Effects of Initial Drivers and Land Use on WRF Modeling for Near-Surface Fields and Atmospheric Boundary Layer over the Northeastern Tibetan Plateau

To improve the simulation performance of mesoscale models in the northeastern Tibetan Plateau, two reanalysis initial datasets (NCEP FNL and ERA-Interim) and two MODIS (Moderate-Resolution Imaging Spectroradiometer) land-use datasets (from 2001 and 2010) are used in WRF (Weather Research and Forecasting) modeling. The model can reproduce the variations of 2 m temperature (T2) and 2 m relative humidity (RH2), but T2 is overestimated and RH2 is underestimated in the control experiment. After using the new initial drive and land use data, the simulation precision in T2 is improved by the correction of overestimated net energy flux at surface and the RH2 is improved due to the lower T2 and larger soil moisture. Due to systematic bias in WRF modeling for wind speed, we design another experiment that includes the Jimenez subgrid-scale orography scheme, which reduces the frequency of low wind speed and increases the frequency of high wind speed and that is more consistent with the observation. Meanwhile, the new drive and land-use data lead to lower boundary layer height and influence the potential temperature and wind speed in both the lower atmosphere and the upper layer, while the impact on water vapor mixing ratio is primarily concentrated in the lower atmosphere

Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition

In recent years, a great deal of attention has been paid to the Transformer network for speech recognition tasks due to its excellent model performance. However, the Transformer network always involves heavy computation and large number of parameters, causing serious deployment problems in devices with limited computation sources or storage memory. In this paper, a new lightweight model called Sim-T has been proposed to expand the generality of the Transformer model. Under the help of the newly developed multiplexing technique, the Sim-T can efficiently compress the model with negligible sacrifice on its performance. To be more precise, the proposed technique includes two parts, that are, module weight multiplexing and attention score multiplexing. Moreover, a novel decoder structure has been proposed to facilitate the attention score multiplexing. Extensive experiments have been conducted to validate the effectiveness of Sim-T. In Aishell-1 dataset, when the proposed Sim-T is 48% parameter less than the baseline Transformer, 0.4% CER improvement can be obtained. Alternatively, 69% parameter reduction can be achieved if the Sim-T gives the same performance as the baseline Transformer. With regard to the HKUST and WSJ eval92 datasets, CER and WER will be improved by 0.3% and 0.2%, respectively, when parameters in Sim-T are 40% less than the baseline Transformer

Unraveling Electronic and Vibrational Coherences Following a Charge Transfer Process in a Photosystem II Reaction Center

A reaction center is a unique biological system that performs the initial charge separation within a Photosystem II (PSII) multiunit enzyme, which eventually drives the catalytic water-splitting in plants and algae. The possible role of quantum coherences coinciding with the energy and charge transfer processes in PSII reaction center is one of the active areas of research. Here, we study these quantum coherences by using a numerically exact method on an excitonic dimer model, including linear vibronic coupling and employing optimal parameters from experimental two-dimensional coherent spectroscopic measurements. This enables us to precisely capture the excitonic interaction between pigments and the dissipation of the energy from electronic and charge-transfer (CT) states to the protein environment. We employ the time nonlocal (TNL) quantum master equation to calculate the population dynamics, which yields numerically reliable results. The calculated results show that, due to the strong dissipation, the lifetime of electronic coherence is too short to have direct participation in the charge transfer processes. However, there are long-lived vibrational coherences present in the system at frequencies close to the excitionic energy gap. These are strongly coupled with the electronic coherences, which makes the detection of the electronic coherences with conventional techniques very challenging. Additionally, we unravel the strong excitonic interaction of radical pair (PD1 and PD2) in the reaction center, which results in a long-lived electronic coherence of >100 fs, even at room temperature. Our work provide important physical insight to the charge separation process in PSII reaction center, which may be helpful for better understanding of photophysical processes in other natural and artificial light-harvesting systems

PAHs in the North Atlantic Ocean and the Arctic Ocean: Spatial Distribution and Water Mass Transport

In the Arctic Ocean, it is still unclear what role oceanic transport plays in the fate of semivolatile organic compounds. The strong-stratified Arctic Ocean undergoes complex inputs and outputs of polycyclic aromatic hydrocarbons (PAHs) from the neighboring oceans and continents. To better understand PAHs’ transport processes and their contribution to high-latitude oceans, surface seawater, and water column, samples were collected from the North Atlantic Ocean and the Arctic Ocean in 2012. The spatial distribution of dissolved PAHs (∑9PAH) in surface seawater showed an “Arctic Shelf \u3e Atlantic Ocean \u3e Arctic Basin” pattern, with a range of 0.3–10.2 ng L−1. Positive matrix factorization modeling results suggested that vehicle emissions and biomass combustion were the major PAHs sources in the surface seawater. According to principal component analysis, PAHs in different water masses showed unique profiles indicating their different origins. Carried by the Norwegian Atlantic Current (0–800 m) and East Greenland Current (0–300 m), PAH individuals’ net transport mass fluxes ranged from −4.4 ± 1.7 to 53 ± 39 tons year−1 to the Arctic Ocean. We suggested the limited contribution of ocean currents on PAHs’ delivery to the Arctic Ocean, but their role in modulating PAHs’ air–sea interactions and other biogeochemical processes needs further studies

Quality of life in rectal cancer patients with permanent colostomy in Xi’an

Purposes: The aim of this study was to observe the quality of life (QOL) in rectal cancer patients with permanent colostomy in different periods after operation. Methods: A 1-,3-,6-month prospective study of QOL in 51 rectal cancer patients with permanent colostomy and 50 ones without permanent colostomy was assessed by using European Organization for Research and Treatment of Cancer (EORTC) QOL-30 and CR38 questionnaires. Results: The variation of QOL in different periods was “v” type. In the 1st postoperative month, these patients had the lowest quality of life scores, accompanied significantly varied functions and severe symptoms. Almost of all indexes of these patients had improved consistently in postoperative periods. The scores of global QOL even better than pre-operative level at 6th months post-operation, but the social function, body image, chemotherapy side effects and financial difficulties had not restored to the baseline level. Patients without permanent colostomy had a better score in most of categories of QOL-30 and CR38. Conclusions: The 1st postoperative month was crucial for patients’ recovery, in which we should pay great attention to these problems which relate to the recovery of rectal cancer patients with permanent colostomy.Keywords: Quality of life, Rectal cancer, Permanent colostomy, EORTC QOL-30 and CR38 questionnairesAfrican Health sciences Vol 14 No. 1 March 201

Overview of predictive maintenance based on digital twin technology

The upgrade and development of manufacturing industry makes predictive maintenance more and more important, but the traditional predictive maintenance can not meet the development needs in many cases. In recent years, predictive maintenance based on digital twin has become a research hotspot in the manufacturing industry field. Firstly, this paper introduces the general methods of digital twin technology and predictive maintenance technology, analyzes the gap between them, and points out the importance of using digital twin technology to realize predictive maintenance. Secondly, this paper introduces the predictive maintenance method based on digital twin (PdMDT), introduces its characteristics, and gives its differences from traditional predictive maintenance. Thirdly, this paper introduces the application of this method in intelligent manufacturing, power industry, construction industry, aerospace industry, shipbuilding industry, and summarizes the latest development in these fields. Finally, the PdMDT puts forwards a reference framework in manufacturing industry, the framework describes the specific implementation process of equipment maintenance, and gives an example of industrial robot using the framework, and discusses the limitations, challenges and opportunities of the PdMDT

Multi-Scale Learning for Multimodal Neurophysiological Signals: Gait Pattern Classification as An Example

Neurophysiological signals are manifestations of the underlying brain activity, and they contain an abundance of neural information. The decoding and understanding of these signals is useful to develop robotic exoskeletons, benefitting device-aided motor rehabilitation. To this date, numerous efforts have been car- ried out to explore the relations between neurophysiological signals and locomotor capacity. Most of these studies focused on a single modality of neurophysiological signal and ignored its multiple modalities. In this study, the modalities from two kinds of biosensors were fused (electroencephalogram (EEG) and electromyogram (EMG)), and a novel deep learning model was proposed (multi-scale learning, MSL) to classify four walking patterns. The EEG and EMG data were collected during a walking experiment, where different walking conditions with and without exoskeleton-aided assistance were implemented (i.e. free-walking and exoskeleton- aided walking at zero, low, and high assistive forces). The performance achieved by the MSL model was compared to that of existing models, and the results show that multimodal MSL achieved the highest performance in terms of clas- sification accuracy (89.33%). Moreover, the comparisons in our study show that an improved classification performance was obtained when a full 62-channel EEG setting was used compared to using a subset of 20 channels located on the senso- rimotor region. This work contributes to the improvement of neurophysiological signal decoding and promotes the development of rehabilitation technologies as well as exoskeleton-aided applications

Stability Analysis and Trigger Control of LLC Resonant Converter for a Wide Operational Range

The gain of a LLC resonant converter can vary with the loads that can be used to improve the efficiency and power density for some special applications, where the maximum gain does not apply at the heaviest loads. However, nonlinear gain characteristics can make the converters unstable during a major disturbance. In this paper, the stability of an LLC resonant converter during a major disturbance is studied and a trigger control scheme is proposed to improve the converter’s stability by extending the converter’s operational range. Through in-depth analysis of the gain curve of the LLC resonant converter, we find that the switching frequency range is one of the key factors determining the system’s stability performance. The same result is also obtained from a mathematical point of view by utilizing the mixed potential function method. Then a trigger control method is proposed to make the LLC resonant converter stable even during a major disturbance, which can be used to extend the converter’s operational range. Finally, experimental results are given to verify the analysis and proposed control scheme