Short-Term Fluctuation of Subjective Age and its Correlates: An Ecological Momentary Assessment of Older Adults

We examined short-term fluctuations of subjective age with data obtained from 123 young-old (Mage = 67.19 years) and 47 old-old adults (Mage = 86.59 years) who reported their momentary subjective age six times a day over seven consecutive days as they were going about their everyday lives. Participants felt younger on a large majority of occasions, and 25% of the total variability in subjective age could be attributed to within-person variation. Those with younger trait subjective ages exhibited larger moment-to-moment variation, while chronological age did not impact variability. Furthermore, we investigated relationships between within-day fluctuations of subjective age and daily cortisol fluctuations. Our findings extend the literature on subjective age by showing that how old people feel can vary on a momentary basis, that state and trait components of subjective age are related, and that fluctuations in subjective age are related to biomarkers of stress.

High-normal blood pressure (prehypertension) is associated with PM2.5 exposure in young adults

We aimed to examine PM2.5 exposure, blood pressure (SBP and DBP) measurement, hypertension risk factors and to assess the association between PM2.5 exposure and hypertension among young adults. The mean SBP was 117.78 mmHg, with 11.22% high-normal blood pressure (prehypertension) and 2.51% hypertension (≥ 140 mmHg). DBP was 75.48 mmHg with 26.37% prehypertension and 4.53% hypertension (≥ 90 mmHg). The median PM2.5 in the past year was 31.79 µg/m3, with highest in winter (49.33 µg/m3), followed by spring (37.34 µg/m3), autumn (29.64 µg/m3) and summer (24.33 µg/m3). Blood pressure was positively correlated with age, height, weight, BMI, daily smoking, alcohol consumption, mental stress and stay-up in the past 1 year. After adjustment for the covariates, each 10 µg/m3 increase in PM2.5 was associated with SBP (Day 1 = 1.07 mmHg, Day 3 = 1.25 mmHg, Day 5 = 1.01 mmHg) and DBP (Day 1 = 1.06 mmHg, Day 3 = 1.28 mmHg, Day 5 = 1.29 mmHg, Day 15 = 0.87 mmHg, Day 30 = 0.56 mmHg). Exposure in winter was associated with 1.17 mmHg increase in SBP. Logistic models showed for every 1 µg/m3 increase of PM2.5, SBP in Day 1 and Day 5 was increased by 6% and 4%, and DPB by 3% and 16%, respectively. SBP was increased by 13% in spring and 7% in winter, and DBP was increased by 12% in winter. Our data suggest a certain prevalence of pre- or hypertension among young population, which is associated with short-term fluctuation and season-specific exposure of PM2.5.

Long-Term Sea Level Cycle Affects Predictions of Future Rise

New research confirms the existence of a regular, long-term fluctuation in sea level, perhaps caused by processes in Earth’s core.

Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling

Trading volume movement prediction is the key in a variety of financial applications. Despite its importance, there is few research on this topic because of its requirement for comprehensive understanding of information from different sources. For instance, the relation between multiple stocks, recent transaction data and suddenly released events are all essential for understanding trading market. However, most of the previous methods only take the fluctuation information of the past few weeks into consideration, thus yielding poor performance. To handle this issue, we propose a graph-based approach that can incorporate multi-view information, i.e., long-term stock trend, short-term fluctuation and sudden events information jointly into a temporal heterogeneous graph. Besides, our method is equipped with deep canonical analysis to highlight the correlations between different perspectives of fluctuation for better prediction. Experiment results show that our method outperforms strong baselines by a large margin.

Does the short-term fluctuation of mineral element concentrations in the closed hydroponic experimental facilities affect the mineral concentrations in cucumber plants exposed to elevated CO2?

Aims Studies dealing with plants’ mineral nutrient status under elevated atmospheric CO2 concentration (eCO2) are usually conducted in closed hydroponic systems, in which nutrient solutions are entirely renewed every several days. Here, we investigated the contribution of the fluctuation of concentrations of N ([N]), P ([P]), and K ([K]) in nutrient solutions in this short period on their concentrations in cucumber plants exposed to different [CO2] and N levels. Methods Cucumber (Cucumis sativus L.) plants were hydroponically grown under two [CO2] and three N levels. [N], [P], and [K] in nutrient solutions and cucumber plants were analyzed. Results The transpiration rate (Tr) was significantly inhibited by eCO2, whereas Tr per plant was increased due to the larger leaf area. Elevated [CO2] significantly decreased [N] in low N nutrient solutions, which imposed an additional decrease in [N] in plants. [P] in nutrient solutions fluctuated slightly, so the change of [P] in plants might be attributed to the dilution effect and the demand change under eCO2. [K] in moderate and high N nutrient solutions were significantly decreased, which exacerbated the [K] decrease in plants under eCO2. Conclusions The short-term fluctuation of [N] and [K] in nutrient solutions is caused by the asynchronous uptakes of N, K, and water under eCO2, which has an appreciable influence on [N] and [K] in plants besides the dilution effect. This defect of the closed hydroponic system may let us exaggerate the negative impact of eCO2 itself on [N] and [K] in plants.

Research on Stock Price Forecast Based on ARIMA-GARCH Model

The stock plays a vital role in economic life, and the economic development of enterprises can be measured by the development and change of stocks. In this paper, the closing price of Ping An stock in China from 2017 to 2019 is selected as the time series empirical analysis data, and the ARIMA-GARCH model is established to predict the law and trend of the stock price change. The results show that the compound model can fit the fluctuation law well, and reasonably predict the short-term fluctuation trend.