Suchergebnisse

Intro -- LONG-TERM CARE: BACKGROUND AND ISSUES -- LONG-TERM CARE: BACKGROUND AND ISSUES -- CONTENTS -- PREFACE -- Chapter 1 LONG-TERM CARE (LTC): FINANCING OVERVIEW AND ISSUES FOR CONGRESS -- SUMMARY -- INTRODUCTION -- WHAT IS LTC? -- SOURCES OF FINANCING FOR LTC -- Public Sources of Financing for LTC -- Medicaid -- Eligibility -- Eligibility Criteria: Group One -- Eligibility Criteria: Group Two -- Other Requirements -- Services -- Mandatory Services -- Optional Services -- Medicare -- Home Health Services -- Skilled Nursing Facility (SNF) Services -- Other Public Payers -- Private Sources of Financing for LTC -- Private LTC Insurance -- Out-of-Pocket Spending -- Other Private Funds -- LTC ISSUES FOR CONGRESS -- Barriers to Public and Private Coverage -- Strain on Federal, State, and Local Budgets -- Better Coordinating Care across Medicaid, Medicare, and Other Public Programs -- Better Coordinating Care across Provider Settings -- Medicaid Spending Still Dominated by Institutional Care -- Navigating the Current LTC System Is Complex -- Limited Supply of the LTC Workforce -- Limited Access to Affordable Housing -- Guaranteeing Quality Can Be Challenging -- End Notes -- Chapter 2 FACTORS AFFECTING THE DEMAND FOR LONG-TERM CARE INSURANCE: ISSUES FOR CONGRESS -- SUMMARY -- INTRODUCTION -- PRIVATE LONG-TERM CARE INSURANCE -- Current Financing of LTC Services -- LTC Insurance Industry Trends -- FACTORS AFFECTING THE DEMAND FOR PRIVATE LTCI -- Cost and Complexity of Long-Term Care Insurance -- The Type of Coverage -- Dollar Amount of Coverage and Annual Inflation Adjustments -- Duration of the Benefit -- Elimination Period -- Adequacy of Consumer Protections For LTCI Policyholders -- State Oversight -- Federal Oversight -- Premium Instability -- Inappropriate Sales Practices -- Inappropriate Denial of Claims -- Solvency of LTC Insurers

Air pollution levels have risen as an outcome of urban and industrial development in so many developing countries. People and governments all around the world are concerned about air pollution, which has a severe influence on both personal health and long-term global development. As a government, it is responsible for preventing and controlling air pollution, as well as monitoring the pollutant's impacts on human health. There are numerous computer models available, ranging from statistics to artificial intelligence. Pollution levels are still out of control in some parts of the world due to a wide range of sources and factors. Because of accurate estimates of future air pollution, the government can take necessary action. Forecasting air pollution levels based on environmental data is becoming increasingly relevant as people become more worried about global warming and urban sustainability. For replicating the complicated linkages between these variables, advanced Deep Learning (DL) algorithms hold enormous promise. The objective of this work is to provide a high level of accurate solution to the air pollution forecasting problem. Kaggle data will be employed to train a DL model that will forecast air pollution levels.

To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan's wind power output datasets.

Abstract
Due to increasing changes in demographics, maintaining cognitive functioning later in life has become both economic and social concerns, and thus finding a cost-effective solution is one of the priorities in research. Factors like physical and intellectual activities have been associated with better cognitive performance in later life. While several studies have considered the impact of short-term physical activity interventions on cognitive functioning, retrospective research focusing on life-time physical activity experience has been sparse. The aim of the study was to determine the relationship between memory performance and whole brain matter integrity in seniors with different regular life-long physical activity experience. Fifty-three Latvian seniors aged 65–85 (M = 72.25, SD = 5.03, 83% female) with no self-reported chronic disease participated in the study. Measures of memory, physical activity and whole brain matter integrity were obtained and analysed. The obtained results indicated no significant relationship between physical activity experience and short and long-term memory and whole brain matter integrity; however, brain matter integrity was significantly correlated with demographic factors like age and education. These results might be related to inadequate physical activity measures, as well as unequal physical activity experience in participants. In the future, more detailed assessment of physical activity experience should be considered.

Political scientists often wish to classify documents based on their content to measure variables, such as the ideology of political speeches or whether documents describe a Militarized Interstate Dispute. Simple classifiers often serve well in these tasks. However, if words occurring early in a document alter the meaning of words occurring later in the document, using a more complicated model that can incorporate these time-dependent relationships can increase classification accuracy. Long short-term memory (LSTM) models are a type of neural network model designed to work with data that contains time dependencies. We investigate the conditions under which these models are useful for political science text classification tasks with applications to Chinese social media posts as well as US newspaper articles. We also provide guidance for the use of LSTM models.

The decline and increase in the price of shares of plantation companies is a problem for investors in making decisions to buy or sell shares. Factors influencing the movement of plantation stock prices include CPO commodity price fluctuations, world oil price fluctuations, Rupiah exchange rate fluctuations, government regulations and policies, demands from importing countries, and climate. Forecasting stock prices is expected to help investors to deal with uncertainty in the movement of plantation stock prices. This study applies the Long Short-Term Memory (LSTM) to predict the stock prices of plantation companies using SSMS, LSIP, and SIMP share price data from the period 1 July 2014 - 22 July 2019. Based on the results of the study it was found that the best LSTM model on SSMS shares by using the RMSProp optimizer and 70 hidden neurons produced an RMSE value of 21,328. Then the best LSTM model on LSIP stock by using Adam optimizer and 80 hidden neurons produces an RMSE value of 33,097. Whereas the best LSTM model on SIMP shares using Adamax optimizer and 100 hidden neurons produced an RMSE value of 8,3337.