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Transcription And Translation Regulation Of Germ Cell To Maternal Transition, Noor Kotb

Electronic Theses & Dissertations (2024 - present)

In sexually reproducing organisms, the process of gametogenesis produces mature eggs and sperms required for fertilization to occur. The egg's quality is crucial for the healthy development of the next generation, making oogenesis a tightly regulated process with multiple benchmarks. To investigate the intricacies of oogenesis, we utilized the Drosophila oogenesis system as our primary model. This choice was motivated by several factors. Firstly, the process of egg development in Drosophila is conserved across species, making it a valuable proxy for understanding oogenesis in other organisms, including humans. Secondly, Drosophila melanogaster has long been established as a powerhouse in genetic …

Stratifying The Social: The Race/Class Dialectic And The Fetishization Of Race In Our Neoliberal Contemporary, Steven D. Delmagori

Electronic Theses & Dissertations (2024 - present)

This dissertation utilizes the lens of Social Reproduction Theory to examine the consequences of viewing race and racism and other systems of oppression like Patriarchy as separate or additive autonomous ontologies. Intersectionality Theory posits oppressions as independent systems that “add” or “intersect” onto oppressed subjects in particular circumstances. On the other hand, responding to Intersectionality Theory, Social Reproduction Theory understands systems of oppression as mutually constitutive social relations. Social Reproduction Theory therefore allows for a more thorough understanding of the relationship between racism and other systems of oppression and their own relationship to capitalism. Rather than theorizing these social relations …

Applying The Fair Use Doctrine In Music Copyright "Borrowing" Infringement Cases, Shea Bettwy

University of Richmond Law Review

Copyright fair use is a broad, statutory doctrine that permits fair use of any type of copyrighted work. But the fair use defense is virtually nonexistent in copyright infringement cases involving musical compositions, such as the famous “Blurred Lines” case. Defendants and courts neglect to consider it for reasons unknown. One might conjecture that this simply reflects a tacit understanding in the music industry that original music should be untouched. In fact, musical borrowing is a longstanding and accepted tradition, which makes copyright fair use particularly amenable to musical compositions. Disconcertingly, ignoring fair use of musical compositions in infringement cases …

Regulation By (Bad) Proxy: How Selective Application Of Transaction Cost Economics Tainted The Ftc's Proposed Ban Of Employee Noncompete Agreements, Alan J. Meese

Faculty Publications

Agencies have imperfect information about conduct they regulate. This problem is particularly acute when identical conduct has differing effects in various markets. Determining the economy-wide impact of such conduct can be difficult or impossible.

The FTC faces such a challenge. The Commission has announced a rule banning the nation’s 30 million employee noncompete agreements (“NCAs”) as unfair methods of competition under Section 5 of the FTC Act. The Commission determined that NCAs likely reduce aggregate wages, helping to establish a presumptive violation. The Commission also found that nearly all NCAs are both procedurally coercive—because employers use overwhelming bargaining power to …

Assessing The Transformative Influence Of Chatgpt On Research Practices Among Scholars In Pakistan, Nayab Arshad, Mehran Ullah Baber, Adnan Ullah

Mesopotamian Journal of Big Data

This article investigates the transformative impact of ChatGPT on research practices within the scholarly community in Pakistan. ChatGPT, a powerful AI language model, has added significant consideration for its possibility of improving academic research. Survey data was gathered via a structured questionnaire distributed to researchers in Pakistan. A total of 278 questionnaires were distributed for the randomly chosen sample, of which 223 were returned. For calculating descriptive statistics, SPSS was utilized.  Results of the study indicated that 90% of scholars are familiar with the practice of ChatGPT in research activities. 86% of scholars used 3.5 (Basic version) of ChatGPT for …

Leveraging Ai And Big Data In Low-Resource Healthcare Settings, Ahmed Hussein Ali, Saad Ahmed Dheyab, Abdullah Hussein Alamoodi, Aws Abed Al Raheem Magableh, Yuantong Gu

Mesopotamian Journal of Big Data

Big data and artificial intelligence are game-changing technologies for the underdeveloped healthcare industry because they help optimize the entire supply chain and deliver more exact patient outcome information. Machine learning approaches that have recently seen more growing popularity include deep learning models that have brought revolution within the healthcare system in the previous years due to more complicated data compared to previous years . Machine learning is an essential data analysis procedure to describe efficient and effective methods to extract hidden information from large amounts of data that it would take logical analytics too long to manage. Recent years have …

Generalized Time Domain Prediction Model For Motor Imagery-Based Wheelchair Movement Control, Z. T. Al-Qaysi, M. S. Suzani, Nazre Bin Abdul Rashid, Reem D. Ismail, M. A. Ahmed, Rasha A. Aljanabi, Veronica Gil-Costa

Mesopotamian Journal of Big Data

Brain-computer interface (BCI-MI)-based wheelchair control is, in principle, an appropriate method for completely paralyzed people with a healthy brain. In a BCI-based wheelchair control system, pattern recognition in terms of preprocessing, feature extraction, and classification plays a significant role in avoiding recognition errors, which can lead to the initiation of the wrong command that will put the user in unsafe condition. Therefore, this research's goal is to create a time-domain generic pattern recognition model (GPRM) of two-class EEG-MI signals for use in a wheelchair control system. This GPRM has the advantage of having a model that is applicable to unknown …

Mlp And Rbf Algorithms In Finance: Predicting And Classifying Stock Prices Amidst Economic Policy Uncertainty, Bushra Ali, Khder Alakkari, Mostafa Abotaleb, Maad M. Mijwil, Klodian Dhoska

Mesopotamian Journal of Big Data

In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and …

Advanced Machine Learning Approaches For Enhanced Gdp Nowcasting In Syria Through Comprehensive Analysis Of Regularization Techniques, Khder Alakkari, Bushra Ali, Mostafa Abotaleb, Rana Ali Abttan, Pushan Kumar Dutta

Mesopotamian Journal of Big Data

This study addresses the challenge of nowcasting Gross Domestic Product (GDP) in data-scarce environments, with a focus on Syria, a country facing significant economic and political instability. Utilizing a dataset from 2010 to 2022, three machine learning algorithms Elastic Net, Ridge, and Lasso were applied to model GDP dynamics based on macroeconomic indicators, commodity prices, and high-frequency internet search data from Google Trends. Among these, the Lasso regression model, noted for its variable selection and sparsity promotion, proved most effective in capturing Syria's complex economic realities, achieving the lowest Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). …

Deep Learning Approaches For Gender Classification From Facial Images, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Reem D. Ismail, Z. T. Al-Qaysi, Fatimah N. Ameen

Mesopotamian Journal of Big Data

Gender recognition on the facial level is considered one of the most important technologies that finds use in such fields as a personalized marketing plan, safe systems of authentication, and effective human-computer interfaces. However, it has the following challenges; variation of lighting, facial movement, and ethnic/age face images. AI and DL has been improving on the effectiveness, flexibility, and speed of the gender classification system. AI enables complex and automatic feature learning in Data, while DL is tailored for handle variants in vision-based data. In this paper, we evaluated several architectures including Efficient Net_B2, ResNet50, ResNet18, and Lightning whilst determining …

Automated Water Quality Assessment Using Big Data Analytics, Yasmin Makki Mohialden, Nadia Mahmood Hussien, Saba Abdulbaqi Salman

Mesopotamian Journal of Big Data

Water is one of the world's most precious resources, essential to life. Industrial waste, agricultural runoff, and urban discharge degrade water, rendering it unfit for consumption. Water quality monitoring and evaluation are more important than ever. Big Data analytics is used to examine water quality utilizing enormous datasets of pH, hardness, solids concentration, chloramine, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. This work classifies water potability, which is vital for human consumption, using strong machine learning on massive datasets. Classifiers were Random Forest, Gradient Boosting, and Support Vector Machine on 3,276 water bodies. The Random Forest classifier obtained the highest …

Anomaly-Based Ids (Intrusion Detection System) For Cyber-Physical Systems, Ahmad Muter Awaad, Khattab M. Alheeti, Abdul Kream A.H. Najem

Mesopotamian Journal of Big Data

Cyber-physical systems (CPS) are critical infrastructures that integrate physical processes with computational components. The security of CPS is paramount, as any breach can lead to severe consequences. Anomaly-based intrusion detection systems (IDS) have emerged as a promising approach to safeguard CPS against cyber threats. This paper presents an anomaly-based IDS designed specifically for CPS, leveraging machine learning techniques to establish a baseline of normal system behaviour and promptly detect deviations indicative of malicious activities. The proposed system incorporates multiple classification techniques, including KNeighbors, RandomForest, XGB, DecisionTree, SGD, SVM, LGBM, AdaBoost, Bagging, and MLP Classifier, to enhance detection accuracy and robustness. …

Age-Related Development Of Bilateral Coordination Of The Upper Limbs In Children And Adolescents, Samuel Nemanich, Sheila M. Schindler-Ivens

Physical Therapy Faculty Research and Publications

Bilateral coordination of the upper limbs (UL) is important for activities of daily living and physical activities. Motor coordination improves from childhood through adolescence. However, age-coordination trajectories for bilateral UL movements are not well-established, and it is unclear if bimanual coordination develops slower than unilateral coordination. In this study we examined age-related changes in UL coordination from childhood to late adolescence. Typically-developing children (N = 29, aged 7–17 years) performed unilateral and bilateral, antiphase cycling tasks with their ULs. Variations in cycling velocity and interlimb phase errors were computed as measures of coordination. Linear regression was used to examine …

The Effects Of Attentional Focus Instructions On Approach Jump Performance, Hanwen Liu, Scott Arnett, Dano Tolusso, Kaylee Woodard

Physical Therapy Faculty Research and Publications

Introduction

Attentional focus has been shown to significantly influence motor learning and performance, with external focus instructions enhancing performance compared to internal focus instructions. Research has also demonstrated that novice performance is improved when instructed to focus on skill execution processes rather than outcomes. However, this effect remains untested in complex jumping skills crucial to numerous sports.

Purpose

This study aimed to investigate the effect of internally and externally directed process and outcome focus instructions on approach jump height and approach speed in novice participants.

Methods

Twelve individuals aged 18 to 22 participated across three sessions: Familiarization, Test Session 1, …

Empirically Assessing 510(K) Device Safety, George Horvath

Akron Law Faculty Publications

Innovations in medical device technology hold the potential to improve health outcomes across the populace. Nearly half of all medical devices that enter the U.S. market each year are regulated by the Food and Drug Administration (FDA) under the 510(k) pathway. If FDA regulation of 510(k) devices stifles innovation, health outcomes will suffer over time because beneficial devices would not reach patients; on the other hand, if regulation facilitates innovation, health outcomes could improve. Most discussions of innovation under the 510(k) pathway have been either excessively reductionist (such as claims that the pathway stifles innovation) or wholly pessimistic (notably, the …

The Patent Law Origins Of Science Fiction, Camilla Hrdy, Daniel H. Brean

Akron Law Faculty Publications

This Article reveals the surprising role of patent law in shaping the literary genre of science fiction. Drawing on previously unpublished sources, the Article shows that Hugo Gernsback—the so-called “father” of science fiction who started the first all-science-fiction magazine in 1926—believed that works of science fiction are analogous to patents. Like patents, science fiction stories can disclose useful information to the public about new inventions. Like patents, science fiction stories can influence future inventors and drive innovation. Gernsback went even further, positing that some of the inventions depicted in science fiction should themselves be patentable. In 1952, he urged Congress …

Dobbs And Democracy, Melissa Murray, Katherine A. Shaw

Articles

In Dobbs v. Jackson Women’s Health Organization, Justice Alito justified the decision to overrule Roe v. Wade and Planned Parenthood of Southeastern Pennsylvania v. Casey with an appeal to democracy. He insisted that it was “time to heed the Constitution and return the issue of abortion to the people’s elected representatives.” This invocation of democracy had undeniable rhetorical power: it allowed the Dobbs majority to lay waste to decades’ worth of precedent, while rebutting charges of judicial imperialism and purporting to restore the people’s voices. This Article interrogates Dobbs’s claim to vindicate principles of democracy, examining both the intellectual pedigree …

Situating The Modern Public Trust Doctrine In Trust Law: The Duty Of Loyalty And The Case For Bifurcated, De Novo Judicial Review, Edward A. Zelinsky

Articles

This article situates the modern public trust doctrine (PTD) in contemporary trust law. Grounding the PTD in trust law leads to two important corollaries. First, the PTD planted in trust law imposes upon government actors and agencies trust law’s fiduciary duty of loyalty. In the context of the PTD, that duty of loyalty runs to the public as the beneficiary of the PTD. Second, faced with plausible claims that this fiduciary duty of loyalty to the public has been violated, courts should apply trust law’s de novo standard of review to those administrative and legislative decisions alleged to impair public …

K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur

Publications

Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to tend to a user’s persona appropriately. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. …

Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth

Publications

Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …

Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy

Publications

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.

Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth

Publications

Current Virtual Mental Health Assistants (VMHAs) primarily offer counseling and suggestive care but do not assist with patient diagnosis due to their lack of training in safety-constrained and specialized clinical process knowledge, referred to as ProKnow. In this work, we define ProKnow as an ordered set of information aligned with evidence-based guidelines or categories of conceptual understanding used by domain experts. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Pro- Know, known as ProKnow-data. We develop a method for natural language question generation (NLG) designed to interactively gather diagnostic information from patients, termed ProKnow-algo. …

A Comprehensive Survey On Rare Event Prediction, Chathurangi Shyalika Jayakody Kankanamalage, Ruwan Wickramarachchi, Amit Sheth

Publications

Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistics and ML. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, …

Active Techniques Of Heat Transfer Enhancement: A Review, Duaa Mraiza, Foad Faraji

Babylonian Journal of Mechanical Engineering

In this work, active methods for improving heat transfer are discussed, with a particular emphasis on new ideas and their applicability in various sectors of different types. Heat transfer efficiency is essential for optimising energy utilisation in industries such as chemical processing, heating, ventilation, air conditioning (HVAC), and renewable energy. Mechanical assistance, surface vibrations, electrostatic fields, and fluid injection are some approaches investigated in this research. The study also investigates the ideas behind these techniques and their practical ramifications. The results of this study indicate that active approaches, even though they often call for more power, have the potential to …

Crop Yield Using Novel Parametric L-System Plant Modelling, Christopher Cameron Napier

Theses: Doctorates and Masters

This research considers a system for the recognition of real plant parts through image analysis based upon synthetic plant modelling. It aims to use data pipelines and synthetic datasets to define recognizable features that assist in the efficient analysis of real plants and plant images. This research asks about the efficacy of L-systems in accurately simulating wheat crop characteristics. It specifically focusses on readable, understandable, accurate, and complex L-system algorithms. The research examines wheat crops in terms of phenotypes and examines the accuracy of a dataset in support of real image annotation. The methodology used was experimental in nature and …

Improving Growth Models Of Cultivated Sugar Kelp, Saccharina Latissima, By Accounting For Intraspecific Variation In Thermal Tolerance, Ruby Krasnow, Sara Gonzalez, Scott Lindell

Student Publications [Scholarly]

Dynamic models of sugar kelp (Saccharina latissima) growth are used to estimate the production potential of seaweed aquaculture in many regions around the world. These models do not currently account for the existence of S. latissima ecotypes that are adapted to regional environmental conditions, particularly temperature. We tested the hypothesis that recalibrating the temperature parameters of a dynamic energy budget model using literature data for S. latissima from regions with a similar climate to the region of interest would result in more accurate predictions than using a general species-wide temperature response curve. Calibrating the model using data from warm regions …

Enhancing Pitch Stability In Floating Wind Platforms: The Hydrodynamic Effects Of Thin Plates, Ignacio Pregnan Johannesen

School of Engineering, Computing and Mathematics Theses

The Floating Offshore Wind Turbines (FOWT) technology is still at an early stage looking for ways to make it competitive. In the case of semi-submersible FOWT the large structure has been pointed as an option to tackle the final energy price and therefore, smaller/cheaper platforms are desired. However, this would have implications on the platform hydrodynamic performance. This thesis discusses the use of thin disks as pitch motion suppression surfaces for FOWT platforms, employing three methodologies; potential flow, Computational Fluid Dynamics (CFD) and Experiments. With NEMOH a boundary element method code, the added mass and excitation forces/moments were calculated for …

Data Security And User Authentication In Public Cloud Computing Environments, Nelson Vasconcelos Soares Dos Santos

School of Engineering, Computing and Mathematics Theses

Cloud computing has become a prevalent paradigm for organisations and individuals, offering a range of services and resources that enable the efficient management, processing and storage of large quantities of data. However, despite its numerous advantages, cloud computing also presents significant challenges related to data security and user authentication. The distributed nature of cloud environments, coupled with the relinquishment of control over data to cloud service providers, has raised concerns regarding data vulnerability to various types of attacks, such as data breaches, malware injection, and denial of service. Moreover, traditional password and token-based authentication methods have been shown to be …

“A View Into The Ccu” – Investigating Visual Analysis And Machine Learning Approaches To Aid Clinicians In Critical Care Units, Christopher Haynes

School of Engineering, Computing and Mathematics Theses

Despite the vast quantities of data stored by critical care units, there is a resistance to applying this to technological innovation. This is limiting the potential to enable the best clinical decision making and increasing time pressures on clinical staff. This thesis examines the current state of data use in two critical care units at University Hospitals Plymouth. Through discussions with clinical staff, the main limitations of their current digital work environment are considered to develop a three-element system. A thorough literature review is then carried out into critical care practices, visualisation principles for multi-variate heterogeneous data, and supervised machine …