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In a dual financial system where both conventional and Islamic financial institutions co-exist, the motives behind customers’ choices of financial products remain a crucial factor to comprehend. Thus, this paper aims to examine the influence of Islamic financial literacy (IFL) and motives (religious, ethical and economic) on the holdings of Islamic financial products (IFPs).

The sample consists of 234 bank customers in Klang Valley, Malaysia, with data obtained through a convenience sampling method. The instrument used was a digital survey that was electronically sent to respondents.

Findings reveal that IFL and religious motives positively influence IFPs, whereas economic motives negatively influence IFPs. Ethical motives have no significant impact on IFPs.

The findings imply that IFPs attract customers due to their adherence to Islamic teachings, indicating strong religious motives. However, the negative leanings of the economic motive suggest that customers may perceive IFPs as less favourable due to higher costs and risks relative to conventional products. Islamic financial institutions must widen their efforts in educating the public regarding IFPs on the benefits of adherence to Shariah principles and at the same time improve their products’ cost-benefits.

This study contributes to the literature by comprehensively examining IFPs in terms of both assets and financing products. In addition, IFL is measured in an all-inclusive way, covering different dimensions of knowledge related to Islamic savings, investments, protection and financing.

This study aims to examine the timing of corporate disclosure in the context of Georgia, an emerging market where a recent reform of corporate financial transparency mandated about 80,000 private sector entities to publicly disclose their annual financial statements.

The main analysis covers more than 4,000 large, medium, small and micro private sector entities, for which the data is obtained from the Ministry of Finance of Georgia. This paper builds an empirical model of logit/probit regression, with industry fixed and random effects to investigate the drivers of the corporate disclosure timing.

Findings suggest that the mean reporting time lag is 279 days after the fiscal year-end, that is nine days after the statutory deadline. Almost one-third (30%) of the entities miss the nine-month statutory deadline, while the timely filers almost unexceptionally file immediately before the deadline. Multivariate tests reveal that voluntarily filing entities completed the process significantly faster than those mandated to do so; audited financial statements take more time to be filed, whereas those with unqualified audit opinion or audited by large/international audit firms are filed faster than their counterparts. The author concludes that despite the overall high filing rates, the timing of corporate disclosure is not (yet) efficiently enforced in practice (but is progressing over time), whereas regulatory incentives prevail over market incentives among the timely filers.

To the best of the author’s knowledge, this is the first study that explores corporate disclosure timing incentives in the context of Georgia. This study extends prior literature on the timing of financial information from an emerging country’s private sector perspective, with juxtaposed market and regulatory incentives.

This study aims to present recent trends in touchscreen research through scientometric analysis. Devices with touchscreen are powerful tools for performing specialized operations. The touch screens of tablets, smartphones, laptops and television play an important role in teaching, learning and research.

The data was collected from Web of Science database from 2011 to 2021 and analysed using MS-Excel and VOSviewer software. After analysing 389 research papers, the authors identified the high impact journals, collaboration of countries, institutions, authors and growth trend of publications. Analysing the most used keywords, country-wise distribution of publications and research collaboration between institutions will help interpret the research trends in the selected time span.

The publications show an increase in number over the years from 2011 to 2021. Among the countries, USA has the highest number of 127 articles published, followed by England (61) and Canada (30). The results showed that the multiple authorship pattern in touchscreen publication is high when compared to single authors. The institutional analysis indicated that the organizations publishing more than five documents in the area were mostly from United Kingdom, Australia, USA and Korea. Timeline visualizations identified prominent keywords like touchscreen, performance, operant platform, Alzheimer’s disease, etc. in the subject. Interdisciplinary research is dominant in the subject, as seen from the most preferred journals and keywords.

The analysis does not include a comprehensive coverage of the research output, as only Web of Science database from 2011 to 2021 in a 10-year period is included.

The study would benefit stakeholders, including manufacturers and researchers alike, to know the future of touchscreen research.

This study is pertinent to socio-psychological fields because touchscreen technology encourages social connection among older persons and may help foster early literacy skills.

This paper will provide an understanding of the global developments in touchscreen research with recommendations for future research.

The energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction.

Data obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel’s environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R²) have been used.

It has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%.

The experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karabük University campus, thereby revealing the energy potential of the Karabük province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.

The purpose of this study is to follow up on the structural and fatigue analysis of car wheel rims with carbon fibre composites in order to ensure the vehicular safety. The wheel is an essential element of the vehicle suspension system that supports the static and dynamic loads encountered during its motion. The rim provides a firm base to hold the tire and supports the wheel, and it is also one of the load-bearing elements in the entire automobile as the car's weight and occupants' weight act upon it. The wheel rim should be strong enough to withstand the load with such a background, ensuring vehicle safety, comfort and performance. The dimensions, shape, structure and material of the rim are crucial factors for studying vehicle handling characteristics that demand automobile designers' concern.

In the present study, solid models of three different wheel rims, namely, R-1, R-2 and R-3, designed for three different cars, are modelled in SOLIDWORKS. Different carbon composite materials of polyetheretherketone (PEEK), namely, PEEK 90 HMF 40, PEEK 450 CA 30, PEEK 450 GL 40 and carbon fibre reinforced polymer-unidirectional (CFRP-UD) are used as rim materials for conducting the structural and fatigue analysis using ANSYS Workbench.

The results thus obtained in the analyses are used to identify the better carbon fibre composite material for the wheel rim such that it gives better structural properties and less fatigue. The R-3 model rim has shown better structural properties and less fatigue with PEEK 90 HMF 40 material.

The carbon composite materials used in this study have shown promissory results that can be used as an alternative for aluminium, steel and other regular materials.