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The integration of big data analytics with effective supply chain practices based on dynamic capability theory is an emerging topic, and the relationship that exists between big data analytics and supply chain dynamic capabilities and supply chain practices, and their impact on supply chain performance, requires further research. This article examines the relationship between three supply chain practices and supply chain performance. The information provided by managers of 415 companies in China was used as a sample and the data collected was empirically tested using partial least squares structural equation modelling. The findings show that different supply chain practices have a positive impact on supply chain dynamic capability and supply chain performance, that supply chain dynamic capability has an indirect mediating effect, and that big data analysis has a positive moderating effect on the relationship between supply chain practices and supply chain performance.

The purpose of this study is to reveal the dark side the Internet and establish a hierarchical framework to provide its governance path based on users' negative psychology. However, this hierarchical framework must consider unnecessary attributes and the interrelationships between the aspects and the criteria. Hence, fuzzy set theory is used for screening out the unnecessary attributes, a decision-making and trial evaluation laboratory (DEMATEL) is proposed to manage the complex interrelationships among the aspects and attributes, and interpretive structural modeling (ISM) is used to divide the hierarchy and construct a hierarchical theoretical framework. The results show that: (1) the institutional system is the driver of Internet governance improvement (2) personal values are the last link in the governance process (3) the governance transition from institutional system to values must cross the barriers of ethics and technology. This paper proposes a more systematic and integrated hierarchical framework which provides theoretical guidance to govern the dark side of the Internet.

This paper aims to expand the acceptance of the AI Virtual Assistant model from the perspective of user’s cognition. Based on the 240 samples, we used multi-layer regression analysis to investigate the influencing factors and differential effects of users' acceptance of AI Virtual Assistant. The results show that functional cognition and emotional cognition of users are important influencing factors for an artificial intelligence virtual assistant. This provides a new perspective for user acceptance processes of the AI Virtual Assistant. We also examined the moderating effect of social norms between user cognition and AI Virtual Assistant. At last, a new AI acceptance model of AI Virtual Assistant was established.

Recently, direct reprogramming between divergent lineages has been achieved by the introduction of regulatory transcription factors. This approach may provide alternative cell resources for drug discovery and regenerative medicine, but applications could be limited by the genetic manipulation involved. Here, we show that mouse fibroblasts can be directly converted into neuronal cells using only a cocktail of small molecules, with a yield of up to >90% being TUJ1-positive after 16 days of induction. After a further maturation stage, these chemically induced neurons (CiNs) possessed neuron-specific expression patterns, generated action potentials, and formed functional synapses. Mechanistically, we found that a BET family bromodomain inhibitor, I-BET151, disrupted the fibroblast-specific program, while the neurogenesis inducer ISX9 was necessary to activate neuron-specific genes. Overall, our findings provide a “proof of principle” for chemically induced direct reprogramming of somatic cell fates across germ layers without genetic manipulation, through disruption of cell-specific programs and induction of an alternative fate.

In explicit-state model checking, system properties are typically expressed in linear temporal logic (LTL), and translated into a Büchi automaton (BA) to be checked. In order to improve performance of the conversion algorithm, some model checkers involve the intermediate automata, such as a generalized Büchi automaton (GBA). The de-generalization is a translation from a GBA to a BA. In this paper, we present a conversion algorithm to translate an LTL formula to a BA directly. A labeling, acceptance degree, is presented to record acceptance conditions satisfied in each state and transition. Acceptance degree is a set of U-subformulae or F-subformulae of the given LTL formula. According to the acceptance degree, on-the-fly degeneralization algorithm, which is different from the standard de-generalization algorithm, is conceived and implemented. On-the-fly de-generalization algorithm is carried out during the expansion of the given LTL formula. It is performed in the case of the given LTL formula contains U-subformulae and F-subformulae, that is, the on-the-fly de-generalization algorithm is performed as required. In order to get a more deterministic BA, the shannon expansion is used recursively during expanding LTL formulae. Ordered binary decision diagrams are used to represent the BA and simplify LTL formulae.We compare the conversion algorithm presented in this paper to previousworks, and show that it is more efficient for five families LTL formulae in common use and four sets of random formulae generated by LBTT (an LTL-to-Büchi translator testbench).

Direct lineage reprogramming, including with small molecules, has emerged as a promising approach for generating desired cell types. We recently found that during chemical induction of induced pluripotent stem cells (iPSCs) from mouse fibroblasts, cells pass through an extra-embryonic endoderm (XEN)-like state. Here, we show that these chemically induced XEN-like cells can also be induced to directly reprogram into functional neurons, bypassing the pluripotent state. The induced neurons possess neuron-specific expression profiles, form functional synapses in culture, and further mature after transplantation into the adult mouse brain. Using similar principles, we were also able to induce hepatocyte-like cells from the XEN-like cells. Cells in the induced XEN-like state were readily expandable over at least 20 passages and retained genome stability and lineage specification potential. Our study therefore establishes a multifunctional route for chemical lineage reprogramming and may provide a platform for generating a diverse range of cell types via application of this expandable XEN-like state.