Risk Analysis of Chemical, Biological, or Radionuclear Threats: Implications for Food Security

If the food sector is attacked, the likely agents will be chemical, biological, or radionuclear (CBRN). We compiled a database of international terrorist/criminal activity involving such agents. Based on these data, we calculate the likelihood of a catastrophic event using extreme value methods. At the present, the probability of an event leading to 5,000 casualties (fatalities and injuries) is between 0.1 and 0.3. However, pronounced, nonstationary patterns within our data suggest that the "reoccurrence period" for such attacks is decreasing every year. Similarly, disturbing trends are evident in a broader data set, which is nonspecific as to the methods or means of attack. While at the present the likelihood of CBRN events is quite low, given an attack, the probability that it involves CBRN agents increases with the number of casualties. This is consistent with evidence of "heavy tails" in the distribution of casualties arising from CBRN events.     

Structural Decomposition of Exchange Rate Shocks in Pakistan: An Empirical Investigation using SVAR Methodology

This study is an attempt to decompose nominal and real exchange rate movements in Pakistan into components induced by real and nominal shocks. Using the Blanchard and Quah (1989), famously known as B-Q, methodology for the period 2000:1–2009:12, the study finds that real shocks affect both nominal and real exchange rate. Similarly, the nominal shocks have a permanent effect on nominal exchange rate but have temporary effect on real exchange rate. Moreover, speed of convergence is different between the two rates affected by the same shock. These results put forward that nominal devaluation is not followed by real devaluation and resultantly will not improve trade balance situation. The policy makers, therefore, need to be careful while taking any decision regarding nominal devaluation as it may also lead to worsening of, instead of improvement in, trade balance.     

Efficient Repeated Implementation

This paper examines repeated implementation of a social choice function (SCF) with infinitely lived agents whose preferences are determined randomly in each period. An SCF is repeatedly implementable in Nash equilibrium if there exists a sequence of (possibly history‐dependent) mechanisms such that its Nash equilibrium set is nonempty and every equilibrium outcome path results in the desired social choice at every possible history of past play and realizations of uncertainty. We show, with minor qualifications, that in the complete information environment an SCF is repeatedly implementable in Nash equilibrium if and only if it is efficient. We also discuss several extensions of our analysis.     

Trade and Wage Inequality in Developing Countries

In this article we provide a theoretical analysis of the possible impact of trade and fragmentation on the skilled–unskilled wage gap in a small developing economy. In particular, we illustrate the possibility of a decline in the relative wage of the unskilled labor following an improvement in the terms of trade. (JEL F1 , F11 , F12 )     

Risks of Paralytic Disease Due to Wild or Vaccine-Derived Poliovirus After Eradication

After the global eradication of wild polioviruses, the risk of paralytic poliomyelitis from polioviruses will still exist and require active management. Possible reintroductions of poliovirus that can spread rapidly in unprotected populations present challenges to policymakers. For example, at least one outbreak will likely occur due to circulation of a neurovirulent vaccine-derived poliovirus after discontinuation of oral poliovirus vaccine and also could possibly result from the escape of poliovirus from a laboratory or vaccine production facility or from an intentional act. In addition, continued vaccination with oral poliovirus vaccines would result in the continued occurrence of vaccine-associated paralytic poliomyelitis. The likelihood and impacts of reintroductions in the form of poliomyelitis outbreaks depend on the policy decisions and on the size and characteristics of the vulnerable population, which change over time. A plan for managing these risks must begin with an attempt to characterize and quantify them as a function of time. This article attempts to comprehensively characterize the risks, synthesize the existing data available for modeling them, and present quantitative risk estimates that can provide a starting point for informing policy decisions.     

Violence against and the Harassment of Women in Canadian Public Housing: An Exploratory Study*

Aucune étude nord-américaine n'a examiné de façon systé'matique l'importance des divers types degressions sexuelles, notamment le harcèlement sexuel et racial, que subissent les femmes dans les logements sociaux. Les logements sociaux et les rues des quartiers pauvres du centre-ville sont les endroits typiques où se jouent les relations de force entre les sexes et où l'influence de la drogue a exacerbé la violence exercée contre les femmes. À l'aide de données provenant du Quality of Neighbourhood Life Survey, dans lequel sont interrogés des ménages habitant dans six logements sociaux situés dans un centre urbain de Test de l'Ontario, cet article permet de combler une lacune importante dans la recherche. On y étudie les données exploratoires sur l'incidence de la violence conju-gale et de la violence extérieure ainsi que trois types de harcèlement vécus par des femmes vivant dans des logements sociaux. No North American study has systematically examined the extent of various types of woman abuse in public housing, including sexual and racial harassment. Public housing communities and inner-city ghetto streets are key arenas where gendered power relations are played out, and where the influx of drugs has exacerbated the degradation and abuse of women. Using data from the Quality of Neighbourhood Life Survey, administered to households in six public housing projects in an urban centre in Eastern Ontario, this paper helps fill a major research gap. Exploratory data on the incidence of intimate violence, stranger violence, and three types of harassment experienced by women in these dwellings are discussed. As men on the street lose their former authoritarian power in the household, they lash out against the women and children they can no longer control. Men are not accepting the new rights and roles that women are obtaining; instead, they are desperately attempting to reassert their grandfathers' lost autocratic control over their households and over public space.     

Diverse classifier ensemble creation based on heuristic dataset modification

Bagging and Boosting are two main ensemble approaches consolidating the decisions of several hypotheses. The diversity of the ensemble members is considered to be a significant element to obtain generalization error. Here, an inventive method called EBAGTS (ensemble-based artificially generated training samples) is proposed to generate ensembles. It manipulates training examples in three ways in order to build various hypotheses straightforwardly: drawing a sub-sample from training set, reducing/raising error-prone training instances, and reducing/raising local instances around error-prone regions. The proposed method is a straightforward, generic framework utilizing any base classifier as its ensemble members to assemble a powerfully built combinational classifier. Decision-tree classifier and multilayer perceptron classifier as some basic classifiers have been employed in the experiments to indicate the proposed method accomplish higher predictive accuracy compared to meta-learning algorithms like Boosting and Bagging. Furthermore, EBAGTS outperforms Boosting more impressively as the training data set gets broader. It is illustrated that EBAGTS can fulfill better performance comparing to the state of the art.     

Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB,ApoA1 and the ratio were determined using a Bayesian approach

We have developed a new approach to determine the threshold of a biomarker that maximizes the classification accuracy of a disease. We consider a Bayesian estimation procedure for this purpose and illustrate the method using a real data set. In particular, we determine the threshold for Apolipoprotein B (ApoB), Apolipoprotein A1 (ApoA1) and the ratio for the classification of myocardial infarction (MI). We first conduct a literature review and construct prior distributions. We then develop classification rules based on the posterior distribution of the location and scale parameters for these biomarkers. We identify the threshold for ApoB and ApoA1, and the ratio as 0.908 (gram/liter), 1.138 (gram/liter) and 0.808, respectively. We also observe that the threshold for disease classification varies substantially across different age and ethnic groups. Next, we identify the most informative predictor for MI among the three biomarkers. Based on this analysis, ApoA1 appeared to be a stronger predictor than ApoB for MI classification. Given that we have used this data set for illustration only, the results will require further investigation for use in clinical applications. However, the approach developed in this article can be used to determine the threshold of any continuous biomarker for a binary disease classification.     

Modeling and optimizing an agro-supply chain considering different quality grades and storage systems for fresh products: a Benders decomposition solution approach

This paper proposes a mathematical model in the context of agro-supply chain management, considering specific characteristics of agro-products to assist purchase, storage, and transportation decisions. In addition, a new method for determining the required quality score of different types of products is proposed based on their loss factors and purchasing costs. The model aims to minimize total cost imposed by purchasing fresh products, opening warehouses, holding inventories, operational activities, and transportation. Two sets of examples, including small and medium-sized problems, are implemented by general algebraic modeling language (GAMS) software to evaluate the model. Then, Benders decomposition (BD) algorithm is applied to tackle the complexity of solving large-sized instances. The results of both GAMS and BD are compared in terms of objective function values and computational time to demonstrate the efficiency of the BD algorithm. Finally, the model is applied in a real case study involving an apple supply chain to obtain managerial insights.

     

A modified DEA cross efficiency method with negative data and its application in supplier selection

Basic Data Envelopment Analysis (DEA) models are designed for non-negative data. However, negative data is inevitably used in many real-world issues. Also, multiple units with a maximum relative performance score (equal to one) can be obtained due to the benevolent view of evaluating Decision Making Units (DMUs) consistent performance. Therefore, the researchers proposed ranking models to differentiate efficient units. Cross efficiency is one of the most useful tools for DMUs ranking in the DEA. There are two major drawbacks to implementing this process. First, it gives different results in the presence of other optimal solutions; second, it does not provide a compelling reason to use the arithmetic mean to aggregate the results of the cross efficiency matrix. In this paper, first a new non-radial model is proposed to evaluate the performance of DMUs in the presence of negative data and then based on this model a new secondary goal model is proposed to eliminate the first drawback in the cross efficiency method. Also, to solve the second drawback in this method, a hybrid Multi-Attribute Decision Making (MADM)-DEA process with the help of fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje method is proposed. Finally, to show the applicability of the proposed methods, the results are used to select the supplier in a real-world problem.