Old Dominion University

The purpose of this article is to examine the content of previously published empirical literature utilizing self-efficacy theory with regard to physical education teachers’ perceived self-confidence to teach students with disabilities in general physical education. Keyword searches were used to identify relevant literature from electronic databases published from 2000 to 2022. Twenty-four articles, from 11 countries, met all inclusion criteria, and relevant data regarding participants, theory, measurement, research design, and dependent variables were extracted. Of the 24 studies, nine were survey validation, eight were experimental, six were cross-sectional, and one was mixed-methods design. Major findings across the examined studies indicate that teachers’ perceptions of training, amount of experience, and support from personnel significantly influence their self-efficacy toward teaching students with disabilities.

Digital Identity Management (DIM) systems have become challenging, especially given the current progression in ubiquitous environments enclosing cooperative Internet of Things (IoT) devices, individuals, and organizations. Self-Sovereign Identity (SSI) has recently surfaced to manifest the notion of decentralized DIMs enlivened by users autonomy. This paper presents a proactive, trustworthy solution for managing digital users data and identity information without risking its integrity, security, privacy, and confidentiality. Accordingly, we propose a decentralized SSI-enabled cloud storage model that enables secure access control and frequent data integrity checking mechanism. The presented model ensures the resiliency of SSI-enabled operations, including users registration, data identification, external information distribution, integrity checking operations, identity information authentication and verification, and decentralized external and shareable operations.

Universal design for learning (UDL) has been advocated for by adapted physical education scholars as a panacea to the challenges associated with teaching disabled and nondisabled students together in physical education. So much so that UDL currently occupies a privileged and largely unquestioned position in adapted physical education scholarship and practice, until now. To move scholarship forward, this article draws on published theoretical and empirical work relating to UDL generally and in physical education in particular to critically discuss the scientific research supporting , or not, the use of UDL as a so-called inclusive approach. We end this article with a call to action for scholars in this field, ourselves included, to conduct theoretically guided and empirically informed research relating to UDL in physical education, which adheres to established hallmarks of research quality that are tied to the ontological and epistemological assumptions of researchers because, at present, it is conspicuous by its absence.

Knowledge discovery in databases (KDD) is crucial in analyzing data to extract valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in diseases with high incidence, mortality, and costs, like cancer. ML techniques can develop more accurate predictive models for cancer patients’ clinical outcomes, aiding informed healthcare decision-making. However, cancer prediction modeling faces challenges because of the unbalanced nature of the datasets, where there is a small minority category of patients with a cancer diagnosis compared to a majority category of cancer-free patients. Imbalanced datasets pose statistical hurdles like bias and overfitting when developing accurate prediction models. This systematic review focuses on breast cancer prediction articles published from 2008 to 2023. The objective is to examine ML methods used in three critical steps of KDD: preprocessing, data mining, and interpretation which address the imbalanced data problem in breast cancer prediction. This work synthesizes prior research in ML methods for breast cancer prediction. The findings help identify effective preprocessing strategies, including balancing and feature selection methods, robust predictive models, and evaluation metrics of those models. The study aims to inform healthcare providers and researchers about effective techniques for accurate breast cancer prediction.

Ultra-endurance running (UER) poses extreme mental and physical challenges that present many barriers to completion, let alone performance. Despite these challenges, participation in UER events continues to increase. With the relative paucity of research into UER training and racing compared with traditional endurance running distance (e.g., marathon), it follows that there are sizable improvements still to be made in UER if the limitations of the sport are sufficiently understood. The purpose of this review is to summarise our current understanding of the major limitations in UER. We begin with an evolutionary perspective that provides the critical background for understanding how our capacities, abilities and limitations have come to be. Although we show that humans display evolutionary adaptations that may bestow an advantage for covering large distances on a daily basis, these often far exceed the levels of our ancestors, which exposes relative limitations. From that framework, we explore the physiological and psychological systems required for running UER events. In each system, the factors that limit performance are highlighted and some guidance for practitioners and future research are shared. Examined systems include thermoregulation, oxygen delivery and utilisation, running economy and biomechanics, fatigue, the digestive system, nutritional and psychological strategies. We show that minimising the cost of running, damage to lower limb tissue and muscle fatigability may become crucial in UER events. Maintaining a sustainable core body temperature is critical to performance, and an even pacing strategy, strategic heat acclimation and individually calculated hydration all contribute to sustained performance. Gastrointestinal issues affect almost every UER participant and can be due to a variety of factors. We present nutritional strategies for different event lengths and types, such as personalised and evidence-based approaches for varying types of carbohydrate, protein and fat intake in fluid or solid form, and how to avoid flavour fatigue. Psychology plays a vital role in UER performance, and we highlight the need to be able to cope with complex situations, and that specific long and short-term goal setting improves performance. Fatigue in UER is multi-factorial, both physical and mental, and the perceived effort or level of fatigue have a major impact on the ability to continue at a given pace. Understanding the complex interplay of these limitations will help prepare UER competitors for the different scenarios they are likely to face. Therefore, this review takes an interdisciplinary approach to synthesising and illuminating limitations in UER performance to assist practitioners and scientists in making informed decisions in practice and applicable research.

The double-spin-polarization observable $${\mathbb {E}}$$ E for $$\vec {\gamma }\vec {p}\rightarrow p\pi ^0$$ γ → p → → p π 0 has been measured with the CEBAF Large Acceptance Spectrometer (CLAS) at photon beam energies $$E_\gamma $$ E γ from 0.367 to $$2.173~\textrm{GeV}$$ 2.173 GeV (corresponding to center-of-mass energies from 1.240 to $$2.200~\textrm{GeV}$$ 2.200 GeV ) for pion center-of-mass angles, $$\cos \theta _{\pi ^0}^{c.m.}$$ cos θ π 0 c . m . , between $$-$$ - 0.86 and 0.82. These new CLAS measurements cover a broader energy range and have smaller uncertainties compared to previous CBELSA data and provide an important independent check on systematics. These measurements are compared to predictions as well as new global fits from The George Washington University, Mainz, and Bonn-Gatchina groups. Their inclusion in multipole analyses will allow us to refine our understanding of the single-pion production contribution to the Gerasimov-Drell-Hearn sum rule and improve the determination of resonance properties, which will be presented in a future publication.

Many individuals with autism spectrum disorder (ASD) are inequitably unemployed due to challenges associated with the social and communication demands of the traditional job interview process. Using a single-case multiple-baseline design replicated across participants, we evaluated the effects of eCoaching with online bug-in-ear (BIE) technology on responses to job interview questions in transition-age students with ASD. Results demonstrated a functional relation between the intervention and target behavior, and the acquired interview skills were maintained up to 6 weeks post-intervention. Social validity findings indicated all participants enjoyed receiving feedback through online BIE while participating in live-streamed mock job interviews and would recommend this intervention to others. This study extends the limited literature on promoting employment opportunities for job-seeking individuals with ASD.

Soil microbiota of the rhizosphere are an important extension of the plant phenotype because they impact the health and fitness of host plants. The composition of these communities is expected to differ among host plants due to influence by host genotype. Given that many plant populations exhibit fine‐scale genetic structure (SGS), associated microbial communities may also exhibit SGS. In this study, we tested this hypothesis using Chamaecrista fasciculata , a legume species that has previously been determined to have significant SGS. We collected genetic data from prokaryotic and fungal rhizosphere communities in association with 70 plants in an area of ~400 square meters to investigate the presence of SGS in microbial communities. Bacteria of Acidobacteria, Protobacteria, and Bacteroidetes and fungi of Basidiomycota, Ascomycota, and Mortierellomycota were dominant members of the rhizosphere. Although microbial alpha diversity did not differ significantly among plants hosts, we detected significant compositional differences among the microbial communities as well as isolation by distance. The strongest factor associated with microbial distance was genetic distance of the other microbial community, followed by geographic distance, but there was not a significant association with plant genetic distance for either microbial community. This study further demonstrates the strong potential for spatial structuring of soil microbial communities at the smallest spatial scales and provides further insight into the complexity of factors that influence microbial composition in soils and in association with host plants.

Conventional probabilistic seismic hazard analysis (PSHA) is often repeated at many locations independently to develop uniform hazard maps. However, such maps are unsuitable for assessing risk to spatially distributed infrastructure because no single event will produce uniform hazard shaking intensities across a broad region. A robust but computationally expensive approach is to analyze spatially distributed infrastructure systems separately for every event considered in the seismic source characterization model used in the PSHA. This approach may not be practical when many scenario events are considered. An alternative is to select a manageable event subset that, in aggregate, approximately matches the hazard for single or multiple ground motion intensity measures across the spatially distributed system preserving contributions of different magnitudes and distances to the PSHA. We present a flexible and efficient regression-based method that meets these requirements using point-based PSHA results as inputs. The approach is illustrated with a case study of distributed infrastructure in southern California. We demonstrate the efficiency of the method by comparing it to a mixed-integer linear optimization method from the literature.

Purpose Lip prints are unique and have potential for use as a human identifier. The purpose of this study was to observe possible cheiloscopy differences of individuals with and without parafunctional oral habits such as smoking, vaping, playing a wind instrument or using an asthma inhaler. Methods This IRB approved blinded cross‐sectional observation pilot study collected lip prints from 66 individuals, three of which were excluded. Participants cleansed their lips, then lipstick was applied to the vermillion zones of the upper and lower lips. Adhesive tape was applied to the lips and prints were transferred to white bond paper for viewing purposes. Each set of included lip prints was divided into quadrants and dichotomized into a group of those with an oral parafunctional habit or with no such habits. Each quadrant sample was then manually analysed and classed according to the gold standard Suzuki and Tsuchihashi system. Results A total of 252 dichotomized lip print quadrants (with habits n = 76, 30.2%, and without habits n = 176, 69.8%) were analysed. Type II patterns were the most common for examined quadrant samples; however, no statistically significant differences (Pearson's chi‐squared test, p = 0.366) were observed between pattern classifications of samples with and without parafunctional oral habits. Conclusions There is no statistically significant difference of lip print patterns between individuals with and without parafunctional oral habits. Further research on populational variations is needed for cheiloscopy to aid in human identifications.

This section offers a muestra—a sampling or display—of major works of Latin American digital poetry. Authored by experts in the field, these 25 short entries analyze the canon of digital poetry from Latin America. Each entry consists of: (1) a technical description of the work; (2) a brief contextualization of the work (referencing typologies, generational schema, and taxonomies); (3) an analysis of the e-poetics of the digital work; (4) minimal (e-) bibliography (about the author/in general); and (5) QR codes linking to the author’s work. This “sample” of trends in Latin American digital poetics is not meant to be exhaustive; rather, it seeks to situate some key features of these emergent forms in larger computational, literary, and cultural contexts.

The current study examined the effects of security score framing, time pressure, and brand familiarity on mobile application choices. Past research has found the framing of safety versus risk scores affects how potential risks for mobile apps is communicated to users. Both time pressure and brand familiarity have been shown to affect consumers’ purchase behaviors but not yet for app-selection decisions. The current study examined the effects of time pressure and brand familiarity on the effectiveness of risk displays (framed as safety or risk) for mobile apps. Participants were shown screenshots of various apps with these factors manipulated, and they were to choose one out of six apps. Our findings indicate that users rely heavily on brand familiarity when choosing apps, which could lead to insecure decisions. Additionally, security scores guided app choices towards more secure apps when framed as safety than when framed as risk, although this advantage was only evident without time pressure and disappeared under time pressure. The design implications call for more careful screening and user education about the potential risks associated familiar apps, as well as the need of new security design solutions to help users under time pressure.

Paper publications are no longer the only form of research product. Due to recent initiatives by publication venues and funding institutions, open access datasets and software products are increasingly considered research products and URIs to these products are growing more prevalent in scholarly publications. However, as with all URIs, resources found on the live Web are not permanent. Archivists and institutions including Software Heritage, Internet Archive, and Zenodo are working to preserve data and software products as valuable parts of reproducibility, a cornerstone of scientific research. While some hosting platforms are well-known and can be identified with regular expressions, there are a vast number of smaller, more niche hosting platforms utilized by researchers to host their data and software. If it is not feasible to manually identify all hosting platforms used by researchers, how can we identify URIs to open-access data and software (OADS) to aid in their preservation? We used a hybrid classifier to classify URIs as OADS URIs and non-OADS URIs. We found that URIs to Git hosting platforms (GHPs) including GitHub, GitLab, SourceForge, and Bitbucket accounted for 33% of OADS URIs. Non-GHP OADS URIs are distributed across almost 50,000 unique hostnames. We determined that using a hybrid classifier allows for the identification of OADS URIs in less common hosting platforms which can benefit discoverability for preserving datasets and software products as research products for reproducibility.

Web archives are sources of big data. When presenting human visitors with archived web pages, or mementos, web archives often apply user interface augmentations to assist them. Unfortunately, these augmentations present challenges for natural language processing, computer vision, and machine learning methods. Thus, big data researchers must apply special techniques to web archives when acquiring mementos. This paper details these techniques so that future projects can more easily create datasets and conduct research. We review 22 web archives and discuss the methods needed to re-synthesize a memento to something close to its original capture without augmentations. We close by discussing options for improving the state of memento sharing for big data efforts.

The relationship between emotions and job satisfaction is widely acknowledged via affective events theory (AET). Despite its widespread use, AET was not designed to address why specific emotions might differentially relate to job satisfaction. We utilize appraisal theory of emotion to refine AET and provide this nuanced theorizing. We meta‐analytically test our ideas with 235 samples across 99 883 individuals and 22 600 intra‐individual episodes. We test two approaches—specific emotion experiences (16 discrete emotions) versus general emotion experiences (positive or negative emotions)—and present empirical evidence of their similarities and differences with job satisfaction. Our findings suggest that specific emotions with circumstance‐agency appraisals (e.g., depression and happiness) have the strongest associations with job satisfaction compared to emotions with self‐ and other‐agency appraisals and general emotion experiences. However, more variability is observed for negative emotions and job satisfaction compared to positive emotions. Further, we address and even challenge influential critiques of emotions and job satisfaction via a meta‐analytic test of five moderators—emotion intensity versus frequency, target of emotion, job satisfaction measure, level of analysis, and time referent for emotion and job satisfaction recall. In sum, we advance academic and practitioner understanding of the relationship between emotions and job satisfaction.

The intestinal pathogen Clostridioides difficile encodes roughly 50 TCS, but very few have been characterized in terms of their activating signals or their regulatory roles. A. G. Pannullo, B. R. Zbylicki, and C. D. Ellermeier (J Bacteriol 205:e00164-23, 2023, https://doi.org/10.1128/jb.00164-23 ) have identified both for the novel C. difficile TCD DraRS. DraRS responds to antibiotics that target lipid-II molecules in the bacterial cell envelope, and regulates the production of a novel glycolipid necessary for bacitracin and daptomycin resistance in C. difficile .

High-dimensional and incomplete (HDI) data usually arise in various complex applications, e.g., bioinformatics and recommender systems, making them commonly heterogeneous and inclusive. Deep neural networks (DNNs)-based approaches have provided state-of-the-art representation learning performance on HDI data. However, most prior studies adopt fixed and exclusive \(L_2\)-norm-oriented loss and regularization terms. Such a single-metric-oriented model yields limited performance on heterogeneous and inclusive HDI data. Motivated by this, we propose a Multi-Metric-Autoencoder (MMA) whose main ideas are two-fold: 1) employing different \(L_p\)-norms to build four variant Autoencoders, each of which resides in a unique metric representation space with different loss and regularization terms, and 2) aggregating these Autoencoders with a tailored, self-adaptive weighting strategy. Theoretical analysis guarantees that our MMA could attain a better representation from a set of dispersed metric spaces. Extensive experiments on four real-world datasets demonstrate that our MMA significantly outperforms seven state-of-the-art models. Our code is available at the link https://github.com/wudi1989/MMA/

Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in IoT environments, and a review of recent research trends is presented.

Objective The objective of this study was to compare the effects of 10 commercially available instrument handle designs' mass and diameter on forearm muscle activity during a simulated periodontal scaling experience. Methods A convenience sample of 25 registered dental hygienists was recruited for this IRB‐approved study. Ten commercially available instruments were categorized into four groups based on their masses and diameters: large diameter/light mass, small diameter/light mass, large diameter/heavy mass and small diameter/heavy mass. Participants were randomized to four instruments, one from each group. Participants scaled with each instrument in a simulated oral environment while muscle activity was collected using surface electromyography. Muscle activity was compared among the four instrument group types. Results Muscle activity of the flexor digitorum superficialis was not significantly influenced by instrument mass ( p = 0.60) or diameter ( p = 0.15). Flexor pollicis longus muscle activity was not significantly influenced by instrument mass ( p = 0.81); diameter had a significant effect ( p = 0.001), with smaller diameter instruments producing more muscle activity. For the extensor digitorum communis and extensor carpi radialis brevis, instrument mass did not significantly affect muscle activity ( p = 0.64, p = 0.43), while diameter narrowly failed to reach significance for both muscles ( p = 0.08, p = 0.08); muscle activity for both muscles increased with smaller diameter instruments. Conclusion Results from this study indicate instrument diameter is more influential than mass on muscle activity generation; small diameter instruments increased muscle activity generation when compared to large diameter instruments. Future research in real‐world settings is needed to determine the clinical impact of these findings.

Researchers have demonstrated that administrative support represents a mitigating factor for teacher attrition and correlates with job satisfaction. The authors’ purpose in this study was to develop and validate the Dimensions of Administrative Support Inventory (DASI) scale to measure the support provided to teachers in school. They employed House’s theoretical framework to assess teachers’ support received from administrators across four domains: emotional support, appraisal support, instrumental support, and informational support. A total of 1,101 special and general education teachers in a southeastern state completed the scale. The authors used exploratory and confirmatory factor analyses to investigate the factor structure of the scale. They also tested measurement invariance through configural, metric, and scalar invariance tests to examine whether differences in construct measurement exist across special and general education teachers. Results indicated that a correlated two-factor model consisting of the Value and Logistics factors was the optimal solution. The latent mean difference testing showed that the two factors were similar across the two groups of teachers. The 26-item DASI scale provides administrators with a valid and reliable measure to quantify the support received by teachers and proactively implement changes to foster a supportive school environment, increase teachers’ job satisfaction, and reduce attrition.