RESUMO
The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure-property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.
RESUMO
BACKGROUND: Our long-term goal is to design and manufacture a customized graft with porous scaffold structure for repairing large mandibular defects using topological optimization and 3D printing technology. The purpose of this study is to characterize the mechanical behavior of 3D printed anisotropic scaffolds as bone analogs by fused deposition modeling (FDM). METHODS: Cone beam computed tomography (CBCT) images were used to reconstruct a 3D mandible and finite element models. A virtual sectioned-block of the mandible was used as the control group and the trabecular portion of the block was modified by topological optimization methods as experimental groups. FDM (FDM) printed samples at 0, 45 and 90 degrees with Poly-lactic acid (PLA) material under a three-point bending test. Finite element analysis was also used to validate the data obtained from the physical model tests. RESULTS: The ultimate load, yield load, failure deflection, yield deflection, stress, strain distribution, and porosity of scaffold structures were compared. The results show that the topological optimized graft had the best mechanical properties. CONCLUSIONS: The results from mechanical tests on physical models and numerical simulations from this study show a great potential for topological optimization and 3D printing technology to be served in design and rapidly manufacturing of artificial porous grafts.
RESUMO
Thermochromic material is a substance that is capable of changing reversibly the color as the temperature rises. Therefore, the optical spectrum of thermochromic material is responsive to the environmental temperature. In this study, the temperature-dependent optical constants of thermochromic pigments over the wavelength of 350-1800 nm were investigated. Three kinds of thermochromic pigments featured with black, blue, and red colors at room temperature were suspended in water and the light reflection and transmission of the suspensions at different temperatures were measured by a multifunctional spectrophotometer. It was found that below the transition temperature of thermochromic material, the refractive index was 2.1-2.5, 2.2-2.6, and 2.0-2.4 over the wavelength range of 350-1800 nm for black, blue, and red thermochromic pigment, respectively, while above the transition temperature it reached 2.3-2.7, 2.4-2.9, and 2.4-2.7, respectively. It was also observed that the relationship between refractive index of thermochromic pigment and wavelength follows the cubic polynomial function. Furthermore, the extinction coefficient is in the range of 1 × 10-5-1.2 × 10-4 for all thermochromic pigments and remains approximately stable at different temperatures. The determination of optical constants of thermochromic pigments provides essential parameters in the modeling of light scattering and absorption by pigment particles to further fine-tune the optical properties of thermochromic coating.
RESUMO
This paper describes an in-vehicle nonintrusive biopotential measurement system for driver health monitoring and fatigue detection. Previous research has found that the physiological signals including eye features, electrocardiography (ECG), electroencephalography (EEG) and their secondary parameters such as heart rate and HR variability are good indicators of health state as well as driver fatigue. A conventional biopotential measurement system requires the electrodes to be in contact with human body. This not only interferes with the driver operation, but also is not feasible for long-term monitoring purpose. The driver assistance system in this paper can remotely detect the biopotential signals with no physical contact with human skin. With delicate sensor and electronic design, ECG, EEG, and eye blinking can be measured. Experiments were conducted on a high fidelity driving simulator to validate the system performance. The system was found to be able to detect the ECG/EEG signals through cloth or hair with no contact with skin. Eye blinking activities can also be detected at a distance of 10 cm. Digital signal processing algorithms were developed to decimate the signal noise and extract the physiological features. The extracted features from the vital signals were further analyzed to assess the potential criterion for alertness and drowsiness determination.
