Physical Sciences and Mathematics Commons^™

The Vertical Evolution And Horizontal Correlation Of Information Science Education Research In China, Siyu Chen, Xiaofeng Zhu, Xumu Jiang

Journal of Scientific Information Research

[Purpose/significance]The comprehensive and accurate visualization of the information science education research in our country will help clarify the research context and inspire future research.[Method/process] Based on the relevant literature on "information education" in the CNKI database from 1980 to 2022, from the high-frequency keywords of the literature, the evolutionary context and development direction of our country's information science education research hotspots were longitudinally studied; from the three levels of system including our county's information science reseanch subject and research authar, our country's information science research research subject and research auther, horizontally explored and analyzed the mutual influence and mutual connection …

Disciplines Different Of Fine-Grained Acknowledged Entity：A Case Of Doctoral Dissertations In Chinese Humanities And Social Sciences, Jiaxin He, Shijin Zhang, Jiayu Zheng, Xinlong Chu, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Thesis acknowledgment is a public text in which the authors express their gratitude to individuals and institutions those support their research. It is helpful to understand the individuals and institutions that play a role in the cultivation of doctoral candidate in different disciplines by extracting the entities of acknowledgment and comparing the distribution of the objects of acknowledgment in the dissertations of different disciplines.[Method/process]In this paper, we gained more than 60 000 Chinese doctoral dissertations acknowledgment of 21 humanities and social sciences disciplines, extracted acknowledged entities from acknowledgment, and constructed a fine-grained classification system of acknowledgment entities. The classification system …

Overhead Based Cluster Scheduling Of Mixed Criticality Systems On Multicore Platform, Amjad Ali, Asad Masood Khattak, Shahid Iqbal, Omar Alfandi, Bashir Hayat, Muhammad Hameed Siddiqi, Adil Khan

All Works

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling …

Lstda: Link Stability And Transmission Delay Aware Routing Mechanism For Flying Ad-Hoc Network (Fanet), Farman Ali, Khalid Zaman, Babar Shah, Tariq Hussain, Habib Ullah, Altaf Hussain, Daehan Kwak

All Works

The paper presents a new protocol called Link Stability and Transmission Delay Aware (LSTDA) for Flying Ad-hoc Network (FANET) with a focus on network corridors (NC). FANET consists of Unmanned Aerial Vehicles (UAVs) that face challenges in avoiding transmission loss and delay while ensuring stable communication. The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network. The protocol uses a Red-Black (R-B) tree to achieve maximum channel utilization and an advanced relay approach. The paper evaluates LSTDA in terms of End-to-End Delay (E2ED), Packet Delivery Ratio …

Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi

All Works

No abstract provided.

Flexible Global Aggregation And Dynamic Client Selection For Federated Learning In Internet Of Vehicles, Tariq Qayyum, Zouheir Trabelsi, Asadullah Tariq, Muhammad Ali, Kadhim Hayawi, Irfan Ud Din

All Works

Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality, …

Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain

All Works

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …

Fuzzy Logic-Based Approach For Location Identification And Routing In The Outdoor Environment, Saravjeet Singh, Jaiteg Singh, Sukhjit Singh Sehra, Babar Shah, Farman Ali, Daehan Kwak

All Works

Finding the precise and accurate location of devices on road networks is challenging in remote areas with poor internet connectivity and Global Positioning System coverage. Navigation applications that completely depend on the internet and reference spatial data for location identification and mapping do not perform well in case of frequent internet disconnection. These reference spatial data sources have many associated challenges like large size, errors in data, and restricted access. To address these challenges, this paper provides an approach for localization and routing using self-generated reference data using likelihood estimation. According to the proposed approach, the trajectory information is used …

Design And Delivery Of National Housing In The Uae: An Alternative Approach, Basem Eid Mohamed, Mohamed Elkaftangui, Rana Zureikat, Rund Hiyasat

All Works

The provision of national housing to citizens in the United Arab Emirates (UAE) is considered a crucial topic. Over the past four decades, the process of developing national housing has emerged into multiple housing programs and schemes, all with the same aim of offering affordable and high-quality housing to citizens, in addition to meeting the needs of local families regarding spatial configurations while maintaining cultural values. However, despite all these efforts, the question has always remained: are the offered housing practices suited for family needs, socioeconomic trends, and environmental challenges? This study aims to offer an alternative approach for the …

Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi

All Works

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big Data, the Internet of Things (IoT), and data streaming, challenges such as monitoring and management remain unresolved. Edge IoT devices produce and stream huge amounts of sample sources, which can incur significant processing, computation, and storage costs during local updates using all data samples. Many research initiatives have improved the algorithm for FL in homogeneous networks. However, in the typical distributed learning application scenario, data is generated …

Covid-19 Pandemic And The Cyberthreat Landscape: Research Challenges And Opportunities, Heba Saleous, Muhusina Ismail, Saleh H. Aldaajeh, Nisha Madathil, Saed Alrabaee, Kim Kwang Raymond Choo, Nabeel Al-Qirim

All Works

Although cyber technologies benefit our society, there are also some related cybersecurity risks. For example, cybercriminals may exploit vulnerabilities in people, processes, and technologies during trying times, such as the ongoing COVID-19 pandemic, to identify opportunities that target vulnerable individuals, organizations (e.g., medical facilities), and systems. In this paper, we examine the various cyberthreats associated with the COVID-19 pandemic. We also determine the attack vectors and surfaces of cyberthreats. Finally, we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals, organizations, and systems.

Air Quality Improvement Following Covid-19 Lockdown Measures And Projected Benefits For Environmental Health, Yuei An Liou, Trong Hoang Vo, Kim Anh Nguyen, James P. Terry

All Works

Many regions worldwide suffer from heavy air pollution caused by particulate matter (PM2.5) and nitrogen dioxide (NO2), resulting in a huge annual disease burden and significant welfare costs. Following the outbreak of the COVID-19 global pandemic, enforced curfews and restrictions on human mobility (so-called periods of ‘lockdown’) have become important measures to control the spread of the virus. This study aims to investigate the improvement in air quality following COVID-19 lockdown measures and the projected benefits for environmental health. China was chosen as a case study. The work projects annual premature deaths and welfare costs by integrating PM2.5 and NO2 …

Design Of Block Copolymer With Tunable Hydrophobic/Hydrophilic/Fluorophilic Interactions, Tatiane De Fatima Dutra

Theses, Dissertations and Culminating Projects

Block copolymers (BCP) with balanced interactions and their resultant bulk-phase self-assembly have become increasingly important in advancing nanotechnology, separation, and energy applications. However, a few reports have addressed the synthesis challenge and bulk-phase self-assembly of such triblock copolymers. This thesis presents a facile route for preparing triblock copolymer via controlled radical and organocatalytic ring-opening polymerization that allows precise control over the incorporation of individual moieties in resultant polymer, responsible for balanced hydrophilic, hydrophobic/lipophilic, and fluorophilic interactions. Synthesized polymer with suggested self-assembled 2D lamellar nanostructure exhibits high-temperature stability. The long-term goal of this work is to selectively use this polymer class …

A Functioning Code May Not Be A Secure Code : A Preliminary Study On The Students' Complacency With Secure Coding, Jeremiah Niiquaye Kotey

Theses, Dissertations and Culminating Projects

Eleanor Roosevelt once said: "Learn from the mistakes of others. You can’t live long enough to make them all yourself". Mistakes are almost inevitable while coding or designing a system. Therefore, patches are created to fix the issues in the code either by a manual review, or through a static analysis tool. Oftentimes, mistakes in programming emanate from lack of skills thus, competence with a particular programming language but negligence also plays a role in other instances. A functioning code that solves a particular problem does not guarantee that the code is secure, hence the code should be structured to …

A Computational Study Of The Copper-Catalyzed Trifluoromethylation Of Boronic Acids, Kevin Nyhuis

Theses, Dissertations and Culminating Projects

This work is a computational study of a reaction mechanism for the trifluoromethylation of boronic acids. Three steps of the proposed reaction mechanism are studied, •CF₃ addition to a copper catalyst center, base promoted transmetalation between copper and aryl boronic acid, and bond forming reductive elimination in which the CF₃ and aryl substituent form a bond. Limited information is known about this mechanism. This study uses computational methods to attempt to elucidate the mechanism and provide the groundwork for potential improvement. Quantum chemical methods in conjunction with tight-binding based conformational sampling methods are used to investigate the possible …

Investigating The Roles Of Active Site Residues K59, K119 And E168 In M. Tuberculosis Indole-3-Glycerol Phosphate Synthase, Maryum M. Bhatti

Theses, Dissertations and Culminating Projects

Tuberculosis (TB) is a disease that is caused by a bacteria called Mycobacterium tuberculosis and primarily attacks the lungs but can spread to other areas of the body as well. TB can develop resistance to drugs that may be used to cure the disease. Multidrug resistant TB is especially a growing issue as it does not respond to the two most powerful anti-TB drugs, isoniazid and rifampicin. For this reason, finding new ways to target TB are critically needed. One possible way to do this is to target the enzymes in the tryptophan biosynthetic pathway of M. tuberculosis. Studies have …

Facial Expression Recognition Using Lightweight Deep Learning Modeling, Mubashir Ahmad, Saira, Omar Alfandi, Asad Masood Khattak, Syed Furqan Qadri, Iftikhar Ahmed Saeed, Salabat Khan, Bashir Hayat, Arshad Ahmad

All Works

Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad, angry, fearful, contemptuous, surprised and disgusted expressions. Accurate detection and classification of human facial expression is a critical task in image processing due to the inconsistencies amid the complexity, including change in illumination, occlusion, noise and the over-fitting problem. A stacked sparse auto-encoder for facial expression recognition (SSAE-FER) is used for unsupervised pre-training and supervised fine-tuning. SSAE-FER automatically extracts features from input images, and …

Is Disclosure And Certification Of The Use Of Generative Ai Really Necessary?, Maura R. Grossman, Paul W. Grimm, Daniel G. Brown

Faculty Scholarship

No abstract provided.

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 …

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 …

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 …

Unlocking The Full Potential Of Spectrum: A Comprehensive Review Of Cognitive Radio Technology, Indu Bala, Maad M. Mijwil

Mesopotamian Journal of Computer Science

Wireless networks are confronted with enormous challenge because of the limited availability of radio spectrum, which has culminated in spectrum congestion and ineffective utilization of the spectrum's resources. This has led to a great deal of innovation in both communication and mobile devices that enable the delivery of high-quality multimedia content from various sources. However, despite these advances, there is still a need to find new ways to utilize existing frequency bands. Recent years have witnessed an awful research on cognitive radio (CR) based networks development. This review paper provides a comprehensive overview of CR technology, its capabilities, and its …

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, …

Study And Analysis Of Ofdm Under Rayleigh Fading Channel Using Various Modulation Methods, Sowmya Kalakoti, Uday Kiran Vashapaka, Mokshith Reddy Gujjula, Harika Surasi, Siddartha Kusuma

Mesopotamian Journal of Computer Science

Orthogonal Frequency Division Multiplexing (OFDM) is well recognized as a very efficient multicarrier technology. OFDM has witnessed a significant increase in its utilization within contemporary wideband digital communication systems. The technology possesses several notable benefits, including its capacity to effectively manage challenging channel circumstances, optimize spectrum utilization, mitigate inter symbol interference (ISI), and support high data transmission rates. Hence, it has been employed in several forthcoming wired and wireless communication systems, such as 4G LTE mobile communications. This work primarily concentrates on the performance assessment and investigation of the OFDM system in the presence of Rayleigh fading channels. By analyzing …

Exploring The Evolution Of Ai Integration In English As A Foreign Language Education: A Scopus-Based Bibliometric Analysis (1997-2023), Mohanad G. Yaseen, Sara S. Alnakeeb

Mesopotamian Journal of Computer Science

Significant scholarly interest has been focused on the use of AI in English as a Foreign Language (EFL) instruction because it presents novel opportunities for improving language learning contexts and pedagogical approaches. This study provides an in-depth investigation into how AI is revolutionizing English as a foreign language (EFL) instruction, with special focus on the ways in which this technology can help students acquire a language in a fun, engaging, and one-on-one setting. This research examines the ethical and sociological implications of the broad integration of AI technology into language learning environments, highlighting major trends and identifying critical gaps through …

Understanding Influencers Of College Major Decision: The Uae Case, Mohammad Amin Kuhail, Joao Negreiros, Haseena Al Katheeri, Sana Khan, Shurooq Almutairi

All Works

This study aims to understand and analyze what influences female students to choose a college major in the United Arab Emirates (UAE). To accomplish our target, we conducted a survey with mostly female first-year undergraduate students (N = 496) at Zayed University to understand the personal, social, and financial factors influencing students’ major choices. Further, this study also asked students to specify their actions before deciding on their major and assessed the information that could be helpful for future students to decide on their majors. Last, the study investigated how Science, Technology, Engineering, and Mathematics (STEM) students differ from other …

Forecasting Networks Links With Laplace Characteristic And Geographical Information In Complex Networks, Muhammad Wasim, Feras Al-Obeidat, Fernando Moreira, Haji Gul, Adnan Amin

All Works

Forecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our method utilizes the adjacency matrix of the network and generates a scoring matrix that captures the similarity between nodes in terms of their geographic location. By transforming the distance matrices into score matrices using exponential …

New Advances In Long-Term Monitoring Of Storm-Deposited Boulder Ridges Along Rocky Shorelines Of San Salvador Island, Bahamas, Bosiljka Glumac, Ursula Miquel, H. Allen Curran

Geosciences: Faculty Publications

Beginning in January 2012, we have monitored two boulder ridges on San Salvador: Singer Bar Point (SBP, length ~790 m) along the reef- and lagoon-protected northern coast and The Gulf (TG, length ~460 m) on the island’s high-energy southern coast. This long-term monitoring aims at documenting changes in ridge morphology and distribution, and the direction and amount of movement of individual boulders to gain insights into the intensity and effects of storms on these coastal areas.

In the initial stage of our investigations, the largest boulders from each site were photographed, GPS-located, measured, and characterized by composition and morphology. Boulders …

Evaluating The Use Of Environmental Tracers To Reduce Conceptual Model Uncertainty Of Hydrogeologic Models, Andrew Nordberg, Jon Graham, W. Payton Gardner

Graduate Student Theses, Dissertations, & Professional Papers

Environmental tracer concentrations for CFC12, SF6, and tritium are used in groundwater simulations to assess the ability of these tracers to reduce conceptual model uncertainty due to uncertainty of a site’s geologic and recharge characterization. The resulting groundwater simulations are characterized by site-specific hydrologic and geologic data, and with coordination from a field team with years of knowledge about the site. First-order (conceptual) uncertainty is directly addressed by using a stochastic modeling approach for spatial variability of the proposed subsurface configurations. Simulations of environmental tracer concentrations and water levels are used to assess six alternate conceptual models that are based …

Synthetic Aperture Radar Observations At Salar De Pajonales, Chile, Michael A. A. Mcinenly

Graduate Student Theses, Dissertations, & Professional Papers

Remotely sensed microwave radars provide the spatial and temporal coverage needed to improve our understanding of the relationship between moisture content and salt pan mineralogy and, ultimately, climate variability. Moisture content in the surface and near-surface crusts found in salt pan environments, such as salt pan, has a significant impact on the backscatter values recorded by synthetic aperture radar (SAR) systems. This is because moisture affects the dielectric constant and surface roughness of the saline surface, which in turn influences the amount of electromagnetic energy reflected back to the SAR sensor. Changes in backscatter values are attributed to seasonal and …