Slow Atomic Motion in Zr-Ti-Cu-Ni-Be Metallic Glasses Studied by NMR

Nuclear magnetic resonance is used for the first time to detect slow atomic motion in metallic glasses, specifically, Be motion in Zr-Ti-Cu-Ni-Be bulk metallic glasses. The observations are not consistent with the vacancy-assisted and interstitial diffusion mechanisms and favor the spread-out free volume fluctuation mechanism for Be diffusion. Comparison with the results of Be diffusion measured by elastic backscattering the NMR results also indicates that the energy barriers for short- and long-range Be motion are the same

Measuring and analysing vibration motors in insoles via accelerometers

Purpose: Falling is a major public health concern among elderly people, and they often cause serious injuries1,2. They most frequently occur during walking and are associated with the chronic deterioration in the neuromuscular and sensory systems, as well as with ankle muscle weakness and lower endurance of these muscles to fatigue1,3. Vibrating insoles, providing a subsensory mechanical noise signal to the plantar side of the feet, may improve balance in healthy young and older people and in patients with stroke or diabetic neuropathy4. The object of this study is to find the most suitable vibrator to put into the insole which can effectively improve the balance control of the elderlies. Method: We choose three different vibration actuators (micro vibration motor, brushless motor and eccentric motor) with two different weights on the insole. First, we put three same motors and two accelerometers on the insole, as shown in Figure1, then attach another layer on both side of the insole. Second, connect the motors to the power supply and the accelerometer to NI PXI-1033 spectrum analyzer which is used to collect the accelerometers' data. At last, using Fast Fourier Transform (FFT) to analyze and compare the results to see which motor is the most stable and suitable to put into the insole. Results & Discussion: The results showed that the most stable one is the brushless motor. The reason why the frequency is stable is that the relationship between voltage and frequency is linear, and the error is small through continuous measurements. On the other hand, when a person weight 55 kg stands on the insole, the frequency isn't affected by the weight. These two results appear very similar to each other, as shown in Figure 2. According to the result, we use the brushless motor to be our vibrator in the insole, and hope this will help the elderlies improve their balance control ability more efficiency

The Making of Cloud Applications An Empirical Study on Software Development for the Cloud

Cloud computing is gaining more and more traction as a deployment and provisioning model for software. While a large body of research already covers how to optimally operate a cloud system, we still lack insights into how professional software engineers actually use clouds, and how the cloud impacts development practices. This paper reports on the first systematic study on how software developers build applications in the cloud. We conducted a mixed-method study, consisting of qualitative interviews of 25 professional developers and a quantitative survey with 294 responses. Our results show that adopting the cloud has a profound impact throughout the software development process, as well as on how developers utilize tools and data in their daily work. Among other things, we found that (1) developers need better means to anticipate runtime problems and rigorously define metrics for improved fault localization and (2) the cloud offers an abundance of operational data, however, developers still often rely on their experience and intuition rather than utilizing metrics. From our findings, we extracted a set of guidelines for cloud development and identified challenges for researchers and tool vendors

Negative Link Prediction in Social Media

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework