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This paper aims to analyze deeply the effect of surface roughness conditions of the common interface of the two-layered riveted cantilever beams on their frictional damping during free lateral vibration at first mode. Here, the product, (µ × α), and damping ratio, ξ, are the parameters whose variations are analyzed in this investigation. For this, the influencing parameters considered are the natural frequency of vibration, f; the amplitude of initial excitation, y; and surface roughness value, Ra.

For experimentally evaluating logarithmic damping decrement, d, the frequency response function analyzer for the case of free lateral vibrations was used. Later, for evaluating the product, µ × α (where µ is the kinematic coefficient of friction and α is the dynamic slip ratio), and then, the damping ratio, ξ, the empirical relation suggested for logarithmic damping decrement, d, of riveted cantilever beams was used. After this, the full and reduced quadratic models of the product, µ × α, ξ, response surface methodology (RSM) with the help of Design Expert 11 software was used. Corresponding main effects plots, surface plots and prediction comparison plots were obtained to observe the variations of the product, µ × α, ξ for the variations of influencing parameters: f, y and Ra. Finally, a machine learning technique such as artificial neural networks (ANNs) using “nntool” present in MATLAB R13a software was used to predict the ξ for the different combinations of f, y and Ra.

The full and reduced quadratic regression models for the product, (µ × α) and the damping ratio, ξ of riveted cantilever beams for free lateral vibrations of the first mode in terms of the parameters: f, y and Ra were obtained. In addition, the main effects plots, surface plots and prediction comparison plots for the product, µ × α, ξ, with the corresponding experimental values of the product, µ × α, ξ, were obtained. Also, the execution of ANNs using MATLAB R13a software is proved to be the more accurate tool for the prediction of damping ratios in comparison to quadratic regression equations obtained from Design Expert 11 software. In the end, the assumption that the effect of surface roughness value on the product, (µ × α), and the damping ratio, ξ, is negligible is proved to be true using the main effects plots for the product, (µ × α) and ξ obtained from the Design Expert 11 software.

Obtaining the full and reduced quadratic regression equations for the product, (µ × α), and ξ of the two-layered riveted cantilever beams in terms of parameters: f, y and Ra was done. In addition, the conditions for the corresponding minimum and maximum values of the product, (µ × α), and ξ were obtained. Later, the main effects plots, surface plots and comparison plots of the predicted product, (µ × α), and ξ versus experimental product, (µ × α), and ξ were also obtained. Finally, the predicted values of the product, (µ × α), and ξ using the ANNs tool are observed to be the more accurate values in comparison to that obtained from RSM using the Design Expert 11 software.

The paper aims at analyzing the co-movements between stock returns and oil prices (West Texas Intermediate, Brent) controlling or not for COVID-19.

It uses continuous wavelet transforms and wavelet coherence over the period July 19, 2019 to August 16, 2021 based on daily data. Continuous wavelet transforms provide an over complete representation of stock returns signals by letting the translation and scale parameters of the wavelets vary continuously.

There are not significant evidence supporting the fact that the COVID-19 has altered the relationship between stock returns and oil prices except perhaps in the case of South Africa. In fact, Southern African Development Community stock markets react more to oil prices than to health shock such as the COVID-19.

The findings of the study are original and have not been published anywhere prior.

The boundary integral method (BIM) is very attractive to practicing engineers as it reduces the dimensionality of the problem by one, thereby making the procedure computationally inexpensive compared to its peers. The principal feature of this technique is the limitation of all its computations to only the boundaries of the domain. Although the procedure is well developed for the Laplace equation, the Poisson equation offers some computational challenges. Nevertheless, the literature provides a couple of solution methods. This paper revisits an alternate approach that has not gained much traction within the community. The purpose of this paper is to address the main bottleneck of that approach in an effort to popularize it and critically evaluate the errors introduced into the solution by that method.

The primary intent in the paper is to work on the particular solution of the Poisson equation by representing the source term through a Fourier series. The evaluation of the Fourier coefficients requires a rectangular domain even though the original domain can be of any arbitrary shape. The boundary conditions for the homogeneous solution gets modified by the projection of the particular solution on the original boundaries. The paper also develops a new Gauss quadrature procedure to compute the integrals appearing in the Fourier coefficients in case they cannot be analytically evaluated.

The current endeavor has developed two different representations of the source terms. A comprehensive set of benchmark exercises has successfully demonstrated the effectiveness of both the methods, especially the second one. A subsequent detailed analysis has identified the errors emanating from an inadequate number of boundary nodes and Fourier modes, a high difference in sizes between the particular solution and the original domains and the used Gauss quadrature integration procedures. Adequate mitigation procedures were successful in suppressing each of the above errors and in improving the solution accuracy to any desired level. A comparative study with the finite difference method revealed that the BIM was as accurate as the FDM but was computationally more efficient for problems of real-life scale. A later exercise minutely analyzed the heat transfer physics for a fin after validating the simulation results with the analytical solution that was separately derived. The final set of simulations demonstrated the applicability of the method to complicated geometries.

First, the newly developed Gauss quadrature integration procedure can efficiently compute the integrals during evaluation of the Fourier coefficients; the current literature lacks such a tool, thereby deterring researchers to adopt this category of methods. Second, to the best of the author’s knowledge, such a comprehensive error analysis of the solution method within the BIM framework for the Poisson equation does not currently exist in the literature. This particular exercise should go a long way in increasing the confidence of the research community to venture into this category of methods for the solution of the Poisson equation.

While urban farming is advocated as a contributor to urban sustainability and resilience, the “informal” household-practiced urban agriculture activities are taking place within urban spaces in most Emirati neighbourhoods but unfortunately without investigating their potential as participatory processes that could efficiently help attain urban sustainability and resilience on the neighbourhood level. So, this research is a humble attempt to bridge the gap of the lack of official recognition of informal residents-led processes and their products in a way that help understand them and their impacts and to explore the possibility of developing them further into wider community shared urban agriculture activities.

The research adopted the case study method and selected a representative neighbourhood to investigate informal residents-led urban agriculture practices. The utilized qualitative–quantitative investigation tools included map analysis, field observation and in-depth interviews with the residents of the selected neighbourhood.

The results of the research have revealed that the residents managed to successfully pursue informal urban farming processes that have led to significant environmental, social and economic sustainability and resilience outcomes. While these informal urban farming activities are performed individually by each household, the interviewed residents have shown enthusiasm to take part in larger-scale collective community urban farming activities, especially in the deserted public and semi-public spaces in their neighbourhood.

The research outcomes significantly contribute to the growing worldwide discourse about urban agriculture/farming, especially in a country like the UAE where such activities are almost overlooked. Based on its findings, the research concludes by proposing a set of recommended actions to legitimatize these informal urban agriculture processes in the urban development regulations and to build on them to encourage the local communities towards more collective urban farming activities.

This paper aims to deal with the grape sourcing strategies of wineries in the Canary Islands.

Sourcing decisions are analysed from official registers of transactions between wineries and their external suppliers. The main sources of information are harvest reports submitted by wineries containing data about observable dimensions of their purchasing decisions. The general behaviour in the wine-grape zones that make up the grape market in the Canary Islands is described, and different strategies of individual wineries are revealed. Grape purchasing decisions are interpreted in terms of the potential explanatory factors involved in the undeclared objectives of wineries’ sourcing strategies. Two research questions are considered in this study: the spatial dimension, which refers to plot location, and the social dimension, which refers to the relationships between wineries and winegrowers.

The location of grape producers is a key factor in achieving the desired wine quality for wineries. The sourcing strategy of wineries is also influenced by size, but the impact of size varies depending on the short and long-term objectives of wineries.

Typically the literature on grape sourcing strategies relies on interviews with winemakers. However, this paper analyses wineries’ sourcing decisions based on records and reports that reveal their decisions in the specific context of the Canary Islands.

New business density (NBD) is the ratio of the number of newly registered liability corporations to the working-age population per year. NBD is critical to assessing a country's business environment. The present work endeavors to discover and gauge the contribution of 28 potential socio-economic enablers of NBD for 2006–2021 across developed and developing economies separately and to make a comparative assessment between those two regions.

Using World Bank data, the study first performs exploratory data analysis (EDA). Then, it deploys a deep learning (DL)-based regression framework by utilizing a deep neural network (DNN) to perform predictive modeling of NBD for developed and developing nations. Subsequently, we use two explainable artificial intelligence (XAI) techniques, Shapley values and a partial dependence plot, to unveil the influence patterns of chosen enablers. Finally, the results from the DL method are validated with the explainable boosting machine (EBM) method.

This research analyzes the role of 28 potential socio-economic enablers of NBD in developed and developing countries. This research finds that the NBD in developed countries is predominantly governed by the contribution of manufacturing and service sectors to GDP. In contrast, the propensity for research and development and ease of doing business control the NBD of developing nations. The research findings also indicate four common enablers – business disclosure, ease of doing business, employment in industry and startup procedures for developed and developing countries.

NBD is directly linked to any nation's economic affairs. Therefore, assessing the NBD enablers is of paramount significance for channelizing capital for new business formation. It will guide investment firms and entrepreneurs in discovering the factors that significantly impact the NBD dynamics across different regions of the globe. Entrepreneurs fraught with inevitable market uncertainties while developing a new idea into a successful new business can momentously benefit from the awareness of crucial NBD enablers, which can serve as a basis for business risk assessment.

DL-based regression framework simultaneously caters to successful predictive modeling and model explanation for practical insights about NBD at the global level. It overcomes the limitations in the present literature that assume the NBD is country- and industry-specific, and factors of the NBD cannot be generalized globally. With DL-based regression and XAI methods, we prove our research hypothesis that NBD can be effectively assessed and compared with the help of global macro-level indicators. This research justifies the robustness of the findings by using the socio-economic data from the renowned data repository of the World Bank and by implementing the DL modeling with validation through the EBM method.

Although numerous studies have explored gamification, its effects on student intrinsic motivation and behavioral engagement remain ambiguous. This study aims to address this gap by investigating the impacts of exogenous and endogenous fantasies on students’ intrinsic motivation, behaviors and perception of learning in gamified, fully online courses.

Using a quasi-experimental design and mixed methods, this study involved two groups of postgraduate students: exogenous fantasy group (N = 23) and endogenous fantasy group (N = 23). Intrinsic motivation was assessed through surveys, while behavioral engagement was tracked over 10 weeks using online trace data. Semi-structured interviews gathered student insights on learning perceptions. The patterns of behavioral engagement in both fantasy groups were analyzed using epistemic network analysis.

Observed behavioral data indicated a significantly higher level of intrinsic motivation in the endogenous fantasy setting. The endogenous group was more engaged in pre-task analysis and post-task reflection, while the exogenous group focused more on quiz work and post-task reflection. Participants in the endogenous fantasy setting also reported increased cognitive engagement and a strong identification with their fictional characters.

Integrating endogenous fantasy into the curriculum can boost students’ intrinsic motivation, behavioral engagement and self-identification. Adopting a first-person perspective that allows students to embody the role of a virtual character is highly recommended. The use of interactive multimedia can greatly enrich the fantasy environment, resulting in a more immersive and engaging learning experience.

The study provides valuable insights into the impact of endogenous and exogenous fantasies on intrinsic motivation and behavioral engagement. It also stands out for its use of epistemic network analysis to assess and compare complex networks of learning task participation in two fantasy settings. Through analyzing these engagement patterns, researchers can obtain a more profound understanding of how each fantasy environment influences student engagement.

This paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic.

The MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons.

The authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons.

The study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios.

This study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.

Transient response of continuous composite material (CCM) fin made of high thermally conductive composite material is presented. The continuously varying effective properties of composite material such as thermal conductivity, heat capacity and density have been modelled using the Mori-Tanaka homogenization theory and rule of mixture. Additionally, temperature dependency of thermal conductivity, heat generation (composite materials) and convection coefficient (fluid properties) have also been incorporated. Different base boundary conditions are addressed such as oscillating heat flow, oscillating temperature, step-changing heat flow and step-changing temperature. At the other boundary, the fin is assumed to have a convective tip.

Lattice Boltzmann method is implemented using an in-house source code for obtaining the numerical solution of typical non-linear heat balance equation of the aforementioned problem under various transient base boundary conditions.

The effects of various thermal parameters such as material diffusivity ratio and conductivity ratio, area ratio and Biot number on transient response of fin and temperature distribution of fins are studied and interpreted. The heat transfer rate and time for attainment of steady state temperature of metal matrix composite (MMC) fin are found to be proportionally dependent on their diffusivity ratio. Additionally for higher values of area ratio and biot number, MMC fins are reported to dissipate the heat more efficiently in comparision to homogeneous fins in terms of time required to attain the steady state and surface temperature.

Response of transient fin associated with advanced class of material can facilitates the practicing engineers for designing high-performance and/or miniaturized thermal management devices as used in electronic packaging industries.

Studies of composite fin consisting of laminating second layer of material over the first layer have been reported previously, however transient response of CCM fin fabricated by continuously varying the volume fraction of two materials along the fin length has not been reported till date. Such material finds its application in thermal management and electronic packaging industries. Results are plotted in form of a graph for different application-wise material combinations that have not been reported earlier, and it can be treated as design data.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.