Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Retrieval of Offshore‐Onshore Correlation Functions With Machine Learning

The retrieval of reliable offshore‐onshore correlation functions is critical to improve our ability to predict long‐period ground motions from megathrust earthquakes. However, localized ambient seismic field sources between offshore and onshore stations can bias correlation functions and generate nonphysical arrivals. We present a two‐step method based on unsupervised learning to improve the quality of correlation functions calculated with the deconvolution technique (e.g., deconvolution functions, DFs). For a DF data set calculated between two stations over a long time period, we first reduce the data set dimensions using the principal component analysis and cluster the features of the low‐dimensional space with a Gaussian mixture model. We then stack the DFs belonging to each cluster together and select the best stacked DF. We apply our technique to DFs calculated every 30 min between an offshore station located on top of the Nankai Trough, Japan, and 78 onshore receivers. Our method removes spurious arrivals and improves the signal‐to‐noise ratio of DFs. Most 30‐min DFs selected by our clustering method are generated during extreme meteorological events such as typhoons. To demonstrate that the DFs obtained with our method contain reliable phases and amplitudes, we use them to simulate the long‐period ground motions from a Mw 5.8 earthquake, which occurred near the offshore station. Results show that the earthquake long‐period ground motions are accurately simulated. Our method can easily be used as an additional processing step when calculating offshore‐onshore DFs and offers a new way to improve the prediction of long‐period ground motions from potential megathrust earthquakes.
Sound localisation ability using cartilage conduction hearing aids in bilateral aural atresia

Abstract Objective Patients with bilateral aural atresia often exhibit poor sound localisation due to bone conduction (BC) features. However, most patients using cartilage conduction (CC) hearing aids reported improvement of sound localisation. Design CC hearing aids were fitted binaurally. Subsequently, sound localisation was evaluated in three conditions: unaided, aided with previously used hearing aids (air conduction or BC hearing aids), and aided with CC hearing aids. Ears were evaluated with eight loudspeakers positioned in a full-circle at 45-degree interval. Loudspeakers were classified into left and right by midline and front and back by horizontal line. The abilities to distinguish left from right and front from back were compared among three conditions. Study sample Thirteen patients with bilateral aural atresia participated. Results The ability to distinguish sounds originating from left or right for participants aided with CC hearing aids was significantly better than that for other conditions (p < 0.05). For distinguishing sounds originating from front or back, unaided ears were significantly better than ears aided with CC hearing aids (p < 0.05). Conclusions CC hearing aids provide the ability to distinguish left from right in patients with bilateral aural atresia.
MACHINE LEARNING FOR DATA CLASSIFICATION IN INDONESIA REGIONAL ELECTIONS BASED ON POLITICAL PARTIES SUPPORT

In this paper, we discuss the implementation of Machine Learning (ML) to predict the victory of candidates in Regional Elections in Indonesia based on data taken from General Election Commission (KPU). The data consist of composition of political parties that support each candidate. The purpose of this research is to develop a Machine Learning model based on verified data provided by official institution to predict the victory of each candidate in a Regional Election instead of using social media data as in previous studies. The prediction itself simply a classification task between two classes, i.e. ‘win’ and ‘lose’. Several Machine Learning algorithms were applied to find the best model, i.e. k-Nearest Neighbors, Naive Bayes Classifier, Decision Tree (C4.5), and Neural Networks (Multilayer Perceptron) where each of them was validated using 10-fold Cross Validation techniques. The selection of these algorithms aims to observe how the data works on different Machine Learning approaches. Besides, this research also aims to find the best combination of features that can lead to gain the highest performance. We found in this research that Neural Networks with Multilayer Perceptron is the best model with 74.20% of accuracy.
Observation of Bone Tissue Metabolism and Bacterial Biofilm in Aqueous Solution Using ASEM

The ASEM ClairScopeTM (JEOL Ltd.) is an inverted scanning electron microscope (SEM) (Fig. 1 left), which realizes SEM of a sample at atmospheric pressure in a readily-accessible, open container called the ASEM dish [1]. The dish has eight 0.25 mm × 0.25 mm, 100 nm-thick SiN film windows and can hold a few milliliters of culture medium for cells. It allows various types of aldehyde-fixed cells and tissues (Fig. 1 right) to be quickly observed in radical scavenger glucose solution at high resolution [1]. An optical microscope (OM) positioned above the ASEM dish can be used to observe wide areas of the sample from above, and the SEM to image specified smaller regions from below through the SiN film in the base of the dish (Fig. 1 left). The optical axes of both microscopes are aligned to image the same area in the center, and the specimen stage can be moved two-dimensionally (x-y) for targeting. The resolution measured between gold particles near the SiN film was 8nm.
Visual Image of Architectural Elements as the Identity of Menanga Traditional Village

The Menanga Village has interesting traditional houses and village layout. There are some unique architectural shapes, easily known and remembered by the observers' minds. This study combines descriptive and graphical method with quantitative analysis to translate the mental map into a variable map, in order to determine the role of architectural elements as a place identity in village space. Based on the mind map, the Lynch five elements in Menanga Village are distributed into four sequences as district and path, edges, nodes, and landmarks. The results of the study show that the district and path elements were remembered by all of the observers, edges were remembered by most observers, while nodes and landmarks were only remembered by a small number of them. The most dominant placemark element is the combination of traditional houses rows with the main corridors forming unique village visual character. The large river flow behind the residential area and the highway in the front of the village has an important role as a boundary line of the village's core. Other physical elements that are supposed functioned as nodes and landmark markers are too weak and unable to be a place marker or as a navigation tool for people who want to go elsewhere in the village core. Therefore, structuring and façade repairing of architectural elements are needed to realize the visual linkage among the elements. It aims to lift the image and identity of Menanga as a traditional architectural village that needs to be preserved.
Designing the QuickCash Payment System prototype by using cash withdrawal services through minimarket as an alternative solution for secure money transfer from bank customers to non-bank customers

In this era wich has many electronic payment facilities, it is noted that the need for cash in Indonesia is still high and bank customers are still relatively small. This shows that Indonesians need money transfer services from bank customers to non-bank customers. As an alternative to meeting these needs, Alhothaily, et al. put forward a QuickCash payment system that allows bank customers to transfer money to non-bank customers. Therefore, in this study, the prototype design of the QuickCash payment system was made to facilitate the transfer of money. According to Alhothaily, et al. QuickCash payment system is a payment system that makes it easy for users to transfer money. To prove this statement, the prototype design of the QuickCash payment system was built using the SSDLC methodology and tested the QuickCash payment system prototype by collecting data using a questionnaire. Based on the test results with a significance level of 5%, all respondents agreed that the QuickCash payment system made it easy for users to make money transfers.
The server's turnover on a federal education institution of Rio de Janeiro: the construction of a proposal of action to aid the reversal of the talent's evasion process

Given the current scenario, where the citizen's level of demand for public services is gradually high, it is impossible to maintain the old inefficient practices in Public Administration. Given this situation, it is necessary to reformulate the methods to improve the final product, moving the current processes to an administration of a more managerial nature, based not only on efficiency, but also on the efficiency of the product returned to society. In this sense, it is conceived that such a reformulation should include Human Resources Management, also directing it to a strategic management model, through which people and, consequently, their motivation, take on a central role for the Institutions, in order to strengthen the degree of commitment between employee and Organization. As an objective, this work aimed at the construction of a proposal of action to assist a Federal Education Institution of Rio de Janeiro in the retention of its servers. In order to do so, the main factors capable of adversely affecting the process of establishment of organizational commitment, as well as the application of a questionnaire to 95 employees of the institution, were identified through bibliographic and documentary exploratory research, in order to identify the degree of relevance of these factors, and the proposition of specific actions, such as the creation of a bank of hours for the servers, in order to mitigate or even reverse the evasion of talent in the Institution.
ANALYSIS OF THE LEAF VEINS AND AREOLES IN DIFFERENT MEDICINAL LEAVES BY USING IMAGE PROCESSING TECHNIQUES

The present study deals with the study of leaf characteristics of medicinal plants of micro and macro levels . The leaves considered are cast roil leaf, betel leaf etc by using the techniques of Image processing. Leaf vein forms the basis of characterization and classification. Different species have different leaf vein patterns. The aim of the work is to classify and authenticate the medicinal plant materials and herbs which are widely used for Indian herbal medicinal products. The quality and authenticity of these leaves are to be ensured for the preparation of herbal medicines. Images play a major role in human perception. The results reveal that is species belonging to the same family exhibit different medicinal properties and species belonging to different families exhibit similar leaf characteristics at the micro and macro levels. The results are presented, discussed and found to be very interesting.
Eine Variante von NEV an ECMO

Patienten an der Extrakorporalen Membranoxygenierung (ECMO) unterliegen einem hohen Risiko für ein akutes Nierenversagen. Wäre es nicht eine Erleichterung für Patienten und Pflegende, wenn die ECMO auch als Plattform für andere Organersatzverfahren eingesetzt werden könnte?
Low power Consumption Through Low-Power and Area-Efficient Shift Register Using Pulsed Latches

: This project proposes a low-force and zone proficient shift register utilizing pulsed latches. The region and force utilization are decreased by supplanting flip-flops with pulsed latches. This technique takes care of the planning issue between pulsed latches using various non-cover postponed pulsed clock flags rather than the regular single pulsed clock signal. The shift register utilizes few the pulsed clock signals by gathering the latches to a few sub shifter registers and utilizing extra transitory stockpiling latches.A 128-bit shift register utilizing pulsed latches was manufactured utilizing a 65nm CMOS process with VDD = 1.0V. The proposed shift register spares zone and force contrasted with the regular shift register comprising of a Flip-flop.
Decision-making in the ICU: An analysis of the ICU admission decision-making process using a ‘20 Questions’ approach

Background Deciding to admit a patient into the intensive care unit (ICU) is a high-stakes, high-stress, time-sensitive process. Elucidating the complexities of these decisions can contribute to a more efficient, effective process. Objectives To explore physicians’ strategic thought processes in ICU triage decisions and identify important factors. Methods Practitioners (N=29) were asked to decide on ICU referrals of two hypothetic cases using a modified ‘20 Questions’ approach. Demographic data, decisions when full information was available, feedback on questions, rating of factors previously identified as important and influence of faith and personality traits were explored. Results Of the 735 questions asked, 95.92% were patient related. There were no significant differences in interview variables between the two cases or with regard to presentation order. The overall acceptance rate was 68.96%. Refusals were associated with longer interview times (p=0.014), as were lower ICU bed capacity (p=0.036), advancing age of the practitioner (p=0.040) and a higher faith score (p=0.004). Faith score correlated positively with the number of questions asked (p=0.028). There were no significant correlations with personality trait stanines. When full information was available, acceptances for Case A decreased (p=0.003) but increased for Case B (p=0.026). The net reclassification improvement index was –0.138 (p=0.248). Non-subspecialists were more likely to change their decisions (p=0.036). Conclusion Limiting information to what is considered vital by using a ‘20 Questions’ approach and allowing the receiving practitioner to create the decision frame may assist with ICU admission decisions. Practitioners should consider the metacognitive elements of their decision-making. Contributions of the study The study used a novel approach to explore physicians’ decision-making process for admitting a patient to the intensive care unit (ICU). Understanding the main factors that influence the decision-making process will allow for streamlining the referral process, more effective selection of patients most likely to benefit from ICU treatment, and prevent inappropriate admissions into the ICU. The findings can also help to improve data capture tools and encourage practitioners to critically reflect on their decision-making processes.
A circular polarized reflectarray antenna with electronically steerable beam and interchangeable polarizations

Abstract The purpose of this paper is the design of a novel single layer reflectarray antenna in X-band, which can electronically steer the antenna beam and change its polarization. Each antenna element includes a circular patch and rings around it, equipped with two varactor diodes, which are positioned perpendicular together to create a circular polarization. First, using these elements, a circular polarized active electromagnetic band-gap (EBG) reflector is implemented. Then, a special feed is placed in a proper distance with respect to EBG reflector and a circular polarized reflectarray antenna is designed, which has capability of switching electronically between right- and left-handed circular polarizations. An electronic active board is designed and fabricated to provide the biasing voltages, control signals, and indicators. The elements of the reflectarray are designed and arranged in such a way that the varactor diodes can be biased independently. Consequently, a 3D beam is created, which can steer up to ±40o. For evaluating the antenna performance, the radiation patterns and the axial ratio of the antenna are determined at the operational frequency of 11.4 GHz. It is illustrated that, increasing the steering angle in θ direction decreases the directivity and the gain of the reflectarray antenna.
Assessment of Health Budgetary Allocation and Expenditure Toward Achieving Universal Health Coverage in Nigeria

Context: The implementation of the Universal Health Coverage (UHC) promotes access to quality health care delivery through cost-effective initiatives to ensure good health and wellbeing without discrimination. This study examines government finance, budgetary allocation, and expenditure as key health development indicators towards achieving the UHC in Nigeria. Evidence Acquisition: Data analyzed in the study were gotten from journal articles, reports and other secondary sources. Searches were conducted in PubMed, Google Scholar, and WHO Library Database with pre-determined search terms. Further publications were identified through snowballing of citations and references. We reviewed only papers written in English with no date restrictions placed on searches. Results: Within the period of analysis, the annual national health budgetary allocation in Nigeria has been below the 2001 Abuja declaration of allocating 15% of the national budget to health. Our analysis also revealed that if the Abuja declaration was implemented, additional allocations of NGN 4.99 trillion should have been injected into the health sector between 2014 and 2020. In addition, Nigeria also lags behind relative to some other low-and middle-income countries in terms of government expenditure to the health sector in achieving the UHC. Conclusions: The inadequate budgetary allocation in Nigeria to healthcare has significantly influenced recurrent and capital health expenditure. It is worthy to note that the insufficient allocation will continue to significantly affect capital expenditure which is a large determinant of the development of any health system. With the current state of healthcare budget allocation in Nigeria, efforts need to be intensified to ensure the achievement of UHC. In the face of achieving UHC, reviewing the system of healthcare financing and ensuring prudent allocation of resources while shifting the focus from out-of-pocket payments for health is essential. We also recommend increase in political commitment towards improving the health of the populace so as to ensure health systems goals of efficiency, equity, quality of care, sustainability, financial risk protection for all citizens are achievable.
Controlling the False Discovery Rate via symmetrized data aggregation based on SLOPE

The symmetrized data aggregation (SDA) method has obvious advantages in controlling false discovery rate (FDR) under dependence, but it is affected by data sparsity level, signal amplitude and feature correlation structures. The FDR control becomes less accurate due to the additional estimation errors. In this paper, we expand the symmetrized data aggregation (SDA) filter by using sorted L-one penalized estimation (SLOPE) method, and in comparison with SDA, SDA-SLOPE method can estimate FDR more accurately and improve the true discovery rate (TDR). Through simulation study, it is found that SDA-SLOPE can adapt to the changes of data sparsity, signal amplitude and feature correlation, and the robustness and effectiveness of this method in FDR control are verified.
Pseudoprospective Evaluation of UCERF3-ETAS Forecasts during the 2019 Ridgecrest Sequence

The 2019 Ridgecrest sequence provides the first opportunity to evaluate Uniform California Earthquake Rupture Forecast v.3 with epidemic-type aftershock sequences (UCERF3-ETAS) in a pseudoprospective sense. For comparison, we include a version of the model without explicit faults more closely mimicking traditional ETAS models (UCERF3-NoFaults). We evaluate the forecasts with new metrics developed within the Collaboratory for the Study of Earthquake Predictability (CSEP). The metrics consider synthetic catalogs simulated by the models rather than synoptic probability maps, thereby relaxing the Poisson assumption of previous CSEP tests. Our approach compares statistics from the synthetic catalogs directly against observations, providing a flexible approach that can account for dependencies and uncertainties encoded in the models. We find that, to the first order, both UCERF3-ETAS and UCERF3-NoFaults approximately capture the spatiotemporal evolution of the Ridgecrest sequence, adding to the growing body of evidence that ETAS models can be informative forecasting tools. However, we also find that both models mildly overpredict the seismicity rate, on average, aggregated over the evaluation period. More severe testing indicates the overpredictions occur too often for observations to be statistically indistinguishable from the model. Magnitude tests indicate that the models do not include enough variability in forecasted magnitude-number distributions to match the data. Spatial tests highlight discrepancies between the forecasts and observations, but the greatest differences between the two models appear when aftershocks occur on modeled UCERF3-ETAS faults. Therefore, any predictability associated with embedding earthquake triggering on the (modeled) fault network may only crystalize during the presumably rare sequences with aftershocks on these faults. Accounting for uncertainty in the model parameters could improve test results during future experiments.
Prediksi Akhir Pandemi COVID-19 di Indonesia dengan Simulasi Berbasis Model Pertumbuhan Parametrik

This research aims to predict the end of the COVID-19 pandemic in Indonesia based on parametric growth models. The models are chosen by considering their fitness with the data of Taiwan which is believed to have passed over the peak of the pandemic and have gone through all phases in the growth curves. The models are parameterized using the nonlinear least squares method. The deviation and confidence interval of each parameter is estimated using the k-fold cross-validation and the bootstrap techniques. Using the total cases per million population data from March 2 to June 18, 2020, it was found that two growth models fit the data, i.e. logistic and modified Gompertz, where the latter performs better. Using the information about the deviation of each model parameter, a simulation model is developed to predict the time at which the total cases curve starts to flatten, which is an indication of the end of the pandemic. It was found with 95% confidence level that based on the modified Gompertz model the pandemic will end somewhere between March 9 – September 7, 2021 with total cases per million of 206 - 555. Meanwhile, based on the logistic growth model, the end of the pandemic is between August 28 – September 23, 2020 with total cases per million of 180 - 375. This model can be extended by making comparative scenario with Taiwan based on measures that represent the quality of the pandemic mitigation such as test ratio and the intensity of social restriction.
Checklist and country status of European bryophytes – update 2020

Tables listing all the bryophyte species recorded in Europe, along with their distribution and Red List status by country/territory, are presented. Status notes give short explanations, justifications, references, etc. for country occurrences in the distribution tables. Taxon details list the naming authorities for each taxon and provide notes and references on nomenclature and synonymy. Nomenclature follows the recently published annotated checklist of bryophytes of Europe, Macaronesia and Cyprus. An extensive bibliography and literature list are provided, followed by an appendix with useful references for Europe as a whole, European regions and individual European countries or territories. Appendices with lists of European bryological societies, journals, floras and the country contacts of the European Committee for Conservation of Bryophytes are presented at the end of the report.