Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ethnolinguistic Perspective on Lexicons of Traditional House in Menyali Village, North Bali

This study aims at identifying the concepts and the lexicons of traditional Balinese houses in Menyali Village, Sawan District, Buleleng Regency. This study is a descriptive qualitative research that involved three respondents, who were selected purposively. The data were collected through interviews and observation and analyzed using an interactive data analysis model. This study found that the Menyali community views that the tri mandala concept underlines its traditional house's spatial pattern. It includes nista (the profane lying), madya (middle lying for living area), and utama (the highest and holiest lying). It is also spiritually connected to two spiritual concepts, kangin-kauh (the directional axis of sunrise and sunset) and kaje-kelod (the directional axis of mountain and sea). This study identified 24 lexicons connected to the utama mandala, 7 to the madya mandala, and 5 to the nista mandala. The lexicon number is influenced by the area function and the rituals held in those areas.
Franken-CT: Head and Neck MR-Based Pseudo-CT Synthesis Using Diverse Anatomical Overlapping MR-CT Scans

Typically, pseudo-Computerized Tomography (CT) synthesis schemes proposed in the literature rely on complete atlases acquired with the same field of view (FOV) as the input volume. However, clinical CTs are usually acquired in a reduced FOV to decrease patient ionization. In this work, we present the Franken-CT approach, showing how the use of a non-parametric atlas composed of diverse anatomical overlapping Magnetic Resonance (MR)-CT scans and deep learning methods based on the U-net architecture enable synthesizing extended head and neck pseudo-CTs. Visual inspection of the results shows the high quality of the pseudo-CT and the robustness of the method, which is able to capture the details of the bone contours despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. The experimental Zero-Normalized Cross-Correlation (ZNCC) reports 0.9367 ± 0.0138 (mean ± SD) and 95% confidence interval (0.9221, 0.9512); the experimental Mean Absolute Error (MAE) reports 73.9149 ± 9.2101 HU and 95% confidence interval (66.3383, 81.4915); the Structural Similarity Index Measure (SSIM) reports 0.9943 ± 0.0009 and 95% confidence interval (0.9935, 0.9951); and the experimental Dice coefficient for bone tissue reports 0.7051 ± 0.1126 and 95% confidence interval (0.6125, 0.7977). The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield Units (m = 0.87; adjusted R2 = 0.91; p < 0.001). The Bland–Altman plot shows that the average of the differences is low (−38.6471 ± 199.6100; 95% CI (−429.8827, 352.5884)). This work serves as a proof of concept to demonstrate the great potential of deep learning methods for pseudo-CT synthesis and their great potential using real clinical datasets.
A Toxic Comment Classification Model Based on Ensemble

Accurate classification of toxic comments in different languages is an important task in today’s international social networking platforms. In order to improve the classification of toxic comments in different languages, this paper combines the advantages of high accuracy of monolingual model and strong generalization ability of multi-language model, and adopts the ensemble of multilingual model and monolingual model to classify toxic comments. For the monolingual model, the monolingual pre-training model is fine-tuned with labeled task data; for the multilingual model, before fine-tuning the model with labeled task data, a further pre-training is applied to train model on unlabeled data, which aims to make full use of the large amount of unlabeled data and reduce the dependency of amount of labeled comment data while improving the classification effect. Comparative experiments on Conversasion AI’s multilingual toxic comment dataset show that the model in this paper has improved results on different evaluation metrics compared to the XLM-RoBERTa multilingual fine-tuning model, illustrating the validity of the model.
Estimation of Extreme Cable Forces of Cable-Stayed Bridges Based on Monitoring Data and Random Vehicle Models

For long-span cable-stayed bridges, cables serve as one of the most important components to guarantee structural integrity. Forces of stay cables indicate not only the performance of cables themselves but also the overall condition of bridges. In order to help stakeholders to make maintenance decisions, an extreme cable force estimation method was proposed based on cable force measurements and traffic data from the weighing system. First, raw monitoring data were preprocessed based on a median filtering to obtain usable cable force signals. The multiresolution wavelet method was used to extract traffic-induced force component from mixed signals. Then, a Monte Carlo-based random vehicle model was developed using traffic data from the weighing system. Based on field temperature measurements and simulation of traffic-induced effects, extreme cable forces with respect to vehicle loads and temperature effects were predicted by extreme value theory. The Generalized Pareto Distribution (GPD) was adopted to establish the probability distribution models of the daily maximum cable force. Then, the extreme value within a return period of 100 years was determined and compared with the design loading demand. Finally, the effectiveness of the proposed method was validated through a cable-stayed bridge in China. As a result, the low-frequency varying component of cable force response had positive correlation with environmental temperatures, and the extreme value of the predicted cable force under prospective traffic volumes was within limit interval value according to the design code. The conclusions can be utilized by bridge owners to make maintenance decisions.
Enhancing science literacy capabilities of prospective primary school teachers through the STEM Project Learning Model

The STEM Project is a learning model that can be used by lecturers in the learning process so that students can get used to analyzing and synthesizing, to come up with an idea that can develop the competence and science literacy abilities of elementary school teacher candidates in the framework of learning in the 21st century. The study aims to improve the ability of the science literacy of Pre-service elementary school teachers by using the STEM Project Learning Model. This study was conducted on Pre-service elementary school teachers as elementary school teacher education students at the University of Halu Oleo Kendari. The results obtained indicate that the science literacy ability of Pre-service elementary school teachers has increased significantly. The increase is based on the results of statistical analysis which shows a significant increase in the ability of the science literacy of Pre-service elementary school teachers, after participating in learning using the STEM Project model. For this reason, the use of the STEM Project model in learning science enables prospective teachers to become accustomed to conducting comprehensive analyzes of problems and their solutions.
A Review of Classical and Nonclassical Nucleation Theories

Nucleation, the initial process in vapor condensation, crystal nucleation, melting, and boiling, is the localized emergence of a distinct thermodynamic phase at the nanoscale that macroscopically grows in size with the attachment of growth units. These phase changes are the result of atomistic events driven by thermal fluctuations. The occurrence of atomistic level events with the length scales on the order of 10–10 m and time scales of 10–13 S equivalent to the vibrational frequency of atoms makes the nucleation a very complicated phenomenon to study. Even though abundant literature is available about fundamental aspects of nucleation, the knowledge on these phenomena is far from complete. The classical pathway to nucleation which was once considered to have general applicability to all nucleating systems is gradually giving way to a nonclassical pathway which is now considered as a dominating mechanism in solution crystallization and other systems. In this review, an attempt is made to compare underlyin.
Determinants of Male Adolescents Smoking Behavior in Indonesia using Negative Binomial Regression

Adolescent smoking habits have become the Ministry of Health's major program associated with tobacco consumption. In 2016, the prevalence of adolescent smoking aged 10-18 years reached 8.8% and were rate increasingly against the Strategic Planning Ministry of Health 2015-2019 target to lower adolescent smoking prevalence to 5.4%. Male adolescents consuming cigarettes are higher than females. Whereas, high consumption of cigarettes in men will increase the risk of impotence and decrease reproductive health quality to affect future generations' quality. This study aims to determine the general picture of smoking behavior in Indonesia's male adolescent in 2018 and any variables that affect the number of cigarettes consumed. The analytical method used is Poisson Regression and Negative Binomial Regression. The data source used is raw data Riskesdas 2018 with the unit of analysis are male adolescent smokers aged 10-18 years. Research indicates that most male adolescents are light smokers. Heavy smokers were dominated by older age, living in a rural area, poorly educated, employed, lived with a household head who was a smoker, and had low education. Age, location of residence, education level, working status, smoking status, and household head education level significantly affect male adolescents' smoking behavior.
Mathematical communications through project based learning based on android

The lack of multimedia applications in schools and the low mathematical communication ability of students, as an alternative to using Android. The purpose of this research is to improve students’ mathematical communication ability through android based learning project. Using the quasi experimental method. This research conducted at Junior High School in Bandung with 55 students as samples, 29 students in class VIII C as an experimental class and 26 students in class VIII D as a control class. Mathematical communication ability test instrument. Data analysis: independent T test, two way ANOVA test. The findings: Improved communication of students who get learning on Android based learning projects was higher than students who get conventional learning. There were no difference in the achievement of mathematical communication between students who get an Android based learning project and conventional learning based on mathematical basic knowledge in the high, medium and low categories. There is the influence of the interaction between the type of project based learning with android applications and expository learning and prior knowledge mathematics category (high, medium, and low) on the ability of students’ mathematical communication. Android based learning project facilitates inventions process, reflective, the material is easy to understanding so that students’ mathematical communication skills increase.
An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study

In recent years, research in the space community has shown a growing interest in Artificial Intelligence (AI), mostly driven by systems miniaturization and commercial competition. In particular, the application of Deep Learning (DL) techniques on board Earth Observation (EO) satellites might lead to numerous advantages in terms of mitigation of downlink bandwidth constraints, costs, and increment of the satellite autonomy. In this framework, the CloudScout project, funded by the European Space Agency (ESA), represents the first time in-orbit demonstration of a Convolutional Neural Network (CNN) applied to hyperspectral images for cloud detection. The first instance of this use case has been done with an INTEL Myriad 2 VPU on board a CubeSat optimized for low cost, size, and power efficiency. Nevertheless, this solution introduces multiple drawbacks due to its design not specifically being for the space environment, thus limiting its applicability to short-lifetime Low Earth Orbit (LEO) applications. The current work provides a benchmark between the Myriad 2 and our custom hardware accelerator designed for Field Programmable Gate Arrays (FPGAs). The metrics used for comparison include inference time, power consumption, space qualification, and components. The obtained results show that the FPGA-based solution is characterized by a reduced inference time, and a higher possibility of customization, but at the cost of greater power consumption and a longer Time to Market. As a conclusion, the proposed approach might extend the potential market of DL-based solutions to long-term LEO or interplanetary exploration missions through deployment on space-qualified FPGAs, with a limited cost in energy efficiency.
Correlating Apollo Soil Mineralogical Data With Kaguya Spectral Data for a Global Mineralogical Classification

Although lunar soils contain rock and mineral components from the breakdown of a mixture of rock types, a classification based on the abundances of the major silicate minerals plagioclase, olivine, low‐Ca pyroxene (LCP) and high‐Ca pyroxene can be used to evaluate the major compositional classes that are represented within a given soil. We studied the compositional classes for Apollo 15, 16, and 17 soil samples based on the mineral modal abundances derived by X‐ray diffraction (XRD). Using the XRD results as a ground truth, we determined the compositional classes of the Apollo 15, 16, and 17 sampling stations using mineral maps from the Kaguya Multiband Imager (MI), then mapped areas having compositional classes similar to the sampling stations on regional and global scales. Global distribution of compositional classes was also mapped using MI mineral maps, and the major compositional classes of lunar nonmare surfaces are noritic anorthosite (40%), anorthositic norite (24%), and anorthosite (23%). Our maps show that the lunar highlands and the South Pole‐Aitken (SPA) basin are enriched with noritic materials, indicating the widespread occurrence of LCP‐rich and olivine‐poor assemblages. In contrast to the SPA basin and the highlands, the basin rings of Serenitatis, Crisium, Humorum, Nectaris, Orientale, and Hertzsprung exhibit higher olivine/pyroxene ratios (>2), and we interpret this signature as reflecting a contribution from olivine‐rich upper mantle components.
Efektivitas Penggunaan Media Pembelajaran Big book Subtema Indahnya Keberagaman Budaya Negeriku

Penelitian ini bertujuan untuk mengetahui efektivitas penggunaan media Big book pembelajaran pada sub tema indahnya keragaman budaya negeriku pada muatan IPS di kelas IV dalam meningkatkan hasil belajar siswa. Penelitian ini menggunakan pendekatan kuantitatif. Metode penelitian ini adalah quasi experiment dengan desain Nonequivalent Control Group Design. Populasi dalam penelitian ini adalah seluruh siswa kelas IV di SD Negeri 026609 Binjai. Sampel dalam penelitian ini dalah siswa kelas IV di SD Negeri 026609 Binjai Penelitian ini dilaksanakan di kelas IV SD Negeri 026609 Binjai Selatan yang beralamat di Jalan Sei Babalan Pujidadi Kecamatan Binjai Selatan. Penelitian ini dilakukan secara bertahap dari bulan Maret sampai bulan April Tahun Pelajaran 2019/2020.Teknik pengumpulan data digunakan tes dan angket. Teknik analisis menggunakan uji-t. Berdasarkan hasil perhitungan nilai gainc score diperoleh nilai rata-rata gain score pada kelas eksperimen sebesar 0,75 dengan kategori tinggi. Sedangkan nilai gains score pada kelas kontrol yaitu 0,46 dengan kategori sedang. Hasil penelitian nilai t hitung (=0,000) sehingga terdapat perbedaan karena sig (2-tailed) < 0,05 dan t hitung bernilai positif sehingga H 0 ditolak. Nilai pretest kelas eksperimen sebelum menggunakan big book rata-rata 45,28. Pada kelas kontrol memperoleh nilai rata-rata sebesar 45,12. Sedangkan nilai postes siswa pada kelas eksperimen setelah menggunakan big book rata-rata 86,80. Nilai posttest kelas kontrol memperole rata-rata sebesar 72,40 Hal ini menunjukkan bahwa penggunaan big book sangat efektif digunakan untuk meningkatkan hasil belajar.
Association of Calcitriol Supplementation with Reduced COVID-19 Mortality in Patients with Chronic Kidney Disease: A Population-Based Study

Treatment with calcitriol, the hormonal form of vitamin D, has shown beneficial effects in experimental models of acute lung injury. In this study, we aimed to analyze the associations between calcitriol supplementation and the risk of SARS-CoV2 infection or COVID-19 mortality. Individuals ≥18 years old living in Catalonia and supplemented with calcitriol from April 2019 to February 2020 were compared with propensity score matched controls. Outcome variables were SARS-CoV2 infection, severe COVID-19 and COVID-19 mortality. Associations between calcitriol supplementation and outcome variables were analyzed using multivariable Cox proportional regression. A total of 8076 patients were identified as being on calcitriol treatment. Advanced chronic kidney disease and hypoparathyroidism were the most frequent reasons for calcitriol supplementation in our population. Calcitriol use was associated with reduced risk of SARS-CoV2 infection (HR 0.78 [CI 95% 0.64–0.94], p = 0.010), reduced risk of severe COVID-19 and reduced COVID-19 mortality (HR 0.57 (CI 95% 0.41–0.80), p = 0.001) in patients with advanced chronic kidney disease. In addition, an inverse association between mean daily calcitriol dose and COVID-19 severity or mortality was observed in treated patients, independently of renal function. Our findings point out that patients with advanced chronic kidney disease could benefit from calcitriol supplementation during the COVID-19 pandemic.
Halogen-free flame-retardant rigid polyurethane foam with a nitrogen–phosphorus flame retardant

A halogen-free flame retardant containing nitrogen and phosphorus, 2-[anilino-(6-oxobenzo[c][2,1]benzoxaphosphinin-6-yl)methyl]phenol (PDOP), has been synthesized by reaction of benzo[c][2,1]benzoxaphosphinine-6-oxide (DOPO) with 2-(N-phenyliminomethyl)phenol. Halogen-free flame-retardant rigid polyurethane foams (RPUF-PDOP) were prepared using PDOP as a flame retardant. The flammability was investigated using limiting oxygen index, a vertical burning test (UL-94), and a cone calorimeter. When PDOP (10 wt%) as flame retardant was added to RPUF (RPUF-PDOP10%), the limiting oxygen index value was increased from 18% to 27%, and a UL-94 V-0 rating was achieved; meanwhile, the peak heat release rate, total heat release, and the average mass-loss rates of RPUF-PDOP10% were reduced from 246 to 207 kW m−2, from 26.9 to 21.0 MJ/m2, and from 0.043 to 0.033 g/s, respectively. Especially, the initial decomposition temperature of RPUF-PDOP10% was decreased from 228°C to 209°C. The final residual char from decomposition of RPUF-PDOP10% was significantly increased up to 35.6%. The addition of PDOP did not markedly decrease the mechanical properties of the resulting flame-retardant RPUFs.
Research on Facial Expression Recognition Method Based on Bilinear Convolutional Neural Network

Facial expressions express different emotions through different degrees of distortion of facial muscles, and the expression images spontaneously generated by people in real scenes are susceptible to interference from different illumination angles and postures. An extraction based on bilinear convolutional neural network is proposed. This is an end-to-end method for emotion classification based on second-order features of expression images. This method optimizes the network structure by adding a batch normalization layer, increasing the maximum pooling core, and replacing the fully connected layer with a global average pooling layer. Finally, the bilinear normalization method is used to convert one-dimensional features into two the dimensional features complete the final classification. Through testing on the FER2013 dataset, this method obtains an accuracy of 73.2%, which is 0.5% higher than the current state-of-the-art method, and proves that extracting image second-order features is more conducive to expression classification than first-order features. In addition, compared with other mainstream methods, the proposed improved model has higher recognition accuracy.
Effect of latency on simulator sickness in smartphone virtual reality

Virtual reality (VR) technologies have experienced rapid development in recent years, leading to low‐cost consumer smartphone VR systems (with 3DoF tracking). One issue with the smartphone VR systems, which rely on existing generic displays, is simulator sickness, which arises due to factors that induce sensory conflicts, especially related to visual–vestibular conflict. Display update latency with respect to head position is one of the most widely implicated factors for simulator sickness. However, studies of the effects of latency on simulator sickness have given inconsistent results. Moreover, these studies were done on systems with latencies much higher than current smartphone VR systems. Therefore, it is hard to generalize those results to today's VR systems. To address this, we present in this paper a study on the effect of latency on simulator sickness for latency levels around 20 ms. We present a systematic evaluation of simulator sickness in smartphone VR, testing for a difference between conditions with latencies of 18 and 28 ms, on a task of natural game playing in smartphone VR. We noted differences between individuals in their reported simulator sickness for the two latency conditions but no sample‐wide significant difference.
Shale Oil Assessment for the Songliao Basin, Northeastern China, Using Oil Generation–Sorption Method

This study was performed in order to evaluate shale oil in the first member of the Qingshankou formation (K2qn1) in the Songliao basin in northeastern China. The evaluation was carried out using immature type I kerogen kinetics in a closed system, two wells drilled into oil prone source rocks, in order to apply Rock-Eval, mineral composition and residual hydrocarbon analysis, and the hydrocarbon sorption method. The “oil windows” for K2qn1 in mudstone and shale wells range from 0.6% to 1.05%, with maximum hydrocarbon generation at around 420 mg of hydrocarbon per gram of total organic carbon. In addition, the brittle mineral contents of K2qn1 conform to the requirements for industrial oilfield development and exploitation. Our results demonstrate that there is clear potential for shale oil exploration in the K2qn1; a highly potential zone is defined as low risk at burial depths of greater than 2065 m for well H14, while well X83 exhibits relatively discrete depth based on oil saturation and production ind.