Groundwater Analysis and Numerical Simulation Based on Grey Theory

Abstract: In view of the deficiency of the traditional methods, based on the grey theory, GM (1, 1) model is established to predict the groundwater level. The proposed model was applied to predict the groundwater level in Xiaonanhai Spring. The prediction result was compared with that of the traditional method and the reported results in the Xiaonanhai Spring. It is indicated that the performance of the proposed model is practically feasible in the application of prediction of groundwater level and its application is simple

Design of compliant parallel grippers using the position space concept for manipulating submillimeter objects

The structure or configuration of compliant mechanisms can be reconfigured through changing the positions of each compliant module thereof within their position spaces. A number of 1-DOF 2-PRRP compliant parallel grippers (CPGs) can be obtained using the structure re-configurability for manipulating sub-millimeter objects. Even with the geometrical parameters for the system’s pseudorigid-body model (PRBM) and each compliant module kept at the same values, the position of each compliant joint can be anywhere within its position space. The performance of the resulting CPG varies with the position of the compliant joint. In this paper two typical CPG designs are presented and analyzed. Comparisons between FEA simulaiton resutls and analytical models show that the input-output kinematic relationship of the non-compact design agrees better with that of the PRBM due to its better load transmissibility. One can design different structures based on specific design requirements

Vertical Velocity Distribution in Open-Channel Flow with Rigid Vegetation

In order to experimentally investigate the effects of rigid vegetation on the characteristics of flow, the vegetations were modeled by rigid cylindrical rod. Flow field is measured under the conditions of submerged rigid rod in flume with single layer and double layer vegetations. Experiments were performed for various spacings of the rigid rods. The vegetation models were aligned with the approaching flow in a rectangular channel. Vertical distributions of time-averaged velocity at various streamwise distances were evaluated using an acoustic Doppler velocimeter (ADV). The results indicate that, in submerged conditions, it is difficult to described velocity distribution along the entire depth using unified function. The characteristic of vertical distribution of longitudinal velocity is the presence of inflection. Under the inflection, the line is convex and groove above inflection. The interaction of high and low momentum fluids causes the flow to fold and creates strong vortices within each mixing layer. Understanding the flow phenomena in the area surrounding the tall vegetation, especially in the downstream region, is very important when modeling or studying the riparian environment. ADV measures of rigid vegetation distribution of the flow velocity field can give people a new understanding

High MB solution degradation efficiency of FeSiBZr amorphous ribbon with surface tunnels

© 2020 by the authors. The as spun amorphous (Fe78Si9B13)99.5Zr0.5 (Zr0.5) and (Fe78Si9B13)99Zr1 (Zr1) ribbons having a Fenton-like reaction are proved to bear a good degradation performance in organic dye wastewater treatment for the first time by evaluating their degradation efficiency in methylene blue (MB) solution. Compared to the widely studied (Fe78Si9B13)100Zr0 (Zr0) amorphous ribbon for degradation, with increasing cZr (Zr atomic content), the as-spun Zr0, Zr0.5 and Zr1 amorphous ribbons have gradually increased degradation rate of MB solution. According to δc (characteristic distance) of as-spun Zr0, Zr0.5 and Zr1 ribbons, the free volume in Zr1 ribbon is higher Zr0 and Zr0.5 ribbons. In the reaction process, the Zr1 ribbon surface formed the 3D nano-porous structure with specific surface area higher than the cotton floc structure formed by Zr0 ribbon and coarse porous structure formed by Zr0.5 ribbon. The Zr1 ribbon\u27s high free volume and high specific surface area make its degradation rate of MB solution higher than that of Zr0 and Zr0.5 ribbons. This work not only provides a new method to remedying the organic dyes wastewater with high efficiency and low-cost, but also improves an application prospect of Fe-based glassy alloys

Different doses of intermittent theta burst stimulation for upper limb motor dysfunction after stroke: a study protocol for a randomized controlled trial

BackgroundUpper limb motor recovery is one of the important goals of stroke rehabilitation. Intermittent theta burst stimulation (iTBS), a new type of repetitive transcranial magnetic stimulation (rTMS), is considered a potential therapy. However, there is still no consensus on the efficacy of iTBS for upper limb motor dysfunction after stroke. Stimulus dose may be an important factor affecting the efficacy of iTBS. Therefore, we aim to investigate and compare the effects and neural mechanisms of three doses of iTBS on upper limb motor recovery in stroke patients, and our hypothesis is that the higher the dose of iTBS, the greater the improvement in upper limb motor function.MethodsThis prospective, randomized, controlled trial will recruit 56 stroke patients with upper limb motor dysfunction. All participants will be randomized in a 1:1:1:1 ratio to receive 21 sessions of 600 pulses active iTBS, 1,200 pulses active iTBS, 1,800 pulses active iTBS, or 1,800 pulses sham iTBS in addition to conventional rehabilitation training. The primary outcome is the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) score from baseline to end of intervention, and the secondary outcomes are the Wolf Motor Function Test (WMFT), Grip Strength (GS), Modified Barthel Index (MBI), and Stroke Impact Scale (SIS). The FMA-UE, MBI, and SIS are assessed pre-treatment, post-treatment, and at the 3-weeks follow-up. The WMFT, GS, and resting-state functional magnetic resonance imaging (rs-fMRI) data will be obtained pre- and post-treatment.DiscussionThe iTBS intervention in this study protocol is expected to be a potential method to promote upper limb motor recovery after stroke, and the results may provide supportive evidence for the optimal dose of iTBS intervention

Text-based Person Search in Full Images via Semantic-Driven Proposal Generation

Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance.However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person retrieval methods mainly focus on the cross modal matching between the query text descriptions and the gallery of cropped pedestrian images. To close the gap, we study the problem of text-based person search in full images by proposing a new end-to-end learning framework which jointly optimize the pedestrian detection, identification and visual-semantic feature embedding tasks. To take full advantage of the query text, the semantic features are leveraged to instruct the Region Proposal Network to pay more attention to the text-described proposals. Besides, a cross-scale visual-semantic embedding mechanism is utilized to improve the performance. To validate the proposed method, we collect and annotate two large-scale benchmark datasets based on the widely adopted image-based person search datasets CUHK-SYSU and PRW. Comprehensive experiments are conducted on the two datasets and compared with the baseline methods, our method achieves the state-of-the-art performance

Developing hierarchically ultra-micro/mesoporous biocarbons for highly selective carbon dioxide adsorption

Activated carbons represent one of the important categories of the adsorbent materials for CO2 capture currently under development. However, the low adsorption capacity and selectivity at low CO2 partial pressure or relatively high flue gas temperatures is the main barrier for carbons to be applied in post-combustion CO2 capture under practical conditions. Here, we report the successful preparation of hierarchical ultra-micro/mesoporous bio-carbons from using a facile one-step method with a low-grade biomass waste as the feedstock. The bio-carbons exhibit high adsorption capacities (1.90 mmol/g) and record-high Henry’s law CO2/N2 selectivities up to 212 at ambient temperature and low CO2 partial pressure. Unlike conventional chemical activation process for manufacturing carbon materials, the integrated compaction-carbonization-activation method proposed endows the biowaste-derived carbons with unique hierarchical bio-modal pore structures, which is highly characterised by their high mesoporosity and high ultra-microporosity with narrow pore size distributions. The results demonstrated that the unique surface textural properties along with the enhanced surface chemistry due to the simultaneously achieved potassium intercalation created favourable conditions for CO2 adsorption with high CO2/N2 selectivity at low CO2 partial pressures, whilst the presence of mesoporosity greatly increased the CO2 adsorption kinetics. Measurements of CO2 adsorption heat confirmed the strong surface affinity of the prepared bio-carbons to CO2 molecules

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519