Slippage of 4He Films and Precursor Phenomenon of Superfluidity

We have carried out quartz crystal microbalance (QCM) experiments for ⁴He films on an exfoliated single-crystalline graphite using a 32 kHz tuning fork, and have measured the temperature dependence of the resonance frequency and the Q value for various areal densities and oscillation amplitudes. Comparing with the previous experiments for Grafoil, the decoupling of the films due to the slippage or the superfluidity was larger than that of Grafoil, and the competition between the slippage and the superfluidity was observed in three-atom thick films. Furthermore, it was found that the slippage is suppressed gradually at higher temperature than the superfluid onset T _c , and that the relaxation time decreases at low temperatures while it obeys the Arrhenius law at high temperatures. These results suggest a precursor to the superfluidity of ⁴He films.     

Maintenance of postharvest quality and bioactive compounds of fresh‐cut sweet leaf bush (Sauropus androgynus L. Merr.) through hot CaCl2 dips

The effects of hot CaCl₂ dips on postharvest quality and bioactive compounds of fresh‐cut sweet leaf bush were investigated. The vegetable was dipped in 0.5% or 1.0% (w/v) of CaCl₂ solutions at 40 °C for 30 s and the control was the untreated sample. Hot CaCl₂ dips significantly delayed the weight loss, maintained the overall quality and inhibited the wiltness (P < 0.05). Both hot CaCl₂ dips had no effect on the changes in lightness (L*) and delayed the decrease in greenness (‐a*) and total chlorophyll content and the increase in both yellowness (b*) and carotenoid content. Hot CaCl₂ dips enhanced total antioxidants, total phenolics content and both AsA‐POD and G‐POD activities from 38.6, 0.44, 15.2 and 22.4 at day 0 to 55.3, 0.55, 50.5 and 362.4 at day 8, respectively. Moreover, hot CaCl₂ dips also delayed the loss of DPPH free radical scavenging activity, total flavonoid, ascorbic acid and CAT activity when compared to other treatments during storage. This study showed that hot CaCl₂ dip maintains postharvest quality and enhanced bioactive compounds of fresh‐cut sweet leaf bush during storage.     

Proof-of-concept study of chitosan-based carbon dioxide indicator for food packaging applications

A chitosan-based carbon dioxide (CO₂) indicator was developed to monitor freshness or quality of packaged foods during their storage, as elevated CO₂ levels inside a package is one of the prime indicators of microbial spoilage of food. The objective of this proof-of-concept study, therefore, was to develop a chitosan-based CO₂ indicator and to investigate its performance in different operating environments. For the enhancement of signal strength of indicator, 2-amino-2-methyl-1-propanol (AMP) was used as an additive to the chitosan solution. Aqueous chitosan–AMP suspensions were prepared by addition of chitosan (0.1%, 0.2% and 0.4%) and AMP (0%, 5%, 10% and 20%) to distilled water at pH 7.0. Thereafter, the aqueous chitosan suspensions were incubated under 100% and 20% CO₂ test atmospheres. The aqueous solubility of chitosan, which in turn impacts the degree of transparency of the suspension, was tested at different pHs and time intervals, spectrometrically. The transparency of 0.2% chitosan aqueous suspension increased dramatically below pH 6.5 under 20% or 100% CO₂ incubation. The addition of a 5% AMP concentration to the chitosan suspension was found optimal for maximum CO₂ absorption and this, therefore, increased transparency by more than 90%.     

Deep Q-network-based auto scaling for service in a multi-access edge computing environment

In 5G networks, it is necessary to provide services while meeting various service requirements, such as high data rates and low latency, in response to dynamic network conditions. Multi-access edge computing (MEC) is a promising concept to meet these requirements. The MEC environment enables service providers to deploy their low latency services that are composed of multiple components. However, operating a service manually and attempting to satisfy the quality of service (QoS) requirements is difficult because many factors need to be considered in an MEC scenario. In this paper, we propose an auto-scaling method using deep Q-networks (DQN), which is a reinforcement learning algorithm, to resize the number of instances assigned to service. In our evaluation, compared to other baseline methods, the proposed approach maintains the appropriate number of instances effectively in response to dynamic traffic change while satisfying QoS and minimizing the cost of operating the service in the MEC environment. The proposed method was implemented as a module running in OpenStack and published as open-source software.     

Adaptive path finding algorithm in dynamic environment for warehouse robot

Neural Computing and Applications - Warehouse robots have been widely used by manufacturers and online retailer to automate good delivery process. One of the fundamental components when designing a...     

How latecomers catch up to leaders in high-energy physics as Big Science: transition from national system to international collaboration

Scientometrics - This study describes the increase of research productivity of latecomer countries (latecomers) in the high-energy physics (HEP) community by research strategies based on a national...     

Developing a custom DSP for vision based human computer interaction applications

Multimedia Tools and Applications - As the computing power of modern devices become greater, computer vision is increasingly adopted as the means of human-computer interaction. The industry is...