This study showed that optimal voriconazole dosage regimens could be determined successfully with prospective population pharmacokinetic analyses and Monte Carlo simulations.

The purpose of this investigation was to describe the effect of antibacterial stewardship and evaluate the trends and correlation of antibacterial resistance and usage from 2009 to 2013 in a tertiary-care teaching hospital in northwest China. Antibacterial usage was expressed as defined daily doses per 100 patients per day (DDDs/100 PDs). Hospital-wide population-level data and time series analysis were used to evaluate the trends and determine associations between antibacterial exposure and acquisition of resistance. Yearly consumption of overall antibacterials significantly decreased from 66.54 to 28.08 DDDs/100 PDs (β = -10.504, p < 0.01). The resistant rates of the five most frequently isolated species (including Escherichia coli, Acinetobacter baumannii, Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae) significantly decreased or remained stable, and none of them showed a statistically significant upward trend. The medical quality indicators got better or remained stable. Autoregressive integrated moving average (ARIMA) models demonstrated that the monthly resistance rate of P. aeruginosa to imipenem was strongly correlated with antipseudomonal carbapenems usage (β = 34.94, p < 0.001), as did the correlation of P. aeruginosa to meropenem with antipseudomonal third-generation cephalosporins usage (β = 32.76, p < 0.01) and K. pneumoniae to amikacin with aminoglycosides usage (β = 22.01, p < 0.001). The decreased antibacterial use paralleled the improved bacterial resistance without deteriorating medical quality indicators during antimicrobial stewardship. It also suggests that optimum antibiotic use is necessary to alleviate the threat posed by resistant microorganisms at the hospital level.

Both mobile computing and cloud computing have experienced rapid development in recent years.Although centralized cloud computing exhibits abundant resources for computation-intensive tasks, the unpredictable and unstable communication latency between the mobile users and the cloud makes it challenging to handle latency-sensitive mobile computing tasks. To address this issue, fog computing recently was proposed by pushing the cloud computing to the network edge closer to the users. To realize such vision, we can augment existing access points in wireless networks with cloudlet servers for hosting various mobile computing tasks. In this paper, we investigate how to deploy the servers in a cost-effective manner without violating the predetermined quality of service. In particular, we practically consider that the available cloudlet servers are heterogeneous, ie, with different cost and resource capacities. The problem is formulated into an integer linear programming form, and a low-complexity heuristic algorithm is invented to address it. Extensive simulation studies validate the efficiency of our algorithm by it performs much close to the optimal solution.

SUMMARYVehicle Ad-Hoc Networks (VANET) enable all components in intelligent transportation systems to be connected so as to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance driving comfort. The vision of all vehicles connected poses a significant challenge to the collection, storage, and analysis of big traffic-related data. Vehicular cloud computing, which incorporates cloud computing into vehicular networks, emerges as a promising solution. Different from conventional cloud computing platform, the vehicle mobility poses new challenges to the allocation and management of cloud resources in roadside cloudlet. In this paper, we study a virtual machine (VM) migration problem in roadside cloudletbased vehicular network and unfold that (1) whether a VM shall be migrated or not along with the vehicle moving and (2) where a VM shall be migrated, in order to minimize the overall network cost for both VM migration and normal data traffic. We first treat the problem as a static off-line VM placement problem and formulate it into a mixed-integer quadratic programming problem. A heuristic algorithm with polynomial time is then proposed to tackle the complexity of solving mixed-integer quadratic programming. Extensive simulation results show that it produces near-optimal performance and outperforms other related algorithms significantly.

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