Evolution of the structure and mechanical performance of Cansas-II SiC fibres after thermal treatment

Herein, an in-depth analysis of the effect of heat treatment at temperatures between 900 and 1500 °C under an Ar atmosphere on the structure as well as strength of Cansas-II SiC fibres was presented. The untreated fibres are composed of β-SiC grains, free carbon layers, as well as a small amount of an amorphous SiC_xO_y phase. As the heat-treatment temperature was increased to 1400 °C, a significant growth of the β-SiC grains and free carbon layers occurred along with the decomposition of the SiC_xO_y phase. Moreover, owing to the decomposition of the SiC_xO_y phase, some nanopores formed on the fibre surface upon heating at 1500 °C. The mean strength of the Cansas-II fibres decreased progressively from 2.78 to 1.20 GPa with an increase in the heat-treatment temperature. The degradation of the fibre strength can be attributed to the growth of critical defects, β-SiC grains, as well as the residual tensile stress.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Effects of non-thermal plasma treating wheat kernel on the physicochemical properties of wheat flour and the quality of fresh wet noodles

The effects of non-thermal plasma (NTP) on the physicochemical properties of wheat flour and the quality of fresh wet noodles ( FWN) were investigated. The results showed that NTP effectively decreased the total plate count (TPC), yeast and mould count (YMC) and Bacillus spp. in wheat flour. Wet gluten contents and the stability time reached the maximum when treated for 20 s. The viscosity of starch increased significantly after treatment due to the increased of damaged starch. The contents of secondary structure were altered to some extent, which was because that the ordered network structure of gluten protein broken. Furthermore, compared with the control, texture properties of FWN were enhanced significantly at 20 s, and the darkening rate of FWN was greatly inhibited due to the low polyphenol oxidase (PPO) activity. Consequently, the most suitable treatment was 500 W for 20 s, providing a basis for the application of NTP in flour products.     

Ir(III) and Ru(II) Complexes in Photoredox Catalysis and Photodynamic Therapy: A New Paradigm towards Anticancer Applications

Individually, photoredox catalysis (PC) and photodynamic therapy (PDT) are well-established concepts that have experienced a remarkable resurgence in recent years, leading to significant progress in organic synthesis for PC and clinical approval of anticancer drugs for PDT. But, very recently, new photoredox catalyst systems based on Ir(III) and Ru(II) complexes have garnered significant interest because they can simultaneously be used as PDT agents apart from their demonstrated PC activity. This highlight discusses the unique PC behavior of emerging Ir(III)- and Ru(II)-based systems while also examining their potential PDT activity in cancer treatment.     

对“垃圾衍生燃料”RDF之探析

当前,我国水泥工业在可燃废弃物应用技术方面都还处于一家一户、自制自用、效率极低的初级阶段。发达国家的替代燃料:“垃圾衍生燃料”RDF、“固体回收燃料”SRF、“次煤”Subcoal和“纸塑垃圾衍生燃料”RPF制成的原材料都是可燃废弃物,只是处理工艺技术不同或者由垃圾中分拣出的可燃废弃物不同,制成颗粒状衍生燃料的品质不同,这些都可以替代部分甚或替代全部化石燃料在水泥窑炉中应用。我国大力发展“替代燃料”产业,有助于水泥工业消纳更多的“可燃废弃物”,为改善环境尤其是城镇环境和面貌,为我国的节能减排和绿色高质量发展发挥更大的作用。     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.     

Treatment of Brain Metastases of Non-Small Cell Lung Carcinoma

Lung cancer is one of the most common malignant neoplasms. As a result of the disease’s progression, patients may develop metastases to the central nervous system. The prognosis in this location is unfavorable; untreated metastatic lesions may lead to death within one to two months. Existing therapies—neurosurgery and radiation therapy—do not improve the prognosis for every patient. The discovery of Epidermal Growth Factor Receptor (EGFR)—activating mutations and Anaplastic Lymphoma Kinase (ALK) rearrangements in patients with non-small cell lung adenocarcinoma has allowed for the introduction of small-molecule tyrosine kinase inhibitors to the treatment of advanced-stage patients. The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein with tyrosine kinase-dependent activity. EGFR is present in membranes of all epithelial cells. In physiological conditions, it plays an important role in the process of cell growth and proliferation. Binding the ligand to the EGFR causes its dimerization and the activation of the intracellular signaling cascade. Signal transduction involves the activation of MAPK, AKT, and JNK, resulting in DNA synthesis and cell proliferation. In cancer cells, binding the ligand to the EGFR also leads to its dimerization and transduction of the signal to the cell interior. It has been demonstrated that activating mutations in the gene for EGFR-exon19 (deletion), L858R point mutation in exon 21, and mutation in exon 20 results in cancer cell proliferation. Continuous stimulation of the receptor inhibits apoptosis, stimulates invasion, intensifies angiogenesis, and facilitates the formation of distant metastases. As a consequence, the cancer progresses. These activating gene mutations for the EGFR are present in 10–20% of lung adenocarcinomas. Approximately 3–7% of patients with lung adenocarcinoma have the echinoderm microtubule-associated protein-like 4 (EML4)/ALK fusion gene. The fusion of the two genes EML4 and ALK results in a fusion gene that activates the intracellular signaling pathway, stimulates the proliferation of tumor cells, and inhibits apoptosis. A new group of drugs—small-molecule tyrosine kinase inhibitors—has been developed; the first generation includes gefitinib and erlotinib and the ALK inhibitor crizotinib. These drugs reversibly block the EGFR by stopping the signal transmission to the cell. The second-generation tyrosine kinase inhibitor (TKI) afatinib or ALK inhibitor alectinib block the receptor irreversibly. Clinical trials with TKI in patients with non-small cell lung adenocarcinoma with central nervous system (CNS) metastases have shown prolonged, progression-free survival, a high percentage of objective responses, and improved quality of life. Resistance to treatment with this group of drugs emerging during TKI therapy is the basis for the detection of resistance mutations. The T790M mutation, present in exon 20 of the EGFR gene, is detected in patients treated with first- and second-generation TKI and is overcome by Osimertinib, a third-generation TKI. The I117N resistance mutation in patients with the ALK mutation treated with alectinib is overcome by ceritinib. In this way, sequential therapy ensures the continuity of treatment. In patients with CNS metastases, attempts are made to simultaneously administer radiation therapy and tyrosine kinase inhibitors. Patients with lung adenocarcinoma with CNS metastases, without activating EGFR mutation and without ALK rearrangement, benefit from immunotherapy. This therapeutic option blocks the PD-1 receptor on the surface of T or B lymphocytes or PD-L1 located on cancer cells with an applicable antibody. Based on clinical trials, pembrolizumab and all antibodies are included in the treatment of non-small cell lung carcinoma with CNS metastases.     

Designing independent water transport channels to improve water flooding in ultra-thin nanoporous film cathodes for PEMFCs

Utilization of 3D nanostructured Pt cathodes could obviously improve performances of proton exchange membrane fuel cells (PEMFCs) owing to the reduced tortuosity and the bi-continuous nanoporous structure. However, these cathodes usually suffer from the flooding problem ascribed to the ionomer-free and nanoscale pores which are more susceptible to water condensation. In this paper, ultra-thin nanoporous metal films (100 nm) were utilized to construct PEMFC cathodes and independent transport channels were designed separately for water and gas aiming at the flooding problem. Nanoporous gold (NPG) film was used as the model support for loading Pt nanoparticles owing to its controllable and stable structure. After optimizing the polytetrafluoroethylene (PTFE) content and carbon loading in the gas diffusion layer (GDL), plasma treatment under O₂ atmosphere was used to pattern the GDL with independent water transport channels. The obtained liquid permeation coefficients and oxygen gains demonstrated the obviously improved water and O₂ transport. By using a home-made optimized GDL and a nanoporous film cathode with pore size ～60 nm, the flooding problem could be facilely solved. With a Pt loading of ～16 μg cm^?2, this 3D nanostructured cathode exhibits a PEMFC performance of ～957 mW cm^?2 at 80 °C. The Pt power efficiency is about 4 times higher than that of the commercial Pt/C cathode (50 μg cm^?2, 756 mW cm^?2). Obviously, this study provides a simple but effective methodology to solve the water flooding problem in the ultra-thin nanoporous film cathodes which is applicable for other types of 3D nanostructured PEMFC cathodes.     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.