The Influence of Rainfall Amounts Not Exceeding Critical Rainfall on Landslide Occurrence in Japan

Critical rainfall events are used in landslide early-warning systems to predict the occurrence and severity of disasters. In this study, past landslide disasters in Japan were identified for which the critical rainfall set for each 1-km grid was exceeded using historical landslide records, radar-based rainfall data over a 1-km grid, and standard rainfall data collected over the past 17 years. It was determined that nearly equal numbers of rainfall events were identified with higher and lower rainfall amounts than the critical rainfall. The probability that a series of rainfall events would cause a landslide was approximately 1.15% when the critical rainfall was exceeded and 0.09% otherwise, a difference of approximately 10 times. It was also found that even if critical rainfall was not exceeded, in the case of debris flow and slope failures, there was rainfall that exceeded the standard rainfall one or two days before. In the case of landslides, there was rainfall that exceeded the critical rainfall one or two weeks before, and if the critical rainfall was exceeded in another rainfall event, a landslide could occur. The operational evaluation of Japanese LEWSs has a recall value of 0.486 as the accuracy of occurrence prediction, which was related to the fact that almost half of the rainfall events occurred in nonexceedance of the reference rainfall. The specificity was 0.935, known as the accuracy of nonoccurrence prediction, which was also greatly influenced by the TN (true negative) data of nonexceeding rainfall events, which accounted for most of the data.

Machine learning for Drug-Virus Prediction

The 2019 Coronavirus (COVID-19) epidemic has recently hit most countries hard. Therefore, many researchers around the world are looking for a way to control this virus. Examining existing medications and using them to prevent this epidemic can be helpful. Drug repositioning solutions can be effective because designing and discovering a drug can be very time-consuming. In this study, we used a binary classifier learning method to predict the drug-virus relationship. The feature vector for each drug-virus pair is based on the similarity between drugs and the similarity between viruses. We calculated the similarities between the drugs using their structural properties (fingerprint) and their phenotype. We also calculated the similarities between viruses based on their genome sequence and the vector encoded by the Biobert model. Finally, using the HDVD dataset, we formed the similarity vectors of each drug-virus pair and considered it as input to neural network and random forest models. In these models, we randomly selected 20% of the positive data and the same amount of negative data. Finally, the performance of the proposed approach for this test data is considered, after five tests, as AUC=0.97 and AUPR = 0.96. We also used the Compressed Sensing (CS) matrix factorization model to predict the drug-virus association. After that, we investigated the importance of drug features in predicting drug-virus association by using Autoencoder and reducing the dimension of drug properties.

Machine learning for Drug-Virus Prediction

The 2019 Coronavirus (COVID-19) epidemic has recently hit most countries hard. Therefore, many researchers around the world are looking for a way to control this virus. Examining existing medications and using them to prevent this epidemic can be helpful. Drug repositioning solutions can be effective because designing and discovering a drug can be very time-consuming. In this study, we used a binary classifier learning method to predict the drug-virus relationship. The feature vector for each drug-virus pair is based on the similarity between drugs and the similarity between viruses. We calculated the similarities between the drugs using their structural properties (fingerprint) and their phenotype. We also calculated the similarities between viruses based on their genome sequence and the vector encoded by the Biobert model. Finally, using the HDVD dataset, we formed the similarity vectors of each drug-virus pair and considered it as input to neural network and random forest models. In these models, we randomly selected 20% of the positive data and the same amount of negative data. Finally, the performance of the proposed approach for this test data is considered, after five tests, as AUC=0.97 and AUPR = 0.96. We also used the Compressed Sensing (CS) matrix factorization model to predict the drug-virus association. We also investigated the importance of drug features in predicting drug-virus association by using Autoencoder and reducing the dimension of drug properties.

Separating Data Examples by Description Logic Concepts with Restricted Signatures

We study the separation of positive and negative data examples in terms of description logic concepts in the presence of an ontology. In contrast to previous work, we add a signature that specifies a subset of the symbols that can be used for separation, and we admit individual names in that signature. We consider weak and strong versions of the resulting problem that differ in how the negative examples are treated and we distinguish between separation with and without helper symbols. Within this framework, we compare the separating power of different languages and investigate the complexity of deciding separability. While weak separability is shown to be closely related to conservative extensions, strongly separating concepts coincide with Craig interpolants, for suitably defined encodings of the data and ontology. This enables us to transfer known results from those fields to separability. Conversely, we obtain original results on separability that can be transferred backward. For example, rather surprisingly, conservative extensions and weak separability in ALCO are both 3ExpTime-complete.

A Class of Exponentiated Regression Model for Non Negative Censored Data with an Application to Antibody Response to Vaccine

In this paper, an asymmetric regression model for censored non-negative data based on the centred exponentiated log-skew-normal and Bernoulli distributions mixture is introduced. To connect the discrete part with the continuous distribution, the logit link function is used. The parameters of the model are estimated by using the likelihood maximum method. The score function and the information matrix are shown in detail. Antibody data from a study of the measles vaccine are used to illustrate applicability of the proposed model, and it was found the best fit to the data with respect to an others models used in the literature.

Novelty Detection via Contrastive Learning with Negative Data Augmentation

Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous generative adversarial networks based methods and self-supervised approaches suffer from instability training, mode dropping, and low discriminative ability. We overcome such problems by introducing a novel decoder-encoder framework. Firstly, a generative network (decoder) learns the representation by mapping the initialized latent vector to an image. In particular, this vector is initialized by considering the entire distribution of training data to avoid the problem of mode-dropping. Secondly, a contrastive network (encoder) aims to ``learn to compare'' through mutual information estimation, which directly helps the generative network to obtain a more discriminative representation by using a negative data augmentation strategy. Extensive experiments show that our model has significant superiority over cutting-edge novelty detectors and achieves new state-of-the-art results on various novelty detection benchmarks, e.g. CIFAR10 and DCASE. Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.

SUGGESTIONS FOR BEHAVIOR-INTERVENTION DESIGN PRACTITIONERS: FROM BEHAVIOR CHANGE MOTIVATIONS FOR CHINESE AGED 18 TO 25

Using applications to change behaviors is a popular trend in recent years as mobiles are the easiest recording medium for users. However, few users can keep the behavior change for a long time. The aim of this study is to investigate motivations of keeping an application-tracked behavior change to provide effective and promote effective and targeted suggestions for application-tracked behavior intervention design practitioners and researchers. A 28-day self-report experiment and following “focus group” discussion have been conducted to detect the possible motivations. The results indicated 8 motivations which can affect maintaining behavior change: cooperation, competition, award, reminder and alarm, trust and willingness, relation with disease information and unplanned events. In addition, the results explore some motivations from negative data in applications or the cheating for good performance data behavior. At the same time, the study suggested the functions needed in future behavior change applications.

Faktor-Faktor yang Berhubungan Dengan Hasil Pemeriksaan IVA Test Pada Wanita Pasangan Usia Subur

ABSTRAK Kanker serviks adalah tumor ganas primer yang berasal dari sel epitel skuamosa serviks atau leher rahim. Penyebab penyakit kanker yang menyerang leher rahim pada organ reproduksi wanita adalah infeksi virus HPV (human papillomavirus). Deteksi dini lesi prakanker pada wanita pasangan usia subur dapat dilakukan dengan menggunakan Teknik IVA test. Puskesmas Padang Pasir adalah salah satu puskesmas yang melakukan pemeriksaan IVA test dengan capaian pemeriksaan tertinggi dibadningkan puskesmas lainnya di Kota Padang. Pada tahun 2018 didapatkan hasil pemeriksaan IVA test positif sebanyak 271 orang. Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor risiko hasil penelitian pemeriksaan IVA test pada wanita pasangan usia subur di Puskesmas Padang Pasir diataranya faktor umur pertama kali ibu melakukan hubungan seksual, jumlah paritas ibu dan riwayat penggunaan kontrasepsi hormonal. Jenis penelitian ini menggunakan desain case control dengan jumlah responden kasus sebanyak 32 orang ibu dengan hasil IVA test positif dan 32 orang ibu dengan hasil IVA test negative. Data yang diambil adalah data rekam medis responden pada tahun 2020 dari bulan Januari sampai dengan bulan Mei. Data dianalisa secara univariat dan bivariat dengan menggunakan uji statistic chi-square. Hasil penelitian menunjukkan terdapat hubungan yang bermakna antara umur ibu dengan hasil IVA test p= 0,024, jumlah paritas dengan hasil IVA test p= 0,003 dan riwayat kontrasepsi hormonal dengan hasil IVA tes p= 0,000. Berdasarkan hasil penelitian ini diharapkan pihak tenaga kesehatan lebih meningkatkan upaya promotif dan preventif pencegahan lesi prakanker serviks dengan penyuluhan faktor risiko kanker serviks, pentingnya vaksinasi dan pemberian vaksin HPV, serta pentingnya pemeriksaan IVA tes secara berkala. Kata Kunci : Kanker Serviks, IVA Test