Climatology and Change of Extreme Precipitation Events in Taiwan Based on Weather Types

Taiwan\u27s most significant natural hazards are caused by hydrological extremes resulting from excessive precipitation. The threat of extreme precipitation is posed by several different types of weather patterns that affect Taiwan. This study examined the bi‐decadal changes in rainfall by defining an extreme precipitation occurrence (EPO) for a range of event durations from 1 to 24 hr. Three major weather types affecting EPO in Taiwan were identified from 1993 to 2015: the front type consisting of either a frontal zone or convective systems developing with an apparent Meiyu cloudband, diurnal rainfall events when no apparent synoptic features are present, and a tropical cyclone (TC) type according to the maximum sustained wind radius of a TC. Results show that TC‐type events have the greatest overall contribution to EPO at longer (\u3e6 hr) durations. Diurnal/afternoon convection events contribute most to the shorter (\u3c3 hr) duration EPO, while frontal/Meiyu systems prevail in the medium (3–6 hr) duration. EPO of almost all durations have experienced an increase, with the 3‐ and 12‐hr EPO having increased by 4.6 days each over the 23 years. However, apparent decadal‐scale variability exists in these EPO associated with the decreasing tendency of EPO after the mid‐2000s, particularly the longer duration (\u3e6 hr) EPO associated with the TC‐type events in summer. The distinction between EPO trends for the entire island of Taiwan and for the Taipei metropolitan area alone (northern Taiwan, population of 7 million) were compared, and an intriguing interannual variation is reported in the TC‐type EPO associated with the TC season 1 year to a year and half just before an El Niño–Southern Oscillation event. The analysis here provides refined statistical distributions of extreme rainfall, and these can contribute to the revision of governmental definitions for weather disasters that are used in mitigation and response strategies

R-process beta-decay neutrino flux from binary neutron star merger and collapsar

This study investigates the antineutrinos production by $\beta$-decay of $r$-process nuclei in two astrophysical sites that are capable of producing gamma-ray bursts (GRBs): binary neutron star mergers (BNSMs) and collapsars, which are promising sites for heavy element nucleosynthesis. We employ a simplified method to compute the $\beta$-decay $\bar\nu_e$ energy spectrum and consider two representative thermodynamic trajectories for $r$-process simulations, each with four sets of $Y_e$ distribution. The time evolution of the $\bar\nu_e$ spectrum is derived for both the dynamical ejecta and the disk wind for BNSMs and collapsar outflow, based on approximated mass outflow rates. Our results show that the $\bar\nu_e$ has an average energy of approximately 3 to 9~MeV, with a high energy tail of up to 20 MeV. The $\bar\nu_e$ flux evolution is primarily determined by the outflow duration, and can thus remain large for $\mathcal{O}(10)$~s and $\mathcal{O}(100)$~s for BNSMs and collapsars, respectively. For a single merger or collapsar at 40~Mpc, the $\bar\nu_e$ flux is $\mathcal{O}(10-100)$~cm$^{-2}$~s$^{-1}$, indicating a possible detection horizon up to $0.1-1$~Mpc for Hyper-kamiokande. We also estimate their contributions to the diffuse $\bar\nu_e$ background. Our results suggest that although the flux from BNSMs is roughly 4--5 orders of magnitude lower than that from the regular core-collapse supernovae, those from collapsars can possibly contribute a non-negligible fraction to the total diffuse $\bar\nu_e$ flux at energy $\lesssim 1$~MeV, with a large uncertainty depending on the unknown rate of collapsars capable of hosting the $r$-process.Comment: 13 pages, 7 figure

A way to learn : the service learning between Indonesia (SCU) and Taiwan (FJU)

Gills and Maclellan (2010) conducted a 1999-2009 systemic literature review service learning in nursing education, outcomes suggest that students who participate in international programs having an increase in self-perceived cultural competency, encouraging lifelong commitment to continue serving, developing students into a positive force of change in healthcare abroad and within their own communities. The purpose of this study is in depth to analyze nursing students\u27 learning process through the service learning. Design: Action research method was used and a total number of 6 nursing students were participated in this study. One Taiwanese and one Indonesia students as a team shared a bed and lived with Indonesia family for 11 days. During this period students logs, field notes, reflection sheets and team working records as raw materials together to be used as content analysis, the reliability and validity is based on Lincoln and Guba (1985) proposed vetting reliability and validity of qualitative research methods. Results: After intensively living and working with Indonesia students and family in this international service learning program, the results come up as an agricultural process. It is improving students\u27 cross-culture communication in spread period. Follow by cultivated period that participants established cross-cultural sensitivities and culture respect. In the final harvest period, through the serving activities, participant fulfilled the role as givers and receivers among Indonesia partners and families and able to understand the meaning of love. In conclusion, this study confirmed the previous studies outcomes and showed the dynamic interaction amongst nursing students who joined service learning, and enhanced students understanding among the cultures, with the respect on inner or outer levels

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

Taiwan Oscillation Network

The Taiwan Oscillation Network (TON) is a ground-based network to measure solar intensity oscillations to study the internal structure of the Sun. K-line full-disk images of 1000 pixels diameter are taken at a rate of one image per minute. Such data would provide information onp-modes withl as high as 1000. The TON will consist of six identical telescope systems at proper longitudes around the world. Three telescope systems have been installed at Teide Observatory (Tenerife), Huairou Solar Observing Station (near Beijing), and Big Bear Solar Observatory (California). The telescopes at these three sites have been taking data simultaneously since October of 1994. Anl – v diagram derived from 512 images is included to show the quality of the data

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

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD); and most existing algorithms are usually designed for the diagnosis (i.e.; feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper; we utilized the MIT-BIH AF Database (AFDB); which is composed of data from normal people and patients with AF and onset characteristics; and the AFPDB database (i.e.; PAF Prediction Challenge Database); which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF); and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction; we regarded diagnosis and prediction as two classification problems; adopted the traditional support vector machine (SVM) algorithm; and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process; the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases; the sensitivity; specificity; and accuracy measures were 99.2% and 99.2%; 99.2% and 93.3%; and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases; respectively. Moreover; the sensitivity; specificity; and accuracy were 94.2%; 79.7%; and 87.0%; respectively; when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels

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