Academic Bridge Program

In this study, we examine the unified progressive hybrid censoring scheme from Topp-Leone models, and we show that while the maximum likelihood estimates of the model parameters are uniquely exist, they cannot be obtained in a closed form. To obtain these estimates, we utilize two frequentist algorithms—the Expectation-Maximization method and an Expectation-Maximization type method. We also derive confidence intervals for the unknown parameters using both asymptotic distributions of the maximum likelihood estimators and bootstrap approaches. Furthermore, we develop sample-based Bayes estimates for the model parameters under different error loss functions, such as square, LINEX, and entropy loss functions. To approximate the Bayes estimates, we apply importance sampling and Metropolis-Hastings algorithms. We also construct Bayes credible intervals for the estimates. To assess the performance of the proposed methods, we conduct a numerical simulation study. Finally, we analyze a real data set to illustrate the practical application of our proposed methods and their findings. The study highlights the usefulness of hybrid censoring schemes and the applicability of various statistical methods for parameter estimation and inference.

Sarawak has a high burglary rate. This project aims to create a low-cost smart home security system based on Zigbee that protects Sarawakian homeowners against flood, smoke, and intrusion. The system includes a hub, a battery-powered sensor node, and an Android application. The sensor node sends sensor output to the hub via Zigbee. If intrusion, flood, or smoke is detected, the hub starts an alarm and triggers the Android application to display an alarm in real time through the Internet via Firebase. If an intrusion is detected, a photo of the intruder is taken and processed via Haar Cascades and Histogram of Oriented Gradient (HOG) to emphasize the face and body of the intruder. The system also provides live mobile monitoring. The system is secure, provides a real-time response, Internet-controlled, and low cost but has low battery life.

Applications of wireless communication technologies are still on the rise since its introduction, known as the Internet of things. These applications often focus on monitoring applications and real-time services. Jetty monitoring becomes one of the suitable situations to implement the Internet of things. For rural areas, implementing a jetty monitoring system can be difficult due to limitations of power supply, wireless access and external environmental conditions. This project proposes a low-cost smart jetty docking monitoring system that is able to conduct smart monitoring for the jetties in rural areas. The system helps to monitor sudden changes in the water stream level. If the water level is too high, the system will alert the administrators, so they can take immediate action. These results are stored in a database for other future environmental research purposes and a web server is used to display the results graphically. Before the system is actually implemented, a model is created to replicate an actual jetty docking area to test the system’s performance. Data correlation algorithms are introduced to improve the system’s sustainability by reducing its power consumption and byte transmission volume. Sensor fault detection algorithm is added to ensure if any damaged sensors in the system is reported immediately for replacement.

We give a counterexample to a trilinear version of the operator space Grothendieck theorem. In particular, we show that for trilinear forms on $\ell_\infty$, the ratio of the symmetrized completely bounded norm and the jointly completely bounded norm is in general unbounded, answering a question of Pisier. The proof is based on a non-commutative version of the generalized von Neumann inequality from additive combinatorics.

The professional skills are the non-technical learning outcomes put forth by the Accreditation Board for Engineering and Technology (ABET) as part of the larger set of student learning outcomes that are required of all ABET accredited programs. The Computing Professional Skills Assessment (CPSA) is the only tool and method that is able to simultaneously assess all six of the computing professional skills at both undergraduate and graduate levels. This paper presents the results from the first comprehensive investigation of student attainment of the professional skills using the CPSA across a UAE-based computing program's 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> year, 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> year, and masters level students. The results show that the students overall are weakest in learning outcomes related to the local and global impact of computing, and the ability to recognise when they need to seek additional knowledge. The proficiency results for each year provide useful insights into what and where weaknesses exist. With this information one can devise interventions in a program so as to improve student learning.

NONSTEROIDAL antiinflammatory drugs (NSAIDs) have been shown to reduce pain and opioid consumption and often accelerate recovery after surgery. However, perioperative inhibition of prostaglandin synthesis by NSAIDs may cause complications, including renal injury, gastric ulceration, and bleeding. Recent molecular studies distinguishing between constitutive cyclooxygenase-1 (COX-1) and inflammation-inducible cyclooxygenase-2 (COX-2) enzymes have led to the exciting hypothesis that the therapeutic and adverse effects of NSAIDs could be uncoupled. The purpose of this article is to review the mechanistic differences between nonselective NSAIDs and selective COX-2 inhibitors (COX-2Is) and to examine currently available COX-2I clinical trials to consider the role of these drugs in postoperative pain management.