An investigastion of factors affecting salaried and waged taxpayer compliance behavior: evidence from Libya

Convincing taxpayers to comply with the tax regulations has been the main challenge of Libyan tax authority. Even though tax is one of the important revenue sources after oil in Libya, over the last five years, tax collection has been on decrease trend. The main purpose of this study is to examine the effect of tax knowledge, tax complexity, public governance quality and perception of government spending on salaried and waged taxpayer compliance behavior in Libya. The study was guided by cognitive theory and social exchange theory which explain the effect of noneconomic factors. Using survey method, a total of 400 questionnaires were distributed among Libyan students in Malaysia since they are considered part of individual taxpayers in Libya. The findings indicate positive and significant relationship between tax knowledge, public governance quality, perception of government spending and taxpayer compliance behavior, except tax complexity that shows a significant negative relationship with taxpayer compliance behavior. The study recommends that the tax legislations should be reviewed and simplified besides promoting tax knowledge among taxpayers. In addition, Libyan government should also place attention on public governance quality and government spending in order to increase taxpayer compliance behavior

Remotality of certain sets Lp(I,X)

Let X be a Banach space and let (I, Ω, µ) be a measure space. For 1 ≤ p < ∞, let Lp (I, X) denote the space of Bochner p−integrable functions defined on I with values in X. The object of this paper is to give sufficient conditions for remotality of L1 (I, H) + L1 (I, G) in L1 (I, X), where H and G are two bounded sets in X which include as a special case remotality of L1 (I) ∧ ⊗ G + H ∧ ⊗ L1 (I) in L1 (I × I).Publisher's Versio

Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-based Cohort

Objectives: Design a machine learning-based prediction framework to predict the presence or absence of Systemic Lupus Erythematosus (SLE) in a cohort of Omani patients. Methods: Records of 219 patients from 2006 to 2019 were extracted from SQU Hospital electronic records, 138 patients have SLE, and the remaining 81 have other rheumatologic diseases. Clinical and demographic features were analyzed to focus on the early stages of the disease. Our design implements Recursive Feature Selection (RFE) to select only the most informative features. In addition, the CatBoost classification algorithm is utilized to predict SLE and an explainer algorithm (SHAP) is applied on top of the CatBoost model to provide individual prediction reasoning which is then validated by rheumatologists. Results: CatBoost achieved an Area Under the ROC curve (AUC) score of 0.95 and a Sensitivity of 92%. Four clinical features (Alopecia, renal disorders, Acute Cutaneous Lupus, and hemolytic anemia) along with the patient’s age were shown to have the greatest contribution to the prediction by the SHAP algorithm. Conclusion: We have designed and validated an explainable framework to predict SLE patients and provide reasoning for its prediction. Our framework enables early intervention for clinicians which leads to positive healthcare outcomes. Keywords: Systemic Lupus Erythematosus; Interpretation; Machine Learning; Supervised; Clinical Decision Support System; Statistical Data; Data Analysis

Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm

Background: Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate the effect of glycated hemoglobin (HbA1c) elevation on the prediction of diabetes onset. However, there is still a need for validation of these models using EHR data collected from different populations. Objective: The aim of this study is to perform a replication study to validate, evaluate, and identify the strengths and weaknesses of replicating a predictive model that employed multiple logistic regression with EHR data to forecast the levels of HbA1c. The original study used data from a population in the United States and this differentiated replication used a population in Saudi Arabia. Methods: A total of 3 models were developed and compared with the model created in the original study. The models were trained and tested using a larger dataset from Saudi Arabia with 36,378 records. The 10-fold cross-validation approach was used for measuring the performance of the models. Results: Applying the method employed in the original study achieved an accuracy of 74% to 75% when using the dataset collected from Saudi Arabia, compared with 77% obtained from using the population from the United States. The results also show a different ranking of importance for the predictors between the original study and the replication. The order of importance for the predictors with our population, from the most to the least importance, is age, random blood sugar, estimated glomerular filtration rate, total cholesterol, non–high-density lipoprotein, and body mass index. Conclusions: This replication study shows that direct use of the models (calculators) created using multiple logistic regression to predict the level of HbA1c may not be appropriate for all populations. This study reveals that the weighting of the predictors needs to be calibrated to the population used. However, the study does confirm that replicating the original study using a different population can help with predicting the levels of HbA1c by using the predictors that are routinely collected and stored in hospital EHR systems

In the Name of God the Most Gracious the Most Merciful

This thesis explains the Islamic law that applies the Quran and Sunnah as a constitution, and the concept of Rahma. It will emphasize this concept by explaining the rigid law of Hudod, then elaborating on Rahma

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records

Background: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records (EHR) data for identifying such patients can ultimately help provide better health outcomes. Objective: Our study investigates the performance of predictive models to forecast HbA1c elevation levels by employing several machine learning models. We also investigate utilizing the patient's EHR longitudinal data in the performance of the predictive models. Explainable methods have been employed to interpret the decisions made by the blackbox models. Methods: This study employed Multiple Logistic Regression, Random Forest, Support Vector Machine and Logistic Regression models, as well as a deep learning model (Multi-layer perceptron) to classify patients with normal (<5.7%) and elevated (≥5.7%) levels of HbA1c. We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large dataset from Saudi Arabia with 18,844 unique patient records. Results: The machine learning models achieved promising results for predicting current HbA1c elevation risk. When employed with longitudinal data, the machine learning models outperformed the Multiple Logistic Regression model employed in the comparative study. The multi-layer perceptron model achieved an accuracy of 83.22% for the AUC-ROC when used with historical data. All models showed close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data. Conclusions: This study shows that machine learning models can provide promising results for the task of predicting current HbA1c levels (≥5.7% or less). Utilizing the patient's longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies

The Role of Community Radio in Livelihood Improvement: The Case of Simli Radio

The present study focuses on the contribution of Simli Radio to the livelihood improvement of the people in the Tolon-Kumbungu and Savelugu-Nanton Districts of the Northern Region of Ghana. A multi-stage sampling technique was used to select 12 communities for the study. Data were gathered on the use of broadcasting as an educational tool, the promotion of traditional culture, communication and information sharing, entertainment and income promotion. The study established that Simli Radio has worked to improve awareness and knowledge of solutions to community development problems ranging from culture, rural development, education, hygiene and sanitation, agriculture to local governance. The station has been an appropriate medium that has facilitated an interface between duty bearers and rights holders. It has promoted small and medium enterprise development by creating market opportunities for Small and Medium Enterprise (SME) operators and consequently improved sales and incomes. It is recommended that regular feedback from the listening public is essential in identifying listeners’ preferences and the taste of various listeners segments (youth, women, men, aged, etc) and to avoid politics and religion

The effect of sulfate contents on the surface properties of iron–manganese doped sulfated zirconia catalysts

The iron–manganese doped sulfated zirconia catalysts were prepared via precipitation method; the sulfation was carried out by impregnation with different amounts of sulfate (4%, 10% and 16% SO4− 2 by weight) with the addition of Fe–Mn doped and calcined at 600 °C for 3 h. The prepared catalysts were characterized by TGA-DTA, XRD, BET, FT-IR, TEM, TPD-NH3 and XPS. XRD and BET results revealed that the addition of sulfate imparts special stabilization to the catalytically active tetragonal phase of zirconia. All the iron–manganese doped sulfated zirconia catalysts were found to have strong acid sites, high surface area and small crystallite size

Cytotoxicity and physicochemical characterization of iron–manganese-doped sulfated zirconia nanoparticles

Iron–manganese-doped sulfated zirconia nanoparticles with both Lewis and Brønsted acidic sites were prepared by a hydrothermal impregnation method followed by calcination at 650°C for 5 hours, and their cytotoxicity properties against cancer cell lines were determined. The characterization was carried out using X-ray diffraction, thermogravimetric analysis, Fourier transform infrared spectroscopy, Brauner–Emmett–Teller (BET) surface area measurements, X-ray fluorescence, X-ray photoelectron spectroscopy, zeta size potential, and transmission electron microscopy (TEM). The cytotoxicity of iron–manganese-doped sulfated zirconia nanoparticles was determined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays against three human cancer cell lines (breast cancer MDA-MB231 cells, colon carcinoma HT29 cells, and hepatocellular carcinoma HepG2 cells) and two normal human cell lines (normal hepatocyte Chang cells and normal human umbilical vein endothelial cells [HUVECs]). The results suggest for the first time that iron–manganese-doped sulfated zirconia nanoparticles are cytotoxic to MDA-MB231 and HepG2 cancer cells but have less toxicity to HT29 and normal cells at concentrations from 7.8 µg/mL to 500 µg/mL. The morphology of the treated cells was also studied, and the results supported those from the cytotoxicity study in that the nanoparticle-treated HepG2 and MDA-MB231 cells had more dramatic changes in cell morphology than the HT29 cells. In this manner, this study provides the first evidence that iron–manganese-doped sulfated zirconia nanoparticles should be further studied for a wide range of cancer applications without detrimental effects on healthy cell functions

Type-2 diabetes mellitus diagnosis from time series clinical data using deep learning models.

Clinical data is usually observed and recorded at irregular intervals and includes: evaluations, treatments, vital sign and lab test results. These provide an invaluable source of information to help diagnose and understand medical conditions. In this work, we introduce the largest patient records dataset in diabetes research: King Abdullah International Research Centre Diabetes (KAIMRCD) which includes over 14k patient data. KAIMRCD contains detailed information about the patient’s visit and have been labelled against T2DM by clinicians. The data is processed as time series and then investigated using temporal predictive Deep Learning models with the goal of diagnosing Type 2 Diabetes Mellitus (T2DM). Long Short-Term Memory (LSTM) and Gated-Recurrent Unit (GRU) are trained on KAIMRCD and are demonstrated here to outperform classical machine learning approaches in the literature with over 97% accuracy