The Current Adoption of Dry-Direct Seeding Rice (DDSR) in Thailand and Lessons Learned for Mekong River Delta of Vietnam

The paper documents the joint study trip, organized by CCAFS Southeast Asia for Vietnamese rice researchers, extension workers, as well as local decision makers, to visit Thailand in April 2018. The goal of the study trip was to observe and learn the experience of Thai farmers on the large-scale adoption process of dry-direct seeding rice (DDSR), a viable alternative to address regional scarcity of fresh water in irrigation caused by the drought and salinity intrusion in the Mekong River Delta

Evolving an optimal decision template for combining classifiers.

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets like those having imbalanced data. Moreover, point estimation by computing the average value on the outputs of base classifiers in the Decision Template method is sometimes not a good representation, especially for skewed datasets. Here we propose to search for an optimal decision template in the combining algorithm for a heterogeneous ensemble. To do this, we first generate the base classifier by training the pre-selected learning algorithms on the given training set. The meta-data of the training set is then generated via cross validation. Using the Artificial Bee Colony algorithm, we search for the optimal template that minimizes the empirical 0–1 loss function on the training set. The class label is assigned to the unlabeled sample based on the maximum of the similarity between the optimal decision template and the sample’s meta-data. Experiments conducted on the UCI datasets demonstrated the superiority of the proposed method over several benchmark algorithms

Hedonic shopping motivations, supermarket attributes, and shopper loyalty in transitional markets: Evidence from Vietnam

Purpose - This study aims to explore the impact of hedonic shopping motivations (HSM) and supermarket attributes (SMA) on shopper loyalty (SLO). Design/methodology/approach A sample of 608 supermarket shoppers in Ho Chi Minh City, Vietnam was surveyed to test the model. Structural equation modeling was used to analyze the data. Findings It was found that SMA and HSM had positive effects on SLO. It was also found that the impact of hedonic motivations on SLO was different between the younger and older, as well as lower and higher income groups of customers. However, no such difference was found between female and male shoppers. Research limitations/implications A major limitation of this study was the use of a sample drawn from one transitional market. Cross-national samples will be a direction for further research. Also, the study focuses on attitudinal loyalty. Behavioral loyalty should be taken into account in future research. Practical implications The findings suggest that the supermarket managers showed concentrate their positioning strategies not only on the utilitarian dimension but also on the hedonic motivations to stimulate SLO, especially for older and higher income segments of customers. Originality/value The major contribution of the study is to empirically examine the role of hedonic motivations in SLO in Vietnam, a transitional market

The role of market and learning orientations in relationship quality: Evidence from Vietnamese exporters and their foreign importers

This study examines the roles of market and learning orientations in relationship quality between exporters in transition economies and their foreign importers and subsequently, export performance. A random sample of 283 export firms in Vietnam provides evidence to support the hypothesized main effects. The results further indicate that learning orientation plays a role in building high-quality relationships for both new and mature relationships. However, the impact of market orientation on relationship quality is found only in the new relationship. In addition, firm-ownership structure does not moderate the relationships between learning orientation, market orientation, relationship quality, and export performance

Estimation of damage location in advanced composite materials

Damage, normally difficult to assess, causes unpredictable behaviours in structural components. To take a decision of whether to repair or retire the part, inspection for damage is important. In engineering, damage modelling is assumed to be driven by a number of unknown parameters, which reduces the inspection to one of parameter estimation, sometimes known as inverse problems

Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods

Effect of sponge volume fraction on the performance

A novel fluidized bed bioreactor (FBBR) was designed by integration of anaerobic granular activated carbon and aerobic sponge reactors. This FBBR was evaluated at different sponge volume fractions for treating a synthetic wastewater. Polyester urethane sponge with cube size of 1 × 1 × 1 cm and density of 28-30 kg/m3 with 90 cells per 25 mm was used as biomass carrier. The results indicate that the FBBR could remove more than 93% of dissolved organic carbon (DOC). The highest nutrient removal efficiencies (58.2% PO4 -P and 75.4% NH4-N) were achieved at 40% sponge volume fraction. The system could provide a good condition for biomass growth (e.g. 186.2 mg biomass/g sponge). No significant different performance in specific oxygen uptake rate was observed between 30, 40, and 50% sponge volume fractions. © IWA Publishing 2013 Water

Robust optimization over time by learning problem space characteristics

Robust optimization over time is a new way to tackle dynamic optimization problems where the goal is to find solutions that remain acceptable over an extended period of time. The state-of-the-art methods in this domain try to identify robust solutions based on their future predicted fitness values. However, predicting future fitness values is difficult and error prone. In this paper, we propose a new framework based on a multi-population method in which sub-populations are responsible for tracking peaks and also gathering characteristic information about them. When the quality of the current robust solution falls below the acceptance threshold, the algorithm chooses the next robust solution based on the collected information. We propose four different strategies to select the next solution. The experimental results on benchmark problems show that our newly proposed methods perform significantly better than existing algorithms