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Business Process Re-engineering Strategies for United Arab Emirates’ Public Organizations
No full textA Master of Science thesis in Engineering Systems Management by Hind Ahmed AlShamsi entitled, “Business Process Re-engineering Strategies for United Arab Emirates’ Public Organizations”, submitted in April 2022. Thesis advisor is Dr. Mahmoud Awad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Organizations in general, and public in specific, are striving to achieve excellence in their performance and gain a competitive edge through process improvement. However, the need for process restructuring is inevitable to overcome contemporary challenges faced within and outside an organization. To date, organizations have been able to achieve significant results with Business Process Re-engineering (BPR) implementation. However, and despite the reported success, many BPR projects in the United Arab Emirates (UAE) public sector do not achieve their improvement objectives. BPR projects are initiated within UAE’s public organizations to reshape their processes and streamline their business structure with the country’s government initiatives. Therefore, it is crucial to establish an understanding on the influential factors that result in BPR projects’ success and failure. The objective of this research study is to explore BPR strategies implemented in the UAE public organizations and investigate the contributing factors of success. Qualitative research through Confirmatory Factor Analysis (CFA) is conducted to explore the relationship between factors and success criteria. The initial model is built on an extensive literature review and interviews with subject matter experts. Results suggest that the success rate of BPR projects in UAE’s public organizations range is 40-75%. Partial Least Squares Structural Equation Model (PLS-SEM) highlight the importance of process owner’s role of motivating and leading performers to define and implement the future state of the process. Results also highlight the importance of data collection in identifying the appropriate process candidates with appropriate metrics to be re-engineered. Moreover, findings indicate that organizations with a smaller size of employees have higher impact on the success rate of BPR projects. Finally, based on the results, a set of recommendations to maximize the benefits of BPR projects is provided.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
Developing a UAE-Based Disputes Prediction Model using Machine Learning
No full textA Master of Science thesis in Construction Management by Ibrahim Wasef Subhi Abu Laila entitled, “Developing a UAE-Based Disputes Prediction Model using Machine Learning”, submitted in April 2022. Thesis advisor is Dr. Sameh El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form)Disputes are a major phenomenon in the construction industry around the world that stems from unsettled disagreements between project stakeholders. These types of disagreements can take place in any project regardless of its size and properties. The UAE is no different. To resolve these disputes, a substantial amount of money and time must be allocated which might cause the project to collapse. As a result, proactive construction management is needed to prevent this issue from arising in the first place. The aim of this research is to develop prediction models for construction disputes, thereby providing early insights to the stakeholders, and thus making precautionary measures that can prevent these disputes from taking place during the project execution. Initially, a literature review was performed to gather the input parameters needed for the prediction models. These parameters were verified by industry experts using a preliminary survey to find out the most important ones. The top three parameters were found to be the general experience and competence of the contractor, the project size, and the level of contract readiness. Moreover, another survey was administered in order to acquire actual project data that will be the input for the proposed prediction models. The sample size was 79 projects, where 67% of these projects faced disputes. These models will be able to predict dispute occurrence, the number of disputes, the impact of disputes on time and cost, the dispute resolution procedure, as well as the time and cost of the disputes resolution. Additionally, three different Machine Learning (ML) algorithms, Artificial Neural Networks (ANN), Support Vectors Machine (SVM), and Random Forests, were used to run these models and perform predictions. It was found that SVM and Random Forests provided better results in terms of accuracy in all of the seven models. Most of the models were well-performing since the testing accuracies lies within the 70-90% range, with disputes resolution duration prediction model even exceeded the 90% mark. Furthermore, as demonstrated in the case study, these models were successful in predicting the different aspects of disputes and demonstrated that they can be implemented in future projects to achieve disputes mitigation.College of EngineeringMultidisciplinary ProgramsMaster of Science in Construction Management (MSCM
The Importance of Maintaining Cultural Capital in Community Psychology and Development
No full textSlides from a presentation entitled "The Importance of Maintaining Cultural Capital in Community Psychology and Development" by Brien K. Ashdown, presented at the 50th anniversary meeting of the Society for Cross-Cultural Research, February 2022 in San Diego, California, USA
The n-zero-divisor graph of a commutative semigroup
No full textLet S be a (multiplicative) commutative semigroup with 0, Z(S) the set of zero-divisors of S, and n a positive integer. The zero-divisor graph of S is the (simple) graph Γ(S) with vertices Z(S) ∗ = Z(S) \ {0}, and distinct vertices x and y are adjacent if and only if xy = 0. In this paper, we introduce and study the n-zero-divisor graph of S as the (simple) graph Γn(S) with vertices Zn(S) ∗ = {x n | x ∈ Z(S)} \ {0}, and distinct vertices x and y are adjacent if and only if xy = 0. Thus each Γn(S) is an induced subgraph of Γ(S) = Γ1(S). We pay particular attention to diam(Γn(S)), gr(Γn(S)), and the case when S is a commutative ring with 1 6= 0. We also consider several other types of “n-zero-divisor” graphs and commutative rings such that some power of every element (or zero-divisor) is idempotent
Tradition and its significance within Islam and the Abrahamic faiths
No full textThe present article looks into the position and significance of tradition within Islam and the Abrahamic Faiths. Tradition in its very theological sense is generally believed to have divine authority albeit not as part of the sacred scripture. The main interest here is to shed light on how different tradition in Islam in comparison to Judaism and Christianity is. Drawn upon examples and evidence, this article thus, studies the idea of tradition and underlines its notional and applied similarities and differences within the said three world religions. Further, this article also investigates and discusses the intricacies of the Islamic tradition
On the precision of full-spectrum fitting of simple stellar populations. IV. A systematic comparison with results from colour-magnitude diagrams
No full textIn this fourth paper of a series on the precision of ages of stellar populations obtained through the full-spectrum fitting technique, we present a first systematic analysis that compare the age, metallicity and reddening of star clusters obtained from resolved and unresolved data (namely colour-magnitude diagrams (CMDs) and integrated-light spectroscopy) using the same sets of isochrones. We investigate the results obtained with both Padova isochrones and MIST isochrones. We find that there generally is a good agreement between the ages derived from CMDs and integrated spectra. However, for metallicity and reddening, the agreement between results from analyses of CMD and integrated spectra is significantly worse. Our results also show that the ages derived with Padova isochrones match those derived using MIST isochrones, both with the full spectrum fitting technique and the CMD fitting method. However, the metallicity derived using Padova isochrones does not match that derived using MIST isochrones using the CMD method. We examine the ability of the full-spectrum fitting technique in detecting age spreads in clusters that feature the extended Main Sequence Turn Off (eMSTO) phenomenon using two-population fits. We find that 3 out of 5 eMSTO clusters in our sample are best fit with one single age, suggesting that eMSTOs do not necessarily translate to detectable age spreads in integrated-light studies.American University of Sharja
Effect of High-Frequency Ultrasound on Targeted Liposomes
No full textDelivering highly toxic drugs inside a safe carrier to tumors while achieving a controlled and effective drug release at the targeted sites represents an attractive approach to enhance drug efficiency while reducing its undesirable side effects. Functionalization of highly biocompatible nanocarriers such as liposomes functionalized with targeting moieties enhances their ability to target specific cancer cells overexpressing the targeted receptors. Furthermore, upon their accumulation at the target site, High-frequency ultrasound (HFUS) can be used to stimulate a controlled release of the loaded drugs. Here, the US-mediated drug release from calcein-loaded non-pegylated, pegylated as well as targeted-pegylated liposomes modified with human serum albumin (HSA) and transferrin (Tf) was investigated. HFUS at two different frequencies (1 MHz and 3 MHz) was found to trigger calcein release, with higher release rates recorded at the lower frequency (1 MHz) compared to the higher frequency (3 MHz) despite a higher power density. Pegylation was found to enhance liposomal sensitivity to HFUS significantly. In addition, targeted pegylated liposomes were more susceptible to HFUS than non-targeted pegylated (control) liposomes. These findings show that pegylation and targeting moieties directly influence liposomal sensitivity to HFUS. Therefore, combining targeted-pegylated liposomes with HFUS represents a promising controlled and effective drug delivery system.American University of SharjahPatient’s Friends CommitteeAlJalila FoundationAl Qasimi FoundationTechnology Innovation Pioneer-Healthcare (TIP) ProgramTakamulSheikh Hamdan Award for Medical SciencesFriends of Cancer Patients (FoCP)Dana Gas Endowed Chair for Chemical Engineerin
Ultrasound-sensitive cRGD-modified liposomes as a novel drug delivery system
No full textTargeted liposomes enable the delivery of encapsulated chemotherapeutics to tumours by targeting specific receptors overexpressed on the surfaces of cancer cells; this helps in reducing the systemic side effects associated with the cytotoxic agents. Upon reaching the targeted site, these liposomes can be triggered to release their payloads using internal or external triggers. In this study, we investigate the use of low-frequency ultrasound as an external modality to trigger the release of a model drug (calcein) from non-targeted and targeted pegylated liposomes modified with cyclic arginine–glycine–aspartate (cRGD). Liposomes were exposed to sonication at 20-kHz using three different power densities (6.2, 9, and 10 mW/cm²). Our results showed that increasing the power density increased calcein release from the sonicated liposomes. Moreover, cRGD conjugation to the surface of the liposomes rendered cRGD-liposomes more susceptible to ultrasound compared to the non-targeted liposomes. cRGD conjugation was also found to increase cellular uptake of calcein by human colorectal carcinoma (HCT116) cells which were further enhanced following sonicating the cells with low-frequency ultrasound (LFUS).American University of SharjahSheikh Hamdan Award for Medical SciencesFriends of Cancer Patient
A Review on Membrane Biofouling: Prediction, Characterization, and Mitigation
No full textWater scarcity is an increasing problem on every continent, which instigated the search for novel ways to provide clean water suitable for human use; one such way is desalination. Desalination refers to the process of purifying salts and contaminants to produce water suitable for domestic and industrial applications. Due to the high costs and energy consumption associated with some desalination techniques, membrane-based technologies have emerged as a promising alternative water treatment, due to their high energy efficiency, operational simplicity, and lower cost. However, membrane fouling is a major challenge to membrane-based separation as it has detrimental effects on the membrane’s performance and integrity. Based on the type of accumulated foulants, fouling can be classified into particulate, organic, inorganic, and biofouling. Biofouling is considered the most problematic among the four fouling categories. Therefore, proper characterization and prediction of biofouling are essential for creating efficient control and mitigation strategies to minimize the damage associated with biofouling. Moreover, the use of artificial intelligence (AI) in predicting membrane fouling has garnered a great deal of attention due to its adaptive capability and prediction accuracy. This paper presents an overview of the membrane biofouling mechanisms, characterization techniques, and predictive methods with a focus on AI-based techniques, and mitigation strategies.American University of SharjahDana Gas Endowed Chair for Chemical Engineerin
Adoption of Industry 4.0 for Sustainable Manufacturing
No full textA Master of Science thesis in Mechanical Engineering by Parham Dadash Pour entitled, “Adoption of Industry 4.0 for Sustainable Manufacturing”, submitted in April 2022. Thesis advisor is Dr. Mohammad Nazzal and thesis co-advisor is Dr. Basil Darras. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The Fourth Industrial Revolution (Industry 4.0) intends to help different industries monitor, control, and run their production systems efficiently. Most of the currently available Industry 4.0 implementation frameworks focus on providing users with an implementation plan that do not include information regarding technology selection or readiness assessment. In this work, a comprehensive Industry 4.0 implementation framework is developed to help manufacturing firms improve their current state of production. The framework developed consists of five main stages. These stages are gap analysis, Industry 4.0 technology selection, Industry 4.0 readiness assessment, Industry 4.0 reference architecture selection, and pilot project assessment. An Industry 4.0 technology selection model is developed that uses Fuzzy Analytical Hierarchy Process (FAHP) to assign weights to the production, social, economic, and environmental indicators. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to aggregate the results and rank the technology alternatives based on their scores. Furthermore, a novel Industry 4.0 readiness tool is developed to assess how capable the facility is to implement Industry 4.0 technologies. A case study was carried out by applying the developed Industry 4.0 technology selection and readiness assessment procedures on an aluminium extrusion factory. Cyber-Physical Systems, Big Data Analytics, and Autonomous/Industrial Robots were the top three ranked technologies to be implemented having closeness coefficient scores of 0.964, 0.928, and 0.601, respectively. The firm obtained a readiness score of 45.8% based on the developed readiness assessment model revealing that the firm is at an intermediate readiness level.College of EngineeringDepartment of Mechanical EngineeringMaster of Science in Mechanical Engineering (MSME