Influences of phase transition and microstructure on dielectric properties of Bi0.5Na0.5Zr1-xTixO3 ceramics

Bismuth sodium zirconate titanate ceramics with the formula Bi0.5Na0.5Zr1-xTixO3 [BNZT], where x = 0.3, 0.4, 0.5, and 0.6, were prepared by a conventional solid-state sintering method. Phase identification was investigated using an X-ray diffraction technique. All compositions exhibited complete solubility of Ti4+ at the Zr4+ site. Both a decrease of unit cell size and phase transition from an orthorhombic Zr-rich composition to a rhombohedral crystal structure in a Ti-rich composition were observed as a result of Ti4+ substitution. These changes caused dielectric properties of BNZT ceramics to enhance. Microstructural observation carried out employing SEM showed that average grain size decreased when addition of Ti increased. Grain size difference of BNZT above 0.4 mole fraction of Ti4+ displayed a significant increase of dielectric constant at room temperature

Case report: Target and immunotherapy of a lung adenocarcinoma with enteric differentiation, EGFR mutation, and high microsatellite instability

BackgroundPulmonary enteric adenocarcinoma (PEAC) is a rare histological subtype of non-small-cell lung cancer (NSCLC) with a predominant (>50%) enteric differentiation component. The frequency of high microsatellite instability (MSI-H) is very low in lung cancer. EGFR tyrosine kinase inhibitors and immunotherapy are standard treatment for NSCLC patients, but their effectiveness in lung adenocarcinoma with pulmonary enteric differentiation is unknown.Case presentationThis report describes a 66-year-old man who was initially diagnosed with metastatic lung adenocarcinoma with EGFR mutation based on pleural fluid. A lung biopsy was obtained after 17 months of first-line icotinib treatment. Histological analysis of biopsy samples and endoscopic examination resulted in a diagnosis of adenocarcinoma with enteric differentiation. Next-generation sequencing of 1,021 genes showed EGFR E19del, T790M, and MSI-H, while immunohistochemical assay showed proficient expression of mismatch repair (MMR) proteins. Consequently, the patient was treated with osimertinib and had a progression-free survival (PFS) of 3 months. His treatment was changed to chemotherapy with/without bevacizumab for 6.5 months. Then, the patient was treated with one cycle of camrelizumab monotherapy and camrelizumab plus chemotherapy, respectively. The tumor continued to grow, and the patient suffered pneumonia, pulmonary fungal infections, and increased hemoptysis. He received gefitinib and everolimus and died 2 months later and had an overall survival of 30 months.ConclusionIn summary, our case describes a rare pulmonary enteric adenocarcinoma with an EGFR-activating mutation and MSI-H, responding to an EGFR tyrosine kinase inhibitor and poorly benefiting from an immune checkpoint inhibitor

DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings

International audienceStore site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models

Impact of the National Reimbursement Drug List Negotiation Policy on Accessibility of Anticancer Drugs in China: An Interrupted Time Series Study

Objective: Since 2016, the Chinese government has been regularly implementing the National Reimbursement Drug List Negotiation (NRDLN) to improve the accessibility of drugs. In the second round of NRDLN in July 2017, 18 anticancer drugs were included. This study analyzed the impact of the NRDLN on the accessibility of these 18 anticancer drugs in China. Methods: National hospital procurement data were collected from 2015 to 2019. As measurements of drug accessibility, monthly average of drug availability or defined daily dose cost (DDDc) was calculated. Interrupted time series (ITS) analysis was employed to evaluate the impact of NRDLN on drug accessibility. Multilevel growth curve models were estimated for different drug categories, regions or levels of hospitals. Results: The overall availability of 18 anticancer drugs increased from about 10.5% in 2015 to slightly over 30% in 2019. The average DDDc dropped from 527.93 CNY in 2015 to 401.87 CNY in 2019, with a reduction of 23.88%. The implementation of NRDLN was associated with higher availability and lower costs for all 18 anticancer drugs. We found an increasing level in monthly drug availability (β2 = 2.1126), which ascended more sharply after the implementation of NRDLN (β3 = 0.3656). There was a decreasing level in DDDc before July 2017 (β2 = −108.7213), together with a significant decline in the slope associated with the implementation of NRDLN (β3 = −4.8332). Compared to Traditional Chinese Medicines, the availability of Western Medicines was higher and increased at a higher rate (β3 = 0.4165 vs. 0.1108). Drug availability experienced a larger instant and slope increase in western China compared to other regions, and in secondary hospitals than tertiary hospitals. Nevertheless, regional and hospital-level difference in the effect of NRDLN on DDDc were less evident. Conclusion: The implementation of NRDLN improves the availability and reduces the cost of some anticancer drugs in China. It contributes to promoting accessibility of anticancer drugs, as well as relieving regional or hospital-level disparities. However, there are still challenges to benefit more patients sufficiently and equally. It requires more policy efforts and collaborative policy combination

There is a Will, There is a Way: A New Mechanism for Traffic Control Based on

Abstract-Traffic light is regarded as one of the most effective ways to alleviate traffic congestion and carbon emission problems. However, traditional traffic light cannot meet the challenges in traffic regulation posed by the fast growing number of vehicles and increasing complexity of road conditions. In this paper, we propose a dynamic traffic regulation method based on virtual traffic light (VTL) for Vehicle Ad Hoc Network (VANET). In our framework, each vehicle can express its "will"-the desire of moving forwardand share among one another its "will"-value and related traffic information at a traffic light controlled intersection. Based on the traffic information collected in real time, the virtual traffic light in our scheme can be adaptive to the changing environment. We conducted a number of simulation experiments with different scenarios using network simulator NS3 combined with traffic simulator SUMO. The results demonstrate the viability of our solution in reducing waiting time and improving the traffic efficiency