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The predictive roles of symptom combination traditionally evaluated in traditional Chinese medicine (TCM) in the treatment of rheumatoid arthritis (RA) were explored. Three hundred and ninety six patients were randomly divided into 197 subjects receiving Western medicine therapy (WM) and 199 subjects receiving TCM therapy (TCM). A complete physical examination and 18 clinical manifestations typically assessed in TCM were recorded before the randomization. The ACR responses were used for efficacy evaluation. ACR20 and 50 responses with WM treatment were higher than in the TCM group. The 18 symptoms in RA could be clustered into 4 symptom combinations with factor analysis, which represent joint symptoms, cold pattern, deficiency pattern and hot pattern in TCM respectively. TCM would be more effective in patients with weak-symptom combination 3 (deficiency pattern in TCM), and WM would be more effective in patients with symptom combination 2 (cold pattern in TCM). Symptom combinations judged with TCM may have influence on the efficacy of therapy in the treatment of RA.

This paper extends the theory of fuzzy diseases predictions in order to detect the causes of business failure. This extension is justified through the advantages of the reference model and its originality. Moreover, the fuzzy model is completed by this proposal and some parts of it have been published in isolated articles. For this purpose, the fuzzy theory is combined with the OWA operators to identify the factors that generate problems in firms. Also, a goodness index to validate its functionality and prediction capacity is introduced. The model estimates a matrix of economic- financial knowledge based on matrices of causes and symptoms. Knowing the symptoms makes it possible to estimate the causes, and managing them properly, allows monitoring and improving the company’s financial situation and forecasting its future. Also with this extension, the model can be useful to develop suitable computer systems for monitoring companies’ problems, warning of failures and facilitating decision-making.

The aim of this paper was to search for alternative hosts to green bean for Erwinia persicina. This enterobacteria, detected for the first time in southeastern Spain at the end of 2003, causes chlorotic and necrotic spots on leaves and pod deformation in green bean. Results showed that E. persicina caused disease in tomato, pepper, melon and cucumber. The most characteristic symptoms observed were: leaf necrosis, adventitious roots, brown colouring along the stem, internerval chlorosis, and curled and blistered leaves. In the case of cucurbits, a noticeable lesion in some areas of the stem was also observed. The presence of the bacteria in each plant was detected by microbiological and immunological analyses (ELISA).

The paper aims at establishing the output-to-input relationship of the real-life adult depression data using a neural network (NN) model. The said model has been developed to diagnose and detect the associated severity (grade) of the illness. An intelligent NN-based reverse model has been trained through batch mode and put to test on another set of real-life data. Reverse mapping of this model has been developed to isolate significantly contributing input components (factors) for any given case to expedite the preventive procedure for further deterioration as well as the start of treatment.