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We present the Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm for automatic inference of the electron number density from plasma wave measurements made on board NASA's Van Allen Probes mission. A feedforward neural network is developed to determine the upper hybrid resonance frequency, fuhr, from electric field measurements, which is then used to calculate the electron number density. In previous missions, the plasma resonance bands were manually identified, and there have been few attempts to do robust, routine automated detections. We describe the design and implementation of the algorithm and perform an initial analysis of the resulting electron number density distribution obtained by applying NURD to 2.5 years of data collected with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite of the Van Allen Probes mission. Densities obtained by NURD are compared to those obtained by another recently developed automated technique and also to an existing empirical plasmasphere and trough density model.
Investigation of the TCA cycle and glycolytic metabolons and their physiological impacts in plants
(2016)
Pollen influx (number of pollen grains cm−2 year−1) can objectively reflect the dispersal and deposition features of pollen within a certain time and space, and is often used as a basis for the quantitative reconstruction of palaeovegetation; however, little is known about the features and mechanisms of vertical dispersal of pollen. Here we present the results from a 5 year (2006–2010) monitoring program using pollen traps placed at different heights from ground level up to 60 m and surface soil samples in a mixed coniferous and deciduous broad-leaved woodland in the Changbai mountains, northeastern China. The pollen percentages and pollen influx from the traps have very similar characteristics to the highest values for Betula, Fraxinus, Quercus and Pinus, among the tree taxa and Artemisia, Chenopodiaceae and Asteraceae among the herb taxa. Pollen influx values vary significantly with height and show major differences between three distinct layers, above-canopy (≥32 m), within the trunk layer (8 ≤ 32 m) and on the ground (0 m). These differences in pollen influx are explained by differences in (i) the air flows in each of these layers and (ii) the fall speed of pollen of the various taxa. We found that the pollen recorded on the ground surface is a good representation of the major part of the pollen transported in the trunk space of the woodland. Comparison of the pollen influx values with the theoretical, calculated “characteristic pollen source area” (CPSA) of 12 selected taxa indicates that the pollen deposited on the ground surface of the woodland is a fair representation with 85–90 % of the total pollen deposited at a wind speed of 2.4 m s−1 coming from within ca. 1–5 km for Pinus and Quercus, ca. 5–10 km for Ulmus, Tilia, Oleaceae and Betula, ca. 20–40 km for Fraxinus, Poaceae, Chenopodiaceae, Populus and Salix, and ca. 30–60 km for Artemisia; it is also a good representation with 90–98 % of the total pollen deposited coming from within 60 km at a wind speed of 2.4 m s−1, or 100 km at a wind speed: 6 m s−1, for the 12 selected taxa used in the CPSA calculation. Furthermore, comparison with the vegetation map of the area around the sampling site shows that the pollen deposited on the ground represents all plant communities which grow in the study area within 70 km radius of the sampling site. In this study, the pollen percentages obtained from the soil surface samples are significantly biased towards pollen taxa with good preservation due to thick and robust pollen walls. Therefore, if mosses are available instead, soil samples should be avoided for pollen studies, in particular for the study of pollen-vegetation relationships, the estimation of pollen productivities and quantitative reconstruction of past vegetation. The results also indicate that the existing model of pollen dispersal and deposition, Prentice’s model, provides a fair description of the actual pollen dispersal and deposition in this kind of woodland, which suggests that the application of the landscape reconstruction algorithm would be relevant for reconstruction of this type of woodland in the past.
Inspired by the application of ultrasonic cavitation based mechanical force (CMF) to open small channels in natural soft materials (skin or tissue), it is explored whether an artificial polymer network can be created, in which shape-changes can be induced by CMF. This concept comprises an interconnected macroporous rhodium-phosphine (Rh-P) coordination polymer network, in which a CMF can reversibly dissociate the Rh-P microphases. In this way, the ligand exchange of Rh-P coordination bonds in the polymer network is accelerated, resulting in a topological rearrangement of molecular switches. This rearrangement of molecular switches enables the polymer network to release internal tension under ultrasound exposure, resulting in a CMF-induced shape-memory capability. The interconnected macroporous structure with thin pore walls is essential for allowing the CMF to effectively permeate throughout the polymer network. Potential applications of this CMF-induced shape-memory polymer can be mechanosensors or ultrasound controlled switches.
Тезисы
(2016)
Nowadays, working in an office environment is ubiquitous. At the same time, progressively more people suffer from occupational musculoskeletal disorders. Therefore, the aim of this pilot study was to analyse the influence of back pain on sitting behaviour in the office environment. A textile pressure mat (64-sensor-matrix) placed on the seat pan was used to identify the adopted sitting positions of 20 office workers by means of random forest classification. Additionally, two standardised questionnaires (Korff, BPI) were used to assess short and long-term back pain in order to divide the subjects into two groups (with and without back pain). Independent t-test indicated that subjects who registered back pain within the last 24 h showed a clear trend towards a more static sitting behaviour. Therefore, the developed sensor system has successfully been introduced to characterise and compare sitting behaviour of subjects with and without back pain. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licensesiby-nc-nd/4.0/).