STEADY-STATE ANALYSIS OF THE GI/M/1 QUEUE WITH MULTIPLE VACATIONS AND SET-UP TIME

In this paper, we consider a GI/M/1 queueing model with multiple vacations and set-up time. We derive the distribution and the generating function and the stochastic decomposition of the steady-state queue length, meanwhile, we get the waiting time distributions. Key words: multiple vacations, set-up time, stochastic decompositio

Maternal mowing effect on seed traits of an invasive weed, Erigeron annuus in Farmland

The effect of maternal mowing on seed traits of an invasive weed, Erigeron annuus, in farmland was discussed by comparing mowing plants with intact (no-mowing) plants. The maternal mowing effect resulted in the decrease of seed mass, achene size, pappus length and germination percentage and the increase of variation in achene size, pappus length, dispersal distance and germination non-uniformity. To some extent, the individuals suffered mowing might accelerate the environmental adaptation through the increase of these variations. Our study indicated the mean of mowing in farmland will restrain the growth and reproduction of weed E. annuus. However, it also increases the diversity of seeds through a more unequal provision to seeds that shares the risk and increases fitness to a wider range of heterogeneity of farmland condition

Apigenin inhibits proliferation and induces apoptosis in human multiple myeloma cells through targeting the trinity of CK2, Cdc37 and Hsp90

<p>Abstract</p> <p>Background</p> <p>Multiple myeloma (MM) is a B-cell malignancy that is largely incurable and is characterized by the accumulation of malignant plasma cells in the bone marrow. Apigenin, a common flavonoid, has been reported to suppress proliferation in a wide variety of solid tumors and hematological cancers; however its mechanism is not well understood and its effect on MM cells has not been determined.</p> <p>Results</p> <p>In this study, we investigated the effects of apigenin on MM cell lines and on primary MM cells. Cell viability assays demonstrated that apigenin exhibited cytotoxicity against both MM cell lines and primary MM cells but not against normal peripheral blood mononuclear cells. Together, kinase assays, immunoprecipitation and western blot analysis showed that apigenin inhibited CK2 kinase activity, decreased phosphorylation of Cdc37, disassociated the Hsp90/Cdc37/client complex and induced the degradation of multiple kinase clients, including RIP1, Src, Raf-1, Cdk4 and AKT. By depleting these kinases, apigenin suppressed both constitutive and inducible activation of STAT3, ERK, AKT and NF-κB. The treatment also downregulated the expression of the antiapoptotic proteins Mcl-1, Bcl-2, Bcl-xL, XIAP and Survivin, which ultimately induced apoptosis in MM cells. In addition, apigenin had a greater effects in depleting Hsp90 clients when used in combination with the Hsp90 inhibitor geldanamycin and the histone deacetylase inhibitor vorinostat.</p> <p>Conclusions</p> <p>Our results suggest that the primary mechanisms by which apigenin kill MM cells is by targeting the trinity of CK2-Cdc37-Hsp90, and this observation reveals the therapeutic potential of apigenin in treating multiple myeloma.</p

Food Image Classification Based on CBAM-Inception V3 Transfer Learning

To improve the accuracy of automatic recognition and classification of food images, a classification model CBAM- InceptionV3 is proposed, which embeds the Convolutional Block Attention Module. The specific method is to split the Inception V3 model with ImageNet pre-trained weight parameters into blocks, embed CBAM modules after each Inception block, and reassemble them into a new model, embedding a total of 11 CBAM modules. This new model is used for transfer learning of Food-101 food image dataset padded and scaled to 299 pixels in both length and width, with the highest accuracy of 82.01%. Compared with the original Inception V3 model, the CBAM module can effectively improve the model's feature extraction and classification capabilities. At the same time, transfer learning can significantly improve the accuracy rate and shorten the training time compared with the training from scratch. Compared with several other mainstream convolutional neural network models, the results show that this new model has higher recognition accuracy and can provide strong support for food image classification and recognition

Application of Local Wave Decomposition in Seismic Signal Processing

Local wave decomposition (LWD) method plays an important role in seismic signal processing for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. The LWD method is an effective way to decompose a seismic signal into several individual components. Each component represents a harmonic signal localized in time, with slowly varying amplitudes and frequencies, potentially highlighting different geologic and stratigraphic information. Empirical mode decomposition (EMD), the synchrosqueezing transform (SST), and variational mode decomposition (VMD) are three typical LWD methods. We mainly study the application of the LWD method especially EMD, SST, and VMD in seismic signal processing including seismic signal de‐noising, edge detection of seismic images, and recovery of the target reflection near coal seams