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Photoelectrochemical (PEC) water-splitting using solar energy holds great promise for the renewable energy future, and a key challenge in the development of industry viable PEC devices is the unavailability of high-efficient photoanodes. Herein, we designed a TiO2 model photocatalyst with nano-groove pattern and different surface orientation using low-energy Ar+ irradiation and photoetching of TiO2, and significantly improved the intrinsic activity for PEC water oxidation. High-resolution transmission electron microscopy directly manifests that the grooves consist of highly stepped surface with steps and well-crystallized. Transient absorption spectroscopy reveals the groove surface that allows for increased recovery lifetime, which ensures promoted electron-hole separation efficiency. Surface photovoltage directly shows the carrier separation and transportation behaviors, verified by selective photodeposition, demonstrating the groove surface on TiO2 contributes to electron-hole separation. This work proposes an efficient and scalable photoanode strategy, which potentially can open new opportunities for achieving efficient PEC water oxidation performance.

Automated detection of cervical cancer cells/clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently there are emerging deep learning-based methods which train Convolutional Neural Networks (CNN) to classify cell patches or to detect cells from the whole image. But the former is computationally expensive, while the latter often requires a large-scale dataset with expensive annotations. In this paper we propose an efficient cervical cancer cells/clumps detection method, called Comparison detector, to deal with the limited data problem. Specifically, we utilize the state-of-the-art proposal-based object detection method, Faster R-CNN with Feature Pyramid Network (FPN) as the baseline and replace the classification of each proposal by comparing it with the prototype representation of each category. In addition, we propose to learn the prototype representation of the background category from data instead of manually choosing them by some heuristic rules. Experimental results show that the proposed Comparison detector yields significant improvement on the small dataset, achieving a mean Average Precision (mAP) of 26.3% and an Average Recall (AR) of 35.7%, both improved by about 20% comparing to the baseline. Moreover, when training on the medium-sized dataset, our Comparison detector gains a mAP of 48.8% and an AR of 64.0%, improving the AR by 5.1% and the mAP by 3.6% respectively. Our method is promising for the development of automation-assisted cervical cancer screening systems. Code and datasets are available at https://github.com/kuku-sichuan/ComparisonDetector .

Quantum key distribution (QKD)1,2 offers a long-term solution to secure key exchange. Due to photon loss in transmission, it was believed that the repeaterless key rate is bounded by a linear function of the transmittance, O(η) (refs. 3,4), limiting the maximal secure transmission distance5,6. Recently, a novel type of QKD scheme has been shown to beat the linear bound and achieve a key rate performance of $$O(\sqrt{\eta })$$ (refs. 7–9). Here, by employing the laser injection technique and the phase post-compensation method, we match the modes of two independent lasers and overcome the phase fluctuation. As a result, the key rate surpasses the linear bound via 302 km and 402 km commercial-fibre channels, over four orders of magnitude higher than existing results5. Furthermore, our system yields a secret key rate of 0.118 bps with a 502 km ultralow-loss fibre. This new type of QKD pushes forward long-distance quantum communication for the future quantum internet. Phase-matching quantum key distribution is implemented with a 502 km ultralow-loss optical fibre. The fluctuations of the laser initial phases and frequencies are suppressed by the laser injection technique and the phase post-compensation method.

A novel and effective method to co-extrude metallic alloys is described which named Direct Extrusion and Bending-Shear Deformation. The compound extrusion plates have cracked at 290 °C and 3 mm/s. According to this phenomenon, a model was built to investigate the crack generation and development between the 6061 Al and AZ31 Mg alloy during the compound extrusion process by DEFORM-3D. The cracking behavior of the Mg/Al composite rod with a soft Mg AZ31 core and a hard Al 6061 sleeve were systematically studied to disclose the influence of microstructure on crack in the different regions. The simulation results show that the distribution of strain and velocity has significant differences due to the influence of dies structure and material properties at different locations in the same region. The experimental results show that in the same conditions, there are differences in recrystallization and texture weakening of AZ31 Mg alloys and 6061 Al alloy, which are important factors for the formation of crack. Both the Mg layer and the Al layer have a homogeneous microstructure in the region d. Keywords: AZ31 Mg alloy, 6061 Al alloy, Compound extrusion, Crack, EBSD

2,5-Diethyl-1,4-benzenedinitrile N-oxide was designed and synthesized to achieve room temperature curing of hydroxyl-terminated polybutadiene binders. The structures and properties of the four cured elastomers having different content of curing agent were investigated. The results showed that the isoxazoline rings from the reaction of nitrile N-oxide with cis-alkene are observed in the four cured elastomers. The results also revealed that the hydroxyl-terminated polybutadiene with the lowest content of curing agent showed the lowest cross-linking density and stress at break. The proper activity and steric hindrance of 2,5‑diethyl-1,4-benzenedinitrile N-oxide gave better mechanical properties and hydrophobic properties of the cured elastomers than nitrile N-oxide prepared by our previous work.

A magnesium alloy, Mg-8Al-0.5Zn-0.2Ag-0.2Mn (AQ80), was extruded by forward extrusion and twist deformation (FETD) at 370°C with 0% and 6% pre-compression. The effect of pre-compression on microstructure and texture of the AQ80 Mg alloy during FETD was systematically investigated. Compared with the 0% pre-compression FETD-ed sample, the 6% pre-compression FETD-ed sample shows a more uniform and finer microstructure and a weaker texture. The effects of pre-twinning on the microstructure and texture during FETD were also investigated. It was found that many {10–12} tension twins were implanted into the as-cast AQ80 Mg alloy by the 6% pre-compression treatment, providing sufficient nucleation sites for subsequent dynamic recrystallization. Combined with microstructure evolution analysis, it can be concluded that the texture development is strongly influenced by the twinning and basal slip during FETD extrusion.

In this paper, we present a fast yet effective method for pixel-level scale-invariant image fusion in spatial domain based on the scale-space theory. Specifically, we propose a scale-invariant structure saliency selection scheme based on the difference-of-Gaussian (DoG) pyramid of images to build the weights or activity map. Due to the scale-invariant structure saliency selection, our method can keep both details of small size objects and the integrity information of large size objects in images. In addition, our method is very efficient since there are no complex operation involved and easy to be implemented and therefore can be used for fast high resolution images fusion. Experimental results demonstrate the proposed method yields competitive or even better results comparing to state-of-the-art image fusion methods both in terms of visual quality and objective evaluation metrics. Furthermore, the proposed method is very fast and can be used to fuse the high resolution images in real-time. Code is available at https://github.com/yiqingmy/Fusion.

The AZ31 Mg alloys were processed by 6% pre-compression and frustum shearing extrusion at various temperatures, and the microstructure, texture and mechanical properties of the resulting alloys are systematically investigated. The results show that the grain size monotonically increases from 6.4 to 12.6 μm and the texture intensity increases from 6.7 to 9.6 with the increase in the extrusion temperature. The combining effect of the pre-twinning and the frustum shearing deformation is found to contribute to the development of the weak basal texture in Mg alloys. The Mg alloy sheet produced at the extrusion temperature of 563 K exhibits excellent mechanical properties. The yield strength, ultimate tensile strength and elongation for the extruded alloys are 189.6 MPa, 288.4 MPa and 24.9%, respectively. Such improved mechanical properties are comparable or even superior to those of the alloys subjected to other deformation techniques, rendering the pre-compression and frustum shearing extrusion a promising way for further tailoring properties of Mg alloys.

Color variations of histological images due to multi-factor hinder the performance of computer-aided diagnosis (CAD) systems. Previous stain normalization methods have achieved excellent results. While in practice, a multidomain stain normalization method is still be needed when more than two color variations exist in dataset. In this paper, we propose a multidomain stain normalization model inspired by MUNIT [1], with the idea of disentangling the representations of content and style. We assume that the latent space of histological images can be decomposed into domain-shared content space and domain-specific style space. The stain normalization aims to transfer the styles cross domains and maintain the contents. In addition, we propose to use the earth mover’s distance(EMD) to evaluate the effectiveness of stain normalization. We evaluate our approach against the state-of-the-art methods quantitatively and qualitatively.

During the process of whole slide imaging, it is necessary to focus thousands of fields of view to obtain a high-quality image. To make the focusing procedure efficient and effective, we propose a novel autofocus algorithm for whole slide imaging. It is based on convolution and recurrent neural networks to predict the out-of-focus distance and subsequently update the focus location of the camera lens in an iterative manner. More specifically, we train a convolution neural network to extract focus information in the form of a focus feature vector. In order to make the prediction more accurate, we apply a recurrent neural network to combine focus information from previous search iteration and current search iteration to form a feature aggregation vector. This vector contains more focus information than the previous one and is subsequently used to predict the out-of-focus distance. Our experiments indicate that our proposed autofocus algorithm is able to rapidly determine the optimal in-focus image. The code is available at https://github.com/hezhujun/autofocus-rnn.

While automation-assisted reading system can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extraction. This paper presents an efficient and totally segmentation-free method for automated cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature. Specifically, we use the state-of-the-art CNN-based object detection methods, YOLOv3, as our baseline model. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade an additional task-specific classifier. We also investigate the presence of unreliable annotations and coped with them by smoothing the distribution of noisy labels. We comprehensively evaluate our methods on our test set which is consisted of 1014 annotated cervical cell images with size of 4000 × 3000 and complex cellular situation corresponding to 10 categories. Our model achieves 97.5% sensitivity (Sens) and 67.8% specificity (Spec) on cervical cell image-level screening. Moreover, we obtain a best mean average precision (mAP) of 63.4% on cervical cell-level diagnosis, and improve the average precision (AP) of hard examples which are the most valuable but most difficult to distinguish. Our automation-assisted cervical cell reading system not only achieves cervical cell image-level classification but also provides more detailed location and category reference information of abnormal cells. The results indicate feasible performance of our method, together with the efficiency and robustness, providing a new idea for future development of computer-assisted reading systems in clinical cervical screening.

A series of ZrN-Ag nano-composite films were deposited using the RF magnetron sputtering system. The microstructure, mechanical properties and tribological performances were investigated. The results showed that ZrN-Ag films were composed of face-centered cubic (fcc)-ZrN and face-centered cubic (fcc)-Ag. With the increase of Ag content, the hardness of ZrN-Ag composite film increased rapidly and then decreased rapidly. The maximum hardness value was 22.8 GPa at 6.1at.% Ag. At room temperature, the coefficient of friction (Cof.) of ZrN-Ag films were lower than the ZrN film. During 25-500°C, the Cof. of ZrN-Ag films at 29.16 at.% Ag were lower than ZrN film, while the wear rate were higher than the ZrN film. In summary, the addition of Ag improved the hardness, and decreased the Cof. of the ZrN-Ag during 25-500°C.

Chlorhexidine (CHX)/β-cyclodextrin (β-CD) inclusion and chitosan nanoparticles containing bovine serum albumin (CS/BSA) were blended into chitosan acetic acid solution respectively or simultaneously to prepare a novel thermosensitive hydrogel dual sustained-release system. Rheological properties, release, cytocompatibility, and antibacterial activity were investigated. The results confirmed that CHX/β-CD and CS/BSA were well dispersed in the solution, and the rheological property of hydrogel was not affected. The system exhibited promising dual sustained release and cytocompatibility. Antibacterial results showed that CHX-loaded hydrogel had a desirable therapeutic effect on Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli).

This paper presents a dynamic trajectory planning scheme for passing through singular orientations of a three degree-of-freedom (DOF) rotational cable-driven parallel robot (CDPR) that uses a rigid link to restrict its translational DOFs. The kinematics, dynamics and actuation singularity loci of this CDPR are analyzed. Singularity loci divide the workspace into four independent parts, which significantly limit the motion performance of the mechanism. To this end, consistency conditions that can allow the robot to steadily pass through singular orientations are studied. The vector inner product form of consistency conditions is derived, which can simplify the determination of the conditions of a more complex parallel robot. A sequence of target orientations are reached by planning dynamic trajectories using the unit quaternion. A modified spherical linear interpolation with a fifth degree polynomial as the time function is used for the generation of a trajectory which does not contain a singular orientation. Moreover, a transition segment using a seventh degree polynomial is designed to merge into the fifth degree polynomial in order to satisfy the consistency conditions and pass through singular orientations. Simulations are provided to verify the proposed technique.

The CaO–B2O3–SiO2 (CBS) glasses with addition of xLa2O3 (0 ≤ x ≤ 20 wt%) were prepared by conventional quenching method, the glass structure, phase transformation and microware dielectric properties were studied. The glass structure analysis by magic angle spinning nuclear magnetic resonance (MAS-NMR) and infrared (IR) spectroscopy indicated that [BO4] transformed into [BO3] and the proportion of Si–O–Si bridge oxygen bond in [SiO4] tetrahedrons decreased obviously with the addition of La2O3. The differential thermal analysis (DTA) revealed that the CBS glass system kept better glass-forming ability and the transition temperature (T g) decreased from 743 to 724 °C after addition of La2O3. As the content of La2O3 increases, the crystallization tendency of CaB2O4 is suppressed, while that for Ca2SiO4 is promoted. With appropriate content of La2O3 (5 wt%), the sample possess excellent dielectric properties (e r ≈ 4.1, tanδ ≈ 1.7 × 10−3 at 15 GHz). The variations of dielectric properties are believed to be appreciably associated with the relative content of the phases present.

A dual-time varying delay complex network system is formed by a plurality of sub-networks. This paper discusses the crossed synchronization stability of such systems on the basis of crossed synchronization definition between the sub-nets. By the Lyapunov stability theory with the Zero Theorem, we obtained a sufficient condition that a synchronization exponentially stable controller exists in the complex network system, with characteristic variable delay. The relationship between the complex network nodes are discussed, and the two nodes must be connected and can interact with each other so it has the actual coupling strength. Finally, combining the given conditions, a numerical simulation illustrates its effectiveness.

The aim of the present work is to investigate the effects of SiO2 content on the structure of CaO–B2O3–SiO2–Al2O3–ZnO glass using the MAS-NMR, the fourier transform infrared spectrometer and differential scanning calorimetry (DSC). The results showed that the majority of Al existed in fourfold coordination and Zn acted as glass modifier in glass structure. With the increasing of SiO2 content, the relative amount of BIII units decreased while the BIV units increased and the proportion of Si–O–Si bridge oxygen bond in [SiO4] tetrahedrons increased obviously. The DSC curves revealed that the glass transition temperature increased from 732 to 773 °C with the increasing of SiO2 content, indicating that SiO2 generating more bridge oxygen can increase the degree of polymerization of glass structure.

The aim of the present work is to investigate the effects of Al2O3 content on both the structure and crystallization behavior of CaO–B2O3–SiO2 (CBS) glass composition using NMR, FT-IR and DSC. The density experimental results showed that the density (ρ) of glasses decreased linearly with increasing Al2O3 content, while the molar volume (Vm) of the glasses increased linearly. The NMR revealed that the Al occurred in the forms of AlIV, AlV and AlVI units. With the increasing Al2O3 content, the amount of Al species show little change, the relative amount of BIIIa and BIIIs units increases while the BIV units decrease, and the Q4 structural units increases clearly but the Q3 decreases. Further, crystallization peaks of CBS glasses shift to higher temperatures, indicating that Al2O3 addition can suppress crystallization of CBS glasses.

Ce-based materials have gained considerable prominence in catalysis as a type of the most valuable material for various applications ranging from automotive emission control to CO2 and water splitting. We here report a novel type-II CeO2/CeF3 composite, composed of two paradigms of Ce-based binary compounds, prepared by a facile one-step in-situ hydrothermal method, and explore its potential as a photocatalyst for solar energy conversion. Primarily, the morphologies, particle size and microstructures of CeO2/CeF3 composite can be modulated by the volume ratio of ethanol to water in the precursor solution. The photocatalytic activities firstly increase to a maximum and then decrease with the increased amount of ethanol. The hybrid CeO2/CeF3 particles with large size and polyhedron morphology exhibit superior photocatalytic activity, having a high rate constant k of MB decomposition about 7.6 times faster than that of the lowest one. A multiple synergetic mechanism in CeO2/CeF3 composite, which is featured by obvious promoted separation of photogenerated electrons and holes at the type-II heterojunction interfaces and in particular between the different facets of microcrystals, and the highly reactive facets exposed, has been proposed to account for its distinguished photocatalytic activity. This work provides some new insights for the synthesis and optimization of novel rare earth Ce-based composite photocatalyst with high photocatalytic performance.

The Ba2+ doped ceramics of Ca1−xBaxCu3Ti4O12 (x = 0.005, 0.01, 0.03, 0.05 and 0.1) were sintered via conventional solid-state reaction at 1050 °C for 8 h in air. The microstructure and electric properties of Ba2+ doping ceramics were investigated. The lattice parameters of Ca1−xBaxCu3Ti4O12 (CBCTO) ceramics decreased with the Ba2+ doping, and Ba1.31Ti8O16 as secondary phase was confirmed at x = 0.1. The doping of Ba2+ resulted in uniformly distribution of small size grain (~4 μm). All the doped samples showed lower value of dielectric constant than CaCu3Ti4O12 (CCTO), due to low resistivity of the grain boundary with the Ba2+ doped samples. The nonlinear coefficient and breakdown field were significantly enhanced by Ba2+ doping.

An ensemble method based on supervised learning for segmenting the retinal vessels in color fundus images is proposed on the basis of previous work of Zhu et al. For each pixel, a 36 dimensional feature vector is extracted, including local features, morphological transformation with multi-scale and multi-orientation, and divergence of vector field which is firstly used to extract feature of retinal image pixels. Then the feature vector is used as input data set to train the weak classifiers by the Classification and regression tree (CART). Finally, an AdaBoost classifier is constructed by iteratively training for the retinal vessels segmentation. The experimental results on the public Digital retinal images for vessel extraction (DRIVE) database demonstrate that the proposed method is efficient and robust on the fundus images with lesions when compared with the other methods. Meanwhile, the proposed method also exhibits high robustness on a new Retinal images for screening (RIS) database. The average accuracy, sensitivity, and specificity of improved method are 0.9535, 0.8319 and 0.9607, respectively. It has potential applications for computer-aided diagnosis and disease screening.

In recent years, how to use renewable energy to reduce the energy cost of internet data center (IDC) has been an urgent problem to be solved. More and more solutions are beginning to consider machine learning, but many of the existing methods need to take advantage of some future information, which is difficult to obtain in the actual operation process. In this paper, we focus on reducing the energy cost of IDC by controlling the energy flow of renewable energy without any future information. we propose an efficient energy dynamic control algorithm based on the theory of reinforcement learning, which approximates the optimal solution by learning the feedback of historical control decisions. For the purpose of avoiding overestimation, improving the convergence ability of the algorithm, we use the double [Formula: see text]-method to further optimize. The extensive experimental results show that our algorithm can on average save the energy cost by 18.3% and reduce the rate of grid intervention by 26.2% compared with other algorithms, and thus has good application prospects.

Diabetic retinopathy (DR) is one of the leading cause of blindness, but the classification of DR requires experienced ophthalmologist to distinguish the presence of various small features, which is time-consuming and difficult. Convolution neural network (CNN), which enables learning hierarchical and discriminative features without experiences of clinicians, is an alternative method to address the above issue. In this paper, we investigate four factors of employing deep CNN to DR classification problem, including network architectures, preprocessing, class imbalance and fine-tuning. The best performance we achieved is an accuracy of 79% opposed to an accuracy of 75% for Harry's scheme [14].

Lead-free (1−x)(Ba 0.85 Ca 0.15 )(Ti 0.9 Zr 0.1 )O 3 -xBa(Mg 1/3 Nb 2/3 )O 3 (BCZT-BMN) (x=0.05, 0.10, 0.15, 0.20) ceramics were fabricated by conventional solid-state reaction. The effect of Ba(Mg 1/3 Nb 2/3 )O 3 (BMN) addition on structure and electrical properties of BCZT ceramics were studied. The results show that the BCZT-BMN ceramics possess rhombohedral phase at x=0.05, orthorhombic and rhombohedral phases coexist at x=0.10, 0.15, 0.20. High Curie temperature (T c ) maintained over 300 °C at the compositions x=0.05 and 0.10. The samples at x=0.05 exhibit good ferroelectric properties. This work offers an effective way to adjust the range of service temperature of BCZT by BMN addition.

With the advent of cloud computing, renewable energy is integrated into data center power supply systems increasingly. The power statistics collection may not be available due to the instability of renewable energy, which results in incomplete data. The incomplete energy data will significantly disturb the management of data centers. We further propose a filling algorithm based on complete tolerance class. The algorithm expands the traditional tolerance relation, and fills the missing values of the energy data, which ensures the data integrity. By taking good advantage of in-Memory Computing, We further parallelize and optimize our algorithm using Spark. The experiment results demonstrate that our algorithm outperforms other general filling algorithms in terms of filling accuracy. The proposed algorithm also shows good performance as the missing rate rises up.

Capillary nonperfusion (CNP), which is one of characteristic features in diabetic retinopathy patients, is an important judgement of the appearance of retinal diseases. This paper presents a novel, fast and hybrid method to detect CNP. This model combines region-based active contour model(ACM), Graph-Cut and Fuzzy Possibilistic C-Means(FPCM). We modify ACM with a new energy function to reduce suspicious pixels, and employ Graph-Cut to replace the level set method to get a faster arithmetic speed, which also make our method insensitive to the initial contour. And FPCM is applied to average grey levels over different clusters instead of the grey levels of the pixel in order to alleviate the influence caused by noise and capillaries. Experimental results demonstrate that the proposed method outperforms other ACM methods in detecting CNP with better accuracy and sensitivity.

Nowadays, humanoids are increasingly expected acting in the real world to complete some high-level tasks humanly and intelligently. However, this is a hard issue due to that the real world is always extremely complicated and full of miscellaneous variations. As a consequence, for a real-world-acting robot, precisely perceiving the environmental changes might be an essential premise. Unlike human being, humanoid robot usually turns out to be with much less sensors to get enough information from the real world, which further leads the environmental perception problem to be more challenging. Although it can be tackled by establishing direct sensory mappings or adopting probabilistic filtering methods, the nonlinearity and uncertainty caused by both the complexity of the environment and the high degree of freedom of the robots will result in tough modeling difficulties. In our study, with the Gaussian process regression framework, an alternative learning approach to address such a modeling problem is proposed and discussed. Meanwhile, to debase the influence derived from limited sensors, the idea of fusing multiple sensory information is also involved. To evaluate the effectiveness, with two representative environment changing tasks, that is, suffering unknown external pushing and suddenly encountering sloped terrains, the proposed approach is applied to a humanoid, which is only equipped with a three-axis gyroscope and a three-axis accelerometer. Experimental results reveal that the proposed Gaussian process regression-based approach is effective in coping with the nonlinearity and uncertainty of the humanoid environmental perception problem. Further, a humanoid balancing controller is developed, which takes the output of the Gaussian process regression-based environmental perception as the seed to activate the corresponding balancing strategy. Both simulated and hardware experiments consistently show that our approach is valuable and leads to a good base for achieving a successful balancing controller for humanoid.

One of the fundamental problems in crowdsourcing is the trade-off between the number of the workers needed for high-accuracy aggregation and the budget to pay. For saving budget, it is important to ensure high quality of the crowd-sourced labels, hence the total cost on label collection will be reduced. Since the self-confidence of the workers often has a close relationship with their abilities, a possible way for quality control is to request the workers to return the labels only when they feel confident, by means of providing unsure option to them. On the other hand, allowing workers to choose unsure option also leads to the potential danger of budget waste. In this work, we propose the analysis towards understanding when providing the unsure option indeed leads to significant cost reduction, as well as how the confidence threshold is set. We also propose an online mechanism, which is alternative for threshold selection when the estimation of the crowd ability distribution is difficult., Comment: 25 pages, 1 figures

Beijing is located on the North China Plain with five rivers, which belong to the Hai River Basin. Its continental monsoon climate with uneven precipitation distribution is extreme likely lead to serious urban flood disasters. According to the disaster results, urban storm flood in Beijing can be classified into four types. Here typical extreme storm flood events and their characteristics in Beijing were analyzed in detail. It showed that storm flood events in recently decades had a trend, which centered in a relatively small area with high intensity and short duration. The main reasons of urban storm flood disaster were urbanization and basic facilities with low flood and drainage standard. Urbanization means land utilization significantly altering hydraulic processes, and extreme storm can easily exceed those facilities capacity. In order to deal with urban storm flood, Beijing government have taken four measurements, which were upgrading and reconstruction of rainwater pumping stations, improving projects of small and medium rivers, building sponge city, and implementing the West Suburb Storm-water Regulation Project. In addition, the flood warning and emergency management system has been established. Furthermore, some measurements were pointed to be done in the future, including improvement of the flood control management system, improvement of flood control plans, strengthening flood warning system, and strengthening social management and public awareness of flood prevention. With these improvements of management and engineering measurements, it can be more secure under intensive storms in Beijing. These experiences of flood control in Beijing can provide references for other cities.

It was found previously by us that the compositions of industrial alloy specializations are related to the chemical short-range ordering in solid solution alloys, which is in accordance with the cluster-plus-glue-atom model. This model identifies short-range-ordered chemical building units in solid solutions, which the specific alloy compositions rely on. For instance, substitutional-type FCC solid solution alloys are described by cluster-based units formulated as [cluster](glue atom)16, where the bracketed cluster is the nearest-neighbor coordination polyhedral cluster, cuboctahedron in this case, and one-to-six glue atoms occupy the inter-cluster sites at the outer-shell of the cluster. In the present paper, we investigate the atomic configurations of these local units in substitutional-type FCC solid solutions by exhausting all possible cluster packing geometries and relevant cluster formulas. The structural model of stable FCC solid solutions is first reviewed. Then, solute distribution configurations in FCC lattice are analyzed by idealizing the measured chemical short-range orders within the first and second neighborhoods. Two key assumptions are made with regards to the cluster distribution in FCC lattice. First, the clusters are isolated to avoid the short-range orders from extending to longer range ones. Second, the clusters are at most separated by one glue atom to confine the inter-cluster distances. Accordingly, only a few structural unit packing modes are identified. Among them, the configurations with glue atoms 0, 1, 3, and 6 show good homogeneities which indicate special structural stabilities. Finally, compositions of FCC Cu-Zn (representative of negative enthalpy systems) and Cu-Ni (positive enthalpy ones) industrial alloys are explained by using the structure units of cluster packing and the cluster formulas, expressed as [Zn-Cu12]Zn1-6 and [Zn-Cu12](Cu, Zn)6, where the cluster is Zn-centered, shelled with Cu atoms, and glued with one to six Zn or with a mixture of six Cu and Zn. In particular, the formula [Zn-Cu12]Zn6, with the highest Zn content, corresponds to the solubility limit in Cu-Zn alpha phase zone, which is also the composition of the specification C27400. The Cu-rich Cu-Ni alloys are explained by cluster formulas [Cu-Cu12](Cu, Ni) 6, where the cluster is Cu centered and glued with a mixture of six Cu and Ni. The Ni-rich Monel alloy is explained by cluster formulas [Ni-Ni12](Cu5Ni)-[Ni-Ni12]Ni6. The present work provides a new approach to alloy composition explanation and eventually to alloy composition design from the perspective of short-range ordering in solid solutions.