Fuzzy case-based reasoning system

In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum degree principle. Thus, we add the "vague" concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.Peer reviewedComputer Scienc

Effects of simulated heat waves on cardiovascular functions in senile mice

The mechanism of the effects of simulated heat waves on cardiovascular disease in senile mice was investigated. Heat waves were simulated in a TEM1880 meteorological environment simulation chamber, according to a heat wave that occurred in July 2001 in Nanjing, China. Eighteen senile mice were divided into control, heat wave, and heat wave BH4 groups, respectively. Mice in the heat wave and heat wave BH4 groups were exposed to simulated heat waves in the simulation chamber. The levels of ET-1, NO, HSP60, SOD, TNF, sICAM-1, and HIF-1a in each group of mice were measured after heat wave simulation. Results show that heat waves decreased SOD activity in the myocardial tissue of senile mice, increased NO, HSP60, TNF, sICAM-1, and HIF-1a levels, and slightly decreased ET-1 levels, BH4 can relieve the effects of heat waves on various biological indicators. After a comprehensive analysis of the experiments above, we draw the followings conclusions regarding the influence of heat waves on senile mice: excess HSP60 activated immune cells, and induced endothelial cells and macrophages to secrete large amounts of ICAM-1, TNF-a, and other inflammatory cytokines, it also activated the inflammation response in the body and damaged the coronary endothelial cell structure, which increased the permeability of blood vessel intima and decreased SOD activity in cardiac tissues. The oxidation of lipoproteins in the blood increased, and large amounts of cholesterol were generated. Cholesterol penetrated the intima and deposited on the blood vessel wall, forming atherosclerosis and leading to the occurrence of cardiovascular disease in senile mice. These results maybe are useful for studying the effects of heat waves on elderly humans, which we discussed in the discussion chapter.Peer reviewedComputer Scienc

Effects of Simulated Heat Waves on Cardiovascular Functions in Senile Mice

The mechanism of the effects of simulated heat waves on cardiovascular disease in senile mice was investigated. Heat waves were simulated in a TEM1880 meteorological environment simulation chamber, according to a heat wave that occurred in July 2001 in Nanjing, China. Eighteen senile mice were divided into control, heat wave, and heat wave BH4 groups, respectively. Mice in the heat wave and heat wave BH4 groups were exposed to simulated heat waves in the simulation chamber. The levels of ET-1, NO, HSP60, SOD, TNF, sICAM-1, and HIF-1α in each group of mice were measured after heat wave simulation. Results show that heat waves decreased SOD activity in the myocardial tissue of senile mice, increased NO, HSP60, TNF, sICAM-1, and HIF-1α levels, and slightly decreased ET-1 levels, BH4 can relieve the effects of heat waves on various biological indicators. After a comprehensive analysis of the experiments above, we draw the followings conclusions regarding the influence of heat waves on senile mice: excess HSP60 activated immune cells, and induced endothelial cells and macrophages to secrete large amounts of ICAM-1, TNF-α, and other inflammatory cytokines, it also activated the inflammation response in the body and damaged the coronary endothelial cell structure, which increased the permeability of blood vessel intima and decreased SOD activity in cardiac tissues. The oxidation of lipoproteins in the blood increased, and large amounts of cholesterol were generated. Cholesterol penetrated the intima and deposited on the blood vessel wall, forming atherosclerosis and leading to the occurrence of cardiovascular disease in senile mice. These results maybe are useful for studying the effects of heat waves on elderly humans, which we discussed in the discussion chapter

Effects of Simulated Heat Waves with Strong Sudden Cooling Weather on ApoE Knockout Mice

This study analyzes the mechanism of influence of heat waves with strong sudden cooling on cardiovascular diseases (CVD) in ApoE−/− mice. The process of heat waves with strong sudden cooling was simulated with a TEM1880 meteorological-environment simulation chamber according to the data obtained at 5 a.m. of 19 June 2006 to 11 p.m. of 22 June 2006. Forty-eight ApoE−/− mice were divided into six blocks based on their weight. Two mice from each block were randomly assigned to control, heat wave, temperature drop, and rewarming temperature groups. The experimental groups were transferred into the climate simulator chamber for exposure to the simulated heat wave process with strong sudden temperature drop. After 55, 59, and 75 h of exposure, the experimental groups were successively removed from the chamber to monitor physiological indicators. Blood samples were collected by decollation, and the hearts were harvested in all groups. The levels of heat stress factors (HSP60, SOD, TNF, sICAM-1, HIF-1α), cold stress factors (NE, EPI), vasoconstrictor factors (ANGII, ET-1, NO), and four items of blood lipid (TC, TG, HDL-C, and LDL-C) were measured in each ApoE−/− mouse. Results showed that the heat waves increased the levels of heat stress factors except SOD decreased, and decreased the levels of vasoconstrictor factors and blood lipid factors except TC increased. The strong sudden temperature drop in the heat wave process increased the levels of cold stress factors, vasoconstrictor factors and four blood lipid items (except the level of HDL-C which decreased) and decreased the levels of heat stress factors (except the level of SOD which increased). The analysis showed that heat waves could enhance atherosclerosis of ApoE−/− mice. The strong sudden temperature drop during the heat wave process increased the plasma concentrations of NE and ANGII, which indicates SNS activation, and resulted in increased blood pressure. NE and ANGII are vasoconstrictors involved in systemic vasoconstriction especially in the superficial areas of the body and conducive to increased blood pressure. The increase in the blood lipid levels of TG, LDL-C, TC, and LDL-C/HDL-C further aggravated CVD. This paper explored the influence mechanism of the heat waves with sudden cooling on CVD in ApoE−/− mice

Precipitation Data Assimilation System Based on a Neural Network and Case-Based Reasoning System

There are several methods to forecast precipitation, but none of them is accurate enough since predicting precipitation is very complicated and influenced by many factors. Data assimilation systems (DAS) aim to increase the prediction result by processing data from different sources in a general way, such as a weighted average, but have not been used for precipitation prediction until now. A DAS that makes use of mathematical tools is complex and hard to carry out. In our paper, machine learning techniques are introduced into a precipitation data assimilation system. After summarizing the theoretical construction of this method, we take some practical weather forecasting experiments and the results show that the new system is effective and promising

Case-Based FCTF Reasoning System

Case-based reasoning uses old information to infer the answer of new problems. In case-based reasoning, a reasoner firstly records the previous cases, then searches the previous case list that is similar to the current one and uses that to solve the new case. Case-based reasoning means adapting old solving solutions to new situations. This paper proposes a reasoning system based on the case-based reasoning method. To begin, we show the theoretical structure and algorithm of from coarse to fine (FCTF) reasoning system, and then demonstrate that it is possible to successfully learn and reason new information. Finally, we use our system to predict practical weather conditions based on previous ones and experiments show that the prediction accuracy increases with further learning of the FCTF reasoning system

Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro) and NFIS-WPM (Ave) are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy

A Neural Network-Based Interval Pattern Matcher

One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising

Effects of Moderate Strength Cold Air Exposure on Blood Pressure and Biochemical Indicators among Cardiovascular and Cerebrovascular Patients

The effects of cold air on cardiovascular and cerebrovascular diseases were investigated in an experimental study examining blood pressure and biochemical indicators. Zhangye, a city in Gansu Province, China, was selected as the experimental site. Health screening and blood tests were conducted, and finally, 30 cardiovascular disease patients and 40 healthy subjects were recruited. The experiment was performed during a cold event during 27–28 April 2013. Blood pressure, catecholamine, angiotensin II (ANG-II), cardiac troponin I (cTnI), muscle myoglobin (Mb) and endothefin-1 (ET-1) levels of the subjects were evaluated 1 day before, during the 2nd day of the cold exposure and 1 day after the cold air exposure. Our results suggest that cold air exposure increases blood pressure in cardiovascular disease patients and healthy subjects via the sympathetic nervous system (SNS) that is activated first and which augments ANG-II levels accelerating the release of the norepinephrine and stimulates the renin-angiotensin system (RAS). The combined effect of these factors leads to a rise in blood pressure. In addition, cold air exposure can cause significant metabolism and secretion of Mb, cTnI and ET-1 in subjects; taking the patient group as an example, ET-1 was 202.7 ng/L during the cold air exposure, increased 58 ng/L compared with before the cold air exposure, Mb and cTnI levels remained relatively high (2,219.5 ng/L and 613.2 ng/L, increased 642.1 ng/L and 306.5 ng/L compared with before the cold air exposure, respectively) 1-day after the cold exposure. This showed that cold air can cause damage to patients’ heart cells, and the damage cannot be rapidly repaired. Some of the responses related to the biochemical markers indicated that cold exposure increased cardiovascular strain and possible myocardial injury

Vegetation change detection using artificial neural networks with ancillary data in Xishuangbanna, Yunnan Province, China

Timely and accurate change detection of the Earth's surface features provides the foundation for better planning, management and environmental studies. In this study ANN change detection was used to perform vegetation change detection, and was compared with post-classification method. Before the post-classification was performed the ANN classification was used to yield multitemporal vegetation maps. ANN were also used to perform a one-pass classification for the images in 2003 and 2004. DEM and slope were used as two extra channels. During the training stage, the training data was separated into 82 subclasses including 36 change subclasses and 46 no change subclasses. Moreover NDVI differencing methods were used to develop the change mask. The result showed that combining the NDVI differencing method with visual interpretation when identifying reference areas can produce more accurate change detection results for the ANN one pass change classification. Moreover, it is effective to use elevation and slope as extra channels together with PCA components, to perform ANN-based change detection in mountainous study areas. It is also important to separate the vegetation transition classes into subclasses based on spectral response patterns, especially for mountainous terrains. This processing can reduce the topographic effect and improve the change detection accuracy