Large-scale Multi-layer Academic Networks Derived from Statistical Publications

The utilization of multi-layer network structures now enables the explanation of complex systems in nature from multiple perspectives. Multi-layer academic networks capture diverse relationships among academic entities, facilitating the study of academic development and the prediction of future directions. However, there are currently few academic network datasets that simultaneously consider multi-layer academic networks; often, they only include a single layer. In this study, we provide a large-scale multi-layer academic network dataset, namely, LMANStat, which includes collaboration, co-institution, citation, co-citation, journal citation, author citation, author-paper and keyword co-occurrence networks. Furthermore, each layer of the multi-layer academic network is dynamic. Additionally, we expand the attributes of nodes, such as authors' research interests, productivity, region and institution. Supported by this dataset, it is possible to study the development and evolution of statistical disciplines from multiple perspectives. This dataset also provides fertile ground for studying complex systems with multi-layer structures

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

BackgroundEssential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients.MethodsThe histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics.ResultsEach classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity.ConclusionOur findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients

Effect of hydrogen addition on criteria and greenhouse gas emissions for a marine diesel engine

Hydrogen remains an attractive alternative fuel to petroleum and a number of investigators claim that adding hydrogen to the air intake manifold of a diesel engine will reduce criteria emissions and diesel fuel consumption. Such claims are appealing when trying to simultaneously reduce petroleum consumption, greenhouse gases and criteria pollutants. The goal of this research was to measure the change in criteria emissions (CO, NOx, and PM 2.5) and greenhouse gases such as carbon dioxide (CO2), using standard test methods for a wide range of hydrogen addition rates. A two-stroke Detroit Diesel Corporation 12V-71TI marine diesel engine was mounted on an engine dynamometer and tested at three out of the four loads specified in the ISO 8178-4 E3 emission test cycle and at idle. The engine operated on CARB ultra-low sulfur #2 diesel with hydrogen added at flow rates of 0, 22 and 220 SLPM. As compared with the base case without hydrogen, measurements showed that hydrogen injection at 22 and 220 SLPM had negligible influence on the overall carbon dioxide specific emission, EFCO2. However, in examining data at each load the data revealed that at idle EFCO2 was reduced by 21% at 22 SLPM (6.9% of the added fuel energy was from hydrogen) and 37.3% at 220 SLPM (103.1% of the added fuel energy was from hydrogen). At all other loads, the influence of added hydrogen was insignificant. Specific emissions for nitrogen oxides, EFNOx, and fine particulate matters, EFPM 2.5, showed a trade-off relationship at idle. At idle, EFNO x was reduced by 28% and 41% with increasing hydrogen flow rates, whilst EFPM2.5 increased by 41% and 86% respectively. For other engine loads, EFNOx and EFPM2.5 did not change significantly with varying hydrogen flow rates. One of the main reasons for the greater impact of hydrogen at idle is that the contribution of hydrogen to the total fuel energy is much higher at idle as compared to the other loads. The final examination in this paper was the system energy balance when hydrogen is produced by an on-board electrolysis unit. An analysis at 75% engine load showed that hydrogen production increased the overall equivalent fuel consumption by 2.6% at 22 SLPM and 17.7% at 220 SLPM. © 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved

Investigation of Flow, Turbulence, and Dispersion within Built Environments

The built environment surrounding arterials impacts the dispersion of vehicular emissions in urban areas, modifying the potential risks to public health, which are not incorporated in current models. In order to study the influence of urban morphometry on flow, turbulence and dispersion of vehicular fine particulate matter emissions, water channel experiments and field measurements were performed. The research started from the investigation of flows through urban-like rectangular arrays of cubical obstacles. Then a mock downtown Los Angeles building cluster was built to simulate the dispersion of vehicular emissions within a complex built environment. Meanwhile, field experiments were carried out in five study areas in the Greater Los Angeles area.For rectangular arrays, Particle Image Velocimetry (PIV) was used for comprehensive flow measurements. A novel flow feature, lateral channeling, observed and quantitatively measured within regular 3 × 3 and 5 × 5 arrays. Low pressure in the wake region drew the fluid through the array, which led to formation of the strongest lateral inflow in front of the last row of buildings.For the complex urban setup, simultaneous Particle Image Velocimetry/Planar Laser Induced Fluorescence (PIV/PLIF) technique was applied to study the impact of an individual tall building on flow characteristics and plume dispersion in built environment. The results suggested that the presence of the tall building increase 16% of vertical plume spread by adding 0.02H* to characteristic length H*. The larger integral time scale of concentration fluctuations Tc below the height of H* indicated that it took longer to get plume well mixed when the tall building was present. The removal of plume from the canyon was driven mainly by the advective process.Field measurements suggested that the observed meteorological data at surface level within the urban canopy has a reasonable agreement with the Monin-Obukhov similarity theory. The generalized additive models showed that urban mixing dominated the variation of roadside particle concentrations regardless of urban mophometry. In Los Angeles, the increasing vertical velocity fluctuation and vertical mean wind speed reduced fine particle concentrations at street level. The estimated fine particle emission factor was 0.021 g/(vehicle⋅km) in the Los Angeles area

Wake patterns of the wings and tail of hovering hummingbirds

The flow fields of slowly flying bats and faster-flying birds differ in that bats produce two vortex loops during each stroke, one per wing, and birds produce a single vortex loop per stroke. In addition, the circulation at stroke transition approaches zero in bats but remains strong in birds. It is unknown if these difference derive from fundamental differences in wing morphology or are a consequence of flight speed. Here, we present an analysis of the horizontal flow field underneath hovering Anna’s hummingbirds (Calypte anna) to describe the wake of a bird flying at zero forward velocity. We also consider how the hummingbird tail interacts with the wake generated by the wings. High-speed image recording and analysis from three orthogonal perspectives revealed that the wing tips reach peak velocities in the middle of each stroke and approach zero velocity at stroke transition. Hummingbirds use complex tail kinematic patterns ranging from in phase to antiphase cycling with respect to the wings, covering several phase shifted patterns. We employed particle image velocimetry to attain detailed horizontal flow measurements at three levels with respect to the tail: in the tail, at the tail tip, and just below the tail. The velocity patterns underneath the wings indicate that flow oscillates along the ventral–dorsal axis in response to the down- and up-strokes and that the sideways flows with respect to the bird are consistently from the lateral to medial. The region around the tail is dominated by axial flows in dorsal to ventral direction. We propose that these flows are generated by interaction between the wakes of the two wings at the end of the upstroke, and that the tail actively defects flows to generate moments that contribute to pitch stability. The flow fields images also revealed distinct vortex loops underneath each wing, which were generated during each stroke. From these data, we propose a model for the primary flow structures of hummingbirds that more strongly resembles the bat model. Thus, pairs of unconnected vortex loops may be shared features of different animals during hovering and slow forward flight

Investigation of Roadside Particulate Matter Concentration Surrounding Major Arterials in Five Southern Californian Cities

Vehicular emissions from arterials may present a risk to public health considering the type of surrounding built environments that can trap pollutants. In order to study the influence of urban morphometry on flow and dispersion of vehicular emissions, field measurements were performed in major arterials in 5 Southern Californian cities with different building geometries. Local mean wind, turbulence, virtual temperature, roadside fine particulate matter (PM2.5) concentration, and traffic flow data were collected in summer 2008. In each city, data were collected for three days, covering two hours during the morning and evening commute and lighter mid-day traffic. First, the observation shows the influence of building geometry on street level concentration of particulates. Tall buildings cause a strong downdraft which upon impinging the street level flushes street canyon from pollutants. Second, field experiments help us understand the influence of local meteorological variables and their interaction with urban canopy to particle concentration. Concentrations at the windward side of buildings within urban canopy are extremely sensitive to wind direction. In addition to wind direction, turbulent flux, sensible heat flux and turbulent velocity are also affecting concentrations by enhancing vertical transport