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In this paper, the impact of rated power and the total capacity of all photovoltaic units on the energy loss reduction of radial distribution networks is investigated. An IEEE 69-node system is supposed to be a radial distribution network in the North and South of Vietnam to take the real mean solar radiations of 1 year from the global solar map for simulation. Five study cases are implemented corresponding to the difference of rated power of five installed photovoltaic units, including optimal size and four fixed sizes of 250, 500, 750 and 1000 kW. The proposed modified slime mould algorithm (MSMA) and three other algorithms including the original Coot optimization algorithm (COA), original Transient search optimization algorithm (TSOA) and original slime mould algorithm (SMA) are used to find optimal solutions of the five study cases. As a result, MSMA is the best method for reaching smaller 1-year energy loss and finding a higher number of more effective solutions than others. In addition, the study also suggests the placement of photovoltaic units should use optimal rated power for each unit and optimal total capacity for all units rather than using the same rated power for each unit and a predetermined total capacity for all units. [ABSTRACT FROM AUTHOR]

This paper proposes a new model to interpolate time series and forecast it effectively for the future. The important contribution of this study is the combination of optimal techniques for fuzzy clustering problem using genetic algorithm and forecasting model for fuzzy time series. Firstly, the proposed model finds the suitable number of clusters for a series and optimizes the clustering problem by the genetic algorithm using the improved Davies and Bouldin index as the objective function. Secondly, the study gives the method to establish the fuzzy relationship of each element to the established clusters. Finally, the developed model establishes the rule to forecast for the future. The steps of the proposed model are presented clearly and illustrated by the numerical example. Furthermore, it has been realized positively by the established MATLAB procedure. Performing for a lot of series (3007 series) with the differences about characteristics and areas, the new model has shown the significant performance in comparison with the existing models via some parameters to evaluate the built model. In addition, we also present an application of the proposed model in forecasting the COVID-19 victims in Vietnam that it can perform similarly for other countries. The numerical examples and application show potential in the forecasting area of this research. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model combining the extreme gradient boosting machine (XGBoost) and the whale optimization algorithm (WOA) to predict the bearing capacity of concrete piles. The XGBoost provides the ultimate prediction from a set of explanatory experiment variables. The WOA, which is configured to search for an optimal set of XGBoost parameters, helps increase the model's accuracy and robustness. The hybrid method is constructed by a dataset of 472 samples collected from static load tests in Vietnam. The results indicate that the hybrid model consistently outperforms the default XGBoost model and deep neural network (DNN) regression. In an experiment of 20 runs, the proposed model has gained roughly 12, 11.7, 9, and 12% reductions in root mean square error compared to the DNN with 2, 3, 4, and 5 hidden layers, respectively. The Wilcoxon signed-rank tests confirm that the proposed model is highly suitable for concrete pile capacity prediction. [ABSTRACT FROM AUTHOR]

Interval-valued intuitionistic fuzzy numbers (IVIFNs) were proposed by Atanassov since the 1980s. They have been applied to many practical problems. One of the problems posed when research is the ranking of interval-valued intuitionistic fuzzy numbers to apply it to decision-making problems. But so far, there has not been a common method to rank two arbitrary IVIFNs. In this paper, we propose a new ranking function based on polynomial and exponential functions to rank fuzzy numbers. The numerical examples are illustrated to show the effectiveness of our proposed score function. In this paper, the multi-criteria decision-making models based on our proposed new score function are also presented namely MOORA models. We apply that model to the selection of materials for growing king oyster mushrooms in Vietnam. [ABSTRACT FROM AUTHOR]

The simplest and effective way to store human knowledge through centuries was using text. Along with the advancement of technology nowadays, the volume of text has grown to be larger and larger. To extract useful information from this amount of text becomes an exceptionally complex task. As an effort to solve that problem, in this paper, we present a pipeline to extract core knowledge from large quantity text using distributed computing. The components of our pipeline are systems that were known to yield good results. The outputs of our proposed system are stored in a knowledge graph. A knowledge graph is a graph for storing knowledge in the form of triples (head, relation, tail). Some of the existing knowledge graphs in the world are Google knowledge graph, YAGO, DBLP, or DBpedia. These knowledge graphs have one thing in common—they are in English. The English language is studied by many researchers in the world and it had become a rich-resource language (with many natural language processing tools and data set). Vietnamese, on the other hand, is a low-resource language. Therefore, we use cross-lingual transfer method to build a Vietnamese knowledge graph. Firstly, we collect data in form of text about Vietnam tourism, which was written mostly in Vietnamese, using Google search and Wikipedia. In the next step, we translate them into English with Google Translate and use English Natural Language Processing tools like Stanford Parser, Co-referencing, ClausIE, MinIE to extract useful triples from this text. Lastly, the triples are translated back to Vietnamese to build a Vietnam tourism knowledge graph. Since we are working with massive text, we develop a distributed algorithm to extract triples from sentences of massive text. This is a distributed version of MinIE, which was originally developed for a single machine model. In Apache Spark framework, we divide massive text into many smaller parts and move them to the worker nodes with distributed MinIE function. Spark distributed MinIE will extract the triples of sentences in the local text of this worker node in parallel. Finally, the result of worker nodes will be sent back to the master node for building the knowledge graph. We conduct experiments with the distributed MinIE on spark cluster to prove the outperformance of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Real estate is believed to grow even stronger in the next few years in Vietnam, so this study is conducted to closely monitor market changes and provide updated findings about this field. However, with such an asset and capital-intensive industry, it is necessary for investors understanding companies' operation efficiency besides their stocks indicators. The paper looks into operation of real estate corporation with highest market capitalization and ranks their performance efficiency using the data envelopment analysis. This study gives a better "past–present–future" insights for decision makers. The findings indicate that the real estate market will continue its upward trend in the next few years. For the policymakers, this study will help short-term development strategies. For companies with low forecasting shown by grey forecasting, they need to apply new projects and methods in their operations and business model to make more progress in the future. In the view of the investors, this research can be used as a source of information in business assessments and investment decisions. [ABSTRACT FROM AUTHOR]

Air-blast overpressure (AOp) is one of the undesirable effects caused by blasting operations in open-pit mines. This side effect of blasting can seriously undermine surrounding residential structures and living quality. To control and mitigate this situation, this study using artificial neural networks to predict AOp implemented at Deo Nai open-pit coal mine, Vietnam. A total of 146 events of blasting were recorded, of which 80% (118 observations) was used for training and 20% (28 observations) was used for testing. A resampling technique, namely tenfold cross-validation, was performed with three repeats to increase the accuracy of the predictive models. In this paper, three different types of neural networks were developed to predict AOp including multilayer perceptron neural network (MLP neural nets), Bayesian regularized neural networks (BRNN) and hybrid neural fuzzy inference system (HYFIS). Each type was tested with ten model configurations to discover the best performing ones based on comparing standard metrics, including root-mean-square error (RMSE), coefficient of determination (R2), and a simple ranking method. Eight parameters were considered for these models, including charge per delay, burden, spacing, length of stemming, powder factor, air humidity, and monitoring distance. The results indicated that MLP neural nets model with RMSE = 2.319, R2 = 0.961 on testing datasets and a total ranking of 12 yielded the most accurate prediction over BRNN and HYFIS models. [ABSTRACT FROM AUTHOR]