Information and communications technology use and income growth: evidence of the multiplier effect in very small island states

ABSTRACT

Very small island states face unique challenges, such as volatile economies, increasing vulnerability to natural disasters, particularly with raising seas, increases their dependence on the world economy. Despite their growing use of ICTs, the results are mixed in terms of the effect of growing ICT usage on income growth. This paper investigates how growth in ICT usage may enable growth in per capita Gross Domestic Product (GDP) in very small island states by analyzing the effects of average ICT usage on GDP growth based on the most recent data available from the World Bank and from the International Telecommunications Union (ITU). Following an analysis of data over four years of 32 very small island states, this paper identifies an ICT multiplier effect that may explain and predict the relationship between average ICT usage and GDP growth. By showing how the ICT multiplier effects may be connected to GDP growth, this paper adds to what we know about the relationship between these two indicators in very small island states. This has implications for how government interventions can enable ICT capacity to bring about GDP growth.     

Initiating software process improvement in very small enterprises: Experience with a light assessment tool

The paper concerns software process improvement in Very Small Enterprises (VSEs). It presents briefly a gradual methodology to initiate software process improvement in VSE through three steps approach and develops the first and most original step. This first step is based on a light evaluation achieved by means of a dedicated Micro-Evaluation approach. It has been experimented during 7 years in 86 organizations from three countries. The experience with that utilization tends to show that such a light approach is practicable and promising, at least for the targeted enterprises.     

基于金属氧化物传感器阵列的小麦霉变程度检测

研制了一套由8个金属氧化物传感器组成、用于检测小麦霉变的电子鼻系统.使用该电子鼻对不同霉变程度和掺入不同百分比含量霉麦的小麦样品进行检测.通过方差分析和主成分分析优化传感器阵列并去掉冗余传感器,对优化后的数据进行主成分分析(PCA)和线性判别分析(LDA),其中PCA的前两个主成分对两类实验结果分析的总贡献率为98.30％和99.27％,LDA前两个判别因子对两类实验结果分析的总贡献率为99.68％和93.30％,且由得分图可知两种方法均能很好地区分不同的小麦样品.利用BP神经网络建立预测模型,对样品菌落总数和掺入样品中霉麦的百分比进行预测.两种预测模型的预测值和测量值之间的相关系数分别为0.91和0.94,表明预测模型具有较好预测性能.     

MEMS微结构变形行为尺度效应的研究进展

微米尺寸驱动结构广泛应用于微机电系统（MEMS）和集成电路等微／纳米系统中。由于这些微米尺寸驱动结构的几何尺寸和其微结构尺寸均在微米至纳米范围,它们的弹性、塑性性能及其变形行为具有明显的尺度效应。简要概述了近年来国内外有关微米尺寸驱动结构的弹性性能和变形特性尺度效应的研究情况,介绍了高阶理论的发展和塑性应变理论与微极理论在尺度效应分析中的应用,并对MEMS今后需要重点研究的方向进行了展望。     

利用图像处理技术解决人脸识别中小样本问题

通过应用PCA及2DPCA算法进行人脸识别,得到了在取不同特征值门限情况下的特征提取维数和识别率,给出了以上两种算法最优特征提取向量的维数和最大特征值门限,并在此基础上应用双线性差值图像旋转处理技术,增加了同一个人较少训练样本情况下的训练样本数量,提高了识别率,从一定程度上解决了小样本问题。如果能从小样本图像中生成出一些新的预测信息,例如,增加同一个训练样本的不同的表情,或改变样本表情的深度,实验的效果可能更加明显。     

Factors responsible for the aggregation behavior of hydrophobic polyelectrolyte PEA in aqueous solution studied by molecular dynamics simulations

Self-association (i.e. interchain aggregation) behavior of atactic poly(ethacrylic acid) PEA in dilute aqueous solution as function of degree-of-neutralization by Na⁺ counter-ions (i.e. charge fraction f) was investigated by molecular dynamics simulations. Aggregation is found to occur in the range 0 ≤ f ≤0.7 in agreement with experimental results compared at specified polymer concentration C_p = 0.36 mol/l in dilute solution. The macromolecular solution was characterized and analysed for radius-of-gyration, torsion angle distribution, inter and intra-molecular hydrogen bonds, radial distribution functions of intermolecular and inter-atomic pairs, inter-chain contacts and solvation enthalpy. The PEA chains form aggregate through attractive inter-chain interaction via hydrogen bonding, in the range f < 0.7, in agreement with experimental observation. The numbers of inter-chain contacts decreases with f. A critical structural transition occurs at f = 0.7, observed via simulations for the first time, in R_g as well as inter-chain H-bonds. The inter-chain distance increases with f due to repulsive interactions between COO− groups on the chains. PEA-PEA electrostatic interactions dominant solvation enthalpy. The PEA solvation enthalpy becomes increasingly favorable with increase in f. The transition enthalpy change, in going from uncharged (acid) state to fully charged state (f = 1) is unfavorable towards aggregate formation.     

Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory

Reliability assessment of composite power systems is a critical and important part of power investigations especially in the market-driven environments. Therefore, the reliability indices as criteria for the comparison of the reliability of the power systems should be evaluated precisely and carefully. Because of the nonlinear behavior of the systems as the effect of different parameters like weather conditions, load pattern changes and some others, reliability indices always contain much uncertainty. In this paper a neuro-fuzzy based method is proposed to reduce the degree of the uncertainty in the reliability indices and therefore to evaluate the reliability of the composite power systems precisely. Fuzzy logic theory makes it possible to make use of the human experts knowledge in the reliability evaluations. Also by the use of RBFNN and its powerful characteristic to learn any nonlinear mapping between two states it would be possible to evaluate the reliability indices for every short time interval needed so that reliability evaluation in real time would be achievable and feasible.In this paper the RBFNN is trained by the training patterns that are achieved by the use of fuzzy logic theory, then the results are examined on a standard Reliability Test System (RTS-96).     

A unified framework for fault detection and isolation of sensor and actuator biases in linear time invariant systems using marginalized likelihood ratio test with uniform priors

This study aims to present a fault detection and isolation (FDI) framework based on the marginalized likelihood ratio (MLR) approach using uniform priors for fault magnitudes in sensors and actuators. The existing methods in the literature use either flat priors with infinite support or the Gamma distribution as priors for the fault magnitudes. In the current study, it is assumed that the fault magnitude is a realization of a uniform prior with known upper and lower limits. The method presented in this study performs detection of time of occurrence of the fault and isolation of the fault type simultaneously while the estimation of the fault magnitude is achieved using a least squares based approach. The newly proposed method is evaluated by application to a benchmark CSTR problem using Monte Carlo simulations and the results reveal that this method can estimate the time of occurrence of the fault and the fault magnitude more accurately compared to a generalized likelihood ratio (GLR) based approach applied to the same benchmark problem. Simulation results on a benchmark problem also show significantly lower misclassification rates.     

Numerical inversions of a source term in the FADE with a Dirichlet boundary condition using final observations

This paper deals with an inverse problem of determining a source term in the one-dimensional fractional advection-dispersion equation (FADE) with a Dirichlet boundary condition on a finite domain, using final observations. On the basis of the shifted Grünwald formula, a finite difference scheme for the forward problem of the FADE is given, by means of which the source magnitude depending upon the space variable is reconstructed numerically by applying an optimal perturbation regularization algorithm. Numerical inversions with noisy data are carried out for the unknowns taking three functional forms: polynomials, trigonometric functions and index functions. The reconstruction results show that the inversion algorithm is efficient for the inverse problem of determining source terms in a FADE, and the algorithm is also stable for additional data having random noises.     

Association of demographics,motives and intensity of using Social Networking Sites with the formation of bonding and bridging social capital in Pakistan

The objectives of this confirmatory study were to investigate the association of socio-economic demographics (age, education of respondent, gender, monthly family income, parentage education), motives (communication and information, self-actualization and outward looking) of using Social Networking Sites (SNSs) and attitudinal and behavior variable (intensity of using SNS, self-esteem, gratification with university life, duration of use, and number of ties) with the formation of bonding and bridging social capital. Total 461 students, aged 18–35 years filled the questionnaire, from randomly selected departments of University of the Punjab, Lahore, Pakistan. Regression analysis was used to assess the association among variables. The study indicated that Facebook is the most popular SNS among university students in Pakistan. Intensity of using SNS, duration of using SNSs, and motives of using SNSs were found to be positively associated with formation of bonding and bridging social capital. Self-esteem and gratifications with university life were found to be significant predictors in formation of bonding social capital only. The demographics variables (education, parentage education, monthly family income) had no influence on formation of both bonding and bridging social capital.