Digital Commons Network^™

The Influence Of Financial Data Subscriptions On Analyst Research, Braiden Coleman, Travis Dyer, Mark Lang

Faculty Publications

We use disclosed “source” data from analyst reports to infer brokerage financial data subscriptions (FDS) and investigate their effects on analyst research. When brokerages add FDS, their analysts’ forecast accuracy increases. Effect sizes are at least as large as those for analyst experience, busyness, and brokerage size. Benefits are largest for less experienced and busier analysts with less private access to management forecasting over longer horizons. Although adding new FDS benefits individual analysts, there is substantial overlap in FDS across brokerages, leading to homogenized market views. Specifically, when brokerages have overlapping FDS, their analysts’ forecasts, timing, boldness, recommendations, report content, …

The Future Performance Implications Of Non-Gaap Motivated Investment, Minkwan Ahn, Theodore E. Christensen, Ryan G. Johnson, Melissa F. Lewis-Western

Faculty Publications

We investigate whether consistent non-GAAP reporting is associated with investment efficiency. Prior research finds a positive association between non-GAAP reporting and investment levels, concluding that it represents overinvestment. We corroborate this positive association, but additional tests are not consistent with the conclusion of inefficient overinvestment. Specifically, we explore the relation between investment and future cash flows as a proxy for the realization of investments in positive net present value projects. We find that the investments of firms that consistently report non-GAAP metrics are associated with similar or higher future cash flows than the investments of firms reporting only GAAP earnings, …

An Accident On Mount Washington: Now I Understand What It's Like To Have Panic Attacks, Chuck Radis

Faculty Publications

This is the story of how Dr. Radis developed PTSD after a mountaineering accident and how it affected him in his personal life and as a physician as he continued to practice and see patients.

Corrigendum: Antioxidant, Anti-Alzheimer’S, Anticancer, And Cytotoxic Properties Of Peanut Oil: In Vitro, In Silico, And Gc-Ms Analysis (Frontiers In Chemistry, (2024), 12, (1487084), 10.3389/Fchem.2024.1487084), Hanène Djeghim, Djamila Benouchenne, El Hassen Mokrani, Huda Alsaeedi, David Cornu, Mikhael Bechelany, Ahmed Barhoum

Research Outputs: 2025-Present

In the published article, there was an error in Affiliation 7. Instead of “GUST, Helwan, Kuwait”, it should be “Functional Materials Group, Gulf University for Science and Technology, Mubarak Al-Abdullah, Kuwait”. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Protective Effects Of Calligonum Comosum As A Natural Remedy To Counteract Pregabalin-Induced Toxicity: Insights From Chemical Profiling, In Vivo, And In Silico Analyses, Smail Mehda, Ibtissam Laib, Feriel Diab, Raounek Attia, Yousef Benaissa, Attia Hanane, Khiari Rayhana, Meriem Bellabidi, Huda Alsaeedi, David Croun, Mikhael Bechelany, Ahmed Barhoum

Research Outputs: 2025-Present

This study explores the therapeutic potential of Calligonum comosum extract in alleviating pregabalin (PGB)-induced toxicity in male Wistar rats, with a focus on hepatic, renal, and reproductive health. PGB exposure led to significant biochemical disturbances, including elevated liver enzymes (AST, ALT, LDH), impaired kidney markers (urea, creatinine, uric acid), reduced reproductive hormones (testosterone, FSH, LH), and notable histopathological damage in liver, kidney, and testicular tissues. Treatment with C. comosum extract effectively restored liver and kidney functions and partially corrected hormonal imbalances. The extract reduced AST, ALT, and LDH levels by 18.5%, 25.2%, and 13.7%, respectively. Similarly, urea, creatinine, and uric …

Carbon Nanotubes And Nanofibers As Building Blocks For The Future: Structure, Synthesis, Properties, And Functionalization Perspectives, Amin Meftahi, Mahshad Sadat Kashef Sabery, Somayeh Alibakhshi, Maureen Walsh, Mikhael Bechelany, Abanoub Naseef, Ahmed Barhoum

Research Outputs: 2025-Present

No abstract provided.

Ceo Turnover And Shareholder Awareness In Cartel Cases, Catarina Marvão, Giancarlo Spagnolo

Research Outputs: 2025-Present

We examine the consequences of cartel detection for the careers of CEOs in the US and the EU; the characteristics of colluding CEOs (e.g., tenure, age); and shareholders’ awareness of management misbehavior. Additionally, we examine at what level cartel decisions are made according to the available case documents. We find that only 4% of all CEOs in convicted EC cartel firms (1998-2020) are explicitly fired due to collusion, and that 30% of CEOs indicted in DOJ cartels (1985-2011) are fired. Often, CEOs remain in place or take on other high-level positions in the firm. This suggests that (most) shareholders are …

Police Records And Rural Crime In 1940s Ireland: The Challenges Of Using Historical Documents As Primary Sources In Sociological Research, Clay Darcy

Research Outputs: 2025-Present

No abstract provided.

Camellia Sinensis Phytochemical Profiling, Drug-Likeness, And Antibacterial Activity Against Gram-Positive And Gram-Negative Bacteria: In Vitro And In Silico Insights, Farouk Boudou, Amal Belakredar, Ahcene Keziz, Huda Alsaeedi, David Cornu, Mikhael Bechelany, Ahmed Barhoum

Research Outputs: 2025-Present

Background: Camellia sinensis extracts have a rich phytochemical profile and therapeutic properties. The plant contains bioactive compounds, such as catechins, flavonoids, and phenolic acids, which are associated with various health benefits, including antioxidant, anti-inflammatory, and anticancer activities. Aim: To investigate the bioactive potential of a Camellia sinensis extract, particularly its antibacterial activity against Gram-positive and Gram-negative bacteria and its drug-like properties. Method: Phenolic compounds in C. sinensis extract were identified and quantified using high-performance liquid chromatography (HPLC). Its antibacterial activity was assessed against both Gram-positive (Staphylococcus aureus) and Gram-negative bacteria (Pseudomonas aeruginosa and Escherichia coli). Drug-likeness, toxicity, and molecular properties …

The Role Of Accelerators In The Success Of Cleantech Firms, Ziqi Liu, Seán O'Reilly, Jim Hanly

Research Outputs: 2025-Present

This paper examines whether accelerator programmes meaningfully influence the performance and development of UK Cleantech firms. Drawing on insights from entrepreneurial ecosystem theory, market validation, and the resource-based view (RBV), we investigate four key outcomes: survival, external equity financing, innovation activity, and commercial milestones (time to revenue and profitability). Utilising propensity score matching and staggered difference-in-differences, complemented by logit and Cox proportional hazards models, we find that accelerator participation significantly increases revenue generation, helps secure external equity financing, and fosters patenting. However, accelerator effects on firm survival and profitability are inconclusive, suggesting that while accelerators can expedite early market traction, …

The Indirect Diversification Benefits Of Investing In Japanese Firms: An Alternative Perspective, Pearlean Chadha, Jenny Berrill

Research Outputs: 2025-Present

This paper examines the role of firm-level multinationality in equity portfolio diversification for Japanese firms from 1998 to 2015. We use a unique multinationality dataset for constituents of the Nikkei 225 based on two measures of sales and subsidiaries. We employ an extended version of the traditional Capital Asset Pricing Model (CAPM) to analyse the exposure of firm returns to various geographical regions. There is evidence that firms are not influenced by the geographic regions where they report operations. The results also indicate that there are benefits from investing in Japanese multinationals but these benefits do not increase with increasing …

Colliding Ideas: Artistic Explorations Of Data Surveillance And Data Protection, Lucy Royal-Dawson, Katherine Nolan, Eugene Mcnamee, Laura O’Connor, Emma Campbell, Anna Pathé-Smith, Kyle Boyd, Daniel Philpott

Research Outputs: 2025-Present

This paper presents a case study of a pilot interdisciplinary collaboration between digital technology law scholars and academic creative practitioners leading to the public art exhibition User Content? in Belfast in the autumn of 2024. Called LawTech Collider to encapsulate the premise that communication across disciplinary boundaries is often unpredictable, this interdisciplinary collaboration was conceptualised as a collision of ideas aimed at generating unforeseeable outcomes for collaborators and audience, that could be harvested, analysed and utilised as jumping off points for further work. The LawTech Collider pilot shared research related to data protection issues, particularly in social media and fertility …

Comparative Analysis Of Real-Time Fluorescence-Based Spectroscopic Instruments: Bioaerosol Detection In The Urban Environment Of Dublin City, Ireland, Jerry Hourihane Clancy, Emma Markey, Moisés Martínez-Bracero, Jose María Maya Manzano, Eoin Mcgillicuddy, Gavin Sewell, Roland Sarda-Estève, Dominique Baisnée, Andrés M. Vélez-Pereira, Gemma Davis, David J. O’Connor

Research Outputs: 2025-Present

No abstract provided.

An Integrative Bioinformatics Approach To Investigating Timp3 And Immune Cell Infiltration: Prognostic And Clinicopathological Implications, Neelam Bhola, Amit K. Jaiswal, Daman Saluja

Research Outputs: 2025-Present

Tissue inhibitor of metalloproteinase 3 (TIMP3) serves as a prominent endogenous inhibitor of matrix metalloproteinases (MMPs), playing a crucial role in inhibiting metastasis, and angiogenesis. However, its exact contributions to colorectal cancer (CRC) remain largely unidentified. We aimed to ascertain the prognostic significance of TIMP3 in CRC patients through a bioinformatic approach. GEPIA, UALCAN, Kaplan–Meier plotter, LinkedOmics, cBioPortal, GeneMANIA, TIMER, TISIDB, the ScTIME database, TISMO, TIDE, CAMOIP, and TISCH2 were employed to comprehensively analyze the differential expression, prognostic value, genetic alterations, signaling pathways, immune cell infiltration, tumor microenvironment (TME) and associated genes of TIMP3 in CRC patients. Compared to adjacent …

Tailoring Innovative Silver Nanoparticles For Modern Medicine: The Importance Of Size And Shape Control And Functional Modifications, Ibtissam Laib, Noura Gheraissa, Abir Benaissa, Latra Benkhira, Manel Azzi, Yousef Benaissa, Ahmed G. Abdelaziz, Furong Tian, Maureen Walsh, Mikhael Bechelany, Ahmed Barhoum

Research Outputs: 2025-Present

Silver nanoparticles (Ag NPs) play a significant role in modern medicine, with their size, shape, and surface chemistry strongly influencing their biological performance. Smaller nanoparticles (1–100 nm) can penetrate cells more easily, while different shapes such as spheres, rods, cubes, triangles, wires, and stars impact their antimicrobial activity and drug delivery efficiency. This review highlights the importance of precise control over these properties during synthesis to optimize their therapeutic potential. Green synthesis methods, including plant-based and microbial approaches, are presented as safer, more cost-effective, and environmentally friendly alternatives to conventional chemical processes. The review also discusses surface functionalization strategies such …

Anatomical Depiction: How Showing A Product's Inner Structure Shapes Product Valuations, Seo Yoon Kang, Junghan Kim, Arun Lakshmanan

Research Collection Lee Kong Chian School Of Business

Anatomical depiction is a technique where the product is decomposed into components that are spatially arranged in a layer-by-layer manner to visually explicate its inner structure. The authors demonstrate that anatomical depiction, compared to non-anatomical depiction, enhances product valuation. This effect occurs because anatomical depiction elicits a ‘coming together’ of the inner components in consumers’ minds thereby evoking a gestalt image of the product – a process labeled simulated assemblage. The elicitation of simulated assemblage in turn boosts their confidence in the product’s performance. Two field experiments first demonstrate that anatomical depiction leads to greater engagement in online settings such …

Interfaces, Social Information Processing, And Diversity Cascades: How Board Diversity Influences Invention Output, Daniel Z. Mack, Guoli Chen, Po-Hsuan Hsu, Yen Teik Lee, Gerard George

Research Collection Lee Kong Chian School Of Business

Although recent studies report a positive relationship between board diversity and firms' invention output, less is known about the mechanisms by which they operate, given that boards are typically far removed from on-the-ground invention activities. Drawing from research on interfaces and social information processing, we propose a theory of diversity cascades across the organizational hierarchy. Specifically, we posit that board diversity has a positive––albeit indirect––influence on firms' invention output. This relationship is mediated by the diversity of top management teams and inventor diversity, as board diversity cascades from the top to lower ranks of an organization. This indirect effect is …

Path Nets: Concurrence And Recurrence In The Dynamics Of Organizing, Brian T. Pentland, Waldemar Kremser, Kenneth T. Goh

Research Collection Lee Kong Chian School Of Business

This article proposes a link between temporal structuring and the dynamics of organizing that is manifest in a fabric of concurrent paths that we call a path net. Path nets are shaped by mechanisms of temporal structuring, such as entrainment, planning, agency, and chance. Path nets materialize the effects of temporal structuring “here” and “now” in the comings and goings of actors and resources, thereby setting the stage for doings and sayings of situated practice and shaping the dynamics of organizing. Path nets offer a parsimonious system of picturing the complexity of organizing that is built on a processual ontology …

Measuring Variation In Gaze Following Across Communities, Ages, And Individuals: A Showcase Of Tango-Cc (Task For Assessing Individual Differences In Gaze Understanding-Open-Cross-Cultural), Julia Christin Prein, Florian M. Bednarski, Ardain Dzabatou, Michael C. Frank, Annette M.E. Henderson, Josefine Kalbitz, Patricia Kanngiesser, Dilara Keşşafoğlu, Bahar Köymen, Maira V. Manrique-Hernandez, Shirley Magazi, Lizbeth Mújica-Manrique, Julia Ohlendorf, Damilola Olaoba, Wesley R. Pieters, Sarah Pope-Caldwell, Umay Sen, Katie Slocombe, Robert Z. Sparks, Roman Stengelin, Jahnavi Sunderarajan, Kirsten Sutherland, Florence Tusiime, Wilson Vieira, Zhen Zhang, Yufei Zong, Daniel B.M. Haun, Manuel Bohn

School of Psychology

Cross-cultural studies are crucial for investigating the cultural variability and universality of cognitive developmental processes. However, cross-cultural assessment tools in cognition across languages and communities are limited. In this article, we describe a gaze-following task designed to measure basic social cognition across individuals, ages, and communities (the Task for Assessing iNdividual differences in Gaze understanding-Open-Cross-Cultural; TANGO-CC). The task was developed and psychometrically assessed in one cultural setting and, with input of local collaborators, adapted for cross-cultural data collection. Minimal language demands and the web-app implementation allow fast and easy contextual adaptations to each community. TANGO-CC captures individual- and community-level variation …

Cross-Cultural Data On Romantic Love And Mate Preferences From 117,293 Participants Across 175 Countries, Marta Kowal, Piotr Sorokowski, Biljana Gjoneska, Katarzyna Pisanski, Gerit Pfuhl, Leonardo Aguilar, Steve M.J. Janssen, Benjamin Gelbart, Patrícia Arriaga, Jan Antfolk, Katarina Zvončáková, Linda H. Lidborg, Jorge Contreras-Garduño, Mikhail V. Kozlov, Taciano L. Milfont, Marco A.C. Varella, Valerija Križanić, Mahmoud Boussena, Tina Kavčič, Diana Ribeiro Da Silva, Brahim Hamdaoui, Fatima Zahra Sahli, Karlijn Massar, Eliane Deschrijver, Tatsunori Ishii, Hakan Cetinkaya, Oksana Senyk, Farida Guemaz, Koen Ponnet, Yahya Don

School of Psychology

Psychological studies on close relationships have often overlooked cultural diversity, dynamic processes, and potentially universal principles that shape intimate partnerships. To address the limited generalizability of previous research and advance our understanding of romantic love experiences, mate preferences, and physical attractiveness, we conducted a large-scale cross-cultural survey study on these topics. A total of 404 researchers collected data in 45 languages from April to August 2021, involving 117,293 participants from 175 countries. Aside from standard demographic questions, the survey included valuable information on variables relevant to romantic relationships: intimate, passionate, and committed love within romantic relationships, physical-attractiveness enhancing behaviors, gender …

From Attention To Action: Advancing Inclusivity In The Landscape Of Anatomy Education, Siobhan M. Moyes, Lily Evans, Katie Snow, Aiden Jayanth

Peninsula Medical School

Conversations about inclusivity in anatomy education are at a critical juncture. While the need for a more equitable discipline is widely recognised, significant challenges remain. As this project aimed to reimagine what a truly inclusive learning experience could like and how it might be best achieved, an inclusive approach to research methodology was essential to ensure robust and representative results. Due to the paucity in anatomical literature on this topic, the authors applied concepts from sociological research methodology and innovative methods of demographic data collection to their own discipline.

The study utilised questionnaires and focus groups to collect diverse data …

A Hybrid Soft Voting And Stacking-Based Meta-Learning Approach For Sentiment Analysis Of Bangkalan Batik, Moh. Imron Wahyudi, Lailil Muflikhah, Rizal Setya Perdana

Knowledge Engineering and Data Science

Sentiment analysis is an important field in Natural Language Processing (NLP) that focuses on processing consumer opinions to gain useful insights. The information generated from sentiment analysis can be used as a basis for business decision-making, service quality evaluation, and the formulation of more effective marketing strategies. In the local context, Bangkalan Batik, as one of Madura's distinctive cultural products, has high economic value and cultural identity. However, consumer reviews available online, for example through Google Maps, are still rarely utilized optimally by MSMEs as a source of strategic information. Therefore, this study was conducted to develop a sentiment classification …

Prediction Of Audit Findings Using Deep Learning With Financial And Non-Financial Data: A Case Study In Province X, Fery Yohan Setiawan, Eko Mulyanto Yuniarno, Reza Fuad Rachmadi

Knowledge Engineering and Data Science

The implementation of the audit from the local government financial statements by The Audit Board of The Republic of Indonesia (BPK RI), especially for the Province X representative, are frequently faced by the various limitations, one of them being the required audit time. At this moment, the BPK RI representative of Province X doesn’t have the tools that are able to help the accurate of sample determination for the pick test, which resulted in this study proposing the application of multi-label classification to predict the findings of financial statement (Laporan Keuangan, LK) audits based on financial and non …

Comparative Analysis Of Deep Learning Algorithms For Predicting Enso Based On Non-Sequential Sampling Procedure Algorithms, Ria Faulina, Siti Hadijah Hasanah, Nuramaliyah Nuramaliyah, Ika Nur Laily Fitriana

Knowledge Engineering and Data Science

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN …

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …

Comparative Performance Of Vgg16 And Efficientnetb0-Based Transfer Learning For Brain Tumor Classification, Huzain Azis, Rizqi Ananda Jalil, Abdul Rachman Manga'

Knowledge Engineering and Data Science

The classification of brain tumors using Magnetic Resonance Imaging (MRI) images is essential for early diagnosis but remains challenging due to tumor diversity. This study evaluates the effectiveness of two distinct architectural approaches for feature extraction: VGG16, representing a classic sequential design, and EfficientNetB0, a modern architecture optimized for parameter efficiency through compound scaling. Using a dataset of 2,870 MRI images categorized into four classes, we implemented a static transfer learning strategy by freezing all pre-trained ImageNet weights to act as fixed feature extractors. Features were extracted from specific layers, the final pooling layer for VGG16 and the Global Average …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

Assessing Deep Learning Models And Hyperparameter Optimization For Stable Time-Series Electricity Load Forecasting, Sukma Patrya, Aji Prasetya Wibawa, Aripriharta Aripriharta

Knowledge Engineering and Data Science

Long-term electricity load forecasting plays an important role in ensuring system reliability, optimizing energy management, and making operational plans in face of continuously rising electricity demands. This study suggests a complete deep learning method for univariate forecasting of future electricity loads based on climatology and electricity consumption data for the period between 2019 and 2023. The initial dataset was cleaned, normalized, and partitioned chronologically into train/test datasets. Four train/test split cases (20/80, 40/60, 60/40, 80/20) were considered to explore the impact of different levels of historical data availability on the performance of the suggested framework from data-poor to data-rich situations. …

Law Enforcement Privilege, Rebecca Wexler

Michigan Law Review

You can’t question a secret you haven’t been told. The criminal legal system depends on fair and open proceedings to expose and regulate unlawful and unconstitutional police conduct through the courts. If police can use claims of secrecy to systematically thwart criminal defendants’ access to evidence, judicial review will fail. And yet that is exactly what is happening under a common-law doctrine called the “law enforcement privilege.” The privilege empowers police and prosecutors to rely on the results of secret investigative methods while withholding information from the defense about how those methods work. It risks perpetuating unconstitutional conduct, enabling wrongful …

Younger And Older Abstention, Fred O. Smith Jr.

Michigan Law Review

When victims of systemic rights violations in state criminal proceedings seek federal court relief, governmental defendants often ask federal courts to abstain for reasons of federalism. These arguments frequently disregard the Supreme Court’s emphasis that abstention is a narrow exception to federal courts’ duty to exercise jurisdiction. Lower federal courts are increasingly employing a form of “free-floating federalism,” diverging from the Supreme Court’s careful balance between comity and individual rights. This has led to lower courts’ significant expansion of criminal abstention doctrine, leaving severe irreparable harm unaddressed in an increasingly broad range of settings, such as pretrial detention and child …