Digital Commons Network^™

The Method Of Power Networks Mode Optimization In Basis Of Genetic Algorithm, Tulkin Shernazarovich Gayibov, Gulnaz Makhmutovna Turmanova

Technical science and innovation

One of the main tasks solved in planning short-term and managing operational modes of electric power systems (EPS) is the optimization of their network modes on the adjustable parameters. For modern complex EPS, this task is often characterized by the multi-extremality of the objective function, the appearance of discontinuous functions, the presence of initial information of a probabilistic and partially uncertain nature. In such conditions, solving the problem by traditional algorithms using mainly linear and nonlinear programming methods, Lagrange, gradient, etc., is associated with a number of difficulties in simplifying them and bringing them to a convenient form for calculations. …

The Challenge Of Achieving Attributability In Multilingual Table-To-Text Generation With Question-Answer Blueprints, Aden Haussmann

International Journal of Undergraduate Research and Creative Activities

Generating faithful text descriptions from data tables is a significant challenge in Natural Language Processing (NLP), especially for the world’s many low-resource languages. This paper investigates whether Question-Answer (QA) blueprints—an intermediate planning step where a model first asks and answers questions about the data—can improve the factual accuracy of multilingual table-to-text generation. This novel approach is tested on the TaTA dataset, which includes several African languages, by finetuning models with and without these blueprints.

The results show a key distinction: while the QA blueprint method improves performance for English-only models, these gains disappear in the multilingual setting. This paper’s analysis …

Introductory Chemistry Student Success: Investigating Study Strategies And Evaluating An Informative Utility Value Intervention, Pallavi Nayyar

USF Tampa Graduate Theses and Dissertations

Improving retention in STEM (science, technology, engineering, and mathematics) programs is essential to meet the growing demand for skilled professionals in the domestic workforce. To achieve this, it is critical to understand and address the factors that influence student persistence in STEM degrees. Gateway courses such as general chemistry often serve as early barriers to STEM degree completion, with high rates of attrition that impact not only individual students but also institutions and society. Therefore, the aim of this work was to examine and influence the factors that contribute to student withdrawal from introductory chemistry courses in order to promote …

Genai Literacy Framework For Library Instruction, Adwoa Boateng, Jennifer Freer, Greyson Pasiak, Erich Short, Ryan Tolnay, Rit Libraries

Presentations and other scholarship

A generative artificial intelligence (genai) framework for library instruction. This short framework is designed to incorporate into existing library instruction across many subject areas. The basic elements of the framework are: know & understand genai, use & evaluate genai, and library research & discovery with genai.

The Pastor As Romantic Author: Ai, Preaching, And The Unacknowledged Inheritance Of Authenticity, Daniel Plate, James Hutson

Faculty Scholarship

This article interrogates contemporary reactions to sermons produced with generative technologies through a historical–conceptual lens, arguing that widespread judgments of such outputs as “soulless,” “generic,” or lacking a “beating heart” are best explained by an unacknowledged inheritance from nineteenth-century Romantic expressivism. Rather than treating resistance to machine authorship as a theological verdict on computational incapacity, the study reconstructs how Romanticism centered authorship in sincere self-expression and solitary genius, displacing earlier heraldic expectations that prized fidelity to a received message. Methodologically, the analysis combines intellectual history with discourse analysis of global Christian experiments in synthetic composition (2020–2025), denominational guidance, and media …

Agentizing Privacy Preferences Without Privatizing Data Protection Policy, Frank Pasquale, Vanna Carter

Cornell Law Faculty Publications

This essay explores how consumers might exercise newly restored agency in a digital environment increasingly shaped by generative AI. Rather than expecting consumers to read privacy-related terms of service, policymakers should encourage the emergence of Automated Consumer Agents (“ACAs”) capable of interpreting privacy provisions and acting on behalf of users according to their stated preferences. These systems could automatically reject objectionable forms of data extraction, translating consumer values into actionable decisions in online transactions.

Despite this promise, the rise of ACAs would also present new problems. Without supportive policy and legal frameworks, agentization of privacy preferences could itself reproduce or …

Analysis Of The Status And Thematic Trends Of Ai For Science Research Abroad From 2015 To 2024, Fangyuan Wang, Huiting Xu, Jinghua Xue

Journal of Scientific Information Research

[Purpose/significance] This paper analyzes the relevant literature in the field of AI for Science（AI4S）in the WoS core database from 2015 to 2024, and sorts out the research status and development trends in this field, aiming to provide forward-looking insights for the application of AI technology in scientific research.

[Method/process] This paper combines bibliometric analysis with the BERTopic model to analyze the publication trends, publishing countries, core authors, and topic identification and development trends in the field of AI4S.

[Result/conclusion] Through bibliometric analysis, this paper reveals the exponential growth trend of AI4S-related literature, and finds that China ranks first in the …

Governance In The Absence Of Government, Tracy Hresko Pearl

Faculty Articles

Artificial intelligence (AI) is advancing at an unprecedented pace, with generative systems exerting growing influence over social, economic, and political life. While Al offers opportunities for innovation and efficiency, it also poses risks ranging from misinformation and job displacement to existential threats if highly autonomous systems evade human control. Across industry, government, and civil society, there is broad consensus that Al requires oversight.

Yet traditional U.S. regulatory approaches face six significant barriers: (1) technology outpacing legislation, (2) limited Al expertise among policymakers, (3) regulatory capture, (4) political gridlock, (5) outdated governance structures, and (6) the inherent complexity of Al. Combined …

Wildfire Emergency Response And Evacuation Framework Using Drones: Phase I, Hovannes Kulhandjian

Mineta Transportation Institute

Wildfires are escalating in frequency and severity due to climate change, posing increasing threats to human life, infrastructure,and ecosystems. Traditional wildfire management systems struggle to respond effectively to rapidly evolving fire conditions. This research presents a novel, AI-powered Wildfire Emergency Response and Evacuation Framework that integrates autonomous unmanned aerial vehicles (UAVs, AKA “drones”), multi-sensor data fusion, and machine learning (ML) for real-time fire detection, evacuation planning, and search and rescue (SAR) operations. Central to the system is the Dynamic Wildfire Response Algorithm (DWRA), a hybrid decision-making framework combining AI-driven techniques including reinforcement learning (RL), genetic algorithms (GA), and deep learning …

Using Some Artificial Intelligence Algorithms To Estimate The Parametric Regression Function For Spatially Dependent Data Of Water Pollution Of The Euphrates River, Ons Edin Musa, Sabah Manfi Redha

Journal of Economics and Administrative Sciences

These models account for the spatial effects resulting from the proximity of events. A compromise exists in the mathematical accuracy of model parameters when spatial correlations are present in the data of the phenomenon. Data reliant on spatial correlations are crucial in statistical modelling, especially in environmental science, economics, epidemiology, and various other disciplines. This study employs and compares three artificial intelligence approaches—the genetic algorithm (GA), the TABU search algorithm (TSA), and the binary firefly algorithm (Binary FFA)—to determine which is the most efficient for estimating the parametric regression function for spatially dependent data. The Mean Absolute Percentage Error numbers …

Ai Exposure And The Future Of Work: Linking Task-Based Measures To U.S. Occupational Employment Projections, Erik Vasilauskas, Michael Horrigan

Reports

No abstract provided.

Drawing On Uncertainty Methodologies Of Neutrosophic Hypersoft Sets In Cognitive Computing-Driven Healthcare Systems, Mona Mohamed, Nurhan Alaa

Neutrosophic Systems with Applications

A new paradigm called cognitive computing simulates human reasoning and decision-making through integrating advanced techniques such as artificial intelligence (AI) and natural language processing (NLP). Cognitive computing systems, in contrast to traditional systems, can handle both structured and unstructured data, adjust to new information, and offer context-sensitive insights. This study examines how cognitive computing improves decision-making, personalization, and human-machine collaboration in various fields. Cognitive computing in the healthcare sector processes clinical notes, imaging data, and electronic health records to help physicians with diagnosis, treatment planning, and patient engagement. This study examines key applications, including their role in diagnostic support, where …

“Made Classes Easier Than A Coloring Sheet”: Student Perceptions And Uses Of Genai, Jessalyn I. Vallade, Renee Kaufmann, Trenton Upchurch

Human-Machine Communication

Student use of GenAI is growing and so are the faculty concerns. With research providing mixed suggestions and approaches, this study sought to understand the student perspective on ethical uses, their own motives and perceived benefits of GenAI use, the risks involved, and their perceptions of susceptibility and severity related to unethical use (i.e., plagiarism/cheating). Results revealed the complexity of student considerations regarding the uses, risks, and benefits of GenAI and its potential for personalized learning enhancement. Students generally view the likelihood of being caught submitting AI-generated work as their own as high and the consequences as severe. The viability …

Amicus Brief In Support Of Ross Intelligence In Ross Intelligence V. Thomson Reuters, Jess Miers, Bryan L. Frye, Mateusz Blaszczyk

Akron Law Faculty Publications

This amici curiae brief, filed in Thomson Reuters Enterprise Centre GmbH and West Publishing Corp. v. ROSS Intelligence Inc. before the U.S. Court of Appeals for the Third Circuit, argues that Thomson Reuters failed to state a viable claim for copyright infringement against ROSS Intelligence. The amici—law professors specializing in copyright and technology—contend that Westlaw’s headnotes are uncopyrightable factual summaries of judicial opinions and that the West Key Number System has long been in the public domain. The brief distinguishes between AI “pretraining” and “fine-tuning,” emphasizing that ROSS’s fine-tuning process relied on expert-created materials rather than wholesale copying. It cautions …

Artificial Intelligence And Collaborative Learning: Impacts On Creativity, Critical Thinking, And Problem-Solving, Abderrahim Agnaou, Hayat El Asri

Journal of Educational Research and Practice

The rapid development of large anguage models (LLMs) has sparked growing interest in their potential to transform education. This study explores how LLMs influence collaborative learning, focusing on student creativity, critical thinking, problem-solving, and collective intelligence. Specifically, it investigates whether artificial intelligence (AI)–assisted group work enhances or hinders these aspects of the learning process. We used a mixed-methods approach, combining quantitative and qualitative data from surveys, structured interviews, and analysis of student project outputs. We collected data from 102 undergraduate students via questionnaires and 15 follow-up interviews, which offered deeper insight into their experiences using LLMs in team-based projects. The …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

Faculty, Staff and Students Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

School of Medicine Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman

SKMC Student Presentations and Publications

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from …

Awareness And Perception Of Artificial Intelligence In Counselling Practice Among Counsellors-In-Training, Margaret Kehinde Soetan, Ayorinde Emola Akinnusi

Buletin Konseling Inovatif

This study investigated awareness and perception of artificial intelligence in counselling practice among counsellors-in-training. Descriptive survey research design was adopted for this study. The population consisted of all counsellors-in-training (CITs) in Adeyemi Federal University of Education, Ondo. Two hundred (200) counsellors-in-training were randomly selected from Degree three (3) and four (4) as sample for the study. Structured questionnaire was used as instrument to collect data for the study. It was validated by experts in Test and Measurement. Reliability of the instrument was ascertained by using Cronbach’s Alpha statistics mean which yielded a coefficient of 0.83. Data collected was analysed by …

Building Multimodal Knowledge Graphs: Automation For Enterprise Integration, Ritvik Garimella, Hong Yung Yip, Revathy Venkataramanan, Amit P. Sheth

Publications

As enterprises increasingly aim to incorporate artificial intelligence into their workflows to tackle complex, multimodal tasks, the demand for intelligent, robust, and trustworthy systems is paramount. While multimodal large language models offer initial capabilities for processing diverse data streams, their dependence on embedding-based representations limit their effectiveness in delivering semantically grounded explanations and reasoning, as well as qualities essential for enterprise-grade applications. Neurosymbolic approaches provide a promising alternative by enabling traceable, context-aware decision making. However, constructing enterprise-level multimodal knowledge graphs (MMKGs) that enable neurosymbolic approaches remains largely impractical. Although prior efforts have explored MMKG construction, they fall short in addressing …

Research And Development Of Intelligent Measurement Systems, Odil Abdujalilovich Jumaev, Mahmudov Giyosjon Baqoyevich

Chemical Technology, Control and Management

The article discusses modern methods for developing intelligent measuring systems. Intelligent measuring systems are systems based on intelligent technologies that not only accurately measure physical or chemical quantities, but also have the ability to self-analyze, diagnose and make management decisions. The article comprehensively examines the architecture, components of such systems, the organization of their software and hardware, the relationship of sensors and artificial intelligence algorithms. It also analyzes the practical application and prospects of intelligent measuring systems in such areas as industry, medicine, energy, ecology, transport.

The God Prompt And Deus Ex Machina: Techno-Theological Tropes And Operational Metaphors In Generative Media, James Hutson

Faculty Scholarship

This study reframes two durable tropes—the ―God Prompt‖ and the deus ex machina—as analytic lenses for understanding how contemporary generative systems stage beginnings and endings of cultural production. The ―God Prompt‖ denotes command-driven synthesis in which minimal textual instructions instantiate content on demand, crystallizing a production loop of input, model execution, and post hoc evaluation that orients anticipation toward instantaneous yield and controllable variation. The deus ex machina names an externally imposed resolution that interrupts causal development—historically a crane-borne god, functionally an algorithmic override—thereby concentrating attention on closure mechanics rather than world-building continuity. Read together, the pair offers a compact …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Futurescape Libraries Ai Toolkit, Keith Webster

Copyright, Fair Use, Scholarly Communication, etc.

A toolkit developed to explore scenario-specific strategies and activities that research libraries can undertake to prepare for various possible AI-influenced futures. The toolkit integrates the ARL/CNI AI Scenarios published in spring 2024 along with priorities trialed and refined by strategic thinkers working directly in, or adjacent to, the research library field during a Strategic Implications forum held December 7–8, 2024, in Washington, DC.

The “Founder’S Gaze”: How The Fourth Amendment Is A Surveillance Technology That Enables Ai To Scale Control Over The Subaltern, Diego H. Alcalá Laboy

Michigan Journal of Race and Law

Much has been written about the rise of artificial intelligence and machine learning applications and how the current Fourth Amendment law has been unable to mitigate the privacy harm that these tools produce. This article explores how the development and usage of AI and machine learning models is dependent on the originalism principles of Fourth Amendment Law. Utilizing Critical Surveillance Studies and Anticolonial Theory, I posit that the Fourth Amendment is a surveillance technology that categorizes conduct, persons, and places to impose the material conditions for the subjugation of historically minoritized communities within the United States. Furthermore, this article explores …

Deep Learning For Land Use Classification: A Systematic Review Of Hs-Lidar Imagery, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, David Blake, Anwaar Ulhaq, Naeem Janjua

Research outputs 2022 to 2026

Remote sensing (RS) technologies have significantly advanced Earth observation capabilities, enhancing the characterization and identification of surface materials through both spaceborne and airborne systems. These advancements are crucial for improving environmental monitoring and urban planning. As RS datasets have become more accessible, their increased complexity has necessitated a shift from traditional machine learning techniques to more robust deep learning approaches, particularly convolutional neural networks (CNNs) and transformer-based models known for their superior feature extraction capabilities. This systematic review focuses on the application of these deep learning techniques in land use classification, emphasizing the fusion of hyperspectral (HS) and LiDAR data. …

Reimagining Academic Assessment In The Age Of Ai, Matthew Hammerton

Research Collection School of Social Sciences

In ‘Reimagining Academic Assessment in the Age of AI’, Matthew Hammerton examines the challenges and opportunities posed by generative AI for higher education assessment. He critiques common responses like banning AI, reverting to in-class exams, or abandoning essays altogether, arguing that they fail to preserve the deeper pedagogical goals of higher order, independent thinking. Instead, Hammerton proposes a guiding principle of intellectual responsibility: students should be accountable for explaining and defending each major choice in their work—regardless of whether they use AI tools. To operationalise this, he advocates for reintegrating oral examinations (‘vivas’) alongside written essays. In this model, students …

Preparing The Next Generation Of Health Education Teachers For The Ethical Use Of Ai, Melissa J. Haithcox-Dennis, Reeshemah Johnson

Journal of Research Initiatives

This article examines how health education teacher preparation programs can prepare future educators to use artificial intelligence (AI) in responsible and effective ways. It begins with a brief overview of the use of AI in education, considering the growing impact of generative AI technologies on teaching and learning. The article outlines international and national standards that govern the application of AI in education, addressing key ethical concerns, including the dissemination of inaccurate health information, violations of student privacy, bias, and inequitable access to AI tools. The article emphasizes the importance of ethical decision-making and professional responsibility when integrating AI into …

Sugar: A Sequence Unfolding Based Transformer Model For Group Activity Recognition, Yash U. Gondkar

Graduate Masters Theses

Large Language Models have improved significantly in the past couple of years due to the adoption of transformers. However, transformers still find it challenging to process videos due to limited context size caused by their quadratic computing cost. Therefore, we studied a booming field in machine learning which powers applications like social scene analysis and video surveillance systems called Group Activity Recognition (GAR). We found that recent models were able to achieve more than 90% accuracy on popular datasets like the Volleyball dataset, however, it turned out that even they relied on transformers.

Therefore, in this work, we developed a …

Can Artificial Intelligence Models Provide Reliable Medical Counselling To Fertility Patients?, Idan Alcalay, Ariel Weissman, Hadas Ganer Herman, Avi Tsafrir, Matan Friedman, Eran Weiner, Raoul Orvieto, Nikolaos P. Polyzos, Michael H. Dahan, Alex Polyakov, Robert Fischer, Sandro C. Esteves, Baris Ata, Jason M. Franasiak, Yossi Mizrachi

Department of Obstetrics and Gynecology Faculty Papers

RESEARCH QUESTION: Can generative artificial intelligence (AI) models provide reliable counselling to fertility patients regarding real-world clinical questions?

DESIGN: In this cross-sectional study, 12 clinical questions were developed to reflect common, real-life dilemmas encountered during fertility workup and treatment. Responses to each question were generated by two experienced fertility specialists, and two AI models - ChatGPT and Gemini. Eight leading internationally recognized fertility experts, blinded to the source of each reply, independently rated all the responses on a scale from 1 (strongly disagree) to 10 (strongly agree). Ratings were compared across all four repliers using non-parametric statistical tests.

RESULTS: The …