and verification system suited to Indian farming conditions, (2) creating income opportunities through carbon credit programs, (3) establishing baseline data on soil organic carbon and health indicators, (4) implementing and evaluating soil management practices, (5) measuring seasonal and long-term changes in soil organic carbon, and (6) developing practical monitoring guidelines. The framework was developed using a doctrinal and conceptual research methodology based on a critical review and synthesis of existing literature, policy documents, carbon market mechanisms, and emerging soil carbon measurement technologies relevant to Indian agriculture. We propose innovative solutions, including affordable low-cost measurement technologies (portable NIR spectroscopy, handheld sensors, smartphone-based tools), a Small Local Carbon Service Organisation (SLCSO) model for accessible carbon measurement services, green incentives with fast remuneration mechanisms featuring tiered payment systems, and a "Carbon Check First" mechanism providing free or subsidised initial baseline assessments. This framework addresses critical barriers preventing smallholder farmer participation in carbon markets, including high measurement costs, complex procedures, and delayed payments. By combining technological innovation with farmer-centric service delivery models, this research aims to transform Indian smallholder agriculture into a viable carbon-sequestration sector while improving farmers' livelihoods and soil health.

DOI: https://doi.org/10.5281/zenodo.21639077

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Authors: Rafiu Kayode.Olalere, Hongming Xu, Sheriff Lamidi, Y.O Bankole

Abstract: The growing demand for carbon-neutral transportation has intensified research into renewable oxygenated fuels capable of improving engine efficiency while reducing exhaust emissions. Among emerging biofuels, biomass-derived 2-methyltetrahydrofuran (MTHF) has attracted considerable attention because of its favourable physicochemical properties. Nevertheless, comprehensive experimental comparisons of neat MTHF with gasoline, 2-methylfuran (MF), and ethanol under identical direct-injection spark-ignition (DISI) engine operating conditions remain limited. This study experimentally investigates the combustion characteristics, engine performance, gaseous emissions, and particulate emissions of neat MTHF relative to commercial gasoline (ULG95), MF, and ethanol using a single-cylinder DISI engine. Experiments were conducted at a constant engine speed of 1500 rpm, stoichiometric operation (λ = 1), and engine loads ranging from 3.5 to 8.5 bar indicated mean effective pressure (IMEP). Ignition timing for each fuel was optimized using the maximum brake torque (MBT) criterion or knock-limited spark advance (KLSA). At 5.5 bar IMEP, MTHF generated peak in-cylinder pressures of about 10%, 15%, and 25% higher than MF, ethanol, and gasoline, respectively. Compared with MF and ethanol, MTHF reduced indicated specific fuel consumption by approximately 8% and 33%, while maintaining thermal efficiency comparable to gasoline. Furthermore, MTHF produced lower total hydrocarbon and particulate emissions than gasoline and MF, together with lower NOₓ emissions than MF. These findings demonstrate that MTHF is a promising renewable gasoline substitute capable of simultaneously enhancing combustion performance and reducing exhaust emissions in modern DISI engines.

DOI: http://doi.org/10.5281/zenodo.21637881

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Authors: Vaibhav Singh Chouhan, Rupali Chaure

Abstract: Image caption: Word Cloud for Explainable A Explainable Artificial Intelligence (XAI) is an important subject area… This extensive review paper provides a meta-analysis of the most prominent explainable artificial intelligence (XAI) frameworks, methodologies and techniques that have been developed in the last decade. We conduct a systematic analysis of > 150 peer-reviewed publications to integrate novel transparent techniques (attention mechanisms, LIME, SHAP, prototype-based methods, counterfactual explanations) and assess their usefulness for promoting interpretability and user trust. Our meta-analysis exposes severe shortcomings in interpretability standardization, validation metrics and real-world applicability. We introduce a universal taxonomy to classify XAI methods based on their explanation scopes, computational complexity and applicability for different application domains. In addition we discuss the accountability-interpretability tradeoff, scalability issues and the need for domain specific explanation frameworks as key challenges still facing this field. Our paper contributes to the field by offering a holistic roadmap that will guide researchers and practitioners to select, implement and evaluate an XAI solution, trace future research paths which are required in order to endow autonomous decision-making systems with trustworthiness when applied on critical infrastructure.

DOI: http://doi.org/10.5281/zenodo.21545756

 

 

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Authors: Om A. Chougule, Vikas A. Patil

Abstract: Stone crusher sites commonly depend on manual supervision, physical registers and reactive security measures, which can lead to theft of equipment or raw material, delayed response to unauthorized movement, incomplete operational records and avoidable downtime. The Automation of Stone Crusher Management System (ASCMS) presented in the attached study is an intelligent automation framework designed to address these problems through integrated IoT sensing, AI-enabled surveillance, real-time dashboards and centralized software control. This professional research paper reorganizes the supplied work into a structured manuscript and presents the system objective, methodology, architecture, implementation process and reported results in a clear academic format. The system uses multiple input sources, including surveillance cameras, RFID and motion sensors, and manual operational inputs. These inputs are processed through a centralized software layer that performs data integration, analytics, machine-learning-based anomaly detection, decision support and alert generation. The output layer provides live dashboards, automated notifications, reports and optimization recommendations. The source results indicate that ASCMS improves security, reduces unauthorized incidents, automates operational logging, supports real-time monitoring and remains scalable for broader industrial applications. The incident comparison reported in the source shows a reduction from 18 theft or intrusion events before implementation to 4 events after ASCMS deployment. Overall, the work demonstrates that a layered AI-IoT architecture can improve safety, accountability and operational efficiency in stone crusher management when it is supported by careful hardware integration, software validation, security testing and continuous feedback-based optimization.

DOI: https://doi.org/10.5281/zenodo.21533791

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Authors: Dr Abhijit Das, Dr Namrata Yadav Das

Abstract: Background: Artificial intelligence (AI) technologies are increasingly integrated into educational settings to enhance student engagement and classroom interaction. However, the evidence base regarding their effectiveness remains fragmented. This systematic review synthesizes empirical evidence on the role of AI in improving student engagement and classroom interaction in K-12 and higher education contexts. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive literature search across multiple databases (SciSpace, Google Scholar, PubMed) from January 2016 to March 2026. Studies were included if they reported empirical evidence on AI interventions targeting student engagement or classroom interaction outcomes in educational settings. Two independent reviewers screened 267 unique records, assessed 204 full-text articles, and included 142 studies in the final synthesis. Risk of bias was assessed using the ROBINS-I tool for six representative studies. Results: From 267 unique records identified, 142 studies met inclusion criteria after title/abstract screening and full-text assessment. Six representative studies (N=50–20,000+ participants) demonstrated that AI interventions—including personalized recommendation systems, intelligent tutoring systems, conversational agents, and adaptive learning platforms—consistently improved student engagement metrics. Two randomized controlled trials showed low risk of bias, while four quasi-experimental studies showed moderate risk. AI-driven interfaces increased engagement by up to 25.13% in large-scale field tests. Personalized AI recommendations significantly improved learning performance and engagement, particularly for students with moderate motivation levels. AI tutors enabled students to learn more than twice as much in less time compared to traditional active learning approaches. Conclusions: The evidence demonstrates that AI technologies can effectively enhance student engagement and classroom interaction across diverse educational contexts. Randomized controlled trials provide the strongest evidence, while quasi-experimental studies show consistent positive effects despite moderate methodological limitations. Future research should prioritize rigorous experimental designs with preregistration, comprehensive reporting of missing data, and investigation of long-term effects and equity considerations.

DOI: http://doi.org/10.5281/zenodo.21509136

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