Terrorism and the Law of Kuwait: the local responses to universal threats and international demands

This thesis will focus on four issues regarding terrorism and counterterrorism in Kuwait. It will first provide a comprehensive understanding of the threats and the phenomenon of terrorism in Kuwait since its independence in 1961. Second, this thesis will discuss the counterterrorism policies and agenda that Kuwait has adopted to react to terrorism. Third, the criminal offences related to terrorism in Kuwait will be examined. Finally, this thesis will evaluate measures intended to thwart financing terrorism in Kuwait before and after the ratification of the International Convention for the Suppression of the Financing of Terrorism (1999). Within these themes, this thesis will assess and evaluate the effectiveness of the abovementioned reactions by the Kuwaiti government. The thesis will also assess whether these reactions have impacted Kuwaiti constitutional values. Therefore, this research project will evaluate the fairness and appropriateness of these reactions with regard to Kuwaiti constitutional law and also with regard to international laws, including human rights. Finally, this thesis will consider the reality that many of the causes of terrorism and many of the possible solutions to these causes do not originate in Kuwait. Nevertheless, Kuwait is not immune to the consequences of terrorism and the efforts of international laws and international partners to stop it. Therefore, this thesis will assess how far Kuwait, as a country in an area of the world that is greatly affected by terrorism, is able to look after its own interests in this regard or is subjected to the wishes of other countries, such as the United States, or the international community. This analysis is especially important, since Kuwait is a small country surrounded by much larger, more powerful, and largely unstable countries, such as Saudi Arabia, Iran, and Iraq

Automatic classification of high resolution satellite imagery - A case study for urban areas in the Kingdom of Saudi Arabia

Updating topographic geospatial databases is often performed based on current remotely sensed images. To automatically extract the object information (labels) from the images, supervised classifiers are being employed. Decisions to be taken in this process concern the definition of the classes which should be recognised, the features to describe each class and the training data necessary in the learning part of classification. With a view to large scale topographic databases for fast developing urban areas in the Kingdom of Saudi Arabia we conducted a case study, which investigated the following two questions: (a) which set of features is best suitable for the classification?; (b) what is the added value of height information, e.g. derived from stereo imagery? Using stereoscopic GeoEye and Ikonos satellite data we investigate these two questions based on our research on label tolerant classification using logistic regression and partly incorrect training data. We show that in between five and ten features can be recommended to obtain a stable solution, that height information consistently yields an improved overall classification accuracy of about 5%, and that label noise can be successfully modelled and thus only marginally influences the classification results

A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis

Over the past few years, much work has been done to develop machine learning models that perform Arabic sentiment analysis (ASA) tasks at various levels and in different domains. However, most of this work has been based on shallow machine learning, with little attention given to deep learning approaches. Furthermore, the deep learning models used for ASA have been based on noncontextualized embedding schemes that negatively impact model performances. This article proposes a novel deep learning-based multilevel parallel attention neural (MPAN) model that uses a simple positioning binary embedding scheme (PBES) to simultaneously compute contextualized embeddings at the character, word, and sentence levels. The MPAN model then computes multilevel attention vectors and concatenates them at the output level to produce competitive accuracies. Specifically, the MPAN model produces state-of-the-art results that outperform all established ASA baselines using 34 publicly available ASA datasets. The proposed model is further shown to produce new state-of-the-art accuracies for two multidomain collections: 95.61% for a binary classification collection and 94.25% for a tertiary classification collection. Finally, the performance of the MPAN model is further validated using the public IMDB movie review dataset, on which it produces an accuracy of 96.13%, placing it in second position on the global IMDB leaderboard

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model

Dermatological Emergencies in Family Medicine: Recognition, Management, and Referral Considerations

Numerous people with skin disorders who have real dermatologic crises show up at the emergency room. Family doctors need to be able to identify potentially fatal dermatological disorders quickly since they could be the first to encounter patients with these illnesses. The purpose of this review is to provide guidance for early recognition, help identify distinct symptoms, and enable early diagnosis of emerging dermatological conditions. Necrotizing fasciitis, Stevens-Johnson syndrome, toxic epidermal necrolysis, Rocky Mountain spotted fever, and other possible emergencies that might manifest as dermatological symptoms are examples of these conditions. In this article we will be discussing the dermatological emergencies present at primary care settings and encountered by family physician, recognition and management of those emergencies, referral considerations, role of family medicine in dermatological emergencies and other topics

Factors associated with poor outcomes among hospitalized patients with COVID-19: Experience from a MERS-CoV referral hospital

BACKGROUND: Coronavirus disease 2019 (COVID-19) has resulted in millions of deaths, including more than 6000 deaths in the Kingdom of Saudi Arabia (KSA). Identifying key predictors of intensive care unit (ICU) admission and mortality among infected cases would help in identifying individuals at risk to optimize their care. We aimed to determine factors of poor outcomes in hospitalized patients with COVID-19 in a large academic hospital in Riyadh, KSA that serves as a Middle East Respiratory Syndrome coronavirus (MERS-CoV) referral center. METHODS: This is a single-center retrospective cohort study of hospitalized patients between March 15 and August 31, 2020. The study was conducted at King Saud University Medical City (KSUMC). COVID-19 infection was confirmed using real-time reverse transcriptase polymerase chain reaction (RT-PCR) for SARS-COV-2. Demographic data, clinical characteristics, laboratory, radiological features, and length of hospital stay were obtained. Poor outcomes were, admission to ICU, need for invasive mechanical ventilation (IMV), and in-hospital all-cause mortality. RESULTS: Out of 16,947 individuals tested in KSUMC, 3480 (20.5%) tested positive for SARS-CoV-2 and of those 743 patients (21%) were hospitalized. There were 62% males, 77% were younger than 65 years. Of all cases, 204 patients (28%) required ICU admission, 104 (14%) required IMV, and 117 (16%) died in hospital. In bivariate analysis, multiple factors were associated with mortality among COVID-19 patients. Further multivariate analysis revealed the following factors were associated with mortality: respiratory rate more than 24/min and systolic blood pressure 37 units/L in the first 48 h of presentation, while a RT-PCR cycle threshold (Ct) value ≤24 was a predictor for IMV. CONCLUSION: Variable factors were identified as predictors of different outcomes among COVID-19 patients. The only predictor of IMV was a low initial Ct values of SARS-CoV-2 PCR. The presence of tachypnea, hypotension, lymphopenia, and elevated AST in the first 48h of presentation were independently associated with mortality. This study provides possible independent predictors of mortality and invasive mechanical ventilation. The data may be helpful in the early identification of high-risk COVID-19 patients in areas endemic with MERS-CoV

HIV-Care Outcome in Saudi Arabia; a Longitudinal Cohort

Background: Clinical characteristics of HIV-1 infection in people inhabiting Western, Sub-Saharan African, and South-East Asian countries are well recognized. However, very little information is available with regard to HIV-1 infection and treatment outcome in MENA countries including the Gulf Cooperation Council (GCC) states. Methods: Clinical, demographic and epidemiologic characteristics of 602 HIV-1 infected patients followed in the adult Infectious Diseases Clinic of King Faisal Specialist Hospital and Research Centre, in Riyadh, Kingdom of Saudi Arabia a tertiary referral center were longitudinally collected from 1989 to 2010. Results: Of the 602 HIV-1 infected patients in this observation period, 70% were male. The major mode of HIV-1 transmission was heterosexual contact (55%). At diagnosis, opportunistic infections were found in 49% of patients, most commonly being pneumocysitis. AIDS associated neoplasia was also noted in 6% of patients. A hundred and forty-seven patients (24%) died from the cohort by the end of the observation period. The mortality rate peaked in 1992 at 90 deaths per 1000 person-year, whereas the mortality rate gradually decreased to <1% from 1993-2010. In 2010, 71% of the patients were receiving highly active retroviral therapy. Conclusions: These data describe the clinical characteristic of HIV-1-infected patients at a major tertiary referral hospital in KSA over a 20-year period. Initiation of antiretroviral therapy resulted in a significant reduction in both morbidity and mortality. Future studies are needed in the design and implementation of targeted treatment and prevention strategies for HIV-1 infection in KSA

First molecular analysis of rabies virus in Qatar and clinical cases imported into Qatar, a case report

Identifying the origin of the rabies virus (RABV) infection may have significant implications for control measures. Here, we identified the source of a RABV infection of two Nepalese migrants in Qatar by comparing their RABV genomes with RABV genomes isolated from the brains of a RABV infected camel and fox from Qatar