Aim/Purpose: This study examined the use of Social Media Applications (SMAs) for accessing health-related information within a heterogeneous population in Jordan. The objective of this study was therefore threefold: (i) to investigate the usage of SMAs, including WhatsApp, Twitter, YouTube, Snapchat, Instagram, and Facebook, for accessing health-related information; (ii) to examine potential variations in the use of SMAs based on demographic and behavioral characteristics; and (iii) to identify the factors that can predict the use of SMAs. Background: There has been limited focus on investigating the behavior of laypeople in Jordan when it comes to seeking health information from SMAs. Methodology: A cross-sectional study was conducted among the general population in Jordan using an online questionnaire administered to 207 users. A purposive sampling technique was employed, wherein all the participants actively sought online health information. Descriptive statistics, t-tests, and regression analyses were utilized to analyze the collected data. Contribution: This study adds to the existing body of research on health information seeking from SMAs in developing countries, with a specific focus on Jordan. Moreover, laypeople, often disregarded by researchers and health information providers, are the most vulnerable individuals who warrant greater attention. Findings: The findings indicated that individuals often utilized YouTube as a platform to acquire health-related information, whereas their usage of Facebook for this purpose was less frequent. Participants rarely utilized Instagram and WhatsApp to obtain health information, while Twitter and Snapchat were very seldom used for this purpose. The variable of sex demonstrated a notable positive correlation with the utilization of YouTube and Twitter for the purpose of finding health-related information. Conversely, the variable of nationality exhibited a substantial positive correlation with the utilization of Facebook, Instagram, and Twitter. Consulting medical professionals regarding information obtained from the Internet was a strong indicator of using Instagram to search for health-related information. Recommendations for Practitioners: Based on the empirical results, this study provides feasible recommendations for the government, healthcare providers, and developers of SMAs. Recommendation for Researchers: Researchers should conduct separate investigations for each application specifically pertaining to the acquisition of health-related information. Additionally, it is advisable to investigate additional variables that may serve as predictors for the utilization of SMAs. Impact on Society: The objective of this study is to enhance the inclination of the general public in Jordan to utilize SMAs for health-related information while also maximizing the societal benefits of these applications. Future Research: Additional research is required to examine social media’s usability (regarding ease of use) and utility (comparing advantages to risks) in facilitating effective positive change and impact in healthcare.

Breast cancer is leading cause of mortality among women compared to other types of cancers. Hence, early breast cancer diagnosis is crucial to the success of treatment. Various pathological and imaging tests are available for the diagnosis of breast cancer. However, it may introduce errors during detection and interpretation, leading to false-negative and false-positive results due to lack of pre-processing of it. To overcome this issue, we proposed a effective image pre-processing technique-based on Otsu's thresholding and single-seeded region growing (SSRG) to remove artefacts and segment the pectoral muscle from breast mammograms. To validate the proposed method, a publicly available MIAS dataset was utilised. The experimental finding showed that proposed technique improved 18% breast cancer detection accuracy compared to existing methods. The proposed methodology works efficiently for artefact removal and pectoral segmentation at different shapes and nonlinear patterns.

The most common kind of blood cancer in people of all ages is leukaemia. The fractional mayfly optimisation (FMO) based DenseNet is proposed for the identification and classification of leukaemia in federated learning (FL). Initially, the input image is pre-processed by adaptive median filter (AMF). Then, cell segmentation is done using the Scribble2label. After that, image augmentation is accomplished. Finally, leukaemia classification is accomplished utilising DenseNet, which is trained using the FMO. Here, the FMO is devised by merging the mayfly algorithm (MA) and the fractional concept (FC). Following local training, the server performs local updating and aggregation using a weighted average by RV coefficient. The results showed that FMO-DenseNet attained maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 94.3%, 96.5% and 95.3%. Moreover, FMO-DenseNet gained minimum mean squared error (MSE) and root mean squared error (RMSE) of 5.7%, 9.2% and 30.4%.

Alzheimer's disease (AD), a leading cause of dementia and mortality, presents a growing concern due to its irreversible progression and the rising costs of care. Early detection is crucial for managing AD, which begins with memory deterioration caused by the damage to neurons involved in cognitive functions. Although incurable, treatments can manage its symptoms. This study introduces a hybrid AlexNet+ResNet-50 model for AD diagnosis, utilising a pre-trained convolutional neural network (CNN) through transfer learning to analyse MRI scans. This method classifies MRI images into Alzheimer's disease (AD), moderate cognitive impairment (MCI), and normal control (NC), enhancing model efficiency without starting from scratch. Incorporating transfer learning allows for refining the CNN to categorise these conditions accurately. Our previous work also explored atlas-based segmentation combined with a U-Net model for segmentation, further supporting our findings. The hybrid model demonstrates superior performance, achieving 94.21% accuracy in identifying AD cases, indicating its potential as a highly effective tool for early AD diagnosis and contributing to efforts in managing the disease's impact.

The novel coronavirus pandemic ravaged the world in 2020, making the world fall into an unprecedented period of stagnation. This research used the Sol-Idea internet public opinion analysis platform to collect, and analyses online public opinion data associated with novel coronavirus. This research finds the following situations: 1) COVID-19 online opinion leaders are more likely to post in major discussion boards. However, opinion leaders of replies but use PTT forum as the main discussion channel; 2) According to the analysis of the content and behaviour of the account 'ebola01', it is found that the content of the posts are mostly news praising the ruling party government or mocking the opposing parties, with the sources mostly coming from media considered to be more pro-ruling party. Therefore, it can be inferred that 'ebola01' may be part of cyber army with a particular political spectrum.

There is a need to emphasise and educate future business leaders on emerging technologies' disruptive and transformative impact on business processes. Allen (2020) suggests the need for a digital mindset and tech literacy in business management education. In our study, we define cyber literacy and cyber ethical mindset emphasising the importance of informing future leaders in business schools about the ethical dilemmas arising while using these emerging technologies. Additionally, we highlight various ethical pitfalls of using technologies enabling disruption (TED). Further, we contribute to the understanding of cyber literacy, cyber ethics and business ethics, how to incorporate cyber ethics into the management curriculum, and why there is a need to integrate cyber ethics into management education.

This conceptual research aims to connect aspects of learning activities of continuing education for professionals (CPE). The objective is to provide conclusions about modes of professional learning within diversity, equity, inclusion, and belonging (DEIB) training. This interpretation is placed in context relating to the process of professional learning objectives. A CPE DEIB training plan is presented as an example of how to provide continuing professional education to adult learners within a DEIB curriculum (El-Amin, 2020). The purpose of incorporating the foundations of CPE into DEIB training permits organisations to strengthening organisational development and productivity. By connecting the foundations of curriculum design, alignment, assessment and mapping, and research-informed innovation, CPE aims to enhance the effectiveness of organisational DEIB initiatives. A CPE DEIB training plan emphasises the importance of accountability, employee involvement, and effective training to drive DEIB initiatives.

In the context of US universities where student evaluations of teaching play an important role in the retention and promotion of faculty, it is important to understand what a student expects in the classroom. This study took the perspective of Perry's cognitive development scheme with the following research question: what is the Perry level of cognitive development of business students? An established survey was used at two different universities. It was found that the median was position 3, and that there was large variation in three dimensions. First is the variation across program levels. Second, there was variation across universities. This becomes an issue when instructors move to a different university and questions the possibility to transfer 'best practices'. Third, variation was found within a specific program level. This means that instructors are faced with students who, from a cognitive perspective, have different demands which are unlikely to be simultaneously met.

Prediction of route based on historical trip observation of users is widely employed in location-based services. This work concentrates on building a route prediction system using Lempel-Ziv technique applied to a historical corpus of user travel data. Huge continuous logs of historical GPS traces representing the user's location in past are decomposed into smaller logical units known as trips. User trips are converted into sequences of road network edges using a process known as map matching. Lempel-Ziv is applied on road network edges to build the prediction model that captures the user's travel pattern in the past. A two-phased model is proposed using a map reduce framework without losing accuracy and efficiency. Model is then used to predict the user's end-to-end route given a partial route travelled by the user at any point in time. The objective of the proposed work is to build a Route Prediction system in which model building and prediction both are horizontally scalable.

This study investigates cross-border commercial air passenger traffic in Africa, focusing on the development of the 15 busiest country pairs during the period 1989 to 2015. It explores dimensions not previously studied by using ICAO's 'Traffic by Flight Stage' (TFS) and data from the CEPII Gravity Dataset. The spatial results show on an uneven geographical distribution of country pairs with the centre of gravity to South, East and North-East Africa, with one long-distance corridor between Egypt and South Africa. Countries in North and West Africa have rather few linkages, except for Egypt. Central African countries are not represented among the 15 country pairs. Although the number of passengers and the rank among the countries have shifted, South Africa and Egypt stand out, as having most country pair connections. Factors such as changing economic, diplomatic and political relations have had an influence on changing country pair connections throughout the period. A number of variables were selected to investigate how they correlated with Africa's commercial passenger traffic. Of the seven variables selected, five did show on a correlation and two did partly so. In that view, Africa's air traffic follows rather typical patterns.

This study aims to identify and understand the perspectives of automotive aftersales stakeholders regarding current challenges posed by decarbonisation strategies. It examines potential responses that the automotive aftersales business could undertake to address these challenges. Semi-structured interviews were undertaken with automotive industry experts from Europe and Latin America. This paper focuses primarily on impacts of decarbonisation upon automotive aftersales and the potential role of data in that business. Results show that investment in technology will be a condition for businesses that want to remain active in the industry. Furthermore, experts agree that incumbent manufacturers are not filling the technology gap that the energy transition is creating in the automotive sector, a consequence of which will be the entrance of new players from other sectors. The current aftersales businesses will potentially lose bargaining control. Moreover, policy makers are seen as unreliable leaders of the transition agenda.