Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent advances in acoustic diagnostics for electrochemical power systems

Over the last decade, acoustic methods, including acoustic emission (AE) and ultrasonic testing (UT), have been increasingly deployed for process diagnostics and health monitoring of electrochemical power devices, including batteries, fuel cells, and water electrolysers. These techniques are non-invasive, highly sensitive, and low-cost, providing a high level of spatial and temporal resolution and practicality. Their application in electrochemical devices is based on identifying changes in acoustic signals emitted from or propagated through materials as a result of physical, structural, and electrochemical changes within the material. These changes in acoustic signals are then correlated to critical processes and the health status of these devices. This review summarises progress in the use of acoustic methods for the process and health monitoring of major electrochemical energy conversion and storage devices. First, the fundamental principles of AE and UT are introduced, and then the application of these acoustic techniques to electrochemical power devices are discussed. Conclusions and perspectives on some of the key challenges and potential commercial and academic applications of the devices are highlighted. It is expected that, with further developments, acoustic techniques will form a key part of the suite of diagnostic techniques routinely used to monitor electrochemical devices across various processes, including fabrication, post-mortem examination and recycle decision support to aid the deployment of these devices in increasingly demanding applications.
Photo Protective Role of Wild Edible Plants on Skin of Mice from Harmful Effects of Ultraviolet Type-B Irradiation

Aim : The aim of this study was to determine the protective antioxidant capacity of edible wild plants in the decreasing effect of ultraviolet B (UVB) on the skin of albino mice. Study Design: Thirty skin biopsies were taken from the mice to detect and compare the hyperplastic effects between the groups. Place and Duration of Study: Animal House of College of Veterinary Medicine, Sulaimani University, Histopathology Lab of Consultative Hospital/Sulaimani Governorate, Anatomy and Histopathology Department, College of Veterinary Medicine, Sulaimani University. Original Research Article Hassan et al.; BJAST, 15(4): 1-10, 2016; Article no.BJAST. 23989 2 Methodology: Thirty mice (Mus musculus species, BALB/c strain) underwent this study and were divided into six groups from A-F, according to the UVB exposure, species of plant, and routes of administration. Mice from all groups (Exposure and plant administration except control group) were subjected to UVB irradiation four days/week (20 minutes/day, four weeks), and treated with edible wild plants (Gundelia tournefortii and Malva sylvestris) by different routes. Results: According to the Student’s t-test (Paired) and Pearson’s correlation coefficients we found that the effect of Gundelia tournefortii and Malva sylvestris were highly significant in reducing the hyperplastic effect of UVB irradiation. Conclusion: This study gives an overview of traditional uses of Gundelia tournefortii and Malva sylvestris against the adverse effects of UVB radiation.
Effects of hydroalcoholic extract of Iranian Propolis on blood serum biochemical factors in streptozotocin-induced diabetic rats

Methods: This study was performed on 40 male wistar rats that ther were divided into 5 groups: control, diabetic, diabetic vehicle, and two groups of Propolis-treated diabetic. Diabetes was induced in the rats by single dose injection of streptozotocin (STZ, i.p., 60 mg/kg bwt). The hydro-alcoholic extracts of Propolis was administered gavages at doses of 100 and 200 mg/kg after diabetes induction for 6 weeks. At the end of administration period, the body weight, kidney weight, levels of lipid profile (LDL, HDL, TG and cholesterol), blood urea nitrogen (BUN), creatinine and glucose were measured . Results: Administration of the hydro-alcoholic extracts of Iranian Propolis significantly increased body weight and reduction kidney weight in treated diabetic rats. In addtion, Propolis significantly inhibited the increasing of glucose, BUN, creatinine, total cholesterol, triglyceride and LDL-c levels and increased HDL-c levels in Propolis-treated diabetic rats as compared untreated diabetics (P<0.05) . Conclusion: The results indicate that hydro-alcoholic extracts of Iranian Propolis can control blood glucose and modulate some of biochemical factors in diabetic rats .
On the illusory connection between cross-conjugation and quantum interference

Quantum interference, be it destructive or constructive, has a substantial influence on the magnitude of molecular conductance and consequently there is significant interest in predicting these effects. It is commonly thought that cross-conjugated paths result in suppressed conductance due to destructive quantum interference. Using H\"uckel theory and DFT calculations we investigate systems that break this cross-conjugation rule of thumb. We predict and rationalize how a class of conjugated molecules containing closed loops can exhibit destructive interference despite being linearly conjugated and exhibit constructive interference despite being cross-conjugated. The arguments build on the graphical rules derived by Markussen et al. [1] and the hitherto neglected effects of closed loops in the molecular structure. Finally, we identify the 1,3 connected Azulene molecule as belonging to the closed-loop class and argue that this explains recent measurements of its electrical conductance.
Preparation and Characterization of 3D Composite Scaffolds Based on Rapid-Prototyped PCL/β-TCP Struts and Electrospun PCL Coated with Collagen and HA for Bone Regeneration

In general, bioceramics such as β-tricalcium phosphate (β-TCP) and hydroxyapatite (HA) are promising biomedical materials for bone tissue regeneration, because of their good osteoconductivity and, in some cases, osteoinductivity, whereas their shortcomings may include low mechanical fracture toughness and brittleness. One reinforcing technique is to use a coating process with various composite systems consisting of synthetic biopolymers and bioceramics. However, the coating ability can be limited, because of the hydrophobic nature of synthetic polymers, such that the coating solution can sporadically clog or block the designed pore structure, and in some cases, a coating layer can become detached from the substrate. In this study, we fabricated rapid-prototyped polycaprolactone (PCL)/β-TCP composite scaffolds, and to increase the biological properties of the composite scaffold, we used a simple coating process to reinforce the scaffolds with a mixture of collagen/HA. To increase the coating efficiency of .
Structural and Surface Study of Praseodymium-Doped SnO2 Nanoparticles Prepared by the Polymeric Precursor Method

In the present study, we report on the successful synthesis of Pr-doped SnO2 (SnO2:Pr) nanoparticles using a polymeric precursor method while setting the Pr-content in the range from 0 to 10.0 mol %. The as-prepared samples were characterized in regard to their structural, morphological and surface properties. X-ray diffraction (XRD) patterns recorded from all samples revealed the tetragonal rutile-type structure with a systematic average size reduction (in the range from 11 to 4 nm) while enhancing the residual strain (in the range of 0.186 to 0.480%) as the Pr-content was increased. From the Rietveld refinement analysis we found that the lattice parameters (a, c, u, and V) showed a linear behavior, indicating a solid solution regimen for the Pr-doping. Transmission electron micrographs provided mean particle sizes of 8.7 ± 0.5 nm, for 2.5 mol % Pr-content, and 5.2 ± 0.5 nm,for 10.0 mol % Pr-content, which are in very good agreement with values obtained from the XRD data analysis: 7.4 ± 1.0 nm and 4.0 ± .
Developing and testing two arts-based knowledge translation tools for parents about pediatric acute gastroenteritis

Characterized by vomiting, fever, abdominal pain and diarrhea, acute gastroenteritis (AGE) is a common illness in pediatric populations. In Canada, pediatric AGE accounts for 200,000 emergency visits, 20,000 hospital admissions, and 30 deaths a year. Yet, there continues to be significant practice variations in the treatment of AGE. Knowledge translation (KT) can help close the research-practice gap. In particular, art and stories are powerful mediums that cut across age, culture, language, literacy, and gender barriers. The purpose of this study was to work with parents to develop an e-Book and whiteboard animation video for parents on pediatric AGE. Using a multi-method research process, we developed a 2 minute 57 second video and 39-page e-Book for pediatric AGE. Both tools underwent usability testing with parents in three Canadian emergency department waiting rooms in urban, rural, and remote regions. Focus groups were also conducted with parents in each of the three regions. Overall, parents felt that digital and paper-based KT tools would be beneficial knowledge dissemination mediums. Our study showed that parents positively rated an e-Book and whiteboard animation video for pediatric AGE. These findings demonstrate how working together with key stakeholders can facilitate the development of KT tools for parents that are usable, relevant, and increase parental confidence. Furthermore, the type of KT tool developed is an important decision that may depend on parental preferences as well as when and where parents access the tools.
Controllable Tuning Various Ratios of ZnO Polar Facets by Crystal Seed-Assisted Growth and Their Photocatalytic Activity

In this paper, Bi2WO6 was, for the first time, utilized as crystal seeds to fine control the ratio of ZnO polar facets under a mild condition. The structure and photocatalytic properties of the ZnO were investigated by powder X-ray diffraction, Raman scattering measurements, X-ray photoelectron spectroscopy, field emission scanning electron microscopy, transmission electron microscopy, UV–vis diffuse reflectance spectroscopy, total organic C analyzer, Brunauer–Emmett–Teller surface areas, and electrochemical workstation. It was found that ZnO architectures were gradually changed from nanorods to nanosheets after adding a certain amount of Bi2WO6 crystal seeds, which led to the change of the ratio of ZnO polar facets. The formation mechanism for this ZnO and the effect of ZnO polar facets on the photocatalytic activity are explored in detail. We find that the nucleation and growth rates of ZnO are controlled by adjusting the quantity of the crystal seeds. Besides, photocatalytic activity of the samples tun.
Screening for the Production of Thermostable Amylase from Bacterial Isolates Recovered From Raw Honey

Twenty sample of bottled raw honey were analyzed for the presence of thermostable amylase producing bacteria by streaking 0.1ml aliquots of suitable dilutions on Nutrient Agar in duplicates. The plates were incubated at 37 0 C for 24 hours. The isolates were sub-cultured and stored in slants for further use. Amylase production was detected by streaking on Starch agar plates and incubating at 55 o C for 48 hours after which the plates were flooded with iodine solution. The amylolytic activity was confirmed by clear zone around the bacterial colonies. Amylase production was carried out in a basal medium containing soluble starch. Bacillus licheniformis was capable of maximum amylase production at 40 o C at pH 7.0 (254μ/ml). Amylase production was highest in fructose (190μ/ml) while peptone was found to be the most suitable nitrogen source for the enzyme activity (17μ/ml). Thermostable α-amylase produced by Bacillus lichenformis had characteristics which makes it suitable for industrial applications and starch
Machine Learning in the Corporate Bond Market and Beyond: A New Classifier

Trade signing algorithms that rely on quote data, tick data or both have been used extensively to assign a trade as either a buy or a sell. Leveraging the availability of a large panel of signed trade data in the corporate bond market, we explore machine learning methods to uncover a new trade signing model that improves upon standard trade classification methods in both the bond and equity markets. We show that both the trading and information environment at the time of the trade critically affect the accuracy of existing trade classification rules in general, and also illustrate the importance of optimizing the feature set in correctly classifying trade direction. Importantly, our approach and the Random Forest algorithm we propose can be used in markets both with and without pre-trade transparency.
Do Retail Investors Matter?

We study the role of retail investors in information discovery and dissemination. Using a unique textual dataset of mandatory online “earnings communication conferences” from China, we find that stock market reaction and future firm performance are positively associated with the tone of questions asked by retail investors in the conferences. When managers dodge the questions from retail investors, as indicated by the high degree of content dissimilarity between investors’ questions and mangers’ answers, the market reacts less favorably and future earnings deteriorates. The effects are stronger for firms with less analyst coverage. Overall, the evidence suggests that retail investors could contribute to the information discovery process by asking value-relevant questions.
The Impact of Selection into the Labor Force on the Gender Wage Gap

We study the impact of selection bias on estimates of the gender pay gap, focusing on whether the gender pay gap has fallen since 1981. Previous research has found divergent results across techniques, identification strategies, data sets, and time periods. Using Michigan Panel Study of Income Dynamics data and a number of different identification strategies, we find robust evidence that, after controlling for selection, there were large declines in the raw and the unexplained gender wage gaps over the 1981-2015 period. Under our preferred method of accounting for selection, we find that the raw median wage gap declined by 0.378 log points, while the median unexplained gap declined by a more modest but still substantial 0.204 log points. These declines are larger than estimates that do not account for selection. Our results suggest that women’s relative wage offers have increased over this period, even after controlling for their measured covariates, including education and actual labor market experience. However, we note that substantial gender wage gaps remain. In 2015, at the median, the selectivity-corrected gaps were 0.242 log points (raw gap) and 0.206 log points (unexplained gap).
Impact of Electronic Human Resource Management on Employee Job Performance in Multinational Entities in Colombo District, Sri Lanka

Competition in the business environment is becoming rigorous over the years. As a result, concern over gaining and retention of the competitive position within the market place become the major concern by the organizations. In order to achieve this objective, employee performance is vital for any organization and ultimately it will generate core competencies within the organization which are hard to imitate by the rival in the short term. This study primarily intends to study the effect of electronic human resource management on employee job performance in special reference with Multi-National Entities in Colombo District, Sri Lanka. The research is carried out as an exploratory cross-sectional study and a self-completion questionnaire is administrated to gather relevant data from the participants. The size of the sample is 152 and the simple random sampling method was employed to create the sample from the population. Factor analysis, frequency distribution analysis, correlation analysis and regression analysis were employed to achieve the objectives of the study. Through this study, it was found that electronic human resource management plays a vital role to influence the job performance of the employees in multinational entities. Moreover, it was found that there is a positive relationship between electronic human resource management with employee job performance. In conclusion, in order to be competitive within the marketplace and to achieve organizational productivity, it is recommended that organization must have sound electronic human resource management practices and also it will permit attracting the best breed towards the particular employer.
Forecasting GDP Growth Rates Using Google Trends in the United States and Brazil

In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed (U.S.) and emerging-market economy (Brazil). Our focus is on the marginal contribution of "Big Data" in the form of Google Trends data over and above that of traditional predictors, and we use a dynamic factor model to handle the large number of potential predictors and the "ragged-edge" problem. We find that factor models based on economic indicators and Google "categories" data provide significant gains compared to models that exclude this information. Using more disaggregated Google Trends data than its "categories" is not helpful. The benefits of using Google Trends data are greater for the U.S. than Brazil.
Experience-Based Heterogeneity in Expectations and Monetary Policy

We show within a New Keynesian model with overlapping generations that experience-based heterogeneity in expectations across age groups affects monetary policy’s effectiveness in stabilising inflation and in closing the output gap. In our model agents only use lifetime observations to forecast macroeconomic variables like inflation or output. A variation in the age distribution has a composition effect on aggregate expectations, which are a size-weighted average over cohort-specific expectations. We show that an increase in the share of old individuals implies an increase in the transmission of monetary policy on inflation but aggravates its stabilisation tradeoff under supply shocks via the composition effect. Thus, the age distribution has a novel impact on monetary policy that is absent from standard models.
Subnational Income Growth and International Border Effects

This paper analyses the effect of international borders and of trade agreements at international borders on subnational (i.e. regional) growth. We construct an extensive panel dataset covering 1,350 regions in 86 countries worldwide between 1950 and 2017. Our results show that international borders decrease regional income per capita, while trade agreements at international borders increase regional income per capita by about the same magnitude. The positive marginal effect of trade agreements on regional income corresponds to at least three fifths of the negative marginal effect of international borders. Thus, trade agreements can compensate negative border effects and explain regional inequalities within countries. An array of robustness tests supports our interpretations.
The Effectiveness of Environmental Management System Standards

This paper investigates the effects of Environmental Management System standards (EMS standards), such as the ISO 14001, on the environmental performance of French manufacturing plants from 2001 to 2017. The impact of EMS certificates depends on the pollutant considered. For instance, adopters of EMS standards lower their water removals by 6.7% and they increase their production of hazardous waste by 8.6% directly after the certification, while there are no significant effects for the CO2 emissions and the water pollution. Moreover, EMS-certified firms reduce their energy bill by 0.6% per additional year following the certification. The absence of clear-cut conclusions regarding the environmental effectiveness of EMS standards question the efficiency of the eventual subsidies.
A model for wind farm management with option interactions

A renewable energy site can expand its power generation capacity by an endogenous amount but may also want to shut down to save on fixed operating costs and interest payments if the market prospects deteriorate. We model such circumstances and derive managerial implications that help us explain real‐world conundrums, illustrating the intricate interactions between the operational decision to build up capacity and the financial decision to exit an industry. Shutting down may be delayed in the hope of expanding capacity upon recovery; an expansion may also be delayed in the presence of a valuable exit option. Numerical extensions provide further managerial insights. In particular, the presence of fixed or proportional financing costs may lead the firm to delay its expansion decision, but the scale of investment will only be affected by proportional costs. If herding behavior causes equipment prices to increase (respectively, decrease) when electricity prices are high (respectively, low), managers should invest earlier (respectively, later) and more (respectively, less) while equipment prices are low (respectively, high). Furthermore, although volume swings (due to capacity decommissionings and expansions) are marked in a homogeneous industry (when the default and expansion thresholds are reached), heterogeneity in the population of wind farms smooths out such effects.
Implications of Tax Loss Asymmetry for Owners of S-Corporations

We study tax loss asymmetry for S corporate owners. These owners use most losses contemporaneously, reducing the tax asymmetry compared to C corporations. However, these owners face distortions due to the progressive individual tax schedule. The value of this asymmetry is approximately $3.5 billion per year. We find that this asymmetry creates disincentives for risky investment and causes allocative inefficiencies among loss and gains owners. Finally, we simulate the effects of certain provisions of the Tax Cuts and Jobs Act; we estimate that these provisions—especially section 199A—reduce the behavioral distortions of the asymmetry for S corporate owners. (JEL D22, G32, H25, H32, K34)
Low-Light Image and Video Enhancement Using Deep Learning: A Survey

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. Recent advances in this area are dominated by deep learning-based solutions, where many learning strategies, network structures, loss functions, training data, etc. have been employed. In this paper, we provide a comprehensive survey to cover various aspects ranging from algorithm taxonomy to unsolved open issues. To examine the generalization of existing methods, we propose a low-light image and video dataset, in which the images and videos are taken by different mobile phones’ cameras under diverse illumination conditions. Besides, for the first time, we provide a unified online platform that covers many popular LLIE methods, of which the results can be produced through a user-friendly web interface. In addition to qualitative and quantitative evaluation of existing methods on publicly available and our proposed datasets, we also validate their performance in face detection in the dark. This survey together with the proposed dataset and online platform could serve as a reference source for future study and promote the development of this research field. The proposed platform and dataset as well as the collected methods, datasets, and evaluation metrics are publicly available and will be regularly updated. Project page: https://www.mmlab-ntu.com/project/lliv_survey/index.html.