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2025

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Amending Rule 15.3 Of The Mississippi Rules Of Criminal Procedure To Fully Capture Boykin V. Alabama And Acknowledge Systemic Coercion Indigent Defendants Face In Mississippi, Sarina P. Larson Jan 2025

Amending Rule 15.3 Of The Mississippi Rules Of Criminal Procedure To Fully Capture Boykin V. Alabama And Acknowledge Systemic Coercion Indigent Defendants Face In Mississippi, Sarina P. Larson

MC Law Review

The U.S. Constitution—not a state—is what governs questions of constitutionality. It follows that for a state court to make a constitutionally sufficient record of a defendant’s waiver at their plea, the complete interpretation as to sufficiency by the United States Supreme Court must be followed. This Comment stands for the proposition that when Rule 15.3 of the Mississippi Rule of Criminal Procedure was adopted in 2017, it misinterpreted the call of the United States Supreme Court in Boykin v. Alabama, 395 U.S. 238 (1969). Ultimately, this Comment suggests amendments to Rule 15.3 that would place it in compliance with Boykin, …

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College Courts: Administrative Abuse Of Title Ix And Its Consequences, Noah Bloomberg Jan 2025

College Courts: Administrative Abuse Of Title Ix And Its Consequences, Noah Bloomberg

MC Law Review

This article examines the contentious application of Title IX in addressing campus sexual assault, tracing its evolution through recent presidential administrations. Initially designed to combat sex-based discrimination in education, Title IX has been expansively interpreted to regulate university adjudication of sexual violence cases. Policies introduced during the Obama administration mandated quasi-judicial procedures, sparking debates over fairness and due process, while Trump-era reforms shifted focus toward protecting the rights of accused students, prompting criticism from victims' advocates. The article argues that these approaches have overstepped Title IX's intended purpose, burdening universities and creating systemic challenges for survivors and the accused alike. …

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Leveraging Artificial Intelligent For Optimized Crop Production: An Ann-Based Approach, Yahya Layth Khaleel, Fadya A. Habeeb, Mustafa Abdulfattah Habeeb, Fatimah N. Ameen Jan 2025

Leveraging Artificial Intelligent For Optimized Crop Production: An Ann-Based Approach, Yahya Layth Khaleel, Fadya A. Habeeb, Mustafa Abdulfattah Habeeb, Fatimah N. Ameen

Mesopotamian Journal of Computer Science

To incite modern day crop production and ensure sustainability, exact crop recommendations are key to the process. This study pays significant attention to the need for the use of big data tools in studies involving comprehensive data sets that contain information on soil and other environmental characteristics. The set of data used in this research includes Nitrogen, Phosphorus, and Potassium content coordinated with Temperature, Humidity, pH Value, and Rainfall. Knowing these factors is to make a favorable decision about improving agricultural products yield, availability and management of the resources, as well as general well-being of the crops. Specialized advisory on …

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A Deep Learning Approach For Identifying Malicious Activities In The Industrial Internet Of Things, Mohammed Amin Almaiah, Fuad Ali El-Qirem, Rami Shehab, Khaled Sulieman Momani Jan 2025

A Deep Learning Approach For Identifying Malicious Activities In The Industrial Internet Of Things, Mohammed Amin Almaiah, Fuad Ali El-Qirem, Rami Shehab, Khaled Sulieman Momani

Mesopotamian Journal of Computer Science

Data-driven decision-making, real-time connectivity, and automation have transformed industrial operations with the Industrial Internet of Things. However, the integration also introduces substantial cybersecurity vulnerabilities, making IIoT networks a prime target for malicious activities. Cyber threats are evolving and becoming more sophisticated, which makes traditional security mechanisms inadequate. An approach using deep learning to detect malicious activities in IIoT environments is examined. It is investigated whether Deep Feed Forward neural networks, autoencoders, and convolutional neural networks are effective at detecting anomalies and mitigating cyber threats. NSL-KDD and UNSW-NB15 benchmark datasets are used to evaluate the proposed model's accuracy, precision, and detection …

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Self-Attention Enhanced Dual Bigru For Arabic Fake News Detection, Baqer M. Merzah, Jafar Razmara, Jaber Karimpour Jan 2025

Self-Attention Enhanced Dual Bigru For Arabic Fake News Detection, Baqer M. Merzah, Jafar Razmara, Jaber Karimpour

Mesopotamian Journal of Computer Science

The rapid proliferation of social media platforms has greatly amplified the dissemination of fake news, representing significant obstacles to public trust and evidence-based decision-making, particularly for the Arabic-speaking population. Meeting the challenge of Arabic fake news detection is a problem compounded by the complex morphological nature of the language, as well as limited resources. This study presents a hybrid deep learning framework that integrates two Bidirectional Gated Recurrent Units (BiGRUs) along with an attention mechanism for efficiently detecting misinformation in Arabic news. The method leverages FastText word embeddings for disambiguating the intricate semantics of the Arabic language. The model is …

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End-To-End License Plate Detection And Recognition In Iraq Using A Detection Transformer And Ocr, Younis Al-Arbo, Hanaa F. Mahmood, Asmaa Alqassab Jan 2025

End-To-End License Plate Detection And Recognition In Iraq Using A Detection Transformer And Ocr, Younis Al-Arbo, Hanaa F. Mahmood, Asmaa Alqassab

Mesopotamian Journal of Computer Science

Automatic License Plate Recognition (ALPR), DEtection TRansformer (DETR), Deep Learning for Object Detection, Optical Character Recognition (OCR), Region-Specific Vehicle Identification

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Enhanced Iot Cyber-Attack Detection Using Grey Wolf Optimized Feature Selection And Adaptive Smote, Sura Abed Sarab Hussien, Mustafa S. Ibrahim Alsumaidaie, Nada Hussein M. Ali Jan 2025

Enhanced Iot Cyber-Attack Detection Using Grey Wolf Optimized Feature Selection And Adaptive Smote, Sura Abed Sarab Hussien, Mustafa S. Ibrahim Alsumaidaie, Nada Hussein M. Ali

Mesopotamian Journal of Computer Science

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrate that GWO reduces features from 32 …

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Agentic Ai-Enhanced Virtual Reality For Adaptive Immersive Learning Environments, Indra Kishor, Udit Mamodiya, Mohammed Almaayah, Amer Alqutaish, Rami Shehab, Theyazn H. H. Aldhyani Jan 2025

Agentic Ai-Enhanced Virtual Reality For Adaptive Immersive Learning Environments, Indra Kishor, Udit Mamodiya, Mohammed Almaayah, Amer Alqutaish, Rami Shehab, Theyazn H. H. Aldhyani

Mesopotamian Journal of Computer Science

Immersive learning using Virtual Reality (VR) has gained prominence for delivering experiential, engaging education. However, most VR learning environments lack real-time adaptability, personalization, and cognitive responsiveness. This study presents an Agentic AI-enabled VR framework that autonomously adjusts pedagogical content, interaction style, and challenge level based on learner behavior, emotions, and performance feedback. The proposed system integrates a reinforcement learning-based agent with a virtual reality module to form an intelligent tutor capable of independent decision-making. A neuro-symbolic model processes multi-modal feedback (gesture, speech, gaze, performance) to determine context-aware pedagogical strategies. The system employs a self-evolving curriculum logic that adapts in real …

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Parkinson's Disease Detection Using Deep Learning Approach Based On Wearable Sensor-Based Daily Monitoring, Bahaulddin N. Adday, Khalid Shaker, Ihsan Salman, Hothefa Shaker Jan 2025

Parkinson's Disease Detection Using Deep Learning Approach Based On Wearable Sensor-Based Daily Monitoring, Bahaulddin N. Adday, Khalid Shaker, Ihsan Salman, Hothefa Shaker

Mesopotamian Journal of Artificial Intelligence in Healthcare

Parkinson's disease (PD) is a movement disorder characterized by motor dysfunction commonly bradyphemia, tremor, rigidity, akinesia, or slowness of movement. Noting that motor states can fluctuate in PD the primary aim of this current paper was to differentiate multiple states using wearable sensors in the patients and detection of this PD based on deep learning (CNN). Methodology: In this paper, the researchers recorded the signals of the accelerometer and gyroscope fixed on the wrist of PD in their regular daily functioning after using this dataset collection. The deep learning architecture developed was to optimize a CNN for analyzing the sensor …

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Models Of Machine Learning To Diagnose Chronic Kidney Disease Using A  Weka-Based Classifier, Shaymaa Adnan Abdulrahman, Sameerah Faris Khlebis Jan 2025

Models Of Machine Learning To Diagnose Chronic Kidney Disease Using A  Weka-Based Classifier, Shaymaa Adnan Abdulrahman, Sameerah Faris Khlebis

Mesopotamian Journal of Artificial Intelligence in Healthcare

In the present day, humans are confronted with a variety of diseases as a result of their lifestyle and the current environmental conditions. Therefore, it is crucial to identify and predict these diseases in their early phases in order to prevent their severe manifestations. Manually identifying maladies is a challenging task for physicians on a regular basis. Predicting chronic illnesses is the aim of this article. This goal is applicable through a state-of-the-art approach to classification correctly identifies people with chronic illnesses. Predicting maladies is also a difficult endeavor. Therefore, disease prediction is significantly influenced by data mining. To get …

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Artificial Intelligence-Powered Robotic Technology For Transforming Palliative Care, Adebo Thomas, Asiku Denis, Wamusi Robert, Simon Peter Kabiito, Zaward Morish, Aziku Samuel, Malik Sallam, Ioannis Adamopoulos Jan 2025

Artificial Intelligence-Powered Robotic Technology For Transforming Palliative Care, Adebo Thomas, Asiku Denis, Wamusi Robert, Simon Peter Kabiito, Zaward Morish, Aziku Samuel, Malik Sallam, Ioannis Adamopoulos

Mesopotamian Journal of Artificial Intelligence in Healthcare

Palliative care seeks to improve the quality of life of patients with life-threatening illnesses by addressing their physical, emotional, and psychological needs. However, global challenges such as workforce shortages, limited access to specialized care, and inconsistent care quality demand innovative solutions. Advances in artificial intelligence (AI)-powered robotics offer transformative potential to overcome these barriers and strengthen palliative care delivery. This study explores how AI-driven robotic technologies support palliative care through applications in symptom monitoring, clinical decision-making, emotional companionship, and personalized care planning. It reviews cutting-edge robotic systems, including assistive, companion, diagnostic, nursing, procedural, service, and rehabilitation robots. Enabled by machine …

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The Role Of Artificial Intelligence In Early Tumor Detection: An Xgboost Risk Assessment Model For Egyptian Patients, Toufik Mzili, Mourad Mzili, Saif Islam Bouderba, Ahmed Abatal, Widi Aribowo, Zahra Oughannou Jan 2025

The Role Of Artificial Intelligence In Early Tumor Detection: An Xgboost Risk Assessment Model For Egyptian Patients, Toufik Mzili, Mourad Mzili, Saif Islam Bouderba, Ahmed Abatal, Widi Aribowo, Zahra Oughannou

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study developed an XGBoost-based risk assessment model to enhance early tumor detection among Egyptian patients, addressing the challenges of late diagnosis and limited healthcare resources. Utilizing a retrospective dataset of 178 patients, the model incorporated demographic, clinical, and biochemical variables, including AFP levels, viral hepatitis status (HBV/HCV), and liver function markers. The model demonstrated strong predictive performance, achieving an accuracy of 0.833, precision of 0.846, and an AUC of 0.86, though recall remained moderate (0.688), indicating room for improvement in identifying high-risk cases. Feature importance analysis highlighted AFP levels and hepatitis status as the most influential predictors, aligning with …

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Heartbeat Sound Classification Using Mel-Spectrogram And Cnn Optimized By Frilled Lizard Algorithm For Cardiovascular Disease Detection, Ahmed T. Alhasani, Zainab H. Albakaa, Shahad A. Alabidi, Osamah Qasim Abd Zaid Gburi, Ammar Kadi, Irina Potoroko Jan 2025

Heartbeat Sound Classification Using Mel-Spectrogram And Cnn Optimized By Frilled Lizard Algorithm For Cardiovascular Disease Detection, Ahmed T. Alhasani, Zainab H. Albakaa, Shahad A. Alabidi, Osamah Qasim Abd Zaid Gburi, Ammar Kadi, Irina Potoroko

Mesopotamian Journal of Artificial Intelligence in Healthcare

Cardiovascular disease (CVD) continues to be the predominant cause of mortality globally, underscoring the critical necessity for prompt and precise diagnostic techniques.  This paper introduces an innovative machine learning framework for categorizing heartbeat sounds into four classifications—normal, murmur, additional heart sound, and artifact—utilizing audio recordings from the PhysioNet/CinC Challenge 2016 dataset.  The methodology employs Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, converting raw heart sound data into comprehensive time-frequency representations.  A Convolutional Neural Network (CNN) is utilized for classification, with its hyperparameters refined by the recently developed Frilled Lizard Optimization (FLO) method, a bio-inspired metaheuristic that emulates the …

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Clinical Data Analysis Using Machine Learning Algorithms To Predict The Progression Of Type 2 Diabetes, Niki Syrou, George Mpourazanis, Panagiotis Tsirkas, Jovanna Adamopoulou, Jovanna Adamopoulou Jan 2025

Clinical Data Analysis Using Machine Learning Algorithms To Predict The Progression Of Type 2 Diabetes, Niki Syrou, George Mpourazanis, Panagiotis Tsirkas, Jovanna Adamopoulou, Jovanna Adamopoulou

Mesopotamian Journal of Artificial Intelligence in Healthcare

Type 2 diabetes mellitus (T2DM) is a growing global health concern requiring early detection strategies. This study applies a Random Forest machine learning model to predict diabetes progression using a structured clinical dataset of 100 patients. The dataset includes demographic, physiological, and biochemical variables such as age, BMI, blood pressure, glucose levels, and lipid profiles. After preprocessing and training, the model achieved strong performance metrics: accuracy of 0.80, precision of 0.84, recall of 0.94, and an AUC of 0.88. Feature importance analysis revealed that systolic blood pressure, fasting glucose, and BMI are the most critical predictors. These findings are consistent …

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An Artificial Intelligence Model For Predicting Hospital Readmission Using Electronic Health Records Data, Rezarta Cara, Klodian Dhoska, Fjona Cara, Fredrick Kayusi, Linety Juma Jan 2025

An Artificial Intelligence Model For Predicting Hospital Readmission Using Electronic Health Records Data, Rezarta Cara, Klodian Dhoska, Fjona Cara, Fredrick Kayusi, Linety Juma

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study investigates the application of a machine learning model—specifically the Light Gradient Boosting Machine (LightGBM)—to predict 30-day hospital readmissions using structured electronic health record (EHR) data. Hospital readmissions remain a critical challenge in healthcare systems, often indicating gaps in continuity of care and contributing to higher costs. By leveraging demographic, clinical, and diagnostic variables from 350 anonymized patient records, the model aimed to accurately identify individuals at high risk of readmission. Key features included age, number of previous admissions, length of stay, number of medications, chronic disease status, and gender. Data preprocessing, model training, and evaluation were conducted using …

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Optimizing Decision Tree Classifiers For Healthcare Predictions: A Comparative Analysis Of Model Depth, Pruning, And Performance, Hussein Alkattan, Ali Subhi Alhumaima, Mohammed Shakir Mohmood, Ghassan Dhahir Mohammed Al-Thabhawee Jan 2025

Optimizing Decision Tree Classifiers For Healthcare Predictions: A Comparative Analysis Of Model Depth, Pruning, And Performance, Hussein Alkattan, Ali Subhi Alhumaima, Mohammed Shakir Mohmood, Ghassan Dhahir Mohammed Al-Thabhawee

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study presents of Decision Tree classifiers for predictive modeling in medicine, focusing on model depth optimization, pruning techniques, and performance evaluation. On the basis of a synthetic healthcare dataset containing over 55,000 records, each with features such as age, gender, blood type, bill amount, and medical condition, we investigate the impact of varying tree depth from 1 to 5 on predictive accuracy, interpretability, and generalizability. Shallow models have strong transparency but poor classification strength, and deep models obtain stronger interactions but suffer from overfitting. With pruning, we find a balance between model simplicity and precision and yield strong classifiers …

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Meta-Learning Approaches For Causal Discovery In Dynamic Healthcare And Robotics Environments, Hayder Abbood Md Facp Jan 2025

Meta-Learning Approaches For Causal Discovery In Dynamic Healthcare And Robotics Environments, Hayder Abbood Md Facp

Mesopotamian Journal of Artificial Intelligence in Healthcare

Healthcare and robotics dynamic task domains are also known to be both complex and time-varying systems where reasoning about causal links are important for making reliable decisions. But causal structures in these domains are difficult to ascertain with limited data, distribution shifts, and changing environments. Meta-learning (or learning to learn) is a promising approach to combating these two obstacles by learning from experience across tasks to quickly adapt to new task environments. In this paper we provide a new framework that combines ideas from meta-learning and causal discovery, whose objective is to discover the underlying cause effect relationships in sequential, …

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Biogpt: A Generative Transformer-Based Framework For Personalized Genomic Medicine And Rare Disease Diagnosis, Ghada Al-Kateb, Emine Cengiz, Murat Gök Jan 2025

Biogpt: A Generative Transformer-Based Framework For Personalized Genomic Medicine And Rare Disease Diagnosis, Ghada Al-Kateb, Emine Cengiz, Murat Gök

Mesopotamian Journal of Artificial Intelligence in Healthcare

This paper introduces BioGPT, a generative transformer-based framework designed to advance personalized genomic medicine and rare disease diagnosis. Unlike conventional models that process either genomic sequences or clinical narratives in isolation, BioGPT employs a cross-modal architecture that effectively fuses both data streams, enabling precise classification and interpretable natural language generation. The model is pre-trained on large-scale genomic and electronic health record datasets and fine-tuned for rare disease tasks. Comprehensive experiments demonstrate BioGPT’s superiority over state-of-the-art biomedical models, including RarePT, BioBERT, and DNABERT, with improvements of up to 10% in F1-score and over 20 BLEU points in justification fluency. Ablation studies …

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Adaboost-Based Classification For Bone Sarcoma Outcome Prediction: A Comparative Machine Learning Approach, El-Sayed M. El-Kenawy, Ahmed Mohamed Zaki, Marwa M. Eid Jan 2025

Adaboost-Based Classification For Bone Sarcoma Outcome Prediction: A Comparative Machine Learning Approach, El-Sayed M. El-Kenawy, Ahmed Mohamed Zaki, Marwa M. Eid

Mesopotamian Journal of Artificial Intelligence in Healthcare

Bone sarcomas represent aggressive malignancies with complex clinical presentations where traditional prognostic methods often lack precision in predicting patient outcomes. This study developed and validated machine learning models for predicting bone sarcoma patient outcomes using a comprehensive dataset from Memorial Sloan Kettering Cancer Center spanning 2010-2020, comparing multiple algorithms including AdaBoost, Logistic Regression, Ridge Classifier, Quadratic Discriminant Analysis, and Linear Discriminant Analysis. AdaBoost demonstrated superior performance with 0.84 accuracy, 0.875 sensitivity, 0.7955 specificity, 0.8448 precision, 0.8333 negative predictive value, and 0.8596 F1 score, outperforming other algorithms which achieved 0.83 accuracy and 0.8496 F1 score. Statistical analysis confirmed significant differences between …

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Data Mining Driven Segmentation Of Health Insurance Policyholders Using K-Means Clustering, Farah Ali Khairi, Laith Farhan, Oluwaseun A. Adelaja Jan 2025

Data Mining Driven Segmentation Of Health Insurance Policyholders Using K-Means Clustering, Farah Ali Khairi, Laith Farhan, Oluwaseun A. Adelaja

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study illustrates a data‐driven approach to the segmentation of health insurance policyholders based on K-Means clustering of an open insurance dataset. Key demographic and financial features like age, body mass index (BMI), dependents, annual medical spending, and premium payment were normalized first to ensure comparability. The optimal number of clusters (k = 3) was determined using silhouette analysis, and three clusters were formed: (1) young, low‐cost individuals, (2) middle‐aged medium‐cost individuals, and (3) old, high‐cost individuals. Cluster centroids provide actionable profiles that can be utilized by insurers for target marketing, risk profiling, and development of customized plans. A set …

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An Iterative Shifting Disaggregation Algorithm For Multi-Source, Irregularly Sampled, And Overlapped Time Series, Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli Jan 2025

An Iterative Shifting Disaggregation Algorithm For Multi-Source, Irregularly Sampled, And Overlapped Time Series, Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency …

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Computer Vision-Based Framework For Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach To Unlocking Financial Insights, Iftakhar Ali Khandokar, Priya Deshpande Jan 2025

Computer Vision-Based Framework For Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach To Unlocking Financial Insights, Iftakhar Ali Khandokar, Priya Deshpande

Electrical and Computer Engineering Faculty Research and Publications

Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and …

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Juvenile Record Expungement Policy In Illinois, Emma Kudelka Jan 2025

Juvenile Record Expungement Policy In Illinois, Emma Kudelka

Res Publica - Journal of Undergraduate Research

This policy paper explores juvenile expungement policies and their broader social impact. Research suggests that lenient expungement laws help prevent discrimination in education, housing, and military service, aiding rehabilitation youth offenders' reintegration. The paper begins with a historical overview of Illinois juvenile law, highlighting its shift from punitive to rehabilitative approaches. It then examines key legislation, including the Juvenile Court Act, Juvenile Justice, and Delinquency Prevention Act, and Youth Opportunity and Fairness Act. Illinois's juvenile expungement laws are analyzed, covering eligibility, process, and timeline. A comparative analysis follows, contrasting Illinois' policies with those of California and Florida to emphasize differences …

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A Critical Review Of Consumer Perception And Environmental Impacts Of Bioplastics In Sustainable Food Packaging, Junjie Lin, Amit K. Jaiswal, Swarna Jaiswal Jan 2025

A Critical Review Of Consumer Perception And Environmental Impacts Of Bioplastics In Sustainable Food Packaging, Junjie Lin, Amit K. Jaiswal, Swarna Jaiswal

Articles

As awareness of environmental issues deepens and the demand for sustainability grows among societies, businesses, and consumers, minimizing the environmental impact of food packaging has become increasingly important. Bioplastics offer a promising solution due to their use of renewable resources (such as plants), their biodegradability, and their ability to retain the protective properties of traditional plastics. This review discusses recent advancements and trends in sustainable food packaging, emphasizing the role of bioplastics. Several typical types of bioplastics are introduced, along with an analysis of their advantages and limitations. Furthermore, this study investigates consumers' attitudes toward bioplastic food packaging, emphasizing their …

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Psychological Reactance Theory And Tax Evasion Intentions, Diana Falsetta, Brian C. Spilker Jan 2025

Psychological Reactance Theory And Tax Evasion Intentions, Diana Falsetta, Brian C. Spilker

Faculty Publications

This study experimentally examines how tax administration service inefficiencies in the form of tax return processing delays from paper-filed tax returns indirectly affect taxpayer tax evasion intentions through taxpayers’ psychological reactance to the delay. We further consider whether this indirect effect is different when the taxpayer willingly chooses to paper file their tax return versus when the taxpayer is forced to do so. Consistent with Psychological Reactance Theory (PRT), we find that when taxpayers receive delayed refunds, they experience psychological reactance (e.g., anger and frustration with the tax administration) that increases their tax evasion intentions. Further, we find that the …

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Foreign Tax Holiday Participation And U.S. Job And Investment Loss, Zackery D. Fox, Linda Krull, Scott G. Rane Jan 2025

Foreign Tax Holiday Participation And U.S. Job And Investment Loss, Zackery D. Fox, Linda Krull, Scott G. Rane

Faculty Publications

We investigate whether foreign tax holiday participation among U.S. multinational companies is associated with offshoring U.S. jobs and other domestic investment activities. We find that foreign tax holiday participation is associated with (1) an increase in offshoring U.S. jobs and (2) a decrease in domestic investment, as proxied by changes in the number of employees, capital expenditures, and R&D activity. Furthermore, we find evidence suggesting that the association between targeted, temporary tax incentives provided by foreign tax holidays and firms’ domestic activities is stronger among firms with a smaller foreign presence and is distinct from the impact of foreign statutory …

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Ai-Based Predictive Analytics For Network Operations, Timur Nikisin, David White Jan 2025

Ai-Based Predictive Analytics For Network Operations, Timur Nikisin, David White

Academic Poster Collection

AI-Based Predictive Analytics for Network Operations

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Instructional Designer Perspectives On The Pursuit Of Quality In Online Course Design, Jason K. Mcdonald, Stephen C. Yanchar Jan 2025

Instructional Designer Perspectives On The Pursuit Of Quality In Online Course Design, Jason K. Mcdonald, Stephen C. Yanchar

Faculty Publications

In this qualitative study we investigated the experiences of instructional designers as they sought to build quality into online courses. Through semi-structured interviews, we explored what enabled and hindered their pursuit of quality, how they experienced their efforts in this regard, what mattered to them, and complexities that accompanied this pursuit. Our analysis of participant experiences suggested four themes: (1) connections between quality and designers’ ability to act autonomously; (2) connections between quality and collaborative, team-based relationships; (3) ambivalence due to tensions between autonomy and collaboration; and (4) ways of coping with limits on autonomy and collaboration. We conclude our …

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Multipath-Enhanced Directional Modulation For Secure Mmwave On-Body Communication, Wasi Ur Rehman Khan, Max J. Ammann, Hossein Raghib Hokmabadi, William Scanlon, Adam Narbudowicz Jan 2025

Multipath-Enhanced Directional Modulation For Secure Mmwave On-Body Communication, Wasi Ur Rehman Khan, Max J. Ammann, Hossein Raghib Hokmabadi, William Scanlon, Adam Narbudowicz

Research Outputs: 2025-Present

This paper proposes Directional Modulation (DM) for secure millimeter-wave (mmWave) on-body communication, where security is enhanced by leveraging multipath propagation. The double two-ray ground reflection model is employed to analyze a two-antenna Multi-Input Single-Output (MISO) system placed on human skin. The system operates at 24 GHz with polarization tangential to the skin. Key parameters such as antenna-separation, antenna-to-skin spacing and phase difference between transmitter antennas are varied to analyze the resulting path gain. The results obtained from analytical model are experimentally validated using Yagi antennas placed over a forearm phantom. Based on the findings, the DM technique is implemented. The …

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Disclosure Benchmarking By Lawyers: Evidence Fromthe Ipo Setting, Michael S. Drake, Jeff Mcmullin, Kenneth Merkley, Chase Potter, John Treu Jan 2025

Disclosure Benchmarking By Lawyers: Evidence Fromthe Ipo Setting, Michael S. Drake, Jeff Mcmullin, Kenneth Merkley, Chase Potter, John Treu

Faculty Publications

Lawyers play an important advisory role in drafting financial reports, yet empirical evidence documenting the influence of external legal counsel on this process remains sparse. This study focuses on a specific aspect of lawyers’ drafting process: the practice of reviewing disclosures previously filed by other issuers—a practice termed “disclosure benchmarking.” Using initial public offering (IPO) disclosures as the setting, we find that disclosure benchmarking is associated with amore efficient Securities and Exchange Commission (SEC) review process. We further find that it is associated with several measures of disclosure quality, including disclosures that are less likely to be revised in subsequent …

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