A Two-Scale Multi-Physics Deep Learning Model for Smart MEMS Sensors

Smart materials and structures, especially those bio-inspired, are often characterized by a hierarchy of length- and time-scales. Smart Micro Electro-Mechanical Systems (MEMS) are also characterized by different physical phenomena affecting their properties at different scales. Data-driven formulations can then be helpful to deal with the complexity of the multi-physics governing their response to the external stimuli, and optimize their performances. As an example, Lorentz force micro-magnetometers working principle rests on the interaction of a magnetic field with a current flowing inside a semiconducting, micro-structured medium. If an alternating current with a properly set frequency is let to flow longitudinally in a slender beam, the system is driven into resonance and the sensitivity to the magnetic field may result largely enhanced. In our former activity, a reduced-order physical model of the movable structure of a single-axis Lorentz force MEMS magnetometer was developed, to feed a multi-objective topology optimization procedure. That model-based approach did not account for stochastic effects, which lead to the scattering in the experimental data at the micrometric length-scale. The formulation is here improved to allow for stochastic effects through a two-scale deep learning model designed as follows: at the material scale, a neural network is adopted to learn the scattering in the mechanical properties of polysilicon induced by its polycrystalline morphology;at the device scale, a further neural network is adopted to learn the most important geometric features of the movable parts that affect the overall performance of the magnetometer. Some preliminary results are discussed, and an extension to allow for size effects is finally foreseen.

Detecting differential expression from RNA-seq data with expression measurement uncertainty

High-throughput RNA sequencing （RNA-seq） has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detect- ing differentially expressed （DE） genes from RNA-seq are based on statistics that compare normalized read counts be- tween conditions. However, there are few methods consider- ing the expression measurement uncertainty into DE detec- tion. Moreover, most methods are only capable of detecting DE genes, and few methods are available for detecting DE isoforms. In this paper, a Bayesian framework （BDSeq） is proposed to detect DE genes and isoforms with consideration of expression measurement uncertainty. This expression mea- surement uncertainty provides useful information which can help to improve the performance of DE detection. Three real RAN-seq data sets are used to evaluate the performance of BDSeq and results show that the inclusion of expression mea- surement uncertainty improves accuracy in detection of DE genes and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to facilitate users.

An enhanced environmental multimedia modeling system based on fuzzy-set approach： II. Model validation and application

Part I of this study develops an enhanced environmental multimedia modeling system （EMMS） based on fuzzy-set approach. Once the model development is complete, the composite module and the entire modeling system need to be tested and validated to ensure that the model can simulate natural phenomena with reasonable and reliable accuracy. The developed EMMS is first tested in a complete case study. And then verification results are conducted to compare with extensively researched litera- ture data. In the third step, the data from an experimental landfill site is used for a pilot-scale validation. The comparisons between EMMS outputs and the literature data indicate that the EMMS can perform accurate modeling simulation. The modules of EMMS could support the entire environmental multimedia modeling system. Further field-scale validation is finished. The results are satisfactory. Most of the modeling yields closely match the monitoring data collected from sites. In addition, with the aid of fuzzy-set approach, EMMS can be a reliable and powerful tool to address the complex environmental multimedia pollution problems and provide an extensive support for decision makers in managing the contaminated environmental systems.