Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Year-Olds’ Eye Movements Reflect Confidence in Their Understanding of Words

We studied the fundamental issue of whether children evaluate the reliability of their language interpretation, that is, their confidence in understanding words. In two experiments, 2-year-olds (Experiment 1: N = 50; Experiment 2: N = 60) saw two objects and heard one of them being named; both objects were then hidden behind screens and children were asked to look toward the named object, which was eventually revealed. When children knew the label used, they showed increased postdecision persistence after a correct compared with an incorrect anticipatory look, a marker of confidence in word comprehension (Experiment 1). When interacting with an unreliable speaker, children showed accurate word comprehension but reduced confidence in the accuracy of their own choice, indicating that children’s confidence estimates are influenced by social information (Experiment 2). Thus, by the age of 2 years, children can estimate their confidence during language comprehension, long before they can talk about their linguistic skills.
Identifying risk factors for mortality in patients admitted to hospital with COVID-19.

This article is an evidence commentary based on the study ‘Predictors of mortality in hospitalised COVID-19 patients: a systematic review and meta-analysis’ by Tian et al, published in volume 92 of the Journal of Medical Virology in 2020. This commentary critically appraises the methods used within this systematic review and meta-analysis and discusses the findings in the context of clinical practice.
A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks

The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN)-based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.
Antimicrobial resistance patterns, virulence gene profiles, and genetic diversity of Salmonella enterica serotype Enteritidis isolated from patients with gastroenteritis in various Iranian cities

Objective(s): This study aimed to evaluate antibiotic resistance profiles and presence of virulence genes among Salmonella enterica serovar Enteritidis (S. Enteritidis) isolated from patients with gastroenteritis in various regions of Iran. Moreover, genetic relatedness among the strains was assessed by pulsed-field gel electrophoresis (PFGE). Materials and Methods: From April through September 2017, 59 Salmonella strains were isolated from 2116 stool samples. Of these strains, 27 S. Enteritidis were recovered. These strains were subjected to disk diffusion tests, polymerase chain reaction (PCR) for detection of virulence genes (invA, hilA, pefA, rck, stn, ssrA, ssaR, sefA, spvC, sipA, sipC, sopB, sopE, and sopE2), and PFGE. Results: High prevalence of resistance towards cefuroxime (n = 20, 74.1%) and ciprofloxacin (n = 13, 48.2%) were demonstrated. All tested strains possessed invA, hilA, sefA, sipA, sopB, and sopE. The least prevalent virulence gene was rck (n = 6; 22.2%). Based on combinations of virulence genes, 12 virulotypes were observed. The most common virulotype was VP2 (n = 12; 44.4%), harboring all of the virulence genes except for rck. PFGE typing showed only two distinct fingerprints among tested strains. Each fingerprint had completely different virulotypes. Notably, VP4 (harboring all genes except for rck and spvC) was only presented in pulsotype A, while VP2 was confined to pulsotype B. Conclusion: S. Enteritidis strains were derived from a limited number of clones, suggesting that it is highly homogenous. Future works should consider combinations of other genotyping methods together with larger sample sizes from more diverse sources.
Surface mass spectrometry as a new approach for the characterisation of coffee

Coffee is the most widely traded tropical agricultural commodity in the world with global production for 2016 of over 151 million 60‐kg bags, according to the International Coffee Organization. Such popularity and its chemical complexity demand the development of analytical chemistry methodologies for the characterisation of different coffee compounds. This paper proposes the use of time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) for this purpose. The main steps involving sample preparation and spectra acquisition are described alongside initial multivariate analysis results.
Application of modified PROMETHEE method for coating quality enhancement in thin film deposition processes

ABSTRACT Thin film coatings are extensively used to enhance the physical and chemical properties of various materials by depositing a thin layer of coatings of different materials onto various substrates. Several deposition processes are available which are widely adopted by many manufacturing industries to meet such demands. To exploit the fullest potential of those deposition processes, it is often advised to operate them with the optimal combination of process parameters. In this research, a modified PROMETHEE (preference ranking organisation method for enrichment evaluation) method as an effective multi-objective optimisation tool is applied for identifying the optimal parametric combinations of two thin film deposition processes: silicon carbonitride thin film coating using a thermal chemical vapour deposition process and zirconium-containing diamond-like-carbon thin film coating using an unbalanced magnetron sputtering process. The derived optimal parametric combinations are validated based on developed regression equations, which exhibit that the proposed approach can attain better response values as compared to other popular optimisation techniques. The analysis of variance results and the developed surface plots further illustrate the influence of various deposition parameters on coating quality.
The Development of Data Journalism in China: Influences, Motivations and Practice

Abstract Using semi-structured interviews with Chinese data journalists across party and commercial media, this article assesses the structure and practice of data journalism in China. In doing this, it responds to calls for further studies of data journalism in non-western contexts. It finds that Chinese data journalists face some of the same pressures and challenges that have been documented in other countries, including limited access to data and the constraints imposed by the screen-size of smartphones. However, these were often exacerbated through a combination of social and systemic factors - to the point that their impact is qualitatively and quantitatively different. Simultaneously, however, we find that in some cases Chinese data journalists, at least amongst party media, were protected from pressures such as audience demand, and encouraged to focus on state-of-the-art work. We conclude that what has emerged is a form of ‘data journalism with Chinese characteristics’, and that these characteristics emerge from the interactions between systemic, newsroom and social factors.
Efficient Data Structures

data type, see interface adjacency list, 145 adjacency matrix, 142 algorithmic complexity attack, 140 amortized cost, 17 amortized running time, 16 ancestor, 73 array circular, 31 ArrayDeque, 33 ArrayQueue, 30 arrays, 23 ArrayStack, 24 asymptotic notation, 9 backing array, 23 Bag, 21 BDeque, 60 Bibliography on Hashing, 136 big-Oh notation, 9 binary heap, 89 binary logarithm, 8 binary search tree, 78 size-balanced, 86 binary search tree property, 78 binary tree, 73 complete, 93 heap-ordered, 90 search, 78 binary-tree traversal, 75 BinaryHeap, 89 BinarySearchTree, 78 BinaryTree, 74 binomial coefficients, 9 binomial heap, 99 bounded deque, 60 breadth-first traversal, 77 breadth-first-search, 148 celebrity, see universal sink ChainedHashTable, 119 chaining, 119 child, 73 left, 73 right, 73 circular array, 31 coin toss, 14 collision resolution, 137 compare(x,y), 7 comparison tree, 110 comparison-based sorting, 101 complete binary tree, 93 complexity space, 16 time, 16 conflict graph, 141 connected components, 153 connected graph, 153 contact list, 1 correctness, 16 counting-sort, 112 CubishArrayStack, 51 cuckoo hashing, 137 cycle, 141
The yield of neuroimaging in patients presenting to the emergency department with isolated neuro-ophthalmological complaints: A retrospective chart review

Neuro-ophthalmological emergencies require prompt assessment and management to avoid vision or life-threatening sequelae. The decision to perform a neuroimaging procedure is based on the clinical judgment of the medical team, without defined indications. This study aims to identify presenting symptoms and physical exam findings associated with relative positive findings on neuroimaging studies. Electronic medical records of patients presenting to the emergency department (ED) with isolated neuro-ophthalmologic complaints between January 1, 2013 and September 30, 2019 were reviewed. We collected data on the clinical presentation, neuroimaging procedures and results, consults, and diagnoses. Two hundred eleven patients’ charts were reviewed. Most presented with unilateral eye complaints (53.6%), and the most common symptoms were blurred vision (77.3%) and headaches (42.2%). A total of 126 imaging procedures were performed of which 74.6% were normal, while 25.4% showed relevant abnormal findings. Complaining of blurry vision (P = .038) or visual field changes (P = .014) at presentation as well as having a visual field defect (P = .016), abnormal pupil reactivity (P = .028), afferent pupillary defect (P = .018), or abnormal optic disc exam (P = .009) were associated with positive findings on imaging. Neuroimaging is more likely to yield positive findings in patients presenting to the ED with visual field irregularities, afferent pupillary defects, or abnormal optic discs. These findings – when combined with the proper clinical setting – should lower the threshold to proceed with neuroimaging in the emergency department. Based on our results, larger-scale studies might lead to a well-structured algorithm to be followed by ED physicians in decision making.
ANTIOXIDANT ACTIVITY DETERMINATION Garcinia dulcis (Roxb.) Kurz, Blumeamollis (D.Don) Merr., Siegesbeckia orientalis L., AND Salvia riparia H.B.K WHICH COLLECTED FROM TAMAN NASIONAL GUNUNG MERAPI USING DPPH (2,2-diphenyl-1-PIKRIL -HIDRAZIL) AND THIN LAYER

Penelitian ini bertujuan untuk menentukan aktivitas antioksidan ekstrak wasbenzen dan metanolik empat jenis tumbuhan, yaitu Garcinia dulcis (Roxb.)Kurz, Blumea mollis (D.Don) Merr. , Siegesbeckia orientalis L. ,dan Salvia riparia H.B. K hasil koleksi dari TNGM berdasarkan metode penangkapan radikal bebas dengan DPPH dan mengetahui profil kromatografi lapis tipisnya. Ekstrak dibuat dengan proses maserasi bertingkat dengan penyari wasbenzen dan metanol, uji aktivitas antioksidan dengan metode DPPH serta analisis kualitatif dengan kromatografi lapis tipis (KLT) untuk tumbuhan dengan aktivitas antioksidan tertinggi. Hasil penelitian menunjukkan bahwa diantara keempat ekstrak metanolik, ekstrak Garcinia dulcis (Roxb.) Kurz mempunyai aktivitas antioksidan tertinggi dengan nilai IC 50 38,613µg/mL. Untuk ekstrak wasbenzen, ekstrak Garcinia dulcis (Roxb.) Kurz juga mempunyai aktivitas tertinggi dengan nilai IC 50 139,381µg/mL. Analisis kualitatif ekstrak metanolik Garcinia dulcis (Roxb.) Kurz dengan kromatografi lapis tipis (KLT) menunjukkan bahwa ekstrak metanolik Garcinia dulcis (Roxb.) Kurz mengandung senyawa golongan fenol dan flavonoid.
Fluorescence quenching and molecular docking study on the binding of four hydroxyanthraquinones to FTO

ABSTRACT In this work, the binding of four hydroxyanthraquinones (HAQs) to fat mass and obesity-associated (FTO) protein has been studied using fluorescence, UV-vis absorption and circular dichroism spectroscopy as well as molecular docking analysis. Analysis of fluorescence data showed that the binding of HAQs to FTO occurred via a static quenching mechanism. Thermodynamic analysis and molecular docking results suggested that hydrophobic force played a major role in stabilising the HAQ-FTO complex. Circular dichroism spectra indicated that the secondary structure of FTO was changed by the addition of HAQs. Results also showed that emodin had the strongest quenching and binding ability among four HAQs.
Physical Activity and Body-Mass-Index: Do Family, Friends and Teachers Restrain the Risk for Physical Inactivity in Adolescents?

Background: The present study investigates the role of different sources of social support in the relationship of BMI and PA in an international sample of children and adolescents. Methods: Data included 170,211 adolescents (51.5% female), aged 10–16 (M = 13.6, SD = 1.64) from 37 European countries/regions and were retrieved from the 2013/14 Health Behaviour in School-aged Children (HBSC) international database. Results: PA levels in adolescents are lower for girls, decrease with age, and are lower for overweight and obese adolescents compared to underweight and normal-weight adolescents. Peer support and teacher support significantly predict PA (ORpeer = 1.11; ORteacher = 0.97) and vigorous PA frequency (ORpeer = 1.13; ORteacher = 0.94). Family support only affected vigorous PA frequency (OR = 1.03). Family support had a moderating effect on PA in overweight (ORfamily*BMI(overweight) = 0.94) and obese (ORfamily*BMI(obese) = 0.90) adolescents. Peer support had a moderating effect on vigorous PA frequency in obese (ORpeer*BMI(obese) = 1.08) adolescents. Teacher support had a moderating effect on PA (ORteacher*BMI(normal) = 1.05; ORteacher*BMI(overweight) = 1.09) and vigorous PA frequency (ORteacher*BMI(normal) = 1.07; ORteacher*BMI(overweight) = 1.08) in normal-weight and overweight adolescents. Conclusion: Social support helps adolescents to be active. Especially for obese adolescents, support by and relations with peers are important to reach a significant amount of PA.
DC-DC Zeta Power Converter: Ramp Compensation Control Design and Stability Analysis

The design of robust and reliable power converters is fundamental in the incorporation of novel power systems. In this paper, we perform a detailed theoretical analysis of a synchronous ZETA converter controlled via peak-current with ramp compensation. The controller is designed to guarantee a stable Period 1 orbit with low steady state error at different values of input and reference voltages. The stability of the desired Period 1 orbit of the converter is studied in terms of the Floquet multipliers of the solution. We show that the control strategy is stable over a wide range of parameters, and it only loses stability: (i) when extreme values of the duty cycle are required; and (ii) when input and reference voltages are comparable but small. We also show by means of bifurcation diagrams and Lyapunov exponents that the Period 1 orbit loses stability through a period doubling mechanism and transits to chaos when the duty cycle saturates. We finally present numerical experiments to show that the ramp compensation control is robust to a large set of perturbations.
Community-Based Survey and Oral Archive Infrastructure in the Archivio Vi.Vo. Project

Audio and audiovisual archives are at the crossroads of different fields of knowledge, yet they require common solutions for both their long-term preservation and their description, availability, use and reuse. Archivio Vi.Vo. is an Italian project financed by the Region of Tuscany, aiming to: (i) explore methods for long-term preservation and secure access to oral sources, and (ii) develop an infrastructure under the CLARIN-IT umbrella offering several services for scholars from different domains interested in oral sources. This paper describes the project’s infrastructure and its methodology through a case study on Caterina Bueno’s audio archive.
Exploring emotions on wine websites: finding joy

ABSTRACT There is little research on the emotions expressed by wine estate websites and whether the emotions conveyed are related to or can significantly predict the positive or negative sentiment articulated by these websites. In order to examine the emotions and sentiment of wine estate websites, content from the websites of 10 wine estates from 5 countries well known for their diverse and high-quality wines (South Africa, Australia, New Zealand, France, the USA) were selected and analyzed using IBM’s Watson. IBM’s Watson assessed the emotions and sentiment expressed on each website, and an output was then used in a multiple regression analysis with emotions predicting sentiment. The findings show that emotions are not related significantly to the overall sentiment on the winery websites. Finally, this research shows how wine estates and wine marketers can test whether the content of their websites mirrors the emotions that they desire to express to consumers and visitors, which can assure that appropriate expectations are set for the consumption experience.
Using Evacuation Drills to Improve Tsunami Evacuation Preparedness and Resilience

This paper presents the use of tsunami evacuation drills within a coastal community in the Cascadia Subduction Zone (CSZ) to better understand evacuation behaviors and thus to improve tsunami evacuation preparedness and resilience. Evacuees' spatial trajectory data were collected by Global Navigation Satellite System (GNSS) embedded mobile devices. Based on the empirical trajectory data, probability functions were employed to model people's walking speed during the evacuation drills. An Evacuation Hiking Function (EHF) was established to depict the speed-slope relationship and to inform evacuation modeling and planning. The regression analysis showed that evacuees' speed was significantly negatively associated with slope, time spent during evacuation, rough terrain surface, walking at night, and distance to destination. We also demonstrated the impacts of milling time on mortality rate based on participants' empirical evacuation behavior and a state-of-the-art CSZ tsunami inundation model. Post-drill surveys revealed the importance of the drill as an educational and assessment tool. The results of this study can be used for public education, evacuation plan assessment, and evacuation simulation models. The drill procedures, organization, and the use of technology in data collection provide evidence-driven solutions to tsunami preparedness and inspire the use of drills in other types of natural disasters such as wildfires, hurricanes, volcanoes, and flooding.
Characteristics of Energy Dissipation Rate Observed from the High-Frequency Sonic Anemometer at Boseong, South Korea

The characteristics of low-level turbulence at Boseong, located on the southern coast of South Korea, were investigated in terms of eddy dissipation rate (EDR) using 1-year (2018) of wind data obtained from the Boseong Meteorological Observatory (BMO), a World Meteorological Organization testbed. At BMO, a 307 m tall tower is installed on which four high-frequency (20 Hz) sonic anemometers are mounted at 60, 140, and 300 m above ground level (AGL). In addition, a sonic anemometer at 2.5 m AGL is located to the south of the tower. EDRs are estimated from the wind measurements based on three different EDR estimation methods. The first two methods use the inertial dissipation method derived from Kolmogorov turbulence theory, and the third uses a maximum likelihood estimation assuming a von Kármán spectral model. Reasonable agreement was obtained between the three methods with various fluctuations, including diurnal variations for all seasons, while the EDR calculated from the third method displayed slightly higher EDR values than the other two methods. The result of the analysis showed that the mean (standard deviations) of logarithms of EDR had larger values as height decreased (increased), and the means were higher in the unstable planetary boundary layer (PBL) than in the stable PBL for this heterogeneous location adjacent to the coastlines. The probability density functions (PDFs) of the EDRs showed that the distribution was well-represented by a lognormal distribution in both the stable and unstable PBL, although the PDFs at the lowest level (2.5 m) deviated from those at other levels due to surface effects. Seasonal variations in the PDFs showed that there was less difference in the shape of the PDFs depending on atmospheric stability in the wintertime. Finally, we calculate the 1-yr statistics of the observed EDR, which will be used for future LLT forecast systems in Korea.
Critical Review of Deep Learning Algorithms for Plant Diseases by Leaf Recognition

The identification and classification of the crop leaf diseases plays an essential role in the cultivation. Plants are the livelihood. Peoples depend entirely on crops for the breathing of their daily lives. Thus, suitable crop caring should take place. Most research suggests that the quality of agricultural commodities can be restricted depending on different factors. Crop diseases include microorganisms and pathogens. The leaf diseases not only reduce crop growth, the cultivation is also destroyed. Several researchers have been identified crop leaf diseases using image processing algorithms but it take more time for detection. Therefore, advanced algorithms are required to identify and classify the crop leaf diseases automatically with higher accuracy. There are different deep learning algorithms using crop leaf images developed for automatically detecting the crop leaf diseases in an efficient manner. In this article, a survey on different deep learning algorithms using image processing for detecting and classifying the crop or plant leaf diseases is presented. Also, the merits and demerits of the surveyed algorithms for crop leaves diseases identification are addressed in a tabular form. Finally, a comprehensive analysis is concluded and future directions are suggested to increase the accuracy of leaf diseases classification