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A novel assessment method for rapid structural safety state assessment is developed based on state-of-the-art machine learning technology. In this paper, a deep neural network (DNN), which originated in computer science, is first introduced, including its concept, structure and related training algorithms. Then the evaluation procedure to determine the structural safety state based on the DNN is developed. In the procedure, the pseudo-acceleration spectra and the structural safety states are selected as the input and output of the DNN model, respectively. Finally, the procedure of the novel assessment method is illustrated using a five-story reinforced concrete (RC) frame as an example. The effectiveness and accuracy are validated, and the influence of the number of hidden layers and size of the training data on the performance of the DDN is also investigated. The results demonstrate that the proposed method can evaluate the structural safety state rapidly based on the DNN. The most appropriate number of hidden layers is two considering both training accuracy and test accuracy. The training accuracy and test accuracy were 93.45% and 93.14%, respectively, using the two-hidden-layer DNN model trained on a training dataset of size 66,612. [ABSTRACT FROM AUTHOR]

One of the major issues confronting the dairy industry is the efficient evaluation of the quality of feed, milk and dairy products. Over the years, the use of rapid analytical methods in the dairy industry has become imperative. This is because of the documented evidence of adulteration, microbial contamination and the influence of feed on the quality of milk and dairy products. Because of the delays involved in the use of wet chemistry methods during the evaluation of these products, rapid analytical techniques such as near-infrared spectroscopy (NIRS) has gained prominence and proven to be an efficient tool, providing instant results. The technique is rapid, nondestructive, precise and cost-effective, compared with other laboratory techniques. Handheld NIRS devices are easily used on the farm to perform quality control measures on an incoming feed from suppliers, during feed preparation, milking and processing of cheese, butter and yoghurt. This ensures that quality feed, milk and other dairy products are obtained. This review considers research articles published in reputable journals which explored the possible application of NIRS in the dairy industry. Emphasis was on what quality parameters were easily measured with NIRS, and the limitations in some instances. [ABSTRACT FROM AUTHOR]

Staple foods, including cereals, legumes, and root/tuber crops, dominate the daily diet of humans by providing valuable proteins, starch, oils, minerals, and vitamins. Quality evaluation of staple foods is primarily carried out on sensory (e.g. external defect, color), adulteration (e.g. species, origin), chemical (e.g. starch, proteins), mycotoxin (e.g.Fusariumtoxin, aflatoxin), parasitic infection (e.g. weevil, beetle), and internal physiological (e.g. hollow heart, black heart) aspects. Conventional methods for the quality assessment of staple foods are always laborious, destructive, and time-consuming. Requirements for online monitoring of staple foods have been proposed to encourage the development of rapid, reagentless, and noninvasive techniques. Spectroscopic techniques, such as visible–infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance spectroscopy, and spectral imaging, have been introduced as promising analytical tools and applied for the quality evaluation of staple foods. This review summarizes the recent applications and progress of such spectroscopic techniques in determining various qualities of staple foods. Besides, challenges and future trends of these spectroscopic techniques are also presented. [ABSTRACT FROM PUBLISHER]