Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream.

Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight power-saving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02~1.91 dB, and those in power are only -0.001~0.008 W.

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.

Investigating mental health outcomes of undergraduates and graduate students in Taiwan during the COVID-19 pandemic.

Objective: This study is an exploration of the major stressors associated with the COVID-19 for students in higher education in Taiwan. Participants: The sample comprised 838 higher education students studying at various Taiwanese universities. Methods: A cross-sectional online survey was administered at different postsecondary institutions during the semi-lockdown period of COVID-19, which mandated online instruction. Machine learning was employed to determine the variables that most highly predicted students' mental health using R. Results: The findings revealed that COVID-19-related experiences, including social interactions, financial conditions, and educational experiences, were significantly associated with mental health outcomes. Particularly, loneliness are significantly related to social interactions and educational experiences. Conclusions: Findings revealed that Covid-19 impacted Taiwanese students' financial conditions, educational experiences, and social interactions, which were significant predictors of their mental health outcomes such as anxiety, loneliness and depression. The current study contributes to the gap in knowledge about mental health issues among postsecondary students during the pandemic.

Integrated Circuit Board Object Detection and Image Augmentation Fusion Model Based on YOLO.

Industry 4.0 has been a hot topic in recent years. The process of integrating Cyber-Physical Systems (CPS), Artificial Intelligence (AI), and Internet of Things (IoT) technology, will become the trend in future construction of smart factories. In the past, smart factories were developed around the concept of the Flexible Manufacturing System (FMS). Most parts of the quality management process still needed to be implemented by Automated Optical Inspection (AOI) methods which required human resources and time to perform second stage testing. Screening standards also resulted in the elimination of about 30% of the products. In this study, we sort and analyze several Region-based Convolutional Neural Network (R-CNN) and YOLO models that are currently more advanced and widely used, analyze the methods and development problems of the various models, and propose a suitable real-time image recognition model and architecture suitable for Integrated Circuit Board (ICB) in manufacturing process. The goal of the first stage of this study is to collect and use different types of ICBs as model training data sets, and establish a preliminary image recognition model that can classify and predict different types of ICBs based on different feature points. The second stage explores image augmentation fusion and optimization methods. The data augmentation method used in this study can reach an average accuracy of 96.53%. In the final stage, there is discussion of the applicability of the model to detect and recognize the ICB directionality in <1 s with a 98% accuracy rate to meet the real-time requirements of smart manufacturing. Accurate and instant object image recognition in the smart manufacturing process can save manpower required for testing, improve equipment effectiveness, and increase both the production capacity and the yield rate of the production line. The proposed model improves the overall manufacturing process.

Detrimental effects of post-treatment with fatty acids on brain injury in ischemic rats.

Studies have illustrated that fatty acids, especially polyunsaturated fatty acids (PUFA), have a role in regulating oxidative stress via the enhancement of antioxidative defense capacity or the augmentation of oxidative burden. Elevated oxidative stress has been implicated in the pathogenesis of brain injury associated with cerebral ischemia/reperfusion (I/R). The objective of this study was to assess whether treatment with fatty acids after focal cerebral I/R induced by occlusion of the common carotid arteries and the middle cerebral artery has effects on brain injury in a rat model. PUFA, including arachidonic acid (AA) and docosahexaenoic acid (DHA), and the saturated fatty acid, stearic acid (SA), were administrated 60 min after reperfusion via intraperitoneal injection. AA and DHA aggravated cerebral ischemic injury, which manifested as enlargement of areas of cerebral infarction and increased impairment of motor activity, in a concentration-dependent manner. However, there were no remarkable differences in post-ischemic alterations between the SA and saline groups. The post-ischemic augmentation of injury in AA and DHA treatment groups was accompanied by increases in the permeability of the blood-brain barrier (BBB), brain edema, metalloproteinase (MMP) activity, inflammatory cell infiltration, cyclooxygenase 2 (COX-2) expression, caspase 3 activity, and malondialdehyde (MDA) production, and by a decrease in the brain glutathione (GSH) content. Furthermore, we found that either AA or DHA alone had little effect on free radical generation in neuroglia, but they greatly increased the hydrogen peroxide-induced oxidative burden. Taken together, these findings demonstrate the detrimental effect of PUFA such as AA and DHA in post-ischemic progression and brain injury after cerebral I/R is associated with augmentation of cerebral I/R-induced alterations, including oxidative changes.

Beneficial effect of docosahexaenoic acid on cholestatic liver injury in rats.

Bile duct obstruction and subsequent cholestasis are associated with hepatocellular injury, cholangiocyte proliferation, stellate cell activation, Kupffer cell activation, oxidative stress, inflammation and fibrosis. Docosahexaenoic acid (DHA) is an essential polyunsaturated fatty acid that has been shown to possess health beneficial effects, including hepatoprotection. However, the molecular mechanism of DHA-mediated hepatoprotection is not fully understood. In the present study, we report the protective effect of DHA on cholestatic liver injury. Cholestasis was produced by bile duct ligation (BDL) in male Sprague-Dawley rats for 3 weeks. Daily administration of DHA was started 2 weeks before injury and lasted for 5 weeks. In comparison with the control group, the BDL group showed hepatic damage as evidenced by histological changes and elevation in serum biochemicals, ductular reaction, fibrosis, inflammation and oxidative stress. These pathophysiological changes were attenuated by chronic DHA supplementation. DHA alleviated BDL-induced transforming growth factor beta-1 (TGF-ß1), intereukin-1beta, connective tissue growth factor and collagen expression. The anti-fibrotic effect of DHA was accompanied by reductions in α-smooth muscle actin-positive matrix-producing cells and Smad 2/3 activity critical to the fibrogenic potential of TGF-ß1. DHA also attenuated BDL-induced leukocyte accumulation and nuclear factor-κB (NF-κB) activation. Further studies demonstrated an inhibitory effect of DHA on redox-sensitive intracellular signaling molecule extracellular signal-regulated kinase (ERK). Taken together, the hepatoprotective, anti-inflammatory and anti-fibrotic effects of DHA seem to be multifactorial. The beneficial effects of chronic DHA supplementation are associated with anti-oxidative and anti-inflammatory potential as well as down-regulation of NF-κB and transforming growth factor beta/Smad signaling probably via interference with ERK activation.