Parallel-Machine Scheduling Problems with Past-Sequence-Dependent Delivery Times and Aging Maintenance

We consider parallel-machine scheduling problems with past-sequence-dependent (psd) delivery times and aging maintenance. The delivery time is proportional to the waiting time in the system. Each machine has an aging maintenance activity. We develop polynomial algorithms to three versions of the problem to minimize the total absolute deviation of job completion times, the total load, and the total completion time

Extended Kalman Filter based Resilient Formation Tracking Control of Multiple Unmanned Vehicles Via Game-Theoretical Reinforcement Learning

In This Paper, We Discuss the Resilient Formation Tracking Control Problem of Multiple Unmanned Vehicles (MUV). a Dynamic Leader-Follower Distributed Control Structure is Utilized to Optimize the Performance of the Formation Tracking. for the Follower of the MUV, the Leader is a Cooperative Unmanned Vehicle, and the Target of Formation Tracking is a Non-Cooperative Unmanned Vehicle with a Nonlinear Trajectory. Therefore, an Extended Kalman Filter (EKF) Observer is Designed to Estimate the State of the Target. Then the Leader of the MUV is Adjusted Dynamically According to the State of the Target. in Order to Describe the Interactions between the Follower and Dynamic Leader, a Stackelberg Game Model is Constructed to Handle the Hierarchical Decision Problems. at the Lower Layer, Each Follower Responds by Observing the Leader\u27s Strategy, and the Potential Game is Used to Prove a Nash Equilibrium among All Followers. at the Upper Layer, the Dynamic Leader Makes Decisions Depending on the Response of All Followers to Reaching the Stackelberg Equilibrium. Moreover, the Stackelberg-Nash Equilibrium of the Designed Game Theoretical Model is Proven. a Novel Reinforcement Learning-Based Algorithm is Designed to Achieve the Stackelberg-Nash Equilibrium of the Game. Finally, the Effectiveness of the Method is Verified by a Variety of Formation Tracking Simulation Experiments

Combined analysis of mRNA expression of ERCC1, BAG-1, BRCA1, RRM1 and TUBB3 to predict prognosis in patients with non-small cell lung cancer who received adjuvant chemotherapy

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to investigate prognostic value of excision repair cross-complementing 1 (ERCC1), BCL2-associated athanogene (BAG-1), the breast and ovarian cancer susceptibility gene 1 (BRCA1), ribonucleotide reductase subunit M1 (RRM1) and class III β-tubulin (TUBB3) in patients with non-small cell lung cancer (NSCLC) who received platinum- based adjuvant chemotherapy.</p> <p>Methods</p> <p>Messenger RNA expressions of these genes were examined in 85 tumor tissues and 34 adjacent tissue samples using semi-quantitative RT-PCR. The expressions of these five genes were analyzed in relation to chemotherapy and progression-free survival (PFS) and overall survival (OS). Seventy-four patients were enrolled into chemotherapy.</p> <p>Results</p> <p>Patients with ERCC1 or BAG-1 negative expression had a significantly longer PFS (<it>P </it>= 0.001 and <it>P </it>= 0.001) and OS (<it>P </it>= 0.001 and <it>P </it>= 0.001) than those with positive expression. Patients with negative ERCC1 and BAG-1 expression benefited more from platinum regimen (<it>P </it>= 0.001 and <it>P </it>= 0.002). Patients with BRCA1 negative expression might have a longer OS (<it>P </it>= 0.052), but not PFS (<it>P </it>= 0.088) than those with BRCA1 positive expression. A significant relationship was observed between the mRNA expression of ERCC1 and BAG-1 (<it>P </it>= 0.042). In multivariate analysis, ERCC1 and BAG-1 were significantly favorable factors for PFS (<it>P </it>= 0.018 and <it>P </it>= 0.017) and OS (<it>P </it>= 0.027 and <it>P </it>= 0.022).</p> <p>Conclusions</p> <p>ERCC1 and BAG-1 are determinants of survival after surgical treatment of NSCLC, and its mRNA expression in tumor tissues could be used to predict the prognosis of NSCLC treated by platinum.</p

Monodispersed Bioactive Glass Nanoclusters with Ultralarge Pores and Intrinsic Exceptionally High miRNA Loading for Efficiently Enhancing Bone Regeneration

Bioactive glass nanoparticles (BGNs) have attracted much attention in drug delivery and bone tissue regeneration, due to the advantages including biodegradation, high bone‐bonding bioactivity, and facile large‐scale fabrication. However, the wide biomedical applications of BGNs such as efficient gene delivery are limited due to their poor pore structure and easy aggregation. Herein, for the first time, this study reports novel monodispersed bioactive glass nanoclusters (BGNCs) with ultralarge mesopores (10–30 nm) and excellent miRNA delivery for accelerating critical‐sized bone regeneration. BGNCs with different size (100–500 nm) are fabricated by using a branched polyethylenimine as the structure director and catalyst. BGNCs show an excellent apatite‐forming ability and high biocompatibility. Importantly, BGNCs demonstrate an almost 19 times higher miRNA loading than those of conventional BGNs. Additionally, BGNCs–miRNA nanocomplexes exhibit a significantly high antienzymolysis, enhance cellular uptake and miRNA transfection efficiency, overpassing BGNs and commercial Lipofectamine 3000. BGNCs‐mediated miRNA delivery significantly improves the osteogenic differentiation of bone marrow stromal stem cells in vitro and efficiently enhances bone formation in vivo. BGNCs can be a highly efficient nonviral vector for various gene therapy applications. The study may provide a novel strategy to develop highly gene‐activated bioactive nanomaterials for simultaneous tissue regeneration and disease therapy.Monodispersed bioactive glass nanoclusters (BGNCs) with ultra‐large mesopores (10–30 nm) are developed for miRNA delivery to enhance bone regeneration. BGNCs demonstrated an ultrahigh miRNA loading and transfection efficiency, overpassing commercial lipofectamine. BGNCs‐mediated miRNA delivery significantly improved osteogenic differentiation of bone marrow stromal stem cells in vitro and enhanced bone formation in vivo.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139128/1/adhm201700630-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139128/2/adhm201700630.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139128/3/adhm201700630_am.pd