10Be和26Ai揭示的合黎山西南部侵蚀速率初步研究

地表侵蚀速率是衡量地貌演化的一个重要因子。本研究利用原地宇宙成因核素 10Be 和 26Al 对合黎山西南部地表岩石侵蚀速率进行了首次测定。结果显示：约 30 ka 以来，合黎山西南部的地表岩石侵速率约为 24 mm∙ka-1。这一结果与已见报道的其他基岩侵蚀速率值一致。这一结果与 Small et al 获得的非干旱地区的基岩侵蚀速率也基本一致，但是显著高于干旱的南极地区和半干旱的澳大利亚。10Be 和26Al 获得的侵蚀速率的良好一致性表明本研究中所用侵蚀模式的有效性。所得的侵蚀速率小于 Palumbo et al 测定的合黎山平均流域侵蚀速率（99 mm∙ka-1），原因解释尚待更多地点和样品的研究。<br style="line-height: normal; text-align: -webkit-auto; text-size-adjust: auto;" /

Fast Distance Protection for Proximal Fault of EHV Transmission Line

As the full length is considered in the traditional EHV line protection, proximal circuit fault can’t be moved in an ideal short time. A fast distance protection algorithm for proximal fault of EHV transmission line is proposed in the paper. Based on differential equation model and power frequency variation principle, the method has fast response speed and small workload. And the protection for proximal fault of long distance EHV transmission lines can be correctly implemented in a very short time. The method is realized with a low pass prefilter and without digital filter. Finally, through the RTDS simulation platform, a variety of fault modes of 500kV transmission lines is realized, and the accuracy and rapidity of the algorithm is verified by the simulation result. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.198

Practical Research of Electronic Transformer Based on Interpolation Algorithm

As a result of the adoption of new photovoltaic technology, electronic transformers have great advantages compared with traditional electromagnetic type, such as anti-saturated, high linearity, compact and lightweight etc. The working principle of sensing head of electronic current/voltage transformers is introduced in the paper. The causes of phase error in electronic transformer are analyzed. And a set of phase compensation methods based on the signal transfer principle of electronic transformer is presented. The phase-difference caused by Rogowski coil and time-delay in signal transferring from high voltage side to merging unit are analyzed, and the higher sampling rate and the method of linear interpolation is used to solve the problem. In the simulation test the phase error compensation effect is very good, and the simulation result shows that the integrated error after compensation is able to meet the requirements of the measurement and protection, and demonstrates the validity of the method. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.198

Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization

Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts. However, most constructive NCO methods cannot solve problems with large-scale instance sizes, which significantly diminishes their usefulness for real-world applications. In this work, we propose a novel Light Encoder and Heavy Decoder (LEHD) model with a strong generalization ability to address this critical issue. The LEHD model can learn to dynamically capture the relationships between all available nodes of varying sizes, which is beneficial for model generalization to problems of various scales. Moreover, we develop a data-efficient training scheme and a flexible solution construction mechanism for the proposed LEHD model. By training on small-scale problem instances, the LEHD model can generate nearly optimal solutions for the Travelling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) with up to 1000 nodes, and also generalizes well to solve real-world TSPLib and CVRPLib problems. These results confirm our proposed LEHD model can significantly improve the state-of-the-art performance for constructive NCO. The code is available at https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/LEHD.Comment: Accepted at NeurIPS 202

Self-Improved Learning for Scalable Neural Combinatorial Optimization

The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design. However, existing methods struggle with large-scale problems, hindering their practical applicability. To overcome this limitation, this work proposes a novel Self-Improved Learning (SIL) method for better scalability of neural combinatorial optimization. Specifically, we develop an efficient self-improved mechanism that enables direct model training on large-scale problem instances without any labeled data. Powered by an innovative local reconstruction approach, this method can iteratively generate better solutions by itself as pseudo-labels to guide efficient model training. In addition, we design a linear complexity attention mechanism for the model to efficiently handle large-scale combinatorial problem instances with low computation overhead. Comprehensive experiments on the Travelling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) with up to 100K nodes in both uniform and real-world distributions demonstrate the superior scalability of our method

DGI: Easy and Efficient Inference for GNNs

While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for up to 94% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. On the other hand, layer-wise inference avoids the neighbor explosion problem by conducting inference layer by layer such that the nodes only need their one-hop neighbors in each layer. However, implementing layer-wise inference requires substantial engineering efforts because users need to manually decompose a GNN model into layers for computation and split workload into batches to fit into device memory. In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution. DGI is general for various GNN models and different kinds of inference requests, and supports out-of-core execution on large graphs that cannot fit in CPU memory. Experimental results show that DGI consistently outperforms layer-wise inference across different datasets and hardware settings, and the speedup can be over 1,000x.Comment: 10 pages, 10 figure

A multi-objective control strategy for three phase grid-connected inverter during unbalanced voltage sag

This paper presents a new multi-objective control strategy for inverter-interfaced distributed generation (IIDG) to ensure its safe and continuous operation under unbalanced voltage sags. The proposed control strategy can effectively improve the low voltage ride through (LVRT) capability, reduce active power oscillations, and limit overcurrent simultaneously, which are marked as the most important control objectives of IIDG during unbalanced voltage sags. The advanced voltage support scheme, which utilizes positive sequence component, is firstly proposed to maximize the LVRT capability of IIDG during unbalanced voltage sags. Then, to ensure the safety of IIDG, the active power oscillation suppression and current limitation algorithm are designed individually. Based on the control algorithms of such objectives, the multi-objective control method, including scenario classification and reference current determination, is then presented to achieve such three objectives under various system conditions simultaneously. Finally, case studies and evaluations based on MATLAB/Simulink are carried out to illustrate the effectiveness of the proposed method

An improved inverse-time over-current protection method for microgrid with optimized acceleration and coordination

This paper presents an improved inverse-time over-current protection method based on the compound fault acceleration factor and the beetle antennae search (BAS) optimization method for microgrid. The proposed method can not only significantly increase the operation speed of inverse-time over-current protection but also improve the protection coordination by considering possible influential factors, in terms of microgrid operation modes, distributed generation (DG) integration status, fault types, and positions, which are marked as the most challenging problems for over-current protection of microgrid. In this paper, a new Time Dial Setting (TDS) of inverse-time protection is developed by applying a compound fault acceleration factor, which can notably accelerate the speed of protection by using low-voltage and short-circuit impedance during the fault. In order to improve protection coordination, the BAS algorithm is then used to optimize the protection parameters of pick up current, TDS and the inverse time curve shape coefficient. Finally, the case studies and various evaluations based on DIgSILENT/Power Factory are carried out to illustrate the effectiveness of the proposed method

Association of CD40 Gene Polymorphisms with Sporadic Breast Cancer in Chinese Han Women of Northeast China

BACKGROUND: Breast cancer is a polygenetic disorder with a complex inheritance pattern. Single nucleotide polymorphisms (SNPs), the most common genetic variations, influence not only phenotypic traits, but also interindividual predisposition to disease, treatment outcomes with drugs and disease prognosis. The co-stimulatory molecule CD40 plays a prominent role in immune regulation and homeostasis. Accumulating evidence suggests that CD40 contributes to the pathogenesis of cancer. Here, we set out to test the association between polymorphisms in the CD40 gene and breast carcinogenesis and tumor pathology. METHODOLOGY AND PRINCIPAL FINDINGS: Four SNPs (rs1800686, rs1883832, rs4810485 and rs3765459) were genotyped by the polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) method in a case-control study including 591 breast cancer patients and 600 age-matched healthy controls. Differences in the genotypic distribution between breast cancer patients and healthy controls were analyzed by the Chi-square test for trends. Our preliminary data showed a statistically significant association between the four CD40 gene SNPs and sporadic breast cancer risk (additive P = 0.0223, 0.0012, 0.0013 and 0.0279, respectively). A strong association was also found using the dominant, recessive and homozygote comparison genetic models. In the clinical features analysis, significant associations were observed between CD40 SNPs and lymph node metastasis, human epidermal growth factor receptor 2 (C-erbB2), estrogen receptor (ER), progesterone receptor (PR) and tumor protein 53 (P53) statuses. In addition, our haplotype analysis indicated that the haplotype C(rs1883832)G(rs4810485), which was located within the only linkage disequilibrium (LD) block identified, was a protective haplotype for breast cancer, whereas T(rs1883832)T(rs4810485) increased the risk in the studied population, even after correcting the P value for multiple testing (P = 0.0337 and 0.0430, respectively). CONCLUSIONS AND SIGNIFICANCE: Our findings primarily show that CD40 gene polymorphisms contribute to sporadic breast cancer risk and have a significant association with clinicopathological features among Chinese Han women from the Heilongjiang Province