In this paper, a temporal probabilistic approach based on the hidden Markov model (HMM), named physically segmented HMM with continuous output, is introduced for continuous tool condition monitoring in machinery systems. The proposed approach has the advantage of providing an explicit relationship between the actual health states and the hidden state values. The provided relationship is further exploited for formulation and parameter estimation in the proposed approach. The introduced approach is tested for continuous tool wear prediction in a computer numerical control milling machine and compared with two well-established neural network (NN) approaches, namely, multilayer perceptron and Elman network. In the experimental study, the prediction results are provided and compared after adopting appropriate hyper-parameter values for all the approaches by cross-validation. Based on the experimental results, physically segmented HMM approach outperforms the NN approaches. Moreover, the prognosis ability of the proposed approach is studied.

Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning. Computer Methods in Applied Mechanics and Engineering

The need to develop protein biomanufacturing platforms that can deliver proteins quickly and cost-effectively is ever more pressing. The rapid rate at which genomes can now be sequenced demands efficient protein production platforms for gene function identification. There is a continued need for the biotech industry to deliver new and more effective protein-based drugs to address new diseases. Bacterial production platforms have the advantage of high expression yields, but insoluble expression of many proteins necessitates the development of diverse and optimised refolding-based processes. Strategies employed to eliminate insoluble expression are reviewed, where it is concluded that inclusion bodies are difficult to eliminate for various reasons. Rational design of refolding systems and recipes are therefore needed to expedite production of recombinant proteins. This review article discusses efforts towards rational design of refolding systems and recipes, which can be guided by the development of refolding screening platforms that yield both qualitative and quantitative information on the progression of a given refolding process. The new opportunities presented by light scattering technologies for developing rational protein refolding buffer systems which in turn can be used to develop new process designs armed with better monitoring and controlling functionalities are discussed. The coupling of dynamic and static light scattering methodologies for incorporation into future bioprocess designs to ensure delivery of high-quality refolded proteins at faster rates is also discussed.

A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, which was then factorized into a subject-specific dictionary matrix and a weight coefficient matrix for sparse representation. In the second stage, all of the dictionary matrices from both tfMRI/rsfMRI data across multiple subjects were composed into another big data-matrix, which was further sparsely represented by a cross-subjects common dictionary and a weight matrix. This framework has been applied on the recently publicly released Human Connectome Project (HCP) fMRI data and experimental results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100% classification accuracy. Moreover, our methods and results can be meaningfully interpreted, e.g., the well-known default mode network (DMN) activities can be recovered from the very noisy and heterogeneous aggregated big-data of tfMRI and rsfMRI signals across all subjects in HCP Q1 release.

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