Rowan University

In matters of great importance that have financial, medical, social, or other implications, we often seek a second opinion before making a decision, sometimes a third, and sometimes many more. In doing so, we weigh the individual opinions, and combine them through some thought process to reach a final decision that is presumably the most informed one. The process of consulting "several experts" before making a final decision is perhaps second nature to us; yet, the extensive benefits of such a process in automated decision making applications have only recently been discovered by computational intelligence community. Also known under various other names, such as multiple classifier systems, committee of classifiers, or mixture of experts, ensemble based systems have shown to produce favorable results compared to those of single-expert systems for a broad range of applications and under a variety of scenarios. Design, implementation and application of such systems are the main topics of this article. Specifically, this paper reviews conditions under which ensemble based systems may be more beneficial than their single classifier counterparts, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined. We discuss popular ensemble based algorithms, such as bagging, boosting, AdaBoost, stacked generalization, and hierarchical mixture of experts; as well as commonly used combination rules, including algebraic combination of outputs, voting based techniques, behavior knowledge space, and decision templates. Finally, we look at current and future research directions for novel applications of ensemble systems. Such applications include incremental learning, data fusion, feature selection, learning with missing features, confidence estimation, and error correcting output codes; all areas in which ensemble systems have shown great promise

We report on the coupling between ferroelectric and magnetic order parameters in a nanostructured BaTiO3-CoFe2O4 ferroelectromagnet. This facilitates the interconversion of energies stored in electric and magnetic fields and plays an important role in many devices, including transducers, field sensors, etc. Such nanostructures were deposited on single-crystal SrTiO3 (001) substrates by pulsed laser deposition from a single Ba-Ti-Co-Fe-oxide target. The films are epitaxial in-plane as well as out-of-plane with self-assembled hexagonal arrays of CoFe2O4 nanopillars embedded in a BaTiO3 matrix. The CoFe2O4 nanopillars have uniform size and average spacing of 20 to 30 nanometers. Temperature-dependent magnetic measurements illustrate the coupling between the two order parameters, which is manifested as a change in magnetization at the ferroelectric Curie temperature. Thermodynamic analyses show that the magnetoelectric coupling in such a nanostructure can be understood on the basis of the strong elastic interactions between the two phases.

Solid organs transport fluids through distinct vascular networks that are biophysically and biochemically entangled, creating complex three-dimensional (3D) transport regimes that have remained difficult to produce and study. We establish intravascular and multivascular design freedoms with photopolymerizable hydrogels by using food dye additives as biocompatible yet potent photoabsorbers for projection stereolithography. We demonstrate monolithic transparent hydrogels, produced in minutes, comprising efficient intravascular 3D fluid mixers and functional bicuspid valves. We further elaborate entangled vascular networks from space-filling mathematical topologies and explore the oxygenation and flow of human red blood cells during tidal ventilation and distension of a proximate airway. In addition, we deploy structured biodegradable hydrogel carriers in a rodent model of chronic liver injury to highlight the potential translational utility of this materials innovation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel virus that causes COVID-19 infection, has recently emerged and caused a deadly pandemic. Studies have shown that this virus causes worse outcomes and a higher mortality rate in older adults and those with comorbidities such as hypertension, cardiovascular disease, diabetes, chronic respiratory disease, and chronic kidney disease (CKD). A significant percentage of older American adults have these diseases, putting them at a higher risk of infection. Additionally, many adults with hypertension, diabetes, and CKD are placed on angiotensin-converting enzyme (ACE) inhibitors and angiotensin II receptor blockers. Studies have shown that these medications upregulate the ACE-2 receptor, the very receptor that the SARS-CoV-2 virus uses to enter host cells. Although it has been hypothesized that this may cause a further increased risk of infection, more studies on the role of these medications in COVID-19 infections are necessary. In this review, we discuss the transmission, symptomatology, and mortality of COVID-19 as they relate to older adults, and possible treatments that are currently under investigation. J Am Geriatr Soc 68:926-929, 2020.

Abstract Review articles or literature reviews are a critical part of scientific research. While numerous guides on literature reviews exist, these are often limited to the philosophy of review procedures, protocols, and nomenclatures, triggering non-parsimonious reporting and confusion due to overlapping similarities. To address the aforementioned limitations, we adopt a pragmatic approach to demystify and shape the academic practice of conducting literature reviews. We concentrate on the types, focuses, considerations, methods, and contributions of literature reviews as independent, standalone studies. As such, our article serves as an overview that scholars can rely upon to navigate the fundamental elements of literature reviews as standalone and independent studies, without getting entangled in the complexities of review procedures, protocols, and nomenclatures.

We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn(++). NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn(++) family of algorithms, that is, without requiring access to previously seen data. Learn(++). NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn(++). NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper.

Using a qualitative content analysis approach, this study reviewed 47 published studies and research on online teaching and learning since 2008, primarily focusing on how theories, practices and assessments apply to the online learning environment. The purpose of this paper is to provide practical suggestions for those who are planning to develop online courses so that they can make informed decisions in the implementation process. Based on the findings, the authors argued that effective online instruction is dependent upon 1) well-designed course content, motivated interaction between the instructor and learners, well-prepared and fully-supported instructors; 2) creation of a sense of online learning community; and 3) rapid advancement of technology. In doing this, it is hoped that this will stimulate an on-going discussion of effective strategies that can enhance universities and faculty success in transitioning to teach online. Under current debates on the cost and quality of higher education, this study could help for the improvement of higher education and student enrollment and retention.

Abstract During its approach to asteroid (101955) Bennu, NASA’s Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft surveyed Bennu’s immediate environment, photometric properties, and rotation state. Discovery of a dusty environment, a natural satellite, or unexpected asteroid characteristics would have had consequences for the mission’s safety and observation strategy. Here we show that spacecraft observations during this period were highly sensitive to satellites (sub-meter scale) but reveal none, although later navigational images indicate that further investigation is needed. We constrain average dust production in September 2018 from Bennu’s surface to an upper limit of 150 g s –1 averaged over 34 min. Bennu’s disk-integrated photometric phase function validates measurements from the pre-encounter astronomical campaign. We demonstrate that Bennu’s rotation rate is accelerating continuously at 3.63 ± 0.52 × 10 –6 degrees day –2 , likely due to the Yarkovsky–O’Keefe–Radzievskii–Paddack (YORP) effect, with evolutionary implications.

We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern classifiers. The proposed algorithm enables supervised NN paradigms, such as the multilayer perceptron (MLP), to accommodate new data, including examples that correspond to previously unseen classes. Furthermore, the algorithm does not require access to previously used data during subsequent incremental learning sessions, yet at the same time, it does not forget previously acquired knowledge. Learn++ utilizes ensemble of classifiers by generating multiple hypotheses using training data sampled according to carefully tailored distributions. The outputs of the resulting classifiers are combined using a weighted majority voting procedure. We present simulation results on several benchmark datasets as well as a real-world classification task. Initial results indicate that the proposed algorithm works rather well in practice. A theoretical upper bound on the error of the classifiers constructed by Learn++ is also provided.

The occurrence of room temperature ferromagnetism is demonstrated in pulsed laser deposited thin films of ${\mathrm{S}\mathrm{n}}_{1\ensuremath{-}x}{\mathrm{C}\mathrm{o}}_{x}{\mathrm{O}}_{2\ensuremath{-}\ensuremath{\delta}}$ ($x<0.3$). Interestingly, films of ${\mathrm{S}\mathrm{n}}_{0.95}{\mathrm{C}\mathrm{o}}_{0.05}{\mathrm{O}}_{2\ensuremath{-}\ensuremath{\delta}}$ grown on $R$-plane sapphire not only exhibit ferromagnetism with a Curie temperature close to 650 K, but also a giant magnetic moment of $7.5\ifmmode\pm\else\textpm\fi{}0.5{\ensuremath{\mu}}_{B}/\mathrm{C}\mathrm{o}$, not yet reported in any diluted magnetic semiconductor system. The films are semiconducting and optically highly transparent.

Abstract Trade credit is created whenever a supplier offers terms that allow the buyer to delay payment. In this paper we document the rich variation in interfirm credit terms and credit policies across industries. We examine empirically the firm's basic credit policy choices: whether to extend credit or to require cash payment; and, if credit is extended, whether to adopt simple net terms or terms with discounts for prompt payment. We also examine determinants of variations in two‐part terms. Results are supportive primarily of theories that explain credit terms as contractual solutions to information problems concerning product quality and buyer creditworthiness.

This article discusses the provision of mental health services in low- and middle-income countries (LMICs) with a view to understanding the cultural dynamics-how the challenges they pose can be addressed and the opportunities harnessed in specific cultural contexts. The article highlights the need for prioritisation of mental health services by incorporating local population and cultural needs. This can be achieved only through political will and strengthened legislation, improved resource allocation and strategic organisation, integrated packages of care underpinned by professional communication and training, and involvement of patients, informal carers, and the wider community in a therapeutic capacity.

The present guide provides a practical guide to conducting latent profile analysis (LPA) in the Mplus software system. This guide is intended for researchers familiar with some latent variable modeling but not LPA specifically. A general procedure for conducting LPA is provided in six steps: (a) data inspection, (b) iterative evaluation of models, (c) model fit and interpretability, (d) investigation of patterns of profiles in a retained model, (e) covariate analysis, and (f) presentation of results. A worked example is provided with syntax and results to exemplify the steps.

This study investigated the relationship between test anxiety and academic performance in 4,000 undergraduate and 1,414 graduate students and found a significant but small inverse relationship between test anxiety and grade point average (GPA) in both groups. Low-test-anxious undergraduates averaged a B+, whereas high-test-anxious students averaged a B. Low-test-anxious female graduate students had significantly higher GPAs than high-test-anxious female graduate students, but there were no significant GPA differences between low- and high-test-anxious male graduate students. Female undergraduates had significantly higher test anxiety and higher GPAs than male undergraduates, and female graduate students had significantly higher test anxiety and higher GPAs than male graduate students.

The prevalence of mobile phones, the internet-of-things technology, and networks of sensors has led to an enormous and ever increasing amount of data that are now more commonly available in a streaming fashion [1]-[5]. Often, it is assumed - either implicitly or explicitly - that the process generating such a stream of data is stationary, that is, the data are drawn from a fixed, albeit unknown probability distribution. In many real-world scenarios, however, such an assumption is simply not true, and the underlying process generating the data stream is characterized by an intrinsic nonstationary (or evolving or drifting) phenomenon. The nonstationarity can be due, for example, to seasonality or periodicity effects, changes in the users' habits or preferences, hardware or software faults affecting a cyber-physical system, thermal drifts or aging effects in sensors. In such nonstationary environments, where the probabilistic properties of the data change over time, a non-adaptive model trained under the false stationarity assumption is bound to become obsolete in time, and perform sub-optimally at best, or fail catastrophically at worst.

HYpothesis testing using PHYlogenies (HyPhy) is a scriptable, open-source package for fitting a broad range of evolutionary models to multiple sequence alignments, and for conducting subsequent parameter estimation and hypothesis testing, primarily in the maximum likelihood statistical framework. It has become a popular choice for characterizing various aspects of the evolutionary process: natural selection, evolutionary rates, recombination, and coevolution. The 2.5 release (available from www.hyphy.org) includes a completely re-engineered computational core and analysis library that introduces new classes of evolutionary models and statistical tests, delivers substantial performance and stability enhancements, improves usability, streamlines end-to-end analysis workflows, makes it easier to develop custom analyses, and is mostly backward compatible with previous HyPhy releases.

This study investigated college experiences and outcomes for low and high SES students utilizing data from a longitudinal database. Low SES students engaged in fewer extracurricular activities, worked more, studied less, and reported lower GPAs than their high SES peers. Nine years after entering college, the low SES students had lower incomes, educational attainment, and graduate school attendance than high SES students. These experiential and outcome differences are tied to differences in cultural capital and habitus.

In this article, we review applications of covariance-based structural equation modeling (SEM) in the Journal of Advertising (JA) starting with the first issue in 1972. We identify 111 articles from the earliest application of SEM in 1983 through 2015, and discuss important methodological issues related to the following aspects: confirmatory factor analysis (CFA), causal modeling, multiple group analysis, reporting, and guidelines for interpretation of results. Moreover, we summarize some issues related to varying terminology associated with different SEM methods. Findings indicate that the use of SEM in the JA contributes greatly to conceptual, empirical, and methodological advances in advertising research. The assessment contributes to the literature by offering advertising researchers a summary guide to best practices and a reminder of the basics that distinguish the powerful and unique approach involving structural analysis of covariances.

This study utilized data from 1,067 firms In six countries to clarify the psychometric properties of the entrepreneurial orientation (EO) measure. The first research question addressed dimensionality and explored whether EO achieved the best model fit when structured as a one-, two-, or three-dimensional measure. The results of a confirmatory factor analysis in LISREL supported modeling entrepreneurial orientation with three sub-dimensions: innovation, proactiveness, and risk-taking. The second research question addressed the extent to which the three sub-dimensions of the entrepreneurial orientation measure co-varied with one another. Correlation analysis revealed that the three sub-dimensions of EO are able to vary independently of one another in many situations. This study also provided strong support for the cross-cultural validity of the Covin and Slevin EO scale.

OBJECTIVE: To provide expert recommendations for the safe and effective application of repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder (MDD). PARTICIPANTS: Participants included a group of 17 expert clinicians and researchers with expertise in the clinical application of rTMS, representing both the National Network of Depression Centers (NNDC) rTMS Task Group and the American Psychiatric Association Council on Research (APA CoR) Task Force on Novel Biomarkers and Treatments. EVIDENCE: The consensus statement is based on a review of extensive literature from 2 databases (OvidSP MEDLINE and PsycINFO) searched from 1990 through 2016. The search terms included variants of major depressive disorder and transcranial magnetic stimulation. The results were limited to articles written in English that focused on adult populations. Of the approximately 1,500 retrieved studies, a total of 118 publications were included in the consensus statement and were supplemented with expert opinion to achieve consensus recommendations on key issues surrounding the administration of rTMS for MDD in clinical practice settings. CONSENSUS PROCESS: In cases in which the research evidence was equivocal or unclear, a consensus decision on how rTMS should be administered was reached by the authors of this article and is denoted in the article as "expert opinion." CONCLUSIONS: Multiple randomized controlled trials and published literature have supported the safety and efficacy of rTMS antidepressant therapy. These consensus recommendations, developed by the NNDC rTMS Task Group and APA CoR Task Force on Novel Biomarkers and Treatments, provide comprehensive information for the safe and effective clinical application of rTMS in the treatment of MDD.