19916 research outputs found
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ML PLAYGROUND: DATA MODIFICATION/PREPROCESSING AND MODEL SIMULATION TOOL
Full text linkThere is a heavy reliance on programming when it comes to learning machine learning (ML). This often creates barriers for students and newcomers unfamiliar with coding. While the lessons you learn in the classroom provide essential foundational understanding, some technical or practical aspects of ML—such as data preprocessing, feature engineering, and model tuning—are best learned through hands-on interaction. ML Playground was developed to act as a proof-of-concept application to address this gap by offering a browser-based, graphical user interface that lets users engage with core ML workflows without writing code. Designed with educational accessibility in mind, the application allows users to modify datasets, apply preprocessing techniques, perform feature engineering, and simulate machine learning models solely through an intuitive interface.
ML Playground provides real-time feedback on how user-controlled changes affect model performance, using metrics such as Mean Squared Error to guide exploration and encourage user experimentation. Built entirely in Python using Streamlit, ML Playground is extremely modular, enabling future extension to support additional models and techniques. While the scope of the current version is limited, it successfully demonstrates how simplifying the ML pipeline through a visual, codeless alternative can lower the barrier to entry and foster understanding and curiosity among learners. The application is ultimately aimed at bolstering traditional ML education with a beginner-friendly environment for experimentation and demonstrations of practical ML controlled entirely by the user.
THE EFFECTS OF ACES ON SUBSTANCE ABUSE IN CALIFORNIA: A SECONDARY DATA ANALYSIS USING CHIS DATA
Full text linkAdverse Childhood Experiences (ACEs) are increasingly recognized as critical determinants of long-term health and behavioral outcomes. Using data from 19,771 adults in the California Health Interview Survey (CHIS), this study examines the relationship between ACEs and substance use in adulthood, emphasizing the role of childhood trauma in shaping later-life health behaviors. Participants reported their ACE exposure through a 10-item ACE scale titled Adverse Childhood Experiences Study Questionnaire developed by Vincent Felitti and Robert Anda. The study assesses the use of various substances, including (a) marijuana, (b) e-cigarettes, (c) CBD, (d) cigarettes, (e) alcohol, and (f) illicit drugs, measured by past use and current use. Demographic variables such as age, gender, race, educational attainment, citizenship, and English proficiency are also included as control variables. Ordinal logistic regressions were conducted using SPSS to examines the association between ACEs and substance use patterns. Findings indicate more ACE exposure predicted increased likelihood of all substance use in adulthood, underscoring the necessity of trauma-informed prevention and intervention strategies. These results contribute to the growing body of evidence linking childhood adversity to long-term substance use behaviors and reinforce the need for public health policies addressing early-life trauma
Access to Mental Health in the Latino Community
Full text linkThis study aims to explore mental health within the Latino community, highlighting the disparities in mental health service utilization and the contributing factors. The key barriers identified include stigma, economic hardships, language barriers, and lack of culturally relevant services, which have disproportionately affected Latinos\u27 access to mental health. The research examines cause-and-effect relationships between interventions and utilization using an explanatory design. Using mixed methods, quantitative and qualitative data is collected to examine participants\u27 knowledge, experiences, and attitudes toward mental health services and their impact on service utilization. Convenience, purposive, and snowball sampling are utilized to recruit 40 participants meeting specific criteria. Data collection will utilize surveys incorporating the Beck Depression Scale and other tailored questions, followed by an intervention that will include either a culturally relevant video or a psychoeducation session. The findings are intended to guide the creation of culturally appropriate interventions to increase access within the Latino community. The significance of this study is to be able to understand the factors that decrease the utilization of mental health services and address the issues to increase utilization. Social workers, led by social justice and ethical practice principles, are important in advocating equitable access to care, addressing health disparities, and empowering vulnerable populations
THE ADVERSE EFFECTS OF SUBSTANCE USE AMONG CAREGIVERS ON CHILDREN IN THE UNITED STATES: A SYSTEMATIC REVIEW
Full text linkIn the United States, Caregiver Substance Use Disorder (SUD) is a significant public health issue that disrupts family dynamics and can severely impact the health of millions of children. The systematic review examines the adverse impact of caregiver substance use on children, with research published from 2020 onwards. The significance of this study is due to the increasing prevalence of substance use disorders among caregivers and their harmful effects on child maltreatment, psychosocial health, physical health, and overall well-being. A systematic review framework will be utilized, synthesizing peer-reviewed studies to describe the prevalence of caregiver substance use, developmental issues for infants and toddlers, and the potential for long-term adverse outcomes of parental substance exposure. Data analysis uses a mixed-methods approach of combining numbers and words to create a holistic view of the problem. However, new research shows that children who live with caregivers who have SUDs are at an increasingly high risk for maltreatment and emotional and physical health problems. The review reveals the significant impact of family dynamics, including the influence of maternal and paternal substance use, and how intergenerational relationships impact child outcomes. In addition, the results highlight the need for more specific interventions and policy changes to address these multifaceted issues. An approach that emphasizes resilience and evidence-based strategies has the potential to shift developmental trajectories for children whose caregivers misuse substances
Incarcerated Parents and their Minor Children: A Study on the Impact of Maintaining Relationships
Full text linkParental incarceration is a social problem that has not received enough attention in the criminal justice literature despite the increase in the prison population over the years and the large number of children adversely impacted by this issue. The purpose of this research proposal is to determine if regular communication between incarcerated parents and their minor children has a positive influence on their rehabilitation and if that communication is also beneficial for the development and well-being of children. Using convenience and snowball sampling methods, this cross-sectional study will recruit approximately formerly incarcerated individuals who had at least one minor child during their incarceration (n = 100). All participants will be 18 or older and have a history in the American criminal justice system. The analysis of variance (ANOVA) will be the most appropriate statistical method for this study. ANOVA is a hypothesis testing procedure used to determine statistically significant differences between the means of three or more groups. The findings of this study will provide valuable insights into how maintaining family connections can contribute to positive outcomes for both parents and their children. The findings of this research will also inform policies and practices that support the rehabilitation of incarcerated individuals and the overall well-being of their children
ESTIMATION OF NITRATES IN SOUTHERN CALIFORNIA WATER RESOURCES
Full text linkThe research examines the water quality of Southern California, focusing on nitrate (NO3) levels. The study aims to provide insights into potential health disparities stemming from disproportionate exposure to these contaminants, particularly in underserved communities. Utilizing advanced analytical tools such as the UV spectrophotometer AquaMate Plus, water samples (n=40) were analyzed to assess nitrate levels compared to previous data (n=70 Approx.). The UV spectrophotometer operates based on measuring the absorbance of light intensity by the analyzed samples, providing valuable data for water quality assessment. Preliminary findings suggest varying levels of nitrate presence across the studied regions (0.4-43 ppm), influenced by agricultural activities, industrial discharge, and urban development. San Bernardino, Riverside, Palm Springs, and Ontario exhibit distinct contamination patterns, with certain areas experiencing higher concentrations of these pollutants.
Addressing water contamination requires collaborative efforts among policymakers, regulatory agencies, and community stakeholders. Strategies such as source water protection, pollution prevention measures, and infrastructure upgrades are essential for mitigating the health risks associated with these contaminants. Furthermore, targeted interventions tailored to the needs of vulnerable populations can help alleviate disparities in water quality- related health outcomes.
This research highlights the importance of mitigating nitrates-related water contamination in Southern California\u27s water sources, especially in places with many health inequalities. Organizations may collaborate to ensure that all citizens have equitable access to clean and safe drinking water by utilizing innovative analytical tools and implementing comprehensive initiatives. This will improve community health and well-being
Numalyze: Numerical Analysis Web Application
Full text linkNumalyze is an online platform that allows users to run and apply different numerical methods in real time. The application is built using Python and the Flask web framework. It provides an interface where users input mathematical functions and parameters to see the results for root-finding and integration methods, and to also perform reductions on matrices. By using a light-weight web framework and self-coded algorithms which removes dependency on massive external libraries—this application connects theoretical concepts to their practical implementation. It enables students and researchers to visualize the series of steps that each algorithm takes to compute results. Moreover, the application can be packaged into a Docker Image which makes it easy for distribution and deployment. The application, once deployed, is available on any device that supports a web browser, which makes it highly suitable for educational demonstrations, self-study, and quick experimentation
Leveraging Secure Generative AI Chatbot for XYZ ORGANIZATION: A Case Research Study
No full textThis project employed a case study research strategy to investigate the integration of Generative Artificial Intelligence (GenAI) within public organization operations, focusing on the XYZ Organization (henceforth XYZ) and its adoption of XYZChat, a GenAI-powered chatbot. The research questions guiding this study were: Q1: What specific factors led XYZ Organization to adopt XYZChat? Q2: How exactly was GenAI integrated into the day-to-day work processes at XYZ Organization? Q3: What were the expected outcomes of XYZChat since its launch, and how do those expectations compare with the actual results observed? Using Yin’s Case Study approach, this study analyzed XYZChat, specifically focusing on its operation within a NIST 800-171 compliant environment designed to process Controlled Unclassified Information (CUI).
The findings of this research were: Q1: XYZ Organization adopted XYZChat primarily to enhance operational efficiency, streamline communication, and ensure stricter compliance with established data security standards. Q2: The integration of GenAI at XYZ Organization involved a carefully planned, multi-phase strategy, including comprehensive training programs for all employees, rigorous system testing, and strategic collaborations with AI vendors to ensure seamless integration with existing workflows. Q3: While XYZ Organization initially expected improved response times and reduced workloads, the actual results showed significant efficiency gains but also revealed challenges such as user adoption issues and occasional system errors, highlighting the need for continuous refinement.
The conclusions are: Q1: XYZ ORGANIZATION’s decision to adopt GenAI was driven by operational needs and strict compliance requirements, demonstrating AI’s potential to transform government operations while safeguarding security and privacy. Q2: Effective GenAI integration requires meticulous planning, stakeholder engagement, and iterative improvements addressing both technical and human factors. Q3: Realizing GenAI’s full potential in government settings demands addressing user concerns, enhancing system reliability, and fostering a culture of innovation and continuous improvement.
Future scope: Q1: Future research should focus on optimizing GenAI adoption in government while mitigating security, privacy, and compliance risks, including exploring innovative AI-driven defense mechanisms. Q2: Studies should develop strategies for improving user adoption and refining AI systems to perform optimally in complex government environments. Q3: To enhance GenAI integration, novel approaches to secure data handling and compliance should be investigated, leveraging cutting-edge AI technologies and collaborative frameworks to boost resilience against evolving cybersecurity threats
On Bed Posture Recognition Using Deep Learning With Pressure Sensors
Full text linkIn healthcare applications such as disease prevention, sleep quality evaluation, and patient monitoring, bed posture recognition is essential. Using pressure sensor arrays placed on top of or embedded in mattresses, this study investigates the application of deep learning models for non-invasive posture classification. Although they have been widely employed, traditional machine learning approaches like support vector machines (SVM) and k-nearest neighbors (KNN) sometimes struggle with feature extraction and real-time performance necessitating considerable processing resources. I implemented a model using conventional approaches to get over these restrictions, then fine-tuned it using the following deep learning architectures for bed posture recognition: ResNet-50, EfficientNet-B0, MobileNetV2, EfficientNet-B4, and ResNet-101.
Our approach involves pressure sensor-based bed posture detection entails identifying and evaluating patterns of pressure distribution through piezo sensor based detectors. When lying on bed in different positions, such as supine, lateral, or fetal, these sensors record changes in the pressure that various body parts exert. A 2D pressure map is created from the gathered data, in order to be understood by machine learning or deep learning models, data is flatted into 1D later utilized to classify the position. Because every position has a different pressure signature, the model can reliably differentiate between them. which capture pressure distribution patterns corresponding to different body postures. The training process includes Feature Extraction and Initial Training, Fine-Tuning for Maximum Accuracy, Evaluation and Performance Optimization.
Among the evaluated models, EfficientNet-B4 and ResNet-101 demonstrated the highest classification accuracy, benefiting from their deep feature extraction capabilities and optimized architecture. MobileNetV2 exhibited the best trade-off between computational efficiency and classification accuracy, making it suitable for real-time applications on embedded systems. Fine-tuning strategies have significantly improved model performance, with accuracy gains of up to 5-10% compared to baseline pre-trained models. The integration of the Nvidia GPU significantly reduced training time while maintaining high classification accuracy increasing the epochs, making it a viable, convenient platform for real-time, on-device bed posture recognition applications
“I HAVE RETURNED HOME”: UNDERSTANDING THE RE-ENTRY EXPERIENCE OF SAUDI FEMALE SCHOLARS AFTER COMPLETING THEIR DOCTORAL STUDIES IN THE UNITED STATES
Full text linkThe experiences of Saudi female scholars returning from abroad remain underexplored. This study investigates how Saudi women in higher education institutions readjust and reintegrate into professional and cultural environments after completing doctoral studies in the United States. Using a narrative approach informed by transformational learning theory (TLT), it examines reverse culture shock and the coping mechanisms employed by these women. Five female participants from various Saudi institutions were interviewed in semi-structured sessions lasting 30 to 60 minutes. Thematic analysis revealed five key themes: Cultural Readjustment, Professional Reintegration, Family and Social Dynamics, Personal Transformation, and Coping Strategies. The findings underscore the challenges faced by participants, including resistance to change within their institutions and a lack of systemic support for knowledge transfer. Despite returning with transformed identities and fresh perspectives, they encountered barriers such as rigid systems and uncooperative colleagues. The study highlights the importance of developing tailored programs to support returning scholars, aiding them in navigating reintegration, managing reverse culture shock, and fostering collaboration. Gradual efforts to introduce change, coupled with environments resembling those abroad, facilitated smoother transitions for the participants. This research provides valuable insights into the transformative experiences of Saudi female returnees and emphasizes the critical role of systemic support in leveraging their potential to advance Saudi higher education