Application of Image Segmentation to Identify In-flight Particles in Thermal Spraying.

In thermal spray process, the characteristics of in-flight particles (velocity and temperature) play an important role regarding the microstructure of the deposit and thus the coating performances. The implementation of diagnostic devices is necessary to measure such characteristics. Many imaging systems and algorithms have been developed for identifying and tracking in-flight particles. However, these current image systems have significant limitations in terms of accuracy for example. One key to solving the tracking problem is to get an algorithm that can effectively distinguish different particles in the same image frame at the same time. This study aims to develop an algorithm capable of identifying a large number of in-flight particles sprayed by thermal process. The results show that the noise and vignettes could be successfully treated, particles are clearly recognized in the background, leading to properly measuring the sizes and positions of the particle versus time. The proposed algorithm has a higher recognition rate and recognition range than other algorithms, which will provide a reasonable basis for subsequent calculation and processing.

Different regulation strategies of anaerobic digestion by AC/CaO2 and Fe3O4/CaO2: Reactive oxygen species induction, methanogenic performance, and microbial response.

This study examined the combination of activated carbon and magnetite with calcium peroxide in enhancing the anaerobic digestion (AD) performance of food waste (FW). The individual mechanisms of these two approaches were also clarified. The results indicated that AC/CaO2 achieved the highest specific methane yield of 434.4 mL/g VS, followed by Fe3O4/CaO2 (416.9 mL/g VS). Both were significantly higher than other groups (control, AC, Fe3O4, and CaO2 were 330.1, 341.4, 342.8, and 373.2 mL/g VS, respectively). Additionally, compared to Fe3O4/CaO2, AC/CaO2 further increased reactive oxygen species (ROS), thereby enhancing the hydrolytic acidification process. Simultaneously, the higher ROS levels of Fe3O4/CaO2 and AC/CaO2 promoted the formation of microbial aggregates and established a more robust enzymatic defense system and unique damage repair strategy. The research comparatively analyzed the synergistic mechanism of iron-based and carbon-based conductive materials with CaO2, providing new perspectives for optimizing the AD of FW.

Causal Optimal Transport for Treatment Effect Estimation.

Treatment effect estimation helps answer questions, such as whether a specific treatment affects the outcome of interest. One fundamental issue in this research is to alleviate the treatment assignment bias among those treated units and controlled units. Classical causal inference methods resort to the propensity score estimation, which unfortunately tends to be misspecified when only limited overlapping exists between the treated and the controlled units. Moreover, existing supervised methods mainly consider the treatment assignment information underlying the factual space, and thus, their performance of counterfactual inference may be degraded due to overfitting of the factual results. To alleviate those issues, we build on the optimal transport theory and propose a novel causal optimal transport (CausalOT) model to estimate an individual treatment effect (ITE). With the proposed propensity measure, CausalOT can infer the counterfactual outcome by solving a novel regularized optimal transport problem, which allows the utilization of global information on observational covariates to alleviate the issue of limited overlapping. In addition, a novel counterfactual loss is designed for CausalOT to align the factual outcome distribution with the counterfactual outcome distribution. Most importantly, we prove the theoretical generalization bound for the counterfactual error of CausalOT. Empirical studies on benchmark datasets confirm that the proposed CausalOT outperforms state-of-the-art causal inference methods.

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Dehydroabietane-type bifunctional organocatalysts in asymmetric synthesis: recent progress.

Dehydroabietane-type bifunctional organocatalysts derived from rosane-type diterpenes of dehydroabietic acid (DHAA) and dehydroabietylamine (DA) have been utilized in a wide variety of highly enantioselective reactions. Since one well-documented review exclusively reported on the development of terpene-derived bifunctional thioureas in asymmetric organocatalysis in 2013, fragmentary progress on the dehydroabietane-type bifunctional thioureas and squaramides has been mentioned in other reviews. In this mini-review, we systematically analyze and reorganize the published literature on dehydroabietane-type bifunctional organocatalysts in the recent decade according to the type of catalysts. Our aim is for this review to provide helpful research information and serve as a foundation for further design and application of rosin-based organocatalysts.

Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr-Cr3C2 Coatings Based on a Hierarchical Neural Network.

High-velocity oxygen fuel (HVOF) spraying is a promising technique for depositing protective coatings. The performances of HVOF-sprayed coatings are affected by in-flight particle properties, such as temperature and velocity, that are controlled by the spraying parameters. However, obtaining the desired coatings through experimental methods alone is challenging, owing to the complex physical and chemical processes involved in the HVOF approach. Compared with traditional experimental methods, a novel method for optimizing and predicting coating performance is presented herein; this method involves combining machine learning techniques with thermal spray technology. Herein, we firstly introduce physics-informed neural networks (PINNs) and convolutional neural networks (CNNs) to address the overfitting problem in small-sample algorithms and then apply the algorithms to HVOF processes and HVOF-sprayed coatings. We proposed the PINN and CNN hierarchical neural network to establish prediction models for the in-flight particle properties and performances of NiCr-Cr3C2 coatings (e.g., porosity, microhardness, and wear rate). Additionally, a random forest model is used to evaluate the relative importance of the effect of the spraying parameters on the properties of in-flight particles and coating performance. We find that the particle temperature and velocity as well as the coating performances (porosity, wear resistance, and microhardness) can be predicted with up to 99% accuracy and that the spraying distance and velocity of in-flight particles exert the most substantial effects on the in-flight particle properties and coating performance, respectively. This study can serve as a theoretical reference for the development of intelligent HVOF systems in the future.

Recombinant Adeno-associated Virus 9-mediated Expression of Kallistatin Suppresses Lung Tumor Growth in Mice.

BACKGROUND: Lung cancer remains the most common cause of cancer-related deaths in China and worldwide. Traditional surgery and chemotherapy do not offer an effective cure, although gene therapy may be a promising future alternative. Kallistatin (Kal) is an endogenous inhibitor of angiogenesis and tumorigenesis. Recombinant adeno-associated virus (rAAV) is considered the most promising vector for gene therapy of many diseases due to persistent and long-term transgenic expression. OBJECTIVE: The aim of this study was to investigate whether rAAV9-Kal inhibited NCI-H446 subcutaneous xenograft tumor growth in mice. METHODS: The subcutaneous xenograft mode was induced by subcutaneous injection of 2×107 H446 cells into the dorsal skin of BALB/c nude mice. The mice were administered with ssrAAV9-Kal (single- stranded rAAV9) or dsrAAV9-Kal (double-stranded rAAV9) by intraperitoneal injection (I.P.). Tumor microvessel density (MVD) was examined by anti-CD34 staining to evaluate tumor angiogenesis. RESULTS: Compared with the PBS (blank control) group, tumor growth in the high-dose ssrAAV9-Kal group was inhibited by 40% by day 49, and the MVD of tumor tissues was significantly decreased. CONCLUSION: The results indicate that this therapeutic strategy is a promising approach for clinical cancer therapy and implicate rAAV9-Kal as a candidate for gene therapy of lung cancer.

Deep learning for misinformation detection on online social networks: a survey and new perspectives.

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

The discovery of SYP-10913 and SYP-11277: novel strobilurin acaricides.

BACKGROUND: As previously reported, methyl (E)-2-[2-(2-phenylamino-6-trifluoromethylpyrimidin-4-yloxymethyl)phenyl]-3-methoxyacrylate has proven to be a new lead with highly acaricidal activity. Following on from this, in an effort to discover new strobilurin analogues with improved activity, a series of substituted pyrimidines were synthesised and bioassayed. RESULTS: All compounds were characterised by (1) H NMR, IR, MS and elemental analysis. Preliminary bioassays demonstrated that some of the title compounds exhibited notable control of Tetranychus cinnabarinus (Boisd.) at 1.25 mg L(-1) . The relationship between structure and acaricidal activity is discussed. CONCLUSION: Two compounds of particular interest, 6j (SYP-10913) and 6k (SYP-11277), exhibited potent acaricidal activity. The acaricidal potencies of these analogues are higher than that of fluacrypyrim in greenhouse applications, and are comparable with those of commercial acaricides such as spirodiclofen and propargite in field trials.