Digital Commons Network^™

Changing The Paradigm For Newly Graduated Nurses’ Intent To Leave Through An Emeritus Rn Virtual Mentoring Program In An Acute Care Hospital Setting, Lacy Eynon

Yale School of Nursing Digital Theses

The U.S. nursing shortage presents a critical challenge to healthcare organizations, withwide-reaching implications for patient safety and financial sustainability. High nurse turnover contributes to increased staffing costs, estimated at $56,300 average cost per nurse replaced. This shortage directly impacts patient outcomes including increased rates of medical errors, patient mortality and decreased overall quality of care. Supporting early-career nurses through mentorship programs is a promising strategy to improve professional development and retention, thereby addressing clinical and economic consequences workforce shortages. This DNP Project developed and implemented an Emeritus RN Virtual Mentoring Program utilizing experienced emeritus nurses to mentor nurses with less …

Empowering Emerging Air Medical Leaders Through A Novel Professional Development Program Adapting National Nursing And Healthcare Management Competencies To A Multi-State Air Medical Service, Christopher Hardman

Yale School of Nursing Digital Theses

Empowering Emerging Air Medical Leaders through a Novel Professional Development Program Adapting National Nursing and Healthcare Management Competencies to a Multi-State Air Medical ServiceHigh turnover in the nursing workforce has led to clinicians with limited experience being promoted to leadership roles without proper training or support. Nurse leaders directly impact clinical outcomes and retention. The financial burden of turnover is significant, as health systems must continually replace employees. Despite this, limited evidence exists on effective professional development and retention strategies in non-traditional, non-hospital settings. This DNP project sought to adapt and implement a novel professional development program, derived from the …

Transgender And Gender Diverse People – Inclusive Healthcare System Access, Dakota Denielle Lawtum

Yale School of Nursing Digital Theses

Transgender and Gender Diverse People – Inclusive Healthcare System AccessPurpose: This DNP project implemented a dual strategy to increase system-wide access to care and awareness of the needs of transgender and gender diverse people. Background: The 2015 U.S. Transgender Survey is the most recent and largest survey of its kind in the United States comprising a total of 27,715 responses. (National Center for Transgender Equality, 2015, U.S. Transgender Survey). The results of this survey are as follows: 1) 26% of the total national respondents experienced insurance issues for gender affirming healthcare or being denied coverage for routine care; 2) 32% …

Anaplastic Large Cell Lymphoma In Children And Adolescents, Eric J. Lowe, Wilhelm Woessmann

Department of Pediatrics Faculty Publications

Anaplastic lymphoma kinase (ALK)-positive anaplastic large-cell lymphoma (ALCL) accounts for >95% of ALCL cases in children and adolescents. The first description of ALCL as a CD30-positive lymphoma in 1985 was followed by the detection of chromosomal translocations involving the ALK gene at chromosome 2p23. The pathogenesis of ALK-positive ALCL is based on signalling from the constitutive active ALK kinase. The clinical characteristics, therapy regimens and outcome data were reported in the 1990s and 2000s. Different chemotherapy regimens led to astonishingly similar long-term event-free survival rates of 70%, independent of the drugs, doses and duration of therapy. Additionally, patients with relapsed …

Delayed Newborn Bathing To Increase Breastfeeding, Nadia Martino, Amanda Bace, Carrie Moneysmith, Mary Brooks, Shelly Wu, Wala Safi

Nurse Residency Posters

Facilitator: Ellie Barte, RN

Promoting A Culture Of Well-Being On A Nursing Unit, Jayla Campbell Rn, Jessica Detty Rn, Rebekah Krider Rn, Abby Winegardner Rn

Nurse Residency Posters

Facilitator: Ellen Villalobos

OhioHealth Berger Hospital

Artificial Intelligence And Digital Technologies In Finance: A Comprehensive Review, Soudeh Pazouki, Mohamad Jamshidi, Mirarmia Jalali, Arya Tafreshi

Finance Faculty Publications

This study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to …

Are Csr Incidents Truly Bad News?, Chen Chen, John A. Doukas, Rongyao Gloria Zhang

Finance Faculty Publications

We revisit whether disclosures of negative Corporate Social Responsibility (CSR) incidents adversely affect firms' stock prices. While univariate tests reveal significant negative abnormal returns around incident announcements, the effect disappears once firm characteristics, industry, and time‐fixed effects are controlled for. We find no robust evidence that CSR incidents or firms' Environmental, Social, and Governance (ESG) commitments influence stock price reactions on the event day or across broader windows. These results suggest that previously documented negative market responses may be attributable to endogeneity. Our baseline results are consistent with informed trading behavior: short‐sellers do not increase activity in incident‐related stocks relative …

Epigenetic Regulation In Fibrosis, Qingping Wei, Xiaoru Hu, Yan Sanders

Department of Biomedical and Translational Sciences Faculty Publications

[Introduction] Fibrosis is one of the pathological conditions underlying the progression of numerous chronic diseases, and it contributes substantially to morbidity and mortality worldwide. Increasing amounts of evidence indicate that epigenetic mechanisms—including DNA methylation, histone modifications, RNA modifications, and non-coding RNAs—play pivotal roles in epithelial cell injury and maladaptive tissue repair, inflammatory cell and fibroblast activation, and extracellular matrix remodeling and accumulation. Compared to genetic alterations, epigenetic changes are relatively reversible, positioning them as promising therapeutic targets. The articles collected in this Research Topic highlight recent advances in understanding how epigenetic regulation shapes fibrotic processes across multiple organ systems and …

Deletion Of Chop In Beta Cell Protects Mice From Cardiovascular Complications In Type 2 Diabetes: Evidence From A Pre-Clinical Mouse Model, Balaji Srinivas, Kiran Alluri, Nour-Eddine Rhaleb, Khalid Matrougui

Department of Biomedical and Translational Sciences Faculty Publications

Introduction

Cardiovascular complications are the leading cause of morbidity and mortality in patients with type 2 diabetes (T2D), which is recognized as a major independent risk factor for coronary artery disease, stroke, peripheral vascular disease, and heart failure. The interrelationship between metabolic dysfunction and cardiovascular disease is complex and multifactorial, involving hyperglycemia, insulin resistance, inflammation, and oxidative stress. Evidence indicates that endoplasmic reticulum (ER) stress and induction of the unfolded protein response (UPR) contribute to metabolic dysregulation and vascular dysfunction. However, the specific role of beta cell-derived UPR mediators, particularly C/EBP homologous protein (CHOP), in coordinating this interorgan axis between …

London's Murderous Mothers: How Charitable And Medical Advancement Influenced Infanticide Indictments During The Long 18th Century, Brandon Gamman

EWU Masters Thesis Collection

No abstract provided.

Reference Interview Recommendations For Text And Data Mining, Laura Egan

Librarian Publications

Text and data mining (TDM) has become increasingly important in research and teaching in higher education. Researchers at all levels are engaging with larger quantities of data than previously feasible. The exponential increase in the availability of text data has resulted in a proliferation of the number of tools that facilitate text data processing and analysis. While TDM skills may have been optional or a disciplinary focus in the past, they are increasingly employed in a broader range of fields. Students and researchers may also be inspired to use TDM tools as they come across examples while looking for new …

Volume Viii, Fall 2025, International Anchoritic Society

International Anchoritic Society Newsletter

No abstract provided.

The Potential For Albedo-Induced Climate Mitigation Using No-Till Management In Midwestern U.S. Croplands, Xian Wang, Mallory Barnes, Landon Yoder, Christopher A. Williams, Jennifer Tank, Todd Royer, Shellye Suttles, Kim Novick

Geography

No-till management is often recognized for its environmental and economic benefits, but its potential to reduce climate warming is still uncertain. Beyond ongoing debate over its effects on soil carbon storage, no-till also leaves plant residue on the surface, which can reflect more sunlight. This increase in surface reflectivity, called albedo, may help mitigate climate change by reducing the energy absorbed by the land. Here, we assessed this climate benefit of no-till across the U.S. Corn Belt using conservation survey records, county-level tillage data, and satellite observations. We found that no-till increased land surface brightness during the dormant season, reducing …

Permafrost And Structural Controls On Holocene Bedrock Landslide Occurrence Around Eyjafjörður, North‐Central Iceland, Adam Booth, Halldór G. Pétursson

Geology Faculty Publications and Presentations

Rapid, transient, landscape‐scale changes associated with deglaciation can condition slopes for failure and trigger bedrock landslides. However, the mechanisms leading to paleo rock slope failures following the last glacial period are challenging to infer because observations of how both landsliding and potential driving factors were distributed in space and time are limited. Here, we map and analyze the spatiotemporal pattern of 676 post‐glacial bedrock landslides around Eyjafjörður in north‐central Iceland using 2‐m resolution digital elevation data generated from optical stereo satellite imagery. Frequency‐ratio analysis demonstrates that after controlling for slope, landslides are most overrepresented within 2.6 km horizontal distances from …

The Geography Of Rock Glaciers In The American West, Allison Reese Trcka, Andrew G. Fountain, Bryce Allen Glenn

Geology Faculty Publications and Presentations

Rock glaciers are relatively common in conterminous United States west of the 100° meridian, yet their distribution and number are not well known. Using aerial and satellite imagery, we inventoried the population of rock glaciers, yielding 2,257 rock glaciers totaling 234.28 km² with an area of uncertainty of 7 percent. Of these, 1,561 (155.44 km²) are considered intact (moving) and 696 (76.84 km²) relict. Based on published estimates of ice content, total water-equivalent volume is roughly 2.52 km³. Rock glacier elevations increase from the northwest (Washington) to the southeast (Colorado), following a spatial …

He's A Builder, But Does Trump Have An Urban Agenda?, Mark Davidson

Geography

For 20 years, turbulence has defined American urbanism. In the late noughties, a global financial crisis pushed many US cities to the brink of bankruptcy. Austerity followed, with belt-tightening squeezing city services. Then came Trump, then came COVID. The pandemic would, unpredictably, make US cities cash rich thanks to the Federal government's largesse. And now we await Trump 2.0. For US cities the prospect of radical change seems real. But what type of reform will Trump bring? Herein lies the difficulty. Trump 2.0 is as hard to predict as the first version was. Immigration, tariff reform and government efficiency are …

Evaluating Exclusion Criteria For Exertional Testing For Mild Tbi In An Outpatient Hospital-Based Organization, Amy Kerstetter Staub Pt, Dpt, Sandra M. Tremblay Pt, Ms, Cws, Mscs

Department of Medicine

No abstract provided.

Peaking Today, Taking It Easy Tomorrow: Daily Performance Dynamics Of Working Long Hours, Lieke L. Ten Brummelhuis, Charles Calderwood, Christopher C. Rosen, Alison S. Gabriel

Management Faculty Publications and Presentations

Underlying the “ideal worker” image that pervades many organizational cultures is the assumption that working longer hours equates to higher performance, despite recovery research that suggests that long work hours might actually impair future work performance. In an effort to reconcile these differences in how long work hours are thought to relate to job performance, we develop and test a conceptual model in which daily boosts in same-day performance associated with working longer hours could be offset by lower next-day performance. More specifically, we examine if working a longer day than usual reduces sleep, which has the potential to diminish …

Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building …

V2vdiscs: Vehicle To Vehicle Distributed Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Sajal K. Das

Computer Science Faculty Research & Creative Works

Electric Vehicles (EVs) have become popular in the domain of Intelligent Transportation Systems for their ability to mitigate increasing environmental concerns by reducing carbon footprints and conserving fossil fuels. Due to the scarcity of static charging stations, Vehicle-to-Vehicle (V2V) charge sharing can facilitate the on-demand charging requirement of EVs. However, most of the V2V charge-sharing solutions are either centralized or semi-centralized, causing long waiting times, huge message overhead, and high infrastructural costs. For a large network, assigning a suitable donor EV for an acceptor EV as well as maximizing the matching cardinality in a distributed environment is a challenging problem. …

Sosta: Skill-Oriented Stable Task Assignment With Bidirectional Preferences In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Traditional task assignment approaches in crowdsourcing platforms have focused on optimizing utility for workers or tasks, often neglecting the general utility of the platform and the influence of mutual preference considering skill availability and budget restrictions. This oversight can destabilize task allocation outcomes, diminishing user experience, and, ultimately, the platform's long-term utility and gives rise to the Worker Task Stable Matching (WTSM) problem. To solve WTSM, we propose the Skill-oriented Stable Task Assignment with a Bi-directional Preference (SoSTA) method based on deferred acceptance strategy. SoSTA aims to generate stable allocations between tasks and workers considering mutually their preferences, optimizing overall …

Pervasive Sensing To Correlate Vehicle Driving Behavior With City-Scale Traffic Dynamics, Debasree Das, Shameek Bhattacharjee, Sandip Chakraborty, Bivas Mitra, Sajal K. Das

Computer Science Faculty Research & Creative Works

Individual driving behavior is a pivotal element that shapes the overall traffic dynamics in a city. In this work, we study and analyze the complex web of relationships between individual driving behaviors and their impact on the overall traffic dynamics of a smart city with two primary objectives: first, understanding the spatial interaction between individual vehicles and their impact on each other, and second, finding anomalous driving behaviors, which lead to congestion and traffic incidents. Specifically, we introduce an overarching modular framework investigating human factors of driver characteristics, vehicle attributes, geographical terrain surrounding the road infrastructure, and environmental conditions. Analyzing …

Remenet: A Memory-Enhanced Gan Model For Intrusion Detection In Transportation Cyber-Physical Systems, Xin Wang, Lianbo Ma, Sajal K. Das, Zhonghua Liu

Computer Science Faculty Research & Creative Works

Ensuring the safety and reliability of Transportation Cyber-Physical Systems (T-CPS) is critical. However, the increasing interconnectedness of T-CPS exposes them to sophisticated cyberattacks, necessitating robust intrusion detection systems (IDS) to safeguard against evolving threats. This paper aims to enhance the security of T-CPS by addressing two key challenges: effective anomaly detection and handling imbalanced datasets in intrusion detection tasks. In this paper, we propose ReMeNet (Reconstruction Memory Network), a novel intrusion detection model that combines a memory module with a GAN-based architecture to enhance anomaly detection and data reconstruction. To address the challenge of imbalanced datasets, we incorporate a Vector …

Safenav: Safe Path Navigation Using Landmark Based Localization In A Gps-Denied Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

In battlefield environments, adversaries frequently disrupt GPS signals, requiring alternative localization and navigation methods. Traditional vision-based approaches like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) involve complex sensor fusion and high computational demand, whereas range-free methods like DV-HOP face accuracy and stability challenges in sparse, dynamic networks. This paper proposes LanBLoc-BMM, a navigation approach using landmark-based localization (LanBLoc) combined with a battlefield-specific motion model (BMM) and Extended Kalman Filter (EKF). Its performance is benchmarked against three state-of-the-art visual localization algorithms integrated with BMM and Bayesian filters, evaluated on synthetic and real-imitated trajectory datasets using metrics including Average Displacement …

Dynamic Anomaly Threshold Based Malicious Behavior Detection In Lora-Assisted Industrial Iot, Subir Halder, Amrita Ghosal, Thomas Newe, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart manufacturing, powered by Long Range (LoRa) communication-assisted Industrial Internet of Things (IIoT), offers significant benefits but also incurs security concerns due to device compromise. In addition, various application scenarios and inherent heterogeneity of IIoT devices induce significant challenges for reliable behavior detection of compromised devices. While existing work is mostly on detecting compromised devices and there exists limited work on modeling system behavior, an open question is how to model the per-device behavior in an IIoT deployment and how behavioral changes can be automatically adapted in different scenarios. This paper proposes Misbehav, a novel self-learning device behavior anomaly detection …

Iterative Recommendations Based On Monte Carlo Sampling And Trust Estimation In Multi-Stage Vehicular Traffic Routing Games, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

Computer Science Faculty Research & Creative Works

The shortest-time route recommendations offered by modern navigation systems fuel selfish routing in urban vehicular traffic networks and are therefore one of the main reasons for the growth of congestion. In contrast, intelligent transportation systems (ITS) prefer to steer driver-vehicle systems (DVS) toward system-optimal route recommendations, which are primarily designed to mitigate network congestion. However, due to misalignment in motives, drivers may exhibit a lack of trust in the ITS. This paper models the interaction between a DVS and an ITS as a novel, multi-stage routing game where the DVS exhibits dynamics in its trust towards the recommendations of the …

Smartsla: Enabling Quality Of Service In Blockchain-Enabled Iot Networks, Kyle M. Whitlatch, Asad Waqar Malik, Sanjay Madria

Computer Science Faculty Research & Creative Works

The significant advancement in Internet of Things (IoT) adoption has enabled Multi-access Edge Computing (MEC) to mitigate IoT sensors limited computational, transmission power constraints, and data distribution overhead. However, integrating MEC with the IoT ecosystem poses several challenges, resulting in integrity issues with the MECs, impacting their capacity to effectively serve users seeking data generated by IoT sensors. To address this, we propose SmartSLA, a blockchain based solution to ensure Quality of Service (QoS) from third party IoT devices. SmartSLA leverages the decentralized and immutable nature of blockchain to combat the shortcomings of MECs. Using smart contracts, we develop a …

Semi-Supervised Multimodal Multi-Instance Learning For Aortic Stenosis Diagnosis, Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes

Computer Science Faculty Research & Creative Works

Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Spectral Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), …

Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates an extra bottleneck layer to learn new knowledge and instill it into the original pre-trained knowledge. The major idea is to …