Digital Commons Network^™

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Gas-Generating Reactive Materials: Design, Evaluation And Effect Of Morphology On Their Ignition And Combustion, Purvam Mehulkumar Gandhi

Dissertations

This work investigates optimizing gas-generating reactive materials, focusing on particle morphology's effect on ignition and combustion behavior. Metal-based gas-generating energetic materials (EMs) are promising alternatives to replace traditional CHNO compounds. Design of advanced metal-based EMs requires consistent ways of evaluating how their characteristics affect their ignition and combustion. Many relevant evaluation approaches exist; however, the results may be difficult to compare directly across different studies. Commonly, experimentalists ignite metal-based EMs in enclosed chambers and report pressures, P. However, direct comparison of these pressures is hindered by variations in the chamber volume, V, and the EM mass, m. To standardize the …

How To Manage Documents & Email In The Law Practice - Getting Your Digital House In Order!, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held August 13, 2024.

Cme Update For Civil Mediators, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held October 24, 2024.

Microsoft Word Automatic Paragraph Numbering, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held January 23, 2024.

Construction Accident And Commercial Premises Liability Cases, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held September 24, 2024.

Understanding Barriers To Optimal Supervision And Delivery Of The Nationalcertificate (Vocational) Curriculum Through Tvet College Lecturers’Reflective Evaluations, Angelona Rewhydah Williams, Karel Prins, Bongani Innocent Nkambule, Sindile Amina Ngubane

REID (Research and Evaluation in Education)

Propounded by reflective theory, this qualitative case study drew on TVET lecturers' reflective evaluations of factors that they considered to have a bearing on optimal supervision and delivery of the National Certificate (Vocational) curriculum. Data were collected from participants across three campuses of a TVET college in the Eastern Cape Province, South Africa. Twelve lecturers of different seniority were purposively sampled and interviewed in two focus group sessions. The first focus session involved six participants: four post-level 2 and two post-level 3 personnel recognized by the South African Council for Educators (SACE) as "office-based lecturers" and classified within the middle …

Ensemble Learning Models For Large-Scale Time Series Forecasting In Supply Chain, Minjuan Zhang

Dissertations

Machine learning and AI techniques are transforming supply chain forecasting, driven by the expanding availability of data assets. These advanced methods offer powerful opportunities to optimize management processes, reduce operational costs, and enhance strategic decision-making, which is crucial for enterprise success. However, conventional statistical approaches, such as Autoregressive Integrated Moving Average Models (ARIMA), dynamic regression, and Unobserved Component Models (UCMs)—which have long dominated time series forecasting—often fall short in accuracy and scalability. These traditional models face limitations in batch processing, handling large-scale data, addressing uncertainty-induced disruptions, and synchronizing demand-supply scenarios.

To address these challenges, a novel class of AI-powered ensemble …

22nd Annual Family Law Institute, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held November 14-15, 2024.

Knowledge Diffusion In Networks Of Artificial Learners, Ehsan Beikihassan

Dissertations

The dissertation draws inspiration from the topic of peer learning in the social sciences and the study of information dissemination and knowledge diffusion in network science. In particular, it introduces and studies a setting involving a population or network of artificial learners, with the objective of optimizing aggregate performance measures under constraints on training resources. In this context, natural knowledge diffusion processes in networks of interacting artificial learners are studied. The term "natural" refers to processes that emulate human peer learning, where the internal state and learning processes of students remain largely opaque, and the main degree of freedom lies …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

Decomposition Of Diisopropyl Methylphosphonate (Dimp) Exposed To Elevated Temperatures And Combustion Products Of Reactive Materials, Elif Irem Senyurt

Dissertations

The safe and efficient destruction of chemical weapon agents (CWAs) stockpiles remains a critical global challenge. Understanding the thermophysical properties, decomposition mechanisms, and interactions of CWAs with their environment is crucial for developing effective strategies to mitigate their threats. Diisopropyl methylphosphonate (DIMP) and Dimethyl methylphosphonate are commonly used nerve agent surrogates. This dissertation focuses on DIMP and DMMP, investigates their properties and behavior under conditions relevant to prompt defeat scenarios.

The thermophysical properties of DIMP and DMMP were experimentally characterized, including vapor-liquid surface tension (3-60 °C) and viscosities of neat and aqueous solutions. Results revealed significant non-ideal behavior in aqueous …

Visual Analytic Techniques For Interpretable Algorithmic Ranking Systems, Jun Yuan

Dissertations

Rankings have a profound impact on the increasingly data-driven society. From leisurely activities like the movies to watch, the restaurants to patronize; to highly consequential decisions, like making educational and occupational choices or getting hired by companies— these are all driven by sophisticated yet mostly opaque algorithmic rankers. A small change in how these rankers order the data items can have profound consequences, like deterioration of the prestige of a university or a job applicant missing out on being on the list of the top candidates for an organization. These scenarios necessitate data-driven and human-centered innovation to make rankers accessible, …

Simulation Of Low-High Method In Adaptive Testing, Rukli Rukli, Noor Azeam Atan

REID (Research and Evaluation in Education)

The era of disruption significantly engineered a classic testing system into an adaptive testing system where each test taker takes a unique test. However, the carrying capacity of the adaptive testing system engineering is experiencing obstacles in terms of the method of presenting the test questions. The study aims to introduce the low-high adaptive tracking method with the item response theory approach, where the difficulty level of the questions is adapted to the test takers' abilities. The number of test questions in the question bank is 400 questions. Data analysis used the Bilog-MG program. The range of the difficulty level …

Construction Of An Instrument For Evaluating The Teaching Process In Highereducation: Content And Construct Validity, Risky Setiawan, Wagiran Wagiran, Yasir Alsamiri

REID (Research and Evaluation in Education)

This study aims to reveal the content validity, construct validity, and reliability of the instrument for evaluating the teaching process in higher education. This research is development research applying the ADDIE model from Molenda. The indicators evaluated consist of context, inputs, processes, and products. The sample consisted of 1200 students from eight faculties, each represented by three study programs. Data analysis uses three stages: content validity test analysis using the V-Aiken method involving six panellists or experts; construct validity test using Confirmatory Factor Analysis (CFA). Quantitative descriptive analysis and interpretive qualitative analysis used the Miles and Huberman method. The results …

Predictor Factors For Teacher Innovative Behavior During Curriculum Reform: The Role Of Cognitive Flexibility, Positive Affect, And Negative Affect, Khairunnisa Syafira Dumbi, Stephanie Yuanita Indrasari

REID (Research and Evaluation in Education)

Teacher innovative behavior is a key capability for maximizing teacher performance as well as the student learning process, especially when teachers face new changes in their education system such as curriculum reform. However, insufficient attention has been given to understanding the interaction of predictor factors to encourage teacher innovative behavior. This study aims to assess the role of cognitive flexibility, positive affect, and negative affect in predicting teacher innovative behavior. A cross-sectional and quantitative design was used for this study. The data collection procedure used convenience sampling, with questionnaires distributed online via social media through several teacher communities. Three instruments …

Automatic Generation Of Physics Items With Large Language Models (Llms), Moses Oluoke Omopekunola, Elena Yu Kardanova

REID (Research and Evaluation in Education)

High-quality items are essential for producing reliable and valid assessments, offering valuable insights for decision-making processes. As the demand for items with strong psychometric properties increases for both summative and formative assessments, automatic item generation (AIG) has gained prominence. Research highlights the potential of large language models (LLMs) in the AIG process, noting the positive impact of generative AI tools like ChatGPT on educational assessments, recognized for their ability to generate various item types across different languages and subjects. This study fills a research gap by exploring how AI-generated items in secondary/high school physics aligned with educational taxonomy. It utilizes …

Utaut And In-App Purchase: Revealing Factors Driving Users To Switch To The Premium Version In Online Dating Apps, Diyanti Pratiwi Ginting, Gita Gayatri

ASEAN Marketing Journal

This study aims to understand the factors influencing purchase intention in premium online dating applications. The main focus is on performance expectancy, effort expectancy, privacy risk, habit, enjoyment, social value, and quality. Data was collected through an online survey in March 2023 with respondents residing in Indonesia. The research method used is descriptive quantitative, based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The variables in this study were measured using a 5-point Likert scale. The sample consisted of online dating app users who had never subscribed to the premium version and had been using the app …

A Society For Professional Archaeologists In Tanzania: A Call For Action, Richard K. Bigambo

Zamani: A Journal of African Historical Studies

This essay discusses the need for an Archaeological Association of Tanzania and provides its general structure. It argues that the proposed association is essential for promoting the country’s development, heritage preservation and conservation, and promotion of archaeological studies. In addition to providing resources and support for research, training, and education, it would facilitate collaboration and networking, help protect and preserve Tanzania's cultural heritage, and increase public awareness and education about archaeology in Tanzania. The essay also discusses the development of archaeology in Tanzania and the history and contributions of archaeological associations worldwide. This structure for the proposed association would enable …

Optimizing Bioethanol Production From Solanum Torvum (Devil's Thorn): An Evaluation Of Conversion Efficiency And Invasive Plant Management Potential, Jaya Ashwin D S, Navaneeth K, Abhay Mahesh Baadkar, Supriya S Sundar, Bhoomika B J

Manipal Journal of Science and Technology

Solanum torvum, commonly known as the Devil’s thorn, is an invasive species present in India that causes negative ecological consequences. However, given its abundance and high starch content in the plant, it could be utilized as a potential feedstock for sustainable biofuel production. We aim to explore the feasibility of bioethanol production from S. torvum and its potential as a means of managing this invasive species. The study will take on a comprehensive approach, including techniques for optimizing starch extraction and improving its accessibility. Fermentation with suitable strains of microorganisms, and analysis of bioethanol yield. In addition, the study will …

Genetic Algorithm-Based Design Solution Of An Area Lighting Scheme - A Case Study, Prabhat Mishra, Arnab Ganguly, Amartya Roy, Mihir Kumar Manna, Abhik Hazra

Manipal Journal of Science and Technology

In this paper, a Genetic Algorithm (GA)-based approach is taken for the lighting design of a specified area. The design of an area lighting scheme primarily depends upon the application of that area and accordingly target values of lighting design parameters are to be decided from relevant BIS (Bureau of Indian Standard) lighting codes. There are several design variables, viz., light distribution, aiming of the luminaire, pole spacing, luminaire mounting height, grid dimension over the field, etc. The task of a lighting designer is to achieve the target design parameters through a suitable combination of set design variables and design …

Field Oriented Control (Foc) Of Permanent Magnet Synchronous Motor (Pmsm) Applied In Electric Vehicle (Ev), Unnikrishnan P C, Delgin Saji, Ashwini M, Georgee Cleetus, Joshua Andrews Chandy

Manipal Journal of Science and Technology

Synchronous motors are the most commonly used steady-state three-phase AC motors in electrical systems. The synchronous speed of these motors remains constant, being equal to the supply frequency and its rotational period corresponding to the integral number of AC cycles. So, these motors are mainly used to improve the power factor in power systems.

The paper focuses on field-oriented control of a permanent magnet synchronous motor to effectively control its speed and torque. AC motors only have stator currents, so separate control mechanisms such as vector controls are required to control the motor’s operation. Field-oriented control is the most commonly …

Contingency Analysis On Transmission Line Of Ieee 9 Bus System, Padmashree K S

Manipal Journal of Science and Technology

Ensuring power system security poses a significant challenge for engineers in the field. Conducting security assessments is crucial as it provides insight into the system's condition in the event of a contingency. The widely employed contingency analysis technique serves to anticipate the impact of outages, such as equipment failures or transmission line disruptions, enabling pre-emptive measures to maintain system reliability. However, analyzing each contingency offline is arduous due to the extensive number of system components, with only select contingencies posing severe threats to the system. Computing performance indices for every scenario is a step in the contingency selection process, which …

An Energy-Efficient Clustering Technique Of Heterogeneous And Homogeneous Wireless Sensor Networks, Ahmad Alkhayyat, Rohit Sharma

NJF Intelligent Engineering Journal

The technology of wireless sensor networks (WSN) has recently gained widespread recognition as an emerging one. A WSN consists of a set of sensors powered by batteries. Inaccessible locations usually make it difficult to replace or recharge sensors' batteries. These networks are plagued by energy consumption, which is the main problem. The clustering algorithms are remarkably effective in dealing with such problems in this regard. As a result, this technique appears to help reduce node energy consumption, which ultimately increases the network's lifespan. Heterogeneous and homogeneous clustering algorithms exist. In homogeneous clustering algorithms, all nodes have the same technical characteristics, …

Optimized Thyroid Disease Classification Using Nature-Inspired Algorithms: Gwo & Woa, Sayan Mondal

NJF Intelligent Engineering Journal

Recently, there has been an upsurge in the number of cases of thyroid disease. Thyroid function is essential for metabolism, making the early diagnosis of thyroid dysfunction an urgent matter. The issue of class imbalance has not been thoroughly examined, even though there are multiple publications on the topic of thyroid disease detection. Furthermore, the binary-class problem has been the primary emphasis of previous research. This study intends to address these concerns by using the suggested strategy, which takes into account ten distinct thyroid illnesses. In order to choose the best features from the Thyroid dataset, this research proposes two …

A Predictive Iot And Cloud Framework For Smart Healthcare Monitoring Using Integrated Deep Learning Model, Preeti Rani, Umesh Chandra Garjola, Haider Abbas

NJF Intelligent Engineering Journal

The researchers developed a deep learning-based smart healthcare monitoring system based on IoT and cloud technology. The proposed system integrates IoT sensors for real-time collection of physiological data, such as ECG, blood pressure, and heart rate, with cloud computing for secure storage and advanced analytics. Utilizing the Bi-LSTM model with fuzzy inference systems (FIS), the framework enhances the accuracy and efficiency of heart disease prediction. According to the evaluation, the model performs better in terms of accuracy, precision, recall, and F1 score than traditional LSTM and FLSTM models. By enabling early detection and personalized interventions, the system aims to reduce …

Real-Time Congestion Control And Load Optimization In Cloud-Manets Using Predictive Algorithms, Preeti Rani, Mohammed Hussien Falaah

NJF Intelligent Engineering Journal

Cloud-MANET environments require a system to balance load and control congestion. As a result of integrating real-time network metrics with predictive traffic algorithms, the proposed model optimizes the management of dynamic topologies, network bandwidth constraints, and fluctuating traffic loads. In addition to energy-aware multi-path routing, the framework incorporates adaptive congestion control mechanisms to ensure data transmission is efficient and stable. This algorithm provides higher packet delivery ratios, reduces end-to-end delays, and increases throughput over existing algorithms, according to the evaluation results. Hybrid Cloud-MANET systems can benefit from this approach by optimizing resource utilization and network performance.

New Forced Convection Flow Of Nanofluid Within A Partially Filled Porous~Straight Channel, Ammar I. Alsabery, Ali Sahib Abosinee

NJF Intelligent Engineering Journal

This study specifically examines how the movement and dispersion of nanoparticles affect heat transfer in a linear channel that contains a partially porous medium. The existing body of literature is lacking a comprehensive understanding of the convective heat transfer of nanofluids in porous channels. This presents an open research topic that demands further investigation. The porous channel is modelled using Finite Element Method (FEM) for steady flow. The assumption of thermal equilibrium model is made between the solid phases and nanofluid. The non-uniform distribution of nanoparticles within the channel is postulated. Consequently, the equation for the distribution of volume fraction …

A Predictive Framework Combining Iot And Machine Learning Regression Models For Smart Precision Farming, Kusum Yadav, Nesreen Abdou El-Hadiede

NJF Intelligent Engineering Journal

In this paper, the Internet of Things (IoT) and machine learning algorithms that incorporate regressor are integrated to improve precision agriculture. Data from Internet of Things sensors like temperature sensors, humidity sensors, and soil sensors can be collected and analysed using machine learning algorithms like Support Vector Machines (SVMs) and Multilayer Perceptrons (MLPs). By using the proposed system, crop yield will be optimised, resource usage will be minimised, and the environmental impact of agriculture will be reduced. A comparison of predictive accuracy and error metrics, such as RMSE, demonstrated the effectiveness of automated monitoring, predicting crop health issues, and implementing …