Categorical observers for metamerism

A method is proposed to generate categorical colour observer functions (individual colour matching functions) for any field size based on the CIE 2006 system of physiological observer functions. The method combines proposed categorical observer techniques of Sarkar et al with a physiologically-based individual observer model of Asano et al and a clustering technique to produce the optimal set of categorical observers. The number of required categorical observers varies depending on an application with as many as 50 required to predict individual observers' matches when a laser projector is viewed. However, 10 categorical observers are sufficient to represent colour-normal populations for personalized colour imaging. The proposed and recommended categorical observers represent a robust and inclusive technique to examine and quantify observer metamerism in any application of colorimetry.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

Friction compensation controller for load varying machine tool feed drive

The load applied to a machine tool feed drive changes during the machining process as material is removed. This load change alters the Coulomb friction of the feed drive. Because Coulomb friction accounts for a large part of the total friction the friction compensation control accuracy of the feed drives is limited if this nonlinear change in the applied load is not considered. This paper presents a new friction compensation method that estimates the machine tool load in real time and considers its effect on friction characteristics. A friction observer based on a Kalman filter with load estimation is proposed for friction compensation control considering the applied load change. A specially designed feed drive testbed that enables the applied load to be modified easily was constructed for experimental verification. Control performance and friction estimation accuracy are demonstrated experimentally using the testbed.     

On one boundary problem for nonlinear stationary controlled system

The object of study is nonlinear stationary controlled system of ordinary differential equations with constant disturbance in the right part. The problem of constructing the synthesising control function providing the transfer of this system from the initial state to the origin is considered. The sufficiently simple for numerical implementation algorithm of solution of the above-mentioned problem is obtained. It is shown that for local null controllability of the considered system, it is sufficient that the conditions of the Kalman's type were satisfied. In addition, the estimates restricting the choice of initial conditions and external disturbances under which the transfer is guaranteed are obtained. The main idea of the method of construction of the desired control function consists in reducing the original problem to stabilisation of a special kind linear non-stationary system and solving the Cauchy problem for an auxiliary system of ordinary differential equations closed by stabilising control. The simplicity of the realisation of this algorithm is determined by the construction of the auxiliary system and its stabilisation that could be obtained by analytical methods. The effectiveness of the method is illustrated by solving the problem of crane control and its numerical simulation.     

Risk factors associated with online game addiction: A hierarchical model

Online gaming addiction has been increasingly recognized as a mental disorder. However, the predictive factors that lead to online gaming addiction are not well established. The aim of this study was to identify factors that may influence the development of online gaming addiction. A total of 263 patients with problematic online gaming addiction (255 males (97%) and 8 females (3%), age: mean = 20.4 ± 5.8 years) and153 healthy comparison subjects (118 males (77%) and 35 females (23%), age: 21.2 ± 5.5 years, range) were recruited for participation in the current study. Hierarchical logistic regression analyses among each set of variables were conducted. Individual factors (sex and age), cognitive factors (IQ and perseverative errors), psychopathological conditions (ADHD, depression, anxiety, and impulsivity), and social interaction factors (family environment, social anxiety, and self-esteem) were evaluated in a stepwise fashion. All four factors were associated with online gaming addiction, with psychopathological conditions being the strongest risk factors for the addiction. Individual factors, psychological factors, and social interactions were associated with the development of pure online gaming addiction. As before, psychological factors (attention, mood, anxiety and impulsivity) were the strongest risk factors for online gaming addiction in patients with pure online gaming addiction. Psychopathologies, including ADHD and depression, were the strongest factors associated with the development of online gaming addiction in individuals.     

Automated network management and configuration using Probabilistic Trans-Algorithmic Search

Online configuration of large-scale systems such as networks requires parameter optimization within a limited amount of time, especially when configuration is needed as a response to recover from a failure in the system. To quickly configure such systems in an online manner, we propose a Probabilistic Trans-Algorithmic Search (PTAS) framework which leverages multiple optimization search algorithms in an iterative manner. PTAS applies a search algorithm to determine how to best distribute available experiment budget among multiple optimization search algorithms. It allocates an experiment budget to each available search algorithm and observes its performance on the system-at-hand. PTAS then probabilistically reallocates the experiment budget for the next round proportional to each algorithm’s performance relative to the rest of the algorithms. This “roulette wheel” approach probabilistically favors the more successful algorithm in the next round. Following each round, the PTAS framework “transfers” the best result(s) among the individual algorithms, making our framework a trans-algorithmic one. PTAS thus aims to systematize how to “search for the best search” and hybridize a set of search algorithms to attain a better search. We use three individual search algorithms, i.e., Recursive Random Search (RRS) (Ye and Kalyanaraman, 2004), Simulated Annealing (SA) (Laarhoven and Aarts, 1987), and Genetic Algorithm (GA) (Goldberg, 1989), and compare PTAS against the performance of RRS, GA, and SA. We show the performance of PTAS on well-known benchmark objective functions including scenarios where the objective function changes in the middle of the optimization process. To illustrate applicability of our framework to automated network management, we apply PTAS on the problem of optimizing link weights of an intra-domain routing protocol on three different topologies obtained from the Rocketfuel dataset. We also apply PTAS on the problem of optimizing aggregate throughput of a wireless ad hoc network by tuning datarates of traffic sources. Our experiments show that PTAS successfully picks the best performing algorithm, RRS or GA, and allocates the time wisely. Further, our results show that PTAS’ performance is not transient and steadily improves as more time is available for search.     

灵芝菌丝体多糖的分离纯化及其单糖组成分析与分子量测定

前期杂交优化后赤芝菌种经液体深层发酵后,提取灵芝菌丝体多糖,并过DEAE-Sepharose Fast Flow柱分离纯化,利用高效体积排阻色谱(HPSEC)检测多糖级分的纯度,采用完全酸水解PMP柱前衍生化RP—HPLC测定多糖级分的单糖组成,多角度光散射仪联用装置(SEC—MALLS)测定其绝对重均分子量(Mw),并且根据分子旋转半径与分子摩尔数的关系曲线斜率初步推断其空间构象。结果显示:分离纯化得到3个多糖级分GLMP1、GLMP2和GLMP3,HPSEC检测其峰面积百分比分别为93.58%,97.64%,99.19%,单糖组成分析结果表明GLMP1、GLMP2和GLMP3均含有甘露糖、鼠李糖、半乳糖醛酸、葡萄糖、半乳糖、木糖、阿拉伯糖和岩藻糖,但单糖摩尔比各异。SEC—MALLS测试GLMP1、GLMP2和GLMP3的Mw分别为4.526×105,4.603×104,3.760×103 g/mol,3个多糖级分构象可能均为高度紧缩且具有分支结构的聚合物。     

Length–weight relationships of five catfish species (Siluriformes) from the Ganga River,India

Knowledge of the length–weight relationships (LWRs) of fish is an important tool to understand fish body form, growth pattern, stock management and their conservation. The present study focused on investigating the length–weight relationships for five catfish species, Pachypterus atherinoides (Bloch, 1794), belonging to family Horabagridae; Batasio batasio (Hamilton, 1822) family Bagridae; Bagarius yarrelli (Sykes, 1839), family Gogangra viridescens (Hamilton, 1822); and Sisor rhabdophorus (Hamilton, 1822) belonging to family Sisoridae. Specimens were collected from the middle stretch of the Ganga River in India from November 2016 to May 2018. A total of 174 specimens of five fish species were collected, and their total lengths were measured to the nearest centimetre and the body weight to the nearest gram. The value of the parameter slope (b) of LWRs of the five species ranged from 2.86 (B. yarrelli) to 3.16 (G. viridescens), with a mean value of 2.99. The results of the present study documented the new maximum total length (TL) for P. atherinoides and S. rhabdophorus. The present study also provides the first reference regarding LWRs for S. rhabdophorus.