temperature instability
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In this study, the instability of extreme temperatures is defined as the degree of perturbation of the spatial and temporal distribution of extreme temperatures, which is to show the uncertainty of the intensity and occurrence of extreme temperatures in China. Based on identifying the extreme temperatures and by analyzing their variability, we refer to the entropy value in the entropy weight method to study the instability of extreme temperatures. The results show that TXx (annual maximum value of daily maximum temperature) and TNn (annual minimum value of daily minimum temperature) in China increased at 0.18 °C/10 year and 0.52 °C/10 year, respectively, from 1966 to 2015. The interannual data of TXx’ occurrence (CTXx) and TNn’ occurrence (CTNn), which are used to identify the timing of extreme temperatures, advance at 0.538 d/10 year and 1.02 d/10 year, respectively. In summary, extreme low-temperature changes are more sensitive to global warming. The results of extreme temperature instability show that the relative instability region of TXx is located in the middle and lower reaches of the Yangtze River basin, and the relative instability region of TNn is concentrated in the Yangtze River, Yellow River, Langtang River source area and parts of Tibet. The relative instability region of CTXx instability is distributed between 105° E and 120° E south of the 30° N latitude line, while the distribution of CTNn instability region is more scattered; the TXx’s instability intensity is higher than TNn’s, and CTXx’s instability intensity is higher than CTNn’s. We further investigate the factors affecting extreme climate instability. We also find that the increase in mean temperature and the change in the intensity of the El Niño phenomenon has significant effects on extreme temperature instability.

Purpose Smart ubiquitous sensors have been deployed in wireless body area networks to improve digital health-care services. As the requirement for computing power has drastically increased in recent years, the design of low power static RAM-based ubiquitous sensors is highly required for wireless body area networks. However, SRAM cells are increasingly susceptible to soft errors due to short supply voltage. The main purpose of this paper is to design a low power SRAM- based ubiquitous sensor for healthcare applications. Design/methodology/approach In this work, bias temperature instabilities are identified as significant issues in SRAM design. A level shifter circuit is proposed to get rid of soft errors and bias temperature instability problems. Findings Bias Temperature Instabilities are focused on in recent SRAM design for minimizing degradation. When compared to the existing SRAM design, the proposed FinFET-based SRAM obtains better results in terms of latency, power and static noise margin. Body area networks in biomedical applications demand low power ubiquitous sensors to improve battery life. The proposed low power SRAM-based ubiquitous sensors are found to be suitable for portable health-care devices. Originality/value In wireless body area networks, the design of low power SRAM-based ubiquitous sensors are highly essential. This design is power efficient and it overcomes the effect of bias temperature instability.

AbstractHome blood pressure (HBP) variability is an important factor for cardiovascular events. While several studies have examined the effects of individual attributes and lifestyle factors on reducing HBP variability, the effects of living environment remain unknown. We hypothesized that a stable home thermal environment contributes to reducing HBP variability. We conducted an epidemiological survey on HBP and indoor temperature in 3785 participants (2162 households) planning to have their houses retrofitted with insulation. HBP was measured twice in the morning and evening for 2 weeks in winter. Indoor temperature was recorded with each HBP observation. We calculated the morning-evening (ME) difference as an index of diurnal variability and the standard deviation (SD), coefficient of variation (CV), average real variability (ARV) and variability independent of the mean (VIM) as indices of day-by-day variability. The association between BP variability and temperature instability was analyzed using multiple linear regression models. The mean ME difference in indoor/outdoor temperature (a decrease in temperature overnight) was 3.2/1.5 °C, and the mean SD of indoor/outdoor temperature was 1.6/2.5 °C. Linear regression analyses showed that the ME difference in indoor temperature was closely correlated with the ME difference in systolic BP (0.85 mmHg/°C, p < 0.001). The SD of indoor temperature was also associated with the SD of systolic BP (0.61 mmHg/°C, p < 0.001). The CV, ARV, and VIM showed similar trends as the SD of BP. In contrast, outdoor temperature instability was not associated with either diurnal or day-by-day HBP variability. Therefore, residents should keep the indoor temperature stable to reduce BP variability.