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Showing papers by "Oklahoma State University–Stillwater published in 2019"


Journal ArticleDOI
TL;DR: It is confirmed that eukaryotes form at least two domains, the loss of monophyly in the Excavata, robust support for the Haptista and Cryptista, and suggested primer sets for DNA sequences from environmental samples that are effective for each clade are provided.
Abstract: This revision of the classification of eukaryotes follows that of Adl et al., 2012 [J. Euk. Microbiol. 59(5)] and retains an emphasis on protists. Changes since have improved the resolution of many ...

750 citations


Journal ArticleDOI
TL;DR: An IGD indicator-based evolutionary algorithm for solving many-objective optimization problems (MaOPs) is proposed and experimental results measured by the chosen performance metrics indicate that the proposed algorithm is very competitive in addressing MaOPs.
Abstract: Inverted generational distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multiobjective and many-objective evolutionary algorithms. In this paper, an IGD indicator-based evolutionary algorithm for solving many-objective optimization problems (MaOPs) has been proposed. Specifically, the IGD indicator is employed in each generation to select the solutions with favorable convergence and diversity. In addition, a computationally efficient dominance comparison method is designed to assign the rank values of solutions along with three newly proposed proximity distance assignments. Based on these two designs, the solutions are selected from a global view by linear assignment mechanism to concern the convergence and diversity simultaneously. In order to facilitate the accuracy of the sampled reference points for the calculation of IGD indicator, we also propose an efficient decomposition-based nadir point estimation method for constructing the Utopian Pareto front (PF) which is regarded as the best approximate PF for real-world MaOPs at the early stage of the evolution. To evaluate the performance, a series of experiments is performed on the proposed algorithm against a group of selected state-of-the-art many-objective optimization algorithms over optimization problems with 8-, 15-, and 20-objective. Experimental results measured by the chosen performance metrics indicate that the proposed algorithm is very competitive in addressing MaOPs.

296 citations


Journal ArticleDOI
TL;DR: The theoretical framework developed identifies IoT priority areas and challenges, providing a guide for those leading IoT initiatives and revealing opportunities for future IoT research.
Abstract: The Internet of Things (IoT) global arena is massive and growing exponentially. Those in the emerging digital world have recently witnessed the proliferation and impact of IoT-enabled devices. The IoT has provided new opportunities in the technology arena while bringing several challenges to an increased level of concern. This research has both practical and theoretical impetus since IoT is still in its infancy, and yet it is considered by many as the most important technology initiative of today. This study includes a systematic review and synthesis of IoT related literature and the development of a theoretical framework and conceptual model. The review of the literature reveals that the number of applications that make use of the IoT has increased dramatically and spans areas from business and manufacturing to home, health care, and knowledge management. Although IoT can create invaluable data in every industry, it does not occur without its challenges. The theoretical framework developed identifies IoT priority areas and challenges, providing a guide for those leading IoT initiatives and revealing opportunities for future IoT research.

259 citations


Journal ArticleDOI
TL;DR: The proposed methodology successfully establishes a map between inputs given by stencils of the vorticity and the streamfunction along with information from two well-known eddy-viscosity kernels, which represents a promising development in the formalization of a framework for generation of heuristic-free turbulence closures from data.
Abstract: In this investigation, a data-driven turbulence closure framework is introduced and deployed for the subgrid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical data are used to inform artificial neural networks for predicting the turbulence source term through localized grid-resolved information. In particular, our proposed methodology successfully establishes a map between inputs given by stencils of the vorticity and the streamfunction along with information from two well-known eddy-viscosity kernels. Through this we predict the subgrid vorticity forcing in a temporally and spatially dynamic fashion. Our study is both a priori and a posteriori in nature. In the former, we present an extensive hyper-parameter optimization analysis in addition to learning quantification through probability-density-function-based validation of subgrid predictions. In the latter, we analyse the performance of our framework for flow evolution in a classical decaying two-dimensional turbulence test case in the presence of errors related to temporal and spatial discretization. Statistical assessments in the form of angle-averaged kinetic energy spectra demonstrate the promise of the proposed methodology for subgrid quantity inference. In addition, it is also observed that some measure of a posteriori error must be considered during optimal model selection for greater accuracy. The results in this article thus represent a promising development in the formalization of a framework for generation of heuristic-free turbulence closures from data.

255 citations


Journal ArticleDOI
Georges Aad1, Alexander Kupco2, Samuel Webb3, Timo Dreyer4  +3380 moreInstitutions (206)
TL;DR: In this article, a search for high-mass dielectron and dimuon resonances in the mass range of 250 GeV to 6 TeV was performed at the Large Hadron Collider.

248 citations


Journal ArticleDOI
TL;DR: Application of exogenous NO alleviates the negative stress effects in plants and improves antioxidant activity in most plant species, and S-nitrosylation and tyrosine nitration are two NO-mediated posttranslational modification.

244 citations


Journal ArticleDOI
TL;DR: A novel algorithm based on particle swarm optimization (PSO), capable of fast convergence when compared with others evolutionary approaches, to automatically search for meaningful deep convolutional neural networks (CNNs) architectures for image classification tasks, named psoCNN.
Abstract: Deep neural networks have been shown to outperform classical machine learning algorithms in solving real-world problems. However, the most successful deep neural networks were handcrafted from scratch taking the problem domain knowledge into consideration. This approach often consumes very significant time and computational resources. In this work, we propose a novel algorithm based on particle swarm optimization (PSO), capable of fast convergence when compared with others evolutionary approaches, to automatically search for meaningful deep convolutional neural networks (CNNs) architectures for image classification tasks, named psoCNN. A novel directly encoding strategy and a velocity operator were devised allowing the optimization use of PSO with CNNs. Our experimental results show that psoCNN can quickly find good CNN architectures that achieve quality performance comparable to the state-of-the-art designs.

240 citations


Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Dale Charles Abbott3  +2936 moreInstitutions (198)
TL;DR: An exclusion limit on the H→invisible branching ratio of 0.26(0.17_{-0.05}^{+0.07}) at 95% confidence level is observed (expected) in combination with the results at sqrt[s]=7 and 8 TeV.
Abstract: Dark matter particles, if sufficiently light, may be produced in decays of the Higgs boson. This Letter presents a statistical combination of searches for H→invisible decays where H is produced according to the standard model via vector boson fusion, Z(ll)H, and W/Z(had)H, all performed with the ATLAS detector using 36.1 fb^{-1} of pp collisions at a center-of-mass energy of sqrt[s]=13 TeV at the LHC. In combination with the results at sqrt[s]=7 and 8 TeV, an exclusion limit on the H→invisible branching ratio of 0.26(0.17_{-0.05}^{+0.07}) at 95% confidence level is observed (expected).

234 citations


Journal ArticleDOI
Georges Aad1, Alexander Kupco2, Samuel Webb3, Timo Dreyer4  +2962 moreInstitutions (195)
TL;DR: In this article, an improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail, including corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies.
Abstract: This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb−1 of proton-proton collision data collected at √s=13 TeV between 2015 and 2017.

227 citations


Journal ArticleDOI
TL;DR: The Hierarchical Taxonomy of Psychopathology (HiTOP) as discussed by the authors is based on empirical patterns of co-occurrence among psychological symptoms, and it has the potential to accelerate and improve research on mental health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
Abstract: For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.

225 citations


Journal ArticleDOI
Helen Phillips1, Carlos A. Guerra2, Marie Luise Carolina Bartz3, Maria J. I. Briones4, George G. Brown5, Thomas W. Crowther6, Olga Ferlian1, Konstantin B. Gongalsky7, Johan van den Hoogen6, Julia Krebs1, Alberto Orgiazzi, Devin Routh6, Benjamin Schwarz8, Elizabeth M. Bach, Joanne M. Bennett2, Ulrich Brose9, Thibaud Decaëns, Birgitta König-Ries9, Michel Loreau, Jérôme Mathieu, Christian Mulder10, Wim H. van der Putten11, Kelly S. Ramirez, Matthias C. Rillig12, David J. Russell13, Michiel Rutgers, Madhav P. Thakur, Franciska T. de Vries, Diana H. Wall14, David A. Wardle, Miwa Arai15, Fredrick O. Ayuke16, Geoff H. Baker17, Robin Beauséjour, José Camilo Bedano18, Klaus Birkhofer19, Eric Blanchart, Bernd Blossey20, Thomas Bolger21, Robert L. Bradley, Mac A. Callaham22, Yvan Capowiez, Mark E. Caulfield11, Amy Choi23, Felicity Crotty24, Andrea Dávalos20, Andrea Dávalos25, Darío J. Díaz Cosín, Anahí Domínguez18, Andrés Esteban Duhour26, Nick van Eekeren, Christoph Emmerling27, Liliana B. Falco26, Rosa Fernández, Steven J. Fonte14, Carlos Fragoso, André L.C. Franco, Martine Fugère, Abegail T Fusilero28, Shaieste Gholami29, Michael J. Gundale, Mónica Gutiérrez López, Davorka K. Hackenberger30, Luis M. Hernández, Takuo Hishi31, Andrew R. Holdsworth32, Martin Holmstrup33, Kristine N. Hopfensperger34, Esperanza Huerta Lwanga11, Veikko Huhta, Tunsisa T. Hurisso14, Tunsisa T. Hurisso35, Basil V. Iannone, Madalina Iordache36, Monika Joschko, Nobuhiro Kaneko37, Radoslava Kanianska38, Aidan M. Keith39, Courtland Kelly14, Maria Kernecker, Jonatan Klaminder, Armand W. Koné40, Yahya Kooch41, Sanna T. Kukkonen, H. Lalthanzara42, Daniel R. Lammel12, Daniel R. Lammel43, Iurii M. Lebedev7, Yiqing Li44, Juan B. Jesús Lidón, Noa Kekuewa Lincoln45, Scott R. Loss46, Raphaël Marichal, Radim Matula, Jan Hendrik Moos47, Gerardo Moreno48, Alejandro Morón-Ríos, Bart Muys49, Johan Neirynck50, Lindsey Norgrove, Marta Novo, Visa Nuutinen51, Victoria Nuzzo, Mujeeb Rahman P, Johan Pansu17, Shishir Paudel46, Guénola Pérès, Lorenzo Pérez-Camacho52, Raúl Piñeiro, Jean-François Ponge, Muhammad Rashid53, Muhammad Rashid54, Salvador Rebollo52, Javier Rodeiro-Iglesias4, Miguel Á. Rodríguez52, Alexander M. Roth55, Guillaume Xavier Rousseau56, Anna Rożen57, Ehsan Sayad29, Loes van Schaik58, Bryant C. Scharenbroch59, Michael Schirrmann60, Olaf Schmidt21, Boris Schröder61, Julia Seeber62, Maxim Shashkov63, Maxim Shashkov64, Jaswinder Singh65, Sandy M. Smith23, Michael Steinwandter, José Antonio Talavera66, Dolores Trigo, Jiro Tsukamoto67, Anne W. de Valença, Steven J. Vanek14, Iñigo Virto68, Adrian A. Wackett55, Matthew W. Warren, Nathaniel H. Wehr, Joann K. Whalen69, Michael B. Wironen70, Volkmar Wolters71, Irina V. Zenkova, Weixin Zhang72, Erin K. Cameron73, Nico Eisenhauer1 
Leipzig University1, Martin Luther University of Halle-Wittenberg2, Universidade Positivo3, University of Vigo4, Empresa Brasileira de Pesquisa Agropecuária5, ETH Zurich6, Moscow State University7, University of Freiburg8, University of Jena9, University of Catania10, Wageningen University and Research Centre11, Free University of Berlin12, Senckenberg Museum13, Colorado State University14, National Agriculture and Food Research Organization15, University of Nairobi16, Commonwealth Scientific and Industrial Research Organisation17, National Scientific and Technical Research Council18, Brandenburg University of Technology19, Cornell University20, University College Dublin21, United States Forest Service22, University of Toronto23, Aberystwyth University24, State University of New York at Cortland25, National University of Luján26, University of Trier27, University of the Philippines Mindanao28, Razi University29, Josip Juraj Strossmayer University of Osijek30, Kyushu University31, Minnesota Pollution Control Agency32, Aarhus University33, Northern Kentucky University34, Lincoln University (Missouri)35, University of Agricultural Sciences, Dharwad36, Fukushima University37, Matej Bel University38, Lancaster University39, Université d'Abobo-Adjamé40, Tarbiat Modares University41, Pachhunga University College42, University of São Paulo43, University of Hawaii at Hilo44, College of Tropical Agriculture and Human Resources45, Oklahoma State University–Stillwater46, Forest Research Institute47, University of Extremadura48, Katholieke Universiteit Leuven49, Research Institute for Nature and Forest50, Natural Resources Institute Finland51, University of Alcalá52, COMSATS Institute of Information Technology53, King Abdulaziz University54, University of Minnesota55, Federal University of Maranhão56, Jagiellonian University57, Technical University of Berlin58, University of Wisconsin-Madison59, Leibniz Association60, Braunschweig University of Technology61, University of Innsbruck62, Russian Academy of Sciences63, Keldysh Institute of Applied Mathematics64, Khalsa College, Amritsar65, University of La Laguna66, Kōchi University67, Universidad Pública de Navarra68, McGill University69, The Nature Conservancy70, University of Giessen71, Henan University72, University of Saint Mary73
25 Oct 2019-Science
TL;DR: It was found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms, which suggest that climate change may have serious implications for earthworm communities and for the functions they provide.
Abstract: Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.

Journal ArticleDOI
Georges Aad1, Alexander Kupco2, Samuel Webb3, Timo Dreyer4  +2961 moreInstitutions (196)
TL;DR: In this article, the ATLAS Collaboration during Run 2 of the Large Hadron Collider (LHC) was used to identify jets containing b-hadrons, and the performance of the algorithms was evaluated in the s...
Abstract: The algorithms used by the ATLAS Collaboration during Run 2 of the Large Hadron Collider to identify jets containing b-hadrons are presented. The performance of the algorithms is evaluated in the s ...

Journal ArticleDOI
TL;DR: A new recurrent unit, gated recurrent multilayer perceptron (GRMLP), is proposed to recursively update the internal memory of CrackNet‐R, a recurrent neural network for fully automated pixel‐level crack detection on three‐dimensional asphalt pavement surfaces.
Abstract: A recurrent neural network (RNN) called CrackNet‐R is proposed in the article for fully automated pixel‐level crack detection on three‐dimensional (3D) asphalt pavement surfaces. In the ar...

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Dale Charles Abbott3  +3001 moreInstitutions (220)
TL;DR: In this paper, the decays of B0 s! + and B0! + have been studied using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016.
Abstract: A study of the decays B0 s ! + and B0 ! + has been performed using 26 : 3 fb of 13TeV LHC proton-proton collision data collected with the ATLAS detector in 2015 and 2016. Since the detector resolut ...

Journal ArticleDOI
TL;DR: It is proposed that phone calls and texting improve well-being, while use of social network sites (SNSs), instant messaging (IM), and online gaming may displace other social contacts and, thereby, impairWell-being.
Abstract: The puzzle of whether digital media are improving or harming psychological well-being has been plaguing researchers and the public for decades. Derived from media richness theory, this study proposed that phone calls and texting improve well-being, while use of social network sites (SNSs), instant messaging (IM), and online gaming may displace other social contacts and, thereby, impair well-being. To test this hypothesis, a meta-analysis of 124 studies was conducted. The results showed that phone calls and texting were positively correlated with well-being, whereas online gaming was negatively associated with well-being. Furthermore, the relationship between digital media use and well-being was also contingent upon the way the technology was used. A series of meta-analyses of different types of SNS use and well-being was used to elucidate this point: interaction, self-presentation, and entertainment on SNSs were associated with better well-being, whereas consuming SNSs’ content was associated with poorer well-being.

Journal ArticleDOI
TL;DR: This study places the origin of Asteraceae at ∼83 MYA in the late Cretaceous and reveals that the family underwent a series of explosive radiations during the Eocene which were accompanied by accelerations in diversification rates.
Abstract: The sunflower family, Asteraceae, comprises 10% of all flowering plant species and displays an incredible diversity of form. Asteraceae are clearly monophyletic, yet resolving phylogenetic relationships within the family has proven difficult, hindering our ability to understand its origin and diversification. Recent molecular clock dating has suggested a Cretaceous origin, but the lack of deep sampling of many genes and representative taxa from across the family has impeded the resolution of migration routes and diversifications that led to its global distribution and tremendous diversity. Here we use genomic data from 256 terminals to estimate evolutionary relationships, timing of diversification(s), and biogeographic patterns. Our study places the origin of Asteraceae at ∼83 MYA in the late Cretaceous and reveals that the family underwent a series of explosive radiations during the Eocene which were accompanied by accelerations in diversification rates. The lineages that gave rise to nearly 95% of extant species originated and began diversifying during the middle Eocene, coincident with the ensuing marked cooling during this period. Phylogenetic and biogeographic analyses support a South American origin of the family with subsequent dispersals into North America and then to Asia and Africa, later followed by multiple worldwide dispersals in many directions. The rapid mid-Eocene diversification is aligned with the biogeographic range shift to Africa where many of the modern-day tribes appear to have originated. Our robust phylogeny provides a framework for future studies aimed at understanding the role of the macroevolutionary patterns and processes that generated the enormous species diversity of Asteraceae.

Journal ArticleDOI
TL;DR: Determining physiological mechanisms and thresholds for climate‐driven tree die‐off could help improve global predictions of future terrestrial carbon sinks and quantifies a continuous probability of mortality risk from hydraulic failure.
Abstract: Determining physiological mechanisms and thresholds for climate-driven tree die-off could help improve global predictions of future terrestrial carbon sinks. We directly tested for the lethal threshold in hydraulic failure - an inability to move water due to drought-induced xylem embolism - in a pine sapling experiment. In a glasshouse experiment, we exposed loblolly pine (Pinus taeda) saplings (n = 83) to drought-induced water stress ranging from mild to lethal. Before rewatering to relieve drought stress, we measured native hydraulic conductivity and foliar color change. We monitored all measured individuals for survival or mortality. We found a lethal threshold at 80% loss of hydraulic conductivity - a point of hydraulic failure beyond which it is more likely trees will die, than survive, and describe mortality risk across all levels of water stress. Foliar color changes lagged behind hydraulic failure - best predicting when trees had been dead for some time, rather than when they were dying. Our direct measurement of native conductivity, while monitoring the same individuals for survival or mortality, quantifies a continuous probability of mortality risk from hydraulic failure. Predicting tree die-off events and understanding the mechanism involved requires knowledge not only of when trees are dead, but when they begin dying - having passed the point of no return.

Journal ArticleDOI
TL;DR: It is concluded that Maxent is robust to predictor collinearity in model training, the strategy of excluding highly correlated variables has little impact because Maxent accounts for redundant variables, and coll inearity shift and environmental novelty can negatively affect Maxent model transferability.
Abstract: University of Arizona Office of Research, Discovery, and Innovation; Oklahoma State University [NSF-OCI 1126330]; University of Tennessee

Journal ArticleDOI
TL;DR: The interplay between the host immune system and the microbiota is reviewed, how commensal bacteria regulate the production of metabolites, and how these microbiota-derived products influence the function of several major innate and adaptive immune cells involved in modulating host immune homeostasis.
Abstract: The gastrointestinal tract is the site of nutrient digestion and absorption and is also colonized by diverse, highly mutualistic microbes. The intestinal microbiota has diverse effects on the development and function of the gut-specific immune system, and provides some protection from infectious pathogens. However, interactions between intestinal immunity and microorganisms are very complex, and recent studies have revealed that this intimate crosstalk may depend on the production and sensing abilities of multiple bioactive small molecule metabolites originating from direct produced by the gut microbiota or by the metabolism of dietary components. Here, we review the interplay between the host immune system and the microbiota, how commensal bacteria regulate the production of metabolites, and how these microbiota-derived products influence the function of several major innate and adaptive immune cells involved in modulating host immune homeostasis.

Journal ArticleDOI
TL;DR: In this paper, the authors develop concepts, priorities, and questions to help guide future research and practice in the field of personal selling and sales management, and summarize their discussion by detailing specific research priorities and questions that warrant further study and development by researchers and practitioners.
Abstract: Recognizing the rapid advances in sales digitization and artificial intelligence technologies, we develop concepts, priorities, and questions to help guide future research and practice in the field of personal selling and sales management. Our analysis reveals that the influence of sales digitalization technologies, which include digitization and artificial intelligence, is likely to be more significant and more far reaching than previous sales technologies. To organize our analysis of this influence, we discuss the opportunities and threats that sales digitalization technologies pose for (a) the sales profession in terms of its contribution to creating value for customers, organizations, and society and (b) sales professionals, in terms of both employees in organizations and individuals as self, seeking growth, fulfillment, and status in the functions they serve and roles they live. We summarize our discussion by detailing specific research priorities and questions that warrant further study and development by researchers and practitioners alike.

Journal ArticleDOI
Morad Aaboud, Alexander Kupco1, Samuel Webb2, Timo Dreyer3  +2969 moreInstitutions (195)
TL;DR: Algorithms used for the reconstruction and identification of electrons in the central region of the ATLAS detector at the Large Hadron Collider (LHC) are presented in this article, these algorithms a...
Abstract: Algorithms used for the reconstruction and identification of electrons in the central region of the ATLAS detector at the Large Hadron Collider (LHC) are presented in this paper; these algorithms a ...


Journal ArticleDOI
TL;DR: In this article, the authors explore the relationships among tourist experience quality, perceived value, perceived price reasonableness, tourist satisfaction with tour experience, and loyalty to an island d...
Abstract: This study aims to explore the relationships among tourist experience quality, perceived value, perceived price reasonableness, tourist satisfaction with tour experience, and loyalty to an island d...

Journal ArticleDOI
TL;DR: In this article, the authors predict that on a daily basis, performance pressure may be appraised as a threat, which promotes self-regulation depletion that explains dysfunctional behavior (i.e., incivility).
Abstract: Performance pressure focuses employee efforts toward enhanced performance. It is unclear, however, whether performance pressure serves as a productive or unproductive strategy for producing beneficial work behavior. Our research provides clarity on the dynamic nature of performance pressure. We theorize that reactions to performance pressure are influenced by daily fluctuations in how the pressure is appraised, and these fluctuations explain why performance pressure can be a double-edged sword, producing bright and dark side effects for organizations. We predict that, on a daily basis, performance pressure may be appraised as a threat, which promotes self-regulation depletion that explains dysfunctional behavior (i.e., incivility); daily performance pressure may also be appraised as a challenge, which elicits engagement that explains enhanced task proficiency and citizenship. Trait resilience is predicted to moderate these effects, promoting performance pressure to be appraised as a challenge rather than ...

Journal ArticleDOI
TL;DR: Highland Hierarchical Taxonomy of Psychopathology represents a viable alternative to classifying mental illness that can be integrated into practice today, although research is needed to further establish its utility.
Abstract: Author(s): Ruggero, Camilo J; Kotov, Roman; Hopwood, Christopher J; First, Michael; Clark, Lee Anna; Skodol, Andrew E; Mullins-Sweatt, Stephanie N; Patrick, Christopher J; Bach, Bo; Cicero, David C; Docherty, Anna; Simms, Leonard J; Bagby, R Michael; Krueger, Robert F; Callahan, Jennifer L; Chmielewski, Michael; Conway, Christopher C; De Clercq, Barbara; Dornbach-Bender, Allison; Eaton, Nicholas R; Forbes, Miriam K; Forbush, Kelsie T; Haltigan, John D; Miller, Joshua D; Morey, Leslie C; Patalay, Praveetha; Regier, Darrel A; Reininghaus, Ulrich; Shackman, Alexander J; Waszczuk, Monika A; Watson, David; Wright, Aidan GC; Zimmermann, Johannes | Abstract: ObjectiveDiagnosis is a cornerstone of clinical practice for mental health care providers, yet traditional diagnostic systems have well-known shortcomings, including inadequate reliability, high comorbidity, and marked within-diagnosis heterogeneity. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a data-driven, hierarchically based alternative to traditional classifications that conceptualizes psychopathology as a set of dimensions organized into increasingly broad, transdiagnostic spectra. Prior work has shown that using a dimensional approach improves reliability and validity, but translating a model like HiTOP into a workable system that is useful for health care providers remains a major challenge.MethodThe present work outlines the HiTOP model and describes the core principles to guide its integration into clinical practice.ResultsPotential advantages and limitations of the HiTOP model for clinical utility are reviewed, including with respect to case conceptualization and treatment planning. A HiTOP approach to practice is illustrated and contrasted with an approach based on traditional nosology. Common barriers to using HiTOP in real-world health care settings and solutions to these barriers are discussed.ConclusionsHiTOP represents a viable alternative to classifying mental illness that can be integrated into practice today, although research is needed to further establish its utility. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Journal ArticleDOI
TL;DR: In this paper, a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows is proposed. But it is not suitable for modeling complex fluid flows.
Abstract: In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equation, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a nonintrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly nonintrusive since any prior information about the underlying governing equations is not required for generating the reduced order model. Our a posteriori analysis shows that the proposed data-driven approach is remarkably accurate and can be used as a robust predictive tool for nonintrusive model order reduction of complex fluid flows.In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equation, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a nonintrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered trul...

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed a computationally economical algorithm for evolving unsupervised deep neural networks to efficiently learn meaningful representations, which is very suitable in the current big data era where sufficient labeled data for training is often expensive to acquire.
Abstract: Deep learning (DL) aims at learning the meaningful representations . A meaningful representation gives rise to significant performance improvement of associated machine learning (ML) tasks by replacing the raw data as the input. However, optimal architecture design and model parameter estimation in DL algorithms are widely considered to be intractable. Evolutionary algorithms are much preferable for complex and nonconvex problems due to its inherent characteristics of gradient-free and insensitivity to the local optimal. In this paper, we propose a computationally economical algorithm for evolving unsupervised deep neural networks to efficiently learn meaningful representations , which is very suitable in the current big data era where sufficient labeled data for training is often expensive to acquire. In the proposed algorithm, finding an appropriate architecture and the initialized parameter values for an ML task at hand is modeled by one computational efficient gene encoding approach, which is employed to effectively model the task with a large number of parameters. In addition, a local search strategy is incorporated to facilitate the exploitation search for further improving the performance. Furthermore, a small proportion labeled data is utilized during evolution search to guarantee the learned representations to be meaningful. The performance of the proposed algorithm has been thoroughly investigated over classification tasks. Specifically, error classification rate on MNIST with 1.15% is reached by the proposed algorithm consistently, which is considered a very promising result against state-of-the-art unsupervised DL algorithms.

Journal ArticleDOI
TL;DR: In this article, a supervised machine learning framework for the non-intrusive model order reduction of unsteady fluid flows is proposed to provide accurate predictions of non-stationary state variables when the control parameter values vary.

Journal ArticleDOI
TL;DR: In this paper, a review of syngas fermentation process development with focus on microorganisms, gas composition, medium design, gas-liquid mass transfer fermentation strategies, technoeconomic analysis and commercialization efforts are critically reviewed.

Journal ArticleDOI
TL;DR: The categorical model of classification in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM) is sorely problematic as mentioned in this paper and a proposed solution is emerging in the form of a qua...
Abstract: The categorical model of classification in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders is sorely problematic. A proposed solution is emerging in the form of a qua...