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Instead of handling fuzzy sets associated with linguistic (L-) labels based on the developers’ intuition immediately, the study follows the hedge algebras (HA-) approach to the time series forecasting problems, in which the linguistic time series forecasting model was, for the first time, proposed and examined in 2020. It can handle the declared forecasting L-variable word-set directly and, hence, the terminology linguistic time-series (LTS) is used instead of the fuzzy time-series (FTS). Instead of utilizing a limited number of fuzzy sets, this study views the L-variable under consideration as to the numeric forecasting variable's human linguistic counterpart. Hence, its word-domain becomes potentially infinite to positively utilize the HA-approach formalism for increasing the LTS forecasting result exactness. Because the forecasting model proposed in this study can directly handle L-words, the LTS, constructed from the numeric time series and its L-relationship groups, considered human knowledges of the given time-series variation helpful for the human-machine interface. The study shows that the proposed formalism can more easily handle the LTS forecasting models and increase their performance compared to the FTS forecasting models when the words’ number grows.

Malnutrition is closely related to the outcome of disease treatment, especially in digestive cancer surgery. The aim of this study was to assess the nutritional condition of pre-operative patients with upper digestive cancers (including stomach and oesophagus) at the Department of General Surgery, Bach Mai Hospital in 2016. We conducted a cross-sectional descriptive analysis of 76 malignancies of the upper gastrointestinal tract with surgical treatments. The results revealed that the weight loss rate of hospitalized patients with gastric cancer and esophageal cancer was 76.6% and 66.7%, respectively. The rate of weight loss above 10% of body weight was 19.7%. The prevalence of chronic energy deficit was 29.9%. The risk of malnutrition according to SGA was 77.6%, of which mild to moderate and severe was 67.2% and 10.4%, respectively. The rate of low blood albumin level (less than 35 g/L) was 36.5%. The average net nutritional value was 1146.3 ± 592.7 Kcal (range 246.7 – 3653.5), which equals to 55.7% of the necessary daily intake. Protein, lipid, and glucid contents reached 73.4%, 57.8%, and 52.1% of the recommended levels, respectively. Conclusion: malnutrition was still prevalent among patients undergoing upper gastrointestinal cancer surgery, and pre-operative nutritional status does not achieve recommended levels.

Dealing with the time series forecasting problem attracts much attention from the fuzzy community. Many models and methods have been proposed in the literature since the publication of the study by Song and Chissom in 1993, in which they proposed fuzzy time series together with its fuzzy forecasting model for time series data and the fuzzy formalism to handle their uncertainty. Unfortunately, the proposed method to calculate this fuzzy model was very complex. Then, in 1996, Chen proposed an efficient method to reduce the computational complexity of the mentioned formalism. Hwang et al. in 1998 proposed a new fuzzy time series forecasting model, which deals with the variations of historical data instead of these historical data themselves. Though fuzzy sets are concepts inspired by fuzzy linguistic information, there is no formal bridge to connect the fuzzy sets and the inherent quantitative semantics of linguistic words. This study proposes the so-called linguistic time series, in which words with their own semantics are used instead of fuzzy sets. By this, forecasting linguistic logical relationships can be established based on the time series variations and this is clearly useful for human users. The effect of the proposed model is justified by applying the proposed model to forecast student enrollment historical data.

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This paper studies the current status of water quality, using both statistical and modelling approaches, in the waterways of the Saigon-Dong Nai river basin in relation to the nature of its environ...

The existing researches have shown that the fuzzy forecasting methods based on the high-order fuzzy time series are better than the fuzzy forecasting methods based on the first-order fuzzy time series. The linguistic forecasting method based on the first-order linguistic time series which can handle directly the word-set of the linguistic variable have been examined by Hieu et al. This paper examines a novel model of high-order linguistic time series with the growth of declared word-set. A procedure for forecasting the enrollments of University of Alabama and Lahi crop production of India is developed based on the proposed model. In the proposed forecasting method, the high-order linguistic logical relationship groups will be established and utilized to calculate the forecasted values based on the quantitative semantics of the used words generated by the hedge algebras structure. The experimental results show that the forecasting accuracy of the proposed high-order forecasting method is better than their counterparts and the growth of the word-set of the linguistic variable is significant in increasing the accuracy of the forecasted results.

Fuzzy time series forecasting model was first introduced by Song and Chissom, in 1993. Then, Chen examined this model, in 1996, applying fuzzy rules in the form A i → A j , where A i and A j are fuzzy sets associated with linguistic labels representing their corresponding values in a time series. Since then, many methods have been proposed to improve the accuracy of time series forecasting results or decrease processing times. In this study, we propose another approach dealing directly with human words with their own inherent semantics. That is, we assume that the above rules are of form X i → X j , in which X i and X j are human expert words describing their corresponding time series values. Therefore, we should deal with a so-called linguistic time series forecasting model using the inherent semantics of words and their quantitative semantics based on the hedge algebras formalism. A comparative experiment is made to show the usefulness of the proposed model.

Urban flooding has become a severe problem in Ho Chi Minh City. Green infrastructure (GI) have been considered as a promising solution. GI is not only beneficial for urban flood reduction but also improved microclimatic conditions, landscape values. Recently, with the advancement of science and technology, the mathematical models have been developed and applied in various fields including urban flood. This paper presents the initial results of applying 2 urban water models, namely Tuflow and SWMM (Stormwater management model). The Tuflow mathematical model was used to simulate 2 dimensional (2D) surface model while the SWMM was developed for 1 dimensional (1D) surface and drainage water system. These models are later served in assessing the effectiveness of green infrastructure development in urban flood reduction. The models were developed at a specific study area (Tham Luong - Ben Cat Catchment) in Ho Chi Minh city. Results can be used for urban planning especially for urban flood management in Ho Chi Minh city.

Organic matter coated Fe nanosized powders were prepared by pulsed wire discharge. X-ray diffraction showed no crystalline phases except for α-Fe. Transmission electron microscopy (TEM) and infrared absorption spectroscopy results indicated that the prepared particles were coated by mineral oil. From the TEM results, the particle size distributions were also obtained and the geometric mean diameters were calculated to be from 20.2 to 42.4 nm. The relation between the geometric mean diameter and theoretical plasma/vapor density was close to that predicted by the known particle size determining equation. Saturation magnetization as high as 172 emu g−1 was obtained for the prepared Fe nanosized powder.