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Hierarchical structures have attracted considerable interest due to their super-oleophobic/super-hydrophobic behavior. However, it is rare to present a novel additive manufacturing (AM) approach to fabricate hierarchical metal structures (HMSs). A micro/nano mixture ink was deposited on a substrate and a laser was used to selectively scan the ink layer. A new layer of ink was deposited on the previous consolidation layer during manufacturing. The surfaces of the as-sintered HMSs exhibit inherently super-hydrophilic and super-oleophobic behavior with a 155° contact angle (CA) with oil. Furthermore, the HMSs were successfully turned into super-hydrophobic and super-oleophilic mode (with a 152° CA) after surface modification with a solvent-free, electrostatic polytetrafluoroethylene particle deposition. They can be used as oil/water separation media by the functional surfaces existing in the pore channels. The experimental study of HMSs shows an effective removal of oil contaminants from water. The developed process also possesses an advantage of AM of HMSs with complex shapes in ambient air under the protection of an organic ink. Importantly, the present approach could be extended to a vast number of HMSs, for the preparation of highly durable functional materials for various applications.

The concept of hidden curriculum has become well established. It addresses the contexts of learning, the actions of students' peers and teachers, and other domains which shape learning but are not part of official syllabuses. The concept of a hidden marketplace for private tutoring, widely known as shadow education, is less established but also becoming part of general understanding of the complementarities of regular and supplementary instruction. This paper brings the two literatures together to examine the values transmitted, mostly unintentionally, by shadow education in Cambodia. Most of this shadow education is delivered by regular teachers, commonly to their existing students and in their existing schools. The paper considers the impact of shadow education not only on the students who do receive it but also on those who do not. Patterns in Cambodia differ from those in more prosperous countries, but have parallels with other low-income countries. The authors suggest that much more attention is needed to the dynamics and impact of shadow education, including relationships between actors and the values that shadow education transmits as part of the hidden curriculum.

Dental caries is a bacterial infectious disease that destroys the structure of teeth. It is one of the main diseases that endanger human health [R. H. Selwitz, A. I. Ismail, and N. B. Pitts, Lancet 369(9555), 51–59 (2007)]. At present, dentists use both visual exams and radiographs for the detection of caries. Affected by the patient's dental health and the degree of caries demineralization, it is sometimes difficult to accurately identify some dental caries in x-ray images with the naked eye. Therefore, dentists need an intelligent and accurate dental caries recognition system to assist diagnosis, reduce the influence of doctors' subjective factors, and improve the efficiency of dental caries diagnosis. Therefore, this paper combines the U-Net model verified in the field of biomedical image segmentation with the convolution block attention module, designs an Attention U-Net model for caries image segmentation, and discusses the feasibility of deep learning technology in caries image recognition so as to prepare for the next clinical verification. After testing, the Dice similarity coefficient, mean pixel accuracy, mean intersection over union, and frequency-weighted intersection over the union of teeth segmentation with Attention U-Net are 95.30%, 94.46%, 93.10%, and 93.54%, respectively. The Dice similarity coefficient, mean pixel accuracy, mean intersection over union, and frequency-weighted intersection over the union of dental caries segmentation with Attention U-Net are 85.36%, 91.84%, 82.22%, and 97.08%, respectively. As a proof of concept study, this study was an initial evaluation of technology to assist dentists in the detection of caries. There is still more work needed before this can be used clinically. [ABSTRACT FROM AUTHOR]

There are limited studies investigating the association between short-term exposure to PM2.5 and incident cardiovascular disease (CVD) cases in China. This study aims to examine the short-term effects of PM2.5 on the incidence of cardiovascular diseases. A combination of Poisson-distribution generalized linear model and distributed lag non-linear model was used to examine the association between short-term exposure to PM2.5 and incident cases of CVD. The results revealed that per 10 µg/m3 increment of PM2.5 would increase the incident CVD cases by 0.147% (Relative Risk: 1.00147, 95% Confidence Interval: 1.00008–1.00286) at a lag of 2 days. The stratified analyses showed higher effects risk in females, older residents (aged 60–75 years), and acute myocardial infarction group (p-value for difference <0.05). This study indicates that short-term exposure to PM2.5 may increase the risk of CVD and highlights the necessity for a higher air quality standard in Yantai, China. [ABSTRACT FROM AUTHOR]

Few studies have investigated the association between PM2.5 and hypertension among floating populations. We therefore examined the relationship using binary logistic regression. Each grade of increment in the annual average PM2.5 (grade one: ≤15 µg/m3; grade two: 15–25 µg/m3; grade three: 25–35 µg/m3 [Excluding 25]; grade four: ≥35 µg/m3) was associated with an increased risk of hypertension (odds ratio [OR] = 1.081, 95% confidence interval (CI): 1.034–1.129). Among the female floating population (OR = 1.114, 95% CI: 1.030–1.204), those with education level of primary school and below (OR = 1.140, 95% CI: 1.058–1.229), construction workers (OR = 1.228, 95% CI: 1.058–1.426), and those living in the eastern region of China (OR = 1.241, 95% CI: 1.145–1.346) were more vulnerable to PM2.5. These results indicate that PM2.5 is positively associated with hypertension in floating populations. Floating populations who are female, less educated, construction workers, and living in the eastern region of China are more vulnerable to the adverse impacts of PM2.5. [ABSTRACT FROM AUTHOR]

In the intelligent production process of wood products, the classification system of wood knot defects is a very practical solution. However, traditional image processing methods cannot handle it well due to the uncertainty of manually extracted features. Therefore, a lightweight and reliable artificial neural network model is proposed to classify and identify our objective. To solve this problem, a wood knot defect recognition model named SE-ResNet18 combining convolutional neural network, attention mechanism, and transfer learning is proposed in this paper. First, the Sequence-and-Exception (SE) module is combined with Basicblock and is constructed as two modules called RBBSE-1 and RBBSE-2. These modules learn to enhance features that are useful for the current task, suppress useless features, and fuse the output features with the original features. Then, the fully connected layer is replaced with a global average pooling layer, which can effectively reduce the parameters of the fully connected layer in the model. Finally, a SE-ResNet18 was constructed by one convolutional layer, five RBBSE-1 modules, and three RBBSE-2 modules of different channels. The SE-ResNet18 has a higher accuracy (98.85%) in the test set compared to the unimproved model ResNet-18. Compared with the previously proposed ReSENet-18, more SE modules are used in SE-ResNet18 to provide a basis for future training on a larger-scale dataset. Based on the same test set, a comparison with other classical models (such as LeNet-5, AlexNet, etc.) was conducted, and the results validated the superiority of the proposed model. The proposed model achieves the expected objective and provides a new way of thinking for non-destructive testing of wood. [ABSTRACT FROM AUTHOR]

Digital fringe projection profilometry based on phase-shifting technology is a reliable method for complex shape measurement, and the phase is one of the most important factors affecting measurement accuracy. The calculation of the absolute phase depends on the calculation of the wrapped phase and encoding technology. In this paper, a technique of obtaining the absolute phase of multi-frequency heterodyne fringe images using the Hilbert transform is presented. Since the wrapped phase can be calculated from only one fringe image of each frequency, the method does not need phase-shifting. The absolute phase can be obtained from the wrapped phase by applying the heterodyne method. The measurement time and computational complexity are dramatically reduced, the measurement efficiency is greatly improved, and this benefit from the number of images is greatly reduced. The experimental results show that the method presented in this paper performs well in the application, and the accuracy is no different from that of the phase-shifting method while the efficiency is greatly improved. [ABSTRACT FROM AUTHOR]

Defects, such as delamination and debonding, are critical to the performance of carbon fiber-reinforced polymer (CFRP) composites. In recent years, non-destructive testing techniques have been improved for the inspection of these defects among CFRPs. In this study, an all-optical and non-destructive laser ultrasonic technique with an optical microphone detection module has been presented to detect the artificial subsurface defects among the CFRP composites. A finite element simulation based on the thermo-mechanical coupling model was used to study the process of nanosecond pulsed laser excitation of the CFRP laminate to produce ultrasound and the propagation behavior of ultrasound among the CFRP laminate. A series of non-contact laser ultrasonic testing experiments were carried out to study the flat bottom holes of different sizes via a laser ultrasonic detection system. The artificial subsurface defects were reliably identified by the presented all-optical laser ultrasonic system imbedded in the optical microphone using four feature images. [ABSTRACT FROM AUTHOR]