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Estimation of solar cell parameters, mathematical modeling and the actual performance analysis of photovoltaic (PV) cells at various ecological conditions are very important in the design and analysis of maximum power point trackers and power converters. This study aims to propose the analysis and modeling of a simplified three-diode model based on the manufacturer’s performance data.

A novel technique is presented to evaluate the PV cell constraints and simplify the existing equation using analytical and iterative methods. To examine the current equation, this study focuses on three crucial operational points: open circuit, short circuit and maximum operating points. The number of parameters needed to estimate these built-in models is decreased from nine to five by an effective iteration method, considerably reducing computational requirements.

The proposed model, in contrast to the previous complex nine-parameter three-diode model, simplifies the modeling and analysis process by requiring only five parameters. To ensure the reliability and accuracy of this proposed model, its results were carefully compared with datasheet values under standard test conditions (STC). This model was implemented using MATLAB/Simulink and validated using a polycrystalline solar cell under STC conditions.

The proposed three-diode model clearly outperforms the earlier existing two-diode model in terms of accuracy and performance, especially in lower irradiance settings, according to the results and comparison analysis.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

The purpose of this study is to investigate the impact of integrating “Licorice powder” into curd balls on their storage stability under refrigeration conditions. Through this examination, this study aims to evaluate the potential effects of licorice powder on extending the shelf life, maintaining quality attributes and preserving the overall stability of curd balls when stored at refrigeration temperatures.

Licorice powder, in varying quantities (1%, 2% and 3%), was incorporated into curd balls alongside a control group lacking licorice (0%). These batches were subsequently stored for 25 days under refrigeration at a temperature of 4 ± 1ºC, using aerobic packaging conditions. During this storage period, the samples were regularly monitored and analyzed for various parameters to assess changes in their properties and qualities.

The findings indicated that in the treatment groups, pH and titratable acidity were notably lower than those in the control group (p = 0.05). Curd balls enriched with licorice powder exhibited significantly higher levels of 2, 2-diphenyl-1-picrylhydrazyl, 2-2-azinobis-3ethylbenthiazoline-6-sulphonic acid and total phenolic contents compared to the control (p = 0.05). Furthermore, curd balls containing licorice powder displayed notably lower levels of peroxide, thiobarbituric acid reactive substances and free fatty acids in comparison to the control (p = 0.05). Among all samples, T3 (3%) demonstrated significantly less microbial growth (p = 0.05) than the other groups. Conversely, the sensory panel rated T2 significantly higher than T3 (p = 0.05).

The investigation highlights that curd balls enriched with 2.0% licorice powder demonstrated significant efficacy in preventing the deterioration of physicochemical attributes, enhancing antioxidant capacity, restraining lipid oxidation, curbing microbial growth and ultimately exhibiting the most favorable organoleptic properties among the tested variations. This finding underscores the potential of incorporating 2.0% licorice powder as an effective agent for bolstering the storage stability and overall quality of curd balls during refrigerated storage.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

One area in which strategy and organizational ecology converge is organizational change. This essay weaves together salient themes in my (and my co-authors’) various writings on organizational change, and is anchored in the research literature of the last twenty years. Among other ideas developed here, I point out that there is now a convergence of agendas in strategy and ecology, with an important role being played by intraorganizational ecology. I develop the distinction between strong and weak selection approaches to organizational ecology. While the strong selection view does not find empirical support, there is stronger support for the weak selection view. I lay out some key features of an emerging evolutionary synthesis for the study of strategy and organization, and develop an evolutionary approach to organizational change.

The purpose of this paper is to attempt the buckling analysis of a laminated composite skew plate using the C₀ finite element (FE) model based on higher-order shear deformation theory (HSDT) in conjunction with minimax probability machine regression (MPMR) and multivariate adaptive regression spline (MARS).

HSDT considers the third-order variation of in-plane displacements which eliminates the use of shear correction factor owing to realistic parabolic transverse shear stresses across the thickness coordinate. At the top and bottom of the plate, zero transverse shear stress condition is imposed. C₀ FE model based on HSDT is developed and coded in formula translation (FORTRAN). FE model is validated and found efficient to create new results. MPMR and MARS models are coded in MATLAB. Using skew angle (α), stacking sequence (Ai) and buckling strength (Y) as input parameters, a regression problem is formulated using MPMR and MARS to predict the buckling strength of laminated composite skew plates.

The results of the MPMR and MARS models are in good agreement with the FE model result. MPMR is a better tool than MARS to analyze the buckling problem.

The present work considers the linear behavior of the laminated composite skew plate.

To the authors’ best of knowledge, there is no work in the literature on the buckling analysis of a laminated composite skew plate using C₀ FE formulation based on third-order shear deformation theory in conjunction with MPMR and MARS. These machine-learning techniques increase efficiency, reduce the computational time and reduce the cost of analysis. Further, an equation is generated with the MARS model via which the buckling strength of the laminated composite skew plate can be predicted with ease and simplicity.

The study aims to use nanofluids as coolants for improving heat transfer peculiarities of plate heat exchangers (PHE). The experimental and numerical investigations are thoroughly performed using distilled water-based Al₂O₃, graphene nanoplatelet (GnP) and multi-walled carbon nanotubes (MWCNT) nanofluids.

The numerical simulation based on Single Phase Model (SPM) was performed on a realistic 3 D model of PHE having similar dimensions as of the actual plate. The standard k-epsilon turbulent model was used to solve the problem. The concentration and flow rate of nanofluids were ranging from 0.1 to 1 Vol.% and 1 to 5 lpm, respectively, at 30°C. Whereas, hot side fluid is distilled water at 2 lpm and 80°C. The heat transfer characteristics such as bulk cold outlet temperature, heat transfer rate (HTR), heat transfer coefficient (HTC), Nusselt number (Nu), pressure drop, pumping power, effectiveness and exergy loss were experimentally evaluated using nanofluids in a PHE.

The experimental results were then compared with the numerical model. The experimental results revealed maximum enhancement in an average heat transfer rate of 9.86, 14.86 and 17.27% using Al₂O₃, GnP and MWCNT nanofluids, respectively, at 1 Vol.%. The present computational fluid dynamics model accurately predicts HTR, and the results deviate <1.1% with experiments for all the cases. The temperature and flow distribution show promising results using nanofluids.

The study helps to visualise heat transfer and flow distribution in PHE using different nanofluids under different operating conditions.

The purpose of this paper is to deal with the springback problems of channel cross-section bars of linear and non-linear work-hardening materials under torsional loading. Using the deformation theory of plasticity, a numerical scheme based on the finite difference approximation has been proposed. The growth of the elastic-plastic boundary and the resulting stresses while loading, and the springback and the residual stresses after unloading are calculated.

The numerical method which has been described in this paper for obtaining the solution of elasto-plastic solution can also be used for other sections. The only care that needs to be taken is to decrease the mesh size near points of stress concentration. The advantage of this technique is that it automatically takes care of all plastic zones developing over the section at different loads and gives a solution satisfying the elastic and plastic torsion equations in their respective regions.

As expected, elastic recovery is found to be more with decreasing values of n and λ. The difference in springback becomes more and more with increasing values of angle of twist. The material will approach an elastic ideally plastic behavior with increasing values of λ and n.

It seems that no attempt has been made to study residual stresses in elasto-plastic torsion of a work-hardening material for a channel cross-section.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.