Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Carbon Black/Polypyrrole Composite Hollow Nanospheres and Performance Evaluation as Electrode Materials for Supercapacitors

Well-defined carbon black/polypyrrole (CB/PPy) composite hollow nanospheres were prepared via in situ chemical oxidative interfacial polymerization of pyrrole in the presence of sulfonic acid-modified carbon black (CB-SO3H) nanoparticles with inert solvent (toluene) as the soft template, in which sodium dodecyl benzenesulfonate (SDBS) was used as both the surfactant for the emulsion and the dopant for the produced polypyrrole (PPy), as well as the CB-SO3H nanoparticles. The effects of the preparation conditions, such as the oil/water ratio and feeding ratio of the CB-SO3H nanoparticles on the morphology, electrical conductivity, and electrochemical properties of the final CB/PPy hollow nanospheres, were investigated. It was found that the CB-SO3H nanoparticles exhibited an obvious influence on the particle size, electrical conductivity, specific capacitance, and cycling stability of the CB/PPy hollow nanospheres. The highest electrical conductivity and specific capacitance of 0.045 S/cm and 29 F/g were ac.
Quarterly Earnings, Operating Cash Flow, and Accruals in Future Performance Assessment

This paper analyzes the informative content of quarterly earnings, operating cash flows, and accruals to assess future performance. The empirical analysis is based on 270 Brazilian non-financial firms listed on B3 from 2005 to 2018. Autoregressive models and variance decomposition were performed. Overall, results show that accounting earnings and accruals have incremental information content over operating cash flows in accessing future financial performance. Firstly, earnings have higher persistence parameters and capture a longer-term perspective showing that earnings, under the accrual regime, are more predictable than cash flows when using their past information. Secondly, the magnitude of accruals positively affects the persistence of earnings. Thirdly, as a component of earnings, operating cash flows are more persistent than the accruals component. Fourthly, high accrual levels are associated with lower future earnings. Finally, earnings have incremental informative power to explain future operating cash flows. This study contributes to the literature by providing evidence of the information content of quarterly performance measures with specific controls for accounting accruals processes, which is useful for financial performance forecasts in markets with low analyst coverage and restricted enforcement mechanisms.
Discrete self-organizing migration algorithm and p-location problems

Mathematical modelling, and integer programming generally, has many practical applications in different areas of human life. Effective and fast solving approaches for various optimization problems play an important role in the decision-making process and therefore, big attention is paid to the development of many exact and approximate algorithms. This paper deals only with a special class of location problems in which given number of facilities are to be chosen to minimize the objective function value. Since the exact methods are not suitable for their unpredictable computational time or memory demands, we focus here on possible usage of a special type of a particle swarm optimization algorithm transformed by discretization and meme usage into so-called discrete self-organizing migrating algorithm. In the paper, there is confirmed that it is possible to suggest a sophisticated heuristic for zero-one programming problem, which can produce near-to-optimal solution in much smaller time than the time demanded by exact methods. We introduce a special adaptation of the discrete self-organizing migration algorithm to the $p$-location problem making use of the path-relinking method. In the theoretical part of this paper, we introduce several strategies of the migration process. To verify their features and effectiveness, a computational study with real-sized benchmarks was performed. The main goal of the experiments was to find the most efficient version of the suggested solving tool.
Contextualizing motivations and perceived barriers of healthy nutrition and lifestyle behaviours among Malaysian adults with metabolic syndrome

Background Nutrition and lifestyle behaviour of individuals have been associated with the risk of metabolic syndrome (MetS). In order to better control the prevalence of MetS nationwide, an effective health promotion program must provide age-appropriate care information that addresses critical factors such as physiological function, social psychology, and emotional and health literacy in order to empower these patients to self-manage their condition and to enhance their self-care-related motivation and confidence. This study qualitatively analysed FGD involving Malaysian adults with MetS to explore their understanding of MetS and their perceived motivation and barriers of healthy nutrition and lifestyle behaviour. Methods A qualitative study with four focus groups (21 purposively sampled respondents) was conducted with adults diagnosed with MetS whom attending MONASH Medical Precinct in 2018. An interview protocol consolidating both responsive interviewing model and health belief model were prepared. The focus groups were audio recorded and transcribed. The data were analysed by emergent themes analysis. Data saturation was achieved in the fourth focus group. Results Seven main themes; three motivations and three perceived barriers and one subtheme on healthy nutrition and lifestyle behaviour were identified in the analysis of FGD. Motivations for healthy nutrition and lifestyles behaviour were (i) weight gain and physical appearances; (ii)personal experience of adverse complications and (iii) good family and social support. The identified perceived barriers were (i) healthcare as a business model; (ii) healthy change is difficult and expensive and (iii) cultural influence on food intake. Inadequate knowledge on MetS as most respondents were unaware of MetS as a clustering of risk factors but were able to identify the components individually was also a strong theme identified in this study. Conclusion Nutrition and lifestyle behaviours of adults with MetS were highly affected by the several potent motivations and perceived barriers among individuals. Information delivery and lifestyle promotion should address these aspects to increase program adoption and adherence, ensuring success of a community-based lifestyle intervention.
Business Climate in the Eye of the Employer

: What constitutes a “good business climate” is often couched in monolithic and diametrically opposed terms of low taxes versus high public services. However, there is likely considerable heterogeneity across ﬁrms in their preferences for the trade-oﬀ between higher taxes and the public services they provide. Using a novel primary data set of ﬁrm expansion and relocation decisions, this analysis investigates how ﬁrms in relatively high-paying sectors express their preferences for a variety of local “business climate” attributes relative to ﬁrms in lower-paying sectors. The ﬁndings show evidence that ﬁrms in low-wage sectors view a “good business climate” diﬀerently than ﬁrms in high-wage sectors.
Macroeconomic Determinants of Households' Indebtedness in Portugal: What Really Matters in the Era of Financialisation?

The objective of this paper is to perform a time series econometric analysis in order to empirically assess the macroeconomic determinants and the corresponding drivers of the Portuguese households’ indebtedness in the period 1988 to 2016. During that period, the Portuguese economy experienced a process of financialisation that contributed to an increase in Portuguese households’ indebtedness to unprecedented levels. The Portuguese households’ indebtedness played a crucial role in the recent sovereign debt crisis. Based on the existing literature, we hypothesize that Portuguese households’ indebtedness was due to seven macroeconomic determinants, notably housing prices, financial asset prices, the degree of personal income inequality, households’ labour income, the importance of welfare state expenditures, the fraction of the working-age population and the level of interest rates. Our findings reveal that financial asset prices, the degree of personal income inequality, households’ labour income and the fraction of the working-age population positively impact Portuguese households’ indebtedness, whereas the housing prices negatively impact Portuguese households’ indebtedness. Our findings also show that the increase in financial asset prices and the decline in housing prices were the main drivers of Portuguese households’ indebtedness in the last few decades.
Patterns of alcohol use among first grade high school students in Novi Sad

Introduction. Alcohol drinking habits are usually formed during adolescence. Adolescents most often drink alcohol to fit in with their peers, but genetics and social modeling are also of great importance. Adolescents who consume alcohol are more prone to mental disorders as well as risky and violent behavior. Material and Methods. A cross-sectional study was conducted among first grade high school students in Novi Sad in December 2017. The final sample included 1,067 participants. For the purpose of this study, the European School Survey Project on Alcohol and Other Drugs questionnaire was used. Data were analyzed using the Statistical Package for the Social Sciences program, version 11.0. Results. Beer is the easiest alcoholic beverage to obtain for young people in Novi Sad, followed by wine and spirits. A great number of participants have consumed alcohol at least once in their lifetime and more than half in the last 30 days, (83.5%) and (52.6%) respectively. More than a third of participants had an episode of excessive drinking during the last month. Early onset of drinking was found among one half of male and a third of female participants. Conclusion. These results indicate an unfavorable situation when it comes to alcohol consumption among young people in Novi Sad. Therefore, efforts of the society that encourage healthy development of adolescents are important, as well as taking preventive measures in order to reduce the risk of alcohol abuse among young people.
A Structural Property of Charging Scheduling Policy for Shared Electric Vehicles With Wind Power Generation

In this work, we focus on the optimization of charging scheduling policy for shared electric vehicles (EVs) integrated with wind power generation. This problem is of significant importance nowadays because of the large adoption of EVs in modern societies and the increasing penetration of renewables. A particular challenge of the problem is the large action space, the size of which may increase exponentially with respect to the number of EVs in the system. This makes the problem difficult to solve in practice. A lot of efforts have been made to overcome the above difficulty. The previous study proposed least-laxity-longer-processing-time-first (LLLP) principle, a rule-based algorithm to schedule EVs’ battery charging. The LLLP principle assigns higher priority to vehicles with less laxity and longer processing time. We extend the LLLP principle and further study the structural property of the charging scheduling problem. The main contributions in this work are as follows. First, we show that the LLLP applies to our problem and may be used to narrow down the action space while preserving the global optimality. Second, we provide a modified LLLP algorithm that may construct a policy in <inline-formula> <tex-math notation="LaTeX">$O(NT)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of the EVs and <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is the number of time steps in the scheduling problem. Third, we use numerical experiments to show that the new algorithm performs better than other existing algorithms, including the least-laxity-shorter-processing-time-first (LLSP) principle, the earliest-deadline-first (EDF) principle, and the latest-deadline-first (LDF) principle. The new algorithm finds near-optimal policies (within 1% performance loss) and is at least 40 times faster than CPLEX. We hope that this work provides insight into the charging scheduling of shared EVs in general.
Use of deep learning methods for hand fracture detection from plain hand radiographs

ABSTRACT BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients with hand trauma. Here, we aim to develop a computer-aided diagnosis (CAD) method to assist physicians in the diagnosis of hand fractures using deep learning methods. METHODS: In this study, Convolutional Neural Networks (CNN) were used and the transfer learning method was applied. There were 275 fractured wrists, 257 fractured phalanx, and 270 normal hand radiographs in the raw dataset. CNN, a deep learning method, were used in this study. In order to increase the performance of the model, transfer learning was applied with the pre-trained VGG-16, GoogLeNet, and ResNet-50 networks. RESULTS: The accuracy, sensitivity, specificity, and precision results in Group 1 (wrist fracture and normal hand) dataset were 93.3%, 96.8%, 90.3%, and 89.7% , respectively, with VGG-16, were 88.9%, 94.9%, 84.2%, and 82.4%, respectively, with Resnet-50, and were 88.1%, 90.6%, 85.9%, and 85.3%, respectively, with GoogLeNet. The accuracy, sensitivity, specificity, and precision results in Group 2 (phalanx fracture and normal hand) dataset were 84.0%, 84.1%, 83.8%, and 82.8%, respectively, with VGG-16, were 79.4%, 78.5%, 80.3%, and 79.7%, respectively, with Resnet-50, and were 81.7%, 81.3%, 82.1%, and 81.3%, respectively, with GoogLeNet. CONCLUSION: We achieved promising results in this CAD method, which we developed by applying methods such as transfer learning, data augmentation, which are state-of-the-art practices in deep learning applications. This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.
Systolic Aortic Root Motion and Angle as novel indices of Left Ventricular Systolic Function

Background Aortic root motion was used only as a surrogate parameter of global left ventricular systolic function depending on its direct proportion to cardiac output. We hypothesize that aortic root motion angle and aortic root motion amplitude may overcome many limitations of EF calculation by M mode and two dimensional methods and are easier and reproducible.Objective The aim of this study is to asses systolic aortic root motion measured by M mode and aortic root motion angle as novel indices of global left ventricular systolic function.Patients and methods one hundred patients were enrolled in this study and divided into four groups: according to their age (above and below 60 years) and EF (above and below50%). They were subjected to full history taking, careful clinical examination, and conventional echo-Doppler study . Systolic aortic root motion obtained from long axis parasternal view by M-mode echo guided by 2D echo, and aortic root motion angle was traced off line and mathematically measured. Also global logitudinal strain (GLS) and global longitudinal strain rate (GLSR) from apical 4,3 and 2 chamber views were measured offline.Results Statistical analysis of collected data show that there are significant differences between control groups and patient groups in aortic root motion angle (t= 16.9 and p value <0.001, and in aortic root motion amplitude (t= 20.1 and p value <0.001). Aortic root motion (cm) and aortic root motion angle have significant positive correlation with EF(Mm), EF(2D), Fs, global longitudinal strain(GLS) and global Strain rate . The best cutoff value of aortic root motion angle was 19.5 degree, with sensitivity of 93.9%, specificity of 96.1. Aortic root motion angle >19.5 predicts systolic function >50% and that<19.5 predicts systolic function <50% The best cutoff value of aortic root SAM was 8.5 mm. An aortic root SAM of ˂ 8.5 mm predicts an LVEF of ˂ 50% with sensitivity of 95.9%, specificity of 96.1%. Conclusion The amplitude of systolic aortic root motion (SARM) by (M-mode) and aortic root motion angle are well- correlated with the EF and GLS and could be considered as novel indices of global left ventricular systolic function with high accuracy and reproducibility .
Implementation of Low Power DFT for ASIC SOC

In System on Chip (SoC), a low power DFT technique plays a crucial role to reduce power consumption. These low power techniques throw a major challenge for the designers and verification engineers. Different low power DFT techniques like multiple Supply Voltages, Power Switches, clock gating, low power isolation cells and so on are applied at various stages of DFT flow. In this brief, the primary focus is to reduce the dynamic power consumption of a digital circuit in the scan Synthesis phase. Here a common clock is connected to all scan flip-flops with a frequency of 40MHz.Without any timing violations, the clock frequency is reduced to 20MHz thereby ensuring that there is a reduction in dynamic power consumption to a value of 50%.
Challenges and prospects of machine learning in visible light communication

Visible light communication (VLC) is a promising research field in modern wireless communication. VLC has its irreplaceable strength including rich spectrum resources, no electromagnetic disturbance, and high-security guarantee. However, VLC systems suffer from the non-linear effects that exist in almost every part of the system. As a part of artificial intelligence, machine learning (ML) is showing its potential in non-linear mitigating for its natural ability to fit all kinds of transfer functions, which may dramatically push the research in VLC. This paper introduces the application of ML in VLC, describes five recent research of deep learning applications in VLC, and analyses the performance.
Hall scattering factors in p-type 4H-SiC with various doping concentrations

The Hall scattering factor (γH) in p-type 4H-SiC with various aluminum doping concentrations of 5.8 × 1014–7.1 × 1018 cm−3 was investigated from 300 to 900 K. γH was determined by comparing the Hall coefficient with the theoretical carrier concentration derived from acceptor and donor concentrations obtained from secondary ion mass spectrometry and capacitance–voltage measurements. γH decreased with increasing temperature or doping concentration; γH = 1–0.4 for the doping concentration of 5.8 × 1014 cm−3 and γH = 0.5–0.2 for the doping concentration of 7.1 × 1018 cm−3. The dependence might be caused by the anisotropic and nonparabolic valence band structure of 4H-SiC.
Lateral vs. Supine Positioning for Femoral Intramedullary Nailing: A Systematic Review of Comparative Studies

Femoral shaft fractures are devastating injuries that usually result from high-energy mechanisms in victims of poly-trauma. Reamed and statically locked intramedullary nailing (IMN) is the definitive treatment modality for femoral shaft fractures. Patients are most commonly positioned either supine or lateral decubitus. There remains considerable concern regarding the safety of lateral positioning in the traumatized patient, particularly in the management of a potentially difficult airway or concomitant C-spine injuries. We therefore undertook a systematic review of intraoperative positioning among patients with femoral shaft fractures following PRISMA guidelines. Title and abstract screening, full text screening, and data abstraction were all completed in duplicate. Methodological Index for Nonrandomized Studies (MINORS) scores were used to evaluate methodological quality. Results: 3018 studies were included in initial screening, with three studies ultimately meeting all inclusion criteria. A total of 1,949 patients were analyzed, with 684 patients treated in lateral positioning and 1,215 patients in supine positioning. Level of agreement was strong across title (κ = 0.872; 95% CI 0.794 to 0.951), abstract (κ = 0.801; 95% CI 0.585 to 1.000), and full-text screening (κ = 1.000). The consensus mean MINORS score of included studies was 17.67 ± 0.58, indicating good to high quality of evidence. Neither patient positioning offered obvious benefits such as fewer complications or shorter operative time. Furthermore, length of admission, days in ICU or on ventilator, and overall morbidity were not found to be significantly different between positions. Lateral positioning for intramedullary nailing of mid-shaft femur fractures appears to be a safe alternative to the standard supine positioning. There is a lack of both prospective and retrospective comparative studies investigating functional clinical outcomes in the literature.
Machine Learning Approach for COVID-19 Detection on Twitter

Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks This study shows that social media analysis can be used as a crisis detector (e g , understanding the sentiment of social media users regarding various pandemic outbreaks) The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020 Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets) For this purpose, we propose herein an intelligent model using traditional machine learning-based approaches, such as support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with the help of the term frequency inverse document frequency (TF-IDF) to detect the COVID-19 pandemic in Twitter messages The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories, namely, confirmed deaths, recovered, and suspected For the experimental analysis, the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy, precision, recall, and F1 score between 70% and 80% and the confusion matrix for machine learning approaches (i e , SVM, NB, LR, RF, and DT) with the TF-IDF feature extraction technique © This work is licensed under a Creative Commons Attribution 4 0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Aid for Trade and Real Exchange Rate in Recipient-Countries

The current analysis contributes to the literature on the real exchange rate (RER) effect of development aid by separating out the RER effect of the development aid allocated to the trade sector - referred to as Aid for Trade (AfT) - from the effect of NonAfT flows. The empirical findings show that AfT flows exert a RER depreciation in recipient-economies, while NonAfT flows are associated with a RER appreciation. From a policy perspective, these results show that scaling-up AfT flows could promote countries' competitiveness in the international trade market through a greater extent of RER depreciation.