Digital Commons Network^™

The Long-Term Effectiveness Of The Anti-Obesity Medication Phentermine (Leap) Trial: Rationale, Design, And Baseline Characteristics, Caroline Blackwell Young, Emily Rives, Kimberly A Gudzune, Byron C Jaeger, Courtney G Simmons, Brian N White, Stephanie A Hooker, Deborah B Horn, Deborah R Young, Jennifer Vesely, Amanda Velazquez, Catherine Price, Shelly D Cook, Katy Martin-Fernandez, Galina Inzhakova, Nicholas M Pajewski, Jamy D Ard, Kristina H Lewis

Faculty, Staff and Student Publications

Background: Current clinical practice guidelines support the long-term use of pharmacotherapy for obesity treatment. Historically, phentermine has been one of the most prescribed obesity medications (OMs), however, its long-term efficacy and safety have never been evaluated in a randomized trial as its market approval predates such requirements. Here we describe the design, rationale, and baseline characteristics for a 24-month, double-blind, randomized controlled trial evaluating the efficacy, cardiovascular risk, and safety of phentermine in adults with overweight or obesity.

Methods: This multicenter trial will compare outcomes among participants randomized to phentermine 24 mg daily versus placebo for 24 months. All participants …

The Importance Of International Exchange In A Time Of Worldwide Economic And Political Crisis And Change, David Anshen

Literatures and Cultural Studies Faculty Publications

This paper argues that international scholarly and cultural exchange has become especially vital in the context of intensifying global economic, political, ecological, and social crises. Drawing on Marxist theory, contemporary political realities in the United States and South Asia, and examples from world literature, the article examines how global capitalism both deepens human suffering and creates new possibilities for solidarity and resistance. Through reflections on immigration, political violence, ecological collapse, and the marginalization of the humanities, the paper highlights the role of international dialogue- facilitated by digital technologies and cross-cultural engagement- as a necessary counterforce to dehumanization and fragmentation. Literary …

Exploring The Value Of International Research Experience For Leadership Development, Lisa Luken

Leadership Studies

The world has become more complex and interconnected, with challenges that span countries and disciplines. To navigate these challenges, individuals are needed who are equipped with skills and competencies to lead in cross-cultural, cross-disciplinary environments. These leadership skills and competencies can be developed through participation in experiential learning programs. However, the impact of international research-based programs remains unexplored. This is a qualitative, phenomenological study within a bounded case that explored the lived experiences of students who participated in an international graduate student research program from a leadership development perspective. Mezirow’s (1978a) transformative learning theory was utilized as a theoretical framework …

Simulation And Analysis Of Very Fast Transient Overvoltage In 1 000 Kv Gis, Shuheng Dan, Shunli Qiao

Journal of Electric Power Science and Technology

In gas insulated switchgear (GIS), very fast transient overvoltage (VFTO) is easily caused by disconnector operation. In order to simulate and analyze this electromagnetic transient phenomenon and its potential harm, the high-frequency oscillation zero-crossing factor is taken into account on the basis of the traditional method considering zero-crossing arc extinguishing logic. A detailed logical judgment process of arc extinguishing and arc reignition is proposed. A 1 000 kV GIS transient circuit model is constructed, and the simulation analysis on the model is carried out. The research results show that when the disconnector reignites the arc, the load side presents a …

Prediction Model For Power Quality Of Distribution Networks Based On Temporal Fusion Transformer, Xiao'e Hou, Ning Wang, Shaoan Shou, Fu Li, Jie Zhang, Wenting Wei

Journal of Electric Power Science and Technology

Anomaly detection of power quality is crucial for the reliable operation of the distribution network, and its accuracy is directly related to the stability and security of the power grid system. In practice, it is not only time-consuming and laborious to analyze the anomaly in the field by manpower but also impossible to manage the anomaly in advance. To this end, a prediction model for power quality anomaly of distribution networks, which combines feature screening, signal decomposition, and an attention-based deep neural network, is proposed. First, the fast correlation-based filter (FCBF) algorithm based on maximal information coefficient (MIC) is combined …

Research On Multi-Objective Planning Of Power Transmission Network Considering Power Angle Stability, Zifa Liu, Haoyu Zheng

Journal of Electric Power Science and Technology

With the continuous improvement of the p enetration level of renewable energy in the power transmission network, the negative resistance effect of the unit side caused by the grid connection of new energy is significantly enhanced, which brings severe challenges to the power angle stability of the power system and then restricts the ability of new energy consumption. In this study, the power angle stability characteristics of multi-node power transmission networks are systematically analyzed by establishing a ternary single-port coupling equivalent model including traditional generator sets, new energy access nodes, and equivalent load networks. Different from the traditional stand-alone system …

Design Of User-Side Allocation Mechanism For Capacity Compensation Cost Based On Entropy Weight Method, Dong Mo, Wenbin Zheng, Mingyuan Chen, Yufu Lu, Youhui Yang, Zikang Ding

Journal of Electric Power Science and Technology

With the continuous and rapid development of new energy represented by wind power and photovoltaic power generation, conventional fossil energy units face difficulties in recovering fixed costs, and the fixed costs of conventional units need to be recovered through capacity prices. However, the current capacity price allocation process does not take load rate-related factors into account, and the allocation results cannot reasonably reflect the actual power supply costs of users with different load rates, leading to unfair allocation of capacity costs among users. To address this issue, we first outline the industry classification methods for electricity users and the commonly …

Reinforced Scan: A Reinforcement Learning Enabled Optimal Laser Scan Path Planning In Laser Powder Bed Fusion Additive Manufacturing, Chaoran Dou, Jihoon Chung, Raghav Gnanasambandam, Yuhao Wu, Jianzhi Li, Zhenyu James Kong

Manufacturing & Industrial Engineering Faculty Publications

Additive Manufacturing is an innovative technology that fabricates parts layer by layer. However, in Laser Powder Bed Fusion (LPBF), printed metal parts often exhibit residual stresses, deformations, and other defects due to non-uniform temperature distribution during the printing process. To mitigate these issues, an optimized scan sequence within each layer can improve thermal uniformity. Traditional optimization methods, which rely on domain knowledge and employ trial-and-error or heuristic approaches, often fail to achieve optimal solutions due to the complex nature of the problem. One major challenge in improving scan strategies lies in the vast search space required to optimize the scan …

Xbrl-Formatted Financial Reporting And The Feedback Effect Of Price, Young Jun Cho, Ying-Chi Huang, Holly I. Yang

Research Collection School Of Accountancy

We examine whether the SEC’s XBRL-formatted financial reporting mandate allows investors to better process public financial information, reducing the extent to which managers learn private information from stock price. We find a significant decrease in the investment-price sensitivity for firms that started to file their 10-Ks in an XBRL format, consistent with a reduction in the cost of information processing crowding out private information in stock price. We also find that the decrease in the investment-price sensitivity associated with XBRL adoption is more pronounced for firms with higher business and information complexity and for firms with higher incentives for managers …

Female Ceos And Esg Visual Expressions, Ziming Guo, Beng Wee Goh, Dan Li, Yuan Wang

Research Collection School Of Accountancy

Prior studies have shown that women possess unique personality traits that cause them to communicate differently than men do. Hence, we investigate whether the presence of female CEOs affects the use of visual expressions in a firm’s ESG report (“ESG visual expressions”). Using detailed disclosure data from ESG reports in China, we find that firms with female CEOs use more ESG visual expressions. This result is stronger when the firm prioritizes ESG performance, when the female CEO is more powerful, and when there are more women on the top management team. ESG visual expressions are also positively associated with the …

Non-Audit Services And Knowledge Spillover: Evidence From Audit Hours And Billing Rates, Jaeyoon Yu, Yongsuk Yun, Yoonseok Zang

Research Collection School Of Accountancy

Using audit hours from firms listed on the Korea Exchange as a direct proxy for audit effort, this study examines whether non-audit services (NAS) purchased from audit firms improve audit efficiency. Our results reveal that the ratio of NAS fees to total fees paid to audit firms is associated with lower audit hours and audit fees, but it is not significantly related to audit billing rates. The reduction in audit effort associated with NAS is more pronounced when NAS provided are audit-related, and for clients who hire Big 4 auditors, have short-tenured auditors, and report a profit. Further, using path …

Contemporary Structures, Processes, And Outcomes Of Critical Care Nursing Education: An Integrative Review, Farida Saghafi, Carol Grech, Belinda Causby, Angelique Clarke, Jacqueline Jauncey-Cooke, Deb Massey, Kaye Rolls, Kylie Thompson, Melanie Greenwood

Research outputs 2022 to 2026

Objective: The critical care workforce requires nurses who are specialised, educated, and adaptable to manage complex situations, with the capability to make informed decisions for optimal patient care. The Australian College of Critical Care Nurses supports evidence-based practice through an education position statement, which is periodically updated to reflect evolving needs. In this review, our aim was to inform revisions to the position statement on critical care nurse education by examining the structure, processes, and outcomes of contemporary education programs designed to enhance the capability of registered nurses working in critical care environments internationally. Review method used: An integrative review …

The Feelit System: Application Content-Aware Perspectives And Challenges On Understanding User Likes In Social Network Posts, Konstantinos Theocharidis, Hady W. Lauw, Panagiotis Karras

Research Collection School Of Computing and Information Systems

In a series of our prior works, we study influence and subscription maximization problems in social networks that are based on posts having influential content; as content we consider a set of features where each feature corresponds to a specific social network page, whereas influence and subscription relate to gaining the postlike and subscription-to-brand page of targeted users, respectively; subscription is conceptually achieved as repetitive influence on users. So, both influence and subscription depend on content that gains the likes of users; however, to be realistic, modeling and estimating such likes is a complex problem that has not been adequately …

Cellscout: Visual Analytics For Mining Biomarkers In Cell State Discovery, Rui Sheng, Zelin Zang, Jiachen Wang, Yan Luo, Zixin Chen, Yan Zhou, Shaolun Ruan, Huamin Qu

Research Collection School Of Computing and Information Systems

Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often this assumption often fails to hold due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective …

Prompt Tuning Without Labeled Samples For Zero-Shot Node Classification In Text-Attributed Graphs, Sethupathy Parameswaran, Suresh Sundaram, Yuan Fang

Research Collection School Of Computing and Information Systems

Node classification is a fundamental problem in information retrieval with many real-world applications, such as community detection in social networks, grouping articles published online and product categorization in e-commerce. Zero-shot node classification in text-attributed graphs (TAGs) presents a significant challenge, particularly due to the absence of labeled data. In this paper, we propose a novel Zero-shot Prompt Tuning (ZPT) framework to address this problem by leveraging a Universal Bimodal Conditional Generator (UBCG). Our approach begins with pre-training a graph-language model to capture both the graph structure and the associated textual descriptions of each node. Following this, a conditional generative model …

Fortifying The Seams Between C/C++ And Rust: Characterizing Bugs In Interop Tools, Xuemeng Cai, Jiakun Liu, Cunyang Liu, Lingfeng Bao, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Rust has become increasingly popular in recent years due to its safety and high performance. Despite these advantages, Rust projects rarely start from scratch in practice, and many Rust-based systems instead use hybrid programming, where Rust interoperates with existing C/C++ code. To reduce the manual effort involved in this interoperation (interop) process, several interop tools have been proposed to facilitate hybrid programming between Rust and C/C++. However, the challenges and limitations of these tools remain largely unexplored, leaving developers unclear about the future directions and users unclear about the appropriate usage scenarios. To fill the gap, we mined 320 bugs …

Mm-Attackg: A Multimodal Approach To Attack Graph Construction With Large Language Models, Yongheng Zhang, Xinyun Zhao, Yunshan Ma, Haokai Ma, Yingxiao Guan, Guozheng Yang, Yuliang Lu, Xiang Wang

Research Collection School Of Computing and Information Systems

Cyber Threat Intelligence (CTI) parsing aims to extract key threat information from massive data, transform it into actionable intelligence, enhance threat detection and defense efficiency, including attack graph construction, intelligence fusion, and indicator extraction. Among these research topics, Attack Graph Construction (AGC) is essential for visualizing and understanding the potential attack paths of threat events from CTI reports. Existing approaches primarily construct the attack graphs purely from the textual data to reveal the logical threat relationships between entities within the attack behavioral sequence. However, they typically overlook the specific threat information inherent in visual modalities, which preserves key threat details …

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …

American Kestrel Population Trends And Vital Rates At The Continental Scale, Paige E. Howell, Abigail J. Lawson, Kristin P. Davis, Guthrie S. Zimmerman, Orin J. Robinson, Matthew A. Boggie, Mitchell J. Eaton, Fitsum Abadi, Jessi L. Brown, Julie A. Heath, John A. Smallwood, Karen Steenhof, Ted Swem, Brian W. Rolek, Christopher J.W. Mcclure, Jean Francois Therrien, Karl E. Miller, Brian A. Millsap

Department of Biology Faculty Scholarship and Creative Works

The American kestrel (Falco sparverius, hereafter referred to as kestrel) has declined across much of its North American range since at least the mid-1960s. Kestrel population dynamics have been explored through a multitude of local studies and two broad reviews of available data. Across large geographic extents, however, the demographic cause(s) of kestrel population declines remain(s) largely unknown. As part of a collaborative effort to elucidate the drivers of kestrel population declines, we developed a continental-scale integrated population model using band-recovery data, productivity data, and Breeding Bird Survey indices from 1986 to 2019 to estimate indices of annual population sizes, …

Michigan's Humble "Rules Of The Road" And How They Drive The Law, Virginia C. Thomas

Law Faculty Research Publications

The author traces the Michigan Secretary of State's publication, What Every Driver Must Know, from its first edition in 1937 to show how it has guided Michiganders' understanding of the state's motor vehicle laws.

Phase Recognition Of Low-Voltage Distribution Network Based On Locally Linear Embedding And Gaussia N Mixture Model Algorithms, Jiayao Hong, Xiangyang Xia, Yunfei Lei, Zejian Yi, Hanqin Zhu, Xiaozhong Hu

Journal of Electric Power Science and Technology

In order to solve the problem of inaccurate user phase recognition in low-voltage station areas, a phase recognition method based on local linear embedding (LLE) dimensionality reduction and Gaussian mixture model (GMM) clustering algorithm based on user voltage data is proposed. In this method, the voltage data in the smart meter of the user in the station area is extracted firstly, and the principal component analysis (PCA) method is used to achieve noise reduction and redundancy. Then, the derivative dynamic time warping (DDTW) method is employed to measure the correlation between user voltages. The LLE dimensionality reduction is used to …

Study On Anomaly Data Detection Of Voltage In Transformer District Of Distribution Network Based On Tfec-Inne, Junsheng Zou, Xiu Yang, Gaiping Sun, Fan Yang, Jun Liu, Bingbing Lu

Journal of Electric Power Science and Technology

A large amount of anomaly data of voltage can be generated during the transformer district operation of distribution network. These anomaly data cannot correctly reflect the operating conditions of the distribution network and can seriously affect the analysis of the voltage characteristics of the transformer district. Therefore, anomaly detection of the voltage operation data in the transformer district is of great significance. Due to the problem of low accuracy of traditional anomaly detection methods, an algorithm, combining trimmed window feature extraction clustering (TFEC) and isolation-based anomaly detection using nearest-neighbor ensembles (INNE), is proposed. The daily features of the initial data …

Method For Anomaly Detection Of Converter Valve Voiceprints Based On Improved Denoising Auto-Encoder, Yisheng Yu, Zhihong Wang, Yunya Zhou, Zhanfan Zhou, Yu Yan, Yu Yu, Mingzhu Tang

Journal of Electric Power Science and Technology

The converter valve, as a key equipment in high-voltage direct current transmission converter stations, may experience an abnormal state when operated for a long time. To address the problem that non-intrinsic background noise in the valve hall may cause voiceprint anomalies and unstable anti-noise performance of the converter valve, an anomaly detection method for the converter valve based on voiceprint signal filter banks (Fbank) features and an improved denoising auto-encoder (IDAE) is proposed. Firstly, the voiceprint data generated during the operation of the converter valve is collected; the Fbank features of the voiceprint data are extracted; the samples containing temporal …

Analysis Method Of Impact Of Complex Electromagnetic Signals On Measurement Errors In Single-Phase Electric Energy Meters, Hongtao Shen, Chong Li, Hao Wang, Juchuan Guo, Penghe Zhang, Cong Wang

Journal of Electric Power Science and Technology

In complex electromagnetic environments, the diverse array of electromagnetic interference signals with varying modulation schemes leads to degraded performance in single-phase electric energy meters. With the random and dynamic characteristics of complex electromagnetic signals, a parametric model is established for m-sequence dynamic test signals. Subsequently, a structured measurement model for single-phase electric energy meters and a mathematical model for the impact of electromagnetic signals on single-phase electric energy meter errors are developed. The impact of electromagnetic signals on random interference in electric energy meters is then simulated. Finally, through simulation and experimental tests, the impacts of electromagnetic signals on measurement …

Default Risk Prediction Model Of Power Trading Service Fees In Spot Market, Wei Yang, Qi Shi, Zhijian Zeng, Xueliang Gong, Jiaxun Liu, Xinlei Wang

Journal of Electric Power Science and Technology

With the further reform of the electricity market in China, electricity trading platforms are confronted with more types of market participants, more categories of trading services, and higher trading frequencies. As the core trading intermediary, power trading institutions bear a great number of operating and management costs and face an increasing trading credit risk, namely the risk that market participants may fail to fulfill their contractual obligations on time. The credit risk resides in trading accounts and is closely related to market fluctuations and the financial status of market users. This paper introduces the traditional credit risk measurement model in …

Research On Transformer Fa Ult Pattern Recognition Based On Double-Layer Xgboost Algorithm Optimiz Ed By Borderline Smote And Ngo, Youcai Li, Weilong Peng, Yuming Zhou, Qiyan Li, Cheng Zeng, Xin Yang

Journal of Electric Power Science and Technology

To enhance the diagnostic accuracy of transformer fault, raise the efficiency of model recognition, and mitigate the adverse effects of imbalanced samples on transformer fault recognition models, a transformer fault pattern recognition model with double-layer extreme gradient boosting (XGBoost) based on borderline synthetic minority over-sampling technique (BSMOTE) and northern goshawk optimization (NGO) algorithms is proposed. Firstly, BSMOTE is used to expand minority class samples, and a balanced dataset is obtained. Secondly, the noncoding ratio method is used to establish multi-dimensional feature quantities, and XGBoost is used to determine the optimal feature subset. Then, an NGO algorithm is used to optimize …

Method For Enhancing Distribution Network Resilience Under Typhoon Disasters Based On Multi-Scale Attention Mechanisms And Multi-Resource Coordinated Optimization, Guilian Wu, Naixuan Zhu, Hao Chen, Nuoling Sun, Jinlin Liao

Journal of Electric Power Science and Technology

In recent years, the increasing frequency of extreme weather events poses a serious threat to the secure operation of distribution networks. To improve the accuracy of typhoon-induced hazard intensity forecasting, an LSTM network enhanced with multi-scale attention (LSTM-MSA) is developed, which overcomes the deficiency of the traditional LSTM model that cannot capture the local attention mechanism. Building on this, a resilience-enhancement strategy is proposed that coordinates diverse flexible resources, including line reinforcement, tie-switch operation, wind power generation, and energy storage system control, to mitigate the impacts of typhoon disasters on the distribution network. Case studies on the IEEE 33-node and …

Weak Point Identification And Risk Assessment Of Distributed Photovoltaic Access To Distribution Network Based On Time-Varying Copula Function, Huanhuan Ye, Linjun Zeng, Qin Yan

Journal of Electric Power Science and Technology

With the continuous development of distributed photovoltaic (PV), the risk of voltage limit violation and the problem of power quality degradation in the distribution network are becoming increasingly serious. Therefore, a method of weak point identification for distributed PV access to the distribution network based on the time-varying Copula function is proposed, and risk assessment is performed on it. The correlation of distributed PV output is analyzed to identify vulnerabilities in power system component states, and the distribution network reliability is enhanced. First, a time-varying Gaussian Copula function is employed to analyze the force output correlation of distributed PV, achieving …

Power Quality Disturbance Recognition Method Based On Spectrogram And Lightweight 2d-Dscnn, Yongqiang Liu, Ng Jinmei Wa, Xuetao Guan, Feng Li

Journal of Electric Power Science and Technology

In response to difficulties in ac curately classifying and recognizing complex power quality disturbance (PQD) signals, this paper proposes a novel PQD recognition method based on spectrogram and lightweight two-dimensional (2D) depth-separable convolutional neural network (2D-DSCNN). Time-frequency analysis is applied to convert PQD signals into spectrograms, so that complex signal data is presented in the form of images. A lightweight 2D-DSCNN model is constructed by using depth-separable convolution technology, and the spectrograms corresponding to different PQD signals are classified and identified. The feasibility and effectiveness of the proposed method are verified through simulation experiments. The experimental results show that the …

Peer-To-Peer Electricity Trading Method Considering Supply-Demand Relationship Tariffs And Tariff Time Period Division, Renjun Zhou, Xin Peng, Jingjie Huang, Xiaojiao Tong

Journal of Electric Power Science and Technology

A two-layer optimization model of a leader-follower game is constructed for peer-to-peer electricity trading between distributed energy power stations and industrial parks. The profit maximization of the distributed energy station is taken as the goal in the upper layer, with decision variables including tariff time period division, tariff pricing, and the charging/discharging power of on-site energy storage equipment. The difference between electricity supply and demand is used as the state variable, and a certain range of difference is used to determine the valley and peak price periods. A tariff response coefficient is designed, which depends on the demand response effect …