Textual analysis in the realm of business depends on text-processing techniques borrowed mainly from information retrieval. Yet, these text-processing techniques are not viable in text-based financial forecasting. In this paper, we suggest developing financial home-grown techniques for processing textual data, specifically in the course of scoring words where standard techniques are not appropriate in financial analysis. On that matter, we pursue two issues. First, we examine major information retrieval heuristics, where we find TF-IDF too facile not only in predicting trends but also in generating accurate results (in terms of errors) on large numbers in text-based financial analysis. Second, we work on a new heuristic satisfying financial concerns. We consider the relationship between the publication rate of information and its importance. The proposed heuristic provides results of unmatchable performance in both predicting trends and precision measures. In an additional analysis, we optimize our scheme using a genetic algorithm as an optimization technique and get greater precision. In comparison with TF-IDF, our proposed heuristic conduces to a 38. 5 percent lower error in closeness measures which is again reduced by 16. 46 percent with the help of a genetic algorithm. Our findings suggest that researchers in the field of financial textual analysis should not rely on standard information retrieval heuristics.

Rainfall intensity at different durations and frequencies (IDF curves) are widely needed in nearly all water projects, e.g., culverts, urban drainage, etc. However, IDF curves are reported at discrete durations and frequencies. To avoid subjective decisions, however, IDF models have been developed and are used universally. In this article, the performance of five commonly used IDF models is analyzed for three synoptic stations in Iran. The results show that there is an overall acceptable performance for all models, as far as the prediction of rainfall intensity from duration and frequency is concerned. However, from a probabilistic point of view, the non-negativity of a cumulative distribution function is not satisfied. Therefore, the non-negativity concept was added as a constraint. Consequently for this case, the performance of the models decreased considerably. It seems that all IDF models currently in use are not efficient, yet more efficient models must be developed to take into account all characteristics of the phenomena.

Introduction: Fasting during Ramadan is a religious obligation and may pose a risk to diabetic patients. This study aimed to stratify diabetic patients based on the International Diabetes Federation- Diabetes and Ramadan International Alliance (IDF- DAR) guideline for the risk of fasting before Ramadan. Methods: A total of 317 diabetic patients attending the endocrinology clinic at Ghaem Hospital, Mashhad, Iran, participated in this cross-sectional study. The American Diabetic Association (ADA) criteria were applied to diagnose diabetes. Various parameters, including diabetes type and duration, complications, and risk factors were recorded to determine IDF-DAR-based risk. Then patients were stratified into high (IDF-DAR>6), medium (IDF-DAR: 3.5-6) and low (IDF-DAR: 0-3) risk groups accordingly. Results: Out of the 317 patients included in the study, 115 (36.3%) patients were categorized as low risk, 127 (40%) as moderate-risk, and 75 (23.7%) as high-risk groups. Conclusions: According to the IDF-DAR guideline, majority of the patients fell into the low and moderate risk categories, suggesting that they should not be entirely exempted from fasting during the holy month of Ramadan. However, the validity of this patient stratification in various fasting populations needs to be evaluated through prospective longitudinal studies.