Digital Commons Network^™

Intercultural Communicative Competence (Icc) Among Efl Learners And Teachers And The Potential For Ai As A Guide, Zeynep Saka

Theses - ALL

This study investigates the Intercultural Communicative Competence (ICC) of English as a Foreign Language (EFL) students and instructors in Turkey by examining both perceived and observed aspects of ICC, as well as the potential role of Artificial Intelligence (AI) in supporting intercultural communicative competence education. Guided by Byram’s (1997) model and informed by Speech Act Theory (Searle, 1969) and Politeness Theory (Brown & Levinson, 1987), the study adopted a mixed-methods design including 89 students and 46 instructors from nine state universities. Data were collected through a 25-item ICC questionnaire and a series of Discourse Completion Tasks (DCTs) completed under self-guided …

Twas-Ctl: A Robust And Efficient Method For Multi-Tissue Transcriptome-Wide Association Studies Using Cross-Tissue Learners, Md Mutasim Billah, Hairong Wei, Fengzhu Sun, Kui Zhang

Michigan Tech Publications

The advent of transcriptome-wide association studies (TWAS) has expanded the classical genome-wide association study (GWAS) framework by integrating gene expression with genetic variation to identify trait-associated variants. While multi-tissue TWAS approaches improve statistical power over single-tissue models, existing methods often lose information during result aggregation and require intensive computation. Here, we present TWAS-CTL (Cross-Tissue Learner), a novel framework that leverages heterogeneous gene expression across tissues by adaptively reweighting and optimizing multiple single-tissue learners. Simulations demonstrate that TWAS-CTL achieves higher statistical power than the leading method, UTMOST, while maintaining proper type I error control and reducing computational time by over half. …

Building Bridges: Industrial Trade Unions In A Changing Climate, Nicole Möller

Dissertations - ALL

This dissertation examines how German industrial trade unions and workers in the automobile and coal industries navigate the profound challenges of the green transition. Workers’ experiences are situated within a broader political-economic context shaped partly by market-led decarbonization efforts, welfare retrenchment/austerity, erosion of progressive institutions in society, and global competition, as well as a global restructuring of value accumulation. The analysis critically engages with unions’ current responses to a market-led transition and their implications for labor’s future. The dissertation develops its argument across three empirical chapters. Chapter 2 investigates workplace-level changes in coal mining and automobile production, analyzing how workers …

3d Point Cloud Analysis With Classification, Segmentation And Few-Shot Learning, Jiajing Chen

Dissertations - ALL

Deep learning for 3D point cloud analysis has made significant progress, yet several critical challenges, including the following, remain underexplored: (1) traditional max-pooling operations discard a substantial portion of learned features, resulting in information loss and inefficient use of computational resources; (2) existing few-shot point cloud classification models lack robustness when faced with occlusion, missing points, and limited training data; (3) semantic segmentation methods often fail to fully exploit background–foreground interactions, leading to reduced accuracy. Moreover, in domains such as gait recognition and visual program synthesis, research has been largely dominated by 2D-based approaches, leaving the potential of point cloud …

Robust Security Assurance In Cyber-Physical Systems: From Attack Diagnosis To Attack Resilience, Zifan Wang

Dissertations - ALL

Cyber-Physical Systems (CPS) have become integral to critical infrastructure, from autonomous vehicles to industrial control systems. However, their increased connectivity and sophistication introduce new vulnerabilities, making security a paramount concern. This dissertation presents a comprehensive pipeline for enhancing CPS security, focusing on accurate diagnosis of trustworthy time frames and subsystems, followed by system resilience measures to restore safe states. The foundation of system resilience lies in an advanced checkpointing protocol for real-time multi-process systems. A novel three-step approach uniquely addresses both logical and timing correctness, which are crucial for modern CPS applications. By partitioning processes into directed acyclic graphs, implementing …

Model Inference And Sparse Network Analysis: Machine Learning In The Gene Regulatory Network Framework, Youchuan Wang

Dissertations - ALL

This thesis explores sparse network inference from high-dimensional, noisy, and underdetermined data—a fundamental challenge in many scientific domains. We focus on the development of evolutionary computation methods for discovering underlying network structures that are both interpretable and biologically plausible. Our methods are applied to the domain of Gene Regulatory Network (GRN) inference, where sparsity, indirect interactions, and limited observations pose significant hurdles. The framework models both steady-state and time-series gene expression data, with particular emphasis on biological sparsity and regulatory dynamics. We approach the problem from three perspectives: (1) edge-level analysis using transitive reduction to distinguish direct from indirect regulation; …

Enzyme Active Bath Affects Protein Condensation, Kevin Ching

Dissertations - ALL

The idea that the activity of an active bath can act as an effective temperature and alter system behavior has been theorized and demonstrated for micron-scale systems, but it has not yet been shown for nanoscale systems. Previous studies have suggested that, although passive and active systems can be combined at the nanoscale, activity from the active system typically disrupts and destroys the passive system. In this work, I attempt to create an enzyme-driven active bath to control the liquid–liquid phase separation (LLPS) and condensation of a protein. I find that enzymatic activity enhances phase separation, effectively driving the LLPS …

Machine-Learning Classification Of Gamma-Producing Neutral Current Interactions In Liquid Argon From A Supernova Neutrino Burst, Sierra Thomas

Dissertations - ALL

With five core-collapse supernova candidates within 1 kiloparsec from Earth, extracting information from the neutrino flux will be important to understanding the explosion mechanism of the star (and its composition). The Deep Underground Neutrino Experiment, (DUNE), will be capable of detecting neutrinos from supernovae, primarily from charged-current interactions due to the electron neutrino flavor. However, considering neutrino oscillations in the presence of matter (MSW effects), the muon neutrinos and tau neutrinos will be undetectable if not for the neutral current channel. About 5% of the neutral current interactions will cause the argon nucleus to excite and release a gamma ray …

Developing Best Practice Fabrication For Niobium Superconducting Devices, Jadrien Timothy-Henke Paustian

Dissertations - ALL

Superconducting qubits are a leading platform for scalable quantum computation. This platform offers high gate speeds and can make use of readily scalable complementary metal-oxide semiconductor (CMOS) fabrication techniques. However, superconducting qubits are susceptible to a variety of losses that arise in systems with superconductors, such as due to ubiquitous two-level systems, quasiparticles, and more. Designing fabrication processes to mitigate and manage these loss sources is critical to achieving scalable quantum computation. However, many of these loss sources are set during fabrication processing, of which there can be many different steps, each with its own parameter space. Exploring this space …

Using A Heuristic Tool To Improve Symptom Self-Management In Adolescents And Young Adults With Cancer: Protocol For A Randomized Controlled Trial., Suzanne Ameringer, Grace Hodges, R K Elswick, Lauri Linder, Catherine Fiona Macpherson, Kristin Stegenga

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Adolescents and young adults (AYAs) with cancer experience multiple distressing symptoms during treatment, yet few developmentally relevant resources have been developed to help them self-manage their symptoms. Empowering patients to have a more active role in self-management during cancer treatment may lessen their symptom severity and distress.

OBJECTIVE: The aim of this study is to test an intervention designed to improve symptom self-management, the Computerized Symptom Capture Tool (C-SCAT), by helping AYAs understand their unique symptom experience and discuss it with their health care providers.

METHODS: We are conducting a multisite, 2-group randomized controlled trial to evaluate the effects …

Mood-Based Thinking And Its Relationship To Decision-Making Among Physical Education Teachers In Baghdad Secondary Schools, Safaa Ali Ghareeb, Husain Abd Al-Zhra Abd Al-Yaim

Modern Sport

The importance of this research is based on investigating mood-based thinking in physical education teachers, as well as the relation to decision making in secondary schools within the Directorate of Education in Baghdad Governorate. Consequently, this contributes to granting teachers a positive role in carrying out their responsibilities correctly and efficiently. The research problem focuses on the two variables of mood-based thinking and decision-making among physical education teachers in secondary schools within Baghdad Governorate, given their positive role and the need to identify the outcomes of such decisions in professional performance and their influence on students. The study therefore sought …

Balancing Robustness And Practicality In Model Compression, Personalization, And Healthcare Interventions, Sawinder Kaur

Dissertations - ALL

Advances in machine learning have enabled significant progress in generating robust solutions. However, designing solutions that balance robustness while meeting the practical constraints of diverse applications, such as limited resources and data, remains a challenge. We explored three key aspects of this interplay: verified robust compressed neural networks, context-wise robust personalization, and reliable counterfactual interventions for healthcare. First, we introduce VeriCompress, a novel framework to streamline the synthesis of compressed neural networks with formal guarantees of adversarial robustness. This enables the deployment of reliable and efficient models in resource-constrained environments, such as smartphones. Second, we developed CRoP (Context-wise Robust Static …

On Detecting Change-Points Of A Segmented Logistic Regression Model, Jingwen Li

Dissertations - ALL

This dissertation investigates the problem of change-point detection in segmented logistic regression models, where the total number of change-points is unknown. We consider both unconstrained models, which allow discontinuities at change-points, and constrained models, which impose continuity across change-points. The central challenge is to determine the number of change-points and to accurately estimate their locations, while ensuring valid statistical inference for the associated regression coefficients. We begin by introducing a set of regularity assumptions that underlie the theoretical analysis throughout this work. For a given number of change-points, we present algorithms to estimate their locations and obtain the maximum likelihood …

Green Ai-Enhanced Deep Learning Model For Breast Cancer Detection And Classification In Mammography Images: Bc-Net-512, Nesma Abd El-Mawla, Mohamed A. Berbar, Nawal A. El-Fishawy, Mohamed A. El-Rashidy

Mansoura Engineering Journal

This study champions a sustainable approach for developing a Deep Learning (DL) model for medical image analysis, specifically focusing on breast cancer (BC) detection in mammograms. By prioritizing low-computing algorithms to achieve high diagnostic accuracy while minimizing the model's environmental footprint, that aligns with the principles of Green AI. In this paper, an innovative architecture called BC-Net-512 was constructed for the classification of BC mammography. It is composed of lightweight Convolutional Neural Network (CNN) blocks for texture, density, and structure feature extraction and detection, a thin, fully connected layer for learning complex patterns and correlations in the extracted features, and …

Variety Of Study Methods In Ophthalmology Residency, Muhammad Rizqy Abdullah, Evelyn Komaratih

Folia Medica Indonesiana

Effective study methods are critical in ophthalmology residency, which must balance cognitive learning with surgical skill development. This systematic review examined non-surgical cognitive strategies and surgical training techniques to inform curriculum design and enhance resident performance. A structured literature search was conducted in PubMed and Scopus (2016–2025) using keywords (“study method” OR “effective study” OR “learning strategy” OR “educational intervention” OR “training approach”) AND (“ophthalmology residency” OR “resident education”). After screening and applying eligibility criteria, 17 studies were included. Methods were categorized as non-surgical (n = 5) and surgical learning (n = 12). This review revealed that non-surgical strategies, including …

How Narrative Storytelling On Social Media Develops Relationships Between Authors And Readers, Madelyn Woodson

Theses - ALL

With the rise of social media being used to sell products and connect communities, it is important to understand how creators are developing relationships with viewers. A specific community where this is evident is BookTok, where readers and authors can connect over shared interests. Understanding how, in this case, relationships are built between readers and authors can be broken down by looking at elements of trust, satisfaction, and commitment which stem from the Organization Public Relationship (OPR) theory developed by Hon and Grunig (1999). Research has shown that personalization and storytelling when creating content and communicating with publics develops a …

Dysconscious Ableism In The Public Library Advancing Disabled Patron And Staff Experiences Toward More Inclusive Programs And Services, William N. Myhill

Dissertations - ALL

This dissertation investigates the operation of dysconscious ableism in public librarianship and its impact on disabled patrons and staff. Drawing on critical disability studies, critical theory, and ethnographic methods, the study explores how public library policies, practices, and professional beliefs perpetuate ableist norms and marginalize disabled individuals. The research is grounded in three central questions: (1) How does dysconscious ableism operate in public librarianship? (2) What are the lived experiences of disabled patrons and staff in public library spaces? (3) What strategies can disrupt dysconscious ableism and promote a more inclusive, anti-ableist praxis? Data were collected through interviews with librarians …

Do Digital Transformation And Macroeconomic Stability Matter For The Asean's Economic Growth?, Sri Andaiyani, Abdul Bashir, Ichsan Hamidi

Bulletin of Monetary Economics and Banking

This work assesses the association between digital transformation, macroeconomic stability, and economic growth. We employ yearly data from 2010 to 2021 in ASEAN countries. The findings show digital transformation and macroeconomic stability can increase in GDP in the long term. Digital transformation contributes favorably and substantially, while macroeconomic stability negatively and significantly affects economic growth in the short-term. The study reveals causal link between digital transformation and macroeconomic stability, causality from digital transformation to economic growth, and economic growth to macroeconomic stability. This implies that digital transformation is the engine of growth.

Video Education On The Knowledge Of Cosmetics And Skin Care Contact Dermatitis: A Cross-Sectional Study, Muhammad Rizkinanda Prasetyo, Damayanti Damayanti, Lilik Herawati, Sylvia Anggraeni

Journal of General - Procedural Dermatology and Venereology Indonesia

Background: Contact dermatitis is an immunologic reaction to irritants, allergens, and is commonly associated with cosmetics and skin-care products. Limited knowledge of this condition increases the risk of improper product use and delays preventive measures. Therefore, education plays a crucial role in fostering a comprehensive understanding of its etiology and prevention. This study aimed to evaluate the effectiveness of a video-based educational intervention in enhancing women’s knowledge regarding contact dermatitis related to cosmetics and skin-care products.

Methods: This cross-sectional study collected primary data from 100 female academics aged 17–45 at Airlangga University, Surabaya, East Java, Indonesia. Knowledge of contact dermatitis …

Learning Engagement In Primary Education: Development And Validation Of A Contextualized Scale For Indonesian Elementary School Students, Meidias Abror Wicaksono, Edy Purwanto, Tri Joko Raharjo, Diani Akmalia Apsari, Bambang Subali, Nuni Widiarti

Jurnal Kajian Bimbingan dan Konseling

This study aims to develop and examine the validity and reliability of a contextual learning engagement scale for elementary school students. The scale was develop based on the learning motivation and behavior theory by Schunk, Pintrich, and Meece, and interest dimensions from Ainley. A quantitative approach using instrument development design was employed. Data analysis was conducted using confirmatory factor analysis (CFA) to evaluate the construct structure. A total of 330 students from grades 4, 5 and 6 were selected through cluster random sampling. The results revealed that the scale consists of four dimensions: learning enthusiasm, learning persistence, learning autonomy, and …

Relational Knowledge Restructuring: Explored Though Experimental And Bayesian Computational Methods, Ashley Nicole Douglass

Dissertations - ALL

Are people rational Bayesian learners that produce a final posterior belief that is a compromise of their prior and the amount of new data? If they are then can one create a model that can predict their final belief given an estimate of their prior belief and the new data? This study aims to explore this. The participants were presented with two relational structures, the initial and the alternative which contradicted each other to a certain degree. In condition 1 the alternative structure was similar to the initial and in condition 2 the structure had the same number of contradictions …

Adaptive Generation In Evolutionary Robotics: From Adversarial Objects To Guided Optimization, Unknown Akshay

Dissertations - ALL

Evolution-inspired algorithms have proven effective for complex optimization problems butsuffer from computational inefficiency due to their reliance on random variation operators. This is problematic in domains where fitness evaluation depends on expensive procedures such as training a neural network or running a robot, either in simulation or on hardware. This dissertation presents novel approaches for evolutionary robotics that replace stochastic evolutionary operations with learned, adaptive strategies using reinforcement learning (RL), significantly improving search efficiency while maintaining population diversity.The first contribution is a voxel-based evolutionary framework for generating adversarial objects that challenge robotic grasping systems. By evolving objects with controlled similarity …

Cultural Mistrust Framework Of Mental Help-Seeking And Internalized Model Minority Myth, Jin Zhao

Dissertations - ALL

Objective: Cultural mistrust can serve as an intermediary variable indirectly facilitating the association between racial microaggressions and unfavorable mental help-seeking attitudes. This study sought to broaden the cultural mistrust framework’s applicability to Asian and Asian American college students by (1) replicating the framework using a national multi-campus sample and (2) integrating internalized model minority myth (i.e., unrestricted mobility and achievement orientation) via a conditional process approach. Methods: This study used cross-sectional data from a national sample of Asian and Asian American college students (N = 180, Mage = 21.47 [SD=1.84]). Path models estimated indirect and direct associations, as well as …

Computationally Efficient Methods For Calculation Of Light-Matter Interaction In Large Semiconductor Quantum Dots, Chandler Martin

Dissertations - ALL

Quantum dots are a class of materials in the nanometer scale that have unique optical and electronic properties due to the quantum confinement effect. Traditional electronic structure computational techniques used for calculating these properties fall to scalability issues as the size of the quantum dot increases, or introduce significant errors into the final result. This makes using computational methods to predict new materials, or verify properties of existing materials, challenging to use for experimental applications of quantum dots. This gap between experiment and computation is due to large basis sets, expensive integrals, and storage and manipulation of tensors that grow …

Studying The Surface Morphology Of The Precipitate Formed Of Cobalt (Ii) Ion And Copper (Ii) Ion By Using Continuous Flow Injection Analysis/ Nag-4sx3-3d Analyzer-Atomic Force Microscopy, Nagham Shakir Turkey, Bakr Sadiq Mohammed, Issam Mohammad Shakir

Baghdad Science Journal

This work aims to use the home-made NAG-4(sources)x3 with three solar cells (NAG-4SX3-3D analyzer), together with AFM and CFIA, in which the morphology of the surface of the Co²⁺ and Cu²⁺ ions precipitates have been determined by determine the roughness parameters and obtain a sufficient amount of weight (Maronite precipitate of Cu(II) ion and bright green precipitate of Co(II) ion) for the AFM sample via the reaction of both ions with Ca₂[Fe(CN)₆] as a precipitating agent. The ability of the suggested method to calculate the quantity of nanoparticles that can fill the vacant surface …

Comparison Of Xrf And Icp-Oes Methods To Determine Trace Element Concentrations Of Kidney Stones In Iraqi Patients, Farah Khalid Denyeif, Saadiyah Ahmed Dhahir

Baghdad Science Journal

Understanding the nature and composition of stone formation as well as developing treatment strategies that prevent stones from forming in kidneys are dependent on researching the chemical makeup of kidney stones. Therefore, 11 samples of kidney stones were collected and digested, and the concentration of metal ions such as (Ag, Al, Cu, Mn, Ni, Ti, Zn, Ca, Na, K and Mg) was measured using the Inductively Coupled Plasma - Optical Emission Spectroscopy method and X-ray Fluorescence. it was found that Ca is the highest concentration and most abundant in all samples in both methods, followed by Na, Mg and Ni. …

Organic And Inorganic Carbon Drives Zooplankton Composition In Tigris River Within Baghdad, Mohammed Hamdan, Batool Kadhim

Baghdad Science Journal

Increasing availability of organic and inorganic carbon in aquatic ecosystems became a common phenomenon that was influenced by manmade activities and ongoing climate change, which could influence zooplankton communities. Zooplankton composition in Tigris River under the effects of dissolved organic and inorganic carbon inputs (DOC and CO₂) has not been studied yet. Hence, we studied those impacts by taking three locations that were distributed in Tigris River within Baghdad City. Eighty-four taxa of zooplankton have been recognized during the study. These taxa were divided into three main groups that are: Rotifera forms 69.04% and Cladocera forms 16.66%, while …

Impact Of Dividend Policy On Share Price Volatility: Evidence From Sri Lanka, Maleesha Piumini Panangala, Neluka Devpura, Ravindra Lokupitiya

Bulletin of Monetary Economics and Banking

This paper investigates how dividend policy affects share price volatility among companies on the Colombo Stock Exchange from 2012 to 2020, including the COVID-19 period. Using a fixed effects model that explains 70% variability, we find both dividend per share and payout ratio significantly reduce share price volatility. Results remain robust even after excluding 2020 data. We identify several policy implications including how our results are useful for policy makers in understanding financial market instability and how investors can factor information in forecasting market prices.

The Impact Of Increasing News Intensity And Number Of Investors On The Relationship Between News Sentiment And Price Movement In The Developing Country: Indonesian Evidence, Zaäfri Ananto Husodo, Muhamad Nagib Alatas

Bulletin of Monetary Economics and Banking

This study examines how news intensity and investor numbers affect the link between news sentiment and equity price movements in Indonesia, using the LQ45 Index. Applying methods such as correlation analysis, CAPM, VAR, Granger causality tests, and rolling correlations, we find that higher news intensity and investor participation strengthen the connection between news sentiment and stock returns while also increasing volatility. The findings suggest that incorporating news sentiment analysis can improve market stability and investment decisions in developing economies.

Phospholipid Scramblase 1 (Plscr1) Regulates Interferon-Lambda Receptor 1 (Ifn-Λr1) And Ifn-Λ Signaling In Influenza A Virus (Iav) Infection, Alina Xiaoyu Yang, Lisa Ramos-Rodriguez, Parand Sorkhdini, Dongqin Yang, Carmelissa Norbrun, Sonoor Majid, Sanghyun Lee, Yong Zhang, Michael Holtzman, David F Boyd, Yang Zhou

2020-Current year OA Pubs

Phospholipid scramblase 1 (PLSCR1) is an interferon-stimulated gene (ISG) that has several known anti-influenza functions. However, the mechanisms in relation to its expression compartment and enzymatic activity have not been completely explored. Moreover, only limited animal models have been studied to delineate its role at the tissue level in influenza infections. Our results showed that influenza A virus (IAV)-infected