Digital Commons Network^™

Empowering Multilingual Families By Reconceptualizing Family Engagement, Melissa J. Cuba, Carolyn Waters, Luciana C. De Oliveira

Journal of English Learner Education

One of the most pressing issues in U.S. public schools is the engagement of multilingual families with children who are multilingual learners. So much so, federal policies, such as the Parent and Family Engagement Provisions in the Every Student Succeeds Act, prioritize and financially support activities and professional development focused on family engagement. In addition, these policies provide opportunities for families to contribute to agreed-upon school district policy that can reshape family engagement. However, as we reflect on the current sociopolitical context in the U.S., how are school systems creating opportunities for multilingual families to contribute and inform local family …

Supporting English Language Learners Through Peer Support, Anny Pedraza Borbon

Journal of English Learner Education

English language learners make up a blossoming part of the student demographic in the United States. (National Center of Educational Statistics, 2024) English language learners are affected by a myriad of circumstances and challenges which can rock their sense of self-efficacy. Self- efficacy has been seen to correlate with success in language acquisition. Teachers can support the development of self- efficacy and language acquisition through the facilitation of peer relationships. Peer relationships have been shown to have positive effects on learners, as evidenced by the social cognitive theory and the sociocultural theory. Teachers should be careful to scaffold these interactions …

The Impact Of Asymmetry In Lower Limb Muscle Strength And Power On Straight-Line Running Speed In Female Soccer Players, Dariusz Skalski, Magdalena Prończuk, Kinga Łosińska, Michał Spieszny, Maciej Kostrzewa, Piotr Aschenbrenner, Adam Maszczyk

Baltic Journal of Health and Physical Activity

The aim of this study was to investigate and determine the impact of asymmetries in muscle strength and power between the right and left lower limbs on straight-line running speed in female soccer players.30 right-footed female soccer players playing in the Ekstraliga participated in the study. The Modulus. Number formula was used to avoid negative results and to assess only the difference in relative strength and power between the lower limbs, not the dominance of one of them. A result equal to or below the threshold defined group G1 "with lower asymmetry," and a result above the threshold defined group …

Evidence For Explicitation: Working From Asl Into English, Campbell Mcdermid, Carrie Humphrey, Anita Harding

Journal of Interpretation

A study was done to examine the explicitation and compression strategies used by interpreters working from American Sign Language (ASL) into spoken English. To date, most research has focused only on their work from English into ASL. A review of the literature identified several expansion and compression strategies that interpreters and translators utilize, and these served as a model of coding the data and for triangulation. The methodology for this study was qualitative and descriptive. Twenty-two interpreters volunteered to simultaneously interpret four ASL recorded texts into spoken English, for a total of 88 target texts. The researchers identified 9 expansions …

Hemophagocytic Lymphohistiocytosis Due To Ehrlichiosis: A Case Series, Ajay Iyer, Mattias D'Anna, Shruti Verma, Thomas Pritchard, Vladimir Begilman, Himal Bajracharya, Kaveh Naemi

HCA Healthcare Journal of Medicine

Background

Hemophagocytic lymphohistiocytosis (HLH) is an immunologic syndrome characterized by excessive inflammation and tissue injury due to uncontrolled activation of the phagocytic system. The underlying mechanism is a lack of downregulation of activated macrophages and lymphocytes by natural killer and T cells. Unfortunately, the diagnosis is often delayed or missed due to the rarity of the disease, decreased awareness, and clinical picture variability. Ehrlichiosis is becoming a more prevalent tick-borne illness in endemic regions and a relatively uncommon but increasingly considered cause of HLH.

Case Presentation

We describe the cases of 2 patients diagnosed with secondary HLH as per the …

A Retrospective Data Analysis On Marine Animal Injuries At A Large, Multi-Site Medical System, Anthony Shadiack, J Burton Banks

HCA Healthcare Journal of Medicine

Background

With increasing numbers of human-animal interactions, there has been an increase in animal-related injuries. While canine bites are the most commonly reported animal injury, little data exists in regard to the other classes of animals, particularly marine life. The last comprehensive report on injuries related to noncanine bites and stings seen in emergency departments (EDs) across the US was between 2011 and 2015.

Methods

We performed a retrospective analysis from 2014-2019 on marine injuries from a large hospital network with over 180 hospitals, 100 freestanding EDs, and 170 urgent care centers to provide an update on the volume and …

Second Generation Antipsychotics And Cardiovascular Adverse Effects: Developing Evidence-Based Recommendations For Primary Care Medicine, Sana Borda, Ahn-Dao Lee, Paul F. Bell

HCA Healthcare Journal of Medicine

Background

Second-generation antipsychotic medications (SGAs) are often used by primary care physicians (PCPs) to treat multiple psychiatric diagnoses. SGAs have been connected to a number of adverse effects, including cardiovascular disease. Currently, there are no published evidence-based recommendations addressing SGAs and cardiotoxicity that are directed toward PCPs. This project aims to fill this gap.

Methods

Relevant search terms related to SGAs and cardiovascular disease were identified and then used to search databases (PubMed, PubMed Central, and AccessMedicine). Research studies obtained from the searches were narrowed to include systematic reviews and meta-analyses. The Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) tool was …

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Improved Rapidly-Exploring Random Tree Using Firefly Algorithm For Robot Path Planning, Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Yuhanis Yusof, Ahmed T. Sadiq, Faiz Al Alawy

Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths …

Satisfaction Of Excellent Service Training Participants With The Kirkpatrick Model At Alia Hospital Depok, Nur Fadilah Dewi

Jurnal Vokasi Indonesia

The study aims to improve the quality of human resources and services at Alia Hospital Depok through evaluation of training programs. The approach used is the Donald Kirkpatrick model evaluation which is a common method used in evaluating the effectiveness of training. The main problem faced by Alia Hospital Depok is the lack of evaluation conducted on the training programs that have been held. Therefore, training evaluation needs to be considered to improve the quality of human resources and services provided. The research method used is a survey by distributing questionnaires to service excellent training participants to evaluate their reactions …

Qualitative Characteristic Of Financial Statement Information And Implementation Psak 73 : Case Study In The Advertising Services Pt Abc, Hasnawati Hasnawati, Marsdenia Marsdenia, Trie Wiesty Cindy Salsabila

Jurnal Vokasi Indonesia

The quality of information presented in the financial statement is the most important thing for stakeholders in the business decision-making context. There has been a significant change in lease accounting practices due to changes in lease accounting standards for all business entities in countries that adopt IFRS. In 2017 the IASB published IFRS 16 Leases as new guidance on leases replacing IAS 17. DSAK IAI made adjustments related to IAS 17 to become PSAK 30, then based on the changes that emerged PSAK 30 was revoked and adjustments were made to IFRS 16 to become PSAK 73. These changes were …

The Importance And Role Of Public Relations In The Marketing Department At Mangkuluhur Artotel Suites, Habibillah Fitran, Mohammad Ridha

Jurnal Vokasi Indonesia

ABSTRACT The purpose of writing this research is to explain the role of Public Relations in the Marketing Communication Department regarding how to improve brand image, improve marketing and advertising to be more optimal, publish various information and promotions at Mangkuluhur ARTOTEL Suites through various information media and social media, and how to respond to customer complaints, as well as obstacles that occur in hotels related to various customer complaints and poor communication by several individuals within the hotel employees are also accompanied by appropriate solutions. To complete this research, the author collected data by conducting observations accompanied by interviews …

Learning Paradigms For Rhythm Detection And Generation Using Mathematical Models, Biophysical And Artificial Neural Networks, Prianka Bose

Dissertations

Humans possess an inherent ability to recognize evenly-spaced rhythms, known as isochronous rhythms, owing to the brain's predisposition to entrain to external auditory stimuli with regular temporal intervals. The central focus of this research is to understand how the brain learns and retains rhythmic time intervals in the context of music. This dissertation studies rhythm detection and generation through mathematical models, biophysical networks, and artificial neural networks, addressing both isochronous and non-isochronous patterns.

A primary focus of the thesis is on isochronous rhythms. In particular, given a perturbation to an isochronous rhythm such as a tempo change or phase shift …

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

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 …

Differential Item Functioning Of The Region-Based National Examinationequipment, Adi Setiawan, Gulzhaina Kuralbaevna Kassymova, Vianney Mbazumutima, Anggit Reviana Dewi Agustyani

REID (Research and Evaluation in Education)

This research aims to detect Differential Item Functioning (DIF) in the 2014/2015 National Examination Questions in mathematics of junior high schools and equivalent- level schools in the Yogyakarta region as a reference group and the South Kalimantan region as a focus group using the Likelihood Ratio Test (LRT) method, Area Measure Raju, and Lord. A sensitivity analysis was conducted to determine the most sensitive method. The data consisted of 5,465 National Examination papers of the students from the two regions who worked on type A questions. A sample of 1,000 exam papers for each region was established using the simple …

Advanced Worker's Compensation, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held July 25-26, 2024.

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

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held September 27, 2024.

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.

Handling Cases Involving Eggshell Plaintiffs, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held April 18, 2024.

Best Practices For Fiduciary Accountings & Procedures In Probate Practice, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held April 30, 2024.

The Ethics Triangle, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held August 29 & October 10, 2024.

The Ethics & Malpractice Risks Of Ai, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held March 28, 2024.

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 …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

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 …