Amblyopia is a common cause of vision damage in children, and some aspects of its etiology are not clear. A number of mineral elements have important effects on the nerve and visual nerve systems. However, little is known about the relationship between amblyopia and nutritional mineral elements. In this study, hair samples were collected from 67 children with amblyopia and 57 age-matched control groups. The height and weight of each child was measured, and body mass index (BMI) was calculated. Mineral elements were determined by atomic absorption spectrometry and inductively coupled plasma-atomic emission spectrometry. The calcium and magnesium levels in the hair of amblyopic children were higher (p < 0.006), but the level of manganese were lower compared with those in the control groups (p < 0.006). Other elements measured were found to have an insignificant difference between the two groups (p > 0.006). The BMI of amblyopic children was higher (p < 0.001). These results show that mineral elements may play an important role in the visual development of children. Therefore, studies should pay more attention to investigating the impact of mineral elements on child vision.

Many techniques for prognostics depend on estimating then forecasting health indicators that reflect the overall health or performance of an asset. For vibration data, health indicators are typically calculated by combining various vibration measurements along with derived features extracted from time, frequency or time-frequency domain analysis. However, selecting or handcrafting good features is a labor-intensive task. On the other hand, deep learning models might be able to learn health indicators automatically from vibration data but require large amount of training data, which are typically hard to obtain from real assets. In this paper, we propose an innovative similarity-based feature extraction method for vibration data which can then be used to learn health indicators and estimate remaining useful life of equipment. The method learns a set of representative templates of frequency spectra for both normal and failure states, and then calculates similarity-based features between new vibration data and the set of learned templates. These features are used to estimate health indicators which are then extrapolated to estimate the future health condition of the asset and its remaining useful life. The proposed method has been tested on the PRONOSTIA bearing dataset provided by FEMTO-ST Institute and achieved a higher accuracy in estimating the remaining useful life of bearings compared to other studies. The results demonstrate the effectiveness of the proposed method for assets with limited training data.

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A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation.