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The main focus is to provide a non-similar solution for the magnetohydrodynamic (MHD) flow of Casson fluid over a curved stretching surface through the novel technique of the artificial intelligence (AI)-based Lavenberg–Marquardt scheme of an artificial neural network (ANN). The effects of joule heating, viscous dissipation and non-linear thermal radiation are discussed in relation to the thermal behavior of Casson fluid.

The non-linear coupled boundary layer equations are transformed into a non-linear dimensionless Partial Differential Equation (PDE) by using a non-similar transformation. The local non-similar technique is utilized to truncate the non-similar dimensionless system up to 2nd order, which is treated as coupled ordinary differential equations (ODEs). The coupled system of ODEs is solved numerically via bvp4c. The data sets are constructed numerically and then implemented by the ANN.

The results indicate that the non-linear radiation parameter increases the fluid temperature. The Casson parameter reduces the fluid velocity as well as the temperature. The mean squared error (MSE), regression plot, error histogram, error analysis of skin friction, and local Nusselt number are presented. Furthermore, the regression values of skin friction and local Nusselt number are obtained as 0.99993 and 0.99997, respectively. The ANN predicted values of skin friction and the local Nusselt number show stability and convergence with high accuracy.

AI-based ANNs have not been applied to non-similar solutions of curved stretching surfaces with Casson fluid model, with viscous dissipation. Moreover, the authors of this study employed Levenberg–Marquardt supervised learning to investigate the non-similar solution of the MHD Casson fluid model over a curved stretching surface with non-linear thermal radiation and joule heating. The governing boundary layer equations are transformed into a non-linear, dimensionless PDE by using a non-similar transformation. The local non-similar technique is utilized to truncate the non-similar dimensionless system up to 2nd order, which is treated as coupled ODEs. The coupled system of ODEs is solved numerically via bvp4c. The data sets are constructed numerically and then implemented by the ANN.

Flow control has a great potential to contribute to a sustainable society through mitigation of environmental burden. However, the high dimensional and nonlinear nature of fluid flows poses challenges in designing efficient control laws using the control theory. This paper aims to propose a hybrid method (i.e. machine learning and control theory) for feedback control of fluid flows, by which the flow is mapped to the latent space in such a way that the linear control theory can be applied therein.

The authors propose a partially nonlinear linear system extraction autoencoder (pn-LEAE), which consists of convolutional neural networks-based autoencoder (CNN-AE) and a custom layer to extract low-dimensional latent dynamics from fluid velocity field data. This pn-LEAE is designed to extract a linear dynamical system so that the modern control theory can easily be applied, while a nonlinear compression is done with the autoencoder (AE) part so that the latent dynamics conform to that linear system. The key technique is to train this pn-LEAE with the ground truths at two consecutive time instants, whereby the AE part retains its capability as the AE, and the weights in the linear dynamical system are trained simultaneously.

The authors demonstrate the effectiveness of the linear system extracted by the pn-LEAE, as well as the designed control law’s effectiveness for a flow around a circular cylinder at the Reynolds number of Re_D = 100. When the control law derived in the latent space was applied to the direct numerical simulation, the lift fluctuations were suppressed over 50%.

To the best of the authors’ knowledge, this is the first attempt using CNN-AE for linearization of fluid flows involving transient development to design a feedback control law.

(1) A mathematical model for the Hybrid nanofluids flow is used as carriers for delivering drugs. (2) The flow conditions are controlled to enable drug-loaded nanofluids to flow through the smaller gap between the two tubes. (3) Hybrid nanofluids (HNFs) made from silver (Ag) and titanium dioxide (TiO₂) nanoparticles are analyzed for applications of drug delivery. (Ag) and (TiO₂) (NPs) are suitable candidates for cancer treatment due to their excellent biocompatibility, high photoactivity, and low toxicity. (4) The new strategy of artificial neural networks (ANN) is used which is machine-based and more prominent in validation, and comparison with other techniques.

The two Tubes are settled in such a manner that the gap between them is uniform. The Control Volume Finite Element Method; Rk-4 and Artificial Neural Network (ANN).

(1) From the obtained results it is observed that the dispersion and distribution of drug-loaded nanoparticles within the body will be improved by the convective motion caused by hybrid nanofluids. The effectiveness and uniformity of drug delivery to target tissues or organs is improved based on the uniform flow and uniform gap. (2) The targeting efficiency of nanofluids is further improved with the addition of the magnetic field. (3) The size of the cylinders, and flow rate, are considered uniform to optimize the drug delivery.

(1)The flow phenomena is considered laminar, one can use the same idea through a turbulent flow case. (2) The gap is considered uniform and will be interesting if someone extends the idea as non-uniform.

(1) To deliver drugs to the targeted area, a suitable mathematical model is required. (2) The analysis of hybrid nanofluids (HNFs) derived from silver (Ag) and titanium dioxide (TiO₂) nanoparticles is conducted for the purpose of drug delivery. The biocompatibility, high photoactivity, and low toxicity of (Ag) and (TiO₂) (NPs) make them ideal candidates for cancer treatment. (3) Machine-based artificial neural networks (ANN) have a new strategy that is more prominent in validation compared to other techniques.

The drug delivery model is a useful strategy for new researchers. (1) They can extend this idea using a non-uniform gap. (2) The flow is considered uniform, the new researchers can extend the idea using a turbulent case. (3) Other hybrid nanofluids flow, in the same model for other industrial usages are possible.

All the obtained results are new. The experimental thermophysical results are used from the existing literature and references are provided.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

The commercialization of research produced by universities constitutes a core facet of academic entrepreneurship (AE). Academic literature reveals the need to shed light on entrepreneurial processes in higher education institutions (HEIs). This study intends to fill this gap by researching the mechanisms for facilitating AE and the variables that can moderate the relationship between such mechanisms and AE in Portuguese HEIs.

Our research model aims to assess the mechanisms of academic entrepreneurship (AE) within a sample of 125 Portuguese public higher education institutions (HEIs). To test our research hypotheses, we employed a structural equation model (SEM) using the partial least squares (PLS) method. Additionally, our evaluation examines the potential moderating effects of incubator programs, support initiatives, and proof-of-concept programs (PoCs). Our research model seeks to evaluate the mechanisms for facilitating AE and explore the effects of including incubator programs, support initiatives, and PoCs as moderators. The seven variables (Research mobilization, Unconventionality, Industry collaboration, University policies, Incubator programs and support initiatives, Proof-of-concept programs, and academic entrepreneurship) were measured using a 7-point Likert scale.

The results revealed that different drivers of AE influence the creation and development of entrepreneurial activities. Our findings also show the moderating effects of incubator programs, support initiatives, and proof-of-concept programs on AE. We find that incubator programs, other support initiatives, and PoCs maintain a moderating effect on AE and benefit their respective HEIs.

The study examines only the Portuguese HEI context. Therefore, generalizing these results necessitates reservations. However, the responses came from various actors in HEIs, from different academic backgrounds and research interests. This makes the results more generalizable. Limitations are evident in external validity, given that we gathered the data over a relatively short period.

Observed factors are explored to gain a deeper understanding of their influence on the mechanisms of AE. The implications arise from the new perspective presented and the methodology used to identify mechanisms capable of fostering AE. We hope this research will encourage other researchers to study this topic further.

the engagement of universities at the global level should be emphasised in future policy. While universities in innovation systems often have a local focus, their engagement in innovation ecosystems transcends the boundaries of geographic locations.

PoCs had a significant positive moderating effect on the impact of research mobilization and university policies on AE. Thus, we find interactions between universities and industry boost AE. This study demonstrates how AE benefits HEIs by extending orientation towards mobilizing research, unconventional approaches, cooperation with industry, and university policy implementation. We thus advocate a new approach, demonstrating the influence that the mobility of research, unconventionality, industry collaboration, and university policies hold over AE.

This study aims to investigate numerically the impact of the three-dimensional convective nanoliquid flow on a rotating frame embedded in the non-Darcy porous medium in the presence of activation energy. The cross-diffusion effects, i.e. Soret and Dufour effects, and heat generation are included in the study. The convective heating condition is applied on the bounding surface.

The control model consisted of a system of partial differential equations (PDE) with boundary constraints. Using suitable similarity transformation, the PDE transformed into an ordinary differential equation and solved numerically by the Runge–Kutta–Fehlberg method. The obtained results of velocity, temperature and solute concentration characteristics plotted to show the impact of the pertinent parameters. The heat and mass transfer rate and skin friction are also calculated.

It is found that both Biot numbers enhance the heat and mass distribution inside the boundary layer region. The temperature increases by increasing the Dufour number, while concentration decreases by increasing the Dufour number. The heat transfer is increased up to 8.1% in the presence of activation energy parameter (E). But, mass transfer rate declines up to 16.6% in the presence of E.

The applications of combined Dufour and Soret effects are in separation of isotopes in mixture of gases, oil reservoirs and binary alloys solidification. The nanofluid with porous medium can be used in chemical engineering, heat exchangers and nuclear reactor.

This study is mainly useful for thermal sciences and chemical engineering.

The uniqueness in this research is the study of the impact of activation energy and cross-diffusion on rotating nanoliquid flow with heat generation and convective heating condition. The obtained results are unique and valuable, and it can be used in various fields of science and technology.

The sustainable development of the platform economy has been hindered by the absence and alienation of platform corporate social responsibility. Previous studies have mainly focused on the contents and governance models for platform corporate social responsibility. This study seeks to explore which strategy participants choose in the governance of platform corporate social responsibility and their influencing factors.

Using a platform ecosystem approach, a quadrilateral evolutionary game model was developed, and the stabilities of subjects’ behavioral strategies and their combinations in various scenarios were analyzed. Additionally, the effects of key parameters on the system’s evolutionary path were simulated.

The ideal steady state system is achieved when platform enterprises, complementors and consumers adopt positive strategies while the government adopts lax regulation. Moreover, the evolutionary strategies of the subjects are influenced by several factors, including the participation costs of governance, the rewards and punishments imposed by platform enterprises, as well as the reputational losses of platform enterprises and complementors due to media coverage.

This study offers insights into improving the governance effectiveness of platform corporate social responsibility for managers and practitioners.

This study contributes to existing literature by considering the rational orientation of platform ecosystem members and revealing the interaction mechanisms among members. Furthermore, this study combines collective action theory and reputation theory to clarify the influencing factors on members’ behaviors.

This paper examines the moderating role of capital structure decisions in the relationship between research and development (R&D) investment and small and medium enterprises (SMEs) performance.

Based on panel data of 1,357 European SMEs from 2014 to 2020, this study employs a generalized method of moments (GMM) regression to examine the R&D-performance link through the moderating role of capital structure.

The results show that R&D investment and equity financing positively and significantly influence SMEs performance. Debt financing, however, is negatively and significantly associated with SME performance. In addition, we show that capital structure choice significantly moderates the relationship between R&D investment and SME performance. Specifically, debt financing attenuates the positive impact of R&D investment on SMEs performance, whereas equity financing accentuates this relationship.

This study helps policymakers formulate appropriate policies to overcome the challenges of underinvestment in R&D projects to enhance SMEs performance.

Our findings provide new evidence on R&D-performance literature by refining the deeper understanding of the role of capital structure, which has previously been examined in partial and fragmented ways.

There will probably be nine billion people on the earth by 2050, meaning food consumption will rise dramatically. Pakistan, the fifth most populous nation in the world, is rapidly expanding its population, making it difficult for the nation to sustain its food supply. Unfortunately, the country's focus on ensuring food security has not kept up with the demographic shifts in its population. However, innovative solutions are sorely needed in the face of several worldwide problems, especially in the crucial agriculture sector. This underscores the need to integrate sustainable financial practices. Considering these circumstances, this research thoroughly examines the intricate relationship inside Pakistan between financial stability (FS), agricultural subsidies, and productivity. Acknowledging the underlying intricacies and asymmetries at work, this study aims to analyze the complex relationships influencing the nation's agricultural production.

The research tries to shed light on the subtle processes at the intersection of financial stability, agricultural subsidies, and agricultural productivity through a comprehensive investigation of these multiple challenges. A non-linear autoregressive distributive lag (NARDL) technique is used, using a dataset from 1980 to 2022.

The results show that FS has a mixed impact on agricultural productivity, both positive and negative. Increasing FS_POS has a beneficial influence on agricultural output, linked to a notable 1.404% increase in output. On the other hand, increasing FS_NEG causes a significant 11.441% decrease in agricultural output, demonstrating its negative impact on output. Subsidies for agriculture also have asymmetric impacts; SUB_POS and SUB_NEG influence variations in agricultural productivity. A substantial 2.414% rise in agricultural output is shown by SUB_POS, demonstrating its noteworthy beneficial influence. Conversely, SUB_NEG adds a relatively small increase of 1.659% in agricultural output. However, the different amounts of each person's contribution show how subtle their effects are.

The current study is limited to the relationship between financial stability, agricultural subsidies, and agricultural productivity, considering the inherent complexity and asymmetries at work in Pakistan only. Further studies are required in Asian markets to have a bigger picture of the agricultural sector.

Considering these critical empirical findings, the report recommends strategic strategies to promote long-term agricultural growth in Pakistan. These include providing integrated financial services customized to farmers' needs, such as credit, insurance, and savings alternatives. Transparency and efficiency in procedural frameworks and the formation of efficient public-private partnerships should be prioritized. Furthermore, improving agricultural subsidy schemes emerges as a crucial priority. Targeting marginalized farmers more effectively and optimizing distribution through transparent, digitally driven systems can significantly improve program performance.

Empirical studies, to date, show that financial inclusion (FI) enhances financial stability (FS) by promoting a large deposit base, reducing information asymmetry, and strengthening market power on the one hand, and leads to financial fragility by expanding credit without proper screening, increasing operational costs, and provoking borrowers' moral hazard on the other. Thus, the most important issue is to maintain FS while extending formal financial services to the impoverished and disadvantaged segments of society. Therefore, this paper investigates the efficacy of macroprudential regulations (MPRs) to align these policy divergences.

To accomplish the objective and facilitate policy implications, the authors use aggregated and disaggregated measures of both FI and MPRs, employ advanced econometric models that minimize endogeneity and ensure robustness, and investigate their joint effectiveness in upholding FS using data of 138 countries spanning the 2004–2017 years.

The findings indicate that the effectiveness of MPRs is instrument specific. Some MPRs that obstruct access to formal financial services, in particular, moderate the advantage of FI in achieving FS, while others boost the effect of inclusion in attaining financial sector stability. Therefore, prudence should be emphasized while designing MPRs as a tool for aligning the policy trade-off between FI and FS.

To the best of the authors knowledge, this paper extends previous empirical research by investigating the conditioning impact of MPRs in the FI-FS nexus.