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Modeling and analysis of the bioconvective flow of nanofluid over a stretching sheet with ThompsonTroian slip condition
In the present study, the flow, heat, and mass transfer characteristics of a bioconvective nanofluid over a stretching plate subjected to an external magnetic field are analyzed. The nonlinear slip at the surface is modeled using the ThompsonTroian velocity slip condition, while convective boundary conditions are applied to account for heat and mass transfer in the thermal and concentration fields. To ensure uniform nanoparticle distribution, motile microorganisms are incorporated into the fluid. These microorganisms help counteract particle aggregation and prevent solidification within the medium. Their motion gives rise to the bioconvection phenomenon, enhancing overall fluid transport. The governing equations for momentum, energy, and species concentration are formulated as partial differential equations (PDEs), incorporating key effects such as viscous dissipation, magnetic field influence, and heat sources. Using similarity transformations, the PDEs are reduced to a system of ordinary differential equations (ODEs). This system is then numerically solved via Python solve_bvp function, which employs a collocation method for boundary value problems. The computed solutions are validated against existing literature, and residual analysis is conducted to ensure accuracy. The results reveal that an increase in magnetic field strength suppresses fluid velocity while simultaneously raising the nanofluid temperature. Additionally, higher critical shear stress associated with the ThompsonTroian slip model further reduces the flow velocity near the surface. Akadiai KiadZrt 2025. -
Modeling and computational fluid dynamic analysis on a non-AC bus coach system
The main objective of this paper is to reduce the drag force and enhance the uniform airflow inside an existing non-air-conditioning bus coach system. The redesigning of an existing bus carried out by considering the forces that reduce the moment of the bus. Modeling and meshing was carried out using solid works and Hypermesh software, respectively. Finally, the problem is simulated using Ansys fluent software and analysis is carried out for different bus models. The noteworthy findings state that the air resistance of the vehicle is found to be 812.74 N and coefficient of drag is 0.67 are less as compared to existing bus model. 2020 Wiley Periodicals LLC -
Modeling Consumer Price Index: A Machine Learning Approach
The change in price of a group of goods and services is reflected in terms of consumer price index (CPI), making it one of the most important economic indicators. This is also the mostly used measure of inflation. Forecasted CPI values help the Government to take corrective measures to control the economic conditions of the country. This paper implements and examines two machine learning models such as artificial neural network (ANN) and ANN model optimized with particle swarm optimization (PSO) known as ANN-PSO to assess the accuracy in predictability of CPI. The data set for four groups such as food and beverages, housing, clothing, and footwear used for the calculation of all India CPI has been taken from the official website of the Government of India. The mean absolute percentage error (MAPE) has been used as the validator for model accuracy. The MAPE calculated for all experiments are less than 10% which indicates that the ANN-PSO models used are highly accurate for prediction of CPI of India. 2022 Wiley-VCH GmbH -
Modeling destination competitiveness: The unfamiliar shift for destination rebranding, restructuring, and repositioning with DMOs
Tourism is a tactical economic practice across the globe, but the urban and provincial transformations in the industry are strongly contemplated in the light of an unfamiliar shift in tourism business. This chapter discusses an integrated concept with a framework relating systematic approach of managing the destination and its competitiveness. An investigation on the impact on tourism and the recent narrative of national, regional, and local planning approach directs towards efficient destination management organizations (DMO) in practice for future development. This has proceeded by the formation of a competitive approach, emphasizing on the DMO roles and responsibilities helpful for a destination management during an unfamiliar business trend. Modeling destination competitiveness demands an absolute mechanism through destination rebranding, restructuring, and repositioning with DMOs for enabling competency. 2018, IGI Global. -
Modeling Environmentally Conscious Purchase Behavior: Examining the Role of Ethical Obligation and Green Self-Identity
Due to environmental degradation, using environment-friendly products has become necessary to reduce carbon emissions. However, the consumption of such products is still below expectations because these products are usually costlier than their traditional counterparts. The current study aims to investigate consumer behavior towards environment-friendly products using Ajzens theory of planned behavior as a theoretical model. The study seeks to examine the role of the key determinates of environmentally conscious purchase behavior, such as ethical obligation and green self-identity. A total of 386 responses were collected from consumers living in a few major cities of northern India using purposive sampling. The data were analyzed using structural equation modeling in Amos 22.0. The results demonstrated that attitudes towards environment-friendly products perceived behavioral control and green self-identity as the major determinants of green purchase intentions. In addition, attitude was reported to mediate the effect of ethical obligation on green purchase intentions and green self-identity was found to moderate the effect of attitude on green purchase intentions. Additionally, green self-identity was also reported to moderate the relationship between ethical obligation and attitude. The study adds value to the existing literature by signifying the role of green self-identity and ethical obligation in stimulating consumers green purchase intentions. The findings of the study are also meaningful for marketers and policymakers. 2023 by the authors. -
Modeling Flood-Induced Cascading Disruptions in the Indian Electronics Supply Chain Using Influence Network Analysis
This study investigates flood induced disruptions in the Indian electronics supply chain using influence network analysis. Monsoon floods are recurring hazards that significantly impact economic activities, logistics, and industrial productivity. This study integrates district-level rainfall data (2020 to 2025) with supply chain network models to quantify cascading failures. The methodology applies rainfall thresholds (? 300 mm/month) to identify flood-prone districts and constructs a stochastic influence matrix representing inter-firm dependencies. Flood propagation dynamics are modeled iteratively with a propagation coefficient (? = 0.6) and convergence threshold (? = 10-4). The resulting disruption profiles are mapped onto company-level revenues calibrated to India-specific scales, adjusted for disruption durations (two months per year). This approach produces district and company-level economic loss estimates consistent with observed flood impacts (e.g., Chennai 2015 flood losses of USD 3 to 5 billion). Key contributions include linking meteorological hazards to systemic supply chain failures, demonstrating economic vulnerabilities at district and sectoral scales, and providing a framework for resilience planning. 2026 Binghamton University Libraries. All rights reserved. -
Modeling of non-Gaussian time series in the presence of measurement error
Measurement error in time series refers to inaccuracies or deviations in recorded data that can distort the true underlying patterns, potentially leading to biased estimates and misleading conclusions in statistical analysis. AR models with non-Gaussian errors are crucial for accurately capturing real-world time series data with skewness, heavy tails, or outliers, leading to better forecasts and more reliable statistical inferences. Our focus in this study is on an optimal estimating function (EF) method that accounts for measurement error while estimating the parameters of AR(1) with logistic innovation. The asymptotic properties of the obtained optimal EF are also proved. The simulation study shows that incorporating measurement error into the model yields better estimates compared to ignoring its presence when measurement error exists. This shows that ignoring the measurement error leads to biased estimates. 2026 Taylor & Francis Group, LLC. -
Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles
Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975. 2022 River Publishers. -
Modeling of the LiouvilleGreen method to approximate the mechanical waves in functionally graded and piezo material with a comparative study
The present research article studies and compares the surface waves transmission through the functionally graded piezoelectric material (FGPM) club between the piezomagnetic (PM) layer -and half-space, and for a comparative study, lower half-space is assumed to be piezoelectric material. The transmission of mechanical waves in a smart structure is analyzed by following the elastic wave theory of magneto-electro-elasticity. The Liouville-Green (LG) approximation technique is used to solve the differential equation in the FGPM stratum, where exponential variation is assumed in material gradients. It is noticed that the material gradients depend considerably on the angular frequency, which should be a crucial factor in regulating the dispersion characteristics of functionally graded materials (FGM) waveguides. In closed determinant form, the dispersion relation has been obtained for FGPM plate for electrically open and short cases. The profound effect of parameters, such as material gradient, a width of the layer on phase velocity, coupled electromechanical factor, and angular velocity, is observed and delineated graphically. Different parametric plots are sub-plotted into a single figure to increase the readability of the graphs. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Modeling Popularity Evolution with Popularity-Augmented Graphs and Dynamic Bayesian PARAFAC
In recent years, social media has evolved as a significant platform for attracting new clients and customers. Every day, a wide range of new offers and products are shared over the social media platforms for buying, selling, promotions, etc., encouraging more and more social engagement. Therefore, it's important to predict high consumer engagement using past interactions. This study proposes a two-stage framework that integrates Popularity Augmented Social Graph construction with Dynamic Bayesian PARAFAC decomposition. The experiments were conducted on the open-source Behance project dataset, which contains interactions from over 85,000 users across 1,326 projects over 60 discrete time intervals. In the first stage, a Popularity Augmented Social Graph (PASG) is constructed using the popularity information. In the second stage, the graph is represented in tensor form and is factorized using Dynamic Bayesian PARAFAC (DBPF), which models latent relationships across users, content, and time. The performance of the model was evaluated using Mean Relative Error, Mean Absolute Error, Root Mean Squared Error, where it consistently outperformed the baseline methods. The results demonstrate the effectiveness of the proposed framework in providing a robust and scalable solution for popularity prediction in social media platforms. 2025 IEEE. -
Modeling requirements with diabetes using supervised machine learning techniques
Diabetes is characterized by either insufficient or inefficient insulin production by the body. High blood glucose levels result from this, which over time can harm a number of tissues and organs in the body. Diabetes can be brought on by a specific age, obesity, inactivity, insufficient physical activity, inherited diabetes, lifestyle, poor diet, hypertension, etc. This chapter explores modeling requirements with diabetes using supervised machine learning techniques. 2023, IGI Global. All rights reserved. -
Modeling the impact of political risk components on major macroeconomic variables
The risks of the political conditions prevailing in an economy are found to have a significant impact on its stock market. Such political risks can distort the entire economy. This study investigated the impact of political risk on major macroeconomic variables which are the indicators of growth in any economy by considering the various components of political risk as given by World Bank's worldwide governance indicators. Using a panel data approach, it modeled the major macroeconomic variables of eleven emerging and frontier Asian economies with various components of political risk. The study found that irrespective of the inter-linkages among different macroeconomic variables, they were not affected by the same political risk components. Most importantly, it revealed that GDP did not respond to any of the political risk components, whereas the exchange rate was found to be affected by all the political risk components. The study also found that FDI, inflation, and real interest rate were affected by one or more political risk components. 2019 AESS Publications. All Rights Reserved. -
Modeling the Intention to Use AI Healthcare Chabots in the Indian Context
Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Modeling User Movement Patterns for Enhanced Internet Experience
Providing an effective quality of service (QoS) can be very challenging, especially in mobile or dynamic environments. The goal of this study is to improve the Internet in a way that anticipates user mobility allowing for more efficient and responsive resource allocation through real-time resource management and connectivity prognostic. The ability for the model to determine typical paths, times, and transition probabilities between access points is accomplished by exploring historical location data, mobility traces, and user activity in a networked environment. The proposed findings can be integrated into network control algorithms requiring spatial predictive data for future user behaviours such as prefetching, intelligent handover, and load balance to configurable infrastructures. User mobility predictions of future mobility are augmented by the combination of machine learning and Markov Chains. Usability testing conducted utilizing real-world mobility datasets is suggestive of many improvements in terms of connection stability, reduction of latency, and increased efficiency of bandwidth utilization. The supporting evidence obtained from this study is supportive of the hypothesis that network management which aware of the mobile user will enhance performance and experience in urban areas and smart cities. This research is important because it further develops a notion of intelligent, human-centric communication systems towards 5G (and beyond) to reframe spatial and temporal user behaviour to be responsive, anticipatory internet service structures and/or systems. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
Modeling, optimizing and diagnosis of chiller systems using machine learning /
Patent Number: 202141035122, Applicant: Dr B S Praveen Kumar.
Modeling, Optimizing and diagnosis of Chiller Systems using Machine Learning The invention aims to create an energy use model for a chiller in heating, ventilation, and air conditioning system using the artificial neural network learning method. Input layers that included several input variables, quantity (percentage) of training data and number of neurons were measured for accuracy by the suggested chiller energy consumption model. -
Modelling and analysis of split parallel hybrid electric vehicle based on 14 degrees of freedom
The paper studies the scope, performs the modelling and validation for conversion of any Convetional Vehicle to a Split Parallel Hybrid Electric Vehicle. The introduction of a smart Energy Management System for sucha setup is also evaluated. The EMS enables load sharing between the IC Engine and the Traction motor based on the gradient of the road. The gradient analysis is performed using the GPS based road gradient database. For the accurate modelling and the dynamic analysis of the designed model the performance of the vehicles Degrees of Freedom (DoF) for the variation in steering angle is analyzed. 14 DoF parameters are considered and the designed vehicle is subjected to variation in steer angle followed by the analysis on the response of the DoF parameters. BEIESP. -
Modelling and CFD simulation of vortex bladeless wind turbine
When the forces act on a bluff body in the wind flow direction, vortices are formed. Vortex bladeless wind turbine oscillates as a result of the vortices generated due to VIV. When the vortex shedding frequency is nearer to the natural frequency of the structure, maximum amplitude of vibration occurs and coincidentally power is generated. 3D models are designed to stimulate flow at a Reynolds number of 50000. This paper focuses on modelling the bladeless wind turbine based on semi-vortex angle and also 1) to study the vortices pattern and vorticity of different models 2) to study the drag and lift coefficients. In this paper vortex turbine is designed with certain parameters of dimension in Solid Edge and CFD analysis is carried out in Simscale software. Different model performance parameters like power, natural frequency and coefficient of power are compared among different models to opt for the best vortex bladeless wind turbine design. 2022 Author(s). -
Modelling and optimization of Rhodamine B degradation over Bi2WO6Bi2O3 heterojunction using response surface methodology
The Bi2O3/Bi2WO6 heterostructures of various compositions are prepared via the surfactant-assisted solgel method, which exhibits enhanced and synergistic photocatalytic activity towards the degradation of Rhodamine B (Rh B) using visible light irradiation. Characterization of these heterostructures has been done using X-ray diffraction, microscopic and spectroscopic methods. The 50% tungstate in bismuth oxide (BWO) nanocomposites having band gap of 2.85eV and an average size of 4080nm shows maximum dye removal up to 87% in 4h compared to pure Bi2O3 and other heterostructures of Bi2O3/Bi2WO6. The reusability studies demonstrate the excellent retention of photocatalytic activity without much loss in activity, implying the stability and efficiency of the prepared catalyst. The degradation of the Rh B dye is modeled mathematically to analyze the interactive effects of the key parameters like the time, amount of catalyst, and dye concentration, and to determine the optimal setting of these parameters to optimize the degradation process using the face-centered Central Composite Design (FC-CCD) of the Response Surface Methodology (RSM) analysis. An accurate full quadratic model has been developed with R2 = 99.41%. The sensitivity of the degradation was evaluated at all levels of the key parameters. At 0.1g of catalyst amount, it was found that the increment of the catalyst amount would be suitable for improved degradation as compared to allowing more time for the degradation. The maximum degradation was obtained for a dye concentration of 5ppm, and 0.1g catalyst for 4h. 2022, King Abdulaziz City for Science and Technology. -
Modelling and simulation of high-pressure hydrogen storage tank with composite reinforcement
The hydro-carbon fuel disadvantages like cost, pollution and non-renewable source made a way to look for the other energy resources. The carbon neutral fuel hydrogen is one of the promising fuels for all types of locomotives. One of the major challenges is safe fuel filling and storage, since the hydrogen is highly volatile fuel. Based on the travel distance, different environmental conditions, the hydrogen fuel tank subjected to the varying pressure and volume, which needs the cost-effective material for the fuel tank. This paper presents a comprehensive modelling and simulation study of a highpressure hydrogen storage tank reinforced with composite materials. The performance analysis of a hydrogen storage tank with composite reinforcement is conducted and compared to a standard aluminium hydrogen tank. 2026 Author(s). -
Modelling bivariate vector autoregressive model using copula approach
In this study, we propose a novel approach to model the relationship between bivariate time series by introducing a bivariate vector autoregressive model with Ali-Mikhail-Haq(AMH) copula, incorporating non-normal errors. The utilization of the Ali-Mikhail-Haq copula will allow for flexible modeling of the dependence structure between the two time series. This copula framework enables us to model the joint distribution of the errors with greater accuracy. Our approach provides a way to capture the relationships between the two time series, making it more suitable for complex data structures where traditional methods based on normal error assumptions may fall short. The Inference Functions for Margins (IFM) technique is employed to estimate both the model parameters and the dependency structure in our proposed model. To evaluate the accuracy of the proposed model, we conduct an extensive simulation study. The results demonstrate that the suggested model performs robustly across different scenarios, effectively capturing the dependence structure and delivering precise parameter estimates. The AMH copula efficiently models moderate levels of both negative and positive dependence. To enhance forecasting performance, we introduce a hybrid extension in which an artificial neural network(ANN) is applied to the residuals of the copula-based AMHVAR model. This hybrid approach captures remaining nonlinear patterns not explained by the linear VAR dynamics and the copula-based dependence structure, leading to improved predictive accuracy. Finally, we apply the proposed models to real-world data, further validating its practical applicability. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026.