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Since the issuance in 2009, the digital currency has enjoyed an increasing popularity and has become one of the most important options for global investors. The purpose of this paper is to propose a hybrid model ( KDJ–Markov chain) which integrates the advantages of the stochastic index (KDJ) and grey Markov chain methods and provide a useful decision support tool for investors participating in the digital currency market.

Taking Litecoin's closing price prediction as an example, the closing prices from May 2 to June 20, 2017, are used as the training set, while those from June 21 to August 9, 2017, are used as the test set. In addition, an adaptive KDJ–Markov chain is proposed to enhance the adaptability for dynamic transaction information. And the paper verifies the effectiveness of the KDJ–Markov chain method and adaptive KDJ–Markov chain method.

The results show that the proposed methods can provide a reliable foundation for market analysis and investment decisions. Under the circumstances the accuracy of the training set and the accuracy of the test set are 76% and 78%, respectively.

This study not only solves the problems that KDJ method cannot accurately predict the next day's state and the grey Markov chain method cannot divide the states very well, but it also provides two useful decision support tools for investors to make more scientific and reasonable decisions for digital currency where there are no existing methods to analyze the fluctuation.

A new approach to analyze the fluctuation of digital currency, in which there are no existing methods, is proposed based on the stochastic index (KDJ) and grey Markov chain methods. And both of these two models have high accuracy.

In this chapter we perform a Monte Carlo simulation study of the errors-in-variables model examined in Ramsey, Gallegati, Gallegati, and Semmler (2010) by using a wavelet multiresolution approximation approach. Differently from previous studies applying wavelets to errors-in-variables problem, we use a sequence of multiresolution approximations of the variable measured with error ranging from finer to coarser scales. Our results indicate that multiscale approximations to the variable observed with error based on the coarser scales provide an unbiased asymptotically efficient estimator that also possess good finite sample properties.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

Modern e‐health systems incorporate different healthcare providers in one system and provide an electronic platform to share medical information efficiently. In cross‐context communications between healthcare providers, the same information can be interpreted as different types or values, so that one patient will be issued different identifiers by different healthcare providers. This paper aims to provide a solution to ensure interoperability so that multiple healthcare providers will be able to collaborate in one e‐health system.

This paper primarily focuses on how different healthcare providers, instead of the patients, are able to interact and share information on a common e‐health platform.

In the course of the work, it was found that previous e‐health solutions mainly have a limited view of patient information, where a user‐centric approach for identity management is usually restricted to a single healthcare provider. Interoperability in an e‐health system becomes more problematic when more actors collaborate, and hence linkability from one context to another should not be straightforward. However, some form of linkability, such as the possibility to follow up a patient's medical treatment, is desirable in the e‐health sector, even when it needs to cross different contexts. Therefore, the authors have designed an identity management mechanism to ensure semantic interoperability when data is exchanged among different authorized healthcare providers.

The paper points out that the next generation of e‐health will move towards federated e‐health and will require user‐centricity and transparency properties so that patients are able to specify and verify the disclosure of their medical information.

This paper proposes a new service for cross‐context identity management in e‐health systems, improving interoperability between agencies when context‐specific information is transferred from one healthcare provider to another. How the proposed cross‐context identity management service can be integrated in an e‐health system is explained with a use case scenario.

This paper aims to evaluate the association between carbohydrate quality index (CQI) and nutrient adequacy in Iranian adults.

A total of 268 men and women with ages ranged from 18 to 70 years were evaluated in a cross-sectional study. The CQI was calculated by adding together the three components, namely, the ratio of solid to total carbohydrate, dietary fiber and glycemic index. The scores of three components were summed to calculate the CQI, with a higher score indicating a higher dietary carbohydrate quality. The odds ratios (ORs) of nutrient adequacy ratio (NAR), defined as the ratio of intake of a nutrient to the age- and gender-specific recommended dietary allowance, for the intake of energy and 10 nutrients across quartiles of the CQI were calculated by logistic regression analysis and expressed with 95% confidence intervals (CIs).

CQI ranged between 3 to 15 (mean ± SD: 9 ± 1.9). Being in top versus bottom quartile of the CQI was associated with a higher NAR of folic acid (OR: 3.20, 95% CI: 1.06–9.62; P-trend: <0.001), vitamin A (OR: 3.66; 95% CI: 1.46–9.17; P-trend: <0.001), magnesium (OR: 5.94; 95% CI; 1.71–20.53; P-trend: <0.001), vitamin C (OR: 7.85; 95% CI; 2.99–20.59; P-trend: <0.001).

A higher CQI was associated with greater micronutrient consumption adequacy in Iranian adults. The results suggest that increasing the consumption of total fiber and solid carbohydrates and decreasing the glycemic index of the diet and liquid carbohydrates can improve micronutrient intake adequacy.

This study aims to identify the relationships among self-efficacy, health literacy, self-care and glycemic control in older people with type 2 Diabetes Mellitus (DM).

This study was a descriptive analytics correlational study with a cross-sectional design. The sampling method was purposive sampling involving 68 older people with type 2 DM.

The results showed that self-efficacy, health literacy and self-care correlated with glycemic control at significant levels of p = 0.020, p = 0.002 and p = 0.022, respectively.

Nurses should help older people with type 2 DM in maintaining their self-efficacy and self-care and increasing their health literacy to ensure their glycemic control is in normal state.

This study showed that self-care, self-efficacy and health literacy had a significant correlation with glycemic control in older people with type 2 DM. It indicates that the better self-care, self-efficacy and health literacy of patients, the more likely the patients’ blood HbA1C level to be in the normal range.