In Vitro Assessment of Salivary Pellicle Disruption and Biofilm Removal on Titanium: Exploring the Role of Surface Hydrophobicity in Chemical Disinfection

Objectives: Peri-implantitis is mostly caused by a pathological biofilm that forms through complex processes, initiated by the formation of the salivary pellicle on implant surfaces. Understanding the nature of these pellicles and biofilm and how to remove them is important for preventing peri-implant infections and improving the success of dental implants. This study explores the characteristics of the salivary pellicle on titanium surfaces and assesses the effectiveness of different decontamination agents in eliminating the salivary pellicle and related microbial contaminations. Materials and Methods: Titanium surfaces were contaminated with salivary pellicles and pathological biofilms. The nature of the salivary pellicle was characterized using X-ray photoelectron spectroscopy (XPS), surface proteomics, contact angle measurements, and fluorescence microscopy. We tested six commonly used decontamination chemicals (chlorhexidine, essential oil-based mouthwash, citric acid, phosphoric acid, saline, and phosphate buffer saline) as well as newly proposed treatments such as surfactants and solvents (acetone, acetic acid, and Tween 20) for their capability to eliminate salivary pellicles and pathogenic biofilms from titanium surfaces. Results: The hydrophobic nature of the salivary pellicle on titanium surfaces limits the efficacy of commonly used hydrophilic solutions in removing pellicles and bacteria. Organic solvents and surfactants, particularly acetic acid and Tween 20, demonstrated superior effectiveness in removing the pellicle and biofilm. Acetic acid was notably effective in restoring surface composition, reducing microbial levels, and removing multispecies biofilms. Conclusions: The use of surfactants and solvents could be a promising alternative for the treatment of biofilms on titanium surfaces. However, further studies are needed to explore their clinical applicability.This work was supported by the Fondation de I'Ordre des dentists du Quebec (FODQ), Le Reseau de recherch\u00E9 en sant\u00E9 buccodentaire et ossseuse (RSBO) (F.T., M.-N.A.), the Fonds de recherche du Qu\u00E9bec\u2014Sant\u00E9 (FRQS) (W.C.), the Fonds de recherche du Qu\u00E9bec\u2014Nature et technologies (FRQNT) (M.-N.A.), the Islamic Development Bank Scholarship (A.A.A.-H.), the Alpha Omega Foundation of Canada (A.A.A.-H.), the Canadian Foundation for Innovation (CFI, F.T.), NSRC-Discovery (F.T.), and Canada Research Chair (Tire 2, F.T.)

COMPARING AL JAZEERA ENGLISH AND CNN ONLINE NEWS COVERAGE OF US-TALIBAN TALKS IN QATAR

The US-Taliban talks in Doha was one of the historic mediations ever hosted by a Gulf state in recent times. This thesis applied Johan Galtung's war and peace journalism framework to analyse how two major news outlets, Al Jazeera English and CNN framed US-Taliban talks in Doha during the period of July 2018 to March 2020. A content analysis of news articles from both media showed that Al Jazeera English was dominated by peace journalism and CNN was dominated by war journalism in their coverage. Additionally, the most salient indicators for the selected media were elite oriented and causes and consequences. When it comes to themes, it was found that both media prioritised peace negotiations and diplomacy followed by current state of war and negotiation and violence. The study also found the tone used by Al Jazeera English was favourable towards peace talks and CNN was unfavourable towards peace talks

Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses

The advancements in autonomous driving technology, coupled with the growing interest from automotive manufacturers and tech companies, suggest a rising adoption of Connected Autonomous Vehicles (CAVs) in the near future. Despite some evidence of higher accident rates in AVs, these incidents tend to result in less severe injuries compared to traditional vehicles due to cooperative safety measures. However, the increased complexity of CAV systems exposes them to significant security vulnerabilities, potentially compromising their performance and communication integrity. This paper contributes by presenting a detailed analysis of existing security frameworks and protocols, focusing on intra- and inter-vehicle communications. We systematically evaluate the effectiveness of these frameworks in addressing known vulnerabilities and propose a set of best practices for enhancing CAV communication security. The paper also provides a comprehensive taxonomy of attack vectors in CAV ecosystems and suggests future research directions for designing more robust security mechanisms. Our key contributions include the development of a new classification system for CAV security threats, the proposal of practical security protocols, and the introduction of use cases that demonstrate how these protocols can be integrated into real-world CAV applications. These insights are crucial for advancing secure CAV adoption and ensuring the safe integration of autonomous vehicles into intelligent transportation systems

MULTI-TASK LEARNING MODEL FOR MOBILE MALWARE DETECTION AND CLASSIFICATION

The rapid growth of mobile devices, especially those running the Android operating system, has made them attractive targets for cybercriminals. The increasing sophistication of mobile malware, including zero-day threats, challenges traditional signature-based detection methods, which struggle to identify newand evolving malware families. To address these limitations, this thesis proposes a multi-task learning (MTL) model capable of simultaneously performing binary classification (malware detection) and multi-class classification (malware family identification) by utilizing shared representations across tasks. Systematic Literature Review (SLR) was conducted to assess the current landscape of MTL applications in cybersecurity. While MTL has shown promise in other areas such as network intrusion detection, a significant research gap was identified in its application to mobile malware detection. This thesis aims to bridge that gap by developing an MTL model that improves both malware detection and classification performance, contributing to advancements in mobile security. The proposedMTLmodelwas trained and evaluated on the CCCS-CIC-AndMal- 2020 dataset, which contains API-based static features of Android applications. To enhance computational efficiency, Principal Component Analysis (PCA) was employed for feature reduction, and class imbalance was mitigated using a weighted loss function. Hyperparameter tuning with Optuna further optimized key parameters, including layer configurations, learning rate, and loss weights, ensuring robust model performance. Experimental results demonstrate that the MTL model outperforms Single-Task Learning models in both malware detection and malware family classification. The model achieved 97% accuracy in detecting malware and 91% accuracy in identifying malware families, demonstrating superior generalization across different malware types. The weighted loss function improved the detection of minority classes, addressing class imbalance challenges, while hyperparameter tuning resulted in reduced validation loss and improved stability. This research contributes to the field of mobile malware detection by introducing an MTL-based model that addresses the shortcomings of STL models. The findings indicate that MTL, combined with feature selection and optimized hyperparameters, provides a scalable and effective solution for improving the accuracy and robustness of malware detection systems. Future work could explore integrating dynamic analysis features and deploying the model for real-time malware detection

Optimizing Soil for Sustainable Agriculture with Treated Wastewater

This research explores the use of treated wastewater as a sustainable resource for agriculture in Oman by assessing its impact on soil properties. However, by conducting the concept in an agricultural region, the research investigates changes in both disturbed and undisturbed soils when treated effluent is used for irrigation. Moreover, laboratory tests were performed to determine the physical and chemical properties of soil and water before and after the application of treated wastewater. Therefore, the results showed that the treated wastewater increased the soil’s hardness due to the migration of salinity elements such as chloride, sodium, and magnesium, enhancing the productivity of the soil. In addition, this study offers significant insights into utilizing treated wastewater for agricultural productivity, supporting sustainable development and groundwater recharge in Oman. In the end, it concludes that treated wastewater when properly filtered through the soil, it can improve soil properties and serve as a valuable resource for sustainable agricultural practices

Envisioning Urban Parks

Urban parks enhance cities' aesthetic appeal and play a crucial role in carbon absorption. However, some of the existing park designs have limited functionality and insufficient integration of educational and recreational elements, besides lacking community engagement, which results in a diminished sense of ownership. Moreover, many designs prioritize neither sustainability in construction nor clean energy (e.g., cement production alone contributes to approximately 90% of its carbon emissions from fossil fuels). To address these concerns, redesigning urban parks as sustainable, circular environments that align with nature, local heritage, and community needs is a priority. This envisioning aims to implement a thorough evaluation of the context and condition of any given park, yielding unique design concepts that adhere to both national and international sustainability standards and support the Sustainable Development Goals (SDGs). The team has done two case studies: (1) envisioning Al-Riyam and Kalbuh Parks as Ophiolite Land and (2) envisioning part of Al-Qurum Park as Blue Carbon. The new philosophy of these parks is based on inspiration from their natural surroundings, emphasizing principles of circularity and decarbonization. Their planning also includes suggesting the most suitable sustainable material alternatives, recycled options, and innovative solutions. Furthermore, there is a focus on the efficiency of resources and waste management, for example, through integrating renewable energy sources, such as solar panels and wind trees, and utilizing processes like electrocoagulation for greywater recycling while incorporating drought-resistant flora. Additionally, the designs will leverage advanced technologies (e.g., AI and hologram) and provide multizones for health, education, and entertainment, featuring diverse facilities and activities. This project serves as a model for envisioning sustainable urban parks worldwide, reflecting the interconnectedness of human experiences and the environment under the title: from human to human by human

مقابلة الدكتور عبدالله صالح الخليفي

تناولت مقابلة الدكتور عبدالله الخليفي أهم الأحداث، المواقف، والتحديات التي واجهت الدكتور طوال مسيرته العلمية والعملية، حيث ترعرع الدكتور الخليفي في منطقة الغانم القديم، وتابع مسيرته العلمية داخل وخارج قطر، وحصل على درجة الدكتوراه في الاقتصاد من جامعة ساذرن الينوي الإمريكية، كما شغل منصب رئيس الجامعة لفترة 1999-2003، وساهم بشكل كبير في اتفاقات وشراكات مع جامعات وجهات متعددة لنفعة المجتمع الجامعي، وقد شغل منصب أمين عام مساعد للشؤون الاقتصادية تلبية لطلب الأمير الوالد حمد بن خليفة ال ثاني

Comparing E-Cigarettes and Traditional Cigarettes in Relation to Myocardial Infarction, Arrhythmias, and Sudden Cardiac Death: A Systematic Review and Meta-Analysis

Background: The use of electronic cigarettes (e-cigarettes) as a perceived safer alternative to traditional cigarettes has grown rapidly. However, the cardiovascular risks associated with e-cigarettes compared to regular cigarettes remain unclear. Objective: To systematically review and compare the cardiovascular outcomes of e-cigarette use versus traditional cigarette use, focusing on the risks of myocardial infarction, arrhythmias, and sudden death. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Peer-reviewed studies published in English were included if they reported cardiovascular outcomes related to e-cigarette or traditional cigarette use. A total of 20 studies were included, covering observational and interventional studies focusing on heart rate variability, myocardial infarction, arrhythmias, and sudden cardiac events. The quality of the evidence was assessed using the GRADE criteria, and data were extracted and analyzed based on the PICOS (Population, Interventions, Comparisons, Outcomes, and Study designs) framework. Results: The systematic review found that both e-cigarettes and traditional cigarettes pose significant cardiovascular risks, with traditional cigarettes linked to a higher incidence of myocardial infarction, arrhythmias, and sudden cardiac death. E-cigarette users also face increased risks of arrhythmias and myocardial infarction compared to non-smokers, primarily due to the constituents of aerosolized e-liquid, including nicotine and flavorings, which contribute to adverse cardiac effects. Regular e-cigarette use, particularly in combination with traditional cigarette use, was associated with a heightened risk of myocardial infarction. Studies also reported heart function abnormalities, such as systolic and diastolic dysfunction, and reduced ejection fractions. Additionally, changes in heart rate variability, heart rate, and blood pressure were observed, indicating both acute and chronic effects of e-cigarettes on cardiovascular autonomic regulation. Conclusions: While e-cigarettes may present a lower cardiovascular risk compared to traditional cigarettes, they are not without harm. Both products are linked to increased risks of myocardial infarction and arrhythmias, though traditional cigarettes pose a higher overall threat. Given the limitations in the current evidence base, particularly concerning the long-term effects of e-cigarette use, further research is needed to clarify these cardiovascular risks and inform public health guidelines.This study is supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1445)

Distributed optimal coverage control in multi-agent systems: Known and unknown environments

This paper introduces a novel approach to solve the coverage optimization problem in multi-agent systems. The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The proposed approach leverages a novel cost function for optimizing the agents' coverage and the cost function eventually aligns with the conventional Voronoi-based cost function. Theoretical analyses are conducted to assure the asymptotic convergence of agents toward an optimal configuration. A distinguishing feature of this approach lies in its departure from the reliance on geometric methods that are characteristic of Voronoi-based approaches; hence it can be implemented more simply. Remarkably, the technique is adaptive and applicable to various environments with both known and unknown information distributions. Lastly, the efficacy of the proposed method is demonstrated through simulations, and the obtained results are compared with those of Voronoi-based algorithms.Scopu

A COMPARISON STUDY: EVALUATING SOME STATISTICAL AND AI TECHNIQUES FOR MEDICAL APPLICATION

Analyzing medical data using artificial intelligence and statistical techniques may contribute to improving healthcare by helping to accurately identify important and irrelevant features in data collection and disease diagnosis. Enhancing the accuracy of collected data through these technologies helps to develop healthcare quality and deliver effective treatment. Python and R-Studio were utilized for medical data analysis, employing machine learning algorithms and statistical techniques for data classification and prediction. The machine learning algorithms included Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbors. Traditional statistical techniques, such as Logistic Regression and Discriminant Analysis, have also been used to evaluate data accuracy and performance of predictive models. The simulation results showed that working with data containing structural issues such as missing data and imbalance between patient and non-patient classes arise. Algorithms such as Random Forest and K-Nearest Neighbors were able to help address these data issues. As the sample size increased, the accuracy of Logistic Regression and Random Forest improved significantly, indicating their ability to handle large datasets. On the other hand, using SMOTE with algorithms reduced accuracy but improved the understanding of rare classes in terms of precision and recall. Discriminant analysis revealed a similarity in the average of variables across classes, which reduced prediction accuracy due to height similarity, thus not providing clear insights. In contrast, Decision Tree algorithms offer better clarity in interpreting variables through the decision tree diagram. Random Forest is the best algorithm for classifying data with missing values and imbalanced medical data. While machine learning is superior in terms of medical data accuracy, statistical techniques remain essential for understanding data and making informed decisions based on precise trends and patterns analysis