Exploring the critical pillars and causal relations within the NRI: An innovative approach

The Global Information Technology Report released by the World Economic Forum (WEF) has employed networked readiness index (NRI) to measure the global competitiveness of a country’s information and communication technologies (ICT) diffusion. The final NRI overall scores were measured by an arithmetic mean aggregation of the composite pillars scores, which implicitly assumed that all the pillars have constant weights. The Report did not explore the critical pillars and causal relations for better decision making. To add values to this Report, the objective of this paper is to propose an innovative approach by using data mining techniques and partial least squares path modeling to scrutinize the critical pillars within the NRI and to further explore the causal relations amongst them. An empirical analysis based on the latest Report (2009-2010) is carried out. The results show that “business usage,” “business readiness,” and “market environment” are the three root drivers—critical pillars to manipulate the NRI overall scores; whereas “government readiness,” which is further mostly affected by the “government usage,” is the foremost enabler to the NRI overall scores. Based on the results, policy makers are suggested to allocate limited resources with priority to the three root drivers and one foremost enabler to frog-leap the global competitiveness of national ICT diffusion.     

One-stage and two-stage DEA estimation of the effects of contextual variables

Two-stage data envelopment analysis (2-DEA) is commonly used in productive efficiency analysis to estimate the effects of operational conditions and practices on performance. In this method the DEA efficiency estimates are regressed on contextual variables representing the operational conditions. We re-examine the statistical properties of the 2-DEA estimator, and find that it is statistically consistent under more general conditions than earlier studies assume. We further show that the finite sample bias of DEA in the first stage carries over to the second stage regression, causing bias in the estimated coefficients of the contextual variables. This bias is particularly severe when the contextual variables are correlated with inputs. To address this shortcoming, we apply the result that DEA can be formulated as a constrained special case of the convex nonparametric least squares (CNLS) regression. Applying the CNLS formulation, we develop a new semi-nonparametric one-stage estimator for the coefficients of the contextual variables that directly incorporates contextual variables to the standard DEA problem. The proposed method is hence referred to as one-stage DEA (1-DEA). Evidence from Monte Carlo simulations suggests that the new 1-DEA estimator performs systematically better than the conventional 2-DEA estimator both in deterministic and noisy scenarios.     

An environment-adjusted evaluation of citizen satisfaction with local police effectiveness: Evidence from a conditional Data Envelopment Analysis approach

Hard data alone are not sufficient to evaluate local police effectiveness in the new age of community policing. Citizens can provide useful feedback regarding strengths and weaknesses of police operations. However, citizen satisfaction indicators typically fail to accurately convey the multidimensional nature of local policing and account for characteristics that are non-controllable for the local police departments. In this paper, we construct a measure of perceived effectiveness of community oriented police forces that accounts for both multidimensional aspects of local policing and exogenous influences. In specific, this paper suggests the use of a multivariate conditional, robust order-m version of a non-parametric Data Envelopment Analysis approach with no inputs. We show the potentiality of the method by constructing and analyzing perceived effectiveness indicators of local police forces in Belgium. The findings suggest that perceived police effectiveness is significantly conditioned by the demographic and socioeconomic environment.     

Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivity growth estimates derived from growth accounting (GA) and frontier-based methods (namely data envelopment analysis-, corrected ordinary least squares-, and stochastic frontier analysis-based malmquist indices) under various conditions. These include the presence of technical inefficiency, measurement error, misspecification of the production function (for the GA and parametric approaches) and increased input and price volatility from one period to the next. The study finds that the frontier-based methods usually outperform GA, but the overall performance varies by experiment. Parametric approaches generally perform best when there is no functional form misspecification, but their accuracy greatly diminishes otherwise. The results also show that the deterministic approaches perform adequately even under conditions of (modest) measurement error and when measurement error becomes larger, the accuracy of all approaches (including stochastic approaches) deteriorates rapidly, to the point that their estimates could be considered unreliable for policy purposes.     

Measuring technical efficiency in the presence of pesticide spillovers and production uncertainty: The case of Dutch arable farms

Pesticides’ dynamic effects and production uncertainty play an important role in farmers’ production decisions. Pesticides have a current production impact through reducing crop damage in the current period and a future impact through impacting the farm biodiversity which alters the future production environment. This study presents the difference in inefficiency arising from models that ignore the dynamic effects of pesticides in production decisions and the impact of production uncertainty. A dynamic data envelopment analysis (DEA) model is applied to outputs, inputs, and undesirables of Dutch arable farms over the period 2003–2007. A bootstrap approach is used to explain farmers’ performance, providing empirical representations of the impact of stochastic elements on production. These empirical representations are used to adjust firms’ inefficiency scores to incorporate production uncertainty in efficiency evaluation. We find that efficiency increased dramatically when a production technology representation that considers both pesticides’ dynamic impacts, and production uncertainty is adopted.     

基于局地和非局地观测算子的GPS 掩星资料后向映射四维变分同化研究

本文通过在区域暴雨预报模式(AREM) 的后向映射四维变分同化系统(AREM-B4DVar) 中引入GPS 掩星折射率局地和非局地两种观测算子, 使得该系统具备了同化全球定位系统(GPS) 掩星折射率资料的能力, 并采用台湾地区与美国联合执行的气象、电离层和气候星座观测系统计划(COSMIC计划) 探测得到的GPS 掩星折射率资料和常规探空资料, 对2007 年7 月4 日至5 日发生在我国江淮流域的暴雨个例进行了同化预报试验. 结果表明, 在同化系统中采用局地和非局地两种观测算子, 加入GPS 掩星折射率资料后, 均可以提高观测资料附近初值的分析质量, 从而在改进24 小时的降水预报中起到正效果; 基于非局地观测算子的掩星折射率资料同化可以通过大气非局地的约束, 进一步改进基于局地观测算子掩星折射率资料同化的不足; 在常规资料的基础上加入掩星折射率资料, 可以使同化系统进一步改进初值分析质量和24 小时预报效果, 尤其能更好地发挥非局地观测算子的作用.     

Method of analyzing the influence of network structure on information diffusion

Social phenomena are affected by the structure of networks consisting of personal relationships. In the present paper, the diffusion of information among people is examined. In particular, the relationship between the network structure and the dynamics is studied. First, several networks are generated using the proposed network model and other network models, such as the WS model and the KE model. By changing the parameters of the network models, networks with different structures are generated. The parameters of the network models determine the topology of the networks and the statistical indicators.     

Determination of dimethoate and phosmet pesticides with surface-enhanced Raman spectroscopy

ABSTRACT

Surface-enhanced Raman spectroscopy spectra of dimethoate and phosmet pesticides were recorded using a Klarite substrate. Significant enhancements were achieved with dimethoate over a concentration range of 0.5–10 µg mL^?1 and phosmet over a concentration range of 0.1–10 µg mL^?1. The best prediction model for dimethoate pesticide was achieved with a correlation coefficient of 0.940 and a root mean square error of prediction of 0.864 µg mL^?1, with the first derivative and standard normalized variate data preprocessing, and the best prediction model of phosmet pesticide was achieved with a correlation coefficient of 0.949 and a root mean square error of prediction of 0.741 µg mL^?1 with the first derivative data preprocessing. Our study shows that pesticides, including dimethoate and phosmet, could be quantitatively measured at as low as 0.5 µg mL^?1 level using surface-enhanced Raman technology coupled with a Klarite substrate and the results indicated that surface-enhanced Raman spectroscopy with a Klarite substrate has potential for the analysis of dimethoate and phosmet residues.     

Attaining and maintaining criticality in a neuronal network model

We propose a cellular automaton model for neuronal networks that combines short-term synaptic plasticity with long-term metaplasticity. We investigate how these two mechanisms contribute to attaining and maintaining operation at the critical point. We find that short-term plasticity, represented in the model by synaptic depression and synaptic recovery, is sufficient to allow the system to attain the critical state, if the level of plasticity is properly chosen. However, it is not sufficient to maintain the criticality if the system is perturbed. But the long time scale change in the short-term plasticity, a change in the way synaptic efficacy is modified, allows the system to recover from perturbation. Working together, these two time scales of plasticity could help the system to attain and maintain criticality, leading to a self-organized critical state.     

High-speed optical digital transmitter/receiver module operating at 1.3 μm wavelength

Taking benefit of optical fibers' wide bandwidth and low attenuation, research and development were conducted in order to apply fiber optics to public telecommunications' networks mainly for long-distance and large-capacity transmissions. However, application of fiber has rapidly spread toward cornrnunication networks having relatively short transmission distances (within several kilometers) such as local area networks (LAN) and computer networks. Thus, requirements for the fiber optic transmitters and receivers used as electro-optic transducers, such as small-size, low-price, high-reliability and low-supply voltage operations, become stricter than those of the public telecommunication network.