Real-Time Warning System of Regional Landslides Supported by WEBGIS and its Application in Zhejiang Province, China

AbstractAs one of the provinces of highest economic growth in coastal China, Zhejiang Province is experiencing serious geological disasters during the past development of economy, which are mainly induced by intensive rainfall during typhoon season or by long-term rainfall from May to June every year. Thus, supported by WEBGIS, a real-time warning system of regional landslides is studied. According to the characteristic of rainfall in Zhejiang province, the study divides the province into typhoon region and non-typhoon region, using statistic approach to study the correlation of regional landslides hazards and rainfall, rainfall intensity of typhoon region and non-typhoon region. By correlation analysis, effective rainfall model is defined, and the thresholds of effective rainfall and rainfall intensity are obtained. Combining these thresholds with spatial prediction production of landslides hazards, predictive models for landslide warning of Zhejiang Province are established. Then a real-time warning system of regional landslides explored by WEBGIS software is successfully developed considering both regional geology and rainfall process information

CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Prediction

Non-communicable disease is the leading cause of death, emphasizing the need for accurate prediction of disease progression and informed clinical decision-making. Machine learning (ML) models have shown promise in this domain by capturing non-linear patterns within patient features. However, existing ML-based models cannot provide causal interpretable predictions and estimate treatment effects, limiting their decision-making perspective. In this study, we propose a novel model called causal trajectory prediction (CTP) to tackle the limitation. The CTP model combines trajectory prediction and causal discovery to enable accurate prediction of disease progression trajectories and uncover causal relationships between features. By incorporating a causal graph into the prediction process, CTP ensures that ancestor features are not influenced by the treatment of descendant features, thereby enhancing the interpretability of the model. By estimating the bounds of treatment effects, even in the presence of unmeasured confounders, the CTP provides valuable insights for clinical decision-making. We evaluate the performance of the CTP using simulated and real medical datasets. Experimental results demonstrate that our model achieves satisfactory performance, highlighting its potential to assist clinical decisions. Source code is in \href{https://github.com/DanielSun94/CFPA}{here}.Comment: 25 pages, 5 figures, 12 table

Construction of a 7-fold BAC library and cytogenetic mapping of 10 genes in the giant panda (Ailuropoda melanoleuca)

BACKGROUND: The giant panda, one of the most primitive carnivores, is an endangered animal. Although it has been the subject of many interesting studies during recent years, little is known about its genome. In order to promote research on this genome, a bacterial artificial chromosome (BAC) library of the giant panda was constructed in this study. RESULTS: This BAC library contains 198,844 clones with an average insert size of 108 kb, which represents approximately seven equivalents of the giant panda haploid genome. Screening the library with 15 genes and 8 microsatellite markers demonstrates that it is representative and has good genome coverage. Furthermore, ten BAC clones harbouring AGXT, GHR, FSHR, IRBP, SOX14, TTR, BDNF, NT-4, LH and ZFX1 were mapped to 8 pairs of giant panda chromosomes by fluorescence in situ hybridization (FISH). CONCLUSION: This is the first large-insert genomic DNA library for the giant panda, and will contribute to understanding this endangered species in the areas of genome sequencing, physical mapping, gene cloning and comparative genomic studies. We also identified the physical locations of ten genes on their relative chromosomes by FISH, providing a preliminary framework for further development of a high resolution cytogenetic map of the giant panda

Effect of the stroke-to-bore ratio on the performance of a dual-piston free piston engine generator

The free piston engine generator (FPEG) is considered as one of the next generation efficient energy conversion device because of its compact structure, high geometric power ratio and low pollution. This paper investigated the effect of stroke-to-bore (S/B) ratio on the system operation characteristics and engine performance, constructed a detailed numerical model in MATLAB/Simulink and verified the experimental data whose difference value could be controlled within 5%. The effect of five S/B ratios (0.84, 0.91, 0.99, 1.07 and 1.14) and three compression ratios (8, 9 and 10) was analysed at a constant bore diameter. The simulation results indicated that the operation frequency increased from 28.2 Hz to 48.3 Hz when the S/B ratio decreased from 1.14 to 0.84. The highest indicated power is 4.1 kW when the S/B ratio is 0.84 and the compression ratio (CR) is 10. While for high thermal efficiency and fuel economy design, larger S/B ratio and higher operating compression ratio should be selected while keeping the periodic energy input unchanged. The heat transfer loss decreased from 29.0% to 20.4% when the S/B ratio increased from 0.84 to 1.14. And in the long stroke, ignition position needs to lean back (from 6.8 mm to 24.8 when S/B increased from 0.84 to 1.14) so as to keep the compression ratio unchanged under different S/B ratios

Provable Routing Analysis of Programmable Photonics

Programmable photonic integrated circuits (PPICs) are an emerging technology recently proposed as an alternative to custom-designed application-specific integrated photonics. Light routing is one of the most important functions that need to be realized on a PPIC. Previous literature has investigated the light routing problem from an algorithmic or experimental perspective, e.g., adopting graph theory to route an optical signal. In this paper, we also focus on the light routing problem, but from a complementary and theoretical perspective, to answer questions about what is possible to be routed. Specifically, we demonstrate that not all path lengths (defined as the number of tunable basic units that an optical signal traverses) can be realized on a square-mesh PPIC, and a rigorous realizability condition is proposed and proved. We further consider multi-path routing, where we provide an analytical expression on path length sum, upper bounds on path length mean/variance, and the maximum number of realizable paths. All of our conclusions are proven mathematically. Illustrative potential optical applications using our observations are also presented

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

The rise of deep neural networks provides an important driver in optimizing recommender systems. However, the success of recommender systems lies in delicate architecture fabrication, and thus calls for Neural Architecture Search (NAS) to further improve its modeling. We propose NASRec, a paradigm that trains a single supernet and efficiently produces abundant models/sub-architectures by weight sharing. To overcome the data multi-modality and architecture heterogeneity challenges in recommendation domain, NASRec establishes a large supernet (i.e., search space) to search the full architectures, with the supernet incorporating versatile operator choices and dense connectivity minimizing human prior for flexibility. The scale and heterogeneity in NASRec impose challenges in search, such as training inefficiency, operator-imbalance, and degraded rank correlation. We tackle these challenges by proposing single-operator any-connection sampling, operator-balancing interaction modules, and post-training fine-tuning. Our results on three Click-Through Rates (CTR) prediction benchmarks show that NASRec can outperform both manually designed models and existing NAS methods, achieving state-of-the-art performance