Ashland University

The planning of sample size for research studies often focuses on obtaining a significant result given a specified level of power, significance, and an anticipated effect size. This planning requires prior knowledge of the study design and a statistical analysis to calculate the proposed sample size. However, there may not be one specific testable analysis from which to derive power (Silberzahn et al., Advances in Methods and Practices in Psychological Science, 1(3), 337356, 2018) or a hypothesis to test for the project (e.g., creation of a stimuli database). Modern power and sample size planning suggestions include accuracy in parameter estimation (AIPE, Kelley, Behavior Research Methods, 39(4), 755–766, 2007; Maxell et al., Annual Review of Psychology, 59, 537–563, 2008) and simulation of proposed analyses (Chalmers & Adkins, The Quantitative Methods for Psychology, 16(4), 248–280, 2020). These toolkits offer flexibility in traditional power analyses that focus on the if-this, then-that approach. However, both AIPE and simulation require either a specific parameter (e.g., mean, effect size, etc.) or a statistical test for planning sample size. In this tutorial, we explore how AIPE and simulation approaches can be combined to accommodate studies that may not have a specific hypothesis test or wish to account for the potential of a multiverse of analyses. Specifically, we focus on studies that use multiple items and suggest that sample sizes can be planned to measure those items adequately and precisely, regardless of the statistical test. This tutorial also provides multiple code vignettes and package functionality that researchers can adapt and apply to their own measures.

Abstract This article offers and defends a reinterpretation of Mark 13, traditionally understood as foretelling the destruction of the temple and the second coming of Christ. It instead argues that the chapter is fundamentally about the cross and the impending passion of both Jesus and his disciples. Contrary to common readings of Mark, the coming of the Son of Man refers not to a future parousia but to Jesus’s exaltation through the crucifixion and resurrection, events that inaugurate the eschatological age. The argument is made that the cosmic imagery in Mark 13 signals the arrival of God’s kingdom through the cross, where conventional notions of power and sovereignty are overturned. This reading is supported by exploring theological and literary connections between Mark 13 and the rest of the gospel, such as the demand for signs, the messianic secret, and sacrifice depicted as an act of violence disguised as religious necessity. Failure to recognize the cross as the decisive historical event has led many interpreters to misinterpret the chapter as a prophecy of historical or future events outside the gospel, missing its deeper theological message.

Bacteriophages largely impact bacterial communities via lysis, gene transfer, and metabolic reprogramming and thus are increasingly thought to alter nutrient and energy cycling across many of Earth's ecosystems. However, there are few model systems to mechanistically and quantitatively study phage-bacteria interactions, especially in soil systems. Here, we isolated, sequenced, and genomically characterized 24 novel phages infecting Klebsiella sp. M5al, a plant growth-promoting, nonencapsulated rhizosphere-associated bacterium, and compared many of their features against all 565 sequenced, dsDNA Klebsiella phage genomes. Taxonomic analyses revealed that these Klebsiella phages belong to three known phage families (Autographiviridae, Drexlerviridae, and Straboviridae) and two newly proposed phage families (Candidatus Mavericviridae and Ca. Rivulusviridae). At the phage family level, we found that core genes were often phage-centric proteins, such as structural proteins for the phage head and tail and DNA packaging proteins. In contrast, genes involved in transcription, translation, or hypothetical proteins were commonly not shared or flexible genes. Ecologically, we assessed the phages' ubiquity in recent large-scale metagenomic datasets, which revealed they were not widespread, as well as a possible direct role in reprogramming specific metabolisms during infection by screening their genomes for phage-encoded auxiliary metabolic genes (AMGs). Even though AMGs are common in the environmental literature, only one of our phage families, Straboviridae, contained AMGs, and the types of AMGs were correlated at the genus level. Host range phenotyping revealed the phages had a wide range of infectivity, infecting between 1-14 of our 22 bacterial strain panel that included pathogenic Klebsiella and Raoultella strains. This indicates that not all capsule-independent Klebsiella phages have broad host ranges. Together, these isolates, with corresponding genome, AMG, and host range analyses, help build the Klebsiella model system for studying phage-host interactions of rhizosphere-associated bacteria.

Anaemia does pose a challenge to the health sector of the world, especially in resource poor settings where most diagnostic techniques are invasive, expensive, and inaccessible. This study aims to develop a very accurate, non-invasive anaemia detection solution using deep learning models on palm images. Such tests, like blood tests, cannot be implemented in many lowresource settings. It is for this reason that the current research gap, particularly as regards the diagnostic capability of palm images using state-of-the-art deep learning methods, falls within the opportunity that may be contemplated here. This work used the ConvNext Tiny model, trained over a dataset of images taken from the palms of people in Ghana using transfer learning and heavy preprocessing of data, other CNN models including ResNet and VGG16. The results showed that the accuracy for ConvNext Tiny was 96.8% and rendered the highest data accuracy and also provided high speed compared with all other models. This approach promotes early detection of anemia, and not only is this a cost-effective method, but it is also accessible enough to be used in such sites of improving healthcare delivery where resources may be scarce.

The enlargement of Artificial Intelligence (AI)-powered privacy preservation techniques leads to new research avenues and innovations. Moreover, in today's data driven world the privacy and data protection are increased. In order to balance both data utility and individual privacy rights. This research paper provides a unique AI-powered privacy preservation framework by using machine learning algorithm that adapts to diverse data scenarios. The proposed framework uses Adaptive Privacy-Preserving Ensemble Learning (APPEL) machine learning algorithms to ensure the optimal data protection while maintaining both data utility and also privacy settings dynamically. Additionally, this paper evaluates the efficacy of data driven applications like Healthcare, social media and Online Platforms, Internet of Things (IoT) and smart devices. This study collected and pre-processed the data from data driven applications to determine trends, insights, and highlighting key challenges and opportunities for improvement. This framework's efficacy in real-world applications demonstrate the experimental results and provides an accurate and promising solution for privacy-preserving data analysis. This research contributes to the development of privacy-preserving AI solutions to safeguard sensitive data in data driven environment.

This article examines the intricate dynamics faced by parents dur-\ning a child protective services (CPS) investigation, highlighting the complexities of navigating the child welfare system within the framework of constitutional exceptions. It outlines the unique challenges parents en- counter, including legal rights, procedural requirements, and the emotional toll of being under investigation. By analyzing relevant legal standards and the roles of various stakeholders, the article provides essential guidance for parents on how to effectively engage with CPS while protecting their family’s interests. Emphasizing the importance of in- formed advocacy, the article aims to equip parents with the knowledge and strategies necessary to navigate this challenging landscape, ensuring the safety and well-being of their children while safeguarding their constitutional rights.

We are delighted to introduce the latest issue of Delos, with Gabriele Belletti stepping in as the new Editor-in-Chief. This transition ensures continuity, as Benjamin Hebblethwaite, our former Editor-in-Chief, will remain as Coeditor, bringing his dedication and infectious enthusiasm to the role. Our journal continues to blend academic rigor with a welcoming spirit of innovation. This rigor is maintained through double peer-reviewed contributions, complemented by comprehensive introductions and critical analyses and thanks to teamwork in collaboration with the Managing Editor, Richard Gray, the Journals Manager Lauren Phillips, and the entire University of Florida Press. Innovation is reflected in our embrace of diverse cultures and languages, as well as various modes of translation across poetry, prose, and other artistic forms, fostering a welcoming attitude towards different perspectives.

In India, around 12 Million people suffer from glaucoma, and 1.5 million are blind due to it. However, more than 75% of glaucoma cases are undiagnosed. Millions of individuals worldwide experience blindness due to the unfortunate reality that they were unaware of their glaucoma diagnosis. As per the World Health Organization (WHO) [1], glaucoma is among the primary causes of blindness and visual impairment, particularly in high-income nations. This motivated us to develop and implement a system advantageous to both healthcare facilities and potential patients (i.e., the general public). We posit that this system will substantially mitigate the disease’s severity, which will be discussed in this paper. Individuals can self-register for the application, or frontline workers, doctors, or hospital management personnel can add them. Upon registration, their medical history is collected, fundus images of their eyes are captured and uploaded, and an immediate prediction of the presence or absence of glaucoma is provided. A report of the findings is shared with the hospital’s doctors, among other actions. Both the patient and the doctor can review the reports. Depending on the severity, doctors can add comments to the reports, which patients can view and respond to. There were apps like the Yanbao app [2], but it only works on iOS, and our app ANSAN works on both Android and iOS platforms and has more personalized features in comparison. The app was shortlisted for the Google Solutions Challenge [3] India Regional Bootcamp when submitted in 2024. We propose two models: one [4] wherein data is stored in a relational database model in the hospital’s server, and another where data is stored in a NoSQL model [5] integrated by Google Firebase tools. The app is scalable on both ends naturally by design. ANSAN can be a real-world application to help doctors, potential patients, and the world in reducing cases where glaucoma is the cause of blindness through early detection.