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In the real decision process, an important problem is how to express the attribute value more efficiently and accurately. In the real world, because of the complexity of decision-making problems and the fuzziness of decision-making environments, it is not enough to express attribute values of alternatives by exact values. For this managing with such sorts of issues, the principle of Linear Diophantine uncertain linguistic set is a valuable and capable technique to manage awkward and inconsistent information in everyday life problems. In this manuscript, we propose the original idea of Linear Diophantine uncertain linguistic set and elaborated their essential laws. Additionally, to determine the association among any numbers of attributes, we elaborated the Linear Diophantine uncertain linguistic arithmetic Heronian mean operator, Linear Diophantine uncertain linguistic weighted arithmetic Heronian mean operator, Linear Diophantine uncertain linguistic geometric Heronian mean operator, Linear Diophantine uncertain linguistic weighted geometric Heronian mean operator, and their properties are also discovered. By using these operators, we utilize the multi-attribute decision-making procedure by using elaborated operators. To determine the consistency and validity of the elaborated operators, we illustrate some examples by using explored operators. Finally, the superiority and comparative analysis of the elaborated operators with some existing operators are also determined and justified with the help of a graphical point of view. [ABSTRACT FROM AUTHOR]

Thailand’s graduate employment data was collected continuously by the Ministry of Higher Education, Science, Research, and Innovation which resulted in a huge, incomplete, and high dimensional data. In this paper, we study a model to predict employability and compare performance usind statistical as well as machine learning models. Our results show that the significant variables are: graduation year, education level, major of study, type of university, ranking, and region. The four models compared are: logistic regression, decision tree, random forest, and KNN models. The comparative result shows that the random forest and KNN models are suitable for employability prediction with 70.554% and 70.158% accuracy, 0.746, and 0.762 in AUC. The Random Forest Model shows the important variables for prediction are: university ranking, master’s degree, statistics major, a national university, and the north-east region. Our study implies that the random forest and KNN models can be used to predict employment status for graduating students, the time when they should apply for work, and the academic staff they worked under. [ABSTRACT FROM AUTHOR]

The Yom River Basin is one of 25 river basins in Thailand. The Yom River Basin experiences perennial droughts and floods that heavily impact the agricultural sector. In order to reduce the impact, water management, including water level estimation, must be applied to critical basins like the Yom River Basin. An important task of management is the quantitative prediction of water levels. Four different modeling approaches were applied to forecast the average monthly water level (AMWL) data from four water level measurement stations for the wet season (May-October) and dry season (November-April) from 2007 to 2020. The forecast patterns obtained from the four approaches were similar to the observed historical values, except the upstream in wet season and downstream in dry season. Furthermore, the artificial neural network (ANN) approach overestimated forecasts for almost every station in both seasons. All four approaches were more accurate in the dry season than the wet season. This study proposed a forecasting method called dynamic seasonal regression (DSR), which was obtained by combining multiple linear regression (MLR) and the autoregressive integrated moving average (ARIMA) model of the random error from MLR. DSR was more efficient than ANN, seasonal-ARIMA (SARIMA) and a hybridized SARIMA and ANN approach (SARIMANN). On average, for all stations in wet and dry seasons, DSR reduced RMSE by over 40.86%, 9.10% and 23.07% with respect to ANN, SARIMA and SARIMANN, and MAPE by over 35.01%, 13.02% and 15.96% with respect to ANN, SARIMA and SARIMANN. The RMSE of upstream was higher than the RMSE downstream in the wet season for all methods, and the MAPE of upstream was lower than the downstream in both seasons for all methods. Moreover, the RMSE of upstream was lower than the downstream in the dry season for all methods except the ANN method. [ABSTRACT FROM AUTHOR]

The Yom River Basin is one of 25 river basins in Thailand. The Yom River Basin experiences perennial droughts and floods that heavily impact the agricultural sector. In order to reduce the impact, water management, including water level estimation, must be applied to critical basins like the Yom River Basin. An important task of management is the quantitative prediction of water levels. Four different modeling approaches were applied to forecast the average monthly water level (AMWL) data from four water level measurement stations for the wet season (May-October) and dry season (November-April) from 2007 to 2020. The forecast patterns obtained from the four approaches were similar to the observed historical values, except the upstream in wet season and downstream in dry season. Furthermore, the artificial neural network (ANN) approach overestimated forecasts for almost every station in both seasons. All four approaches were more accurate in the dry season than the wet season. This study proposed a forecasting method called dynamic seasonal regression (DSR), which was obtained by combining multiple linear regression (MLR) and the autoregressive integrated moving average (ARIMA) model of the random error from MLR. DSR was more efficient than ANN, seasonal-ARIMA (SARIMA) and a hybridized SARIMA and ANN approach (SARIMANN). On average, for all stations in wet and dry seasons, DSR reduced RMSE by over 40.86%, 9.10% and 23.07% with respect to ANN, SARIMA and SARIMANN, and MAPE by over 35.01%, 13.02% and 15.96% with respect to ANN, SARIMA and SARIMANN. The RMSE of upstream was higher than the RMSE downstream in the wet season for all methods, and the MAPE of upstream was lower than the downstream in both seasons for all methods. Moreover, the RMSE of upstream was lower than the downstream in the dry season for all methods except the ANN method. [ABSTRACT FROM AUTHOR]

An element a of a semigroup S is called left [right] magnifying if there exists a proper subset M of S such that S = aM [S = Ma]. Let L(V ) be the linear transformation semigroup on a vector space V. It is well-known that L(V ) contains left [right] magnifying elements if and only if the dimension of V is infinite. In case its dimension is infinite, α is left magnifying if and only if α is surjective and not injective and α is right magnifying if and only if α is injective and not surjective. To generalize this results, let W be a subspace of V and L(V,W) = {α ... L(V ) / im α ... W}. Then L(V,W) is a subsemigroup of L(V ) and if W = V, then L(V,W) = L(V ). Our purpose in this paper is to give necessary and sufficient conditions for elements in L(V,W) to be left or right magnifying. [ABSTRACT FROM AUTHOR]

Our previous study showed that young coconut juice (YCJ) at a high dose of 100 mL/kgBW had many health benefits e.g. it delayed Alzheimer's pathologies, preserved neuronal cells, accelerated wound healing and prevented osteoporosis. However, such a large dose of YCJ over a period of time started to have unfavourable side effects e.g. the deposition of glycogen in the liver. Therefore, our aim in the present study was to investigate the lowest neuroprotective dose of YCJ that would cause the least side effects for long-term consumption by postmenopausal women, using ovariectomized (ovx) rats as a model for postmenopausal women. Three lower doses of YCJ (10, 20 and 40 mL/kg body weight) were applied. The results clearly showed that the OY10 group was the best dose to help to preserve neuronal cells in both the hippocampus and the prefrontal cortex with cell numbers being higher than for the ovx group at various degrees of significance in each brain region. After 10 weeks of treatment, the circulating levels of BUN, creatinine, cholesterol, triglyceride, HDL, LDL, AST, ALT, ALP, total protein, albumin, calcium and phosphorus of the OY10 group were not significantly different from those of the sham and ovx groups. This study has confirmed that feeding YCJ had beneficial effects on the serum lipid profile, and maintained liver and renal functions for up to 10 weeks after administration. YCJ consumption at 10 mL/kgBW/day for 10 weeks, however, did increase body weight and serum glucose when compared with the control groups. Therefore, supplementation with YCJ in postmenopausal women with a diabetic condition should only be allowed under supervision by a physician. [ABSTRACT FROM AUTHOR]