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Through a comparative analysis, we confirm that the value of the dark channel pixels of the smoke image is higher than the non-smoke image. It means that the dark channel of the smoke image has more elaborate information of the smoke, which is of great benefit to our detailed feature extraction of smoke. On this background, we propose a dual convolution network using dark channel prior for image smoke classification (DarkC-DCN) for the image smoke classification. In DarkC-DCN, basing on the AlexNet, and through continuous structural improvement and optimization, we improve a detailed CNN to extract the detailed features of dark channel images. Similarly, to extract the general features in the image, we further design another residual network based on the AlexNet, which is the main framework of the entire network. To ascertain the robustness of the network, the two channels are trained separately for various inputs. In addition, we perform feature fusion before the common fully connected layer. In the experiment, we also add some non-smoke data similar to smoke in the public smoke data set for data expansion. The experimental results indicate that the model has a good performance in general. The accuracy value reaches 98.56%. INDEX TERMS Dark channel prior, dual convolution network, image smoke classification, AlexNet.

The importance of closed-loop supply chains has been widely recognized both in academic communities and in industrial sectors. This paper starts from the traditional supply chains and the new self-supply chain of GREE to extract realistic problems, to mainly investigating two noncooperative dynamic pricing policies in a dual-channel closed-loop supply chain consisting of a manufacturer and a retailer. Then, it studies the influence of different channel power structures on dynamic decisions and their complexities. Furthermore, the reference price affects the purchase decisions of consumers. Therefore, the model takes into account the influence of reference price of the market demands. Results show that the manufacturer who opens up a direct channel can make a huge profit in the game. In the dynamic game evolution process, the game leader is in a more advantageous position when the system is in a stable region; once entering into the bifurcating region or chaotic region, the game follower needs to adjust his price to follow the leader’s decision in order to make a profit. In addition, the system’s stable region becomes smaller when the market demand becomes more sensitive to the difference between the reference price and the actual price. In this model, if the manufacturer acts as a leader, he is in a more advantageous position when the market is sensitive to channel competition in the stable stage while the result is opposite in the unstable stage.

In this paper, we present further study on the interlayer exchange coupling of [Pt/Co]n/MgO/[Co/Pt]2 perpendicular magnetic tunnel junctions. Antiferromagnetic interlayer couplings in [Pt/Co]n/MgO/[Co/Pt]2 are observed. The strength of antiferromagnetic coupling oscillates irregularly with the repetition number n, that may be related to the Ruderman-Kittel-Kasuya-Yosida (RKKY)-type ferromagnetic interlayer coupling existing in the [Pt/Co]n hard layer. The interlayer coupling of [Pt/Co]9/MgO(22 Å)/[Co/Pt]2 magnetic tunnel junction reaches a maximum at 200 K, and decreases gradually with increasing temperature. This thermal behavior of interlayer coupling may be related to the enhanced perpendicular magnetic anisotropy of hard layer with decreasing temperature.

With the advent of the digital music era, digital audio sources have exploded. Music classification (MC) is the basis of managing massive music resources. In this paper, we propose a MC method based on deep learning to improve feature extraction and classifier design based on MIDI (musical instrument digital interface) MC task. Considering that the existing classification technology is limited by the shallow structure, it is difficult for the classifier to learn the time sequence and semantic information of music; this paper proposes a MIDIMC method based on deep learning. In the experiment, we use the MC method proposed in this paper to achieve 90.1% classification accuracy, which is better than the existing classification method based on BP neural network, and verify the music with its classification accuracy. The key point is that the music division method used in this paper has correct MC efficiency. However, due to the limited ability and time involved in the interdisciplinary field, the methodology of this paper has certain limitations, which still needs further research and improvement.

Background. Scutellaria baicalensis Georgi (SBG) has been widely shown to induce apoptosis and inhibit invasion and migration of various cancer cells. Increased evidence shows that SBG may be useful to treat oral squamous cell carcinoma (OSCC). However, the biological activity and possible mechanisms of SBG in the treatment of OSCC have not been fully elucidated. This study aimed to clarify the bioactive component and multitarget mechanisms of SBG against OSCC using network pharmacology and molecular docking. Methods. Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to predict the active components in SBG, and putative molecular targets of SBG were identified using the Swiss Target Prediction database. OSCC-related targets were screened by GeneCards, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). Then, we established protein-protein interaction (PPI), compound-target-disease (C-T-D), and compound-target-pathway (C-T-P) networks by Cytoscape to identify the main components, core targets, and pharmacological pathways of SBG against OSCC via applying data mining techniques and topological parameters. Metascape database was utilized for Gene Ontology (GO) and pathway enrichment analysis. The potential interaction of the main components with core targets was revealed by molecular docking simulation, and for the correlation between core targets and OSCC prognosis analysis, the Kaplan–Meier Plotter online database was used. Results. There were 25 active compounds in SBG and 86 genes targeted by OSCC. A total of 141 signaling pathways were identified, and it was found that the PI3K-Akt signaling pathway may occupy core status in the anti-OSCC system. GO analysis revealed that the primary biological processes were related to apoptosis, proliferation, and migration. Molecular docking results confirmed that core targets of OSCC had a high affinity with the main compounds of SBG. Conclusion. Our study demonstrated multicomponent, multitarget, and multipathway characteristics of SBG in the treatment of OSCC and provided a foundation for further drug development research.

PURPOSE: Early palliative care, concomitant with disease-directed treatments, is recommended for all patients with advanced cancer. This study assesses population-level trends in palliative care use among a large cohort of commercially insured patients with metastatic cancer, applying an expanded definition of palliative care services based on claims data. METHODS: Using nationally representative commercial insurance claims data, we identified patients with metastatic breast, colorectal, lung, bronchus, trachea, ovarian, esophageal, pancreatic, and liver cancers and melanoma between 2001 and 2016. We assessed the annual proportions of these patients who received services specified as, or indicative of, palliative care. Using Cox proportional hazard models, we assessed whether the time from diagnosis of metastatic cancer to first encounter of palliative care differed by demographic characteristics, socioeconomic factors, or region. RESULTS: In 2016, 36% of patients with very poor prognosis cancers received a service specified as, or indicative of, palliative care versus 18% of those with poor prognosis cancers. Being diagnosed in more recent years (2009-2016 v 2001-2008: hazard ratio [HR], 1.8; P < .001); a diagnosis of metastatic esophagus, liver, lung, or pancreatic cancer, or melanoma ( v breast cancer, eg, esophagus HR, 1.89; P < .001); a greater number of comorbidities (American Hospital Formulary Service classes > 10 v 0: HR, 1.71; P < .001); and living in the Northeast (HR, 1.43; P < .001) or Midwest ( v South: HR, 1.39; P < .001) were the strongest predictors of shorter time from diagnosis to palliative care. CONCLUSION: Use of palliative care among commercially insured patients with advanced cancers has increased since 2001. However, even with an expanded definition of services specified as, or indicative of, palliative care, < 40% of patients with advanced cancers received palliative care in 2016.

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