3854 research outputs found
Sort by
May 20, 2021: Covid-19 Campus Update
Get PDFTopics in this Update:
Updated Safety Guidelines
Commencement
Free Vaccination Clinic
6/9/2021: UCC SoM Certificate in Data Analytics
No full textIn order to increase the market attractiveness of the SoM graduate degree programs in general and Tech MBA-Data Analytics & Big Data program in particular, we are developing a ‘stackable’ credential in the form of a Certificate that students can earn along the way to earning their MBA (on-campus and online), TECH MBA (on-campus only), MS Engineering Management (on-campus and online), MS Operations Management (on-campus and online), MS Supply Chain Management (online only) and MS Lean Manufacturing (online only). This certificate bundles courses that are required for the program and allows students to complete portions of the degree program and earn a Certificate
Stochastic Modeling in the Internet of Things
No full textThe current technology has permeated everything people do in their day-to-day activities. Since the introduction of the term “Internet of Things” (IoT ) by Ashton, this concept has become a hot topic in almost all fields. As more and more people use Internet for business, recreation, education, health care, and other purposes, it is not surprising to see the exponential explosion in the number of devices being used. While IoT has been studied in many perspectives such as cloud layer, fog layer, and device layer, the key thing in IoT is to apply analytic to big data to get insights and take appropriate actions promptly. One of the main purposes of IoT analytic is to create and sustain smarter environments such as smarter homes, smarter hospitals, smarter service sectors, and so on to provide timely and quality services to people. Toward this end, one has to develop suitable queueing models
Testing Processing Methods to Enhance Anaerobic Co-Digestion of Orange Peels
No full textAnaerobic co-digestion is a process where microorganisms break down municipal wastewater in combination with other organic materials, such as food waste, under oxygen-free conditions. Not only diverting wastes from going to landfills, this process results in the production of biogas for energy. Over 51 million tons of oranges were produced globally from 2017 to 2018 with an anticipated 4.2 million ton expansion the following year. Orange peel wastes account for about 44% of the total mass of an orange. Orange waste has been shown to inhibit anaerobic digestion due to it containing high concentrations of D-limonene, an antimicrobial compound. Different methods for acclimating anaerobic digesters to orange peel waste were tested. The effectiveness of biological pretreatment of homogenized orange peels was assessed using a biomethane potential test (BMP). Adaptation of an anaerobic digester microbe culture in a continuously stirred tank reactor (CSTR) to increasing levels of Dlimonene was also assessed and CSTRs with and without d-limonene were monitored for gas production and chemical composition over 100 days. BMP results show a significantly higher biomethane potential for the biologically pretreated orange peel. The CSTR results demonstrate the ability to add up to 1000ppm D-limonene in the reactor utilizing an acclimation process. Overall, these results support different processing methods that can enable efficient utilization of homogenized orange peels for anaerobic co-digestion
Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study
No full textA tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes
Identify Challenges in Vibration Measurements for Rotating Tyres Using a Finite Element Model
No full textIn the current paper, the measurements from a tyre testing on a dynamometer are replicated using a finite element analysis to identify the feasibility of obtaining mode shapes using rotating tyre data. Performing vibration analysis using computer-aided engineering (CAE) reduces the human effort of executing the complex experimental setup and performing the tests. Furthermore, it provides test engineers with an understanding of the dynamic performance of tyres. In this work, an explicit finite element analysis was performed on the rolling tyre. Using the data from the simulation, an operational modal analysis was performed to extract the modal parameters of the tyre in the rolling condition. The results are analysed with those obtained from the experimentation of the Kettering Formula SAE (FSAE) tyre