(1S,4S)-2-(2,4-Difluoro­phen­yl)-5-[(4-methyl­phen­yl)sulfon­yl]-2,5-diaza­bicyclo­[2.2.1]hepta­ne

In the title mol­ecule, C18H18F2N2O2S, the two benzene rings, which are oriented in opposite directions with respect to the rigid 2,5-diaza­bicyclo­[2.2.1]heptane core, form a dihedral angle of 17.2 (1)°. Weak inter­molecular C—H⋯O, C—H⋯F and C—H⋯N contacts consolidate the crystal packing

Microwave-Assisted Synthesis of Co/CoOx Supported on Earth-Abundant Coal-Derived Carbon for Electrocatalysis of Oxygen Evolution

The evident demand for hydrogen as the ultimate energy fuel for posterity calls for the development of low-cost, efficient and stable electrocatalysts for water splitting. Herein, we report the synthesis of Co/CoOx supported on coal-derived N-doped carbon via a simple microwave-assisted method and demonstrate its application as an efficient catalyst for the oxygen evolution reaction (OER). With the optimal amount of cobalt introduced into the N-doped coal-derived, the developed catalyst achieved overpotentials of 0.370 and 0.429 V during water oxidation at current densities of 1 mA cm(-2) and 10 mA cm(-2), respectively. There was no noticeable loss in the activity of the catalyst during continuous galvanostatic polarization at a current density of 10 mA cm(-2) for a test period of 66 h. The synergistic interaction of the Co/CoOx moieties with the pyridinic and pyrollic nitrogen functional groups in the N-doped carbon, as well with the other heteroatoms species in the pristine coal favored enhancement of the OER electrocatalytic performance. (C) The Author(s) 2019. Published by ECS

NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca2+ imaging

The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process

An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model

The Yangtze River Economic Belt (YREB) is a major national strategic development area in China, and the development of the YREB will greatly promote the development of the entirety China, so research on its agricultural production efficiency is also of great significance. This paper is committed to studying the agricultural production efficiency of 11 provinces in the YREB and adopts a combination of the Data Envelopment Analysis (DEA) model and the Malmquist index to make a dynamic and static analysis on the YREB’s agricultural production efficiency from 2010 to 2019. Then, a three-stage DEA Malmquist model that eliminates the factors of random interference and management inefficiency is compared to a model without elimination. The results show that the adjusted technological efficiency changes, technological progress, and total factor productivity increased by −0.1%, 0.24%, and 0.22%, respectively. When comparing these values to the pre-adjustment values, the results indicate that the effect of environmental variables cannot be ignored when studying the agricultural production efficiency of the YREB. At the same time, the differences in the agricultural production efficiency in the YREB are reasonably explained, and feasible suggestions are put forward

Magnetic Resonance Compatibility Analysis Method of Surgical Robotic System Based on Image Quality Evaluation

To ensure the safety and precision of surgery, the robotic system applied in magnetic resonance (MR) image-guided robot-assisted surgery should be MR-compatible. In terms of this issue, a MR compatibility analysis method for surgical robotic system based on image quality evaluation is proposed in this paper, and the image sets are extended. The image quality evaluation model is constructed by combining evaluation parameters such as signal-to-noise ratio of MR images, image change factor and MR image distortion. The model can analyze the effect of the robot component and robot motion on image quality, forming a basis for image quality evaluation. The experimental results show that the image quality evaluation method can fully analyze the MR compatibility of the robotic system component, and provide an evaluative method and theoretical basis for the MR compatibility analysis of other kinds of medical robotic systems

A Cuboid Model for Assessing Surface Soil Moisture

This study proposes a cuboid model for soil moisture assessment. In the model, the three edges were the meteorological, soil, and vegetation feature parameters highly related to soil moisture, and the edge lengths represented the degree of influence of each feature parameter on soil moisture. Soil moisture is assessed by the cuboid diagonal, which is referred to as the cuboid soil moisture index (CSMI) in this paper. The model was applied and validated in the Huang-Huai-Hai Plain. The results showed that (1) the difference in land surface temperature between day and night (ΔLST), land surface water index (LSWI), and accumulated precipitation (AP) were most closely correlated with soil moisture observation data in our study area, and were therefore selected as soil, crop, and meteorological system parameters to participate in CSMI calculations, respectively. (2) CSMI-1, with a cuboid length coefficient of 2/1/2, was the best model. The correlation of soil moisture derived from CSMI-1 with observed values was 0.64, 0.60, and 0.52 at depths of 10 cm, 20 cm, and 50 cm, respectively. (3) CSMI-1 had good applicability to the evaluation of soil moisture under different vegetation coverage. When the normalized difference vegetation index (NDVI)was 0–0.7, CSMI-1 was highly correlated with soil moisture at a significance level of 0.01. (4) The three-dimensional (3D) CSMI model can be easily converted to a two-dimensional (2D) model to adapt to different surface conditions (as long as the weight coefficient of one parameter is set to 0). Irrigation information (if available) can be considered as artificial recharge precipitation added in the AP to improve the accuracy of soil moisture inversion. This study provides a reference for soil moisture inversion using optical remote sensing images by integrating soil, vegetation, and meteorological feature parameters

Mechanical Properties and Axial Compression Deformation Property of Steel Fiber Reinforced Self-Compacting Concrete Containing High Level Fly Ash

The cement industry has brought serious environmental pollution problems. In the background of ecological civilization, accelerating rational use of waste resources plays an important role in protecting the environment. In this study, self-compacting concrete (SCC) is prepared using fly ash and lime powder as supplementary cementitious materials by replacing 50%, 60%, and 70% of ordinary Portland cement. By systematically analyzing the influence of the fly ash replacement rate on the workability and mechanical properties of SCC, steel-fiber-reinforced SCC containing 60% fly ash is chosen for further study, and steel fiber is added at the percentages of 0.25%, 0.50%, 0.75%, and 1.00%. The performances in fresh and hardened states are investigated in terms of workability, compressive strength, splitting tensile strength, flexural strength, and axial compression deformation property. The obtained outcomes indicate that although the incorporation of fly ash can improve the workability of the mixture, there is a negative correlation between the mechanical properties of SCC and the fly ash replacement rate. For steel-fiber-reinforced SCC containing 60% fly ash, when the content of steel fibers exceeds 0.75%, the workability decreases sharply, and even when the volume fraction is 1.00%, the passing ability cannot meet the requirements of the technical specifications for applications of self-compacting concrete. The analysis results for mechanical properties show that compressive strength is not changed significantly with increasing percentage of steel fibers. The steel fibers strengthen splitting tensile strength and flexural strength significantly, and compared with that of without steel fibers, they increased by 22% and 58%, respectively, with steel fibers up to 1.00%. Additionally, the parameters of the axial compression deformation property are improved by introducing steel fibers, especially the strain energy (Vε) and relative toughness (Γ) of steel-fiber-reinforced SCC containing a high level of fly ash

Preparation of a Novel Organic Phosphonic Acid Intercalated Phosphate Tailings Based Hydrotalcite and Its Application in Enhancing Fire Safety for Epoxy Resin

Phosphate tailings (PTs) are solid waste, which is produced by phosphate flotation. In this work, PTs were used as raw materials for the preparation of diethylenetriamine pentamethronic acid (DTPMP) intercalated trimetal (Ca-Mg-Al) layered double hydroxides (TM-DTPMP LDHs) by co-precipitation method. TM-DTPMP LDHs were characterized by X-ray diffraction, fourier-transform infrared spectroscopy, scanning electron microscopy, differential thermal gravimetric analysis, X-ray photoelectron spectroscopy and applied as a flame retardant to improve the fire safety of epoxy resin (EP). The results showed that the composite materials exhibited obvious layered structure. After intercalation, layer spacing increased from 0.783 to 1.78 Å. When the amount of TM-DTPMP LDH in EP was 8%, the limitted oxygen index of the composite material increased from the original 19.2% to 30.2%. In addition, Cone calorimeter (CC) and Raman spectrum results indicated that with the addition of TM-DTPMP LDHs, the value of heat release rate peak (pHRR) and total heat release (THR) were reduced by more than 43% and 60%, while the value of smoke formation rate (pSPR) and the total smoke production (TSP) decreased nearly 64% and 83%, respectively. The significant reduction in the release of combustion heat and harmful smoke during EP combustion may be attributed to the synergistic flame-retardant effect between hydrotalcite and DTPMP. This work exhibited great potential for the green recycling of PTs and the enhancement of the fire safety of EP