University of Missouri System

This review proposes that physical inactivity could be considered a behavior selected by evolution for resting, and also selected to be reinforcing in life-threatening situations in which exercise would be dangerous. Underlying the notion are human twin studies and animal selective breeding studies, both of which provide indirect evidence for the existence of genes for physical inactivity. Approximately 86% of the 325 million in the United States (U.S.) population achieve less than the U.S. Government and World Health Organization guidelines for daily physical activity for health. Although underappreciated, physical inactivity is an actual contributing cause to at least 35 unhealthy conditions, including the majority of the 10 leading causes of death in the U.S. First, we introduce nine physical inactivity-related themes. Next, characteristics and models of physical inactivity are presented. Following next are individual examples of phenotypes, organ systems, and diseases that are impacted by physical inactivity, including behavior, central nervous system, cardiorespiratory fitness, metabolism, adipose tissue, skeletal muscle, bone, immunity, digestion, and cancer. Importantly, physical inactivity, itself, often plays an independent role as a direct cause of speeding the losses of cardiovascular and strength fitness, shortening of healthspan, and lowering of the age for the onset of the first chronic disease, which in turn decreases quality of life, increases health care costs, and accelerates mortality risk.

Psychological theories are statements of constraint. The role of hypothesis testing in psychology is to test whether specific theoretical constraints hold in data. Bayesian statistics is well suited to the task of finding supporting evidence for constraint, because it allows for comparing evidence for 2 hypotheses against each another. One issue in hypothesis testing is that constraints may hold only approximately rather than exactly, and the reason for small deviations may be trivial or uninteresting. In the large-sample limit, these uninteresting, small deviations lead to the rejection of a useful constraint. In this article, we develop several Bayes factor 1-sample tests for the assessment of approximate equality and ordinal constraints. In these tests, the null hypothesis covers a small interval of non-0 but negligible effect sizes around 0. These Bayes factors are alternatives to previously developed Bayes factors, which do not allow for interval null hypotheses, and may especially prove useful to researchers who use statistical equivalence testing. To facilitate adoption of these Bayes factor tests, we provide easy-to-use software.

Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis. To develop accurate spectral unmixing and endmember estimation methods, a number of approaches that account for spectral variability have been developed. This article motivates and provides a review for methods that account for spectral variability during hyperspectral unmixing and endmember estimation and a discussion on topics for future work in this area.

Although the measurement of working memory capacity is crucial to understanding working memory and its interaction with other cognitive faculties, there are inconsistencies in the literature on how to measure capacity. We address the measurement in the change detection paradigm, popularized by Luck and Vogel (Nature, 390, 279-281, 1997). Two measures for this task-from Pashler (Perception & Psychophysics, 44, 369-378, 1988) and Cowan (The Behavioral and Brain Sciences, 24, 87-114, 2001), respectively-have been used interchangeably, even though they may yield qualitatively different conclusions. We show that the choice between these two measures is not arbitrary. Although they are motivated by the same underlying discrete-slots working memory model, each is applicable only to a specific task; the two are never interchangeable. In the course of deriving these measures, we discuss subtle but consequential flaws in the underlying discrete-slots model. These flaws motivate revision in the modal model and capacity measures.

Malaria is a major global health threat. The standard way of diagnosing malaria is by visually examining blood smears for parasite-infected red blood cells under the microscope by qualified technicians. This method is inefficient and the diagnosis depends on the experience and the knowledge of the person doing the examination. Automatic image recognition technologies based on machine learning have been applied to malaria blood smears for diagnosis before. However, the practical performance has not been sufficient so far. This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected. In a ten-fold cross-validation based on 27,578 single cell images, the average accuracy of our new 16-layer CNN model is 97.37%. A transfer learning model only achieves 91.99% on the same images. The CNN model shows superiority over the transfer learning model in all performance indicators such as sensitivity (96.99% vs 89.00%), specificity (97.75% vs 94.98%), precision (97.73% vs 95.12%), F1 score (97.36% vs 90.24%), and Matthews correlation coefficient (94.75% vs 85.25%).

In recent years convolutional neural networks (CNNs) have been successfully applied to various applications that are appropriate for deep learning, from image and video processing to speech recognition. The advancements in both hardware (e.g. more powerful GPUs) and software (e.g. deep learning models, open-source frameworks and supporting libraries) have significantly improved the accuracy and training time of CNNs. However, the high speed and accuracy are at the cost of energy consumption, which has been largely ignored in previous CNN design. With the size of data sets grows exponentially, the energy demand for training such data sets increases rapidly. It is highly desirable to design deep learning frameworks and algorithms that are both accurate and energy efficient. In this paper, we conduct a comprehensive study on the power behavior and energy efficiency of numerous well-known CNNs and training frameworks on CPUs and GPUs, and we provide a detailed workload characterization to facilitate the design of energy efficient deep learning solutions.

This article presents a model of college outcomes for adult undergraduate students to address the key elements that affect their learning and to stimulate research and theory building about adults' experience in college. It provides a review of the literature and a comprehensive model that considers the relationships between six major elements related to adults' undergraduate collegiate experiences: (a) prior experiences; (b) orienting frameworks such as motivation, self-confidence, and value system; (c) adult's cognition or the declarative, procedural, and self regulating knowledge structures and processes; (d) the "connecting classroom" as the central avenue for social engagement and for negotiating meaning for learning; (e) the life-world environment and the concurrent work, family, and community settings; and (I) the different types and levels of learning outcomes experienced by adults.

We design optimal harmonic-trap trajectories to transport cold atoms without final excitation, combining an inverse engineering technique based on Lewis-Riesenfeld invariants with optimal control theory. Since actual traps are not really harmonic, we keep the relative displacement between the center of mass of the transport modes and the trap center bounded. Under this constraint, optimal protocols are found according to different physical criteria. The minimum time solution has a ``bang-bang'' form, and the minimum displacement solution is of ``bang-off-bang'' form. The optimal trajectories for minimizing the transient energy are also discussed.

During the spring of 2020, institutions of higher education (IHEs) closed their buildings but continued to offer instruction through emergency remote teaching procedures in the United States of America. Although students and teaching faculty received much attention for their mutual under preparedness for remote learning using online tools, instructional designers at IHEs were working to support both groups. Using grounded theory, the purpose of this research was to identify instructional designers' perceptions of their abrupt shift in roles and responsibilities during this transition. We also gathered data to understand instructional designers' expectations for supporting future course design and delivery as IHEs revise their distance education plans to prepare for re-opening in the fall of 2020 and beyond in the United States of America. Overall, their role shift focused on building relationships within the university community. Specific efforts included gathering, organizing, and distributing resources, designing faculty course development workshops, providing technology support, and advocating for students and for their profession.

The new phenomenon of sharenting, the overuse of social media by parents who share content of their children, has increased children's digital representations on social media, especially Instagram. In order to understand how they are digitally portrayed, a content analysis of 510 Instagram photos was conducted. Applying self-categorization theory, this exploratory study focused on how parents are gender stereotypically and racially categorizing their children into ingroups and outgroups. Results indicated that although the visibility of marginalized groups, such as females and ethnic minorities, has increased, they were also shown to sustain gender and racial stereotypes that can be in traditional media. This study discusses the importance of studying children on social media and how their representations can question digital ethics and importance of digital literacy skills.

UNLABELLED: We proposed and tested a novel geometry for PET system design analogous to pinhole SPECT called the virtual-pinhole PET (VP-PET) geometry to determine whether it could provide high-resolution images. METHODS: We analyzed the effects of photon acolinearity and detector sizes on system resolution and extended the empiric formula for reconstructed image resolution of conventional PET proposed earlier to predict the resolutions of VP-PET. To measure the system resolution of VP-PET, we recorded coincidence events as a (22)Na point source was stepped across the coincidence line of response between 2 detectors made from identical arrays of 12 x 12 lutetium oxyorthosilicate crystals (each measuring 1.51 x 1.51 x 10 mm(3)) separated by 565 mm. To measure reconstructed image resolution, we built 4 VP-PET systems using 4 types of detectors (width, 1.51-6.4 mm) and imaged 4 point sources of (64)Cu (half-life = 12.7 h to allow a long acquisition time). Tangential and radial resolutions were measured and averaged for each source and each system. We then imaged a polystyrene plastic phantom representing a 2.5-cm-thick cross-section of isolated breast volume. The phantom was filled with an aqueous solution of (64)Cu (713 kBq/mL) in which the following were imbedded: 4 spheric tumors ranging from 1.8 to 12.6 mm in inner diameter (ID), 6 micropipettes (0.7- or 1.1-mm ID filled with (64)Cu at 5x, 20x, or 50x background), and a 10.0-mm outer-diameter cold lesion. RESULTS: The shape and measured full width at half maximum of the line spread functions agree well with the predicted values. Measured reconstructed image resolution (2.40-3.24 mm) was +/-6% of the predicted value for 3 of the 4 systems. In one case, the difference was 12.6%, possibly due to underestimation of the block effect from the low-resolution detector. In phantom experiments, all spheric tumors were detected. Small line sources were detected if the activity concentration is at least 20x background. CONCLUSION: We have developed and characterized a novel geometry for PET. A PET system following the VP-PET geometry provides high-resolution images for objects near the system's high-resolution detectors. This geometry may lead to the development of special-purpose PET systems or resolution-enhancing insert devices for conventional PET scanners.

Falling is a common health problem for elderly. It is reported that more than one third of adults 65 and older fall each year in the United States. To address the problem, we are currently developing an acoustic fall detection system, FADE, which automatically detects a fall and reports it to the caregiver. In a previous version, FADE used a 3-microphone linear array to eliminate the false alarms produced by sounds produced well above the floor level. To improve the fall detection in noisy and reverberant environments, we replaced the linear array by an 8-microphone circular array that can provide a better 3-D estimation of the sound location. Preliminary experiments show that the sound location estimation performed by the circular array is reliable and robust to interference. We obtained encouraging classification results on a pilot dataset with 55 falls and 120 non-fall sounds.

Purpose The purpose of this paper is to examine the impact of engagement on job burnout and turnover intention (plan to leave the organization) among blue-collar workers in manufacturing facilities. In addition, this study also explores the role of happiness as a moderator in explaining the effect of engagement on burnout and turnover intention. Design/methodology/approach The data required to examine the hypothesis were collected using well-established research instruments from 1,197 blue-collar employees working at three manufacturing facilities that are owned and operated by the same company in India. The hypotheses were examined and the conceptual model was validated using structural equation modeling. The statistical analyses were conducted using two statistical packages, namely, SPSS and SPSS–AMOS. Findings The results indicate that a disengaged employee is at higher risk of burnout and is likely to leave the organization in the near future. Furthermore, employee burnout was positively associated with turnover intentions. Happiness was established as a significant moderating factor in the relationship between employee engagement and burnout and turnover intention. Besides, the prevalence of happiness and turnover intention was higher in males. Practical implications The results showed the importance of engagement and happiness on reducing burnout and turnover intention. Organizations could capitalize on these findings by implementing new and improving their existing quality management initiatives, which, in turn, could improve the employee’s organizational commitment. Originality/value This study contributes to the industry and academia by exploring the perceptions of working-class, blue-collar employees, which has received limited attention till date, despite specific negative job characteristics.

The Higher Education Act of 1992 increased the availability of student loan funds dramatically. Using the National Student Postsecondary Aid Study, this study examines the difference in effects of background, achievement and aspiration, college experience, price variables, and accumulated debt in 1993 as compared to 1996 on student persistence decisions. In contrast with previous studies using NPSAS:87, the authors find that there is more financial aid available, albeit in the form of loans. Current year subsidies are positively associated with persistence, but the opposite is true for accumulated debt, except for higher debt levels in 1996. The authors provide context and explanation for these findings.

Solar irradiance prediction has a significant impact on various aspects of power system generation. The predictive models can be deployed to improve the planning and operation of renewable systems and can improve the power purchase process and bring several advantages to the power utilities. The irradiance is affected by several factors, such as clouds and dust, and it becomes challenging for physical models to predict and capture the dynamics. The statistical methods are commonly used to predict the irradiance. These methods include autoregressive moving average, support vector machine, and artificial neural network. Deficiencies and challenges of existing methods include low prediction accuracy, low scalability for big data, and inability to capture long-term dependencies. In this paper, a deep recurrent neural network is used to predict the solar irradiance. Deep recurrent neural network (DRNN) is an artificial neural network with more hidden layers to improve the complexity of the model and enable the extraction of high-level features. The neural network is trained, tested, and validated using real data from the National Resources in Canada. The simulation and experimental results are compared to other methods to illustrate the advantages using the proposed approach.

Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy -the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time. Method: OCT is an emerging imaging technique to obtain 3-dimensional (3D) "optical biopsies" of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas.

Despite new technologies that make face detection and recognition more sophisticated, long-recognized problems in security, privacy, and accuracy persist. Refining this technology and introducing it into new domains will require solving these problems through focused interdisciplinary efforts among developers, researchers, and policymakers.

Following G. T. Fechner (1966), thresholds have been conceptualized as the amount of intensity needed to transition between mental states, such as between states of unconsciousness and consciousness. With the advent of the theory of signal detection, however, discrete-state theory and the corresponding notion of threshold have been discounted. Consequently, phenomena such as subliminal priming and perception have a reduced theoretical basis. The authors propose a process-neutral definition of threshold that allows for graded perception and activation throughout the system. Thresholds correspond to maximum stimulus intensities such that the distribution of mental states does not differ from that when an appropriate baseline stimulus is presented. In practice, thresholds are maximum intensities such that the probability distribution on behavioral events does not differ from that from baseline. These thresholds, which the authors call task thresholds, may be estimated with modified item response psychometric measurement models.

Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative minibatch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables high-quality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixed-point convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.

One of the most influential findings in the study of recognition memory is that receiver operating characteristic (ROC) curves are asymmetric about the negative diagonal. This result has led to the rejection of the equal-variance signal detection model of recognition memory and has provided motivation for more complex models, such as the unequal-variance signal detection and dual-process models. Here, the authors test the possibility that previous demonstrations of ROC asymmetry do not reflect mnemonic process but rather reflect distortions due to averaging data over items. Application of a hierarchical unequal-variance signal detection model reveals that asymmetries are in fact a real phenomenon and do not reflect distortions from averaging data. (PsycINFO Database Record (c) 2009 APA, all rights reserved).