Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

In order to ensure the benign operation of the social security fund system, it is necessary to understand the social security fund facing all aspects of the risk, more importantly to know the relationship between different risks. Based on RBF, the interpretative structure model is applied to draw the risk correlation hierarchy diagram, which provides a scientific risk management method for the social security fund. RBF neural network is used to build the risk warning model of social security fund operation. Then, put forward the corresponding risk treatment scheme to the warning signal. Finally, the RBF neural network is used for comprehensive risk warning. In this paper, the risk warning of social security fund operation is the research object, and the corresponding risk treatment scheme is put forward for the warning signal. This paper uses an improved ant colony algorithm to optimize the parameters of the RBF neural network, which overcomes the shortcomings of the traditional RBF neural network such as slow convergence, ease of falling into local extremes, and low accuracy, and improves the generalization ability of the RBF neural network. It has the characteristics of good output stability and fast convergence speed. On this basis, the prediction model based on the improved ANT colony-RBF neural network is established, and the MATLAB software calculation tool is used for accurate calculation, which makes the prediction results of coal mine safety risk more accurate and provides more reliable decision basis for decision makers. The results show that the network has small calculation error, fast convergence, and good generalization ability.

Description of the characteristics of the clinical incidents reported in 2020 to the voluntary reporting system of a pediatric hospital in Costa Rica

The objective of this manuscript was to describe the clinical incidents that were sent to the voluntary reporting system during 2020 at the National Children's Hospital of Costa Rica, belonging to the Costa Rican Social Security Fund. A descriptive observational study of the consolidated data that was sent during the months of January to December of the year 2020 was carried out. During 2020, 1.6% of the patients treated in the hospital experienced some type of clinical incident. The total discharges decreased by 38.4% compared to the discharges of the year 2019, however, the reported clinical incidents increased in the year 2020 by 37.6%, especially from the month of August. Sentinel events were not reported this year. The services that made the highest number of reports were Intensive Care (14.3%), General Surgery (12%), Neonatology (9.8%) and Infectiology (9%). The day on which the most incidents were reported was Wednesday (27.8%), in the first hospital shift most of the cases were reported (48.1%) and these incidents occurred predominantly to male individuals (66%). Regarding the age of the patients, the majority were in the age range from 1 year to less than 5 years (36.1%), followed by the age range from over 29 days to under 1 year (24, 1%). Most of the cases were related to the care provided to the patient (63.9%). 41.4% of the incidents required clinical measures but the sequelae were transitory. 51.1% of the cases merited some type of additional medical care to their therapeutic scheme upon admission. 96% of clinical incidents were reported by nursing staff. Most of the clinical incidents (35.3%) in this period were errors related to notes in the digital file.

Investigating the Impact of Firm Specific Determinants of Non-Pension Fund on Property Investment Decisions: An Evidence from Zanzibar Social Security Fund in Tanzania

The aim of this study was to look at the impact of firm specific determinants of non-pension fund on property investment decisions, a case study of Zanzibar Social Security Fund (ZSSF) in Tanzania. The unit root test, co-integration, and vector error correction model (VECM) were used for estimation in the linear econometric model equation, which looked at the impact of three firm specific determinants of non-pension fund on property investment decisions: urbanization (URB), inflation rate (INF) and interest rate (IR). The estimated result showed that, there was presence of long-run relationship at equilibrium between property investment decisions (PID) in ZSSF and all tested determinants of property investment decisions. The results revealed that urbanization (URB) had positive significant long run relationship with property investment decisions in ZSSF. But it was further revealed that the inflation rate (INF) and interest rate (IR) had negative relationship with PID at ZSSF, though they were statistically significant. The results also revealed unidirectional causality relationship whereby PID causes IR. Furthermore, the results revealed unidirectional causal relationship from URB to PID at 5% level of significant. However, the result revealed that PID and INF were not granger cause each other in a short run. The study then recommends among others, that management of ZSSF has to consider these determinants when they make property investment decisions, this including, the need for management of Zanzibar Social Security Fund to work together with financial institutions like banks to develop a working formula on how they can facilitate mortgage facility at reasonable rate for residential and commercial properties, this will help to increase demand for real estate to society.

Taxation of the Self‑employed in Poland and other EU Countries – a Comparative Analysis

The article aims to compare the taxation of the self‑employed in Poland and other EU countries. We show that, for years, Poland has been at the forefront of EU countries with the highest self‑employment rates. Our analysis indicates that many people in Poland chose the status of self‑employed, guided by tax optimization. Due to large differences in the burden of income tax and social security contributions of people working full‑time and choosing self‑employment, there are strong incentives to move from employment to fictitious self‑employment. Our study shows that this significantly affects the revenues of the state budget and social security fund in Poland.

Construction of Social Security Fund Cloud Audit Platform Based on Fuzzy Data Mining Algorithm

Guided by the theories of system theory, synergetic theory, and other disciplines and based on fuzzy data mining algorithm, this article constructs a three-tier social security fund cloud audit platform. Firstly, the article systematically expounds the current situation of social security fund and social security fund audit, such as the technical basis of cloud computing and data mining. Combined with the actual work, the necessity and feasibility of building a cloud audit platform for social security funds are analyzed. This article focuses on the construction of the cloud audit platform for social security funds. The general idea of using fuzzy data mining algorithm to build the social security fund audit cloud platform is to compress the knowledge contained in a large number of data into the weights between nodes and optimize the weights through the learning of the neural network system. Through the optimization function, the information contained in the neural network is stored in a few weights as far as possible. The main information is further highlighted by network clipping and removing weights that have little impact on the output.

Research on Prediction of Investment Fund’s Performance before and after Investment Based on Improved Neural Network Algorithm

There are more and more popular investment fund projects in the continuous economic development; the prediction and performance continuity become hot topics in the financial field. Scholars’ enthusiasm for this also reflects the domestic fund primary stage progress, and there is a huge application demand in China. The prediction of fund performance can help investors to avoid risks and improve returns and help managers to learn more unknown information from the prediction for the sake of guide market well and manage the market orderly. In the past research, the traditional way is to use the advantages of neural network to build a model to predict the continuous trend foundation performance, but the author found that the traditional single neural network (NN) algorithm has a large error value in the research. With the discussion, the particle swarm optimization (PSO) algorithm is added to the radial basis function (BRF) neural network, and PSO is conditioned to optimize and improve the RBF NN combining the advantages of both sides; a new set of PSO-RBF neural network security fund performance prediction method is summed up, which optimizes the structure and workflow of the algorithm. In the research, the author takes the real data as the reference and compares the prediction results with the traditional method RBF and the improved PSO-RBF. In the prediction results of the continuous trend, the highest value, and the lowest value in the period of the security fund performance, the new PSO-RBF has a good prediction in the fund performance prediction, and its accuracy rate is greatly improved compared with the traditional method Sheng, with good application value, and is worth popularizing.

Policies to control the COVID-19 pandemic in Costa Rica

The objective of this paper is to present a series of policies for the control of the COVID-19 pandemic by the Costa Rican authorities. An exhaustive review of the pandemic control policies was made in the official government media, mainly the Ministry of Health and the Costa Rican Social Security Fund and some collective media. The first wave of the pandemic in Costa Rica was quite mild, allowing the government to address it with a series of quite effective suppression and mitigation measures, which had the unrestricted support of the population. The second wave grew aggressively, causing social discontent due to the economic impact. Due to the ineffectiveness of the “hammer and dance” strategy, the Costa Rican government has rethought that strategy, lifting certain restrictions while recognizing the risk involved in terms of the increase in cases of COVID-19 in cases and deaths.

Efficiency and productivity change of public hospitals in Panama: Do management schemes matter?

Background In Latin American and Caribbean countries the main concern of public health care managers has been traditionally placed on problems related to funding, payment mechanisms and equity of access. However, more recently, there is a growing interest in improving the levels of efficiency and reducing costs in the provision of health services. In this paper we focus on measuring the efficiency of public hospitals in Panama, where no such studies have been conducted so far. One of the most interesting features of the public hospital system in this country is that there are two different management schemes that coexist, thus we can make a distinction between hospitals operating under the Social Security Fund (SSF) and those belonging to the Ministry of Health (MoH). Methods Our dataset includes data about 22 public hospitals (11 for each model) during the period 2005–2015. We rely on the use of Data Envelopment Analysis and the Malmquist Productivity index to calculate technical efficiency and productivity change of hospitals. In addition, we also apply bootstrapping techniques to calculate confidence intervals for the obtained efficiency and productivity scores in order to obtain more robust results. Results We find that, until the period 2012–2013, the performance of hospitals belonging to different systems experienced a similar trend. However, during the last years of the evaluated period the hospitals operating under the Social Security Fund clearly outperformed Ministry´s hospitals. The main explanation for these divergences seems to be the growth of technological change. Nevertheless, the use of bootstrapping to make statistical inferences reveals that some differences detected may not be significant. Conclusion We demonstrate that the results of traditional DEA and Malmquist index analyses need to be tested for statistical significance. Otherwise, the conclusions reached could be wrong.

Research on the Social Security Fund Risk Control and ManagementMeasures Based on Cloud Audit and Big Data Era

Affected by the continuous development of China's national economy and other aspects, the number of people participating in social security and the amount of money in China have continued to expand. As a result, the scale of social security fund financial data will continue to expand. In such cases, traditional auditing techniques are often used to standardize this data. With the continuous development of big data technology and cloud audit technology, it has provided corresponding technical support for the development of China's social security fund audit technology. However, the risks involved cannot be ignored. This paper is based on the social security fund system in the era of cloud auditing and big data. Based on the analysis of the problems, it puts forward corresponding measures on how to better control and manage the risks in order to provide corresponding management for the financial data risk management control of social security funds in China. reference.