ObjectiveTo estimate individual and household economic impact of cardiovascular disease (CVD) in selected low- and middle-income countries (LMIC).BackgroundEmpirical evidence on the microeconomic consequences of CVD in LMIC is scarce.Methods and FindingsWe surveyed 1,657 recently hospitalized CVD patients (66% male; mean age 55.8 years) from Argentina, China, India, and Tanzania to evaluate the microeconomic and functional/productivity impact of CVD hospitalization. Respondents were stratified into three income groups. Median out-of-pocket expenditures for CVD treatment over 15 month follow-up ranged from 354 international dollars (2007 INT$, Tanzania, low-income) to INT$2,917 (India, high-income). Catastrophic health spending (CHS) was present in >50% of respondents in China, India, and Tanzania. Distress financing (DF) and lost income were more common in low-income respondents. After adjustment, lack of health insurance was associated with CHS in Argentina (OR 4.73 [2.56, 8.76], India (OR 3.93 [2.23, 6.90], and Tanzania (OR 3.68 [1.86, 7.26] with a marginal association in China (OR 2.05 [0.82, 5.11]). These economic effects were accompanied by substantial decreases in individual functional health and productivity.ConclusionsIndividuals in selected LMIC bear significant financial burdens following CVD hospitalization, yet with substantial variation across and within countries. Lack of insurance may drive much of the financial stress of CVD in LMIC patients and their families.

Purpose This paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions. Design/methodology/approach This study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case. Findings The results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning. Research limitations/implications There are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions. Originality/value Prior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.

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Abstract-Memory accesses form an important source of timing unpredictability. Timing analysis of real-time embedded software thus requires bounding the time for memory accesses. Multiprocessing, a popular approach for performance enhancement, opens up the opportunity for concurrent execution. However due to contention for any shared memory by different processing cores, memory access behavior becomes more unpredictable, and hence harder to analyze. In this paper, we develop a timing analysis method for concurrent software running on multi-cores with a shared instruction cache. Communication across tasks is by message passing where the message mailboxes are accessed via interrupt service routines. We do not handle data cache, shared memory synchronization and code sharing across tasks. Our method progressively improves the lifetime estimates of tasks that execute concurrently on multiple cores, in order to estimate potential conflicts in the shared cache. Possible conflicts arising from overlapping task lifetimes are accounted for in the hit-miss classification of accesses to the shared cache, to provide safe execution time bounds. We show that our method produces lower worst-case response time (WCRT) estimates than existing shared-cache analysis on a real-world embedded application.