bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural and electrical characterizations of the as grown and annealed Au/MοO3/In/MoO3/C bandpass filters
Authors: Khanfar Hazem K. , Qasrawi Atef , Daraghmeh Masa , Abusaa Muayad 
Published: August 2019

AbstractIn this work, the structural, morphology, and electrical properties of two 500 nm thick molybdenum trioxide layers that are sandwiched with indium slab of thickness of 200 nm (MoO3/In/MoO3 [MIM]) to form a bandpass filter are investigated by X‐ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X‐ray spectroscopy (EDX), and impedance spectroscopy techniques, respectively. The MIM films which coated onto Au thin film substrates by the thermal evaporation technique under vacuum pressure of 10−5 mbar was post annealed at 250°C in air atmosphere. While the XRD analysis revealed polycrystalline hexagonal lattice structure of the Au/MLM samples, the SEM and EDS analysis displayed grains of sizes of 350 nm and stoichiometric structure of MoO3. Electrically, indium layer which caused n‐type conduction with donor level of 299 meV, forced the material to exhibit negative capacitance (NC) effect at high frequencies (above 1.1 GHz). The impedance spectroscopy which was recorded in the frequency domain of 0.01 to 1.80 GHz, also revealed low pass and high pass filters characteristics in the low and high frequency domains, respectively. The annealing of the Au/MIM samples, decreased the crystallite and grain sizes and increased the microstrain, the defect density and the stacking faults. Small amount of excess oxygen and some indium deficiency are observed upon annealing. In addition, the annealing shifted the donor level closer to the bottom of the conduction band and inverted the NC effect from high to low frequency regions. The study indicates the applicability of the Au/MIM/C structures as microwave cavities and parasitic capacitance cancellers in electronic circuits.
MR fingerprinting as a diagnostic tool in patients with frontotemporal lobe degeneration: A pilot study
Authors: Keil Vera Catharina , Bakoeva Stilyana Peteva , Jurcoane Alina , Doneva Mariya , Amthor Thomas , Koken Peter , Mädler Burkhard , Block Wolfgang , Fimmers Rolf , Fliessbach Klaus , Hattingen Elke 
Published: August 2019

Several very rare forms of dementia are associated with characteristic focal atrophy predominantly of the frontal and/or temporal lobes and currently lack imaging solutions to monitor disease. Magnetic resonance fingerprinting (MRF) is a recently developed technique providing quantitative relaxivity maps and images with various tissue contrasts out of a single sequence acquisition. This pilot study explores the utility of MRF‐based T1 and T2 mapping to discover focal differences in relaxation times between patients with frontotemporal lobe degenerative dementia and healthy controls. 8 patients and 30 healthy controls underwent a 3 T MRI including an axial 2D spoiled gradient echo MRF sequence. T1 and T2 relaxation maps were generated based on an extended phase graphs algorithm‐founded dictionary involving inner product pattern matching. A region of interest (ROI)‐based analysis of T1 and T2 relaxation times was performed with FSL and ITK‐SNAP. Depending on the brain region analyzed, T1 relaxation times were up to 10.28% longer in patients than in controls reaching significant differences in cortical gray matter (P = .047) and global white matter (P = .023) as well as in both hippocampi (P = .001 left; P = .027 right). T2 relaxation times were similarly longer in the hippocampus by up to 19.18% in patients compared with controls. The clinically most affected patient had the most control‐deviant relaxation times. There was a strong correlation of T1 relaxation time in the amygdala with duration of the clinically manifest disease (Spearman Rho = .94; P = .001) and of T1 relaxation times in the left hippocampus with disease severity (Rho = .90, P = .002). In conclusion, MRF‐based relaxometry is a promising and time‐saving new MRI tool to study focal cerebral alterations and identify patients with frontotemporal lobe degeneration. To validate the results of this pilot study, MRF is worth further exploration as a diagnostic tool in neurodegenerative diseases.
Organizational identity and the state of organizational identification in nursing organizations
Authors: Tsukamoto Naoko , Hirata Akemi , Funaki Yuka 
Published: August 2019

AbstractAimThis study qualitatively identified the organizational identity of a nursing organization and determined the state of organizational identification of staff in hospital wards.DesignCross‐sectional descriptive survey study.MethodsInterviews were conducted using interview guides; a qualitative inductive analysis was performed for the three attributes of organizational identity (central, distinctive and enduring). The study included three head nurses working in different facilities and three teams comprising three nurses each, who worked under each of the head nurses (12 nurses total).ResultsCentrality comprised two subcategories: “ward work attributes” and “ward care attributes”. Clear centrality originating from a head nurse showed a strong influence on organizational culture in a hospital ward. As young staff is identified by distinctiveness in wards, it is important to clarify distinctiveness. When centrality and distinctiveness were not clear, enduring was weak.
Comparison of text processing methods in social media–based signal detection
Authors: Gavrielov‐Yusim Natalie , Kürzinger Marie‐Laure , Nishikawa Chihiro , Pan Chunshen , Pouget Julie , Epstein Limor BH , Golant Yan , Tcherny‐Lessenot Stephanie , Lin Stephen , Hamelin Bernard , Juhaeri Juhaeri 
Published: August 2019

AbstractPurposeAdverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co‐occurrence‐based ML methods are rather basic, as they identify simultaneous appearance of drugs and clinical events in a single post. In contrast, statistical learning methods involve more complex NLP and identify drugs, events, and associations between them. We aimed to compare the ability of co‐occurrence and NLP to identify AEs and signals of disproportionate reporting (SDR) in patient‐generated SM. We also examined the performance of lift in SM‐based signal detection (SD).MethodsOur examination was performed in a corpus of SM posts crawled from open online patient forums and communities, using the spontaneously reported VigiBase data as reference data set.ResultsWe found that co‐occurrence and NLP produce AEs, which are 57% and 93% consistent with VigiBase AEs, respectively. Among the SDRs identified both in SM and in VigiBase, up to 55.3% were identified earlier in co‐occurrence, and up to 32.1% were identified earlier in NLP‐processed SM. Using lift in SM SD provided performance similar to frequentist methods, both in co‐occurrence and in NLP‐processed AEs.ConclusionOur results indicate that using SM as a data source complementary to traditional pharmacovigilance sources should be considered further. Various levels of SM processing may be considered, depending on the preferred policies and tolerance for false‐positive to false‐negative balance in routine pharmacovigilance processes.
Effects of antibullying programs on teachers’ interventions in bullying situations. A meta‐analysis
Authors: van Verseveld Marloes D. A. , Fukkink Ruben G. , Fekkes Minne , Oostdam Ron J. 
Published: August 2019

AbstractEven though teachers are key figures of a program's effectiveness, most intervention studies have not focused explicitly on the effects of antibullying programs at teacher level. We conducted a meta‐analysis into the effects of school‐based antibullying programs on determinants of teacher intervention, including teachers’ attitudes towards bullying, their self‐efficacy and knowledge regarding intervention strategies, and the effects on teachers’ bullying intervention itself. Following the PRISMA guidelines, 13 peer‐reviewed papers were retrieved that reported outcomes on teachers, staff, and students (N = 948, 2,471, and 138,311, respectively). Antibullying programs had a significant moderate effect on determinants of teacher intervention (g = 0.531) and a significant small to moderate effect on teacher intervention in bullying situations (g = 0.390). Results of the meta‐analysis indicate that the effectiveness of antibullying programs may increase when components are included to reinforce teachers’ attitudes, subjective norms, self‐efficacy, knowledge, and skills towards reducing bullying in the school.
Feedback stabilization of multi‐DOF nonlinear stochastic Markovian jump systems
Authors: Hu Rongchun , Dong Hao , Gu Xudong , Deng Zichen 
Published: August 2019

SummaryA feedback control strategy is designed to asymptotically stabilize a multi‐degree‐of‐freedom (DOF) nonlinear stochastic systems undergoing Markovian jumps. First, a class of hybrid nonlinear stochastic systems with Markovian jumps is reduced to a one‐dimensional averaged Itô stochastic differential equation for controlled total energy. Second, the optimal control law is deduced by applying the dynamical programming principle to the ergodic control problem of the averaged systems with an undetermined cost function. Third, the cost function is determined by the demand of stabilizing the averaged systems. A Lyapunov exponent is introduced to analyze approximately the asymptotic stability with probability one of the originally controlled systems. To illustrate the application of the present method, an example of stochastically excited two coupled nonlinear oscillators with Markovian jumps is worked out in detail.
Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming
Authors: Lottes Philipp , Behley Jens , Chebrolu Nived , Milioto Andres , Stachniss Cyrill 
Published: August 2019

AbstractConventional farming still relies on large quantities of agrochemicals for weed management which have several negative side‐effects on the environment. Autonomous robots offer the potential to reduce the amount of chemicals applied, as robots can monitor and treat each plant in the field individually and thereby circumventing the uniform chemical treatment of the whole field. Such agricultural robots need the ability to identify individual crops and weeds in the field using sensor data and must additionally select effective treatment methods based on the type of weed. For example, certain types of weeds can only be effectively treated mechanically due to their resistance to herbicides, whereas other types can be treated trough selective spraying. In this article, we present a novel system that provides the necessary information for effective plant‐specific treatment. It estimates the stem location for weeds, which enables the robots to perform precise mechanical treatment, and at the same time provides the pixel‐accurate area covered by weeds for treatment through selective spraying. The major challenge in developing such a system is the large variability in the visual appearance that occurs in different fields. Thus, an effective classification system has to robustly handle substantial environmental changes including varying weed pressure, various weed types, different growth stages, changing visual appearance of the plants and the soil. Our approach uses an end‐to‐end trainable fully convolutional network that simultaneously estimates plant stem positions as well as the spatial extent of crop plants and weeds. It jointly learns how to detect the stems and the pixel‐wise semantic segmentation and incorporates spatial information by considering image sequences of local field strips. The jointly learned feature representation for both tasks furthermore exploits the crop arrangement information that is often present in crop fields. This information is considered even if it is only observable from the image sequences and not a single image. Such image sequences, as typically provided by robots navigating over the field along crop rows, enable our approach to robustly estimate the semantic segmentation and stem positions despite the large variations encountered in different fields. We implemented and thoroughly tested our approach on images from multiple farms in different countries. The experiments show that our system generalizes well to previously unseen fields under varying environmental conditions—a key capability to deploy such systems in the real world. Compared to state‐of‐the‐art approaches, our approach generalizes well to unseen fields and not only substantially improves the stem detection accuracy, that is, distinguishing crop and weed stems, but also improves the semantic segmentation performance.
CROSS SECTIONS AND REACTION RATES OF RELEVANCE TO AERONOMY
Authors: Fox J. L. 
Published: August 2019

AbstractExperimental and theoretical data relevant to models and measurements of the chemical and thermal structures and luminosity of the thermospheres of the Earth and planets published during the last four years are surveyed, and those in which one or more authors is affiliated with a U.S. institution are indicated. Among chemical processes, we include ion‐molecule reactions, dissociative recombination of molecular ions, and reactions between neutral species. Both reactions between ground state species and species in excited states are considered, including energy transfer and quenching. Measured and calculated cross sections for interactions of solar radiation with atmospheric species, such as photoabsorption, photoionization, and photodissociation and related processes are surveyed. Measurements and calculations of cross sections for elastic and inelastic scattering of particles, including electrons, protons, alpha particles, heavier ions, and neutral atoms are also surveyed.
How freshwater biomonitoring tools vary sub‐seasonally reflects temporary river flow regimes
Authors: White James C. , Armitage Patrick D. , Bass Jon A.B. , Chadd Richard P. , Hill Matthew J. , Mathers Kate L. , Little Sally , Wood Paul J. 
Published: August 2019

AbstractCharacterizing temporary river ecosystem responses to flow regimes is vital for conserving their biodiversity and the services they provide to society. However, freshwater biomonitoring tools rarely reflect community responses to hydrological variations or flow cessation events, and those available have not been widely tested within temporary rivers. This study examines two invertebrate biomonitoring tools characterizing community responses to different flow‐related properties: the “Drought Effect of Habitat Loss on Invertebrates” (DEHLI) and “Lotic‐invertebrate Index for Flow Evaluation” (LIFE), which, respectively reflect community responses to habitat and hydraulic properties associated with changing flow conditions. Sub‐seasonal (monthly) variations of LIFE and DEHLI were explored within two groundwater‐fed intermittent rivers, one dries sporadically (a flashy, karstic hydrology—River Lathkill) and the other dries seasonally (a highly buffered flow regime—South Winterbourne). Biomonitoring tools were highly sensitive to channel drying and also responded to reduced discharges in permanently flowing reaches. Biomonitoring tools captured ecological recovery patterns in the Lathkill following a supra‐seasonal drought. Some unexpected results were observed in the South Winterbourne where LIFE and DEHLI indicated relatively high‐flow conditions despite low discharges occurring during some summer months. This probably reflected macrophyte encroachment, which benefitted certain invertebrates (e.g., marginal‐dwelling taxa) and highlights the importance of considering instream habitat conditions when interpreting flow regime influences on biomonitoring tools. Although LIFE and DEHLI were positively correlated, the latter responded more clearly to drying events, highlighting that communities respond strongly to the disconnection of instream habitats as flows recede. The results highlighted short‐term ecological responses to hydrological variations and the value in adopting sub‐seasonal sampling strategies within temporary rivers. Findings from this study indicate the importance of establishing flow response guilds which group taxa that respond comparably to flow cessation events. Such information could be adopted within biomonitoring practices to better characterize temporary river ecosystem responses to hydrological variations.
Enantioselective Assembly of Tricyclic Tetrahydroquinoline Derivatives
Authors: Kazancioglu Mustafa Z. , Kalay Erbay , Kazancioglu Elif Akin , Peshkov Vsevolod A. 
Published: August 2019

AbstractA three‐step synthetic route towards enantioenriched tricyclic tetrahydroquinoline derivatives has been investigated. The first step involved the assembly of 4‐amino‐tetrahydroquinoline core through the asymmetric three‐component Povarov reaction of benzyl (E)‐prop‐1‐en‐1‐ylcarbamate and p‐anisidine with aliphatic aldehydes of various length bearing protected hydroxyl group. The alcohol was then deprotected and employed in an intramolecular reaction with a secondary amine of tetrahydroquinoline moiety producing desired tricyclic scaffolds.
Climate and society in long‐term perspective: Opportunities and pitfalls in the use of historical datasets
Authors: van Bavel Bas J. P. , Curtis Daniel R. , Hannaford Matthew J. , Moatsos Michail , Roosen Joris , Soens Tim 
Published: August 2019

Recent advances in paleoclimatology and the growing digital availability of large historical datasets on human activity have created new opportunities to investigate long‐term interactions between climate and society. However, noncritical use of historical datasets can create pitfalls, resulting in misleading findings that may become entrenched as accepted knowledge. We demonstrate pitfalls in the content, use and interpretation of historical datasets in research into climate and society interaction through a systematic review of recent studies on the link between climate and (a) conflict incidence, (b) plague outbreaks and (c) agricultural productivity changes. We propose three sets of interventions to overcome these pitfalls, which involve a more critical and multidisciplinary collection and construction of historical datasets, increased specificity and transparency about uncertainty or biases, and replacing inductive with deductive approaches to causality. This will improve the validity and robustness of interpretations on the long‐term relationship between climate and society.This article is categorized under:

Climate, History, Society, Culture > Disciplinary Perspectives
Effect of foundation type and modelling on dynamic response and fatigue of offshore wind turbines
Authors: Løken Ingrid B. , Kaynia Amir M. 
Published: August 2019

AbstractThis paper presents dynamic response and fatigue analyses of several bottom‐mounted offshore wind turbine (OWT) models, simulated in the aero‐hydro‐servo‐elastic simulation tool FAST. The distinction between the models is the foundations, which are modelled with different methods, concepts, and dimensions. The US National Renewable Energy Laboratory has developed a 5‐MW reference turbine supported on a monopile, the NREL 5MW, which was used as a reference model in this paper. The paper presents the implementation and comparison of two different foundation modeling methods, referred to as the simplified apparent fixity method and the improved apparent fixity method. Furthermore, sensitivity analyses of different monopile dimensions were performed, followed by sensitivity analyses of suction caisson foundations of different dimensions. The final part of the paper presents fatigue analyses for the foundation models considered in this study subjected to 17 load cases. Fatigue damage, fatigue life, and damage equivalent loads were calculated, as well as the relative fatigue contribution from each load case.
A method for mapping hunting occurrence using publicly available, geographic variables
Authors: Burke Conner R. , Peterson M. Nils , Sawyer David T. , Moorman Christopher E. , Serenari Christopher , Pacifici Krishna 
Published: August 2019

ABSTRACTThe spatial distribution of land available for hunting has received scant attention in the literature, but it fundamentally affects the feasibility of wildlife management. Modeling the distribution of hunting lands can be logistically difficult because of data requirements and the dynamic nature of landscapes and landowner preferences. We describe one approach to address this challenge using spatially explicit logistic regression models that accurately predict whether each parcel of land in North Carolina, USA, was hunted using free and publicly available geographic predictors. We collected data to develop and validate models from surveys of nonindustrial (n = 1,936) and industrial (n = 670) private landowners conducted during 2016 in North Carolina. Property size and housing and road density predicted whether hunting occurred correctly on 96% of nonindustrial parcels. Property size, housing and road density, and distance to the nearest city correctly predicted whether hunting occurred for 94% of industrial parcels. These results suggest wildlife managers may be able to accurately map and quantify where hunting occurs using relatively few publicly available geographic predictors. Future refinement of the methodology and model parameters is likely needed in different regions, with independent data sets, before adopting widespread implementation of underlying methods. This mapping method will facilitate assessing the efficacy of hunting as a wildlife management tool for overabundant species. Similarly the mapping approach would improve wildlife population estimates based on hunter harvest data by providing a more rigorous estimate of land that is huntable per harvested animal reported. © 2019 The Wildlife Society.