Innovations in financial services have given rise to Financial Technology (FinTech) as a disruptive technology which is now shaping the future of the financial sector through services like crowdfunding, peer-to-peer loans, and alternative underwriting platforms. Given the status of the web as a vital information resource, accessible and usable online FinTech services can aid people with disabilities in taking full advantage of the presented financial services to support them in basic needs like accessing high-quality care. This study set out to analyze the accessibility and usability of 32 Saudi FinTech websites by means of various automated tools and manual inspection. The results revealed the investigated websites to have a significant number of accessibility and usability problems that cause prejudice against disabled individuals and foster an overall poor user experience. Thus, more work is needed to disseminate knowledge about the importance of developing suitable FinTech websites and services to assist disabled people.

In the logistics service industry, the rapid advancement of Information Technology (IT) has cultivated a competitive dynamic that promotes uptake of logistics information technologies by Logistics Service Providers (LSPs). However, there remain many LSPs who have yet to embrace IT and thus have not realized their fullest competitiveness and financial success. This research conceptualizes the role of IT in logistics operations and investigates the degree to which IT, its usage, and its capabilities can affect LSP performance and competitiveness. The research interviews a logistics operations manager and the findings support that a firm’s performance and competitive advantage are influenced by IT usage and (especially) capability. As IT capability complements service processes and allows for innovation in services, this research and suggested future research provide not only theoretical and technological but also managerial implications.

The population models allow for a better understanding of the dynamical interactions with the environment and hence can provide a way for understanding the population changes. They are helpful in studying the biological invasions, environmental conservation and many other applications. These models become more complicated when accounting for the stochastic and/or random variations due to different sources. In the current work, a spectral technique is suggested to analyze the stochastic population model with random parameters. The model contains mixed sources of uncertainties, noise and uncertain parameters. The suggested algorithm uses the spectral decompositions for both types of randomness. The spectral techniques have the advantages of high rates of convergence. A deterministic system is derived using the statistical properties of the random bases. The classical analytical and/or numerical techniques can be used to analyze the deterministic system and obtain the solution statistics. The technique presented in the current work is applicable to many complex systems with both stochastic and random parameters. It has the advantage of separating the contributions due to different sources of uncertainty. Hence, the sensitivity index of any uncertain parameter can be evaluated. This is a clear advantage compared with other techniques used in the literature.

Recent advancements have focused on developing automated diagnostic systems for detecting schizophrenia (SZ) and bipolar disorder using medical imaging techniques, particularly MRI. However, acquiring MRI scans is costly and requires patients to remain still, which poses significant challenges for individuals with SZ and bipolar disorder. Additionally, many developing countries face a shortage of mental health professionals, further complicating the diagnosis of these conditions. Research has also consistently highlighted motor abnormalities in SZ and bipolar disorder since their earliest descriptions. Notably, recent studies have identified substantial statistical differences in handwriting features between patients with these disorders and healthy individuals. Moreover, it is well-established that machine learning models trained on imbalanced datasets often exhibit biased performance. To address these challenges, we propose a novel three-stage machine learning framework called SRF-SMOTE-NET, which integrates statistically robust features method and oversampling techniques with neural network. In the first stage, we apply the Kruskal-Wallis statistical test to analyze kinematic, complexity, and geometrical features from the handwriting data and identify statistically significant features, termed Statistically Robust Features (SRF). The second stage involves applying the SMOTE technique to address class imbalance, ensuring a fair training process. Finally, in the third stage, we develop a neural network (NET) model to perform unbiased classification. The proposed model is validated using a handwriting dataset, achieving an overall classification accuracy of 96%, and Mathews correlation co-efficient of 0.919.

The COVID-19 pandemic remains an immediate and present concern, yet as of now there is still no approved therapeutic available for the treatment of COVID-19.This study aimed to investigate and report evidence concerning demographic characteristics and currently-used medications that contribute to the ultimate outcomes of COVID-19 ICU patients. A retrospective cohort study was conducted among all COVID-19 patients in the Intensive Care Unit (ICU) of Asir Central Hospital in Saudi Arabia between the 1st and 30th of June 2020. Data extracted from patients’ medical records included their demographics, home medications, medications used to treat COVID-19, treatment durations, ICU stay, hospital stay, and ultimate outcome (recovery or death).Descriptive statistics and regression modelling were used to analyze and compare the results. The study was approved by the Institutional Ethics Committees at both Asir Central Hospital and King Khalid University. A total of 118 patients with median age of 57 years having definite clinical and disease outcomes were included in the study. Male patients accounted for 87% of the study population, and more than 65% experienced at least one comorbidity. The mean hospital and ICU stay was 11.4 and 9.8 days, respectively. The most common drugs used were tocilizumab (31.4%), triple combination therapy (45.8%), favipiravir (56.8%), dexamethasone (86.7%), and enoxaparin (83%). Treatment with enoxaparin significantly reduced the length of ICU stay (p = 0.04) and was found to be associated with mortality reduction in patients aged 50−75 (p = 0.03), whereas the triple regimen therapy and tocilizumab significantly increased the length of ICU stay in all patients (p = 0.01, p = 0.02 respectively). COVID-19 tends to affect males more significantly than females. The use of enoxaparin is an important part of COVID-19 treatment, especially for those above 50 years of age, while the use of triple combination therapy and tocilizumab in COVID-19 protocols should be reevaluated and restricted to patients who have high likelihood of benefit.