A Networking-based View of Business Model Innovation: Theory and Method

After reviewing the literature, the concept of Business Model or E-Business Models are discussed in Ms paper. Firms keep interacting with each other and build all kinds of relationships among them. Based on the theory of business networks, we put forward a networking-based view of business model analysis frame. When we make decisions to choose one or other business models, first, the source of value should be found. Second, business network models, which show the procedure of value creation and distribution, should be analyzed. Third, we choose the feasible network among networks we analyzed. Finally, the firms\u27 strategy to ensure the location in business networks and turn the business model to a series of business decision are analyzed

An Examination of the Compositionality of Large Generative Vision-Language Models

With the success of Large Language Models (LLMs), a surge of Generative Vision-Language Models (GVLMs) have been constructed via multimodal instruction tuning. The tuning recipe substantially deviates from the common contrastive vision-language learning. However, the performance of GVLMs in multimodal compositional reasoning remains largely unexplored, as existing evaluation metrics and benchmarks focus predominantly on assessing contrastive models like CLIP. In this paper, we examine the potential evaluation metrics to assess the GVLMs and hypothesize generative score methods are suitable for evaluating compositionality. In addition, current benchmarks tend to prioritize syntactic correctness over semantics. The presence of morphological bias in these benchmarks can be exploited by GVLMs, leading to ineffective evaluations. To combat this, we define a MorphoBias Score to quantify the morphological bias and propose a novel LLM-based strategy to calibrate the bias. Moreover, a challenging task is added to evaluate the robustness of GVLMs against inherent inclination toward syntactic correctness. We include the calibrated dataset and the task into a new benchmark, namely MOrphologicall De-biased Benchmark (MODE). Our study provides the first unbiased benchmark for the compositionality of GVLMs, facilitating future research in this direction. We will release our code and datasets

The juxtamembrane and carboxy-terminal domains of Arabidopsis PRK2 are critical for ROP-induced growth in pollen tubes.

Polarized growth of pollen tubes is a critical step for successful reproduction in angiosperms and is controlled by ROP GTPases. Spatiotemporal activation of ROP (Rho GTPases of plants) necessitates a complex and sophisticated regulatory system, in which guanine nucleotide exchange factors (RopGEFs) are key components. It was previously shown that a leucine-rich repeat receptor-like kinase, Arabidopsis pollen receptor kinase 2 (AtPRK2), interacted with RopGEF12 for its membrane recruitment. However, the mechanisms underlying AtPRK2-mediated ROP activation in vivo are yet to be defined. It is reported here that over-expression of AtPRK2 induced tube bulging that was accompanied by the ectopic localization of ROP-GTP and the ectopic distribution of actin microfilaments. Tube depolarization was also induced by a potentially kinase-dead mutant, AtPRK2K366R, suggesting that the over-expression effect of AtPRK2 did not require its kinase activity. By contrast, deletions of non-catalytic domains in AtPRK2, i.e. the juxtamembrane (JM) and carboxy-terminal (CT) domains, abolished its ability to affect tube polarization. Notably, AtPRK2K366R retained the ability to interact with RopGEF12, whereas AtPRK2 truncations of these non-catalytic domains did not. Lastly, it has been shown that the JM and CT domains of AtPRK2 were not only critical for its interaction with RopGEF12 but also critical for its distribution at the plasma membrane. These results thus provide further insight into pollen receptor kinase-mediated ROP activation during pollen tube growth

Luteoloside Inhibits Proliferation of Human Chronic Myeloid Leukemia K562 Cells by Inducing G2/M Phase Cell Cycle Arrest and Apoptosis

Purpose: To investigate the effects of luteoloside on the proliferation of human chronic myeloid leukemia K562 cells and whether luteoloside induces cell cycle arrest and apoptosis in K562 cells.Methods: Luteoloside’s cytotoxicity was assessed using a cell counting kit. Cell cycle distribution was analysed by flow cytometry after propidium iodide (PI) staining. Cell apoptosis was assayed with apoptosis detection kit and Hoechst staining followed by observation under a fluorescence microscope. The expression of cell cycle- and apoptosis-related proteins was examined by Western blot analysis.Results: Luteoloside inhibited the proliferation of K562 cells in a dose- and time- dependent manner (IC50 = 30.7 μM) with less toxicity in a normal human cell line (IC50 = 91.8 μM). Moreover, antiproliferative effect of luteoloside was accompanied with G2/M phase arrest（p ＜ 0.05 or p＜0.01） and apoptosis（p ＜ 0.01 or p ＜ 0.001）. Further studies revealed that the expression level of cyclinB1 was down-regulated by luteoloside treatment. Furthermore, luteoloside treatment also increased proapoptotic protein Bax expression and decreased anti-apoptotic protein Bcl-2 expression.Conclusion: These results suggest that the inhibitory effect of luteoloside on K562 cell proliferation is associated with inducing G2/M phase arrest and apoptosis, and that luteoloside is worth further studying for anticancer potential.Keywords: Luteoloside, Myeloid leukemia, Proliferation, Cell cycle arrest, Apoptosis, Anticance

trans-Diaqua­bis­[2-(2-pyrid­yl)acetato-κ2 N,O]nickel(II)

In the centrosymmetric title complex, [Ni(C7H6NO2)2(H2O)2], the NiII atom, located on an inversion center, is six-coordinated in a distorted octa­hedral geometry defined by two N and four O atoms from the two chelating 2-(2-pyrid­yl)acetate ligands and two aqua ligands. The mol­ecules form a three-dimensional framework by O—H⋯O hydrogen bonds and aromatic π–π stacking inter­actions, with a centroid–centroid distance of 3.506 (3) Å

Spatial Aided Decision-making System for E-Government

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