Miracle-dz/UniFAR-FTUs huggingface dataset:
Patient – physician conversations about life-sustaining treatment: Treatment preferences and participant assessments

Objective. In 2019, the Danish parliament issued legislation requiring Danish physicians to clarify and honor seriously ill patients ’ treatment preferences. The American POLST (Physician Orders for Life-Sustaining Treatment) document could be a valuable model for this process. The aim of the study was to examine patients ’ preferences for life-sustaining treatment and participant assessment of a Danish POLST form. Methods. The study is a prospective intervention based on a pilot-tested Danish POLST form. Participant assessments were examined using questionnaire surveys. Patients with serious illness and/or frailty from seven hospital wards, two general practitioners, and four nursing homes were included. The patients and their physicians completed the POLST form based on a process of shared decision-making. Results. A total of 95 patients (aged 41 – 95) participated. Hereof, 88% declined cardiopulmonary resuscitation, 83% preferred limited medical interventions or comfort care, and 74% did not require artificial nutrition. The preferences were similar within age groups, genders, and locations, but with a tendency toward younger patients being more in favor of full treatment and nursing home residents being more in favor of cardiopulmonary resuscitation. Questionnaire response rates were 69% (66/95) for patients, 79% (22/28) for physicians, and 31% (9/29) for nurses. Hereof, the majority of patients, physicians, and nurses found that the POLST form was usable for conversations and decision-making about life-sustaining treatment to either a high or very high degree. Significance of results. The majority of seriously ill patients did not want a resuscitation attempt and opted for selected treatments. The majority of participants found that the Danish POLST was usable for conversations and decisions about life-sustaining treatment to either a high or a very high degree, and that the POLST form facilitated an opportunity to openly discuss life-sustaining treatment.
Hydroxychloroquine for COVID-19: A Review

In dealing with the COVID-19 pandemic, hydroxychloroquine (HCQ) as a drug captured serious worldwide attention. Some in-vitro and in-vivo studies recently showing possible inhibition of SARS-CoV-2 by the use of HCQ. At same time, however, a few cases research demonstrating NO medical benefit/ poor clinical result with major adverse effects through the use of HCQ in coronavirus disease-2019 treatment. Therefore, the use of HCQ (in treatment with COVID-19) is of considerable international concern, although a consensus has not yet been reached. Further proof to prove HCQ 's efficacy against COVID-19 is still needed. In view of this, the current analysis illustrates the ongoing work on use of HCQ in the management of coronavirus disease-2019. The present analysis will address the potential in-vitro study of HCQ's anti-viral function, prophylaxis strategy and impact of HCQ on SARS-CoV-2 viruses. This review also sums up and discusses all clinical practice trials / HCQ use research studies against COVID-19 (with clinical results). Finally, considering the public health and pharmacovigilance concerns, possible detrimental effects are also discussed.
Moss biomonitors for heavy metal pollution in soils of Manganese Carbonate Mine across ecological succession stages

Many plants have been widely used in monitoring and assessing heavy metal pollution in soil, air and water. However, few studies have considered the unique value of bryophyte communities in monitoring manganese ore pollution and the diversity characteristics of bryophytes in different natural succession stages. Tongluojing Manganese Mine in Guizhou Province, China, was chosen for identifying bryophytes and statistical analyses. In total 61 species of mosses in 21 genera of 7 families were identified, including 8 dominant species, primarily representing the turf life-form. Following the successive stages from bare rock to woodland, single-species moss communities decreased while multi-species communities increased, with increment of the α diversity index. However, the β diversity index showed the opposite trend. The moss similarity index was the highest (0.43) and Cody index lowest (7.5) on bare rock, while the indices were the lowest (0.17) and the highest (18), respectively, in woodland. The Nemerow pollution index decreased gradually with the successive stages, with the bare rock area being the most polluted. The soil was polluted by Pb, Cr, Zn, Cd and Mn to varying degrees, among which Mn was the biggest pollutant with a concentration 129 times higher than the background value of the soil in Guizhou Province. There was a positive correlation between the contents of Cd, Cr, Zn and Mn in Weissia planifolia Dix and those in the substrate, suggesting that W. planifolia can be used as an indicator plant. This study highlighted multiple effects of mosses on heavy metal absorption, which could be used as pioneer plants for vegetation restoration in the manganese ore waste rock accumulation area.
Exploring Domain-Speciﬁc Architectures for Network Protocol Processing

—Existing architectures for network protocol processing incur bottlenecks in communication-intensive cloud applications. Software processing competes for scarce CPU cycles with applications, ﬁxed protocol ofﬂoads such as RDMA lack required ﬂexibility, and SmartNICs are inefﬁcient, expensive, or power-hungry. The design space for alternative architectures remains largely unexplored, and choosing a suitable architecture is complicated by rapidly evolving application and transport protocols. We draw parallels to machine learning acceleration and argue for a similar approach: conﬁgurable domain-speciﬁc architectures (DSAs) combined with a domain-speciﬁc high-level programming model decoupling applications from architectures. We propose Kugelblitz, an implementation and design exploration framework for network DSAs enabling ﬂexible and efﬁcient protocol ofﬂoad. Kugelblitz comprises abstractions for separately specifying hardware conﬁguration and protocols, a hardware RTL generator, and a compiler to generate runtime conﬁgurations to implement speciﬁc protocols on speciﬁc hardware conﬁgurations.
Effect of Solvent, Temperature and pH on Anthocyanin Pigments from Red Dragon Fruit Peel Extract (Hylocereus Polyrhizus)

Red dragon fruit peel (Hylocereus polyrhizus) is an agricultural waste that contains quite high natural anthocyanin pigments. The purpose of this study was to determine how the effect of solvent, temperature and pH on the total content and stability of anthocyanins from liquid extract of red dragon fruit peel. Extraction of anthocyanins from red dragon fruit peel using maceration method with solvent distilled water, distilled water: acetic acid 10% (1:6), and distilled water: citric acid 10% (1:6) and testing was carried out in the form of anthocyanin stability tests at pH 1, 2, 3, 4, 5 and temperatures 0°C, 10°C, 20°C, 30°C, 40°C, 50°C. The results obtained showed that the use of distilled water:citric acid 10% (1:6) as a solvent resulted in the highest total anthocyanin content with 18.034 Proceeding of Mulawarman Pharmaceuticals Conferences Pengaruh Pelarut, Suhu dan pH Terhadap Pigmen Antosianin dari Ekstrak Kulit Buah Naga Merah (Hylocereus Polyrhizus) 14 Proc. Mul. Pharm. Conf. 2021.
Polygenic scores in disease prediction: evaluation using the relevant performance metrics

We examine the performance of polygenic scores in screening and disease prediction using metrics which are firmly established for non-genetic tests but surprisingly, rarely used in evaluations of polygenic scores. Using reported metrics from the Polygenic Score Catalog (odds and hazard ratios, area under the receiver operating characteristic curve or C-index), we calculate the sensitivity or detection rate (DR5) for polygenic score cut-offs that define a 5% false positive rate (FPR). To examine clinical and public health relevance, we use information on disease incidence to calculate the odds of being affected given a positive result (OAPR; the ratio of true to false positives) both for an individual and a population. Polygenic scores typically detect 7.6-16.4% (and therefore miss 83.6-92.4%) of affected individuals with a 5% FPR. For a polygenic score for coronary artery disease (CAD) with a DR5 of 12% (88% of cases missed), the OAPR is 1:3.75 if used in a population with an average 10-year CAD risk of 10%, and 1:41 in a population with an average 10-year CAD risk of 1%. For a polygenic score for breast cancer also with a DR5 of 12% and a population average 10-year risk of 1 in 65 (odds =1:64), odds are reduced to 1:91 for a woman with a polygenic score at the 25th centile and increased to 1:53 for a woman with a result at the 75th centile. Analysis using the relevant metrics reveals the weak predictive performance of polygenic risk scores, which limits their effectiveness in screening and disease prevention.
LABELLING HUMAN KINEMATICS DATA USING CLASSIFICATION MODELS

The goal of this study is to develop a classification model that can accurately and efficiently label human kinematics data. Kinematics data provides information about the movement of individuals by placing sensors on the human body and tracking their velocity, acceleration and position in three dimensions. These data points are available in C3D format that contains numerical data transformed from 3D data captured from the sensors. The data points can be used to analyse movements of injured patients or patients with physical disorders. To get an accurate view of the movements, the datasets generated by the sensors need to be properly labelled. Due to inconsistencies in the data capture process, there are instances where the markers have missing data or missing labels. The missing labels are a hindrance in motion analysis as it introduces noise and produces incomplete datapoints of sensor positioning in 3 dimensional space. Labelling the data manually introduces substantial effort in the analysis process. In this paper, we will describe approaches to pre-process the kinematics data from its raw format and label the data points with missing markers using classification models.
THE METHOD OF SPLATTING THE FILTER-BASED WEIGHTED AVERAGE

Annotation. Modern laser and optical scanners require scene rendering techniques that process millions of points with high-resolution textures. This paper describes a method for rendering and filtering textures based on the weighted average filter (EWA) and splatting. The method provides high quality anisotropic texture filtering, removal of hidden surfaces, antialiasing and transparency.
The Diversity and Classification of Tea Germplasm Based on Morpho-Agronomic Characteristics

The information on diversity needs to be disclosed as a basis for consideration in the use of genetic resources. The objective of this study is to reveal the diversity and the classification of tea germplasm based on morpho-agronomic characteristics. The experiment was carried out from June to December 2018 in Gunung Putri Experimental Garden, Cianjur, West Java, Indonesia. The materials of the study used were 13 first generation tea clones. The research used was the observation method.s. The level of kinship among the clones was calculated using cluster analysis with the PBSTAT program. The results of observations showed that there was a diversity of leaf margin characteristics, leaf shape, mature leaf color, and immature leaf color. The high diversity was seen in pecco+2 weight, pecco+3 weight, and petiole length. The highest weight of pecco+3 was found in clone Cin 1. The coefficient of dissimilarity of 0.34 was divided into 2 main groups. Group I consisted of the clone Cin 1 and the clone Cin 28. The second group consisted of 11 tea clones. The clone Cin 1 and the clone Cin 28 and the clone Mal 17 are the tea clones with the smallest similarity values.
Characterization and Modeling of SiC MOSFET Modules for High-voltage Applications

This paper presents a detailed characterization and modeling of 6.5kv SiC MOSFET half-bridge module. Based on the equivalent circuit topology, the model parameters are extracted from the data manual and test data, and the behavior model is constructed to describe the static and dynamic characteristics of the device. This paper used behavioral model functions, and carries out parameter extraction and model fitting for new devices. The model is verified by double pulse test under inductive load. The verification results show that the modeling work is correct and effective, it can predict the switching performance of devices in high voltage applications.
Retraction: Comparative analysis of CNN and Viola-Jones for face mask detection (J. Phys.: Conf. Ser. 1916 012043)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv:2107.06751v1 Retraction published: 23 February 2022
Research on the Semantic Composite Network Recognition Method of Power Distribution DTU Automation Acceptance Virtual Seat System

Aiming at semantic inaccurate recognition problem caused by the lack of professional vocabulary in the existing corpus, a composite network recognition method for the semantics of the power distribution DTU acceptance virtual agent system is proposed. This method is used to extract and merge the entity, attribute and relationship information of power equipment from multiple data sources to construct a power distribution DTU acceptance knowledge graph. In addition, the graph database is the main body and the SQL database is the extension, and the corpus is stored in a distributed manner. The acceptance test results show that the overall recognition rate of the system platform for the instructions issued by the acceptance personnel in the complex environment is 92.8%, which reduces the acceptance time by more than 70%.
The Role of Learning Styles in the Uptake of Index Insurance: Evidence from Kenya

Index insurance has a huge potential to increase the income of households in developing countries by shielding them against various shocks and by facilitating technology adoption. Despite this theoretical promise, however, the uptake of insurance has turned out to be disappointingly low. One of the key barriers in the adoption of this financial technology has been learning difficulty. Index insurance can be considered a relatively complex product especially in the context of low financial literacy in developing countries. Despite the relevance of learning in the diffusion of the product, however, previous studies on the subject have been quite limited. This study attempts to address this gap by testing the effect of two learning methods, a 9min video and a computer-simulated insurance game. The study relies on experimental data from Samburu County of Kenya on the adoption of IBLI (IndexBased Index Livestock Insurance) that insures against drought for pastoral households. The two training methods, or treatments, are tested on 1743 households. The main finding of the study is that both types of interventions could enhance the uptake of index insurance. The insurance game increases the uptake of index insurance provided that it is framed as insurance for household rather than for livestock. The effect is a 3-4 percentage point (pp) increase in uptake and only appears for sales windows right after the treatment. The video intervention has opposite effects depending on the gender of the treated household head. For maleheaded households, the effect is a 2-3 pp increase in uptake while for females, it is about a 6 pp decrease in uptake. In general, the results suggest that the insurance game has a stronger and consistent effect on the uptake than index insurance.
ALLIE: Active Learning on Large-scale Imbalanced Graphs

Human labeling is time-consuming and costly. This problem is further exacerbated in extremely imbalanced class label scenarios, such as detecting fraudsters in online websites. Active learning selects the most relevant example for human labelers to improve the model performance at a lower cost. However, existing methods for active learning for graph data often assumes that both data and label distributions are balanced. These assumptions fail in extreme rare-class classification scenarios, such as classifying abusive reviews in an e-commerce website. We propose a novel framework ALLIE to address this challenge of active learning in large-scale imbalanced graph data. In our approach, we efficiently sample from both majority and minority classes using a reinforcement learning agent with imbalance-aware reward function. We employ focal loss in the node classification model in order to focus more on rare class and improve the accuracy of the downstream model. Finally, we use a graph coarsening strategy to reduce the search space of the reinforcement learning agent. We conduct extensive experiments on benchmark graph datasets and real-world e-commerce datasets. ALLIE out-performs state-of-the-art graph-based active learning methods significantly, with up to 10% improvement of F1 score for the positive class. We also validate ALLIE on a proprietary e-commerce graph data by tasking it to detect abuse. Our coarsening strategy reduces the computational time by up to 38% in both proprietary and public datasets.
A comparative study of predictive models for pharmaceutical sales data

—To provide their patients with the care they need as quickly as possible, pharmacies are supplied by wholesaler-distributors who provide them with a half-day delivery guarantee for most product references. For this purpose, they have set up an efficient and complex supply chain. To further improve the efficiency of their delivery services, some of them want to use machine learning tools to predict future orders and anticipate their inventory needs. This paper investigates different machine learning models for the prediction of sales on molecules of a French wholesaler-distributor. This paper focuses on four molecules and compares the results of the models predictions on these molecules.
Retraction Retraction: 5G Antipodal Reconfigurable Base Station

This paper presents antipodal dipole antenna which is also a base station antenna which is highly suitable for tracking an automobile especially cars/trucks. This antenna contains 4 planes, including the reflector plane, brick on which two poles are present. The Gain of the antenna is around 6.9 to 7.1 dB based on the width of the dipole of the antenna. The directivity remains the same in all width of the dipoles and the S11 parameters values changes as of the width of the dipole. Comparatively the antenna has a good gain and S11 values in frequency 6.5 GHz at the dipole width of 4mm. This antenna finds its way in medical fields to improve the communication inside the automotive vehicles. The proposed antenna is a reconfigurable antenna, which has good gain at multiple frequencies. The reconfigurable is made possible using Metamaterials. The Antipodal structure of the antenna is due to presence of multiple planar types in the antipodal structure of the antenna. Even if the frequency not properly received by the antenna, the reflector plane reflects the frequency towards the one plane at the back of the brick, so the gain is maintained at a desired level.
Corpus Creation and Baseline System for Aggression Detection in Telugu-English Code-Mixed Social Media Data

The Internet has emerged as a powerful tool for people across the globe to communicate freely and to convey their feelings, opinions, and views. A massive surge is observed in the number of people voicing their opinions on social media sites like Facebook, Twitter and other blogging sites over the past few years. This led to a tremendous increase in web content and simultaneously an increase in the rate of misuse of these platforms by various individuals, communities, and organizations is observed. Directly and indirectly, aggressive tweets are being posted against these stakeholders. The task of automatically detecting aggression became signiﬁcantly important for various bodies. This paper attempts to describe an open source corpus of Telugu-English code-mixed tweets annotated with Overtly Aggressive, Covertly Aggressive and Non-Aggressive tags along with the language tags. This work is ﬁrst of its kind to give corpus and to describe experiments to provide a baseline classiﬁcation system which identiﬁes aggressive tweets in Telugu-English code-mixed data with an F1-score of 0.67.
―Fog computing for securing data on Cloud–A Review‖

— Cloud computing is using as a delivery platform which is a promising way for storing user data and provides a secure access to personal and business information. The users are provided with on-demand services through the Internet. But there is a risk of the involvement of a third party which makes it difficult to trust that user data is secure enough and will not be misused. To deal with such malicious intruders there are some technology which are used to secure user data called ―Fog computing‖. It is gaining attention of the cloud users nowadays. This paper review the work that has been done using this technology and discussed the paradigm for preventing misuse of user data and securing information.
Investigation of China's nanotechnology study based on frequency analysis of key words

Based on two databases, Tsinghua Tongfang's CJFD (title) and China Patent Information Database, this paper has shown the distributions of China's nanotechnology papers and patents by topic, which draw the outline of the nanotechnology field where Chinese scientists have been exploring over the past decade. The hot topics and weaknesses of Chinese nanotechnology study are analyzed. The method we used is frequency analysis of keywo rds. The definition of nanotechnology research in this study is derived from 79 keyw ords provided by the National Research Council of Canada.