Influence of Fiber Type and Length on Mechanical Properties of MICP-Treated Sand.

Fibers are applied in construction work to improve the strength and avoid brittle failure of soil. In this paper, we analyze the impact mechanism of fiber type and length on the immobilization of microorganisms from macroscopic and microscopic perspectives with fibers of 0.2% volume fraction added to microbial-induced calcite precipitation (MICP)-treated sand. Results show the following: (1) The unconfined compressive strength (UCS) of MICP-treated sand first increases and then decreases with increasing fiber length because short fiber reinforcement can promote the precipitation of calcium carbonate, and the network formed between the fibers limits the movement of sand particles and enhances the strength of the microbial solidified sand. However, the agglomeration caused by overlong fibers leads to uneven distribution of calcium carbonate and a reduction in strength. The optimal fiber length of polypropylene, glass, and polyvinyl alcohol fiber is 9 mm, and that of basalt fiber is 12 mm. (2) The UCS of the different fiber types, from small to large, is basalt fiber < polypropylene fiber < glass fiber < polyvinyl alcohol fiber because the quality of the fiber monofilament differs. More fibers result in more a evident effect of interlacing and bending on sand and higher strength in consolidated sand.

Treatment initiation prediction by EHR mapped PPD tensor based convolutional neural networks boosting algorithm.

Electronic health records contain patient's information that can be used for health analytics tasks such as disease detection, disease progression prediction, patient profiling, etc. Traditional machine learning or deep learning methods treat EHR entities as individual features, and no relationships between them are taken into consideration. We propose to evaluate the relationships between EHR features and map them into Procedures, Prescriptions, and Diagnoses (PPD) tensor data, which can be formatted as images. The mapped images are then fed into deep convolutional networks for local pattern and feature learning. We add this relationship-learning part as a boosting module on a commonly used classical machine learning model. Experiments were performed on a Chronic Lymphocytic Leukemia dataset for treatment initiation prediction. Experimental results show that the proposed approach has better real world modeling performance than the baseline models in terms of prediction precision.

An Optimized Method for Bayesian Connectivity Change Point Model.

The brain undergoes functional dynamic changes at all times. Investigating functional dynamics has been recently verified to be helpful for detecting psychological conditions and powerful for analyzing disease-related abnormalities of the brain. This article aims to detect functional dynamics. Specifically, we focus on how to effectively distinguish corresponding functional connectivity and change points from functional magnetic resonance imaging (fMRI) data. By combining Bayesian connectivity change point model (BCCPM), a modified genetic algorithm (GA) is presented to optimize the evolutionary procedure toward the most probable distributions of real change points in fMRI. We randomly initialize different binary indicator vectors to represent different distributions of change points. Each indicator vector represents an individual in GA, and together they form an initial population. Then we calculate Bayesian posterior probability and use it as the fitness of each individual. Finally, we evolve individuals of current generation toward the next higher fitness generation by a series of modified genetic operators. After several evolutionary procedures, individuals in the final generation may have outstanding fitness and the one with highest fitness can represent the most likely change point distribution in the corresponding fMRI data. Furthermore, the most probable change point distribution could be resolved. We test the optimized method for BCCPM on several synthesized data sets, and the experimental results verify that the proposed model produces higher accuracy results with lower time consumption. Also, we apply the new model to real block-designed task-based fMRI data set and excellent results are obtained.