Digital Commons Network^™

Heaven? Is It Worth It?: The Effect Of Belief On Religious Participation And Signaling, Claudia R. Chin

Scripps Senior Theses

Religion has long been a subject that baffles economists because of its seemingly inefficient requirements. It is hard to get one's mind around why an individual would be willing to give up secular commodities for religion, or face the strict entry costs and stigma society associates with the religion. Although many reasons have been suggested, the most widely accepted solution to why we engage in these behaviors is Religious Signaling Theory (RST). RST argues that people engage in these ineffective costs as a way to signal their commitment to the religion, and religions were utilized by society to enforce these …

Interpersonal Emotions As Emergent Phenomena: Social Neuroscience Beyond Western Cultural Constructions, Kaitlyn Penchina

Scripps Senior Theses

Because science as it exists today is a cultural construction of the West, studies of neuroscience have often been limited by Western perspectives. In particular, the Western proclivity towards individualism has led to a field of neuroscience which has historically focused on studying single individuals, as opposed to social or collective neuroscience. For the most part, it has just been assumed that collective phenomena such as interpersonal emotions must be able to be reduced in terms of individual phenomena such as individual emotions. However, closer review reveals that interpersonal emotions have emergent properties that individual emotions alone do not account …

Wireless Investors & Apathy Obsolescence, Sergio Alberto Gramitto Ricci, Christina M. Sautter

Hofstra Law Faculty Scholarship

This Article discusses how a subgenre of retail investors makes investors' apathy obsolete. In prior work, we dub retail investors who rely on technology and online communications in their investing and corporate governance endeavors "wireless investors." By applying game theory, this Article discusses how wireless investors' global-scale online interactions allow them to circulate information and coordinate, obliterating collective action problems.

Data Driven Method To Assess Safety And Energy On Freeway And Intersection Performance Using Probe Vehicle, Rezwana Kabir

Wayne State University Dissertations

Traffic congestion in cities is becoming a more pressing issue as travel demand increases, leading to safety and mobility problems and wasteful use of vehicle energy. Both freeways and signalized corridors are important in creating an efficient transportation network, and congestion on these infrastructure types presents a significant mobility concern in the United States. With advances in sensing technologies for intelligent transportation systems, probe vehicles equipped with GPS-enabled devices such as cell phones and in-vehicle telematics or navigation systems are increasingly being used to gather more information about traffic conditions. While fixed point detectors can be limited by their geographical …

Exploring Faculty Perspectives On Open Access At A Medium-Sized, American Doctoral University, Rachel E. Scott, Julie Murphy, Cassie Thayer-Styes, Chad E. Buckley, Anne Shelley

Faculty and Staff Publications – Milner Library

Faculty hold widely varying perspectives on the benefits and challenges afforded by open access (OA) publishing. In the United States, conversations on OA models and strategy have been dominated by scholars affiliated with Carnegie R1 institutions. This article reports findings from interviews conducted with faculty at a Carnegie R2 institution, highlighting disciplinary and individual perspectives on the high costs and rich rewards afforded by OA. The results reiterate the persistence of a high degree of skepticism regarding the quality of peer review and business models associated with OA publishing. By exploring scholars’ perceptions of and experiences with OA publishing and …

Exploring New Horizons: Surgical Robots Supported By Artificial Intelligence, Aish Hussein, Mohamed Elsayed Sallam, Mohamed Yehia Ahmed Abdalla

Mesopotamian Journal of Artificial Intelligence in Healthcare

Artificial intelligence-assisted surgical robots combine the agility and accuracy of robotic systems with the cutting-edge capabilities of AI technology. These advanced surgical robots are made to help surgeons carry out difficult surgical procedures more precisely and effectively. Surgical robots with AI algorithms built in can analyse enormous volumes of patient data, make choices in real-time, and adjust to changing circumstances. The surgical process and patient safety could both be considerably improved by this technology. Several important advantages of surgical robots powered by AI include Enhanced Accuracy Surgical robots can make extremely precise motions with little error because of the capabilities …

Probabilistic Solutions Of Fractional Differential And Partial Differential Equations And Their Monte Carlo Simulations, Tamer Oraby, Erwin Suazo, Harrinson Arrubla

School of Mathematical & Statistical Sciences Faculty Publications

The work in this paper is four-fold. Firstly, we introduce an alternative approach to solve fractional ordinary differential equations as an expected value of a random time process. Using the latter, we present an interesting numerical approach based on Monte Carlo integration to simulate solutions of fractional ordinary and partial differential equations. Thirdly, we show that this approach allows us to find the fundamental solutions for fractional partial differential equations (PDEs), in which the fractional derivative in time is in the Caputo sense and the fractional in space one is in the Riesz–Feller sense. Lastly, using Riccati equation, we …

Long-Time Asymptotics Of A Complex Cubic Camassa-Holm Equation, Hongyi Zhang, Yufeng Zhang, Zhijun Qiao

School of Mathematical & Statistical Sciences Faculty Publications

n this paper, we study the Cauchy problem of the following complex cubic Camassa-Holm (ccCH) equation mt=bux+12[m(|u|2−|ux|2)]x−12m(uu¯x−uxu¯),m=u−uxx,

where b is an arbitrary real constant. %By applying ∂¯-steepest descent method, l Long-time asymptotics of the equation is obtained through the ∂¯-steepest descent method. Firstly, based on the spectral analysis of the Lax pair and scattering matrix, the solution of the equation is able to be constructed %can be expressed by %the solution of via solving the corresponding Riemann-Hilbert problem (RHP). Then, we present %obtain different long time asymptotic expansions of the solution u(y,t) in different space-time solitonic regions of ξ=y/t. The …

Quantization Dimension For Inhomogeneous Bi-Lipschitz Ifs, Amit Priyadarshi, Mrinal Kanti Roychowdhury, Manuj Verma

School of Mathematical & Statistical Sciences Faculty Publications

Let ν be a Borel probability measure on a d-dimensional Euclidean space Rd, d≥1, with a compact support, and let (p0,p1,p2,…,pN) be a probability vector with pj>0 for 1≤j≤N. Let {Sj:1≤j≤N} be a set of contractive mappings on Rd. Then, a Borel probability measure μ on Rd such that μ=∑Nj=1pjμ∘S−1j+p0ν is called an inhomogeneous measure, also known as a condensation measure on Rd. For a given r∈(0,+∞), the quantization dimension of order r, if it exists, denoted by Dr(μ), of a Borel probability measure μ on Rd represents the speed at which the nth quantization error of order r …

Quantization For A Set Of Discrete Distributions On The Set Of Natural Numbers, Juan Gomez, Haily Martinez, Mrinal Kanti Roychowdhury, Alexis Salazar, Daniel J. Vallez

School of Mathematical & Statistical Sciences Faculty Publications

The quantization scheme in probability theory deals with finding a best approximation of a given probability distribution by a probability distribution that is supported on finitely many points. In this paper, first we state and prove a theorem, and then give a conjecture. We verify the conjecture by a few examples. Assuming that the conjecture is true, for a set of discrete distributions on the set of natural numbers we have calculated the optimal sets of n-means and the nth quantization errors for all positive integers n. In addition, the quantization dimension is also calculated.

Note On Illuminating Constant Width Bodies, Alexey Glazyrin

School of Mathematical & Statistical Sciences Faculty Publications

Recently, Arman, Bondarenko, and Prymak constructed a constant width body in R n whose illumination number is exponential in n. In this note, we improve their bound by generalizing the construction. In particular, we construct a constant width body in R n whose illumination number is at least (τ + o(1))n, where τ ≈ 1.047.

Constrained Quantization For Probability Distributions, Megha Pandey, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, for a Borel probability measure P on a Euclidean space Rk, we extend the definitions of nth unconstrained quantization error, unconstrained quantization dimension, and unconstrained quantization coefficient, which traditionally in the literature known as nth quantization error, quantization dimension, and quantization coefficient, to the definitions of nth constrained quantization error, constrained quantization dimension, and constrained quantization coefficient. The work in this paper extends the theory of quantization and opens a new area of research. In unconstrained quantization, the elements in an optimal set are the conditional expectations in their own Voronoi regions, and it is not true …

Furthering Usability And Efficiency Of Massively Parallel Gaussian Process Regression As Applied To Multi-Beam Sonar Data, Phillip M. Parisi

Open Access Master's Theses

Massively Parallel Gaussian Process Regression (MP-GPR) is a parallelized programmatic implementation of a machine learning algorithm used to process multi-beam sonar data and generate 2.5D terrain maps in close to real-time. This thesis furthered the capabilities of MP-GPR in two ways. The first introduced data reduction techniques to reduce the quantity of data inputted to the MP-GPR algorithm for computational efficiency. Various approaches were analyzed to determine an optimal data minimization strategy. The second informed the variances of regression estimates by considering beam angle and range uncertainty of the multi-beam sonar data being analyzed. In tandem, the presented methodologies decreased …

Power And Sample Size Calculations For Evaluating Spillover Effects In Networks With Non-Randomized Interventions, Ke Zhang

Open Access Master's Theses

Determining the appropriate sample size that is needed to achieve a desired level of statistical power is an integral aspect of study design, as it ensures that studies are adequately powered and that their results are reliable and meaningful. Adequate statistical power can also address some practical and ethical considerations. Various methodologies for power and sample size calculations have been developed for distinct types of studies. However, the implications for power when estimating spillover effects in sociometric network-based studies with non-randomized interventions (SNS-NRIs) remain inadequately explored. In this study, we first conducted a series of simulations to evaluate how the …

Recovering The Full Network Using A Sampling Bias Adjustment In Sampled Network, Ryan Humphreys

Open Access Master's Theses

This project aimed to investigate the differences in terms of network structural characteristics between full and sampled networks and to develop a new weighted generalized estimating equations model (WGEE) in order to mitigate the sampling bias and obtain less biased sample estimates for model coefficients and standard errors. The applications of such an approach are of interest in the field of network analysis and public health studies. The Transmission Reduction Intervention Project (TRIP) is an HIV study using risk networks and is a key motivator for this work. Two other motivating examples were constructed where it was found that sampled …

Examination Of Varying Pair Programming Strategies In Computer Science Laboratory Environments, Lily Sisouvong

Open Access Master's Theses

Pair programming is a technique within the computer science space in which two programmers are paired on one computer to solve a related programming task. This technique is often practiced in both the industry and the academic setting, as it has a consistent history of positively impacting working professionals and students regarding personal experience and performance in programming. As a result, there has been a large focus within the computer science education field on how and why pair programming is a successful technique. More specifically, what traits of pairing students together make pair programming successful. At the University of Rhode …

Analysis Of Turkey Reovirus Using Machine Learning, Maryam Kafikang

Open Access Master's Theses

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus (TERV) has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus tentatively named as turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV) are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, increased morbidity and mortality, and reduced marketability of commercial turkeys. To combat these issues, it …

Neural Network Based Generation Of Radar Heatmaps For Advanced Driver Assistance Systems (Adas), Angela Klassen

Open Access Master's Theses

Radar sensors are an essential component of Advanced Driver Assistance Systems (ADAS). They use radio waves to monitor the surroundings of a vehicle, capturing data that can be utilized to provide assistance to the driver. Major challenges of this procedure are the high complexity and the considerable power consumption that the transfer of radar data to subsequent processing units requires. This work addresses these issues by using deep learning architectures to compress the raw data captured by radar before it is being transmitted. As proceeding step so-called Range-Angle (RA) Heatmaps are reconstructed from the compressed data. These heatmaps present an …

Navigating The Void: Uncovering Research Gaps In The Detection Of Data Poisoning Attacks In Federated Learning-Based Big Data Processing: A Systematic Literature Review, Mohammad Aljanabi, Hijaz Ahmad

Mesopotamian Journal of Big Data

This systematic literature review scrutinizes the landscape of research at the intersection of federated learning, big data processing, and data poisoning attacks. Employing a meticulous search strategy across multiple databases, the studyunveils a surge in annual scientific production, emphasizing a growing interest in federated learning and related fields. However, a critical research gap becomes evident during the investigation of data poisoning attacks specifically in the context of federated learning when processing big data. The most relevant keywords and a visually compelling word cloud further illuminate the prevailing themes and emphases within the literature, emphasizing the lack of explicit focus on …

Design And Development Of Hybrid Spectrum Access Technique For Cr-Iot Network, Sandeep Singh

Mesopotamian Journal of Computer Science

The radio spectrum is an underutilized natural resource with significant untapped potential. The rapid proliferation of Internet of Things (IoT) devices is driving a dramatic increase in radio spectrum demand. These devices can utilize cognitive radio (CR) technology to access the bandwidth left unused by licensed spectrum users, also known as primary users (PUs), to meet end users spectrum requirements efficiently. However, because PUs are given priority, CR-enabled Internet of Things (CR-IoT) devices, also known as secondary users (SU), must frequently communicate with one another in an opportunistic manner or under stringent power constraints. This can complicate CR-IoT device communication, …

Role Of Chatgpt In Computer Programming., Som Biswas

Mesopotamian Journal of Computer Science

Purpose: The purpose of this abstract is to outline the role and capabilities of ChatGPT, a language model developed by OpenAI for computer programming. Methodology: ChatGPT is a large language model that has been trained on a diverse range of texts and can perform a variety of programming-related tasks. These tasks include code completion and correction, code snippet prediction and suggestion, automatic syntax error fixing, code optimization and refactoring suggestions, missing code generation, document generation, chatbot development, text-to-code generation, and answering technical queries. Results: ChatGPT can provide users with explanations, examples, and guidance to help them understand complex concepts and …

The Ethical Implications Of Dall-E: Opportunities And Challenges, Kai-Qing Zhou, Hatem Nabus

Mesopotamian Journal of Computer Science

Artificial intelligence (AI) images, like those produced by DALL-E, have seen explosive growth in the past several years and have the potential to disrupt numerous markets. While the technology offers exciting opportunities for creativity and innovation, it also raises important ethical considerations that must be addressed. These ethical implications include issues related to bias and discrimination, privacy, job displacement, and unintended consequences. To mitigate these challenges, a multi-disciplinary approach is needed, including the development of effective regulations and governance frameworks, the creation of unbiased algorithms, responsible data management practices, and educational and training programs. Additionally, encouraging ethical discussions and debates …

Infusing K-Means For Securing Iot Services In Edge Computing, Tam Sakirin, Iqra Asif

Mesopotamian Journal of Computer Science

Accurate, timely, and safe administration of data from IoT devices is made possible by intelligent computing. As the number of IoT devices proliferates, more and more data will be collected, adding depth and breadth to the existing range of IoT services. Technology that incorporates entire systems on a single integrated circuit has improved to the point where more and more consumer electronics can support full-fledged operating systems. It is impractical to use a single computing model for the entire planet since doing so would cause severe network congestion and security holes. In order to solve this problem, we present a …

Enhancing Hybrid Spectrum Access In Cr-Iot Networks: Reducing Sensing Time In Low Snr Environments, Nthatisi Margaret Hlapisi

Mesopotamian Journal of Computer Science

The current utilization of the licensed spectrum band is not optimal. the abundance of Internet of Things (IoT) gadgets could lead to congestion in the unlicensed spectrum band. A potential solution is to integrate cognitive radios into IoT devices, specifically by developing CR-IoT (cognitive-radio-enabled IoT) devices that leverage the hybrid spectrum access (HSA) technique to access the licensed spectrum band and employ energy detectors for spectrum sensing. While HSA can enable high data throughput for CR-IoT networks, environments with low signal-to-noise ratio (SNR) may experience reduced performance. Particularly, in low SNR environments with SNR values ranging from -20dB to -24dB, …

Predicting Carbon Dioxide Emissions With The Orange Application: An Empirical Analysis, Israa Ezzat, Alaa Wagih Abdulqader

Mesopotamian Journal of Computer Science

The effects of climate change, such as droughts, storms, and extreme weather, are increasingly being felt around the world. Greenhouse gases are the primary contributors to climate change, with carbon dioxide (CO2) being the most significant. In fact, CO2 accounts for a significant percentage of all greenhouse gas emissions. As a result, reducing CO2 emissions has become a critical priority for mitigating the impacts of climate change and preserving our planet for future generations. Based on simulation and data mining technologies that use historical data, CO2 is expected to continue to rise. Around the world, 80% of CO2 emissions come …

Student’S Rating System For Teachers: A Tool For Teacher Scheduling Consideration, Naomi Bajao, Jose Primo Bardoquillo, Jhay Concha, Mae Fatima Monsanto, Ma.Chrisfie Karen Mojar

Mesopotamian Journal of Computer Science

The end-of-term ratings are one of the most crucial components of improving educational institutions' ability to assess teachers' performance. The paper-based evaluation system for teachers at Cebu Technological University's Tuburan Campus is time-consuming and laborious.  During the flexible learning, the school uses Google Forms to create surveys to rate the teachers which also has shortcomings since Google Forms has limited functions. To solve these issues, the proponents created a web-based evaluation system that makes it simple for students to rate their professors and generates evaluation reports at ease. Additionally, the system gives the system administrator the ability to control colleges, …

The Evolving Role Of Artificial Intelligence In The Future Of Distance Learning: Exploring The Next Frontier, Maad M. Mijwil, Guma Ali, Emre Sadıkoğlu

Mesopotamian Journal of Computer Science

In recent years, education has become especially related to the applications provided by artificial intelligence technology through a digital environment that includes a set of tools that assist in processing and storing information. Artificial intelligence techniques contribute to the development of students' skills by providing them with advanced scientific content and building their mental capabilities faster. Moreover, these techniques support analysing student data and suggest suitable educational materials and activities for them. Artificial intelligence is a noteworthy tool for the growth of distance education, especially after the development of expert systems that have become a human advisor in many domains, …

Machine Learning Application On Employee ‎Promotion, Muhannad Ilwani, Ghalia Nassreddine, Joumana Younis

Mesopotamian Journal of Computer Science

Any company's most valuable asset, its workforce, is its employees. As a result, the company's primary goal should be to develop an excellent strategy for supporting and investing in its employees and staff by providing the best training and development. Employee Promotion denotes the advancement of an employee's rank. It raises the salary, position, duties, and benefits. It is a part of the job that propels employees to the highest commitment and loyalty to their organizations. Employee morale and loyalty are two critical components of any successful business. Employee promotion is the key to improving employee performance and engagement. It …

Clustering Algorithms And Comparisons In Vehicular Ad Hoc Networks, Radhakrishna Karne, Sreeja Tk

Mesopotamian Journal of Computer Science

Vehicular Ad hoc Network (VANET) is a new era in the transmission of dynamic information across communities. Intelligent Transportation Systems is only one of the many applications for VANET (ITS). The topology of VANET is extremely dynamic, and connections are irregular. These features cause information transmission in the VANET to be unreliable. Vehicle clustering is a successful strategy to increase the network's scalability and connection dependability. Characteristics of the VANET have an impact on clustering performance as well. An extensive explanation of VANET clustering algorithms is given in this article. A complete evaluation of clustering in VANETs is provided based …

Artificial Neural Networks: An Overview, Roheen Qamar, Baqar Ali Zardari

Mesopotamian Journal of Computer Science

Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by the human brain, and they replicate the way real neurons communicate with one another. Artificial neural networks (ANNs) are massively parallel systems comprised of a huge number of interconnected basic processors. This paper discuss about the artificial neural network and its basic types. This article explains the ANN and its basic outlines the fundamental neuron and the artificial computer model. It describes network structures …