A Data-Driven Approach to Morphogenesis under Structural Instability

Morphological development into evolutionary patterns under structural instability is ubiquitous in living systems and often of vital importance for engineering structures. Here we propose a data-driven approach to understand and predict their spatiotemporal complexities. A machine-learning framework is proposed based on the physical modeling of morphogenesis triggered by internal or external forcing. Digital libraries of structural patterns are constructed from the simulation data, which are then used to recognize the abnormalities, predict their development, and assist in risk assessment and prognosis. The capabilities to identify the key bifurcation characteristics and predict the history-dependent development from the global and local features are demonstrated by examples of brain growth and aerospace structural design, which offer guidelines for disease diagnosis/prognosis and instability-tolerant design

Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting

Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of material microstructures and service conditions, making diagnosis and prognosis of fatigue damage challenging. We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties. Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations. Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events. The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns. The end-to-end approach is validated by representative examples with fatigue cracks in plates, which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making

An Expectation Maximization Algorithm to Model Failure Times by Continuous-Time Markov Chains

In many applications, the failure rate function may present a bathtub shape curve. In this paper, an expectation maximization algorithm is proposed to construct a suitable continuous-time Markov chain which models the failure time data by the first time reaching the absorbing state. Assume that a system is described by methods of supplementary variables, the device of stage, and so on. Given a data set, the maximum likelihood estimators of the initial distribution and the infinitesimal transition rates of the Markov chain can be obtained by our novel algorithm. Suppose that there are m transient states in the system and that there are n failure time data. The devised algorithm only needs to compute the exponential of m×m upper triangular matrices for O(nm2) times in each iteration. Finally, the algorithm is applied to two real data sets, which indicates the practicality and efficiency of our algorithm

Three-Dimensional Passive Source Localisation using the Flank Array of an Autonomous Underwater Vehicle in Shallow Water

Researchers have become interested in autonomous underwater vehicles equipped with various kinds of sonar systems that can perform many of underwater tasks, which is encouraged by the potential benefits of cost reduction and flexible deployment. This paper proposes an approach to three-dimensional passive source localisation with the flank array of an autonomous underwater vehicle in shallow water. The approach is developed based on matched-field processing for the likelihood of passive source localisation in the shallow water environment. Inter-position processing is also used for the improved localisation performance and the enhanced stability of the estimation process against the lack of spatial gain due to the small physical size of the flank array. The proposed approach is presented and validated through simulation and experimental data. The results illustrate the localisation performance at different signal-to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the autonomous underwater vehicle cruises at a low speed along a straight line at a constant depth.Defence Science Journal, 2013, 63(3), pp.323-330, DOI:http://dx.doi.org/10.14429/dsj.63.301

Intercalated water layers promote thermal dissipation at bio–nano interfaces

The increasing interest in developing nanodevices for biophysical and biomedical applications results in concerns about thermal management at interfaces between tissues and electronic devices. However, there is neither sufficient knowledge nor suitable tools for the characterization of thermal properties at interfaces between materials of contrasting mechanics, which are essential for design with reliability. Here we use computational simulations to quantify thermal transfer across the cell membrane–graphene interface. We find that the intercalated water displays a layered order below a critical value of ∼1 nm nanoconfinement, mediating the interfacial thermal coupling, and efficiently enhancing the thermal dissipation. We thereafter develop an analytical model to evaluate the critical value for power generation in graphene before significant heat is accumulated to disturb living tissues. These findings may provide a basis for the rational design of wearable and implantable nanodevices in biosensing and thermotherapic treatments where thermal dissipation and transport processes are crucial.MIT-China seed fundNational Natural Science Foundation of China (Grant No. 11472150)National Natural Science Foundation of China (Grant No. 2015CB351900)United States. Office of Naval Research (Grant No. N00014-16-1-233)United States. Office of Naval Research. Presidential Early Career Award for Scientists and Engineers (Grant No. N00014-10-1-0562)United States. Air Force. Office of Scientific Research. FATE MURI (Grant No. FA9550-15-1-0514)United States. Defense Advanced Research Projects AgencyMIT Energy InitiativeNational Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (award number DMR-0819762

Viremia Associated with Fatal Outcomes in Ferrets Infected with Avian H5N1 Influenza Virus

Avian H5N1 influenza viruses cause severe disease and high mortality in infected humans. However, tissue tropism and underlying pathogenesis of H5N1 virus infection in humans needs further investigation. The objective of this work was to study viremia, tissue tropism and disease pathogenesis of H5N1 virus infection in the susceptible ferret animal model. To evaluate the relationship of morbidity and mortality with virus loads, we performed studies in ferrets infected with the H5N1 strain A/VN/1203/04 to assess clinical signs after infection and virus load in lung, brain, ileum, nasal turbinate, nasal wash, and blood. We observed that H5N1 infection in ferrets is characterized by high virus load in the brain and and low levels in the ileum using real-time PCR. In addition, viral RNA was frequently detected in blood one or two days before death and associated with symptoms of diarrhea. Our observations further substantiate pathogenicity of H5N1 and further indicate that viremia may be a bio-marker for fatal outcomes in H5N1 infection