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1. Collaborative and Efficient Personalization with Mixtures of Adaptors

Author

Almansoori, Abdulla Jasem, Horváth, Samuel, and Takáč, Martin

Subjects
Computer Science - Machine Learning
Abstract

Non-iid data is prevalent in real-world federated learning problems. Data heterogeneity can come in different types in terms of distribution shifts. In this work, we are interested in the heterogeneity that comes from concept shifts, i.e., shifts in the prediction across clients. In particular, we consider multi-task learning, where we want the model to adapt to the task of the client. We propose a parameter-efficient framework to tackle this issue, where each client learns to mix between parameter-efficient adaptors according to its task. We use Low-Rank Adaptors (LoRAs) as the backbone and extend its concept to other types of layers. We call our framework Federated Low-Rank Adaptive Learning (FLoRAL). This framework is not an algorithm but rather a model parameterization for a multi-task learning objective, so it can work on top of any algorithm that optimizes this objective, which includes many algorithms from the literature. FLoRAL is memory-efficient, and clients are personalized with small states (e.g., one number per adaptor) as the adaptors themselves are federated. Hence, personalization is--in this sense--federated as well. Even though clients can personalize more freely by training an adaptor locally, we show that collaborative and efficient training of adaptors is possible and performs better. We also show that FLoRAL can outperform an ensemble of full models with optimal cluster assignment, which demonstrates the benefits of federated personalization and the robustness of FLoRAL to overfitting. We show promising experimental results on synthetic datasets, real-world federated multi-task problems such as MNIST, CIFAR-10, and CIFAR-100. We also provide a theoretical analysis of local SGD on a relaxed objective and discuss the effects of aggregation mismatch on convergence., Comment: 36 pages, 10 figures

Published
2024

2. Alignment with Preference Optimization Is All You Need for LLM Safety

Author

Alami, Reda, Almansoori, Ali Khalifa, Alzubaidi, Ahmed, Seddik, Mohamed El Amine, Farooq, Mugariya, and Hacid, Hakim

Subjects
Computer Science - Machine Learning
Abstract

We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achieve a significant boost in global safety score (from $57.64\%$ to $99.90\%$) as measured by LlamaGuard 3 8B, competing with state-of-the-art models. On toxicity benchmarks, average scores in adversarial settings dropped from over $0.6$ to less than $0.07$. However, this safety improvement comes at the cost of reduced general capabilities, particularly in math, suggesting a trade-off. We identify noise contrastive alignment (Safe-NCA) as an optimal method for balancing safety and performance. Our study ultimately shows that alignment techniques can be sufficient for building safe and robust models.

Published
2024

3. Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes

Author

Demidov, Dmitry, Shtanchaev, Abduragim, Mihaylov, Mihail, and Almansoori, Mohammad

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The emerging task of fine-grained image classification in low-data regimes assumes the presence of low inter-class variance and large intra-class variation along with a highly limited amount of training samples per class. However, traditional ways of separately dealing with fine-grained categorisation and extremely scarce data may be inefficient under both these harsh conditions presented together. In this paper, we present a novel framework, called AD-Net, aiming to enhance deep neural network performance on this challenge by leveraging the power of Augmentation and Distillation techniques. Specifically, our approach is designed to refine learned features through self-distillation on augmented samples, mitigating harmful overfitting. We conduct comprehensive experiments on popular fine-grained image classification benchmarks where our AD-Net demonstrates consistent improvement over traditional fine-tuning and state-of-the-art low-data techniques. Remarkably, with the smallest data available, our framework shows an outstanding relative accuracy increase of up to 45 % compared to standard ResNet-50 and up to 27 % compared to the closest SOTA runner-up. We emphasise that our approach is practically architecture-independent and adds zero extra cost at inference time. Additionally, we provide an extensive study on the impact of every framework's component, highlighting the importance of each in achieving optimal performance. Source code and trained models are publicly available at github.com/demidovd98/fgic_lowd., Comment: Main paper and Appendices

Published
2024

4. Firmamento: a multi-messenger astronomy tool for citizen and professional scientists

Author

Tripathi, Dhurba, Giommi, Paolo, Di Giovanni, Adriano, Almansoori, Rawdha R., Hamly, Nouf Al, Arneodo, Francesco, Macciò, Andrea V., Puccetti, Goffredo, de Almeida, Ulisses Barres, Brandt, Carlos, Di Pippo, Simonetta, Doro, Michele, Israyelyan, David, Pollock, Andrew M. T., and Sahakyan, Narek

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Firmamento (https://firmamento.hosting.nyu.edu) is a new-concept web-based and mobile-friendly data analysis tool dedicated to multi-frequency/multi-messenger emitters, as exemplified by blazars. Although initially intended to support a citizen researcher project at New York University-Abu Dhabi (NYUAD), Firmamento has evolved to be a valuable tool for professional researchers due to its broad accessibility to classical and contemporary multi-frequency open data sets. From this perspective Firmamento facilitates the identification of new blazars and other multi-frequency emitters in the localisation uncertainty regions of sources detected by current and planned observatories such as Fermi-LAT, Swift , eROSITA, CTA, ASTRI Mini-Array, LHAASO, IceCube, KM3Net, SWGO, etc. The multi-epoch and multi-wavelength data that Firmamento retrieves from over 90 remote and local catalogues and databases can be used to characterise the spectral energy distribution and the variability properties of cosmic sources as well as to constrain physical models. Firmamento distinguishes itself from other online platforms due to its high specialization, the use of machine learning and other methodologies to characterise the data and for its commitment to inclusivity. From this particular perspective, its objective is to assist both researchers and citizens interested in science, strengthening a trend that is bound to gain momentum in the coming years as data retrieval facilities improve in power and machine learning/artificial intelligence tools become more widely available, Comment: Accepted for publication in AJ

Published
2023

5. Byzantine-Tolerant Methods for Distributed Variational Inequalities

Author

Tupitsa, Nazarii, Almansoori, Abdulla Jasem, Wu, Yanlin, Takáč, Martin, Nandakumar, Karthik, Horváth, Samuel, and Gorbunov, Eduard

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract

Robustness to Byzantine attacks is a necessity for various distributed training scenarios. When the training reduces to the process of solving a minimization problem, Byzantine robustness is relatively well-understood. However, other problem formulations, such as min-max problems or, more generally, variational inequalities, arise in many modern machine learning and, in particular, distributed learning tasks. These problems significantly differ from the standard minimization ones and, therefore, require separate consideration. Nevertheless, only one work (Adibi et al., 2022) addresses this important question in the context of Byzantine robustness. Our work makes a further step in this direction by providing several (provably) Byzantine-robust methods for distributed variational inequality, thoroughly studying their theoretical convergence, removing the limitations of the previous work, and providing numerical comparisons supporting the theoretical findings., Comment: NeurIPS 2023; 69 pages, 12 figures

Published
2023

6. DDAM-PS: Diligent Domain Adaptive Mixer for Person Search

Author

Almansoori, Mohammed Khaleed, Fiaz, Mustansar, and Cholakkal, Hisham

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Person search (PS) is a challenging computer vision problem where the objective is to achieve joint optimization for pedestrian detection and re-identification (ReID). Although previous advancements have shown promising performance in the field under fully and weakly supervised learning fashion, there exists a major gap in investigating the domain adaptation ability of PS models. In this paper, we propose a diligent domain adaptive mixer (DDAM) for person search (DDAP-PS) framework that aims to bridge a gap to improve knowledge transfer from the labeled source domain to the unlabeled target domain. Specifically, we introduce a novel DDAM module that generates moderate mixed-domain representations by combining source and target domain representations. The proposed DDAM module encourages domain mixing to minimize the distance between the two extreme domains, thereby enhancing the ReID task. To achieve this, we introduce two bridge losses and a disparity loss. The objective of the two bridge losses is to guide the moderate mixed-domain representations to maintain an appropriate distance from both the source and target domain representations. The disparity loss aims to prevent the moderate mixed-domain representations from being biased towards either the source or target domains, thereby avoiding overfitting. Furthermore, we address the conflict between the two subtasks, localization and ReID, during domain adaptation. To handle this cross-task conflict, we forcefully decouple the norm-aware embedding, which aids in better learning of the moderate mixed-domain representation. We conduct experiments to validate the effectiveness of our proposed method. Our approach demonstrates favorable performance on the challenging PRW and CUHK-SYSU datasets. Our source code is publicly available at \url{https://github.com/mustansarfiaz/DDAM-PS}., Comment: Accepted in WACV-2024. Code is here at \url{https://github.com/mustansarfiaz/DDAM-PS

Published
2023

8. Dapagliflozin mitigates cellular stress and inflammation through PI3K/AKT pathway modulation in cardiomyocytes, aortic endothelial cells, and stem cell-derived β cells

Author

Fatmah R. Alsereidi, Zenith Khashim, Hezlin Marzook, Ahmed M. Al-Rawi, Tiana Salomon, Mahra K. Almansoori, Moustafa M. Madkour, Ahmed Mohamed Hamam, Mahmoud M. Ramadan, Quinn P. Peterson, and Mohamed A. Saleh

Subjects
Dapagliflozin, Sodium-glucose cotransporter, Cardiomyocyte, Inflammation, AKT signaling, Endothelial cells, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Abstract Dapagliflozin (DAPA), a sodium-glucose cotransporter 2 (SGLT2) inhibitor, is well-recognized for its therapeutic benefits in type 2 diabetes (T2D) and cardiovascular diseases. In this comprehensive in vitro study, we investigated DAPA’s effects on cardiomyocytes, aortic endothelial cells (AECs), and stem cell-derived beta cells (SC-β), focusing on its impact on hypertrophy, inflammation, and cellular stress. Our results demonstrate that DAPA effectively attenuates isoproterenol (ISO)-induced hypertrophy in cardiomyocytes, reducing cell size and improving cellular structure. Mechanistically, DAPA mitigates reactive oxygen species (ROS) production and inflammation by activating the AKT pathway, which influences downstream markers of fibrosis, hypertrophy, and inflammation. Additionally, DAPA’s modulation of SGLT2, the Na+/H + exchanger 1 (NHE1), and glucose transporter (GLUT 1) type 1 highlights its critical role in maintaining cellular ion balance and glucose metabolism, providing insights into its cardioprotective mechanisms. In aortic endothelial cells (AECs), DAPA exhibited notable anti-inflammatory properties by restoring AKT and phosphoinositide 3-kinase (PI3K) expression, enhancing mitogen-activated protein kinase (MAPK) activation, and downregulating inflammatory cytokines at both the gene and protein levels. Furthermore, DAPA alleviated tumor necrosis factor (TNFα)-induced inflammation and stress responses while enhancing endothelial nitric oxide synthase (eNOS) expression, suggesting its potential to preserve vascular function and improve endothelial health. Investigating SC-β cells, we found that DAPA enhances insulin functionality without altering cell identity, indicating potential benefits for diabetes management. DAPA also upregulated MAFA, PI3K, and NRF2 expression, positively influencing β-cell function and stress response. Additionally, it attenuated NLRP3 activation in inflammation and reduced NHE1 and glucose-regulated protein GRP78 expression, offering novel insights into its anti-inflammatory and stress-modulating effects. Overall, our findings elucidate the multifaceted therapeutic potential of DAPA across various cellular models, emphasizing its role in mitigating hypertrophy, inflammation, and cellular stress through the activation of the AKT pathway and other signaling cascades. These mechanisms may not only contribute to enhanced cardiac and endothelial function but also underscore DAPA’s potential to address metabolic dysregulation in T2D. Graphical abstract

Published
2024
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9. Multiperiod optimization of cooling water system with flexible topology network

Author

Bo Liu, Ruimei Zhang, Ye Liu, Yufei Wang, Ali Almansoori, and Xiao Feng

Subjects
Cooling water systems, Flexible network, Multiperiod optimization, Air cooler, Chemical engineering, TP155-156, Biochemistry, QD415-436
Abstract

Cooling water systems (CWSs) are extensively utilized in various industries to eliminate the excess heat and converse energy. Studies on CWSs mainly concentrated on finding the optimal cooler network structure. In addition, some works also considered the optimal design under varied operation conditions. However, in these works, once the optimal design of the cooler's network is determined, its arrangement remains fixed and cannot be adapted to accommodate diverse operating conditions. In this work, a flexible topology network concept is proposed to make the adjustment of network structure possible under different operation conditions. The CWS with integrated air cooler and flexible topology network has better overall performance, represented by a mixed integer nonlinear programming (MINLP) model that require advanced tools such as GAMS software. Case studies revealed that the proposed methodology can realize better energy-saving performance, and improve the economic performance under varied operation conditions. The impact of critical flexible nodes on system configuration and economy is achieved by sensitivity analysis.

Published
2024
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12. High-quality genome assembly and annotation of five bacteria isolated from the Abu Dhabi sabkha-shore region

Author

Sarfraz, Beenish, Tuyisabe, Jean, De Montfort, Louis, Ibrahim, Abdulrahman, Abdulkreem Almansoori, Shamma Z., Alajami, Haya, Almeqbaali, Asma, Kundu, Biduth, Nath, Vishnu Sukumari, Saeed, Esam Eldin, Mishra, Ajay Kumar, Hazzouri, Khaled Michel, Almaskari, Raja, Sharma, Abhishek Kumar, Sudalaimuthuasari, Naganeeswaran, and Amiri, Khaled M. A.

Published
2024
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14. High-quality genome assembly and annotation of five bacteria isolated from the Abu Dhabi sabkha-shore region

Author

Beenish Sarfraz, Jean Tuyisabe, Louis De Montfort, Abdulrahman Ibrahim, Shamma Z. Abdulkreem Almansoori, Haya Alajami, Asma Almeqbaali, Biduth Kundu, Vishnu Sukumari Nath, Esam Eldin Saeed, Ajay Kumar Mishra, Khaled Michel Hazzouri, Raja Almaskari, Abhishek Kumar Sharma, Naganeeswaran Sudalaimuthuasari, and Khaled M. A. Amiri

Subjects
Bacillus spizizenii, Illumina, MinION, Priestia Aryabhattai, Pelagerythrobacter marensis, Salt flat, Genetics, QH426-470
Abstract

Abstract Objectives Sabkhas represent polyextreme environments characterized by elevated salinity levels, intense ultraviolet (UV) radiation exposure, and extreme temperature fluctuations. In this study, we present the complete genomes of five bacterial isolates isolated from the sabkha-shore region and investigate their genomic organization and gene annotations. A better understanding of the bacterial genomic organization and genetic adaptations of these bacteria holds promise for engineering microbes with tailored functionalities for diverse industrial and agricultural applications, including bioremediation and promotion of plant growth under salinity stress conditions. Data description We present a comprehensive genome sequencing and annotation of five bacteria (kcgeb_sa, kcgeb_sc, kcgeb_sd, kcgeb_S4, and kcgeb_S11) obtained from the shores of the Abu Dhabi Sabkha region. Initial bacterial identification was conducted through 16 S rDNA amplification and sequencing. Employing a hybrid genome assembly technique combining Illumina short reads (NovaSeq 6000) and Oxford Nanopore long reads (MinION), we obtained complete annotated high-quality gap-free genome sequences. The genome sizes of the kcgeb_sa, kcgeb_sc, kcgeb_sd, kcgeb_S4, and kcgeb_S11 isolates were determined to be 2.4 Mb, 4.1 Mb, 2.9 Mb, 5.05 Mb, and 4.1 Mb, respectively. Our analysis conclusively assigned the bacterial isolates as Staphylococcus capitis (kcgeb_sa), Bacillus spizizenii (kcgeb_sc and kcgeb_S11), Pelagerythrobacter marensis (kcgeb_sd), and Priestia aryabhattai (kcgeb_S4).

Published
2024
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15. PaDPaF: Partial Disentanglement with Partially-Federated GANs

Author

Almansoori, Abdulla Jasem, Horváth, Samuel, and Takáč, Martin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Federated learning has become a popular machine learning paradigm with many potential real-life applications, including recommendation systems, the Internet of Things (IoT), healthcare, and self-driving cars. Though most current applications focus on classification-based tasks, learning personalized generative models remains largely unexplored, and their benefits in the heterogeneous setting still need to be better understood. This work proposes a novel architecture combining global client-agnostic and local client-specific generative models. We show that using standard techniques for training federated models, our proposed model achieves privacy and personalization by implicitly disentangling the globally consistent representation (i.e. content) from the client-dependent variations (i.e. style). Using such decomposition, personalized models can generate locally unseen labels while preserving the given style of the client and can predict the labels for all clients with high accuracy by training a simple linear classifier on the global content features. Furthermore, disentanglement enables other essential applications, such as data anonymization, by sharing only the content. Extensive experimental evaluation corroborates our findings, and we also discuss a theoretical motivation for the proposed approach., Comment: 29 pages, 21 figures. Published at TMLR 04/2024

Published
2022

16. Streamlining management in thoracic trauma: radiomics- and AI-based assessment of patient risks

Author

Ashraf F. Hefny, Taleb M. Almansoori, Darya Smetanina, Daria Morozova, Roman Voitetskii, Karuna M. Das, Aidar Kashapov, Nirmin A. Mansour, Mai A. Fathi, Mohammed Khogali, Milos Ljubisavljevic, and Yauhen Statsenko

Subjects
blunt chest trauma, injury severity score (ISS), abbreviated injury scale (AIS), radiomics, chest CT scans, regression models, Surgery, RD1-811
Abstract

BackgroundIn blunt chest trauma, patient management is challenging because clinical guidelines miss tools for risk assessment. No clinical scale reliably measures the severity of cases and the chance of complications.AimThe objective of the study was to optimize the management of patients with blunt chest trauma by creating models prognosticating the transfer to the intensive care unit and in-hospital length of stay (LOS).MethodsThe study cohort consisted of 212 cases. We retrieved information on the cases from the hospital’s trauma registry. After segmenting the lungs with Lung CT Analyzer, we performed volumetric feature extraction with data-characterization algorithms in PyRadiomics.ResultsTo predict whether the patient will require intensive care, we used the three groups of findings: ambulance, admission, and radiomics data. When trained on the ambulance data, the models exhibited a borderline performance. The metrics improved after we retrained the models on a combination of ambulance, laboratory, radiologic, and physical examination data (81.5% vs. 94.4% Sn). Radiomics data were the top-accurate predictors (96.3% Sn). Age, vital signs, anthropometrics, and first aid time were the best-performing features collected by the ambulance service. Laboratory findings, AIS scores for the lower extremity, abdomen, head, and thorax constituted the top-rank predictors received on admission to the hospital. The original first-order kurtosis had the highest predictive value among radiomics data. Top-informative radiomics features were derived from the right hemithorax because the right lung is larger. We constructed regression models that can adequately reflect the in-hospital LOS. When trained on different groups of data, the machine-learning regression models showed similar performance (MAE/ROV≈8%). Anatomic scores for the body parts other than thorax and laboratory markers of hemorrhage had the highest predictive value. Hence, the number of injured body parts correlated with the case severity.ConclusionThe study findings can be used to optimize the management of patients with a chest blunt injury as a specific case of monotrauma. The models we built may help physicians to stratify patients by risk of worsening and overcome the limitations of existing tools for risk assessment. High-quality AI models trained on radiomics data demonstrate superior performance.

Published
2024
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17. Studying the Hygrothermal Effects on the Impact Responses of Composite Materials Under Various Environmental Conditions

Author

Hasan Abbas Flayyih, Fadhel Abbas Abdulla, and Alaa Almansoori

Subjects
Impact, Composite material, Hygro-thermal, Nanocomposites, Engineering (General). Civil engineering (General), TA1-2040
Abstract

The current study focuses on the effects of different weather conditions, including temperature and humidity, on the impact energy properties of composite materials tested using a Charpy impact tester connected to a special hygro-thermal device. This is a unique device, handcrafted and equipped with sensors and control panels The composite materials, used in this study, combine polyester and reinforced with fiberglass in various shapes and loadings. Nanopowder materials including titanium oxide nanoparticles (TiO2) and aluminum oxide nanoparticles (Al2O3) were also added to the composite materials and their effects were assessed. The obtained results showed that all the different factors studied in this research have a positive effect on the impact test, except the nanomaterials in both cases (Al2O3 and TiO2 nanopowders) at 2 wt.% of the sample and at approximately 10 wt.%. Hence, at this percentage, there is not enough effect on the impact energy

Published
2024
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18. Modulation of antioxidant defense and PSII components by exogenously applied acetate mitigates salinity stress in Avena sativa

Author

Sajeesh Kappachery, Mohamed AlHosani, Tanveer Alam Khan, Sara Nouh AlKharoossi, Nemah AlMansoori, Sara Ali Saeed AlShehhi, Hamda AlMansoori, Maha AlKarbi, Shina Sasi, Sameera Karumannil, Sampath Kumar Elangovan, Iltaf Shah, and Mayank Anand Gururani

Subjects
Medicine, Science
Abstract

Abstract Salinity stress has detrimental effects on various aspects of plant development. However, our understanding of strategies to mitigate these effects in crop plants remains limited. Recent research has shed light on the potential of sodium acetate as a mitigating component against salinity stress in several plant species. Here, we show the role of acetate sodium in counteracting the adverse effects on oat (Avena sativa) plants subjected to NaCl-induced salinity stress, including its impact on plant morphology, photosynthetic parameters, and gene expression related to photosynthesis and antioxidant capacity, ultimately leading to osmoprotection. The five-week experiment involved subjecting oat plants to four different conditions: water, salt (NaCl), sodium acetate, and a combination of salt and sodium acetate. The presence of NaCl significantly inhibited plant growth and root elongation, disrupted chlorophylls and carotenoids content, impaired chlorophyll fluorescence, and down-regulated genes associated with the plant antioxidant defense system. Furthermore, our findings reveal that when stressed plants were treated with sodium acetate, it partially reversed these adverse effects across all analyzed parameters. This reversal was particularly evident in the increased content of proline, thereby ensuring osmoprotection for oat plants, even under stressful conditions. These results provide compelling evidence regarding the positive impact of sodium acetate on various plant development parameters, with a particular focus on the enhancement of photosynthetic activity.

Published
2024
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19. Relationship between admission selection tools and student attrition in the early years of medical school

Author

Ashraf F. Hefny, MSc, MRCS, MMEd (Dundee), FACS, Taleb M. Almansoori, MD, Margaret El-Zubeir, PhD, Alia AlBawardi, MD, Sami Shaban, PhD, Mohi Eldin Magzoub, PhD, Taoufik Zoubeidi, PhD, and Nirmin A. Mansour, MD

Subjects
Attrition, College admission test, Dropouts, Education, Medical school, Medicine (General), R5-920
Abstract

الملخص: أهداف البحث: يعد الالتحاق بكليات الطب أمرا مطلوبا للغاية في جميع أنحاء العالم مع وجود منافسة شرسة بين المتقدمين. ومع ذلك، فإن بعض أفضل الطلاب ينسحبون بعد قبولهم في كلية الطب. الهدف من هذه الدراسة هو دراسة تناقص الطلاب الأوائل خلال العامين الأولين في كلية الطب وتحديد علاقته بأدوات اختيار القبول. طريقة البحث: تم إجراء بحث كمي في كلية الطب والعلوم الصحية بجامعة الإمارات العربية المتحدة خلال الفترة من 2016 حتى 2020 حيث تم جمع وتحليل بيانات القبول الروتينية ونتائج امتحانات الطلاب للسنتين الأولى. النتائج: بلغ معدل التناقص خلال فترة الدراسة 31.7٪. كانت درجات الامتحانات الكتابية في المدارس الثانوية والكليات مرتبطة بشكل كبير باجتياز برنامج ما قبل الطب. سجلت الطالبات درجات أعلى بكثير في المقابلات المصغرة المتعددة مقارنة بنظرائهن من الذكور. ومع ذلك، فإن الاختلاف في نتيجة المقابلات المصغرة المتعددة لم يكن مرتبطا بتناقص الطلاب. الاستنتاجات: سبب التناقص المبكر معقد ولا يمكن إرجاعه إلى عامل واحد. كانت نتيجة الدراسة الثانوية في المرحلة الجامعية وامتحان القبول الكتابي من العوامل ذات الأهمية الإحصائية فيما يتعلق بمعدل تناقص الطلاب وانخفاض الأداء الأكاديمي. وأظهرت الدراسة أن الطالبات سجلن درجات أعلى بكثير في اختبارات المقابلة المصغرة المتعددة مقارنة بالطلاب الذكور. ومع ذلك، فإن نتيجة المقابلات المصغرة المتعددة وحدها لم تكن مرتبطة بشكل كبير بتناقص الطلاب. Abstract: Objectives: Placement in medical schools is highly sought after worldwide with fierce competition among applicants. However, some of the best students withdraw after being accepted to medical school. The aim of this study was to investigate early student attrition within the first 2 years of medical school and determine its relationship to admission selection tools. Methods: Quantitative research was conducted at the College of Medicine and Health Sciences from 2016 until 2020, during which time routine admission data and students’ examination results for the first 2 years were collected and analyzed. Results: The attrition rate during the study period was 31.7%. High school and college written examination scores were significantly related to completing the premedical program (p = 0.001 and p = 0.002, respectively). Female students scored significantly higher in multiple mini interviews (MMIs) compared with male counterparts (p

Published
2024
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20. Predicting early Alzheimer’s with blood biomarkers and clinical features

Author

Muaath Ebrahim AlMansoori, Sherlyn Jemimah, Ferial Abuhantash, and Aamna AlShehhi

Subjects
Alzheimer’s disease, Machine learning, Blood biomarkers, Clinical features, Medicine, Science
Abstract

Abstract Alzheimer’s disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.

Published
2024
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21. Stochastic Gradient Methods with Preconditioned Updates

Author

Sadiev, Abdurakhmon, Beznosikov, Aleksandr, Almansoori, Abdulla Jasem, Kamzolov, Dmitry, Tappenden, Rachael, and Takáč, Martin

Subjects
Mathematics - Optimization and Control, Computer Science - Machine Learning
Abstract

This work considers the non-convex finite sum minimization problem. There are several algorithms for such problems, but existing methods often work poorly when the problem is badly scaled and/or ill-conditioned, and a primary goal of this work is to introduce methods that alleviate this issue. Thus, here we include a preconditioner based on Hutchinson's approach to approximating the diagonal of the Hessian, and couple it with several gradient-based methods to give new scaled algorithms: Scaled SARAH and Scaled L-SVRG. Theoretical complexity guarantees under smoothness assumptions are presented. We prove linear convergence when both smoothness and the PL condition are assumed. Our adaptively scaled methods use approximate partial second-order curvature information and, therefore, can better mitigate the impact of badly scaled problems. This improved practical performance is demonstrated in the numerical experiments also presented in this work., Comment: 40 pages, 2 new algorithms, 20 figures, 4 tables

Published
2022
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22. A narrative review of wastewater surveillance: pathogens of concern, applications, detection methods, and challenges

Author

Surabhi Singh, Amina Ismail Ahmed, Sumayya Almansoori, Shaikha Alameri, Ashraf Adlan, Giovanni Odivilas, Marie Anne Chattaway, Samara Bin Salem, Grzegorz Brudecki, and Wael Elamin

Subjects
wastewater surveillance, infectious disease, pathogens, detection methods, challenges, public health, Public aspects of medicine, RA1-1270
Abstract

IntroductionThe emergence and resurgence of pathogens have led to significant global health challenges. Wastewater surveillance has historically been used to track water-borne or fecal-orally transmitted pathogens, providing a sensitive means of monitoring pathogens within a community. This technique offers a comprehensive, real-time, and cost-effective approach to disease surveillance, especially for diseases that are difficult to monitor through individual clinical screenings.MethodsThis narrative review examines the current state of knowledge on wastewater surveillance, emphasizing important findings and techniques used to detect potential pathogens from wastewater. It includes a review of literature on the detection methods, the pathogens of concern, and the challenges faced in the surveillance process.ResultsWastewater surveillance has proven to be a powerful tool for early warning and timely intervention of infectious diseases. It can detect pathogens shed by asymptomatic and pre-symptomatic individuals, providing an accurate population-level view of disease transmission. The review highlights the applications of wastewater surveillance in tracking key pathogens of concern, such as gastrointestinal pathogens, respiratory pathogens, and viruses like SARS-CoV-2.DiscussionThe review discusses the benefits of wastewater surveillance in public health, particularly its role in enhancing existing systems for infectious disease surveillance. It also addresses the challenges faced, such as the need for improved detection methods and the management of antimicrobial resistance. The potential for wastewater surveillance to inform public health mitigation strategies and outbreak response protocols is emphasized.ConclusionWastewater surveillance is a valuable tool in the fight against infectious diseases. It offers a unique perspective on the spread and evolution of pathogens, aiding in the prevention and control of disease epidemics. This review underscores the importance of continued research and development in this field to overcome current challenges and maximize the potential of wastewater surveillance in public health.

Published
2024
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23. Evaluating deposited radiation energy amount and collision quantities of small-molecule radiosensitizers through Monte Carlo simulations

Author

Ghada ALMisned, Ceyda Sibel Kilic, Asma Almansoori, A. Mesbahi, Mawieh Hamad, and H.O. Tekin

Subjects
Radiosensitizers, Radiation therapy, Monte Carlo simulation, NVX-108, Cancer treatment, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

This study investigates the photon interaction mechanism of various small molecule radiosensitizers, including Hydrogen Peroxide, Nimorazole, 5-Fluorouracil, NVX-108, and others, using the MCNP 6.3 Monte Carlo simulation code. The simulations focused on quantifying the linear attenuation coefficients, mean free path, and accumulation factors of these radiosensitizers, as well as their interactions in a simulated spherical water phantom irradiated with a 100 keV mono-energetic X-ray source. Our findings reveal significant variations in deposited energy, collision events, and mean free path among the radiosensitizers, indicating different efficacy levels in enhancing radiation therapy. Notably, NVX-108 demonstrated the highest energy deposition, suggesting its potential as a highly effective radiosensitizer. The study also examined the individual attenuation properties of these radiosensitizers against energetic photons, with NVX-108 showing the highest attenuation coefficient and a shorter mean free path, further supporting its superior potential in effective radiosensitization. It can be concluded that NVX-108 has higher interaction tendency with the energetic photons comparing other small-molecules under investigation.

Published
2024
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24. Do mobile phone surfaces carry SARS-CoV-2 virus? A systematic review warranting the inclusion of a '6th' moment of hand hygiene in healthcare

Author

Matthew Olsen, Thibaut Demaneuf, Gobinddeep Singh, Adrian Goldsworthy, Peter Jones, Mark Morgan, Rania Nassar, Abiola Senok, Rose Ghemrawi, Reem Almheiri, Hussain Al Marzooqi, Sumaya Almansoori, Abdullah Albastaki, Rashid Almansoori, Simon McKirdy, Rashed Alghafri, and Lotti Tajouri

Subjects
SARS-CoV-2, COVID-19, Coronavirus, Mobile phones, Cell phones, Fomite, Infectious and parasitic diseases, RC109-216, Public aspects of medicine, RA1-1270
Abstract

Background: Mobile phones, used in billions throughout the world, are high-touch devices subject to a dynamic contamination of microorganisms and rarely considered as an important fomite to sanitise systematically. The emergence of SARS-CoV-2 resulted in the COVID-19 pandemic, arguably the most impactful pandemic of the 21st century with millions of deaths and disruption of all facets of modern life globally. Aim: To perform a systematic review of the literature exploring SARS-CoV-2 presence as a contaminant on mobile phones. Methods: A systematic search (PubMed and Google Scholar) of literature was undertaken from December 2019 to March 2023 identifying English language studies. Studies included in this review specifically identified or tested for the contamination of the SARS-CoV-2 virus or genome on mobile phones while studies testing for SARS-COV-2 in environments and/or other fomites samples than but not mobile phones were excluded. Results: A total of 15 studies with reports of SARS-CoV-2 contamination on mobile phones between 2020 and 2023 were included. Amongst all studies, which encompassed ten countries, 511 mobile phones were evaluated for the presence of SARS-CoV-2 contamination and 45% (231/511) were positive for SARS-CoV-2. All studies were conducted in the hospital setting and two studies performed additional testing in residential isolation rooms and a patient’s house. Four studies (3 in 2020 and one in 2021) reported 0% contamination while two other studies (in 2020 and 2022) reported 100% of mobile phone contamination with SARS-COV-2. All other studies report mobile phones positive for the virus within a range of 4–77%. Conclusion: A total of 45% of mobile phones are contaminated with SARS-CoV-2 virus. These devices might be an important fomite vector for viral dissemination worldwide. Competent health authorities are advised/recommended to start a global implementation of mobile phone decontamination by introducing regulations and protocols in public health and health care settings such as the 6th moment of hand washing.

Published
2023
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25. The impact of integrating Jolly Phonics Lessons application into English literacy lessons on UAE preschoolers’ phonics skills

Author

Noora Almansoori, Robin Ogdol, and Aisha Alteneiji

Subjects
jolly phonics lessons application, letter-sound identification, letter formation, phoneme blending, phoneme segmenting, Education (General), L7-991
Abstract

The Jolly Phonics Lessons application is a digital tool that is designed to enhance the preschoolers’ reading and writing skills through synthetic phonics approach. This study examined the impact of integrating Jolly Phonics Lessons application into literacy lessons on the emergent reading and writing skills among kindergarten students, ages 4-6, in the United Arab Emirates through a comparative study between pupils (Group-A), whose literacy classes were integrated with the application, and pupils (Group-B) who were taught using the traditional method of teaching literacy. Mixed methods research design was followed to analyze the effects of integrating the application. The Phonemic Awareness Assessment Inventory (PAAI) tool, field notes, and artifacts were utilized to measure the development of students’ letter-sound identification, letter formation, phoneme blending and segmenting abilities. In addition, the paper compared the growth on phonics skills between students in Groups A and B. The results showed that students in Group-A outperformed pupils who received literacy instruction through traditional methods. The findings from the PAAI scores exhibited dramatic growth in letter-sound recognition and letter formation, and substantial increase in phoneme blending and segmenting abilities of Group-A. The T-statistic for differences between two means at 95% confidence revealed that there is a significant difference between the performance of students in Groups-A and B. Implications from this study highlight the positive impact of incorporating Jolly Phonics Lessons application into literacy classes on low-achieving students. On this bases, future research of Jolly Phonics Lesson application to support mixed-ability classes or students with dyslexia.

Published
2024
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26. SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING AN ENHANCED ENCODER-DECODER ARCHITECTURE

Author

N. Aburaed, M. Al-Saad, M. Q. Alkhatib, M. S. Zitouni, S. Almansoori, and H. Al-Ahmad

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Semantic segmentation is one of most the important computer vision tasks for the analysis of aerial imagery in many remote sensing applications, such as resource surveys, disaster detection, and urban planning. This area of research still faces unsolved challenges, especially in cluttered environments and complex sceneries. This study presents a repurposed Robust UNet (RUNet) architecture for semantic segmentation, and embeds the architecture with attention mechanism in order to enhance feature extraction and construction of segmentation maps. The attention mechanism is achieved using Squeeze-and-Excitation (SE) block. The resulting network is referred to as SE-RUNet. SE is also tested with the classical UNet, termed SE-UNet, to verify the efficiency of introducing SE. The proposed approach is trained and tested using “Semantic Segmentation of Aerial Imagery” dataset. The results are evaluated using Accuracy, Precision, Recall, F-score and mean Intersection over Union (mIoU) metrics. Comparative evaluation and experimental results show that using SE to embed attention mechanism into UNet and RUNet significantly improves the overall performance.

Published
2023
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27. A Novel Data Analytics Methodology for Analyzing Real Estate Brokerage Markets with Case Study of Dubai

Author

Ahmed Saif Al Abdulsalam, Maged Mohammed Al-Baiti Al Hashemi, Mohammed Zayed Sulaiman Aleissaee, Abdelaziz Saleh Husain Almansoori, Gurdal Ertek, and Thouraya Gherissi Labben

Subjects
real estate brokerage firms, brokerage markets, statistical analysis, data analytics, multi-dimensional scaling (MDS), Building construction, TH1-9745
Abstract

Despite the vast economic impact of real estate markets worldwide, research on real estate brokerage markets remains limited. Specifically, there are few studies that provide a systematic, integrated, and replicable analytical methodology to analyze and benchmark a given real estate brokerage market. To this end, this paper introduces a data analytics methodology for analyzing real estate brokerage markets, integrating various statistical and analytical methods to extract insights from market data, supporting real estate investment decisions. The applicability of the methodology is demonstrated with a case study analyzing data from the top 50 real estate brokerage firms in Dubai, UAE. As shown in the case study, applying this methodology to brokerage market data enables the visual benchmarking of firms, identification of similarities between them, profiling and comparison of clusters of firms, and exploration of the impacts of various categorical and numerical attributes on performance. A notable finding for the Dubai real estate brokerage market is that it takes a minimum of 700 days for a brokerage firm to mature and advance to the next level of business success.

Published
2024
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29. Do mobile phone surfaces carry SARS-CoV-2 virus? A systematic review warranting the inclusion of a “6th” moment of hand hygiene in healthcare

Author

Olsen, Matthew, Demaneuf, Thibaut, Singh, Gobinddeep, Goldsworthy, Adrian, Jones, Peter, Morgan, Mark, Nassar, Rania, Senok, Abiola, Ghemrawi, Rose, Almheiri, Reem, Marzooqi, Hussain Al, Almansoori, Sumaya, Albastaki, Abdullah, Almansoori, Rashid, McKirdy, Simon, Alghafri, Rashed, and Tajouri, Lotti

Published
2023
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31. An evaluation of the interconnection between change management strategies and New Public Management Model among Dubai police force

Author

Almansoori, Khalid

Subjects
Human resources management, new public management
Abstract

This thesis is concerned with understanding the New Public Management Model vis-à-vis the change management strategy employed by the Dubai police force. The study also focuses on reviewing the different Change Management Strategies employed by the management of the Dubai police force. The study further seeks to explore the policy's success. The main objectives are however concerned with analysing how successful the New Public Management Model has been vis-à-vis the Change Management Strategies deployed by the Dubai police force. This question was raised as a result of the transformation in Dubai and the need to review management practice in light of the current models of "best fit" and "best practice." The study aimed to achieve this analysis by addressing five specific objectives, namely: to identify the strategies that makeup change management; to explore the extent to which management strategies influence the application of the New Public Management Model in the public sector; to explore how the interconnection between Change Management Strategies and New Public Management Model impact on the outcome of the activities of the police force; to explore the reaction of public sector employee to the New Public Management Model, and to determine the success of the implementation of the New Public Management Model. The study was based on case study research conducted among members of the Dubai police force. The participants in the study include both members of the management team, officers and men of the Dubai police force. A snowball sampling technique was employed in the study. Semi-structured interviews were used to obtain an in-depth response from participants about the New Public Management Model and the change management strategy deployed. vi This study revealed that the concept of change management involves several processes and the need to combine resources and strategies to smooth the process of organizational change. The study further revealed that cultural factors tend to influence the choice of strategies and have significant implication for management decisions within the Dubai police force. Align with issues around culture is the innovative practice that is now available due to the use of new technologies and the application of artificial intelligence. Technology has enhanced communication in a less intrusive way. It also aids the accomplishment of set tasks within a realistic time frame.

Published
2022
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35. Hydroxyapatite-Based Natural Biopolymer Composite for Tissue Regeneration

Author

Wasan Alkaron, Alaa Almansoori, Katalin Balázsi, and Csaba Balázsi

Subjects
hydroxyapatite, biopolymers, composite, tissue regeneration, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85
Abstract

Hydroxyapatite (HAp) polymer composites have gained significant attention due to their applications in bone regeneration and tooth implants. This review examines the synthesis, properties, and applications of Hap, highlighting various manufacturing methods, including wet, dry, hydrothermal, and sol–gel processes. The properties of HAp are influenced by precursor materials and are commonly obtained from natural calcium-rich sources like eggshells, seashells, and fish scales. Composite materials, such as cellulose–hydroxyapatite and gelatin–hydroxyapatite, exhibit promising strength and biocompatibility for bone and tissue replacement. Metallic implants and scaffolds enhance stability, including well-known titanium-based and stainless steel-based implants and ceramic body implants. Biopolymers, like chitosan and alginate, combined with Hap, offer chemical stability and strength for tissue engineering. Collagen, fibrin, and gelatin play crucial roles in mimicking natural bone composition. Various synthesis methods like sol–gel, hydrothermal, and solution casting produce HAp crystals, with potential applications in bone repair and regeneration. Additionally, the use of biowaste materials, like eggshells and snails or seashells, not only supports sustainable HAp production but also reduces environmental impact. This review emphasizes the significance of understanding the properties of calcium–phosphate (Ca-P) compounds and processing methods for scaffold generation, highlighting novel characteristics and mechanisms of biomaterials in bone healing. Comparative studies of these methods in specific applications underscore the versatility and potential of HAp composites in biomedical engineering. Overall, HAp composites offer promising solutions for improving patient outcomes in bone replacement and tissue engineering and advancing medical practices.

Published
2024
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36. Effects of Maritime Mortgages on Creditors and Debtors under the UAE Law

Author

Ramzi Madi and Esmaeel Almansoori

Subjects
maritime mortgage, creditor (mortgagee), debtor (mortgagor), uae, Law
Abstract

This article aims to investigate the definition of mortgage and will clarify and address the Importance of mortgage and the application of maritime mortgage (vessel and ancillaries thereof). It is known that contracts affect the parties thereto and third parties. Therefore, we explored the effects of the mortgage contract on the creditor (mortgagee) in terms of the assignment of the mortgage right to a third party, the method of enforcement on the mortgaged vessel, and his right in priority and tracking. Moreover, the article examines the effects of the maritime mortgage on the debtor (mortgagor). We also explained that the debtor (mortgagor) is the principal party in the mortgage contract, amongst the effects thereof, to manage the mortgaged vessel with ordinary diligence, exert the effort required to maintain it, and clarify the disposals, which may be effected on the mortgaged vessel. We concluded that the maritime mortgage rules outlined in the UAE Maritime Commercial Law (26) of 1981 must be updated to promote foreign investment in the State, particularly since the said law was enacted in 1981 and various changes have taken place from that time in the field of maritime commerce.

Published
2023
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38. Clinical Manifestations Of Otolaryngology In COVID-19

Author

Firas Mowaffak Hassan Alkhamaisi , Yasir L. Hassoun Almansoori, Ahmed adhab mayan alzubaidi , Laith Altamimi and Adhwaa Razzaq Muslim Abdulrasool

Subjects
Covid-19 Infection, Medicine
Abstract

Background: In late 2019, a new coronavirus known as SARS-CoV-2 emerged, causing an acute respiratory disease called COVID-19. The outbreak originated in China and quickly gained a global attention. COVID-19 can cause various upper respiratory tract symptoms such as sore throat, nasal obstruction, and loss of smell function. Patients and Methods: this is a cross-sectional descriptive study done during the COVID-19 pandemic from June 1st, 2020 to August 31st, 2020. The study was carried out in some isolation hospitals located in Baghdad and Najaf cities. The collected data are from laboratory-confirmed COVID-19 positive patients by using a pre-designed questionnaire which included demographic information such as age, gender, occupation, and place of residence. All participants were specifically asked about their symptoms related to the ear, nose, and throat (ENT) system. Results: A Total number of 408 confirmed Covid-19 cases had been included in the current study. Age range of participants was from 12 to 86 years with average of 51.42 years. The study has revealed that sore throat is the predominant ENT symptom in COVID-19 cases, while ear symptoms are uncommon. The most common nasal manifestations observed in COVID-19 patients were anosmia/ hyposmia and nasal obstruction. Discussion: This study demonstrated the different ENT manifestations occurred COVID-19 patients. These manifestations involved both lower and upper respiratory tract symptoms. Looking to these different manifestations with more interest can help in the early diagnosis and treatment of COVID-19 cases.

Published
2023
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39. Scanning electron microscopy as a valuable tool to optimize the properties of the polymer/clay nanocomposites

Author

Mohammed M. Sabri, Alaa M. Almansoori, and Riyadh Aziz Ghadban

Subjects
scanning electron microscope, polymers/clay interfaces, polymer nanocomposites, plasma treatment, Engineering (General). Civil engineering (General), TA1-2040
Abstract

AbstractThe current research utilizes a low voltage scanning electron microscopy (LV-SEM) with an electron beam along with low loading energy of lower than 2.2 KeV to minimize damage and specimen surface charging. The NovaSEM, is used as an efficient tool in the current study due to the high resolution information it can gather as images and its high magnification nanometers. Polyamide 12 (PA12), as a polymer matrix, and Cloisite 30B (C30B) nanoclay, as a filling material, were the materials tested in this study. From the results obtained, CBS was found to be a significant and valuable tool for certain complex tasks when studying and analyzing polymer/clay interfaces. CBS in conjunction with beam deceleration in a LV-SEM was used to map the C30B clay distribution on PA12 particles and within PA12-nanoclay nanocomposites manufactured from the latter’s clay distribution particles within the polymer particles’ surfaces. This SEM experimentation has demonstrated that using the clay’s air plasma design preceding the composite preparation resulted in removing the large clay assemblages. The plasma treatment has improved the interfacial adhesion and dispersion in the nanoclay/PA12 composite, resulting in similar maximum stress values which were both higher than the pure PA12. Thus, the mechanical tests exhibited performance enhancement for the resulting composites defined in the present work, and the enhancement of this method can be identified via SEM imaging.

Published
2023
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41. Brassinosteroid Signaling Pathways: Insights into Plant Responses under Abiotic Stress

Author

Tanveer Alam Khan, Sajeesh Kappachery, Sameera Karumannil, Mohamed AlHosani, Nemah Almansoori, Hamda Almansoori, Mohammad Yusuf, Lam-Son Phan Tran, and Mayank Anand Gururani

Subjects
abiotic stress, abscisic acid, brassinosteroids, ethylene, hydrogen peroxide, signaling pathway, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

With the growing global population, abiotic factors have emerged as a formidable threat to agricultural food production. If left unaddressed, these stress factors might reduce food yields by up to 25% by 2050. Plants utilize natural mechanisms, such as reactive oxygen species scavenging, to mitigate the adverse impacts of abiotic stressors. Diverse plants exhibit unique adaptations to abiotic stresses, which are regulated by phytohormones at various levels. Brassinosteroids (BRs) play a crucial role in controlling essential physiological processes in plants, including seed germination, xylem differentiation, and reproduction. The BR cascade serves as the mechanism through which plants respond to environmental stimuli, including drought and extreme temperatures. Despite two decades of research, the complex signaling of BRs under different stress conditions is still being elucidated. Manipulating BR signaling, biosynthesis, or perception holds promise for enhancing crop resilience. This review explores the role of BRs in signaling cascades and summarizes their substantial contribution to plants’ ability to withstand abiotic stresses.

Published
2023
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44. Numerical Simulation of Dispersion Patterns and Air Emissions for Optimal Location of New Industries Accounting for Environmental Risks

Author

Ali Bseibsu, Chandra Mouli R. Madhuranthakam, Kaan Yetilmezsoy, Ali Almansoori, and Ali Elkamel

Subjects
AERMOD, air pollution, dispersion, environmental risks, optimization, simulation, Environmental pollution, TD172-193.5
Abstract

One of the main reasons for air pollution is industrial plants releasing huge amounts of air pollutants in the form of gas emissions. The different chemical pollutants and their corresponding levels present in these emissions, and their proximity to the industrial source, have serious effects on the nearby ecosystems. Some of the industrial nuisances include noise, smoke, dirt, dust, odor and noxious gases, which have to be minimized (if possible, eliminated), especially if the location is desired to be used as a community site. When choosing locations at which to build either new industrial plants or new community sites, software can be used to assess both the short-term and long-term concentration profiles of the various detrimental air pollutants. In this study, the AERMOD model was used to find an optimal location to build a new plant in Toledo, Ohio, USA. Simulations were performed to study the pollutant emissions and their dispersion patterns for four different geographic locations situated away from an existing plant in this region. The AERMOD model, along with the IRAP-h View model, which is approved by the US Environmental Protection Agency (EPA), has been successfully used to assess the fate and transport of pollutants from the proposed new industrial plants. The hazard quotients from the analysis of the results for these four different geographic locations were assessed. The highest total non-cancer hazard indices of 18.7 and 13.2 were obtained for fisher adult and fisher child, respectively, in one of the four locations. The acute inhalation quotient risk was less than the target hazard index of 0.25 for all the four locations. With respect to the concentrations of several chemicals of potential concern (COPC), such as soil, produce, beef, chicken, milk and pork, the fourth location (farthest east) recorded the minimum range values compared to the other three locations.

Published
2022
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45. Public value of the e-Sick Leave system in Emirate of Abu Dhabi

Author

Almansoori, Mouza and Brookes, Stephen

Subjects
658.3
Abstract

The e-Sick Leave system is a new information system in the Health Sector in the Emirate of Abu Dhabi. Until now, no other country has implemented a programme focused on digitalisation, controlling, and regulating the issuance and attestation of sick leave certificates. This thesis takes a unique approach in describing the implementation and evaluation of this medico-legal system. In 2012, the Department of Health (DOH) in Abu Dhabi commissioned a medico-legal system, called the e-Sick Leave system, to control abuses and protect the rights of employees, employers, and doctors. This study sought to develop a framework for understanding the influence of the e-Sick Leave system on sick leave service performance, using the public value approach. No previous research has investigated the public value of such a system, and this study addressed the question of how the e-Sick Leave system can create such public value. The five research objectives were to define and describe a modern, digital healthcare initiative by analysing the characteristics of an e-Sick Leave system; to develop a benefits map to connect the enablers of e-Sick Leave benefits with its public value; to assess the influence of e-Sick Leave system on transaction costs; to assess the influence of e-Sick Leave on controlling different actors and agencies; and to develop recommendations that would improve the framework for understanding this healthcare initiative as well as to improve its public value. To this end, the research used a phased, mixed-research approach. First, to extend and verify the public value of e-Sick Leave from the users' perspective, 16 policy-makers in the DOH were interviewed to define e-Sick Leave, to understand its mechanisms for controlling and orchestrating its users, and to define its public value from the perspective of its sponsor and owner. Fifty doctors from different hospitals, 18 policy-makers, 15 patients and ten employers were interviewed. To verify and generalise the perceptions, two surveys were conducted, which collected 223 and 230 questionnaires, respectively, from doctors and patients. E-Sick Leave system is (my own definition) 'digital cybernetic platform that integrates different systems in different institutions to understand, and so to enable, regulation of the behaviour of different sub-systems and their players'. To understand the public value of this system, a benefits map was developed to connect enablers with the outcomes and benefits. The enablers here were both digital and cybernetic. The outcomes and benefits are the control of the fabrication of sick leave certificates, less paperwork, the removal of unnecessary and duplicated activities, a simplified sick leave process, standardised processes and sickness leave criteria, governing the medical decision-making process, reducing doctors' undesired behaviours (i.e. prescribing long periods off work to patients, and unnecessary examinations and medications), reducing undesirable patient behaviour (e.g. fabrication of data, visiting different doctors to obtain the longest period of sick leave, and exploiting informal relationships with doctors), and controlling disease behaviour. E-Sick Leave's public value dimensions are its economic and welfare benefits. The economic benefits take the form of reduced total controlling costs (i.e. the cost of controlling abusers, and the cost of abusers), reduced transaction costs (i.e. search costs, negotiation costs, and enforcement costs), and operational costs (e.g. process costs). The welfare benefits are in improving morale (i.e. accountability and the giving of false/misleading information), improving public health, total system satisfaction (i.e. satisfaction of employees, employers, doctors, and the DOH as a win-win scenario), improved services (i.e. greater speed, accessibility, and accuracy), protecting rights (i.e. transparency, fairness and trust), and improving relationships between different actors. To consolidate the two functionalities of the e-Sick Leave system effectively, and to be more decentralised while reducing transaction costs, a blockchain system is proposed. Because of other operational and governance issues, to achieve the full potential of benefits as a cybernetic system and a digital platform, a new, independent board was proposed to manage these benefits. This research filled several knowledge gaps. It was the first to define the e-Sick Leave system from a cybernetic perspective, and hence is considered to be a contribution that introduced cybernetics into the healthcare sector. It was also the first to use the benefits maps methodology in mapping the relations between the enablers (i.e. the functionalities of the system) and the benefits. Finally, this research was the first to apply a public value framework to a medico-legal system in the Emirate.

Published
2019

46. Shaping the future through strategic change management framework in the UAE Law Enforcement Agency

Author

Almansoori, Saeed

Subjects
710
Abstract

Change is an essential part of life as well as that of any organisation. Change may be undertaken for different purposes, but the need for improvements tend to be the overarching aim for change. While most private organisations utilise change management model to influence the process, limited studies on change management in the Law Enforcement Agency (LEA) across the world reveal lack of change management model or framework. This observation is also true in the United Arab Emirates (UAE) even though significant change had been undertaken in the past decades. Not without its challenges, barriers to accepting change and in sustaining change management emphasise the importance of drivers of change as well as commitments of all levels of management in the LEA to change process. Thus, change management models are critically examined with the view to identify and determine factors that may facilitate change in LEA in the UAE. The Ministry of Interior (MOI) is the LEA for the UAE which limits the scope of this study to one organisation in the UAE. MOI is the LEA case examined in the UAE which limits the scope of this study to an organisation. Through extended literature review, the definitions, concepts and theories of change management are examined to identified essential elements for successful change management. This was done by evaluating factors that drive change in each change management model, a process that proved useful in developing a conceptual framework applicable in LEA. Framework developed helped to benchmark change management status in MOI using survey and semi-structured interviews. 14 directors and senior officers in the LEA in the UAE were interviewed, while survey was completed by 290 staff at lower management levels in the same organisation. The result reveals that change management status in the LEA needs improvements even though drivers exist in the organisation. Active involvement of lower management is lacking in the change process nor is sufficient hard and soft variables present to support strategic change management. Gaps identified led to the review of framework which is further validated by 10 experienced officers in LEA. The validation indicates that the strategic change management framework if appropriately applied is able to improve, advance and sustain successful future change process in the LEA. The revised strategic framework is a major contribution to both knowledge and practice in change management in LEA, UAE public sector and other LEA across the world with similar security and safety system to the UAE.

Published
2019

47. Major crimes police interviewing in Dubai : an examination of the Investigative Interviewing Triangle

Author

Almansoori, Rashid Ahmad and Milne, Rebecca Jane

Subjects
363.25095357
Abstract

Most studies on police interviewing practices have been conducted in Western countries. The results of which may not be generalisable to other countries whose social, cultural, and legal norms differ. The research conducted for this thesis was the first to explore major crime interviewing in Dubai (and indeed the whole region) from the perspectives of three main parties in criminal investigations, namely, (i) police officers, (ii) victims, and (iii) suspects. These parties form what was termed the Investigative interviewing Triangle. In the present thesis, after a review of the investigative interviewing literature (Chapter 2) and an overview of the social and legal aspects of Dubai (Chapter 3), five empirical studies were conducted to explore the perspectives of the the three parties of the interviewing triangle. The first empirical study (N=10) (Chapter 4) explored how police officers in Dubai conduct major crime interviews and how they viewed them, with the aim of understanding in-depth, major crime interviewing in Dubai and providing an empirical basis for the subsequent empirical studies. Informed by empirical Study One, empirical Study Two (N=62) (Chapter 5) aimed to understand the perspectives of a wider range of police officers, and helped form a broader picture of the practices of major crime interviewing in Dubai. Chapter 6 (empirical Study Three) (N= 16) examined the perspectives of major crime victims in Dubai, and how their experiences compared between being interviewed in a police station and being interviewed by a specialist unit, both of which they had experienced. Chapter 7 (empirical Study Four) (N=28) explored convicted offenders’ perceptions of their own police interviews, and whether their perceived police interviewing practices effected their resultant interview outcomes. Finally, empirical Study Five (Chapter 8) examined (utilising a vignette experiment) the potential effects of police interviewing styles on interview outcomes, from the perspectives of convicted offenders (N=28). Overall, the findings from these studies were in line with much of the Western research findings, especially concerning the positive effects of a humanitarian based approach. However, there were a few discrepancies (e.g. it was found that victims and suspects in this research who reported experiencing attitudes and responses related to a humanitarian interview style, also experienced a more formal police interviewer), and those differences could be attributed to Dubai having a unique social, cultural, and legal norms.

Published
2019

48. Case report: Birk–Landau–Perez syndrome linked to the SLC30A9 gene—identification of additional cases and expansion of the phenotypic spectrum

Author

Praseetha Kizhakkedath, Watfa AlDhaheri, Ibrahim Baydoun, Mohammed Tabouni, Anne John, Taleb M. Almansoori, Saeed Al-Turki, Fatma Al-Jasmi, and Hiba Alblooshi

Subjects
hyperechogenic kidneys, ataxia, oculomotor apraxia, developmental regression, tubulointerstitial nephritis, zinc transporter, Genetics, QH426-470
Abstract

Birk–Landau–Perez syndrome (BILAPES) is an autosomal recessive cerebro-renal syndrome associated with genetic defects in the SLC30A9 gene, initially reported in 2017 in six individuals belonging to a large Bedouin kindred. The SLC30A9 gene encodes a putative mitochondrial zinc transporter with ubiquitous expression, the highest found in the brain, kidney, and skeletal muscle. Since the first report, only one additional affected patient has been described, but there were some inconsistencies, such as hearing loss, failure to thrive, and neuroimaging findings between the clinical presentation of the disease in the Bedouin family and the second patient. Here, we present two more patients from a consanguineous Middle Eastern family with features of chronic kidney disease, neurodevelopmental regression, ataxia, hearing loss, and eye abnormalities, which were largely consistent with BILAPES. Whole-exome sequencing detected a homozygous in-frame deletion c.1049_1051delCAG (p.Ala350del) in the SLC30A9 gene, which was the same variant detected in the patients from the primary literature report and the variant segregated with disease in the family. However, in the patients described here, brain MRI showed cerebellar atrophy, which was not a cardinal feature of the syndrome from the primary report. Our findings provide further evidence for SLC30A9-associated BILAPES and contribute to defining the clinical spectrum.

Published
2023
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49. Multimodal diagnostics in multiple sclerosis: predicting disability and conversion from relapsing-remitting to secondary progressive disease course – protocol for systematic review and meta-analysis

Author

Linda Östlundh, Yauhen Statsenko, Fatmah Al Zahmi, Tetiana Habuza, Klaus Neidl-Van Gorkom, Teresa Arora, Darya Smetanina, Gillian Lylian Simiyu, Sarah Meribout, Tatsiana Talako, Fransina Christina King, Iryna Makhnevych, Juri George Gelovani, Karuna M Das, Taleb M Almansoori, Miklós Szólics, Fatima Ismail, and Milos Ljubisavljevic

Subjects
Medicine
Abstract

Background The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS.Methods and analysis We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS.Ethics and dissemination The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences.PROSPERO registration number CRD42022354179.

Published
2023
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50. Complete genome sequence of Vibrio gazogenes PB1: an estuarine bacterium capable of producing prodigiosin from starch or cellulose

Author

Bincy Baby, Dhanya Vijay, Pretty S. Philip, Afra A. Alnuaimi, Hessa M. Almansoori, Sarah O. Areidat, Gulfaraz Khan, Ranjit Vijayan, and M. Kalim Akhtar

Subjects
high-value compound, polysaccharide, CRISPR, marine, virulence, degradation, Science, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

Vibrio is a genus of gram-negative, rod-shaped, motile bacteria commonly found in saltwater. One species in particular, Vibrio gazogenes PB1, sourced from an estuarine environment, is known to produce the secondary metabolite, prodigiosin. This high-value compound has potential uses as an antibiotic, a fungicide, and an anti-cancer agent. To further explore its metabolic and genetic features for biotechnological purposes, the complete genome sequence of V. gazogenes PB1 was determined by Illumina and Pacbio sequencing. Two chromosomes were assembled with a mean coverage of 293x. Chromosome 1 is 3.5 Mbp in size with 45.3% GC content and chromosome 2 is 1.2 Mbp in size with 45.1% GC content. The entire genome harbours 4178 genes, of which 3988 are protein-coding and 114 are RNA-coding. A total of 55 virulence-related genes, 38 antimicrobial resistance genes, 48 transposase sequences, 2 intact prophage regions, and 10 genomic islands were present within the genome. Six genes associated with the degradation of cellulose and starch were also identified within the genome. Four of them were strongly up-regulated, as confirmed by RT-qPCR, thus providing strong evidence for their involvement in starch and cellulose degradation. Quite importantly, we demonstrate for the first time that starch and cellulose is associated with the synthesis of prodigiosin in a native prodigiosin-producing bacterium. The prodigiosin titres obtained in the presence of cellulose were on par with glucose as the carbon source which lends further support in the use of V. gazogenes PB1 as a biotechnological host for prodigiosin production.

Published
2023
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