Digital Commons Network^™

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 …

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 …

Prisrv+: Privacy And Usability-Enhanced Wireless Service Discovery With Fast And Expressive Matchmaking Encryption, Yang Yang, Guomin Yang, Yingjiu Li, Pengfei Wu, Rui Shi, Minming Huang, Jian Weng, Hwee Hwa Pang, Deng, Robert H.

Research Collection School Of Computing and Information Systems

The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …

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, …

February 2026 Table Of Contents Newsletter - Artificial Intelligence, Alicia Ikerd

Table of Contents Newsletter

While artificial intelligence (AI) takes the world by storm, students and faculty may notice AI popping up in library databases like weeds in the spring. Variety in AI functionality in scholarly databases remains limited, with the majority of AI being used to power tools called “reading assistants. ” Reading assistants attempt to summarize text, recommend adjacent sources, or extract search terms.

While these tools can be useful, they are not foolproof and are still prone to hallucination, thus it is always important to read and cite the actual source material. Additionally, the ability to use these tools can depend on …

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, …

Chanticleer | February 2026, Jacksonville State University

Chanticleer

No abstract provided.

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 …

Active Power Balance Service Product System Design Based On Signal Time-Frequency Domain Analysis, Keyi Yin, Tianyao Ji, Ng Chao Zha, Peng Zou, Xiran He, Zhaoxia Jing, Xiaorui Cui, Rui Li

Journal of Electric Power Science and Technology

In the context of the energy internet, traditional active power balance regulation products fail to meet the reliability, flexibility, and low-carbon demands of new power systems. To address the problems that domestic regulation products are single in configuration and ill-equipped to handle the increasing volatility of generation, the concept of balance demand is introduced, and an active power balance service product system based on signal time-frequency domain analysis is proposed. The method uses K-shape clustering to identify typical daily balance demands, applies ensemble empirical mode decomposition (EEMD) for multi-level decomposition of regulation signals, and standardizes product design through time-frequency domain …

Iowa Waste Reduction Center Newsletter, February 2026, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- Cedar Rapids Linn County SWA Wins Award
--- Energy Savings Made Simple: What is An Energy Audit?
--- New IEDA Grant Helps Rural Grocers Power Up
--- Dan Nickey Gets Thermography Certified
--- You’re Invited to UNI Day of Service
--- Minor Source Emission Inventory
--- IStorm 26
--- Industry News

Keynoter, February/March 2026, Boise State University Association Of Office Professionals

Boise State University Association of Office Professionals - Keynoter

President's Message

Get Better Soon

Professional Development Day

Recipes

Enrichment Program

COHS Lunch & Learn

PSP Certification

NAEOP

Readership

General AOP Info

Treasurer's Report

Membership

Calendar

Somatic Deficiency Of The Human E3 Ubiquitin Ligase Cbl In Leukocytes Impairs B Cell But Not T Cell Development And Function., Taja Vatovec, Anna-Lena Neehus, Katherine J L Jackson, Danielle T Avery, Ivan Bagarić, Lucia Erazo, Carlos A Arango-Franco, Masato Ogishi, Syed F Ahmed, Axel Cederholm, Amanda J Russell, Erika Della Mina, Dena Al-Rifai, Rowena Bull, Lori Buetow, Steicy Sobrino, Allison Zhang, Lara Wahlster, Marine Michelet, Nima Parvaneh, Jessica Peel, Federica Barzaghi, Davide Leardini, Quentin Philippot, Francesco Saettini, Jacques Dutrieux, Benedicte De Muylder, Francesca Vendemini, Francesco Baccelli, Albert Catala, Eleonora Gambineri, Marinella Veltroni, Vignesh Pandiarajan, Yurena Aguilar, Filomeen Haerynck, Michael Elliott, Stuart Turville, Fabienne Brillot, Taushif Khan, Filippo Consonni, Laureline Berteloot, William A Sewell, Geetha Rao, Laetitia Largeaud, Francesca Conti, Cecile Roullion, Cécile Masson, Francesco Pegoraro, Tianyi Ye, Samantha Joubran, Emily Villalpando, Boris Bessot, Yoann Seeleuthner, Tom Le Voyer, Jérémie Rosain, Hailun Li, Zarah Janda, Edoardo Muratore, Camille Soudée, Eric Delabesse, Claire Goulvestre, Mohammad Shahrooei, Anne Puel, Isabelle André, Christine Bole-Feysot, Laurent Abel, Miriam Erlacher, Vivien Béziat, Chantal Lagresle-Peyrou, Remi Cheynier, Emmanuelle Six, Nico Marr, Marlène Pasquet, Laia Alsina, Christopher C Goodnow, Nils Landegren, Alessandro Aiuti, Peng Zhang, Riccardo Masetti, Danny T Huang, Cindy S Ma, Jean-Laurent Casanova, Vijay G Sankaran, Jacinta Bustamante, Stuart G Tangye, Jonathan Bohlen

Faculty Research 2026

The E3 ubiquitin ligase Casitas B-lineage lymphoma (CBL) promotes positive selection and antigen responses in mouse T lymphocytes by ubiquitinating ZAP70. Conversely, mouse CBL and CBL-B mutually redundantly regulate SYK ubiquitination and B cell receptor signaling. Here we studied individuals with somatically homozygous CBL loss-of-function variants in leukocytes. Human CBL is largely redundant for the development and function of human T cells. Conversely, B cell development is altered at the immature stage, with a tenfold increase in transitional cells, enhanced survival of autoreactive clones and impaired tolerance manifested by autoantibody production. B cell maturation is intrinsically impaired by reduced apoptosis …

Greater Newark Stem Ecosystem - 2026 Convening - Small Group Outcomes & Action Plan, Brad Schenker

STEM for Success Showcase

In January 2026, GNSE convened ecosystem partners for a co-design session focused on identifying shared challenges, opportunities, and near-term actions to strengthen STEM education and career-connected learning. Participants engaged in small-group discussions to surface barriers, enablers, and potential areas for coordinated action.

Coe Annual Technical Review 2025, Jianshun Zhang, Bing Dong

SyracuseCoE

No abstract provided.

Mapping Of Wetland And Riparian Habitat For The National Wetland Inventory: Wetland-31 Utah 2, Emma R. Chantegros, Ryhan T. Sempler, Melissa L. Moser, Faith O. Hardin, Ella S. Hartshorn, Miles L. Fenolio, Liana N. Heberer, Sara K. Owen, Kay L. Hajek

EMMA Publications

This report details the wetland and riparian mapping methodologies implemented for the Wetland-31 Utah 2 project. The project area consists of 70 1:24,000 US Geological Survey (USGS) quads across Southeastern Utah with quads in Grand, San Juan, Emery, and Sevier counties, and Western Colorado with quads in Mesa County.

Contents, North Carolina Law Review

North Carolina Law Review

No abstract provided.