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With the incessant fluctuations in oil prices and increasing stress from environmental pollution, renewed attention is being paid to the microbial production of biofuels from renewable sources. As a gasoline substitute, butanol has advantages over traditional fuel ethanol in terms of energy density and hygroscopicity. A variety of cheap substrates have been successfully applied in the production of biobutanol, highlighting the commercial potential of biobutanol development. In this review, in order to better understand the process of acetone-butanol-ethanol production, traditional clostridia fermentation is discussed. Sporulation is probably induced by solvent formation, and the molecular mechanism leading to the initiation of sporulation and solventogenesis is also investigated. Different strategies are employed in the metabolic engineering of clostridia that aim to enhancing solvent production, improve selectivity for butanol production, and increase the tolerance of clostridia to solvents. However, it will be hard to make breakthroughs in the metabolic engineering of clostridia for butanol production without gaining a deeper understanding of the genetic background of clostridia and developing more efficient genetic tools for clostridia. Therefore, increasing attention has been paid to the metabolic engineering of E. coli for butanol production. The importation and expression of a non-clostridial butanol-producing pathway in E. coli is probably the most promising strategy for butanol biosynthesis. Due to the lower butanol titers in the fermentation broth, simultaneous fermentation and product removal techniques have been developed to reduce the cost of butanol recovery. Gas stripping is the best technique for butanol recovery found so far.

With the rapid development of Internet of Things (IoT) devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in the paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real-time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network (CNN) model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient.

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The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to selfdriving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in the paper to give autonomous vehicles the ability to make decisions like a human. In our method, a Convolutional Neural Network (CNN) model is used to detect, recognize and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decisionmaking system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.

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Electrical load forecasting is still a challenging open problem due to the complex and variable influences, e.g. weather and time. Although, with the recent development of Internet of Things (IoT) and smart meter technology, people have obtained the ability to record relevant information on a large scale, traditional methods struggle in analyzing such complicated relationships for their limited abilities in handling non-linear data. In the paper, we introduce an IoT-based deep learning system to automatically extract features from the captured data, and ultimately, give an accurate estimation of future load value. One significant advantage of our method is the specially designed two-step forecasting scheme, which significantly improves the forecasting precision. Also, the proposed method is able to quantitatively analyze the influences of some major factors, which is of great guiding significance to select attribute combination and deploy on-board sensors for smart grids with vast area, variable climates and social conventions. Simulations demonstrate that our method outperforms some existing approaches, and can be well applied in various situations. Index Terms-Smart grid, Internet of things (IoT), load forecasting, metering infrastructure, big data.