Students Partnering with Faculty Awards 2024
2024 SpF Awards
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Using Augmented Reality to enhance the student learning outcomes in Introductory Programming Augmented Reality (AR) is a next-generation platform where digital content is seamlessly Recently, research on using Augmented Reality to enhance student learning has showed The goal of this project is to evaluate the effectiveness of using Augmented Reality to increase A quasi-experimental design will be used to strategically evaluate the effectiveness of AR |
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Trustworthy Weakly Supervised Breast Cancer Detection in Ultrasound Imaging Breast cancer has continued to be a significant health issue in recent years, being the most frequently diagnosed cancer among women in the United States. Early-stage breast cancer lacks apparent symptoms in the early stage of breast cancer; thus, many patients miss the best chance to cure it. Breast Ultrasound (BUS) imaging has emerged as a crucial diagnostic tool. Yet, the inherent challenges in BUS imaging, such as low contrast and noise, impede accurate breast cancer diagnosis by doctors. Computer-aided-diagnosis (CAD) systems are proposed to help radiologists interpret BUS images, make a more precise diagnosis, and reduce their workload. Breast cancer detection plays a crucial role in the CAD system. Training a fully supervised model for detecting breast cancer necessitates many manual annotations. Several weakly supervised frameworks for object detection have been developed to minimize the requirement for extensive annotations. During the SpF 2023 award period, the research team developed two such frameworks for natural images and breast cancer detection. However, trustworthiness is the most critical aspect of smart health applications. Constructing a trustworthy deep-learning model requires extensive datasets. Additionally, previous methods do not offer a qualitative assessment of the models' trustworthiness. To overcome these hurdles, the researchers propose a new, trustworthy, weakly supervised framework for breast cancer detection named BUSwiNet. This framework utilizes only image-level labels (cancer/non cancer). It is capable of identifying the bounding boxes of breast tumors in breast ultrasound (BUS) images, as well as determining whether the tumor is cancerous. Moreover, the research aims to develop a method using Bayesian Neural Networks to evaluate the trustworthiness of breast cancer detection models quantitatively. Objective: The project is focused on two primary objectives: 1) to develop an innovative weakly supervised framework for breast cancer detection named BUSwiNet. 2) to employ Bayesian Theory to evaluate the trustworthiness of BUSwiNet and the models developed during the SpF 2023 period to identify the most reliable model. Furthermore, in response to the growing interest in AI and smart health, the research team intends to establish a summer learning environment for three undergraduate students. It is designed to spark their interest in AI and healthcare and prepare them for future careers. Significance and expected outcomes: The importance of this project lies in several key areas: Firstly, it is expected to not only increase the accuracy of breast cancer diagnosis but also has the potential to be adapted for other types of cancers, such as lung and brain cancers. Secondly, the method for estimating trustworthiness could provide a strategy applicable across various contexts. Thirdly, this project offers an excellent opportunity to mentor undergraduate students in my initial year as a faculty member at Kean University, engaging in research collaboration with students and enhancing faculty-student relationships. The expected research outputs include 1) our proposed BUSwiNet to outperform existing methods on three public BUS datasets and 2) a publicly available software tool for breast cancer detection and trustworthiness evaluation, 3) three peer-reviewed papers: two conference papers addressing objective one and a journal article focusing on objective 2. |
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Technical and environmental study of using worm castings in hydroponics production This project proposes research to test organic nutrients (worm castings) used to grow lettuce in hydroponic systems at Kean University. The project will include three objectives:
This project focuses on water-based farming, specifically hydroponics, a technique gaining prominence in sustainable agriculture globally. While hydroponic production offers many advantages, the use of synthetic nutrients hinders hydroponic produce from being classified as organic food. In order to grow plants organically, the project PI conducted a 2023 summer test on vegetable growth in hydroponics with compost tea—a natural nutrient derived from compost. The experiment showed that the in-vessel compost from Kean led to plant death in NFT hydroponics due to the presumed low nutrients and high pH of the compost tea. In contrast, using purchased worm castings for nutrient liquid resulted in robust plant growth, suggesting that the digestive process of the worm improves nutrients’ accessibility and advances plants' health. The project PI, therefore, proposed to purchase a worm composter to make worm castings with in-vessel compost. The in-vessel compost will be nourished and become more accessible for plants to uptake in hydroponics. The project will examine if the technique is practical and viable under available resources. More importantly, a LCA is conducted to assess environmental impacts from resource extraction to composting to hydroponic production. Successful project completion will result in a waste-to-food hydroponic demonstration system installed at Kean. It will extend the end use of in-vessel compost produced at Kean. Also, a peer-reviewed journal article will be published to present the study's outcomes, contributing to a better understanding of organic nutrient application in diverse hydroponic systems. This initiative can address "food deserts" by providing nutritious options to urban areas, aligning with Kean's commitment to sustainable agriculture. Ultimately, the project aims to enhance efficiency, engage communities in hydroponics, and underscore Kean's role in sustainable urban agriculture. |
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EHR-based Mental Health Risk Assessment via LLM In the realm of mental health care, Electronic Health Records (EHRs) have become a fundamental part of patient care. These records, often extensive and text-heavy, encompass critical information about patient histories, including mental health assessments, substance use, and treatment protocols, presenting a significant challenge in risk assessment. The complexity and length of these records often impede efficient and accurate risk identification, crucial in mental healthcare management. Addressing this challenge, this proposal outlines a novel methodology leveraging Large Language Models (LLMs) for the categorization and evaluation of patient risk across over 30 critical mental health categories. The current scenario in mental health care necessitates meticulous risk management, encompassing various domains such as substance use-related psychosis, sleep disturbances, medication adherence, and potential violent behaviors. Traditional approaches, reliant on manual review of EHRs, are not only labor-intensive but also prone to errors and omissions. The proposed solution introduces an innovative use of LLMs to analyze, interpret, and categorize risks from EHRs, transforming extensive textual data into actionable insights. The project consists of several objectives: Analysis of EHR Data: This objective involves the deployment of LLMs to systematically dissect and analyze EHR content, with a particular focus on extracting data pertinent to mental health risks. This process is designed to distill complex and extensive EHR texts into essential elements that highlight key risk factors, ensuring a comprehensive understanding of each patient’s unique mental health profile. Risk Management: The aim here is to develop advanced algorithms that systematically assess patients’ risk profiles based on their EHR content. This involves a detailed algorithmic analysis that not only identifies but also quantifies the severity and nature of the risks associated with mental health conditions. The end goal is to provide a nuanced risk assessment that aids in effective patient management. Validation and Enhancement: The project encompasses an extensive validation phase, utilizing real-world EHR data for rigorous testing. This phase guarantees the model’s precision and reliability in identifying and assessing risk factors. The impact of this project is manifold. It promises to revolutionize mental health risk assessment by providing healthcare professionals with a tool for swift and precise risk evaluation from EHRs. This advancement is expected to significantly improve patient care quality, prevent potential adverse events, and enable more effective resource allocation. Furthermore, the application of this technology could extend beyond mental health to other healthcare sectors where risk assessment is critical. In conclusion, the integration of LLMs in mental health risk assessment within EHRs marks a substantial progression in healthcare technology. By efficiently converting extensive EHR data into categorized risk assessments, this project will bolster healthcare professionals’ decision making processes, leading to enhanced patient care and more streamlined healthcare services. |
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Roots and Routes: Designing for Wellness Agriculture and wellness have traditionally been considered independent categories, especially because we imagine agriculture as an integral part of the rural landscape and separated from the cities and the suburban areas. However, the urban gardens that bloomed during the struggling financial environment of New York City in the 70s are an example of safe spaces where the community could gather and grow. They bridged the gap between production and consumption of food while creating a culture of place based on agriculture and health. Drawing inspiration from these early ecological initiatives, our research will focus on Kean's Main Campus in Union. We will identify and designate underutilized lots for micro-orchards, thus creating a wellness corridor that runs behind the educational buildings, through parking lots and service areas, connecting hidden spaces and transforming them into healing gathering hubs for the student body. These community spaces will act as therapeutical spots where students, faculty, and employees can disconnect from the academic pressure and prioritize their mental health by planting, growing, cooking, and sharing recipes. We will analyze the pedestrian and vehicular flows as well as the circulation of waste to integrate these factors into our network. By introducing a new way-finding system, we will also trace alternative routes to connect these public spaces so that they act as catalysts of change across the whole campus. Our initiative will combine micro-agriculture and wellness through a system that is open to the students and faculty, local food producers, non-profit organizations, and local residents in Elizabeth encouraging a new understanding of nutrition, health, and urban development. As part of the transformation of the lots, we will design the following integral elements:
By focusing the project on food and wellness, we hope to develop a strategy that places the students and the local community first. We will research the current food and well-being services on campus to identify gaps that can be solved through the conversion of low-use areas to provide individuals with spaces to relax, learn, harvest, and cook. Our research and resulting proposal will ensure long-term benefits for the health of the student community by establishing an agenda for design interventions in the future years. We will create a platform to sustain dialogue with the community inside the campus and in Elizabeth to develop a body of research that will extend beyond the initial timeframe. It will be a commitment to explore spatial solutions that connect Kean University to its surroundings. |
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Bridging Agendas: New Models for Affordable Housing in Underutilized Urban Spaces “There is no state where a renter working full-time at minimum wage can afford a two-bedroom apartment.” Our project aims to seamlessly integrate the agendas of environmental sustainability and affordable housing by establishing a study that meticulously documents successful funding models for industrial conversion projects worldwide. Focusing specifically on the unique challenges faced by New Jersey, we aspire to contribute valuable insights into innovative funding approaches for affordable housing that can be utilized by local municipalities and planners towards supporting adaptive reuse and affordable housing initiatives within the state. Housing affordability is a major issue nationwide. In 2023, just 15% of homes for sale were affordable for the typical U.S. household according to analysis of the U.S. Census Bureau data, that number decreasing to 7% for Black households. In NJ, 54% of Low Income and 84% of Extremely Low Income renters are cost burdened, spending more than 30% of their income on housing. Meanwhile, the adaptive reuse or conversion of vacant industrial buildings into affordable housing provides a sustainable opportunity to celebrate the cultural and industrial heritage of northern New Jersey. Despite this theoretical potential and the various innovative design solutions that have been put forward by architects nationwide, the financial intricacies in the U.S. hinder the realization of these projects. As a result, existing design proposals often remain in the ideation phase due to funding constraints, emphasizing the pressing need for a scalable financial model. Our research identifies a crucial gap: the absence of a comprehensive affordable housing study tracking successful design solutions and their associated costs. The project will take an interdisciplinary approach incorporating methods and inputs from economics, public policy, sociology, engineering and the applied arts. Our project's outcomes include: 1) a survey of global affordable housing solutions and associated costs, 2) an exhibition in collaboration with the Passaic County Department of Engineering and the New Jersey Historical Society in support of their ongoing affordable housing and adaptive reuse initiatives. |
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Tracking Migration of Passerines in the NJ Skylands and Meadowlands Using the Motus Network This project will fill in current gaps of knowledge in these areas of avian ecology: (1) breeding ecology and success of native New Jersey birds, (2) migratory routes and timing of neotropical migrants native to New Jersey, and (3) the suitability of pristine deep woods versus historically contaminated urban sites as breeding and stopover habitat for species of conservation concern. The study proposes to track local movements and migration of passerines in real time using the North American Motus wildlife tracking system. Following a successful pilot season in 2023, the project’s goals are to (1) run concurrent bird banding operations during breeding season and fall migration at Kean Skylands and in the New Jersey Meadowlands, continuing to develop a robust data set to determine breeding and migratory success of species that utilize these locations, (2) affix nanotags to species of conservation concern present at both sites to track their migratory timing and routes using the North American Motus network, and (3) train two Kean University students in all aspects of bird banding operations from identifying and safely handling local birds to analyzing data and presenting research to the greater scientific community. This project seeks to quantify the avian biodiversity at these sites in the breeding season and during migration, as well as breeding success, the length of stopover during migration, and differences between migratory routes and timing of individuals from both sites. Radio-tracking nanotags will be used to follow migratory routes and successful completion of migration. A previous iteration of this project focused on Savannah Sparrows (Passerculus sandwichensis) and wood warblers (Parulidae). This project focuses on Gray Catbirds (Dumetella carolinensis) and Northern Waterthrushes (Parkesia noveboracensis), additional species of conservation interest. Nanotags allow close monitoring of land use patterns including locations and length of stopovers. In this project, three sites – Kean Skylands campus as well as Harrier Meadow and Erie Landfill in the New Jersey Meadowlands – will be assessed to determine habitat use and breeding success of native birds in the summer and usefulness of these sites as stopovers during fall migration, as well as differences in the timing or routes of migration. The Kean site consists of upland mixed forest with considerable edge habitat. Harrier Meadow is a saltmarsh between a tidal marsh and a non-tidal impoundment while capped Erie Landfill has midsuccessional habitat dominated by invasive plants. Meadowlands sites have undergone restoration efforts but remain contaminated with heavy metals and industrial pollutants. Data since 2020 indicate that these sites host a wide variety of native species including several of conservation concern during the breeding and migration seasons. Pilot data suggest migratory timing and routes of conspecifics may differ between the two locations though they are only ~30 miles apart. Additionally, it is unknown which habitat type is preferred by each species, which provides higher breeding or stopover success, and if migratory routes or timing differ significantly between the habitats. Finally, pilot data suggest the two Meadowland sites host significantly different species assemblages though they are adjacent. |
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Optimizing Airborne Surveillance Operations with Multimodal ChatGPT and Transformer Technology This research became possible after attaining access to the ChatGPT-vision model and allocating 20,000 USD Azure credits, granted by Microsoft, to PI Y. Kumar as a part of the Accelerate Foundation Models Research – Minority Serving Institutions Program. Our project embarks on an ambitious journey to redefine AI and Machine Learning (AI/ML) in airborne surveillance, integrating the cutting-edge multimodal capabilities of ChatGPT alongside advanced transformer technologies. This research focuses on validating and advancing AI/ML algorithms and systems, emphasizing their mathematical interpretation, cyberinfrastructure integration, uncertainty management, and functional correctness. Our multifaceted approach includes:
Our project, "Enhancing Airborne Surveillance with Multimodal ChatGPT and Transformer Technologies," focuses on pioneering motion detection techniques, transitioning from traditional object detection methodologies. This includes: Innovative Motion Detection Techniques: Employing methods like Background Subtraction, Frame Differencing, Optical Flow, and Deep Learning-Based Methods adapted for motion detection. We will explore how multimodal ChatGPT can augment these techniques, providing a more comprehensive understanding of aerial dynamics. Hybrid Approaches and Experimentation: Combining traditional computer vision techniques with advanced deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Siamese Networks. Integrating multimodal ChatGPT will be critical in understanding the complex interplay of spatial and temporal data in drone surveillance. Research Collaboration and Validation: Continuing our collaboration with renowned institutes, we aim to validate the latest AI/ML research, such as Dr. Cynthia Rudin’s ProtoTree, and apply these findings to our motion detection project. Research Conferences and Publications: Our 2024 plan includes active participation in ACM/IEEE ICSE/AST2024, ACM SIGCSE2024, and other IEEE/ACM conferences, focusing on validating AI algorithms. We anticipate publishing our findings in Q1/Q2 journals. Recent Achievements: Our team has made significant progress with Transformer Neural Networks and Large Language Models, contributing to various conferences and publications. We have explored diverse applications, from AI linguistic systems to addressing biases in deep learning models. Our drone-related research, presented at conferences like URTC MIT 2023, emphasizes our commitment to applying these advanced technologies in practical, real-world scenarios. A full list of contributions is available at the end of this proposal. |
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The Relationship between Emotion Regulation and Antiracism Efforts Racism is a public health issue (APA, 2021); the impact is profound and associated with numerous adverse outcomes. The history of clinical psychology is closely tied to Western traditions and connected to white supremacist beliefs (e.g. eugenics and intelligence testing). A long tradition exists of these beliefs influencing the study and practice of clinical psychology well into the 21st Century (McManus, et al., 20023). They continue to impact clients, students, and trainees. Adopting an antiracist stance requires clinicians to increase awareness of their own biases and perpetuate a more inclusive science. Emotion regulation is considered to be one way in which people are able to modulate their emotional responses such that they can continue to pursue their goals (Gross, 1998; Linehan, 1993. The concept of emotion regulation offers a way to conceptualize how people respond to stressful events. Prior research indicates that effective emotion regulation strategies (e.g. seeking support) can buffer the impact of a racist experience (Graham et al., 2015; LoPresti et al., 2023). Research is limited with respect to how emotion regulation operates in these situations and the selection of emotion regulation strategies across ethnic groups (each with a different community history and history of racism in the US). Relatedly, ineffective emotion regulation strategies can make it difficult to point out when a racist event occurs and then engage in the necessary discussion to repair the relationship and recover from the harm. This project seeks to address these gaps by examining the role of emotion regulation in response to racist events and gathering information from different ethnic groups to provide information from diverse population. The current study examines the role of emotion regulation in connection with racism and racist experiences. Prior research suggests that it plays a role in both protecting against the effects of chronic racism and confers an inability to have necessary, difficult conversations. The two aims are to: 1) evaluate the relationship between emotion regulation and racist experiences, and 2) examine differences between ethnic groups in utilization of different emotion regulations strategies (e.g. avoidance vs. thought suppression). This research project contributes to the literature by studying emotion regulation in a culturally responsive way, advancing the discussion on antiracist efforts in clinical psychology, and influencing training considerations. |
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Does Collective Punishment Work? Testing the role of anticipated intragroup retribution Collective punishment has been common throughout history and is present even in modern societies that champion individualism. Theoretical works are divided about the effectiveness of collective punishment, and the empirical evidence so far is scarce and inconclusive. Here, we propose a project to advance the theory and research of collective punishment by empirically testing the effectiveness of collective punishment against individual punishment in deterring negative behavior. Building on research on intragroup processes, we hypothesize that people’s fear of intragroup retribution can render collective punishment effective. The findings of this project can contribute to the long-lasting interdisciplinary debate about the effectiveness of collective punishment, aiming to reveal the psychological processes explaining the effect. The project would also advance my programmatic scholarly work on collective responsibility processes. The key to the project’s success, as well as the student researchers’ development, will be the close mentoring relationship with the student researchers, which will be built through regular meetings and the students’ high-level involvement at every phase of the project. Accordingly, the student researchers will play an integral part in every element of this project. As a matter of fact, the three students have already been involved in the conception and design of the study, in planning the experimental protocol, and in preparing study materials. The student researchers will be responsible for the data collection by running the experimental sessions. They will gain experience in data analysis, contribute to the manuscript preparation, and collaborate in the writing process. To disseminate the findings and practice presentation skills, the students will present their work at Kean Research Days, submit proposals for presentation at a national conference, and collaborate on the manuscript to be submitted for publication in a Q1 journal. |
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Expanding the Reach of Kean University/Community Access Unlimited Concert Series Since 2015, the Kean University music education program has forged a meaningful partnership with area K-12 music programs and the Academy for Continuing Education (ACE), a division of Community Access Unlimited (CAU) dedicated to assisting adults with physical and intellectual disabilities. The primary goal of this collaboration has been to champion inclusion and equity through joint performances involving ACE participants and their communities. To date, this alliance has culminated in seven concerts, with the most recent one taking place in January 2024. These concerts and associated activities have not only enriched the lives of ACE participants but have also served as a platform for creativity, collaboration, and community building. The musical activities, ranging from singing to playing instruments like rock bands, percussion, and radio batons, have allowed CAU individuals to make creative choices, add expressive elements, and contribute meaningfully to discussions about the significance and context of music. This empowerment is a transformative experience that has often eluded them throughout their lives. Beyond the impact on ACE participants, the Kean/CAU initiative has effectively inspired and guided Kean music education interns. These interns have been motivated to apply their skills and knowledge by modeling best practices, especially when working with diverse populations. Studies have consistently shown the interest of interns in gaining exposure and experience in diverse settings, aligning with best practices that emphasize the integration of theory and practice in working with diverse populations. The Kean music education students involved in this initiative demonstrated heightened awareness, openness, and commitment to diversity and community. Their experiences provided valuable insights into the power of music in a group context, engaging learners in creative music making. Moreover, they realize the need to build constructive music learning communities. The purpose of this project is to enhance the Kean/CAU Collaborative Concert series by involving adult day programs and extending its reach to schools catering to students with disabilities. Currently confined to Kean or CAU campuses during late afternoons or evenings, expanding the scope to other organizations in nearby communities, such as the Morris-Union Jointure Commission, The Opportunity Project, The Cerebral Palsy League, The ARC, The New Jersey Institute for Disabilities, and JESPY House, would make the concerts more accessible. This expansion also allows ACE participants to "pay it forward" by sharing the joy of live performances with those who may not regularly experience such events. |
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Computer-aided Repurposing of Approved Small Molecules to Discover Drug for Zika Virus The Zika virus (ZV) is primarily transmitted by Aedes mosquitoes and is known for causing symptoms like rash, fever, conjunctivitis, muscle and joint pain, malaise, and headache. However, most people infected with the ZV do not develop symptoms. When symptoms do occur, they are generally mild and last for about 2–7 days [1]. The virus is particularly concerning for pregnant women as it can cause congenital malformations such as microcephaly in infants. Additionally, Zika virus infection is associated with neurological conditions like Guillain-Barré syndrome, neuropathy, and myelitis in adults and children [2]. The risk of congenital malformations following infection in pregnancy is estimated to be between 5–15% for infants born to women infected with Zika virus during pregnancy [3]. In terms of mortality, deaths due to the Zika virus are generally low, but the virus remains a significant concern due to its potentially severe impact on pregnant women and their babies. However, in severe cases, the virus can cause complications leading to death, particularly in cases involving Guillain-Barré syndrome or severe birth defects in infants. For instance, in Brazil, the estimated case fatality rate for Zika virus has been reported as around 8.3% in infants [4]. A series of drugs are tested and under clinical trials, but till today nothing sees the daylight of hope. There is no US Food and Drug Administration (FDA)-approved drugs and vaccines for the Zika virus to date. Due to the unavailability of small drug molecules or vaccines, Zika has the potential to make a future pandemic as mutations of genomic sequences can increase transmissibility as well as mortality over time. Although several vaccine candidates have been tested in clinical trials, showing promise in terms of safety and the ability to induce neutralizing antibodies, there are no small drug molecules under clinical trials [5]. In the realm of small molecule drug discovery for ZV, five key target proteins are identified due to their crucial roles in the virus's life cycle and pathogenesis. The major target proteins typically include (1) NS3 protein Protease/Helicase plays a vital role in the replication and maturation of the ZV; (2) NS5 RNA-Dependent RNA Polymerase (RdRp) is essential for the replication of the Zika virus RNA genome; (3) NS5 Methyltransferase is involved in capping viral RNA, which is crucial for RNA stability and immune evasion; (4) Capsid Protein is involved in the assembly and packaging of the viral genome; and (5) Envelope (E) Protein is critical for the virus's ability to infect host cells [3, 6]. Each of these targets presents unique challenges and opportunities for drug development. The focus of research is often on finding molecules that can effectively inhibit these proteins without being toxic to human cells. Among the five major target proteins of the ZV for drug discovery, the NS5 RdRp is often considered the most critical due to the following reasons: (a) central role in viral replication, (b) highly conserved structure among flaviviruses, (c) potential for selective targeting as the RNA polymerase of RNA viruses has distinct features not found in human DNA polymerases. Thus, NS5 RdRp will be used as a target and US FDA approved small molecules will be employed for the repurposing technique to find a new drug for ZV. Computational methods like docking, molecular dynamics, machine learning (ML), and ADMET profiling followed by MM-GBSA/PBSA will be utilized to identify potential new drug molecules for ZV. The proposed research can enhance the whole drug discovery process up to many folds compared to traditional drug discovery processes in terms of time and economy followed by saving late-stage drug failure substantially enhancing its success rate, and ultimately contributing to the enhancement of healthcare quality in the United States. |
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Effect of speech intervention on social confidence in elementary school children who stutter This study intends to evaluate how speech treatment affects elementary school students and speech therapists with speech-language impairments and social confidence. Previous research demonstrates the importance of early intervention, teamwork between educators and therapists, and self-efficacy as confidence boosters (e.g., Yates et al., 2018). These themes provide information on improving kids' social skills and lowering anxiety levels when communicating. Children who stutter can have severe effects on their self-esteem, which can result in low self-worth assessments and heightened susceptibility to social anxiety. When messages reinforce a stutter to stop, relax, and slow down, a person who stutters feels ashamed about their ability to communicate as they may interpret the encouragement counter-intuitively (Yates et al., 2018). Other researchers agree that in the long run, stuttering children and even adults have higher rates of anxiety disorder that contributes to lower self-esteem (Brignell et al., 2021; Iverach et al., 2016; Smith et al., 2014; Yaruss and Quesal, 2004). However, there is a significant vacuum in the research because most studies haven't specifically examined the complex subtleties of confidence in the setting of stuttering therapy. Examining confidence, which includes self efficacy and self-esteem, is essential to developing a thorough grasp of the psychological and emotional factors that affect the general well-being of kids with speech difficulties. Furthermore, a significant lacuna in the current body of literature is the dearth of studies examining speech pathologists' perspectives on how students' self-confidence evolves during therapy. Although prior research has illuminated the effectiveness of many therapeutic modalities and the significance of cooperative tactics in fostering social confidence in kids with speech impairments, the viewpoints of speech pathologists themselves are mainly untapped. To do this, we adopt a quantitative approach. We will gather information about the social confidence levels of elementary school students who are receiving therapy over the course of one month. Surveys are administered to students that will ask about their self-esteem and their self efficacy. In addition, we compare the output with that of their SLP’s perspective to examine disparities in students’ self-identified progress versus that of their provider. |
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Investigating solar energy technology adoption and diffusion using the agent-based model with GIS Current personal, technical, social, socio-demographic, political, economic, and environmental issues have made energy source selection a delicate decision. These pressures concurrent with the decrease in sustainable energy costs have increased the interest in cleaner alternative energies such as solar and wind to mitigate the environmental consequences. Among these energies, Solar Photovoltaic energy is one of the most promising sources of power providing 12.8% of the annual energy in 2022 in the US. The study attempts to investigate the interrelationships between income, education, and solar adoption using the US census data and Geographic Information Systems (GIS) tool with an agent-based model on the East Coast of the U.S. This research aims to identify adoption hotspots and recommend particular areas in New Jersey and other states that are ideal for energy-related organizations to promote, educate, and incentivize “go solar” programs to promote the adoption and diffusion of solar photovoltaics. This study introduces a combined methodology to help policymakers distribute financial incentives for residential solar electric systems in particular areas. |