1393 research outputs found
Sort by
Toward extracting scattering phase shifts from integrated correlation functions. IV. Coulomb corrections
Get PDFThe formalism developed in Guo and Gasparian [Toward extracting the scattering phase shift from integrated correlation functions, Phys. Rev. D 108, 074504 (2023)]; Guo [Toward extracting the scattering phase shift from integrated correlation functions. II. A relativistic lattice field theory model, Phys. Rev. D 110, 014504 (2024)]; and Guo and Lee [Toward extracting scattering phase shift from integrated correlation functions. III. Coupled channels, Phys. Rev. D 111, 054506 (2025)] that relates the integrated correlation functions for a trapped system to the infinite volume scattering phase shifts through a weighted integral is further extended to include Coulomb interaction between charged particles. The original formalism cannot be applied due to different divergent asymptotic behavior resulting from the long-range nature of the Coulomb force. We show that a modified formula in which the difference of integrated correlation functions between particles interacting with Coulomb plus short-range interaction and with Coulomb interaction alone is free of divergence, and has rapid approach to its infinite volume limit. Using an exactly solvable model, we demonstrate that the short-range potential scattering phase shifts can be reliably extracted from the formula in the presence of Coulomb interaction
An Analysis of Executive Managers Acceptance of Cyber Security Risk Management – A Systematic Review
No full textInformation security is vital for safeguarding critical assets and services from cyber threats, but it incurs significant organizational costs and technological reliance, raising questions about its value and necessity. Security is not merely a technical issue but a strategic one requiring executive managers\u27 involvement. This study examines how executive management\u27s participation in information security risk management (ISRM) affects organizational security. A systematic literature review of 69 articles identifies the aspects and impacts of executive managers\u27 (EM) involvement in cybersecurity risk management (CRM). Findings indicate that EM involvement is crucial for corporate strategy and business success, enhancing security, visibility and accountability at higher levels. EMs play a key role in protecting critical assets, aligning security strategy with business goals, and fostering a culture of awareness and responsibility. The paper proposes a best practice framework for maintaining EM involvement in CRM, aligning cybersecurity strategy with organizational goals while balancing costs and benefits
Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda
Get PDFThis systematic literature review rigorously evaluates the impact of Generative AI (GenAI) on academic integrity within higher education settings. The primary objective is to synthesize how GenAI technologies influence student behavior and academic honesty, assessing the benefits and risks associated with their integration. We defined clear inclusion and exclusion criteria, focusing on studies explicitly discussing GenAI’s role in higher education from January 2021 to December 2024. Databases included ABI/INFORM, ACM Digital Library, IEEE Xplore, and JSTOR, with the last search conducted in May 2024. A total of 41 studies met our precise inclusion criteria. Our synthesis methods involved qualitative analysis to identify common themes and quantify trends where applicable. The results indicate that while GenAI can enhance educational engagement and efficiency, it also poses significant risks of academic dishonesty. We critically assessed the risk of bias in included studies and noted a limitation in the diversity of databases, which might have restricted the breadth of perspectives. Key implications suggest enhancing digital literacy and developing robust detection tools to effectively manage GenAI’s dual impacts. No external funding was received for this review. Future research should expand database sources and include more diverse study designs to overcome current limitations and refine policy recommendations
A Survey-Based Quantitative Analysis of Stress Factors and Their Impacts Among Cybersecurity Professionals
Get PDFThis study investigates the prevalence and underlying causes of work-related stress and burnout among cybersecurity professionals using a quantitative survey approach guided by the Job Demands-Resources model. Analysis of responses from 50 cybersecurity practitioners reveals an alarming reality: 44% report experiencing severe work-related stress and burnout, while an additional 28% are uncertain about their condition. The demanding nature of cybersecurity roles, unrealistic expectations, and unsupportive organizational cultures emerge as primary factors fueling this crisis. Notably, 66% of respondents perceive cybersecurity jobs as more stressful than other IT positions, with 84% facing additional challenges due to the pandemic and recent high-profile breaches. The study finds that most cybersecurity experts are reluctant to report their struggles to management, perpetuating a cycle of silence and neglect. To address this critical issue, the paper recommends that organizations foster supportive work environments, implement mindfulness programs, and address systemic challenges. By prioritizing the mental health of cybersecurity professionals, organizations can cultivate a more resilient and effective workforce to protect against an ever-evolving threat landscape
Strategic Leadership in Cybersecurity Risk Management: Elevating the Role of Executive Managers
Get PDFAs the frequency and sophistication of cyber threats continue to escalate, cybersecurity has evolved from a technical concern to a central pillar of strategic enterprise governance. This TREO Talk presents the findings of a rigorous systematic literature review (SLR) encompassing 69 scholarly publications selected using the PRISMA 2020 framework. The study investigates how executive managers (EMs) influence the success of cybersecurity risk management (CRM) programs and frames their participation as both a requirement and a competitive advantage. Despite global investment in security infrastructure, many organizations remain vulnerable to breaches, largely due to the absence of sustained executive leadership in CRM. This research exposes a persistent gap between technical risk solutions and the strategic insight required at the executive level to integrate these solutions across business functions. Drawing from multidisciplinary sources, this work identifies that EMs who actively shape and lead CRM policies achieve superior outcomes in resilience, compliance, stakeholder trust, and organizational agility. The study introduces the \u27Action and Remediation Zone Framework\u27—a novel conceptual model that delineates two interconnected zones of executive involvement: (1) the Action Zone, encompassing proactive functions such as risk appetite definition, cybersecurity budgeting, strategy alignment, and enterprise-wide awareness cultivation; and (2) the Regeneration Zone, focusing on post-incident leadership roles including crisis communication, accountability, trust rebuilding, and systemic improvement. The framework synthesizes the best practices from ISO/IEC 27001, COBIT, and the NIST Cybersecurity Framework, reframing them to center executive agency in CRM decision-making. This TREO Talk will showcase how EMs can influence technical decisions through strategic foresight and governance alignment, reinforcing that their presence is indispensable at every critical juncture—from setting organizational tone to navigating breach recovery. It will also demonstrate that cybersecurity success depends not only on tools and technologies but on visionary leadership that treats CRM as a shared, cultural priority. Furthermore, the session will engage the IS community in discussing future research directions, including empirical validation of the proposed framework, metrics for assessing executive involvement, and longitudinal studies exploring CRM maturity across sectors. By placing EMs at the heart of CRM, this work lays a foundation for redefining cybersecurity leadership in the age of digital transformation and persistent threats. Attendees will gain both theoretical frameworks and actionable insights to influence policy, shape organizational culture, and architect resilient security strategies from the top down
Towards Adaptive Learning: A Review of Machine Learning on LMS Data
Get PDFThis study presents a literature survey on the application of machine learning (ML) in learning management system (LMS) data analytics, aiming to provide insights into adaptive learning development and propose an agenda for future research. The literature survey is based on a proposed adaptive learning framework and critically analyzes the results within this context. The results reveal that machine learning methods can be used to evaluate the effectiveness of instructional interventions and combining online behaviors with textual data can improve the outcome of performance prediction. Key findings also highlight several open issues, including using small datasets and the need for comprehensive ML methods and algorithm development. Future research directions include improving the accuracy of student performance prediction, supporting instructional interventions, enriching student engagement through multimodal LMS data analytics, and leveraging big data and ML approaches for learning behavior pattern detection
Latent subtypes of Comorbidities in Multiple Sclerosis patients: Insights from Social Media
Get PDFMultiple Sclerosis is a chronic neurological disease associated with various physical and cognitive impairments. Individuals with MS might also experience medical and psychiatric comorbidities that can exacerbate the severity of the disease and lower their overall well-being. Therefore, it becomes essential to identify these co-occurring health conditions at the early stages to optimize treatment and improve patient outcomes. Consequently, the objective of this study is to explore the commonly occurring comorbidities among MS patients around the globe from social media discourse. Furthermore, it aims to unveil public perceptions, providing insights that might not be captured via clinical research methods. The results indicate that psychiatric and autoimmune comorbidities are the most prevalent, whereas visual disorders are the least common among MS patients. Understating such patterns can help prioritize and guide interventions aimed at tailoring MS comorbidity management strategies to address the specific needs of MS patients
Impact of Data Characteristics on the effectiveness of multi-stage transfer learning using MRI medical images
Get PDFRecent innovations in the field of deep learning and computational power, both in terms of hardware and software technology gave much-needed attention. Implementation of models by using the techniques has attracted a lot of researchers and is still one of the most widely used choices of cross-sectional researchers from medical as well as information system and science. The inherent characteristics of the data make contribution in determining the effectiveness of multi-stage learning in brain tumor classification by MRI medical images. Transfer learning models’ direct application is limited by variations in brain tumor MRI dataset’s image quality, resolution, contrast, noise, and anatomical diversity. Existing approaches often ignore how these data characteristics influence the model\u27s ability to generalize and maintain high classification performance. In other words, CNN or any deep learning model demands sufficient pre-training work effectively in picture analysis. The use of transfer learning turned out to be an efficient approach in Learning models from scratch with high accuracy. Real images that have been used in the training of models during their learning on ultrasound do not seem to be as effective as in the non-medical images.
The dissertation discusses deep learning models for medical image classification and introduces an EfficientNetB2-GRU hybrid model with improved transfer learning methods. The ensemble method performed better than all the individual base classifiers trained on the whole MNIST dataset. A comparative analysis of three well-known CNN models—VGG-16, VGG-19, and ResNet50, which have all performed well in medical image classification and segmentation tasks. All the models have been trained on MNIST datasets to determine the importance of TL model effectiveness with the use of OCL and how it affects the data characteristics. The EfficientNetB2+GRU model is employed for the proposed hybrid model using the two-stage transfer learning method. In order to achieve the research goal, an initial timeline mapping background study was done first for the knowledge base of developing deep learning models and made acquainted with the recent advancement and issues of the field. The second activity entails analyzing and comparing another set of state-of-art deep learning models to ascertain the best of kind deep learning models and then proceeded to design an IT artifact that adhered to design science method. Lastly, an experiment was conducted for the test of the model trained sequentially for the identification of the Optimum Cutoff Layer (OCL) of the model, which was really used to compare the model of various image quantities of variable sizes to determine the effect on the efficiency of models.
The dissertation provides multiple contributions in the deep learning field and medical image analysis. In light of the new developments in model, the dissertation developed a new IT artifact to improve the deep learning models for the use of decreasing computationally intensive procedure involved in training of the small sample of the image data. From the theoretical perspective – a robust review of literature using the timeline method will contribute to enhancing the domain knowledge. From a methodological perspective – conceptualization of a unique IT artifact will encourage the model applicability for optimum training. From application perspective a detailed comparative analysis of medical image characteristics over the effectiveness of the deep learning model will provide informed decision-making capability for the benchmark utilization of the various image characteristics
Using LLMs to Synthesize Product Desirability Datasets
Get PDFThis research investigates the use of large language models (LLMs) to generate synthetic datasets for the evaluation of product desirability using the Product Desirability Toolkit (PDT). The study aims to identify if LLMs can effectively create cost-efficient and scalable datasets to enhance sentiment analysis and user experience design.https://scholar.dsu.edu/research-symposium/1061/thumbnail.jp
Investigating the energetics and feeding ecology of a range of Azhdarchid Pterosaurs
Get PDFHere we aim to gain insight into the ecology of Azhdarchid pterosaurs. By exploring their genetic requirements and feeding capabilities, we gain a deeper understanding of what their day-to-day life may have looked like.https://scholar.dsu.edu/research-symposium/1060/thumbnail.jp