Forecasting the Health Transition and Medical Expenditure of the Future Elderly in China: A Longitudinal Study Based on Markov Chain and Two Part Model

Set in the rapid development of population aging, this study focuses on the relationship between health and medical expenditure of the elderly population. Taking the health and medical expenditure of the elderly as the research object, this study analyzes the characteristics and the intrinsic relationship between them. Based on the future elderly model, this study calculates the transition probability of the elderly's self-assessment health state using the Health Transition Model and estimates the medical expenditure of the elderly by the Two-Part Model. Based on the above, this study predicts the trend of the population size and medical expenditure of the elderly in the next 15 years (2020–2035). Based on the results, the policy suggestions are put forward. To begin with, strengthening health management and health services for the elderly in the construction of healthy China. Next, building a comprehensive system of health care for the elderly in government, society, family, and individual. Then, establishing a long-term care service system as soon as possible. In addition, it is better to establish lifelong health consciousness and cultivate healthy accomplishment behavior. Finally, it is necessary to promote gender mainstreaming in the health field.

Advanced ant colony algorithm for high dimensional abnormal data mining in Internet of things

This paper proposes a deep mining method of high-dimensional abnormal data in Internet of things based on improved ant colony algorithm. Preprocess the high-dimensional abnormal data of the Internet of things and extract the data correlation feature quantity; The ant colony algorithm is improved by updating the pheromone and state transition probability; With the help of the improved ant colony algorithm, the feature response signal of high-dimensional abnormal data in Internet of things is extracted, the judgment threshold of high-dimensional abnormal data in Internet of things is determined, and the objective function is constructed to optimize the mining depth, so as to realize the deep data mining. The results show that the average error of the proposed method is only 0.48%.

Composite pulses for high fidelity population transfer in three-level systems

In this work, we propose a composite pulses scheme by modulating phases to achieve high fidelity population transfer in three-level systems. To circumvent the obstacle that not enough variables are exploited to eliminate the systematic errors in the transition probability, we put forward a cost function to find the optimal value. The cost function is independently constructed either in ensuring an accurate population of the target state, or in suppressing the population of the leakage state, or both of them. The results demonstrate that population transfer is implemented with high fidelity even when existing the deviations in the coupling coefficients. Furthermore, our composite pulses scheme can be extensible to arbitrarily long pulse sequences. As an example, we employ the composite pulses sequence for achieving the three-atom singlet state in an atom-cavity system with ultrahigh fidelity. The final singlet state shows robustness against deviations and is not seriously affected by waveform distortions. Also, the singlet state maintains a high fidelity under the decoherence environment.

VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost

Vesicular transport proteins are related to many human diseases, and they threaten human health when they undergo pathological changes. Protein function prediction has been one of the most in-depth topics in bioinformatics. In this work, we developed a useful tool to identify vesicular transport proteins. Our strategy is to extract transition probability composition, autocovariance transformation and other information from the position-specific scoring matrix as feature vectors. EditedNearesNeighbours (ENN) is used to address the imbalance of the data set, and the Max-Relevance-Max-Distance (MRMD) algorithm is adopted to reduce the dimension of the feature vector. We used 5-fold cross-validation and independent test sets to evaluate our model. On the test set, VTP-Identifier presented a higher performance compared with GRU. The accuracy, Matthew’s correlation coefficient (MCC) and area under the ROC curve (AUC) were 83.6%, 0.531 and 0.873, respectively.

Multivariate Markov Chain Model for Sales Demand Estimation in a Company

Estimation of the number of demands for a product must be done correctly, so that the company can get maximum profit. Therefore, this study discusses how to estimate the amount of sales demand in a company correctly. The model that will be used to estimate sales demand is the Multivariate Markov Chain Model. This model can estimate the future state by observing the present state. The model requires parameter estimation values ​​first, namely the transition probability matrix and the weighted Markov chain, where in previous studies an estimation of the transition probability matrix has been carried out, so that in this study we will continue to estimate the weighted Markov chain parameters. This model is compatible with 5 data sequences (product types) defined as product 1, product 2, product 3, product 4, and product 5, with 6 conditions (no sales volume, very slow-moving, slow-moving, standard, fast moving, and very fast moving). As the result, the state probability for product 1, product 2 and product 3 in company 1 are stationary at state 6 (very fast moving), product 4 and product 5 are stationary at state 2 (very slow moving).

The Markov Process as a Model of Migration Based on the Example of the Movement of Banknotes

This paper presents a way of using the Markov chain model for the analysis of migration based on the example of banknote migration between regions in Poland. We have presented the application of the methodology for estimating one-step transition probabilities for the Markov chain based on macro-data gathered during the project conducted in the National Bank of Poland (NBP) in the period of December 2015–2018. We have shown the usefulness of state-aggregated Markov chain not only as a model of banknote migration but as migration in general. The banknotes are considered here as goods, so their migration is strictly related to, inter alia, the movement of people (commuting to work, business trips, etc.).Thus, the gravity-like properties of cash migration pointed to the gravity model as one of the most pervasive empirical models in regional science. Transition probability of the Markov chain expressing the attractive force between regions allows for estimating the gravity model for the identification of relevant reasons of note and, consequently, people migration.

Description of the energy levels and electric quadrupole transitions of the 92Nb and 92Mo nuclei using nuclear shell model

In current study ,92Nb and 92Mo isotopes have been determined for calculating energy levels and electric quadrupole transition probabilities. Two interaction have been applied in this study are surface delta and modified surface delta interactions. The calculations have been achieved by using appropriate effective charges for proton and neutron as well as parameter length of harmonic potential. Computed results have been compared with the experimental values. After this comparison, energy and the transition probability values have a good agreement with the experimental values, also there are values of the total angular momentum and parity are determined and confirmed for some of the experimental energies, undetermined and unconfirmed experimentally. Theoretically, new values of quadrupole electric transition probabilities have been explored which have not been known in the experimental data.

Incorporating rainy season and reproductive phenology into the survival and transition rates of the invasive species Sambucus nigra: An approximation with multi-state models

Although it has been demonstrated that environmental changes within a year can affect the reproduction, survival, and growth of invasive species, these factors have rarely been incorporated into the demographic analysis. Therefore, we applied multi-state demographic models (based on capture–recapture animal methods accounting for imperfect detectability of individuals in natural conditions) to evaluate the effects of reproductive phenology and rainy season on the survival and transition/retrogression rates among stage categories of Sambucus nigra (L)—an invasive tree species, widely distributed in temperate forests of Europe and America. In the Abies religiosa temperate forest, Mexico City, a multi-state demographic model of S. nigra was built using bi-monthly censuses during a year. We selected the best-fitting model according to Akaike’s information criterion (AICc). We determined the response of reproductive phenology of S. nigra to the rainy season for two years through repeatability and phenotypic plasticity indexes. Our results showed that the reproductive phenology of S. nigra has a low repeatability index and a high phenotypic plasticity index. We demonstrated that additive and interactive effects of reproductive phenology and rainy season promote changes in survival and transition/retrogression rates among stage categories. During the rainy season, the survival probability of seedlings and transition probability towards the adult category increased. Therefore, our study represents a significant contribution to the knowledge of the demographic dynamics of invasive species on an intra-annual scale.

A Predictive Energy Management Strategy for Multi-Energy Source Vehicles Based on Full-Factor Trip Information

To achieve the real-time application of a dynamic programming (DP) control strategy, we propose a predictive energy management strategy (PEMS) based on full-factor trip information, including vehicle speed, slip ratio and slope. Firstly, the prediction model of the full-factor trip information is proposed, which provides an information basis for global optimization energy management. To improve the prediction’s accuracy, the vehicle speed is predicted based on the state transition probability matrix generated in the same driving scene. The characteristic parameters are extracted by a feature selection method taken as the basis for the driving condition’s identification. Similar to speed prediction, regarding the uncertain route at an intersection, the slope prediction is modelled as a Markov model. On the basis of the predicted speed and the identified maximum adhesion coefficient, the slip ratio is predicted based on a neural network. Then, a predictive energy management strategy is developed based on the predictive full-factor trip information. According to the statistical rules of DP results under multiple standard driving cycles, the reference SOC trajectory is generated to ensure global sub-optimality, which determines the feasible state domain at each prediction horizon. Simulations are performed under different types of driving conditions (Urban Dynamometer Driving Schedule, UDDS and World Light Vehicle Test Cycle, WLTC) to verify the effectiveness of the proposed strategy.