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Under the constraint of carbon emission, measuring and analyzing the spatial-temporal evolution characteristics of urban land use efficiency in the Yangtze River Economic Belt is the inherent requirement of its ecological protection and sustainable development. In this paper, we calculated the urban land use efficiency of 107 cities in the Yangtze River Economic Belt from 2006 to 2020 by using the SBM-Undesirable model with unexpected output, and analyzed its temporal evolution trend and spatial correlation relationship by using kernel density and spatial autocorrelation method. The results showed that: except in 2020, the urban land use efficiency was generally low due to the COVID-19 epidemic, and the urban land use efficiency in other years was mostly concentrated in the middle levels, and showed a trend of slow fluctuation and rise year by year. The difference of urban land use efficiency level between regions increased, and the dispersion degree in upstream, midstream and downstream increased with each passing year. Urban land use efficiency spatial imbalance was significant, and the urban land use efficiency level of large and medium-sized cities was generally lower than that of cities with low economic development level. The spatial correlation was weak, and the global spatial autocorrelation was basically insignificant, while the local spatial agglomeration areas were mainly distributed in the upstream and downstream regions, with a small distribution range and weak spatial interaction. The distribution areas of the standard deviation ellipse were gradually flattened, and the center of gravity as a whole shift significantly to the southwest. The research results are helpful to understand the development history and future trend of urban land use efficiency in various regions, and propose that cities should consider the impact of public crisis events in advance, reasonably control the scale of land expansion, and lead coordinated development and other reasonable suggestions when formulating land use policies.

Bulk dense anisotropic rare earth (RE)-Fe-B magnets synthesized by hot pressing and subsequent hot deforming melt-spun powders have attracted much attention because of their good all-around properties. It is well known that the formation of the desired crystallographic texture is critical to obtain good magnetic properties in the preparation process of anisotropic RE-Fe-B magnets. Melt-spun ribbons with nominal compositions of Nd13Fe78Co3B6 and Ce7Cu3 were consolidated into isotropic nanocrystalline NdFeB hot-pressed magnets by the spark plasma sintering technique, and the microstructure and magnetic properties of the magnets were investigated. Results demonstrate that the coercivity of the magnets is enhanced dramatically by the grain boundary diffusion of the low-melting-point CeCu alloy. With 10 wt% CeCu addition, the coercivity increases from 7.5 to 12.44 kOe. The excessive CeCu, however, reduces the remanence of the NdFeB magnets from 7.76 to 5.66 kGs. Moreover, an appropriate holding time effectively improves the magnetic properties of the magnets. The microstructure indicates that the main phase NdFeB with a grain thickness of 20 $\mu \text{m}$ , and the rest are Nd-rich phase and CeCu grain boundary phase. The intergranular phase of CeCu acts as a diffusion channel, which improves the diffusion efficiency and the uniform distribution of elements. The magnets sintered by spark plasma form a special two-zone structure with a coarse grain region and a fine grain region, and the CeCu alloy effectively inhibits the growth of the grains.

The influence of Ce-Co alloy addition and sintering holding time on permanent magnetic properties and microstructure of nanocrystalline Nd-Fe-B bulk alloy were investigated. The coercivity of Nd-Fe-B bulk alloy can be enhanced greatly by more than 100% after adding Ce-Co powders. However, when the concentration of Ce-Co is up to 30 wt%, the density of the magnet can reach the maximum value of 7.58 g/cm3, but the coercivity does not increase significantly. On the other hand, with the increase of holding time to 10 min, the density and coercivity of magnets increase gradually, reaching up to 7.55 g/cm3 and 1134.3 kA/m, respectively. After the addition of Ce-Co alloy, Ce-Co may easily diffuse into the Nd-Fe-B matrix during hot-pressing and under the high pressure and temperature, thus increasing the content of grain boundary phase and the pinning effect of grain boundary, which leads to the increase of coercivity. The extension of the hot-pressing holding time may be more conducive to the diffusion of Ce-Co into the Nd-Fe-B matrix. In addition, the effect of Ce-Co addition on the magnetic properties of Nd-Fe-B with different content of rare earth was also studied. The addition of Ce-Co can effectively increase the coercivity of nanocomposite Nd2Fe14B/α-Fe magnets. The addition of Nb to the parent alloy can further improve the coercivity. For Nd11Fe81.5Nb1Ga0.5B6 alloy with 10 wt% Ce-Co addition, the coercivity can increase from 740.28 to 1098.48 kA/m.

Additional file 1: Scheme S1. Schematic elastration of ROS production mechanism. Figure S1. A, B and C The comparison of standard and extracted Chlorophyll (Chl) and confirm the loading Chl on V2C. Scheme S1: The ROS production mechanism scheme. Figure S2. Zeta potential analysis. Figure S3. A, B and C: The EDX analysis. Figure S4. A, B and C: Loading efficiency of of chlorophyll (Chl) on V2C SNs. (B) temperature increasing (Chl) and (C) oxygen generation measurement with various loading concentration of Chl. Figure S5. A, B: To measure the photothermal conversion efficiency of Chl/V2C NSs. Figure S6. Cell viabilities performance of Chl/V2C for different cell lines. Figure S7. A, B: The intracellular and hypoxia quantification analysis of Chl/V2C. Figure S8. A, B: Biodistribution and) Blood circulation of Mg concentrations measured. Figure S9. Hypoxia analysis of tumours untreated and treated with Chl/V2C SNs. Figure S10. Statistical analysis Ki-67 A and B TUNEL assessment of all the tumors.

Due to the complexity of the anatomical structure for human organs, medical image segmentation is always a challenging computer vision task. The Convolutional Neural Network (CNN) requires a rich feature representation, which not only needs the convolutional layers from shallow to deep,but also requires the resolution from small to large. Although CNN can be used to fuse mid-level features that are employed short-cutting, this just is a simple “shallow” connection. Thus, how to obtain useful features and how to utilize these features to improve the segmentation processes are still the key issues. In this paper, Multi-features Refinement and Aggregation (MRA) makes full use of hierarchical features by using the features fusion on several levels, and reveal the importance of refinement and aggregation of features in the medical image segmentation process. The network get low-level, high-level and even mid-level features by sampling. After aggregation and re-extraction, these features are more effectively combined. Experiment results show that our method can significantly improve segmentation accuracy compared to existing feature fusion schemes. And our approach is generalized to different backbone networks with consistent accuracy gain in brain segmentation, and it sets a new state-of-the-art in the Brat-2015 benchmarks.

In the present work, anisotropic Nd2Fe14B/Sm2Co17 hybrid-bonded magnets were prepared with different Nd-Fe-B contents. It is found that the particle distributions and ratios between the two magnetic phases have important roles in the magnetic properties, microstructures and thermal stability of the magnets. With increase of Nd-Fe-B content, the saturation magnetization of the anisotropic hybrid magnet increases significantly, however, coercivity decreases, and the demagnetization curves show magnetically single-phase behavior. The anisotropic Nd2Fe14B/Sm2Co17 hybrid-bonded magnets exhibit a maximum energy product and remanence of 14.15 MGOe and 99.53 A·m2/kg, respectively, when the Nd-Fe-B content is 70 wt% at room temperature. Furthermore, the hybrid magnets also have better thermal stability at elevated temperatures due to the interaction between the two magnetic particles.

The monodisperse Fe@SiO2 core-shell nanocapsules were synthesised via hydrothermal reaction followed with heat treatment. Nanostructures were characterised by X-ray diffraction, scanning electron microscopy and transmission electron microscopy. The magnetic properties of Fe@SiO2 nanocapsules were evaluated with magnetic property measurement system. The results show that Fe@SiO2 core-shell nanocapsules are highly monodispersed. The silica thickness of Fe@SiO2 nanocapsules increased from 10–20 to 25–35 nm with increasing tetraethyl orthosilicate (TEOS) amount. In the Fe@SiO2 nanocapsules prepared with 900 μl TEOS, as the reaction temperature increases, the mean particle size of Fe@SiO2 nanocapsules increases from 328 to 546 nm. It is remarkable that the saturation magnetisation of Fe@SiO2 nanocapsules decreases with increasing silica thickness. However, the coercivity of nanocapsules has less influence with the variation of silica thickness and particles’ length.

Brain tumor segmentation plays an important role in diagnosing brain tumor. Nowadays, intense interest has been received in applying convolution neural networks in medical image analysis, but its performance is restricted by the limitation of the depth of the network. And how to accelerate the information propagation and make full use of all the hierarchical features in the network is also of vital importance. To address these problems, this paper proposed Deep Residual Dilate Network with Middle Supervision (RDM-Net), which combines the residual network with dilated convolution. It can solve the problem of vanishing gradient and increase the receptive field without reducing the resolution. During images processing, some information of regions of small tumor could be discarded, for its resolution is attenuated to a single pixel by continuously convolutional operations. Therefore, the spatial fusion block, consisting of a pixel discriminator and a region discriminator, has been designed to reserve the detailed information in the region of small tumor. It evaluates the relationship between this single pixel and its adjacent region to obtain the spatial structure information of brain tumors. Furthermore, the middle supervision block consisting of proposal pyramid and multi-hierarchical loss is proposed, which shortens the distance of information path and reduces cumulative errors during the network training. The proposal pyramid is inspired by the idea of boost learning, which fuses each proposal at multiple resolution level to ensure that the network produces better predictions. Multi-hierarchical loss combines the loss of intermediate proposal in the middle layers and the loss of prediction of the output layer to achieve the effect of middle supervision. The results of experiments illustrate that our framework can effectively propagate features of each layer and increase the diversity of information to enhance feature hierarchy for medical image recognition. Compared to other state-of-the-art methods, our framework has performed well in the BRATS2015 challenge. In summary, the main contribution in our paper is that this work is an early attempt to adopt the concept of “middle supervision” on multi-modal brain tumor segmentation.

SmCo7/Co nanocomposite powders were prepared by high-energy ball milling of SmCo5 modified by adding Co and annealed at different temperatures. The phase evolution, microstructure and magnetic properties of the nanocomposite powders were analyzed. The results have shown that the annealing temperature and the addition of Co significantly affected the phase structures and magnetic properties of the powders. The nanocomposite powders were completely composed of the SmCo7 phase and the face-centered cubic Co phase. When annealing the SmCo5 powder at 750 °C, the SmCo7 phase decomposed into Th2Zn17-type Sm2Co17 and CaCu5-type SmCo5 phases with an average grain size of 13.9 nm, and the maximum energy product and remanence had maximum values of 10.51 MGOe and 77.92 emu/g, respectively. However, adding 28 wt% Co effectively prevented growth of the grain (11.5 nm) and enhanced the decomposition temperature (850 °C), resulting in a higher maximum magnetic energy product, coercivity, and remanence. The SmCo7 lattice contracted as Co content increased.

Background: Stable angina pectoris (SAP) is one of the important causes and harbingers of disability and mortality worldwide in the cardiovascular diseases. Acupuncture has been widely applied in the treatment and prevention of cardiovascular diseases in recent years. This systematic review protocol aims to analyze different acupuncture and related therapies to treat SAP, with a view to providing an evidence-based basis for clinical implementation of treatment for patients with SAP. Methods and analysis: The electronic databases of PubMed, EMBASE, The Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Wanfang database, Chinese Science and Technology Periodical Database (VIP), and China Biology Medicine Database (CBM) will be searched from inception to November 2020. The outcome measures were angina attack frequency, ECG changes, angina pain intensity, performance on the Six-Minute Walk Test (6-MWT) and reported adverse events. Study inclusion, data extraction and quality assessment will be performed independently by 2 reviewers. STATA 14.0 will be used to perform pairwise meta-analysis. STATA 13.0 and WinBUGS 1.4.3 will be used to perform pairwise meta-analysis and will be used to conduct network meta-analyses. Results: The results of this review will generate a comprehensive review of current evidence and be published on a peer-reviewed journal. Conclusions: The result of this network meta-analysis is expected to provide a possible ranking for acupuncture treatment methods of stable angina pectoris and offer better options for patients with stable angina pectoris. Ethics and dissemination: Ethical approval is not necessary since this protocol is only for systematic review and does not involve privacy data or conduct an animal experiment. This protocol will be disseminated by a peer-review journal or conference presentation. Trial registration number: INPLASY2020110035.

This paper studies the problem of distributed weighted least-squares (WLS) estimation for an interconnected linear measurement network with additive noise. Two types of measurements are considered: self measurements for individual nodes, and edge measurements for the connecting nodes. Each node in the network carries out distributed estimation by using its own measurement and information transmitted from its neighbours. We study two distributed estimation algorithms: a recently proposed distributed WLS algorithm and the so-called Gaussian Belief Propagation (BP) algorithm. We first establish the equivalence of the two algorithms. We then prove a key result which shows that the information matrix is always generalised diagonally dominant, under some very mild condition. Using these two results and some known convergence properties of the Gaussian BP algorithm, we show that the aforementioned distributed WLS algorithm gives the globally optimal WLS estimate asymptotically. A bound on its convergence rate is also presented., 6 pages

The identification of nerve is difficult as structures of nerves are challenging to image and to detect in ultrasound images. Nevertheless, the nerve identification in ultrasound images is a crucial step to improve performance of regional anesthesia. In this paper, a network called Brachial Plexus Multi-instance Segmentation Network (BPMSegNet) is proposed to identify different tissues (nerves, arteries, veins, muscles) in ultrasound images. The BPMSegNet has three novel modules. The first is the spatial local contrast feature, which computes contrast features at different scales. The second one is the self-attention gate, which reweighs the channels in feature maps by their importance. The third is the addition of a skip concatenation with transposed convolution within a feature pyramid network. The proposed BPMSegNet is evaluated by conducting experiments on our constructed Ultrasound Brachial Plexus Dataset (UBPD). Quantitative experimental results show the proposed network can segment multiple tissues from the ultrasound images with a good performance.

The electronic structure and thermoelectric property of SnSe are calculated by first-principles methods and Boltzmann transport theory. The deformation potential approximation and single parabolic band model are employed to compute the relaxation time. Owing to the accurate electronic structure calculated by Tran-Blaha modified Becke-Johnson (TB-mBJ) potential, the theoretical data (Seebeck coefficient, electrical conductivity and figure of merit ZT) are in good agreement with experimental data. Results show that the uniaxial strain not only affects the electrical structure, but also changes the thermal conductivity. As the uniaxial strain increases, the band gap, density of states of mass ( m dos ) and inertia mass ( m I ) also increases, indicating that the Seebeck coefficient S increases, while the electrical conductivity σ decreases. At carrier concentration of 3 × 10 18 cm−3, 4 × 10 18 cm−3, 5 × 10 18 cm−3, 6 × 10 18 cm−3 and 7 × 10 18 cm−3, the corresponding optimal value of ZT is 1.91, 2.78, 3.51, 4.17 and 4.73, respectively. These results show that the strain not only leads to a lower elastic property, which causes the phonon modes softening and phonon propagation slowing down, but also enhances the conduction band minimum (CBM) convergence, which improves the Seebeck coefficient. This work provides insightful data to understand the enhanced thermoelectric properties of SnSe induced by the strain.

The electronic structure and thermoelectric property of GeTe are calculated by the First-principles calculations and Boltzmann transport theory. The deformation potential approximation and single parabolic band model are employed to compute the relaxation time. Owing to the accurate electronic structure calculated by Tran-Blaha modified Becke-Johnson (TB-mBJ) potential, the theoretical data (Seebeck coefficient, electrical conductivity and ZT) are in good agreement with experimental data. The thermoelectric property of n type is found to be significantly better than that of p type for temperature between 300 K and 800 K and carrier concentration between 1020cm−3 and 1022cm−3. For n type, the largest ZT value of 5.05 could be obtained at 2.5 × 1021cm−3 and 800 K, while for p type, the largest ZT value of 3.32 could be obtained at 7.0 × 1020cm−3 and 800 K. Our results give a valuable theoretical guidance for the high performance of GeTe through n doping.

The aim of this study is to present the Moving Bed Biofilm (MBB) technology as an alternative and successful method to treating different kinds of effluents under different conditions. In the past few years this technology has become more common and widely used in the world because the need for clean water is rapidly increasing as the world’s population grows by each year, so many wastewater treatment facilities are needed to being expanded to provide additional capacity with least possible cost. This review covered the most important processes on MBB such as basic treatment process, kinetics of biofilm, growth and detachment of particles, modeling of MBB and affecting of carrier type and filling ratio. The review also includes many relevant researches carried out at the laboratory and pilot scales.

2 Abstract: This review paper intends to provide an overall vision of biofilm technology as an alternative method for treating waste waters. This technology has been gaining popularity through the years, mainly because many wastewater treatment plants, which are still used Activated Sludge Process (AS) are present some shortcomings when exposed to increased hydraulic and organic loads. Fundamental research into biofilms is presented in three sections, Biofilm Types and Characterization, Advantages and Drawbacks and Design Parameters. The reactor types covered in this review are: un-submerged fixed film systems (trickling filters and rotating biological contactors) and submerged fixed film systems (biological aerated flooded filters, submerged aerated filters, biofilm up-flow sludge blanket, fluidized bed, expanded granular sludge blanket, biofilm airlift suspension, internal circulation, moving bed biofilm and membrane biofilm) reactors.

2 Abstract: A combined runoff-sediment model is used to simulated soil erosion processes and predict soil loss of land surface. The Stanford Watershed Model (SWM) is used as runoff generator for this model. Effects of sensitive parameters errors on soil erosion characteristics are investigated. These characteristics include peak erosion and mean monthly erosion rates. Three different methods are used for the analysis, namely; first-order uncertainty analysis method; direct investigation technique and mean-maximum likelihood method. The aim is to quantify sensitive parameters errors propagation and to gain an appreciation of the approximate magnitudes of model output uncertainty caused by different levels of sensitive parameters uncertainty. Model output uncertainty ranges between (4.000-83.115)% for mean monthly erosion against (4.680-83.098)% for peak erosion. Uncertainty in simulated erosion due to sensitive parameters uncertainty is subsequently analyzed. The probability of peak erosion values occurrence due to sensitive parameters error are investigation. Based on the result obtain, high and moderately parameters are identified. Appropriate conclusion are drawn and suggestion for future work are introduced.