Bis(μ-2-hydroxy­benozato)-κ3 O,O′:O′;κ3 O:O,O′-bis­[(2-hydroxy­benozato-κ2 O,O′)(1,10-phenanthroline-κ2 N,N′)cadmium(II)]

The dinuclear title compound, [Cd2(C7H5O3)4(C12H8N2)2], is located on a crystallographic rotation twofold axis. The two CdII ions are connected by two tridentate bridging 2-hydroxy­benzoate anions. Each CdII ion is seven-coordinated by five O atoms from three 2-hydroxy­benzoate ligands and two N atoms from 1,10-phenanthroline. The 2-hydroxy­benzoate mol­ecules adopt two kinds of coordination mode, bidentate chelating and tridentate bridging–chelating. Intra­molecular hydrogen bonds between hydr­oxy and carboxyl­ate groups from 2-hydroxy­benzoate groups and π–π stacking interactions between parallel 1,10-phenanthroline ligands [centroid–centroid distances = 3.707 (3) and 3.842 (3) Å] are observed. Furthermore, adjacent benzene rings from 2-hydroxy­benzoate ligands are involved in π–π inter­actions with inter­planar distances of 3.642 (3) Å, thereby forming a chain along the a axis direction

White privilege and teacher perceptions of teacher-child relationship quality

In this study, we investigated differences in teachers’ perceptions of the teacher-child relationship from kindergarten through second grade as a function of child race and gender from the perspective of critical race theory and the cultural synchrony hypothesis. Given the extensive evidence of White privilege and anti-Black racism in the US education system, we expected that teachers, particularly White teachers, would perceive their relationships with White children more positively than with Black children. Controlling for family SES and child gender, results supported this hypothesis. Black boys had the highest risk of being perceived by teachers as having poor relationships with teachers in kindergarten (highest conflict and lowest closeness) and White girls had the lowest risk. In addition, teachers perceived relationships with Black boys as increasing in conflict across first and second grades at higher rates than with White and female children. These findings remained after examining teacher-child racial match as a moderator. Our results indicate that racism and sexism work together to explain the perceptions teachers have of children in the early elementary grades. Implications for training teachers and school psychologists on anti-racism and cultural competency are discussed

Characterization of Al-Doped ZnO Transparent Conducting Thin Film Prepared by Off-Axis Magnetron Sputtering

The off-axis sputtering technique was used to deposit Al-doped ZnO (AZO) films on glass substrates at room temperature. For the illustration of the sample position in the sputtering chamber, the value of R/r is introduced. Here, r is the radius of AZO target and R is the distance between the sample and the center of substrate holder. A systematic study for the effect of deposition parameters on structural, optical, and electrical properties of AZO films has been investigated in detail. As the sample position of R/r is fixed at 1.8, it is found that the as-deposited AZO film has relatively low resistivity of 2.67 × 10−3 Ω-cm and high transmittance above 80% in the visible region. Additionally, after rapid thermal annealing (RTA) at 600°C with N2 atmosphere, the resistivity of this AZO film can be further reduced to 1.19 × 10−3 Ω-cm. This indicates the AZO films prepared by off-axis magnetron sputtering and treated via the appropriate RTA process have great potential in optoelectronic applications

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%