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.

Abstract: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.

No abstract

Abstract: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" OPEN ACCESSAtmosphere 2014, 5 789 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.

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.