Cow's milk is an important foodstuff and beneficial to human health. In the present study, commercial milks from China and Japan and raw milk from Inner Mongolia of China were collected. The contents of 18 elements were determined using inductively coupled plasma-optical emission spectrometry (ICP-OES), atomic absorption spectrometry (AAS) and atomic fluorescence spectrometry (AFS). Our analysis showed both Chinese and Japanese milks are rich in macroelements, such as calcium, potassium. However, the milk contents of chromium, manganese and zinc, which belong to microelements, were higher in Chinese commercial milks than in Japanese commercial milks. Heavy metals in food pose potential healthy risk. Although lead and cadmium contents in Chinese milk did not exceed the tolerance limits of Chinese National Standards, they were higher than those in Japanese commercial milks. Based on the high contents of some microelements and heavy metals in Chinese milk, we should innovate the technology and improve quality control for milk process and decrease environmental pollution.

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Device-free passive detection is an emerging technology to detect whether there exist any moving entities in the areas of interest without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, and so forth. Despite the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to a finer-grained channel descriptor at the physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored the full potential of CSI for human detection. Moreover, space diversity supported by nowadays popular multiantenna systems are not investigated to a comparable extent as frequency diversity. In this article, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both full information (amplitude and phase) of CSI and space diversity across multiantennas in MIMO systems are exploited to extract and shape sensitive metrics for accuracy and robust target detection. We prototype PADS on commercial WiFi devices, and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.

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Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

An experimental device, based on the light-gas gun technology, was set up to realize high speed cutting over a wide range of cutting speeds from 30 m/s to 200 m/s. High-speed cutting experiments were performed on AISI 1045 steels. The investigation of chip morphology, micro-structures, micro-hardness and the finished surface integrity were carried out, focusing on the physical phenomena accompanying the saw-tooth chip formation. The results reveal that, with increasing the cutting speed, the transition of chip morphology from continue to saw-tooth could be attributed to repeated thermoplastic shearbanding rather than periodic cracking. In particular, a severe material flow leading to mass transfer of heat was observed at very high cutting speed. The effect of mass transfer of heat on thermoplastic shear instability was further investigated, which implies that the mass transfer of heat would retard the formation of saw-tooth chip. Finally, the influence of cutting speed and mass transfer on the temperature distribution during high speed machining was briefly discussed.