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The magic in the works of Ben Okri is closely related to the influence of Yoruba culture in Nigeria. With his keen observation, rich imagination and inexhaustible creativity, he draws on the experience of African myths and legends, traces its origin to African culture, and presents an African picture with a combination of magic and realism. Based on Africa, Ben Okri portrays the real image of Africa from an objective and intuitive perspective. As the son of Africa, he tells a thought-provoking African story in an enlightening voice of Africa with a sense of responsibility, so as to realize his original intention of writing.

During the era of global warming and highly urbanized development, extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and property. Although with the vast development of numerical simulation of atmosphere, there still exists substantial forecast biases objectively. To predict extreme weather, severe air pollution, and abrupt climate change accurately, the numerical atmospheric model requires not only to simulate meteorology and atmospheric compositions and their impacts simultaneously involving many sophisticated physical and chemical processes but also at high spatiotemporal resolution. Global atmospheric simulation of meteorology and atmospheric compositions simultaneously at spatial resolutions of a few kilometers remains challenging due to its intensive computational and input/output (I/O) requirement. Through multi-dimension-parallelism structuring, aggressive and finer-grained optimizing, manual vectorizing, and parallelized I/O fragmenting, an integrated Atmospheric Model Across Scales (iAMAS) was established on the new Sunway supercomputer platform to significantly increase the computational efficiency and reduce the I/O cost. The global 3-km atmospheric simulation for meteorology with online integrated aerosol feedbacks with iAMAS was scaled to 39,000,000 processor cores and achieved the speed of 0.82 simulation day per hour (SDPH) with routine I/O, which enables us to perform 5-day global weather forecast at 3-km horizontal resolution with online natural aerosol impacts. The results demonstrate the promising future that the increasing of spatial resolution to a few kilometers with online integrated aerosol impacts may significantly improve the global weather forecast.

MongoDB is a NoSQL database which stores the data in form of key-value pairs. It is an open-source, document database which is being used in many data-center applications (e.g. Google, Facebook, etc.) because of its high performance, high availability and automatic scaling. For this kind of data intensive applications, low latency and high throughput are extremely important. However, the existing MongoDB is built upon Boost. Asio, which is a cross-platform C++ library for network and low-level I/O. It can provide a great degree of portability, but at the price of performance due to the limitation of Ethernet network and TCP/IP protocol. This makes MongoDB hard to meet the performance requirements of data intensive applications. The High Performance Computing(HPC) domain has developed high performance networks such as InfiniBand for many years, which provides higher bandwidth and lower latency than Ethernet. These kind of networks also provide advanced features, such as Remote Direct Memory Access(RDMA), to achieve better performance. In this paper, we present a modern RDMA capable design of MongoDB. The performance evaluation on QDR (32 Gbps) shows that our RDMA design achieves 2.84X and 1.93X peak speedup over 1 Gigabit Ethernet (1 GigE) and IP-over-InfiniBand (IPoIB) in all experiments. To the best of our knowledge, this is first MongoDB design utilizing high performance RDMA capable interconnects.

Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi-temporal data encounters a serious problem known as a “dimension disaster”. Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto-encoders (S-SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi-temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S-SAE, a three-hidden-layer S-SAE network was built to reduce the dimensionality and extract effective features to manage the “dimension disaster” caused by excessive scattering parameters, especially for multi-temporal, quad-pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi-temporal Sentinel-1 data for two experimental sites established by the European Space Agency (ESA). The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short-term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi-temporal crop classification methodology in terms of classification accuracy, even with small training ratios.

The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for understanding their exotic properties. Here we present PEPS++, a novel computational paradigm for simulating frustrated magnetic systems and other strongly correlated quantum many-body systems. PEPS++ can accurately solve these models at the extreme scale with low cost and high scalability on modern heterogeneous supercomputers. We implement PEPS++ on Sunway TaihuLight based on a carefully designed tensor computation library for manipulating high-rank tensors and optimize it by invoking various high-performance matrix and tensor operations. By solving a 2D strongly frustrated $J_1$-$J_2$ model with over ten million cores, PEPS++ demonstrates the capability of simulating strongly correlated quantum many-body problems at unprecedented scales with accuracy and time-to-solution far beyond the previous state of the art., Comment: IEEE TPDS (in press)

Frequency domain reflectometry (FDR) is an inexpensive and attractive methodology for repeated measurements of soil water content (θ). Although there are some known measurement limitations for dry soil and sand, a fixed-frequency method is commonly used with commercially available FDR probes. The purpose of our study was to determine if the soil dielectric spectrum could be used to measure changes in θ. A multifrequency FDR probe was constructed with a 6-mm diameter, and a soil dielectric spectrum was obtained. Using the dielectric spectrum, the dielectric dispersion frequency (fd) was determined. It was discovered that changes in fd were highly correlated with changes in θ, and a third-order polynomial equation (R² = 0.96) was developed describing the relationship. The effectiveness of fd for θ measurement was evaluated for three soils and a sand across a range of θ. The effects of soil temperature and soil salinity were also evaluated. Accurate measurements of θ were obtained even in dry soil and sand. The root mean square error of the θ estimated by the fd measurement was 0.021. The soil temperature and soil salinity had no measureable effects on θ determination. The use of fd for θ determination should be an effective and accurate methodology, especially when dry soils, soil temperature, and/or soil salinity could potentially cause problems with the θ measurements.

To improve the measurement accuracy of T-TDR (Thermo-Time Domain Reflectometry) sensors and optimize their structural parameters, we studied the heat conduction process of T-TDR sensors using the two-dimensional soil heat transfer equation. By analyzing the initial boundary value problem of T-TDR probe heat transfer, we simulated the dynamic process of heat transfer and the spatial distribution of the thermal field of the sensor in soils with different ambient temperature, thermal conductivities and volumetric heat capacities, and we determined that the thermocouple junction’s ideal location should be 2 mm below the probe midpoint. Three types of T-TDR sensors were manufactured with designed thermocouple junctions at the probe mid-point or 2 mm below the probe mid-point and a reduced junction volume by using spot welding. Experiments were performed to measure the thermal property curves in sand, sandy loam and clayey loam with five different water contents with these sensors and to calculate the water contents. The measurement accuracy was verified using a drying method. The results indicate that the experimental data were consistent with the numerical simulation results; the water content measurement results of the optimized T-TDR sensors were well correlated with the measurements obtained with the drying method (R 2 = 0.981, RMSE = 0.0152), indicating improvement in the sensor measurement accuracy compared with sensors using the conventional structural parameters. The research method and results of this paper provided guidance for the optimization of the T-TDR sensor parameters.

The theoretical relationship between several hydraulic performance parameters of variable-rate contour-controlled sprinklers for high uniformity precision irrigation was researched. The operational equation that describes the internal connection of flow rate, rotating speed and throw distance of the variable-rate contour-controlled sprinkler was derived using mathematical theory of limitation and double integral. The derived operational equation indicates that the flow rate of the variable-rate contour-controlled sprinkler is proportional to the product of rotating speed and square throw distance. The square wetted area sprinklers were used to illustrate the application of the operational equation of the variable-rate contour-controlled sprinkler. The theoretical throw distance equation for the square wetted area sprinkler was built. With the operational equation and theoretical throw distance equation, the theoretical flow rate and rotating speed equations of the square wetted area sprinkler were derived. The results of this research provide fundamental principles for the design of variable-rate contour-controlled sprinklers and square wetted area sprinklers.

An evaluation method, with accompanying software, was developed to precisely calculate uniformity from catch-can test data, assuming sprinkler distribution data to be a continuous variable. This has value in assessing application uniformity of sprinkler irrigation designs. Two interpolation steps are required to compute unknown water application depths at grid distribution points from radial distribution of catch-cans’ data: using both radial and peripheral interpolations. Interpolation by cubic splines was used to give accurately interpolated values. In theory, this method has higher accuracy compared with conventional methods to analyze catch-can data. Water application depths were calculated at each grid point and uniformity coefficients were computed from the grid distribution maps of water application depths.