Authors: Dr.Sumbul Alam

Abstract: Food waste management is a contemporary global issue. According to UNDP, food waste constitutes 40-51% of the waste in the Kingdom, followed by paper, cardboard, plastics, and other materials. Consequently, an effective waste segregation system is essential to address this problem. This research proposal aims to develop a mechanism for the segregation of decayed fruits and vegetables from the shelves, storage, and inventories of retailers such as Hyper Panda, Carrefour , and Lulu hypermarkets, utilising a hybrid machine learning algorithm comprising Linear SVM and PCA, alongside YOLO for real-time detection. Following segregation, this organic waste may be transported to a biorefinery where, using anaerobic digestion technology, it can be turned into methane gas. Currently, the door-to-door accessibility of processed foods, consumer products, and groceries is in great demand. Consequently, this methane gas may be utilised by their own delivery trucks and vehicles, so minimising food waste reduction in retail, promoting energy sustainability, and decreasing pollution.

DOI: http://doi.org/10.5281/zenodo.21676317

Published by: vikaspatanker

Authors: Mrs.Aruna C, Rajeswary Nair

Abstract: With the advent of digital services, various vulnerabilities in centralized identity management systems such as Single Point of Failure (SPOF), data leakage, and user privacy invasion are evident. In this paper, an architecture framework for designing Decentralized Identity Management System (DIDMS) by leveraging blockchain technologies is proposed for privacy preserving authentication in digital age. The DIDMS framework makes use of Decentralized Identifier (DID) with Verifiable Credentials (VC) and Zero Knowledge Proof (ZKP) techniques to implement the concept of self-sovereign identity. Four phases of framework including registration, credential issuance, authentication and storage is designed and tested. The performance results obtained from the experimentation indicate that the proposed framework gives an authentication latency of 320 ms, storage overhead reduction of 42%, and verification accuracy of 98.6% with 31% gas optimization over conventional smart contract approaches. The comparative evaluation with existing frameworks indicates improvement in the areas of privacy protection, scalability and user control.

DOI: http://doi.org/10.5281/zenodo.21676274

Published by: vikaspatanker

Authors: Dr. R. Bhaskar Reddy, Dr. A. Kumaraswamy

Abstract: Due to the increasing demands for energy in the world coupled with increased environmental regulations, it is now necessary to use sustainable energy sources instead of the traditional fossil fuels for internal combustion engines. In this paper, a study on performance improvement in compression ignition engines through the use of alternative biofuels has been presented. The study focuses on hydrotreated vegetable oil (HVO), biodiesel blends and the use of dual fuel where the two fuels used include biogas and hydrogen. The results of experimental and computer simulations show that the use of HVO leads to reduction of NOx and CO by 9.0% and 38.7%, respectively, compared to fossil diesel. Turbocharged B20 biodiesel blends lead to fuel consumption reduction of 20% and brake torque increase of 75%. The canola biodiesel gives brake thermal efficiency of 31.2% and brake-specific fuel consumption of 0.26 kg/kWh.

DOI: http://doi.org/10.5281/zenodo.21675388

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

DOI:

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker

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

Published by: vikaspatanker