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For decades, although the world has made significant progress in the fight against hunger, food insecurity has been considered as the top concern of countries, especially developing countries. In this study, we use panel data of 10 Southeast Asian countries for the period from 2000-2015 to evaluate the effect of international trade on food security. Applying panel data regression methods such as fixed effects model (FE), random effects model (RE), and feasible generalized least squares model (FGLS), the results show the positive impact of international trade on food security on all three aspects of food availability, stability, and access at each country. Besides, other factors such as agricultural productivity, the share of agricultural land over the total land area, percentage of farmers in total population, and inflation also affect to food security of Southeast Asian countries on several aspects. Based on the results, we propose some policy implications for Southeast Asian countries to deal with food insecurity problems.

Structural balance theory assumes triads in networks to gravitate towards stable configurations. The theory has been verified for undirected graphs. Since real-world networks are often directed, we introduce a novel method for considering both transitivity and sign consistency for calculating balance in signed digraphs. We test our approach on graphs that we constructed by using different methods for identifying edge signs: natural language processing to infer signs from underlying text data, and self-reported survey data. Our results show that for various social contexts and edge sign detection methods, balance is moderately high, ranging from 67.5% to 92.4%., 27 pages including figures, tables, references

Supplemental material, OnlineAppendices_2020.04.03_FINAL for Exploring the Relationship Between Interdisciplinary Ties and Linguistic Familiarity Using Multilevel Network Analysis by William C. Barley, Ly Dinh, Hallie Workman and Chengyu Fang in Communication Research

This study has classified and predicted the water quality for the Dong Nai river at different times. The data mining techniques were used to screen out unreliable data. The neural network model FFNN in the form of architecture I(8)-HL(9)-O(1) has been successfully developed to classify the water quality of the Dong Nai river during the period from 2012 to 2019. For this model, the RMSE values for the training, validation, and test sets are equal to 0.0392, 0.0411, and 0.0423, respectively. Also, the MAPE values for those data sets are 1.241, 1.444, and 1.511, respectively. The models ARIMA(0,1,3)(1,1,1), NAR, NAR-MA, LSTM, and LSTM-MA were used to forecast the water quality according to different time series (months) k = 12 and k = 24 at the monitoring points. The LSTM-MA hybrid model provided faster training time and more reliable forecasts than the ARIMA, NAR, NAR-MA, and LSTM models. The RMSE and MAPE values of the LSTM-MA hybrid model for predicting the forecast set are smaller than those from other models. The LSTM-MA hybrid model was used to classify the water quality over the years 2020 to 2022. The water quality classification derived from the LSTM-MA hybrid model in close agreement with the actual monitoring data. This model becomes reliable and useful for the water quality classification of the Dong Nai River.

This paper analysed temporal trend possibilities of hydro-meteorological records in Vu Gia Thu Bon watershed during 1979-2014 by employing the recently innovative-Sen trend methodology in combination with the classical/modified Mann-Kendall tests. The outcomes show that most of statistically significant increasing trends were found in annual rainfall, extreme temperatures, relative humidity and stream flow, whereas opposite trend direction was predominantly observed in evapotranspiration and sunshine duration over the 36-year period. Particularly, the greatest warming trends in maximum and minimum temperature were 0.019 and 0.024°C/year respectively. The estimated slope of significant positive trends in rainfall varied approximately from 29.7 to 43.6 mm/year. This study also demonstrates high applicability of the innovative-Sen approach to overall and partial trend identification. Generally, it is expected that these findings will be conducive to climate-induced risks and water resources management for the study area under climate emergency.

3-Methyl-2-geranylfuran (I, R = CH2CH:CMeCH2CH2CH:CMe2, R1 = Me) was prepd. by treating I (R = Li, R1 = Br) [prepd. from I (R = H, R1 = Br) and (Me2CH)2NLi] with BrCH2CH:CMeCH2CH2CH:CMe2 and methylating with MeI in THF contg. BuLi. I (R = CH2CH:CMe2, R1 = Et, Cl, Br, Me; R = Me, CH2CH:CMe2, R1 = H; R = CH2OH, CH2OMe, R1 = Br) were prepd. similarly. [on SciFinder (R)]