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Does Green Financing affect the Sustainable Economic Growth of Emerging Economies? Evidence from Panel ARDL Model
This study examines the nexus between green finance determinants and sustainable economic growth in Brazil, India, China, and South Africa using a panel Autoregressive Distributed Lag (ARDL) approach. These rapidly developing countries face the dual challenge of maintaining economic growth while addressing environmental sustainability. The analysis focuses on five key independent variables: Comparative Advantage in Low Carbon Technology Products, Total Trade in Low Carbon Technology Products, Trade Balance in Low Carbon Technology Products, Annual CO2 Emissions, and Lack of Coping Capacity. Short-run results indicate that Total Trade in Low Carbon Technology Products negatively affects GDP, suggesting that while green trade is expanding, it currently lacks stable, revenue-generating mechanisms. Annual CO2 Emissions and Lack of Coping Capacity positively influence GDP in the short term, reflecting continued dependence on emission-intensive industries and limited infrastructure for resilience. Comparative Advantage and Trade Balance in Low Carbon Technology Products are statistically insignificant in the short run, implying delayed economic benefits. In the long run, none of the green finance indicators show a significant relationship with GDP, possibly due to the substantial upfront investments required for green projects, which delay economic returns. The study underscores the need for strategic investments in technology, infrastructure, and governance to align economic growth with long-term sustainability goals. 2025 Sathish Pachiyappan et al., published by Oikos Institut d.o.o. -
REGRESSION WITH VOLATILE ERRORS IN THE PRESENCE OF MEASUREMENT ERRORS
This study explores the estimation and testing of regression models with volatile errors when measurement errors are present. The presence of measurement error in models with heteroscedastic disturbances, such as those following an autoregressive conditional heteroscedasticity (ARCH) or Generalized ARCH (GARCH) structure, can lead to biased estimates and misleading inferences. To address this, we develop an estimation framework that accounts for both heteroscedasticity and mismeasured observations, ensuring consistent and asymptotically normal parameter estimates. We estimate the parameters using corrected score estimation and weighted linear regression, which effectively mitigate the impact of measurement error and hetroscedasticity. Additionally, we perform a Likelihood Ratio (LR) test to assess the significance of measurement errors in regression models with volatile errors. Through Monte Carlo simulations, we analyze the bias and efficiency of traditional estimators and demonstrate the robustness of our proposed approach. Finally, the methodology is applied to real-life economic and financial data, illustrating its practical relevance and effectiveness in empirical research. The findings contribute to improving statistical inference in models where measurement error and volatility coexist, ensuring more reliable and accurate parameter estimation. 2025, Gnedenko Forum. All rights reserved. -
MULTI REFERENCE SKIP-LOT SAMPLING PLAN
Skip-lot sampling plans have become significant in modern quality control due to rising production volumes and the demand for cost-effective inspection methods that will yield high-quality outputs. When inspecting a submitted lot, a skip-lot plan is economically favourable and guarantees high quality. Thus, this approach benefits both producers and consumers. The skip-lot sampling plan generally utilizes the same sampling plan as the reference plans for both skipping and normal inspection. However, using the same plan in both phase favours either the producer or the consumer in the most essential situations. This article introduces a novel approach, the Multi Reference Skip-Lot Sampling Plan with the provision of having two different reference plans in the normal and skipping phases of the skip-lot plan. The paper explores the efficacy of this approach by deriving performance measures using a power series approach. To evaluate the proposed plan, a comparison is made with existing skip-lot sampling plans that use single sampling plans or double sampling plans as reference plans. This comparison is based on operational characteristics and average sample number values, accompanied by graphical representations. The comparative analysis demonstrates that the new plan effectively balances the satisfaction of both producers and consumers. Additionally, the study offers a strategy for selecting the plan parameters using the unity value approach, supported by a table providing unity values. 2025, Gnedenko Forum. All rights reserved. -
A SIGNIFICANT STUDY ON ROBUST MEASURE OF LOCATION PARAMETERS USING DATA DEPTH APPROACHES
Data depth procedures are statistical methods used to measure the centrality or depth of a point within a multivariate dataset. These procedures provide a way to quantify how deep or outlying a point is relative to the overall distribution of the data. This study explores various data depth procedures to find reliable location estimations in cases like with and without outliers. In this paper, various depth procedures, such as Mahalanobis depth, Halfspace depth, Euclidean depth, Simplicial depth, and Projection depth, are studied and compared. The efficiency of these depth functions is evaluated using real datasets and simulation studies with R software. 2025, Gnedenko Forum. All rights reserved. -
BAYESIAN SPATIAL TEMPORAL TREND ANALYSIS FOR DECISION MAKING AND RISK ASSESSMENT IN DENGUE INCIDENCE STUDIES: A CASE OF TAMILNADU
This study presents a Bayesian spatial-temporal analysis for studying Dengue incidence in Tamil Nadu, aiming to provide insights into decision-making and risk assessment strategies. Statistical models that allow a more accurate depiction of true disease rates by borrowing information from neighboring regions will help mitigate the effects of sparsely populated regions and deliver better inference. Perhaps the most conspicuous manner of modeling spatial dependence is to introduce spatially associated random effects within a Bayesian hierarchical setting. The Bayesian modeling and inferential framework are flexible and extremely rich in its capabilities to accumulate various scientific hypotheses and assumptions. The spatial and spatial temporal epidemiology is concerned with the description and analysis of spatial and spatial temporal variations in disease risk with respect to risk factors. As the primary aim of this work is to quantify the spatial disease pattern of dengue incidences apart from the mapping of disease modelling the disease and finding spatial clusters/hotpots is one important aspect in epidemiology is to find the temporal trends in or outside of clusters. In this study, a spatial-temporal trends model is fitted using the Leroux CAR priors set up for studying the spatial-temporal disease patterns with the estimation of the temporal trends with reference to dengue incidences in Tamil Nadu, India. 2025, Gnedenko Forum. All rights reserved. -
Decision-Making Models for Efficient Outbreak Response: A Management-Orientated Approach to Dengue Control in Andhra Pradesh, India
Dengue remains a serious health challenge across India, and Andhra Pradesh faces repeated outbreaks that put a heavy strain on hospitals, clinics, and communities. Combating this disease isnt just about tracking casesits about making quick, smart decisions to control its spread effectively. This study looks into different decision-making approaches that can help improve how Andhra Pradesh responds to dengue outbreaks, making actions faster and more targeted. Using a mix of existing epidemiological data, interviews with health officials and community leaders, and simulated scenarios, the research explores how tools like Multi-Criteria Decision Analysis (MCDA), the Analytic Hierarchy Process (AHP), and Decision Tree Analysis can assist in choosing the best strategies. These models help prioritise interventions such as resource distribution, vector control efforts, and public awareness campaigns, especially when dealing with uncertainties like limited resources or unpredictable case surges. The findings indicate that integrating these decision-making frameworks into public health planning can foster better coordination among policymakers, healthcare workers, and local authorities. This improved coordination can lead to quicker responses, more effective use of resources, and ultimately, a reduction in dengue cases and their impact on communities. The study emphasises that combining management science tools with traditional epidemiology isnt just helpfulits essential for strengthening outbreak preparedness. Plus, these approaches can be adapted to tackle other communicable diseases in India and similar settings worldwide, paving the way for smarter, more resilient public health systems. 2025, Indian Society for Malaria and Communicable Diseases. All rights reserved. -
PRODUCTIVITY LOSS LINKED TO NON-COMMUNICALE DISEASES ACROSS SOCIO-DEMOGRAPHIC PROFILES: EVIDENCE FROM SEDENTARY OCCUPATION EMPLOYEES DURING COVID-19
BACKGROUND: The COVID-19 pandemic has significantly transformed work dynamics, leading to a notable shift towards remote work, particularly for those in sedentary roles. This change has been linked to a heightened risk of Non-Communicable Diseases (NCDs), many of which stem from lifestyle-related factors. Such health challenges can adversely affect productivity in the workplace, causing both absenteeism and presenteeism. AIM: This study examines the costs of presenteeism and absenteeism related to non-communicable diseases (NCDs) across socio-demographic variables. METHODS: Using stratified and purposive sampling, a cross-sectional study was conducted with 426 employees in sedentary occupations in the Delhi-NCR region. Productivity losses from presenteeism and absenteeism were assessed using the WHO HPQ Questionnaire. Additionally, the General Linear Model (GLM) was utilised to analyse the relationship between loss productive time (LPT) costs associated with presenteeism and absenteeism across disease categories and socio-demographic factors. RESULTS: Employees diagnosed with 'NCDs Category I', 'NCDs Category II', and those with 'comorbid' conditions were estimated to lose between 40 and 48 workdays each year. Absenteeism accounts for a greater portion of productivity losses than presenteeism in all disease categories. Comorbidities contribute to the most significant losses, with costs surpassing those associated with CDs by INR 51.78 thousand (932.04 AUD) for presenteeism and INR 226.47 thousand (4,076.46 AUD) for absenteeism. Additionally, every extra year of education corresponds to an increase of INR 4.96 thousand (89.28 AUD) in costs related to LPT due to presenteeism and a reduction of INR 15.68 thousand (282.24 AUD) in absenteeism-related LPT costs. CONCLUSION: The research indicates that NCDs, particularly in the presence of comorbid conditions, have a substantial effect on workplace productivity. Notably, individuals with higher levels of education and Income exhibit elevated presenteeism costs, which may be attributed to the influence of remote work arrangements. Conversely, absenteeism rates appear to be lower among highly educated employees in similar settings. 2026, Australasian College of Health Service Management. All rights reserved. -
Research Advances on Foreign Portfolio Investments: A Bibliometric and Thematic Analysis
[No abstract available] -
Optimal Reactive Power Compensation in Indian Urban Electrical Distribution Networks Using Hybrid Starfish Optimization Algorithm
This paper presents an efficient hybrid optimization approach for optimal reactive power compensation (ORPC) problem in electrical distribution networks (EDNs) using a Hybrid Starfish Optimization Algorithm (HSFOA). A Voltage Stability Index (VSI) is integrated to identify critical buses and narrow the search space, improving solution quality and convergence efficiency. The proposed method determines the optimal locations and sizes of capacitor banks (CBs) and Distribution static synchronous compensators (DSTATCOMs) to minimize real power losses and enhance voltage stability. The effectiveness of the HSFOA is evaluated first on the IEEE 33-bus benchmark system. The results demonstrate that the proposed approach provides superior improvements compared to conventional techniques. Later, the approach is implemented on 106-bus and 85-bus real-time Indian urban distribution networks. For the 106-bus system, losses decrease from 644.768 kW (base case) to 495.273 kW with CBs and to 487.933 kW with DSTATCOMs, corresponding to 23.25% and 24.32% reductions. In the 85-bus system, real power losses are reduced by 34.56% with CBs and 34.44% with DSTATCOMs, while the VSI improves by 15.05% and 20.70%, respectively. Similar improvements were recorded for the IEEE 33-bus system. Overall, the findings confirm that HSFOA offers a robust and effective solution for optimal reactive power planning and enhanced operational performance in modern EDNs. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. https://creativecommons.org/licenses/by-sa/4.0/ -
Combined Enzymatic Action of Pseudomonas psedoalcaligenes Protease and Stutzerimonas xanthomarina Amylase in Textile Blood Stain Removal
Blood is a body fluid. When it contacts oxygen, it can clot, forming a dark solid substance. In accidents involving wounds, blood is released and can stain clothing when absorbed by fabrics. Removing blood stains from fabrics is challenging with various commercial detergents. This study aimed to determine the efficacy of commercially available detergent powder supplemented with amylase and protease enzymes in removing bloodstains from textiles. Stutzerimonas xanthomarina and Pseudomonas pseudoalcaligenes were isolated from soil and examined for their enzymatic activity. The results demonstrated that these enzymes can effectively remove bloodstains from cotton fabric, with maximum activity observed at 50% saturation with ammonium sulfate and specific activity of protease at 93.45 2.17 U/mg and amylase at 36.28 1.36 U/mg. This study suggests that the addition of enzymes to chemical detergents can enhance their surface impact and improve their effectiveness. These findings indicate a potential for the development of more efficient detergents. The Author(s) 2025. -
Exploring Star Fruit Extract as an Alternative Substrate for Polyhydroxyalkanoates Production by Bacillus licheniformis NJ04
Polyhydroxyalkanoates (PHA) production from diverse group of microorganisms has been a topic for extensive research for several decades. Despite this extensive research explorations, commercialization of PHA is still facing major hurdles, mainly due to the high cost involved in PHA production and recovery. This study was designed to determine a sustainable approach to produce PHA using an underutilized fruit extract. The major novelty of this research work is the use of starfruit (Averrhoa carambola L.), a tropical fruit, as a substrate for PHA production employing Bacillus licheniformis NJ04. Commercialization of PHA production can help to tackle global issues like raising microplastic pollution and biomagnification. The maximum PHA production reported in this work was 3.8 g/L under optimized conditions like temperature of 37 C, pH 7 under shaking conditions (120 rpm) with 2% glycerol and starfruit extract as a carbon source after 72 h of incubation. The extracted PHA was further characterized through (FTIR) Fourier-transform infrared radiation, differential scanning calorimetry (DSC), Thermogravimetric analysis (TGA), X-Ray diffraction (XRD), and Proton Nuclear magnetic resonance (1H NMR). Thus, the present work highlights a novel strategy for using starfruit waste as a cost-effective substrate for PHA production. The Author(s) 2025. -
Antibacterial Efficacy of Oreochromis niloticus Mucus and its Characterization
Investigating new antimicrobial agents from various biological sources is necessary due to the rise of bacteria that are resistant to drugs. A neglected source of bioactive substances with possible antibacterial qualities is fish, especially Oreochromis niloticus (Nile tilapia). This study identifies the bioactive components of extracts made from O. niloticus and examines their antibacterial activity. PBS and sterile water were used to remove fish mucus using a solvent. Using the agar well diffusion method, extracts were tested for antibacterial activity against a panel of Gram-positive bacteria (Staphylococcus aureus, B. cereus), Gram-negative bacteria (Klebsiella pneumoniae, P. aeruginosa), and fungus (Candida albicans). The presence of hydroxyl and amide functional groups, which are suggestive of proteins and polyphenolic substances, was further validated by Fourier-transform infrared spectroscopy (FTIR). The study demonstrates O. niloticuss antibacterial potential, with its mucus being an exceptionally abundant source of bioactive substances. These results highlight the potential of fish-derived antimicrobial compounds as substitutes for traditional antibiotics. The isolation, structural clarification, and possible therapeutic uses of these chemicals should be the main areas of future study. Furthermore, to guarantee the efficacy and safety of these natural compounds, knowledge of their toxicity profiles and mechanisms of action is crucial. In the fight against antibiotic resistance, this study helps to establish sustainable bioresources and expands our knowledge of O. niloticuss antibacterial potential. The Author(s) 2025. -
Extended-spectrum ?-Lactamase (ESBL) Producing Bacterial Pathogens Associated with Respiratory Tract Infections
Respiratory tract infections (RTIs) have been critically associated with health care problems globally. Subsequently, increased antibiotic resistance rates have limited treatment options that are further exaggerated due to lack of newer novel drugs and therapies. Current study highlights, antibiotic resistance profiling along with extended-spectrum beta-lactamase (ESBL) producers of RTI pathogens from Bengaluru. During June 2020-May 2021, 1016 clinical samples collected, prevalence rate of 22.4% was exhibited, with highest in male (74.5%). Following age group, 30-35 years displayed highest (24.1%) though, lowest was in 45-50 years (1.3%). The standard microbiological characterization revealed Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii as predominant bacterial pathogens associated with RTIs. While, Antibiotic susceptibility test (AST) exhibited highest resistance rates for different antibiotics in the following pathogens, as K. pneumoniae for ampicillin (74.8%), P. aeruginosa for doripenem (66.6%), A baumannii to piperacillin/tazobactam (76.9%), E. coli for penicillin and ?-lactamase inhibitors ranging between 56-92%, E. cloacae to ticarcillin/clavulanic acid besides cefuroxime (100%). However, prevalence of Gram-positive strains were lowest and exhibited highest resistance to penicillin, and fluoroquinolone (83.3%). ESBL producers were predominantly K. pneumoniae, followed by E. coli, and E. cloacae with 21.9%, 6.5% and 1.3%, respectively. Notably, all the Gram-negative strains showed 100% sensitivity towards colistin with remarkable sensitivity was observed in oxazolidinone, glycopeptides by S. aureus and Coagulase-neagtive Staphylococcus aureus (CoNS). The study emphasizes increased antimicrobial resistance antimicrobial and ESBL resistance, suggesting AST as a systematic approach for apprising treatment guidelines in current scenario. The present study denotes polypeptide colistin as choice of drugs for treating RTI pathogens, however its not recommended in all cases. The Author(s) 2025. -
Indias credit growth and asset prices movements; Does the global financial cycle have a moderating role to play?; [Evoluci del crecimiento del crito y de los precios de los activos en la India; Desempe el ciclo financiero mundial un papel moderador?]
This study examines the effect of the global financial cycle on different financial indicators of the Indian economy through experimental analysis. It detects evidence of a connection between contemporaneous changes in capital flows, asset prices, and credit growth, which are related to the Global Financial Cycle (GFCy). The evolution of the cycle is largely driven by the monetary policy decisions of the Federal Reserve, and existing studies have examined the influence of these decisions in different contexts. The current study experimentally examines the effect of the global financial cycle on credit growth and asset prices in India during the period 2010-2023. For the purpose of achieving its goals, the study utilizes advanced time-series econometric techniques, such as the Granger Causality Test, Vector Autoregression (VAR) methodology, and the Impulse Response Function (IRF) test. The outcomes show that the global financial cycle has significant effects on the stock market, as confirmed by the Granger causality and IRF findings. 2019 Universidad Nacional Automa de Mico, Facultad de Contadur y Administraci. This is an open access article under the CC BY-NC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/) -
Investor's behaviour to COVID-19 vaccination campaign; An event study and panel data analysis in the southeast asian region
This study examines how the COVID-19 immunization campaign has influenced the stock market responses in the WHO Southeast Asian Region. The effects of the immunization campaign on the WHO Southeast Asian countries were different, and the study used event study techniques and panel-data regression models to investigate the impact of the WHO South-East Asian capital market. Some countries like India, Sri Lanka, and South Korea had positive markets that responded to the news, while others did not. The findings of this study suggest that investors make fair assessments and respond to events and announcements, but they tend to have a more visible reaction to negative incidents than to positive news/events. However, after 51 days, the WHO South East region as a whole had internalized the encouraging news. The study has a few limitations, such as a small dataset and period, only a few variables and models, and so on. Future studies could include a few additional countries and periods to produce more significant results. Originality/value- This study contributes to the existing knowledge about the impact of drugs and vaccinations on stock markets. It is the first study to investigate how the WHO Southeast Asian Region's COVID-19 immunization program affects the stock market reaction. The study used keywords such as Immunization campaign, abnormal returns, Cumulative average abnormal returns, Event Study, and WHO Southeast Asian Region. 2025 Universidad Nacional Autonoma de Mexico. All rights reserved. -
Urban Heat Dynamics in Pune: The Influence of Land Cover and Local Climate
Urban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (Tmax; 0.13? year?1 to 1.63? year?1) at almost each study site and minimum temperature (Tmin; 0.06? year?1 to 0.23? year?1) at a specific location during night. The mutual effect of land cover changes and meteorological conditions have evidenced the heat islands with varying intensities (2? to 8?) at four of the six sites, with significantly intensifying rates from 0.05? year?1 to 0.39? year?1. The estimation of dominating land cover type for the formation of heat islands demonstrated a significant simple determination (r2 = 0.001 to 0.013) and probability (P < 7.910?13 to 2.330?5) with heat island temperature identifying urban land cover as the primary factor at two sites, while the other two were affected by mixed land covers influenced by local meteorological characteristics. The outcomes of this study offer valuable insights into the development of heat islands in Pune and could guide strategies for alleviating urban heat, ultimately improving climate resilience and thermal comfort citywide. 2025, Binghamton University Libraries. All rights reserved. -
Analysis of Systematic Trade-offs between Military and Healthcare Expenditure alongside GDP Growth of Select Asian and Western Exporting Economies in the 21st Century
This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality between GDP growth and healthcare expenditure, reflecting economic stability and balanced allocation patterns. In contrast, developing Asian nations display fragmented and volatile relationships due to resource constraints and inefficiencies. Outlier analysis reveals country-specific dynamics, such as conflict-driven spending in Afghanistan and Myanmar and growth-focused strategies in China. Temporal trends show that economic crises, like the COVID-19 pandemic, significantly disrupt GDP growth but have limited long-term effects on healthcare or military expenditures. Clustering analysis identifies distinct groups of nations, shaped by economic capacity and geopolitical pressures. The findings emphasize the need for tailored policy frameworks to balance national security and human well-being, particularly in developing nations facing structural challenges. For sustainable development, policies must align resource allocation with economic priorities, geopolitical contexts, and societal needs. 2025, Binghamton University Libraries. All rights reserved. -
Greening the Workplace: Can Sustainable Practices Reduce Anxiety and Enhance Meaningful Work Engagement?
This academic research examines the relationship between job engagement, green work climate, job-related anxiety, meaningfulness at work within the organization. It draws attention to identify the significant relations among all these factors and highlights the role of a green work climate in promoting meaningful work and alleviating job-related anxiety. The research emphasizes a diverse sample of employees from various organisations using structural modelling to find the mediating roles of job engagement and work meaningfulness in the correlation between organizational practices, environmental sustainability, and employee satisfaction. The study finds that a green work climate significantly enhances meaningful work experiences and reduces job-related anxiety. It emphasises the mediating role of job engagement and work meaningfulness in linking organizational practices focused on environmental sustainability with employee satisfaction. The results of the study give practical understanding for organizations aiming to create resilient and committed workforces. Aligning green initiatives with strategies to boost individual fulfilment and reducing anxiety which can strengthen employee engagement and improve overall organizational outcomes. This study underscores the importance for an integrated approach to workplace management that blends environmental sustainability with employee well-being. It offers valuable contributions to understand how organizational practices can promote sustainability and promote a motivated and satisfied workforce. 2025, Binghamton University Libraries. All rights reserved. -
Impact of Node Failures on Productivity in Multilayer Supply Chain Networks: An Influence Network Analysis in the Indian Electronics Sector
Supply chain networks are essential for the delivery of goods and information, but disruptions such as natural disasters or trade embargoes can severely impact them. Resilience of entire networks under different types of disruptions when nodes or edges fail has been extensively studied. However, the extent to which the failure of a particular company affects another company of interest within a network has not been widely explored. To address this, we created a multilayer physical supply chain network of companies in the Indian electronics industry. Through systematic node removal simulations, we examined how the productivity of one company is impacted by the removal of another. Extending these simulations to include all possible combinations of companies yielded an influence network that represents interdependence among nodes in terms of productivity. We observed that removing a critical node could lead to not only a decrease but, quite counter-intuitively, an increase as well in the productivity of affected nodes. This study identifies the factors that influence these productivity changes and offers insights to supply chain managers to maintain network resilience in the face of node failures. 2025, Binghamton University Libraries. All rights reserved. -
A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis
Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC) and Diffusion-Weighted Imaging (DWI). Multimodal data gives a comprehensive understanding of the brain and the stroke lesions. Complex algorithms and processing steps are required to ensure that the data is prepared for further processing. The objective of this experimental research is to create a novel multimodal preprocessing model that can be used for the preprocessing of the multimodal data from various MRI modalities (FLAIR, DWI and ADC). The proposed model supports the automatic removal of artefacts from the multimodal data, by identifying and applying the best preprocessing techniques for Image Registration (Affine or non-rigid transformations), Normalization (Z Score or min-max normalizations), Denoising Techniques (Gaussian, Median, Non-Local Means, or Anisotropic Diffusion filters) and Bias Field correction. The best technique is identified using the evaluation techniques of Dice Coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Root Mean Squared Error (RMSE). Preprocessing is critical process to improve the outcome of the subsequent analysis including segmentation. Here, we propose an Enhanced Image Registration and Artefact Correction (EIRAC) model with Best Image Registration Technique (BIRT) and Multiple Orientation Normalization Denoising and Bias field correction Parallelly (MONDBP) algorithms for the preprocessing of multimodal MRI images to provides better results for the segmentation of stroke lesions through Machine Learning models. 2025, Binghamton University Libraries. All rights reserved.