How swelling debts give rise to a new type of politics in Vietnam

Vietnam has seen fast-rising debts, both domestic and external, in recent years. This paperreviews the literature on credit market in Vietnam, providing an up-to-date take on the domesticlending and borrowing landscape. The study highlights the strong demand for credit in both therural and urban areas, the ubiquity of informal lenders, the recent popularity of consumer financecompanies, as well as the government’s attempts to rein in its swelling public debt. Given thehigh level of borrowing, which is fueled by consumerism and geopolitics, it is inevitable that theamount of debt will soon be higher than the saving of the borrowers. Unlike the conventional wisdom that creditors have more bargaining power over the borrowers, we suggest that—albeitlacking a quantitative estimation—when the debts pile up so high that the borrowers could not repay, the power dynamics may reverse. In this new politics of debt, the lenders fear to lose the money's worth and continue to lend and feed the insolvent debtors. The result is a toxic lending/borrowing market and profound lessons, from which the developing world could learn

Sustainable Growth and Ethics: a Study of Business Ethics in Vietnam Between Business Students and Working Adults

Sustainable growth is not only the ultimate goal of business corporations but also the primary target of local governments as well as regional and global economies. One of the cornerstones of sustainable growth is ethics. An ethical organizational culture provides support to achieve sustainable growth. Ethical leaders and employees have great potential for positive influence on decisions and behaviors that lead to sustainability. Ethical behavior, therefore, is expected of everyone in the modern workplace. As a result, companies devote many resources and training programs to make sure their employees live according to the high ethical standards. This study provides an analysis of Vietnamese business students’ level of ethical maturity based on gender, education, work experience, and ethics training. The results of data from 260 business students compared with 704 working adults in Vietnam demonstrate that students have a significantly higher level of ethical maturity. Furthermore, gender and work experience are significant factors in ethical maturity. While more educated respondents and those who had completed an ethics course did have a higher level of ethical maturity, the results were not statistically significant. Analysis of the results along with suggestions and implications are provided

The spindle of oocytes observed by polarized light microscope can predict embryo quality

Background: The aim is to evaluate spindle position of metaphase II oocyte and the development of embryos originated from oocytes with spindle and without spindle.Methods: Cross-sectional analysis Research: 250 MII oocytes were analyzed with polarized microscope in Military Institute of Clinical Embryology and Histology, Vietnam Military Medical University.Results: Spindles were detected in 170 (77.98%) of 218 metaphase II oocytes, 115 spindles (67.65%) of MII oocytes is beneath or adjacent to the first polar body, 55 oocytes had the spindle located between 300 and 1800 away from the first polar body. Fertilization rate and the rate of good quality embryos in oocytes with a visible spindle (77.98% and 61.02%) were higher than those in oocytes without a visible spindle (22.02% and 36.84%), the difference was statistically significant with p <0.001 and p <0.05.Conclusions: The spindle position of metaphase II oocytes is not always beneath or adjacent to the first polar body. Fertilization rate and the rate of good quality embryos in oocytes with a visible spindle were higher than those in oocytes without a visible spindle

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the pruned physics-aware neural network (P2N2), is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one-year smart meter data of load consumptions for three-phase power flow is developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS in terms of data redundancy and estimation accuracy

Voltage Stability Monitoring based on Adaptive Dynamic Mode Decomposition

This paper develops a new voltage stability monitoring method using dynamic mode decomposition (DMD) and its adaptive variance. First, state estimation (SE) is used to estimate the voltage in the system. Then, the measured voltages from the phasor measurement units (PMU) and estimations from SE are used as the inputs for DMD to predict the long-term voltage dynamic. Furthermore, to improve the prediction performance, the normal DMD is improved by adaptively changing the size of input samples based on the error in the training phase, named adaptive DMD (ADMD). The effectiveness of the proposed method is validated on the Nordic32 test system, which is recommended as the test system for voltage stability studies. Different contingency scenarios are used, and the results show that the proposed method is able to monitor the voltage stability after a disturbance (i.e., 4.3x10-4 MAPE for a stable case and 0.0041 MAPE for an unstable case). Furthermore, the results from a scenario in which a real-world oscillation event is used show high accuracy in voltage stability monitoring of the proposed ADMD method

FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Co-Training

We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients to collaboratively train a large deep learning model by dividing it into an ensemble of several small sub-models and train them on multiple devices in parallel while maintaining privacy. In this co-training process, clients from the same cluster can also learn from each other, further improving their ensemble performance. In the aggregation stage, the server takes a weighted average of all the ensemble models trained by all the clusters. FedDCT reduces the memory requirements and allows low-end devices to participate in FL. We empirically conduct extensive experiments on standardized datasets, including CIFAR-10, CIFAR-100, and two real-world medical datasets HAM10000 and VAIPE. Experimental results show that FedDCT outperforms a set of current SOTA FL methods with interesting convergence behaviors. Furthermore, compared to other existing approaches, FedDCT achieves higher accuracy and substantially reduces the number of communication rounds (with $4-8$ times fewer memory requirements) to achieve the desired accuracy on the testing dataset without incurring any extra training cost on the server side.Comment: Under review by the IEEE Transactions on Network and Service Managemen

Deep Autoencoder for Recommender Systems: Parameter Influence Analysis

Recommender systems have recently attracted many researchers in the deep learning community. The state-of-the-art deep neural network models used in recommender systems are multilayer perceptron and deep autoencoder (DAE). In this work, we focus on the DAE model due to its superior capability to reconstruct the inputs, which works well for recommender systems. Existing works have similar implementations of DAE but the parameter settings are vastly different for similar datasets. In this work, we have built a flexible DAE model, named FlexEncoder that uses configurable parameters and unique features to analyze the parameter influences on the prediction accuracy of recommendations. Extensive evaluation on the MovieLens datasets are conducted, which drives our conclusions on the influences of DAE parameters. We find that DAE parameters strongly affect the prediction accuracy of the recommender systems, and the effect remains valid for bigger datasets in the same family

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

publishedVersio

Self-adaptive Controllers for Renewable Energy Communities Based on Transformer Loading Estimation

In this paper, self-adaptive controllers for renewable energy communities based on data-driven approach are proposed to mitigate the voltage rise and transformer congestion at the community level. In the proposed approach, the transformer loading percentage is estimated by the trained data-driven model, which uses the extreme gradient boosting regression algorithm based on a measurement set acquired from critical coupling points of the communities. To avoid voltage rise issues, the droop control parameters (i.e., voltage threshold for P - V, Q - V curves) are adaptively tuned based on the solar irradiance availability and estimated transformer loading. The proposed approach has been tested in the IEEE European LV distribution network. Results showed that the control approach could effectively reduce 22.2 % of the total overloaded instances, while still keeping voltage magnitude in the operation range. This method can help DSOs manage voltage violation and congestion without further communication