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We have developed in this paper a morphological disambiguation hybrid system for the Arabic language that identifies the stem, lemma and root of a given sentence words. Following an out-of-context analysis performed by the morphological analyser Alkhalil Morpho Sys, the system first identifies all the potential tags of each word of the sentence. Then, a disambiguation phase is carried out to choose for each word the right solution among those obtained during the first phase. This problem has been solved by equating the disambiguation issue with a surface optimization problem of spline functions. Tests have shown the interest of this approach and the superiority of its performances compared to those of the state of the art.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

This paper aims to determine causal relationships between the level of productive credit, real deposits and money demand – all of them in real terms – and Gross National Product between 2006 and 2020.

The vector autoregressive technique (VAR) was used, where data from real macroeconomic aggregates published by the Central Bank of Ecuador (BCE) are correlated, such as productive credit, gross domestic product (GDP) per capita, deposits and money demand.

The results indicate that there is no causal relationship, in the Granger sense, between GDP and financial activity, but there is between the growth rate of real money demand per capita and the growth rate of total real deposits per capita.

The study shows that bank credit mainly finances the operations of current assets and/or liabilities. In addition, economic agents use the banking system mainly to carry out transactional and precautionary activities.

This study empirically tests the use of loan loss provisions (LLPs) for earnings and capital smoothing when emphasis is laid on banks' riskiness and adoption of the International Financial Reporting Standards (IFRSs) in Nigeria.

Annual bank-level data are hand-extracted between 2007 and 2017 from annual reports of a sample 16 deposit money banks (DMBs), and analysed using appropriate panel regression models subsequent to a number of diagnostic tests including heteroscedasticity, autocorrelation and cross-sectional dependence. The use of both reported LLPs (TLLP) and discretionary LLPs (DLLP) for earnings and capital management is tested to advance the practice in the literature.

Generally, the study finds that Nigerian DMBs manage capital via LLPs, while mixed results are obtained for earnings smoothing. However, during IFRS, Nigerian DMBs' management of capital is identifiable with TLLP, while smoothing of earnings is peculiar to DLLP. Additionally, evidence of the improvement in loan loss reporting quality expected during IFRS for riskier Nigerian DMBs, could not be attained. This is corroborated by the study's findings of the use of both TLLP and DLLP for earnings and capital management during IFRS by DMBs in solvency crisis against the only use of TLLP to manage capital found for the entire period.

The evidential capital and earnings lopsidedness may subject Nigerian DMBs' going-concern to a lot of questions.

The study sets a foremost record in the empirical test of managerial opportunistic behaviour embedded in earnings and capital concurrently while accounting for loan losses by all categories of Nigerian DMBs in terms of riskiness, following accounting regime change.

Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

We aimed to identify factors that influence student engagement in distance learning.

The research involved a group of 671 students from economic and technical higher education institutions in Poland. We collected the data with the CAWI technique and an original survey. Next, we processed the data using principal component analysis and then used the extracted components as predictors in the induced smoothing LASSO regression model.

The components of the students’ attitude toward remote classes learning conditions are: satisfaction with teachers’ approach, attitude to distance learning, the system of students’ values and motivation, IT infrastructure of the university, building a network of contacts and communication skills. The final model consisted of seven statistically significant variables, encompassing the student’s sex, level of studies and the first five extracted PCs. Student’s system of values and motivation as well as attitude toward distance learning, were those variables that had the biggest influence on student engagement.

The research result suggests that in addition to students’ system of values and motivation and their attitude toward distance learning, the satisfaction level of teachers’ attitude is one of the three most important factors that influence student engagement during the distance learning process.

The main value of this article is the statistical model of student engagement during distance learning. The article fills the research gap in identifying and evaluating the impact of various factors determining student engagement in the distance learning process.

Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data.

The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered.

The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed.

The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.

This article aims to identify and review existing studies on the adoption and compliance of International Financial Reporting Standards (IFRS) in Africa.

The methodology involves a sole focus on studies conducted with an African sample, using a bibliometric method and data from the Web of Science (WoS) database. Visualizations from VOSViewer and Biblioshiny software are employed to identify the dominant authors, journals and countries contributing to research in the region.

The findings reveal existing collaborations among authors in the field. However, the study emphasizes the need for additional research to enhance the intellectual structure of the research domain, as the majority of related documents are concentrated within twenty articles with at least one citation.

The practical implications underscore the importance of collaboration in practice, emphasizing the need for cooperation among corporations, experts and regulatory agencies involved in IFRS adoption and compliance in Africa. By fostering collaborative efforts and knowledge-sharing among corporations, experts and regulatory agencies, practitioners can enhance their understanding, streamline implementation processes and improve compliance methods.

This review is one of the few to explicitly conduct a bibliometric review of IFRS adoption and compliance studies in Africa, providing a foundation for future research to determine the current direction of IFRS studies in this region.

This study aims to investigate how online teaching of faculty members is affected by technological readiness (TR) of using online teaching platforms. The study sheds light on how many faculty members were ready to use different online platforms during COVID-19 period.

This study used TR measures to determine the impact of optimism regarding the perceived usefulness and ease of usage, impact of innovativeness in terms of perceived usability and ease of use, the influence of discomfort on perceived usefulness and ease of usage, the effect of uncertainty on perceived usefulness and ease of use and the influence of perceived usefulness and ease of use on behavior. An online questionnaire survey was conducted among 255 faculty members of different private universities of Bangladesh. The sample was chosen based on a convenience method. The responses were analyzed using partial least square (PLS) approach with the help of software Smart PLS 3.

The finding supported all of the hypotheses except that discomfort and insecurity have a positive relationship with ease of use and usefulness.

The study will help faculty members in developing their competency in using technologies in their pedagogy. Also, this study will provide some guidelines to the university management in developing adequate technological infrastructure to aid teaching.

The aim of the study was to investigate the faculty members' readiness level with respect to online teaching. The technology assessment model (TAM) was used to determine the readiness index. The study intended to validate the hypotheses regarding the extent to which the faculty members perceived that TAM factors affect Ease of Use and Usefulness of online teaching. Also, this research analyzed the perception of faculty members that Ease of Using online teaching affects its Usefulness. Lastly, the study examined how their perception of Ease of Use and Usefulness affect Intention to Use online as a mode of teaching. It was found from the study that each of the TAM factors, Optimism, Innovativeness, Insecurity and Discomfort has positive and significant contribution on the Ease of Use. On the other hand, Optimism, Innovativeness, Insecurity and Discomfort have positive and significant contributions on the Usefulness. The study also revealed that Ease of Use has positive and significant contribution on the Usefulness. Lastly, it was found that Ease of Use and Usefulness have positive and significant contribution on the Intention to use. Teaching remotely is still a novel concept, and it is more difficult for people who have not done it before. Many teachers became burned out as a result of trying to adjust to new teaching methods, especially after the lockdown began. They were having a difficult time since there was so much ambiguity. When a teacher is well-versed in communication tools, it can improve learning efficiency. When they are properly trained, deploying engaging features of virtual learning, such as audio-visual lessons, quizzes, and so on, becomes simple, and students become eager to learn more. Teachers can plan their classes, prepare and master technology and create innovative and stimulating discussion topics (Mishra et al., 2020). They need to utilize a variety of technological options. They can rehearse virtual classroom management with colleagues if they face any difficulty. All of the aforementioned abilities can be honed with the assistance of an integrated academic system. Teachers can be trained by educational institutions to ensure a smooth learning process through the use of ICT (information and communication technologies) (Scherer et al., 2021; Mishra et al., 2020). The training will assist teachers in efficiently taking online classes. Institutions should ensure that teachers are well-suited to teach online and are skilled at keeping students engaged during remote learning. To make every chapter engaging, aspects such as videos, slides, images and digital copies of books and workbooks can be used. This allows students to receive personalized support and counseling in order to maintain their motivation (Sahu et al., 2022; Lapitan et al., 2021). Every other day, group doubt resolution classes ensure that there are no gaps in learning (Lapitan et al., 2021). All teachers require is a digital mindset, the appropriate tools and a committed approach (Sahu et al., 2022). If teachers can hold their students' attention, they can easily deliver an effective learning experience (Lapitan et al., 2021).

This study was conducted to identify technological preparedness of faculty members of private universities in Bangladesh during COVID-19 period. Some studies were there to assess such kind of preparedness but none of those used TAM and technology readiness model either in isolation or in combination. Also, this paper focused on teachers' readiness in contrast to students' readiness specific to private universities.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.