Deep Clustering Survival Machines with Interpretable Expert Distributions

Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance

Experimental investigations on the correlations between the structure and thermal-electrochemical properties of over-discharged ternary/Si-C power batteries

© 2021 John Wiley & Sons Ltd. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1002/er.7274The thermal safety of power lithium-ion batteries（LIBs） has seriously affected the booming development of electric vehicles (EVs). Especially, owing to the requirement of high energy density, thermal runaway (TR) easily occurs in LIBs, resulting in a higher heat generation rate. Over-discharging is recognized as a common cause for TR. In the present research, the correlations between the structure and thermal-electrochemical properties of an over-discharged ternary/Si-C battery at room and high temperatures were investigated. The heat generation mechanisms of the batteries due to the maximum surface temperature and peak temperature difference variations during fast charging and discharging processes were investigated. Moreover, the electrochemical performances parameters of the batteries, such as voltage changing trend, discharge time, discharge capacity, internal resistance, electrochemical impedance spectroscopy (EIS) spectra, were analyzed. When the battery was discharged at 2.0C and 55°C, its maximum temperature and highest temperature difference reached 91.34°C and 13.24°C, respectively, finally resulting in a sharp decline in electrochemical performance. Furthermore, the root reasons for performance degradation and heat generation intensification of the over-discharged battery (ODB) were analyzed by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The cause of the aforementioned phenomenon is due to irreversible damage of the electrode materials. This research not only reveals the relevant relationship between the thermal behavior and the microscopic structure of the over-discharged ternary/Si-C battery under various temperature conditions but also provides valuable insights for improving the safety of LIBs modules even packs.Peer reviewe

Simulation and Analysis of Fluid-Solid Coupling of Wave Impact Sandcastle Based on COMSOL

It is a complex problem to study the interaction between sand castle and flowing water, which needs to consider the complexity of seawater flow and the stress of sand castle structure. The authors use the fluid-solid coupling model to establish the connection between the fluid field and the structural mechanical field, and use the finite element analysis to complete the simulation modeling of the transient process of wave impact and sandcastle foundation deformation. This paper analyzes the stress and the first principal strain of the sand castle foundation in the direction of flow velocity when the sand castle foundation is hit by waves, as a method to judge the strength of the sand castle.The best shape: the boundary value of sand castle collapse caused by strain have been determined, so as to obtain the maximum stress that a sand castle foundation can bear before collapse, which makes it possible to use the fatigue strength calculation theory of sand castle solid to carry out the quantitative calculation of sand castle durability. At the same time, the impact of waves is abstracted as wave motion equation. Finally, the finite element analysis technology is adopted to calculate the main strain of sandcastles of different shapes under the impact of the same wave, and through the comparison of the main strain, the authors get the sandcastle shape with the strongest anti-wave impact ability, which is the eccentric circular platform body.Affected by rain: the authors considered the effect of rainwater infiltration on the sandcastle's stress, and simplified the process of rain as a continuous and uniform infiltration of rain into the sandcastle's surface. The rain changes the gravity of the sand on the castle's surface. Simulation analysis is adopted to calculate the surface stress of sand castle with different degree of water seepage and different geometry. By comparison, it has been found that the smooth cone is more able to withstand the infiltration of rain without collapse.

Flexible Coherent Optical Access: Architectures, Algorithms, and Demonstrations

To cope with the explosive bandwidth demand, significant progress has been made in the ITU-T standardization sector to define a higher-speed passive optical network (PON) with a 50Gb/s line rate. Recently, 50G PON becomes mature gradually, which means it is time to discuss beyond 50G PON. For ensuring an acceptable optical power budget, beyond 50G PON will potentially use coherent technologies, which can simultaneously promote the applications of flexible multiple access such as time/frequency-domain multiple access (TFDMA). In this paper, we will introduce the architectures, algorithms, and demonstrations for TFDMA-based coherent PON. The system architectures based on an ultra-simple coherent transceiver and specific signal spectra are designed to greatly reduce the cost of ONUs. Meanwhile, fast and low-complexity digital signal processing (DSP) algorithms are proposed for dealing with upstream and downstream signals. Based on the architectures and algorithms, we experimentally demonstrate the first real-time TFDMA-based coherent PON, which can support at most 256 end users, and peak line rates of 100Gb/s and 200Gb/s in the upstream and downstream scenarios, respectively. In conclusion, the proposed technologies for the coherent PON make it more possible to be applied in the future beyond 50G PON.Comment: The paper has been submitted to the Journal of Lightwave Technolog

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.</p

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Dynamic recrystallization analysis of reduction pretreatment process by multi-phase field method

The reduction pretreatment (RP) process is an effective method to improve billet quality, and the deformation recrystallization plays an important role in the process. Exploring the RP process parameters, a dynamic recrystallization model of GCr15 steel was established by the phase-field method and physical simulation. The recrystallization kinetics and flow stress curves during hot compression were simulated by using this mode. The effects of deformation parameters and initial grain size on the dynamic recrystallization were investigated. Moreover, by using the results obtained by Finite element method (FEM), dynamic recrystallization during the RP process was investigated though this model. It was found that increasing the deformation temperature, deformation rate and decreasing the initial grain size can promote the dynamic recrystallization kinetics. Large Zener-Hollomon parameters can enhance recrystallized grain refinement, while the recrystallized grain size was not affected by the initial grain size. During the RP process, when the reduction is insufficient (10%), partial recrystallization occurs in the billet. With the increase of reduction from 10% to 16%, the area of complete recrystallization increases gradually. When the reduction is the same, the recrystallization in the billet center increases with the decrease of casting speed. When the reduction is 10%, partial recrystallization occurs in the billet center at a casting speed of 0.7 m min ^−1 , and fully recrystallization occurs in the billet center with a casting speed of 0.5 m min ^−1 . Thus, when the reduction is difficult to increase further, the recrystallization in the billet center can be improved by decreasing the casting speed

Design and Numerical Study of Argon Gas Diversion System Using Orthogonal Experiment to Reduce Impurities in Large-Sized Casting Silicon

To reduce oxygen and carbon impurities while casting silicon, an argon gas diversion system is proposed. A series of two-dimensional global transient numerical simulations are carried out using Fluent software according to the orthogonal experimental design, including heat transfer, convection of silicon melt and argon gas, and the fully coupling transport of impurities. The numerical results show that when the distance between the outer tube outlet and the cover is 10 mm, the backflow is inhibited by lateral outflow, thus the generation of CO is suppressed and the penetration of impurities into the silicon melt is decreased. The larger the flow rate, the more obvious the effect is. When the outer tube outlet is far from the cover, the effect of removing impurities is no longer significant. In addition, too large or too small an inner tube flow rate is not conducive to impurity reduction. The optimal parameter combination of outer tube flow rate, inner tube flow rate, and the distance between outer tube outlet and the cover are determined by the orthogonal experiment. Compared with the original furnace, the average concentration of oxygen and carbon in casting silicon ingots could be decreased by 7.4% and 59.9%, respectively, by using the optimized argon gas diversion system