Baseline characteristics and treatment pattern of type 2 diabetes patients in Jordan: analysis from the DISCOVER patient population

Introduction: Jordan has limited published data on T2DM and its treatment patterns. This analysis of the DISCOVER study, focusing on Jordan, is aimed at describing the characteristics of patients and treatment patterns according to the real-world setting in T2DM patients initiating a second-line antidiabetic treatment Methods: The DISCOVER study is an ongoing, multi-country, multicenter, observational, prospective, and longitudinal cohort study. The baseline data of patients’ characteristics, clinical and laboratory variables, micro- and macro-complications, and treatment choices were captured on a standardized case report form. Results: Two hundred and seventy-one patients were enrolled from 13 different clinical sites in Jordan. Sixty percent of the patients were male. The participants overall mean age was 53.8 ± 11.3 years with a mean BMI 30.8 ± 5.0 kg/m 2. The mean duration of T2DM was almost 6 years and the mean documented HbA1c and fasting plasma glucose were e 8.4% ± 1.6 and 180.9 ± 63.7 mg/dL, respectively, at the initiation of second-line antidiabetic treatment. Almost 25% of the participants were reported to be either current smokers or ex-smokers. More than 40% of patients had comorbidities such as hypertension or dyslipidemia. Diabetes related microvascular and macrovascular complications were documented in 10.3% and 12.5% of patients, respectively. Metformin (MET) alone was used as a first-line therapy in almost one-half of the patients and in combination with sulfonylurea (SU) in approximately one-third of the patients. The most commonly used second-line therapy was the combination of MET and dipeptidyl peptidase-4 inhibitors (DPP-4i) with 29.9% followed by the triple therapy of MET, SU, and DPP-4i with 28%. Conclusion: A substantial number of patients were young with uncontrolled diabetes and at high risk for micro- and macrovascular complications. Therefore, a comprehensive management with early treatment intensification and risk factors modifications are required to achieve target goals

Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department

As the COVID-19 pandemic has afflicted the globe, health systems worldwide have also been significantly affected. This pandemic has impacted many sectors, including health in the Kingdom of Jordan. Crises that put heavy pressure on the health systems’ shoulders include the emergency departments (ED), the most demanded hospital resources during normal conditions, and critical during crises. However, managing the health systems efficiently and achieving the best planning and allocation of their EDs’ resources becomes crucial to improve their capabilities to accommodate the crisis’s impact. Knowing critical factors affecting the patient length of stay prediction is critical to reducing the risks of prolonged waiting and clustering inside EDs. That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. Therefore, patients’ length of stay in EDs across waiting time duration is categorized as (low, medium, and high) using supervised machine learning (ML) approaches. Unsupervised algorithms have been applied to classify the patient’s length of stay in local EDs in the Kingdom of Jordan. The Arab Medical Centre Hospital is selected as a case study to justify the performance of the proposed ML model. Data that spans a time interval of 22 months, covering the period before and after COVID-19, is used to train the proposed feedforward network. The proposed model is compared with other ML approaches to justify its superiority. Also, comparative and correlation analyses are conducted on the considered attributes (inputs) to help classify the LOS and the patient’s length of stay in the ED. The best algorithms to be used are the trees such as the decision stump, REB tree, and Random Forest and the multilayer perceptron (with batch sizes of 50 and 0.001 learning rate) for this specific problem. Results showed better performance in terms of accuracy and easiness of implementation

Internationalization as a strategy for small and medium‐sized enterprises and the impact of regulatory environment: An emerging country perspective

This study focuses on identification, categorisation and comparison of regulatory barriers to internationalisation for the SMEs from an emerging market context. Primary data were collected to develop and validate a structural model to assess the salient regulatory barriers of internationalisation with a particular attention to the SMEs in Bangladesh. Structured questionnaire has been used to collect data from 212 SMEs operating in Bangladesh. The results indicate that both administrative and economic regulatory barriers are significant for the internationalisation of SMEs whereby administrative regulatory barriers are slightly more substantial. This study provides further discussion from both theoretical and methodological aspects. By developing and validating structural model, this study contributes to the literature on small business and regulation with particular attention to the emerging markets

Improving the Efficiency of Median Filters Using Special Generated Windows

Digital images of various types, gray and color, are used in many vital applications, necessitating the need to rid them of the noise that can infect them during the messaging process. One of the most important types of noise that negatively affects the characteristics of the digital image is salt and pepper noise, which leads to changing some of the pixels in the digital image to values of 0 or 255. The negative effect of this noise increases with the increase in the noise ratio (the number of affected pixels). This paper will discuss a new method to reduce the adverse effects of salt and pepper noise. This research aims to provide an effective way to deal with the noise of salt and pepper, especially if the noise ratio is higher than 50%, which the rest of the filters cannot deal with. This method will be used to treat the affected pixels only by using six matrices with specific dimensions divided into two types: the examination (checking) matrix (WC) and the execution (processing) matrix (WP); where these two types of matrices are used to process the noise-forming pixels. The six generated matrices are used in the first round (3 of each type), while the first two matrices (one of each type) are used again in the second round to eliminate the adverse effects of noise. The proposed method will be implemented, and the obtained experimental results will be compared with median and average filters to show how the proposed method will enhance the quality of the processed noisy image; a visual and statistical analysis will be performed to prove the quality provided by the proposed method. The proposed method will be compared with other existing methods, such as MDBUT MF, MDBPT GMF, AWM F, and AAMF. MSE, PSNR, SSIM, and CC parameters will be used for comparison purposes

Organizational culture and affective commitment to e-learning’ changes during COVID-19 pandemic: The underlying effects of readiness for change.

Higher education institutions (HEIs) have been embracing digital transformation for years, but the disruptive influence of the global COVID-19 pandemic has accelerated it. Despite the importance of organizational culture (OC) for the successful delivery of e-learning, empirical studies looking at its impact on academics’ readiness and affective commitment to e-learning-induced changes are scant. This study unveils the underlying impacts of multiple employee readiness for change (ERFC) dimensions in the OC-employee affective commitment to change (EACC) relationship. Survey data were obtained from 1,200 Jordanian public HEIs’ academics. Structural equation modelling was used to analyze the data, testing the study’s six hypotheses. The findings offer a novel contribution by showing that OC types influence different dimensions of ERFC, each having a distinctive impact on EACC. It further shows that two ERFC dimensions, namely self-efficacy and personal valence, function as full mediators in the relationships between group culture/adhocracy culture and EACC