Analysis of a high-pier railway bridge under spatial stochastic stationary and non-stationary earthquake excitations

Ciljevi ovoga rada su provesti komparativnu analizu velikog sustava željezničkog mosta na visokim stupovima izloženog stacionarnim i ne-stacionarnim prostorno promjenjivim uzbudama potresa primjenom metode pseudo-uzbude (pseudo-excitation method - PEM), te procijeniti može li se ili ne može ne-stacionarna stohastička analiza željezničkih mostova na visokim stupovima izloženih trosmjernim prostornim podzemnim gibanjima zamijeniti jednostavnijom stacionarnom slučajnom analizom kako bi se izbjegla prekomjerna računanja. Zasnovane na ANSYS softveru konačnih elemenata, analize stacionarnih i ne-stacionarnih stohastičkih uzbuda mosta na visokim stupovima pretvorile su se u harmonične analize i determinističke prijelazne analize u našem istraživanju, primjenom PEM-a. Učinak prolaza vala i učinak nekoherentnosti modelirani su kao ključni čimbenici, a ukupno je razmotreno dvanaest slučajeva u svrhu ispitivanja učinka prolaza vala i učinka nekoherentnosti na seizmičku reakciju željezničkog mosta na visokim stupovima izloženog stacionarnim i ne-stacionarnim uzbudana potresa. Rezultati pokazuju da je reakcija konstrukcije pod stacionarnom uzbudom veća nego kod ne-stacionarne uzimajući u obzir bilo učinak prolaza vala ili učinak nekoherencije. Kad se uspoređuju reakcije konstrukcije pod stacionarnom pobudom s onima kod ne-stacionarne, sve stope rasta su manje od 25 %, što je u tehnici prihvatljivo, a to znači da se ne-stacionarna stohastička analiza željezničkih mostova na visokim stupovima pri trosmjernim prostornim gibanjima u zemlji može pojednostavniti u stacionarnu analizu kako bi se izbjeglo prekomjerno računanje.The objectives of this paper are to perform a comparative analysis of the large-scale system of a high-pier railway bridge subjected to stationary and non-stationary spatially varying earthquake excitations using the pseudo-excitation method (PEM), and to estimate whether or not the non-stationary stochastic analysis of the high-pier railway bridges under tri-directional spatial ground motions can be simplified into a stationary random analysis to avoid excessive computation. Based on the finite element software ANSYS, the stationary and non-stationary stochastic excitations analyses of a high-pier bridge were transformed into harmonic analyses and deterministic transient analyses in the study, respectively, by using PEM. The wave-passage effect and the incoherence effect were modelled as the key factors, a total of twelve cases were considered to investigate the wave-passage effect and incoherence effect on the seismic response of a high-pier railway bridge under stationary and non-stationary earthquake excitations. Results show that structural responses under stationary excitation are larger than those under non-stationary by considering either the wave-passage effect or the incoherence effect. Through comparing structural responses under stationary excitation with those under non-stationary one, all the growth rates are less than 25 %, which is acceptable in engineering, meaning that a non-stationary stochastic analysis of high-pier railway bridges under tri-directional spatial ground motions can be simplified into a stationary analysis to avoid excessive computation

Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems

Visualizing Exotic Orbital Texture in the Single-Layer Mott Insulator 1T-TaSe2

Mott insulating behavior is induced by strong electron correlation and can lead to exotic states of matter such as unconventional superconductivity and quantum spin liquids. Recent advances in van der Waals material synthesis enable the exploration of novel Mott systems in the two-dimensional limit. Here we report characterization of the local electronic properties of single- and few-layer 1T-TaSe2 via spatial- and momentum-resolved spectroscopy involving scanning tunneling microscopy and angle-resolved photoemission. Our combined experimental and theoretical study indicates that electron correlation induces a robust Mott insulator state in single-layer 1T-TaSe2 that is accompanied by novel orbital texture. Inclusion of interlayer coupling weakens the insulating phase in 1T-TaSe2, as seen by strong reduction of its energy gap and quenching of its correlation-driven orbital texture in bilayer and trilayer 1T-TaSe2. Our results establish single-layer 1T-TaSe2 as a useful new platform for investigating strong correlation physics in two dimensions

Dynamic Responses of Continuous Girder Bridges with Uniform Cross-Section under Moving Vehicular Loads

To address the drawback of traditional method of investigating dynamic responses of the continuous girder bridge with uniform cross-section under moving vehicular loads, the orthogonal experimental design method is proposed in this paper. Firstly, some empirical formulas of natural frequencies are obtained by theoretical derivation and numerical simulation. The effects of different parameters on dynamic responses of the vehicle-bridge coupled vibration system are discussed using our own program. Finally, the orthogonal experimental design method is proposed for the dynamic responses analysis. The results show that the effects of factors on dynamic responses are dependent on both the selected position and the type of the responses. In addition, the interaction effects between different factors cannot be ignored. To efficiently reduce experimental runs, the conventional orthogonal design is divided into two phases. It has been proved that the proposed method of the orthogonal experimental design greatly reduces calculation cost, and it is efficient and rational enough to study multifactor problems. Furthermore, it provides a good way to obtain more rational empirical formulas of the DLA and other dynamic responses, which may be adopted in the codes of design and evaluation

Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome