Potential predictability of seasonal extreme precipitation accumulation in China

Abstract The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960–2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.</jats:p

A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.Comment: To Appear in Proceedings of the IEE

Deep learning based lithology classification using dual-frequency Pol-SAR data

ABSTRACT: Lithology classification is a crucial step in the prospecting process, and polarimetric synthetic aperture radar (Pol-SAR) imagery has been extensively used for it. However, despite significant improvements in both information content of Pol-SAR imagery and advanced classification approaches, lithology classification using Pol-SAR data may not provide satisfactory classification accuracy due to high similarity of certain classes. In this paper, a novel Pol-SAR lithology classification method based on a stacked sparse autoencoder (SSAE) is proposed. By using superpixel segmentation, new features can be extracted from dual-frequency Pol-SAR data, which can increase the class separability of the input data. Then, these features and the coherency matrices are incorporated into SSAE to classify the lithology. The classification performance is evaluated on an SIR-C dataset acquired over Xinjiang, China. The experimental result shows that this method is effective for lithology classification and can improve the overall accuracy up to 98.90%

Single-cell genetic models to evaluate orphan gene function: The case of QQS regulating carbon and nitrogen allocation

We demonstrate two synthetic single-cell systems that can be used to better understand how the acquisition of an orphan gene can affect complex phenotypes. The Arabidopsis orphan gene, Qua-Quine Starch (QQS) has been identified as a regulator of carbon (C) and nitrogen (N) partitioning across multiple plant species. QQS modulates this important biotechnological trait by replacing NF-YB (Nuclear Factor Y, subunit B) in its interaction with NF-YC. In this study, we expand on these prior findings by developing Chlamydomonas reinhardtii and Saccharomyces cerevisiae strains, to refactor the functional interactions between QQS and NF-Y subunits to affect modulations in C and N allocation. Expression of QQS in C. reinhardtii modulates C (i.e., starch) and N (i.e., protein) allocation by affecting interactions between NF-YC and NF-YB subunits. Studies in S. cerevisiae revealed similar functional interactions between QQS and the NF-YC homolog (HAP5), modulating C (i.e., glycogen) and N (i.e., protein) allocation. However, in S. cerevisiae both the NF-YA (HAP2) and NF-YB (HAP3) homologs appear to have redundant functions to enable QQS and HAP5 to affect C and N allocation. The genetically tractable systems that developed herein exhibit the plasticity to modulate highly complex phenotypes

Finite-Time Distributed Cooperative Guidance Law with Impact Angle Constraint

The problem of the distributed cooperative guidance law of multiple missiles attacking a stationary target with impact angle constraint is investigated. A distributed cooperative guidance law, which consists of a nonsingular terminal sliding mode component for ensuring finite time convergence to the desired LOS angle and a coordination component for realizing finite time consensus of time-to-go estimates, is proposed. Analysis shows that the guidance law designed in this study can ensure that missiles’ time-to-go estimates represent real times to go once all the missiles fly along the desired LOS. Therefore, simultaneous arrival can be guaranteed. Furthermore, it is modified to accommodate the communication failure cases. Compared with existing results, this guidance law owns faster convergence rate and can satisfy large impact angles. Numerical simulations are performed to demonstrate the effectiveness of the proposed guidance law

Direct Printing of Stretchable Elastomers for Highly Sensitive Capillary Pressure Sensors

We demonstrate the successful fabrication of highly sensitive capillary pressure sensors using an innovative 3D printing method. Unlike conventional capacitive pressure sensors where the capacitance changes were due to the pressure-induced interspace variations between the parallel plate electrodes, in our capillary sensors the capacitance was determined by the extrusion and extraction of liquid medium and consequent changes of dielectric constants. Significant pressure sensitivity advances up to 547.9 KPa−1 were achieved. Moreover, we suggest that our innovative capillary pressure sensors can adopt a wide range of liquid mediums, such as ethanol, deionized water, and their mixtures. The devices also showed stable performances upon repeated pressing cycles. The direct and versatile printing method combined with the significant performance advances are expected to find important applications in future stretchable and wearable electronics

Research on product appearance patent spatial shape recognition for multi-image feature fusion

In the process of patent retrieval, the traditional content-based single image retrieval method mainly has the following two reasons: a) semantic deviation caused by text description, b) the similarity of a single pixel in the image is high but the whole is inconsistent. Low accuracy leads to unsatisfactory retrieval results, which makes it difficult to obtain product design information timely and effectively and reduces design efficiency. How to obtain data quickly and accurately has become a challenging problem. In this paper, by analyzing the problems existing in Locarno classification method, combined with the characteristics of the patent image, a new improved method is proposed. Firstly, the structural features of product parts are extracted through segmentation. Subsequently, combine it with multi-view image fusion to determine the spatial shape of product parts jointly. Finally, the spatial shape of key structures is confirmed to refine the specific search range as well as improve the search accuracy. The feasibility and effectiveness of the proposed method are verified by taking a shower appearance patent as an example

Development Overview of Flight Loads

Flight load design is an important part of aircraft design. It is a bridge connecting the general aerodynamic design and structure stress design. In this paper, the development of flight load design is reviewed including the specifications to be followed in flight load design, the design method of flight load, the selection of severe load, and the verification of flight load. The development direction in the future is looked forward

Effect of Mg Addition on Inclusions in the Welding Heat-Affected Zone of Pressure Vessel Steels

With the development of the pressure vessel industry, high-energy wire welding has a great future. However, this means higher demands on the weldability of pressure vessel steels. Controlling inclusions via oxidative metallurgy is a reliable method of improving the weldability of pressure vessel steels. Hence, in this paper, experimental steels with different Mg element mass fractions were prepared using vacuum metallurgy. Simulated welding for high-heat input welding was carried out using the Gleeble-2000 welding thermal simulation test machine. The inclusions in the welding heat-affected zone (HAZ) in the experimental steels were observed using an optical microscope (OM) and scanning electron microscope (SEM). The compositions of the inclusions were analyzed using an energy-dispersive spectrometer (EDS). The research results indicated that the addition of Mg could increase the number density of the inclusions in the welding HAZ. With the addition of Mg from 0 to 5 wt.%, the total number density of the inclusions increased from 133 to 687 pieces/mm2, and the number density of the inclusions with a size of 0–5 μm2 increased from 122 to 579 pieces/mm2. The inclusions in the experimental steel welding HAZ with Mg elements were mainly elliptical composite inclusions composed of (Mg-Zr-O) + MnS. Moreover, MnS precipitated on the surface of the Mg-containing inclusions in the welding HAZ. Intragranular acicular ferrite (IAF) nucleation was primarily induced via the minimum lattice mismatch mechanism, supplemented with stress-strain energy and inert interface energy mechanisms