Prediction of Fatigue on Rotating-Shift Workers

Rotating shifts have become prevalent in many industries, leading to a growing concern about the impact of fatigue on workers performance and safety. Thus, it is useful to develop a method to predict the fatigue of workers with rotating shifts. This thesis aims at contributing to the development of such method by building data-driven models to predict level of fatigue. We use random forest classifier and random forest regressor to build two fatigue prediction models. A third model is built by a combination of random forest classifier and regressor. Two imbalanced datasets from different groups of workers in the same industry are used. We explore two strategies to deal with imbalanced datasets: random over-sampling and class weights. We select features with feature importance of random forest and discover that a set of 19 features, selected from 38 original features, gives best performance. We obtain good prediction accuracy on both datasets. The combined model reaches mean absolute error of 0.93 and 0.83 on two datasets, on a 9-level scale of fatigue. In the area of high level of fatigue, which in real work is of particular interest, our model can predict with average 85\% confidence that the true level falls into +-1 range of prediction. We conclude that fatigue can be predicted with high confidence, based on a dataset of sleep patterns, work schedules and demographic data. Future work will focus on model generalization to datasets from different industries or geographical areas; and the discovery of other sets of features that give better prediction

Factors Affecting Members’ Satisfaction With High-Tech Agriculture Cooperatives in Vietnam’s Northern Key Economic Region

Purpose: This study aims to determine the factors affecting cooperates’ satisfaction when joining high-tech agricultural cooperatives in Vietnam’s Northern Key Economic Region.   Theoretical framework: The study absorbed previous studies and expert opinions to determine the member' satisfaction coming to high-tech agricultural cooperatives.   Design/methodology/approach: Primary data was collected through a direct survey of 395 members participating in high-tech agricultural cooperatives. Then, the study uses the method of partial least squares structural equation modeling to test the hypothesis and assess the satisfaction level of the members.   Finding: The research results show that the factors affecting the satisfaction of cooperative members include 7 factors: working environment, trust, managers, level of participation, perceived benefits, support policies, and service capacity.   Research, Practical & Social Implication: Based on the results of this study, several solutions to improve the current association activities with the role of the cooperative model as the core are proposed. People are encouraged to participate more in the association by improving the management skills of cooperatives, strengthening support activities, creating jobs, and raising incomes for people.   Originality/value: Members' satisfaction will promote the activities of linking production and consumption with the main role of the cooperative, not just stopping at the immediate propaganda activities. Since then, these activities also improve people's confidence when participating in the association. Specifically, collective economic development must come from the needs of the people and participating organizations

Proposed ASTM Standard for the Stokoe-type Resonant Column Torsional Shear Device

Resonant Column Torsional Shear (RCTS) testing has become one of the most commonly used methods for determining laboratory soil stiffness and soil damping. The RCTS test has been accepted and is commonly utilized during the permitting of new nuclear facilities. However, there is still no available public standard for performing RCTS tests using the Stokoe-type device. Therefore, an ASTM standard for calibration and performance of RCTS tests using the Stokoe-type RCTS device is presented herein. Data collected using the Stokoe-type RCTS devices at the University of Arkansas (UofA) and at the Norwegian Geotechnical Institute (NGI) also aided in the development of this standard. By following the proposed standard to calibrate the RCTS Stokoe-type device, the mass polar moment of inertia value, Jo, for the UofA drive plates were found to be similar but smaller than to Jo values found by other authors. The proximeter calibration factor, KP, was determined to be valid because the obtained results were consistent for the linear calibration method and for the rotational calibration method (0.0028 rad/V). The torque calibration factor, KT, was also determined to be valid with the obtained value of 0.1347 N·m/V. To validate the proposed ASTM standard, RCTS tests following the standard were performed on Ottawa Sand specimens using the Stokoe-type devices at the UofA. The obtained modulus reduction curves and damping curves were compared with curves developed at the University of Texas. The UofA obtained modulus reduction curves were found to plot at higher values than the Texas curves, but both curves followed the same trend. The UofA damping curves compared well with the Texas curves at shear strain levels less than 10-2 percent, but it was above the Texas obtained curves at shear strain levels greater than 10-2 percent. Shear wave velocity values for the Ottawa Sand specimens from the RCTS tests were also compared with results obtained from bender element test performed on similar specimens at the same confining pressure. A only five (5) percent difference in shear wave velocities was observed between the bender element obtained values (178 m/s) and the resonant column obtained values (187 m/s)

Improving GAN with neighbors embedding and gradient matching

We propose two new techniques for training Generative Adversarial Networks (GANs). Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of $30.80$ for the STL-10 dataset. Our code is available at: https://github.com/tntrung/ganComment: Published as a conference paper at AAAI 201

On the existence and exponential attractivity of a unique positive almost periodic solution to an impulsive hematopoiesis model with delays

In this paper, a generalized model of hematopoiesis with delays and impulses is considered. By employing the contraction mapping principle and a novel type of impulsive delay inequality, we prove the existence of a unique positive almost periodic solution of the model. It is also proved that, under the proposed conditions in this paper, the unique positive almost periodic solution is globally exponentially attractive. A numerical example is given to illustrate the effectiveness of the obtained results.Comment: Accepted for publication in AM

An optimal control approach for the treatment of hepatitis C patients

In this article, the feasibility of using optimal control theory will be studied to develop control theoretic methods for personalized treatment of HCV patients. The mathematical model for HCV progression includes compartments for healthy hepatocytes, infected hepatocytes, infectious virions and noninfectious virions. Methodologies have been used from optimal control theory to design and synthesize an open-loop control based treatment regimen for HCV dynamics.Comment: Accepted for oral presentation at the ICCSE 2014, Ho Chi Minh City, Vietna

Topological Lifshitz phase transition in effective model of QCD with chiral symmetry non-restoration

The topological Lifshitz phase transition is studied systematically within an effective model of QCD, in which the chiral symmetry, broken at zero temperature, is not restored at high temperature and/or baryon chemical potential. It is found that during phase transition the quark system undergoes a first-order transition from low density fully-gapped state to high density state with Fermi sphere which is protected by momentum-space topology. The Lifshitz phase diagram in the plane of temperature and baryon chemical potential is established. The critical behaviors of various equations of state are determined.Comment: 8 pages, 10 figure

19世紀ベトナム紅河デルタ沿岸低地における干拓過程 : タイビン省ティエンハイ県の事例から

紅河デルタはメコンデルタとならぶベトナムの二大穀倉地帯となっている沖積デルタで,農業活動が中心となっている。紅河デルタは人口密度がきわめて稠密で多くの過剰人口をかかえている。その沿岸部のニンビン省,ナムディン省,タイビン省などは海面干拓の適地となっている。これらの地域では古くから干拓が過剰人口問題を解決するひとつの手段となってきた。本論文の目的は,1)19世紀グエン朝の土地政策を概観し,2)干拓計画やその技術を評価し,3)事例としてタイビン省ティエンハイ県の干拓過程の特色を詳述することである。以下がその結果である。ティエンハイ県では18,979マウ(mau)の干拓地,71の村が2,350人の労働力によって生まれた。自然要素としての広大な干潟,社会的には卓越した個人による組織力,技術的には築堤技術や村づくりの手法,経済的には国家的な財政やインフラ援助などがうまく複合して成し遂げられた。さらに干拓請負人のモチベーションを促進したことも成功の一要因であった。1828年から19世紀末までに小さな干拓計画がいくつもこの地域に簇生した。とりわけ,重要なのは,ティエンハイ県南部に位置するドンタイン(Dong Thanh)行政村の例である。干拓地の灌漑施設の建設とその管理運用が重要である。その後も干拓は継続し,ベトナム語で"Dan thuy nhap dien"という,潮汐の差を利用した干拓様式は,灌漑に海水が入り込まないような工夫がされたものである。そこでは堤防の下に設けられた樋門を干満のタイミングにあわせて開閉することが要諦であり,樋門管理人の役割が非常に重要となる

Global exponential stability of a class of neural networks with unbounded delays

In this paper, the global exponential stability of a class of neural networks is investigated. The neural networks contain variable and unbounded delays. By constructing a suitable Lyapunov function and using the technique of matrix analysis, some new sufficient conditions on the global exponential stability are obtained.Досліджено глобальну експоненціальну стійкість одного класу нейронних сіток. Нейронні сітки містять змінні та необмежені загаювання. На основі побудови відповідної функції Ляпунова та техніки матричного аналізу отримано нові достатні умови глобальної експоненціальної стійкості

Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique

Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 38% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses