For the vast majority of stars in the second Gaia data release, reliable distances cannot be obtained by inverting the parallax. A correct inference procedure must instead be used to account for the ...nonlinearity of the transformation and the asymmetry of the resulting probability distribution. Here, we infer distances to essentially all 1.33 billion stars with parallaxes published in the second Gaia data release. This is done using a weak distance prior that varies smoothly as a function of Galactic longitude and latitude according to a Galaxy model. The irreducible uncertainty in the distance estimate is characterized by the lower and upper bounds of an asymmetric confidence interval. Although more precise distances can be estimated for a subset of the stars using additional data (such as photometry), our goal is to provide purely geometric distance estimates, independent of assumptions about the physical properties of, or interstellar extinction toward, individual stars. We analyze the characteristics of the catalog and validate it using clusters. The catalog can be queried using ADQL at http://gaia.ari.uni-heidelberg.de/tap.html (which also hosts the Gaia catalog) and downloaded from http://www.mpia.de/~calj/gdr2_distances.html.
The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine delay or human factors, it is difficult to ...use a single real-number to express the processing and completion time of the jobs. JSP with fuzzy processing time and completion time (FJSP) can model the scheduling more comprehensively, which benefits from the developments of fuzzy sets. Fuzzy relative entropy leads to a method that can evaluate the quality of a feasible solution following the comparison between the actual value and the ideal value (the due date). Therefore, the multiobjective FJSP can be transformed into a single-objective optimization problem and solved by a hybrid adaptive differential evolution (HADE) algorithm. The maximum completion time, the total delay time, and the total energy consumption of jobs will be considered. HADE adopts a mutation strategy based on DE-current-to-best. Its parameters (CR and F ) are all made adaptive and normally distributed. The new individuals are selected according to the fitness value (FRE) obtained from a population consisting of N parents and N children in HADE. The algorithm is analyzed from different viewpoints. As the experimental results demonstrate, the performance of the HADE algorithm is better than those of some other state-of-the-art algorithms (namely, ant colony optimization, artificial bee colony, and particle swarm optimization).
The result of the title is: an archimedean \\ell \-group with weak unit A is (isomorphic to) \C(\mathcal {L})\ for some (identifiable) locale \\mathcal {L}\ (or, \\mathbb {R}\mathcal {L}^{\mathrm ...{op}}\, \\mathcal {L}^{\mathrm {op}}\ the opposite frame) iff A is divisible and “\*\-complete,” (a type of sequential completeness). This is from Ball and Hager (Positivity 10:165–199, 2006), and is revisited here, with streamlined proof.
Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing ...system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.
Following the publication of this article, the authors have requested that the Acknowledgements section be amended to thank Weidi Yang for his assistance with their Bostrychus sinensis photograph ...that was chosen for the front cover of the January 2018 issue of the journal. This error has been corrected in both the PDF and HTML versions of the paper. Also, the legends for Supplementary Figures 1 and 2 were not posted online. This error has been corrected in the HTML version of the paper.
Abstract
Immune checkpoint inhibitors (ICIs) targeting PD-L1 and PD-1 have improved survival in a subset of patients with advanced non-small cell lung cancer (NSCLC). However, only a minority of ...NSCLC patients respond to ICIs, highlighting the need for superior immunotherapy. Herein, we report on a nanoparticle-based immunotherapy termed ARAC (Antigen Release Agent and Checkpoint Inhibitor) designed to enhance the efficacy of PD-L1 inhibitor. ARAC is a nanoparticle co-delivering PLK1 inhibitor (volasertib) and PD-L1 antibody. PLK1 is a key mitotic kinase that is overexpressed in various cancers including NSCLC and drives cancer growth. Inhibition of PLK1 selectively kills cancer cells and upregulates PD-L1 expression in surviving cancer cells thereby providing opportunity for ARAC targeted delivery in a feedforward manner. ARAC reduces effective doses of volasertib and PD-L1 antibody by 5-fold in a metastatic lung tumor model (LLC-JSP) and the effect is mainly mediated by CD8+ T cells. ARAC also shows efficacy in another lung tumor model (KLN-205), which does not respond to CTLA-4 and PD-1 inhibitor combination. This study highlights a rational combination strategy to augment existing therapies by utilizing our nanoparticle platform that can load multiple cargo types at once.
Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce ...response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.
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