Digital Commons Network^™

Mathematical Analysis Of Hemoglobin Target In Univentricular Parallel Circulation, Mubbasheer Ahmed, Sebastian I Acosta, George M Hoffman, James S Tweddell, Nancy S Ghanayem

Faculty, Staff and Students Publications

Objective: The hemoglobin threshold for a decision to transfuse red blood cells in univentricular patients with parallel circulation is unclear. A pediatric expertise initiative put forth a "weak recommendation" for avoiding reflexive transfusion beyond a hemoglobin of 9 g/dL. We have created a mathematical model to assess the impact of hemoglobin thresholds in patients with parallel circulation.

Methods: A univentricular circulation was mathematically modeled. We examined the impact on oxygen extraction ratios and systemic and venous oxygen saturations by varying hemoglobin levels, pulmonary to systemic blood flow ratios, and total cardiac output.

Results: Applying a total cardiac index of 6 …

Factors Associated With Extraction Versus Non-Extraction Treatment Among Orthodontic Patients At Marquette University, Kelley Dentino

Master's Theses (2009 -)

Objective: This study aimed to use the available database (2017-2023) of the patients treated in the orthodontic graduate clinic at Marquette University School of Dentistry to survey epidemiologic characteristics and to determine the contributing diagnostic factors and their weights in decision-making for extraction vs. non-extraction treatment plans. Methods: Data were deidentified and extracted from the orthodontic module of the internal clinical records database in axiUm. We evaluated patients who presented between 2017-2023 for comprehensive treatment and collected the following parameters: age, race, gender, anteroposterior occlusal classification (Class I, II, III), skeletal vertical classification (high, normal, or low mandibular plane angle), …

Dynamic Tire Response And The Effect On Roughness-Induced Vehicle Energy Dissipation, Gabriela Ann Lukanus

Master's Theses (2009 -)

Vehicle dynamics models rely on accurate inputs to quantify dynamic loading and energy dissipation to design pavement structures and assess the environmental impacts. The tire’s response to loading is one of the inputs used in these models, and it is typically represented by a linear spring and dashpot connected in parallel. However, tires are made of rubber, a viscoelastic material, so their load response depends on the excitation frequency. Yet, existing models do not incorporate this parameter. This thesis will first examine the load response and its variation with excitation frequency of two truck tires – a dual tire assembly …

Current Vehicle Fleet Inventory And Future Implementation Of A Centralized Electric Fleet At Portland State University, Dane Kovaleski

Environmental Science and Management Professional Master's Project Reports

As the effects of climate change continue to impact the world, many institutions have developed climate action goals to reduce their effects on the environment. Portland State University (PSU) has committed to an 80% reduction of greenhouse gas emissions by 2030 and carbon neutrality by 2040. A part of this commitment must include looking at the contributions of transportation on campus to reduce carbon emissions. According to a greenhouse gas emissions report done by the Campus Planning and Sustainability Office in 2016, transportation contributed to 12% of total greenhouse gas emissions on campus.

This project aims to evaluate the management …

Machine Learning For Ecg Diagnosis And Risk Stratification Of Occlusion Myocardial Infarction, Salah S Al-Zaiti, Christian Martin-Gill, Jessica K Zègre-Hemsey, Zeineb Bouzid, Ziad Faramand, Mohammad O Alrawashdeh, Richard E Gregg, Stephanie Helman, Nathan T Riek, Karina Kraevsky-Phillips, Gilles Clermont, Murat Akcakaya, Susan M Sereika, Peter Van Dam, Stephen W Smith, Yochai Birnbaum, Samir Saba, Ervin Sejdic, Clifton W Callaway

Faculty, Staff and Students Publications

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived …

The Effectiveness Of Nedam's Constructive Model In Teaching Technology To Develop Productive Thinking Skills And Technological Awareness Among Middle School Students In Palestine, Mohamed S. Sharaf

Journal of Educational and Psychological Studies

The study aimed to reveal the effectiveness of using Needham's Constructivist Model in teaching technology to develop productive thinking skills and technological awareness among middle school students in Palestine. The study followed the semi-experimental approach. In order to achieve these goals, two study tools were prepared, which are a test of productive thinking skills, and a measure of technological awareness. The sample consisted of (90) female students, (45) students for the experimental group, and the other (45) students for the control group, from the ninth grade students at Ramez Fakhira Basic School for Girls. A teachers’ guideline was developed to …

Designing An Educational Program Based On (Micro: Bit) And Its Effectiveness In Acquisition Of Basic Programming Skills And Computational Thinking Skills For Primary School Students, Abdulaziz A. Alnamlah, Abdulrahman A. Alothman

Journal of Educational and Psychological Studies

The study aimed to design an educational program based on the Micro: Bit and measure its effectiveness in acquisition of basic programming skills and computational thinking skills for primary school students. The quasi-experimental method was used. The sample consisted of (22) students. After verifying the releablity and validity of the study tools (the observation card for basic programming skills, and the computational thinking scale), they were applied as pre-test, then (15) lessons were implemented using project-based learning for a period of two months. After that, the post-test was applied. The results showed that there were statistically significant differences at the …

Transprofessional Competencies Of Information Managers And The Challenges Of The New Normal, Adeoye Augustine Bamgbose, Hassan Mallam Ibrahim, Sa’Ad Madaki Adamu

Library Philosophy and Practice (e-journal)

Librarians and other information professionals are required to possess transprofessional competencies in the form of additional education or knowledge to increase the range of services that could be provided for improved efficiency of information service delivery in the new normal. This study investigates the transprofessional competencies of librarians and their perceptions of what barriers prevent librarians from developing transprofessional knowledge in this changing and knowledge-driven environment. The study is a qualitative research, and the phenomenography method was adopted for this study; The participants constitute librarians and information managers in four academic libraries in Lagos and Oyo states, Nigeria. A semi-structured …

Er Translocon Inhibitor Ipomoeassin F Inhibits Triple-Negative Breast Cancer Growth Via Blocking Er Molecular Chaperones, Shishi Tao, Eun Ju Yang, Guanghui Zong, Pui Kei Mou, Guowen Ren, Liang Chen, Ho Jeong Kwon, Jianhong Zhou, Zhijian Hu, Arman Khosravi, Qingyang Zhang, Yuchun Du, Wei Q. Shi, Joong Sup Shim

Biological Sciences Faculty Publications and Presentations

Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer where no effective therapy has been developed. Here, we report that the natural product ER translocon inhibitor ipomoeassin F is a selective inhibitor of TNBC cell growth. A proteomic analysis of TNBC cells revealed that ipomoeassin F significantly reduced the levels of ER molecular chaperones, including PDIA6 and PDIA4, and induced ER stress, unfolded protein response (UPR) and autophagy in TNBC cells. Mechanistically, ipomoeassin F, as an inhibitor of Sec61α-containing ER translocon, blocks ER translocation of PDIA6, inducing its proteasomal degradation. Silencing of PDIA6 or PDIA4 by RNA interferences …

Encoding And Storage Of Information In Mechanical Metamaterials, Zhiqiang Meng, Hujie Yan, Mingchao Liu, Wenkai Qin, Guy M Genin, Chang Qing Chen

2020-Current year OA Pubs

Information processing using material's own properties has gained increasing interest. Mechanical metamaterials, due to their diversity of deformation modes and wide design space, can be used to realize information processing, such as computing and storage. Here a mechanical metamaterial system is demonstrated for material-based encoding and storage of data through programmed reconfigurations of the metamaterial's structured building blocks. Sequential encoding and decoding are achieved in the three-dimensional (3D) printed pixelated mechanical metamaterial via kirigami-based "pixels" with programmable, temperature-dependent bistability. The mechanical metamaterial is demonstrated via a multistep deformation of encoding messages of texts and surfaces with arrays of binary data, …

Investment And The Long Swings Of Unemployment, Hian Teck Hoon, Margarita Katsimi, Gylfi Zoega

Research Collection School Of Economics

We estimate the medium-term relationship between investment and unemployment over the period 1960–2017 in 20 OECD countries, a period that includes the years of the Great Recession. While neoclassical growth theory typically assumes full employment—with no effect of investment on unemployment—and New-Keynesian models assume that such a relationship only exists in the short term at business cycle frequencies, we find that over our sample period covering more than 5 decades, a statistically significant negative relationship does exist: in decades when investment fell, unemployment increased. Panel estimation, using a measure of stock market volatility as an instrument for investment, confirms the …

Generative Ai And Chatgpt: Applications, Challenges, And Ai-Human Collaboration, Fiona Nah, Ruilin Zheng, Jingyuan Cai, Keng Siau, Langtao Chen

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) has elicited much attention across disciplines and industries (Hyder et al., Citation2019). AI has been defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, Citation2019, p. 15). AI has gone through several development stages and AI winters. In the first two decades (i.e., 1950s and 1960s), AI demonstrated success which included programs such as General Problem Solver (Newell et al., Citation1959) and ELIZA (Weizenbaum, Citation1966). However, limitations in processing capacity and reduced spending on …

Augmenting Fake Content Detection In Online Platforms: A Domain Adaptive Transfer Learning Via Adversarial Training Approach, Ka Chung Ng, Ping Fan Ke, Mike K. P. So, Kar Yan Tam

Research Collection School Of Computing and Information Systems

Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content. Yet, online platforms face an operational challenge in implementing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limited and costly to obtain. To address this issue, we propose a domain adaptive transfer learning via adversarial training approach to augment fake content detection with collective human intelligence. We first start with a source domain dataset containing deceptive and trustworthy general news constructed from a large collection of labeled news …

Duplicate Bug Report Detection: How Far Are We?, Ting Zhang, Donggyun Han, Venkatesh Vinayakarao, Ivana Clairine Irsan, Bowen Xu, Thung Ferdian, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient comparison among them, and it is unclear how far we have been. This work fills this gap by comparing the aforementioned techniques. To compare them, we first need a benchmark that can estimate how a tool would perform if applied in a realistic setting today. Thus, we first investigated potential biases that affect the fair comparison of the accuracy of DBRD techniques. Our experiments suggest that data age and issue tracking system choice cause a significant …

Barriers And Self-Efficacy: A Large-Scale Study On The Impact Of Oss Courses On Student Perceptions, Larissa Salerno, Simone De França Tonhão, Igor Steinmacher, Christoph Treude

Research Collection School Of Computing and Information Systems

Open source software (OSS) development offers a unique opportunity for students in Software Engineering to experience and participate in large-scale software development, however, the impact of such courses on students’ self-efficacy and the challenges faced by students are not well understood. This paper aims to address this gap by analyzing data from multiple instances of OSS development courses at universities in different countries and reporting on how students’ self-efficacy changed as a result of taking the course, as well as the barriers and challenges faced by students

A Comprehensive Study On Quality Assurance Tools For Java, Han Liu, Sen Chen, Ruitao Feng, Chengwei Liu, Kaixuan Li, Zhengzi Xu, Liming Nie, Yang Liu, Yixiang Chen

Research Collection School Of Computing and Information Systems

Quality assurance (QA) tools are receiving more and more attention and are widely used by developers. Given the wide range of solutions for QA technology, it is still a question of evaluating QA tools. Most existing research is limited in the following ways: (i) They compare tools without considering scanning rules analysis. (ii) They disagree on the effectiveness of tools due to the study methodology and benchmark dataset. (iii) They do not separately analyze the role of the warnings. (iv) There is no large-scale study on the analysis of time performance. To address these problems, in the paper, we systematically …

Knowledge-Enhanced Mixed-Initiative Dialogue System For Emotional Support Conversations, Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam

Research Collection School Of Computing and Information Systems

Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation. In this work, we study the problem of mixed-initiative ESC where the user and system can both take the initiative in leading the conversation. Specifically, we conduct a novel analysis on mixed-initiative ESC systems with a tailor-designed schema that divides utterances into different types with speaker roles and initiative types. Four emotional support metrics are proposed to evaluate the mixed-initiative interactions. The analysis reveals the necessity …

Towards Robust Personalized Dialogue Generation Via Order-Insensitive Representation Regularization, Liang Chen, Hongru Wang, Yang Deng, Wai-Chung Kwan, Zezhong Wang, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Generating persona consistent dialogue response is important for developing an intelligent conversational agent. Recent works typically fine-tune large-scale pre-trained models on this task by concatenating persona texts and dialogue history as a single input sequence to generate the target response. While simple and effective, our analysis shows that this popular practice is seriously affected by order sensitivity where different input orders of persona sentences significantly impact the quality and consistency of generated response, resulting in severe performance fluctuations (i.e., 29.4% on GPT2 and 83.2% on BART). To mitigate the order sensitivity problem, we propose a model-agnostic framework, ORder Insensitive Generation …

Product Question Answering In E-Commerce: A Survey, Yang Deng, Wenxuan Zhang, Qian Yu, Wai Lam

Research Collection School Of Computing and Information Systems

Product question answering (PQA), aiming to automatically provide instant responses to customer’s questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing …

Goal Awareness For Conversational Ai: Proactivity, Non-Collaborativity, And Beyond, Yang Deng, Wenqiang Lei, Minlie Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems are envisioned to provide social support or functional service to human users via natural language interactions. Conventional conversation researches mainly focus on the responseability of the system, such as dialogue context understanding and response generation, but overlooks the design of an essential property in intelligent conversations, i.e., goal awareness. The awareness of goals means the state of not only being responsive to the users but also aware of the target conversational goal and capable of leading the conversation towards the goal, which is a significant step towards higher-level intelligence and artificial consciousness. It can not only largely improve …

Strategy‑Aware Bundle Recommender System, Yinwei Wei, Xiaohao Liu, Yunshan Ma, Xiang Wang, Liqiang Nie, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

A bundle is a group of items that provides improved services to users and increased profits for sellers. However, locating the desired bundles that match the users' tastes still challenges us, due to the sparsity issue. Despite the remarkable performance of existing approaches, we argue that they seldom consider the bundling strategy (i.e., how the items within a bundle are associated with each other) in the bundle recommendation, resulting in the suboptimal user and bundle representations for their interaction prediction. Therefore, we propose to model the strategy-aware user and bundle representations for the bundle recommendation.Towards this end, we develop a …

Singapore's Ai Applications In The Public Sector: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

Steven M. Miller describes six instances in which Singapore has applied AI in the public sector, illustrating different ways of improving its engagement with the public by making government services more accessible, anywhere, anytime, and speeding its responses to public processes and feedback. He illustrates how its leaders made the city a living lab for AI use, and what they learned.

Finding Causally Different Tests For An Industrial Control System, Christopher M. Poskitt, Yuqi Chen, Jun Sun, Yu Jiang

Research Collection School Of Computing and Information Systems

Industrial control systems (ICSs) are types of cyber-physical systems in which programs, written in languages such as ladder logic or structured text, control industrial processes through sensing and actuating. Given the use of ICSs in critical infrastructure, it is important to test their resilience against manipulations of sensor/actuator inputs. Unfortunately, existing methods fail to test them comprehensively, as they typically focus on finding the simplest-to-craft manipulations for a testing goal, and are also unable to determine when a test is simply a minor permutation of another, i.e. based on the same causal events. In this work, we propose a guided …

A Lightweight Privacy-Preserving Path Selection Scheme In Vanets, Guojun Wang, Huijie Yang

Research Collection School Of Computing and Information Systems

With the rapid development of edge computing, artificial intelligence and other technologies, intelligent transportation services in the vehicular ad hoc networks (VANETs) such as in-vehicle navigation and distress alert are increasingly being widely used in life. Currently, road navigation is an essential service in the vehicle network. However, when a user employs the road navigation service, his private data maybe exposed to roadside nodes. Meanwhile, when the trusted authorization sends the navigation route data to the user, the user can obtain all the road data. Especially, other unrequested data might be related to the military. Therefore, how to achieve secure …

A Hierarchical Optimization Approach For Dynamic Pickup And Delivery Problem With Lifo Constraints, Jianhui Du, Zhiqin Zhang, Xu Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider a dynamic pickup and delivery problem (DPDP) where loading and unloading operations must follow a last in first out (LIFO) sequence. A fleet of vehicles will pick up orders in pickup points and deliver them to destinations. The objective is to minimize the total over-time (that is the amount of time that exceeds the committed delivery time) and total travel distance. Given the dynamics of orders and vehicles, this paper proposes a hierarchical optimization approach based on multiple intuitive yet often-neglected strategies, namely what we term as the urgent strategy, hitchhike strategy and packing-bags strategy. These multiple strategies …

Qebverif: Quantization Error Bound Verification Of Neural Networks, Yedi Zhang, Fu Song, Jun Sun

Research Collection School Of Computing and Information Systems

To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge devices, quantization is widely regarded as one promising technique. It reduces the resource requirements for computational power and storage space by quantizing the weights and/or activation tensors of a DNN into lower bit-width fixed-point numbers, resulting in quantized neural networks (QNNs). While it has been empirically shown to introduce minor accuracy loss, critical verified properties of a DNN might become invalid once quantized. Existing verification methods focus on either individual neural networks (DNNs or QNNs) or quantization error bound for partial quantization. In this work, we propose …

Augmenting Low-Resource Text Classification With Graph-Grounded Pre-Training And Prompting, Zhihao Wen, Yuan Fang

Research Collection School Of Computing and Information Systems

ext classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with few or no labeled samples, poses a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) …

Towards Omni-Generalizable Neural Methods For Vehicle Routing Problems, Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce …

Forward/Backward And Content Private Dsse For Spatial Keyword Queries, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Yinbin Miao, Yang Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Spatial keyword queries are attractive techniques that have been widely deployed in real-life applications in recent years, such as social networks and location-based services. However, existing solutions neither support dynamic update nor satisfy the privacy requirements in real applications. In this article, we investigate the problem of Dynamic Searchable Symmetric Encryption (DSSE) for spatial keyword queries. First, we formulate the definition of DSSE for spatial keyword queries (namely, DSSESKQ) and extend the DSSE leakage functions to capture the leakages in DSSESKQ. Then, we present a practical DSSESKQ construction based on geometric prefix encoding inverted-index and encrypted bitmap. Rigorous security analysis …

Information Screening Whilst Exploiting! Multimodal Relation Extraction With Feature Denoising And Multimodal Topic Modeling, Shengqiong Wu, Hao Fei, Yixin Cao, Lidong Bing, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and external-information exploiting. First, we represent the fine-grained semantic structures of the input image and text with the visual and textual scene graphs, which are further fused into a unified cross-modal graph (CMG). Based on CMG, we perform structure refinement with the guidance of the graph information bottleneck principle, actively denoising the less-informative features. Next, we perform topic modeling over the input image and text, incorporating latent multimodal …