Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

Split Hamiltonian Monte Carlo

We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by “splitting” the Hamiltonian in a way that allows much of the movement around the state space to be done at low computational cost. One context where this is possible is when the log density of the distribution of interest (the potential energy function) can be written as the log of a Gaussian density, which is a quadratic function, plus a slowly-varying function. Hamiltonian dynamics for quadratic energy functions can be analytically solved. With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics. Another context where splitting helps is when the most important terms of the potential energy function and its gradient can be evaluated quickly, with only a slowly-varying part requiring costly computations. With splitting, the quick portion can be handled with a small stepsize, while the costly portion uses a larger stepsize. We show that both of these splitting approaches can reduce the computational cost of sampling from the posterior distribution for a logistic regression model, using either a Gaussian approximation centered on the posterior mode, or a Hamiltonian split into a term that depends on only a small number of critical cases, and another term that involves the larger number of cases whose influence on the posterior distribution is small.     

Generalized Estimating Equations for Symmetric Distributions Observed with Admixture

A semiparametric two-component mixture model is considered, in which the distribution of one (primary) component is unknown and assumed symmetric. The distribution of the other component (admixture) is known. Generalized estimating equations are constructed for the estimation of the mixture proportion and the location parameter of the primary component. Asymptotic normality of the estimates is demonstrated and the lower bound for the asymptotic covariance matrix is obtained. An adaptive estimation technique is proposed to obtain the estimates with nearly optimal asymptotic variances.     

Generalized estimating equations for mixtures with varying concentrations

A finite mixture model is considered in which the mixing probabilities vary from observation to observation. A parametric model is assumed for one mixture component distribution, while the others are nonparametric nuisance parameters. Generalized estimating equations (GEE) are proposed for the semi‐parametric estimation. Asymptotic normality of the GEE estimates is demonstrated and the lower bound for their dispersion (asymptotic covariance) matrix is derived. An adaptive technique is developed to derive estimates with nearly optimal small dispersion. An application to the sociological analysis of voting results is discussed. The Canadian Journal of Statistics 41: 217–236; 2013 © 2013 Statistical Society of Canada     

Unemployment and Well-Being of Europeans Across the Life Cycle: The Role of Countries’ Macroeconomic Situation

Social Indicators Research - Unemployment impairs individuals’ well-being and health and there is some empirical evidence showing that macroeconomic conditions can moderate these effects....     

Citizens’ Perception of Past Environmental Damage and Liability in Countries with Transition: Evidence from Kemerovo,Russia

This paper analyzes citizens’ perception of past environmental damage and liability through the estimation of willingness to pay for the implementation of decontamination and land rehabilitation of the damaged site. The paper takes the Russian Federation as a case of country in transition and focuses on Kemerovo region as one of the most affected regions in the country. The major analytical tool employed in the study is choice experiment, data for which was collected through a household survey. Primary conclusion of the analysis is that citizens' willingness to financially support the implementation of environmental quality improvement projects is not high though individuals in the studied area express high level of concern over the issue in focus.     

Framing and counter-framing a Peace March in Russia: the use of Twitter during a hybrid war

ABSTRACT

Framing is vital to the capacity of social movements to enlist popular support and sustain contentious collective action. Using the case of a Peace March held in Moscow on 21 September 2014, the article examines how antiwar activists and their opponents framed a protest against Russia’s intervention in Ukraine. The study argues that different interpretations of patriotism underpinned divergent evaluations of the conflict and the construction of opposing identities. An analysis of Twitter posts on the eve of the march shows that peace activists positioned themselves as citizens with high moral standards and a healthy dose of patriotism, criticized the Russian government for military intervention in Ukraine, and called for a peaceful conflict resolution. In turn, opponents of the march considered themselves as real patriots and their adversaries as national traitors, denied Russia’s military presence in Ukraine, and fomented an attack on critics of Russian foreign policy. The study contributes to social movement literature by analyzing the framing of antiwar activism on a social media platform in the midst of a hybrid war, marked by a great deal of ambiguity and deception about causes, dynamics, and consequences of military operations by state and non-state actors.     

Association between serum levels of testosterone and biomarkers of subclinical atherosclerosis

Objective: To investigate the association between serum levels of testosterone and biomarkers of subclinical atherosclerosis based on data from 119 middle-aged men of the general population.

Methods: Testosterone, Apolipoprotein A-1 (ApoA-1), Apolipoprotein B (ApoB), Apolipoprotein B-to-Apolipoprotein A-1 ratio (ApoB-to-ApoA-1), high-sensitive C-reactive protein (hsCRP), and fibrinogen levels were measured. Data were also gathered based on age, BMI, waist circumference, smoking, alcohol consumption, and family history of cardiovascular diseases. Men were classified into two groups based on testosterone levels: hypogonadal (testosterone ≤12?nmol/L) and eugonadal men (testosterone >12?nmol/L).

Results: When compared to eugonadal, the hypogonadal men were significantly older (56?years vs. 55?years, p?=?.03), had greater BMI (28?kg/cm² vs. 26?kg/cm², p?=?.01), and higher waist circumference (104?cm vs. 100?cm, p?=?.01). Moreover, ApoB, ApoB-to-ApoA-1 ratio, and hsCRP were significantly higher in hypogonadal men compared to eugonadal men (1.1?g/L vs. 1.0?g/L, p?=?.03), (0.8 vs. 0.7, p?=?.03), (3.3?mg/L vs. 2.0?mg/L, p?=?.01), respectively. On the other hand, ApoA-1 and fibrinogen levels did not differ significantly between groups (p?>?.05). In an adjusted multivariate regression analysis model, only ApoB showed a significant negative association with testosterone levels (β?=??0.01; 95% CI?=??0.02, ?1.50; p?=?.04).

Conclusion: Testosterone levels showed an inverse relation to ApoB, a biomarker implicated in subclinical atherosclerosis. These findings support the hypothesis that low testosterone levels play a role in atherosclerosis.     

Hamiltonian Monte Carlo acceleration using surrogate functions with random bases

For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computational efficiency of Bayesian inference. Here we propose an efficient and scalable computational technique for a state-of-the-art Markov chain Monte Carlo methods, namely, Hamiltonian Monte Carlo. The key idea is to explore and exploit the structure and regularity in parameter space for the underlying probabilistic model to construct an effective approximation of its geometric properties. To this end, we build a surrogate function to approximate the target distribution using properly chosen random bases and an efficient optimization process. The resulting method provides a flexible, scalable, and efficient sampling algorithm, which converges to the correct target distribution. We show that by choosing the basis functions and optimization process differently, our method can be related to other approaches for the construction of surrogate functions such as generalized additive models or Gaussian process models. Experiments based on simulated and real data show that our approach leads to substantially more efficient sampling algorithms compared to existing state-of-the-art methods.     

Cannabis Use and Antisocial Behavior among Youth

Despite the numerous efforts to curb substance use and abuse through legislation and interventions, marijuana consumption continues to be a major social problem, particularly among young adults in the United States. We provide new information on the relationship between cannabis use and antisocial behavior by analyzing a sample of young adults (aged 18–20) from the National Epidemiological Survey of Alcohol and Related Conditions (NESARC). We examine a broad set of cannabis use patterns and multiple dimensions of antisocial behaviors and test the empirical importance of two prominent criminological theories—general strain and social bond—in explaining associations between cannabis use and antisocial behavior. We include important socioeconomic, demographic, health and health behaviors, and contextual information in all regressions to control for confounding factors. Our results imply that cannabis use is positively and significantly related to antisocial behavior among young adults, and general strain and social bond theories cannot fully explain our findings. As expected, the estimated association with antisocial behavior is stronger for more frequent cannabis users.