The Mx/G/1 queue with queue length dependent service times

We deal with the MX/G/1 queue where service times depend on the queue length at the service initiation. By using Markov renewal theory, we derive the queue length distribution at departure epochs. We also obtain the transient queue length distribution at time t and its limiting distribution and the virtual waiting time distribution. The numerical results for transient mean queue length and queue length distributions are given.Bong Dae Choi, Yeong Cheol Kim, Yang Woo Shin, and Charles E. M. Pearc

Efficiency Analysis of Project Management Offices for Large-scale Information System Projects: Insights for Construction Megaprojects

In this study, the efficiencies of Project Management Offices (PMOs) in large-scale information system (IS) projects are addressed by using data envelopment analysis. Moreover, the potential improvement levels for each input and output factors of inefficient PMOs are examined. The effects of performance levels of PMO functions on project outcomes with respect to efficiency levels are also analyzed. A total of forty-nine PMOs are analyzed for this study. The result shows that twenty-four PMOs are found to be efficient. As a result of analyzing the impact of efficiency on project performance depending on the functional levels of PMOs, those groups with a high degree of efficiency show higher outcomes compared with the groups with a low degree of efficiency regardless of the functional levels of PMOs. Furthermore, the gap in outcome between the groups with a high degree of efficiency and the groups with a low degree of efficiency is maintained at almost the same level, regardless of the functional levels of PMOs, with the exception of the case of practice management. This indicates that even those groups with a low degree of efficiency could expect high outcomes in terms of schedule and cost compliance if their level of practice management is high

Predictive Coding Strategies for Developmental Neurorobotics

In recent years, predictive coding strategies have been proposed as a possible means by which the brain might make sense of the truly overwhelming amount of sensory data available to the brain at any given moment of time. Instead of the raw data, the brain is hypothesized to guide its actions by assigning causal beliefs to the observed error between what it expects to happen and what actually happens. In this paper, we present a variety of developmental neurorobotics experiments in which minimalist prediction error-based encoding strategies are utilize to elucidate the emergence of infant-like behavior in humanoid robotic platforms. Our approaches will be first naively Piagian, then move onto more Vygotskian ideas. More specifically, we will investigate how simple forms of infant learning, such as motor sequence generation, object permanence, and imitation learning may arise if minimizing prediction errors are used as objective functions

Antitumor activity of sorafenib-incorporated nanoparticles of dextran/poly(dl-lactide-co-glycolide) block copolymer

Sorafenib-incoporated nanoparticles were prepared using a block copolymer that is composed of dextran and poly(DL-lactide-co-glycolide) [DexbLG] for antitumor drug delivery. Sorafenib-incorporated nanoparticles were prepared by a nanoprecipitation-dialysis method. Sorafenib-incorporated DexbLG nanoparticles were uniformly distributed in an aqueous solution regardless of the content of sorafenib. Transmission electron microscopy of the sorafenib-incorporated DexbLG nanoparticles revealed a spherical shape with a diameter < 300 nm. Sorafenib-incorporated DexbLG nanoparticles at a polymer/drug weight ratio of 40:5 showed a relatively uniform size and morphology. Higher initial drug feeding was associated with increased drug content in nanoparticles and in nanoparticle size. A drug release study revealed a decreased drug release rate with increasing drug content. In an in vitro anti-proliferation assay using human cholangiocarcinoma cells, sorafenib-incorporated DexbLG nanoparticles showed a similar antitumor activity as sorafenib. Sorafenib-incorporated DexbLG nanoparticles are promising candidates as vehicles for antitumor drug targeting