Miracle-dz/UniFAR-FTUs huggingface dataset:
APPLICATION OF RAMAN SPECTROSCOPY FOR THE CHARACTERISATION OF SYNTHETIC NON-METALLIC INCLUSIONS FOUND IN AL- KILLED CALCIUM TREATED STEELS

Non-metallic inclusions form an essential parameter for assessing steel cleanliness. The chemical composition of the inclusions and content found in the steel matrix can be detrimental to both the steelmaking process and steel properties. Therefore, a characterisation technique that has features such as an improved duration for sample preparation, a non-destructive and relatively fast result analysis is of high interest to both researchers and steelmakers. The main objective of this study is to use Raman spectroscopy as an analytical technique to characterise synthetic non-metallic inclusions mostly associated with aluminium-killed calcium treated steels. The work first examines the possibility of estimating individual phase content for synthetic inclusion mixtures consisting of CA, C12A7, C3A, MgO.Al2O3 and CaS phases by using Raman spectroscopy. Observations made from the Raman spectra measured provide qualitative information for the specific phases present in the samples. The results from the study using calibration models are satisfactory in quantifying the specific phase content present in the sample mixtures based on the Raman spectral data. Additionally, this study investigates the potential use of Raman spectroscopy as a characterisation technique for inclusion evolution studies and to predict the liquidus region within a CaO–Al2O3 binary system under steelmaking temperatures. This is done by using different synthetic inclusion phases to generate the sample matrix that has varying phase content for possible inclusion evolution routes. The experimental measurements performed with Raman spectroscopy prove to have a relatively short duration for sample preparation, fast results, and the synthetic inclusion phases can be distinguished based on their characteristic Raman bands features. Consequently, this study shows that Raman spectroscopy can potentially be used as an analytical technique for inclusion studies in steel samples since the steel matrix is Raman inactive.
Sweetness or Mouthfeel: A corpus-based study of the conceptualization of taste

Zhong, Yin and Chu-Ren Huang. 2020. Sweetness or Mouthfeel: A corpus-based study of the conceptualization of taste. Linguistic Research 37(3): 359-387. The sensory lexicon plays a pivotal role in bridging our cognitive system to the physical world. In this role, it has been the focus of recent interdisciplinary investigations on cognition, language, culture, and their interactions. Recent studies on linguistic synesthesia and sensory modality exclusivity showed unequivocally that cross-modality usages of sensory words are the norm rather than the exception. Given the dominance of cross-modality uses, the null hypothesis that the five senses are separate but equal modules merits a closer examination. In this paper, we focus on the gustatory quality of sweetness because of its universal appeal as well as the well-attested cultural influence on the gustatory lexicon. Based on an analysis of online food reviews containing descriptions of desserts, we show that mouthfeel, a multisensory concept, is strongly preferred over sweetness. Mouthfeel is associated with words from all the sensory domains, including both sensory and abstract (e.g., mental state) concepts. The highly non-exclusive characteristic of gustatory sensation suggests that it might be the most connected sensory modality, and the cross-modality expressions indicating personal preferences further imply the subjective propensity of the gustatory sense. Our study adds to the existing literature the interrelationship among sensory modalities through language use, and further sheds light on the interactions between language, cognition, and culture. (The Hong Kong Polytechnic University)
GRAVITY+: Towards Faint Science, All Sky, High Contrast, Milli-Arcsecond Optical Interferometric Imaging

A. Amorim2,12, W. Brandner8, M. Carbillet13, V. Cardoso2,14, Y. Clénet10, R. Davies1, D. Defrère15, T. de Zeeuw1,16, G. Duvert9, A. Eckart11,17, S. Esposito18, M. Fabricius1,19, N.M. Förster Schreiber1, P. Gandhi6, E. Gendron10, S. Gillessen1, D. Gratadour10, K. -H. Hofmann17, M. Horrobin11, M. Ireland20, P. Kervella10, S. Kraus21, S. Lacour10, O. Lai22, B. Lopez22, D. Lutz1, F. Martinache22, A. Meilland22, F. Millour22, T. Ott1, R. Oudmaijer23, F. Patru10, K. Perraut9, G. Perrin10, R. Petrov22, S. Quanz24, S. Rabien1, A. Riccardi18, R. Saglia1,19, J. Sánchez Bermúdez8,25, D. Schertl17, J. Schubert1, F. Soulez26, E. Sturm1, L.J. Tacconi1, M. Tallon26, I. Tallon-Bosc26, E. Thiébaut26, F. Vidal10, G. Weigelt17, A. Ziad13
Children’s School Subjective Well-Being: The Importance of Schools in Perception of Support Received From Classmates

Besides educational results, a comprehensive view of childhood should include children’s opinions on their well-being in school. The objective of this study is to determine whether school subjective well-being of children varies according to the school they attend, which would justify identifying related factors (school perceptions, individual affection, and socioeconomic composition). The 3,962 answers of children from Barcelona (Mage = 10.74) in 2017 to the International Survey of Children’s Well-being are analysed. The multilevel analysis shows that classmates play an essential role in school experience: in those schools where more children are very satisfied with their life as students, children have more confidence in receiving support from their classmates if they have a problem and feel less stressed. This has important implications for learning, coexistence, and participation. As the impact of social inequalities on school experience has not been identified, research focused on schools facing situations of social vulnerability is required.
Market Equilibrium Models in Large-Scale Internet Markets

Markets and their corresponding equilibrium concepts have traditionally been used as very powerful building blocks to ﬁnd allocations and prices. This chapter provides examples of the use of Fisher markets in the technology industry. We focus on Internet advertising auctions, fair division problems, content recommendation systems, and robust abstractions of large-scale markets. After introducing these markets, we describe how these models ﬁt the relevant application domains and what insights they can generate, exhibiting the most important theoretical and computational results from the recent literature on these topics
Accumulated Heating and Chilling Are Important Drivers of Forest Phenology and Productivity in the Algonquin-to-Adirondacks Conservation Corridor of Eastern North America

Research Highlights: Forest phenology and productivity were responsive to seasonal heating and chilling accumulation, but responses differed across the temperature range. Background and Objectives: Temperate forests have responded to recent climate change worldwide, but the pattern and magnitude of response have varied, necessitating additional studies at higher spatial and temporal resolutions. We investigated climatic drivers of inter-annual variation in forest phenology and productivity across the Algonquin-to-Adirondacks (A2A) conservation corridor of eastern North America. Methods: We used remotely sensed indices from the AVHRR sensor series and a suite of gridded climate data from the Daymet database spanning from 1989–2014. We used random forest regression to characterize forest–climate relationships between forest growth indices and climatological variables. Results: A large portion of the annual variation in phenology and productivity was explained by climate (pR2 > 80%), with variation largely driven by accumulated heating and chilling degree days. Only very minor relationships with precipitation-related variables were evident. Conclusions: Our results indicate that anthropogenic climate change in the A2A has not yet reached the point of triggering widespread changes in forest phenology and productivity, but the sensitivity of forest growth to inter-annual variation in seasonal temperature accumulation suggests that more temperate forest area will be affected by climate change as warming continues.
Forecasting Rainfall in Tanzania Using Time Series Approach Case Study: Dar es Salaam

The prediction of rainfall on monthly time scale has been attempted by a number of researchers by using different time series techniques at different time periods around the world. It is challenging to forecast rainfall at monthly time scale because of spatial and temporal random variation caused by a numbers of dynamic and environmental factors. In this paper, an attempt has been made to develop a Seasonal Autoregressive Integrated Moving Average (SARIMA) Model to analyze long term monthly rainfall data of Dar es Salaam region in Tanzania for the period of fifty three years (1961 to 2014). Rainfall observations were discovered to have Seasonality and also non-stationarity and hence differencing and Seasonal differencing was used to attain stationarity. Rainfall data were found to have two seasons namely October to December (OND) and March to May (MAM) . The analysis exhibited that the Seasonal ARIMA model which is satisfactory in describing the monthly rainfall data in Dar es Salaam Tanzania is SARIMA (2, 1, 1)(1, 1, 1)12. The model was then used for predictions of monthly rainfall values from January 2015 to December 2024. The forecasting results showed that monthly rainfall values have a decreasing trends, hence that may be a threat to agriculturists and water managers in the region. The study will be useful to decision makers for the region of Dar es Salaam to establish priorities and strategies based on the impacts posed by the variation of rainfall.
Effects of the Global Financial Crisis on the V4

The aim of the research is to explore the development of trade relations between the Visegrád countries and their major Western European partners since accession to the European Union. The topic is currently an important one, as the Visegrád region is highly dependent on Western European countries, especially in the area of foreign trade. The research analyzed how the 2008-2009 global economic crisis and the subsequent sovereign debt crisis in Europe affected these trade relationships, i.e. did it cause significant changes in levels of relationship and/or trends. To answer this question, this paper used a time-series analysis method called Interrupted Time Series Analysis. As a result, statistically significant changes in the level and trend of foreign trade relations between the Visegrád countries and their Western European partners were detected. Finally, a more detailed breakdown of product groups also revealed which product groups are most responsible for slowing down the growth of trade relations.
Structural Behavior of Unbalanced Section self-compacting reinforced concrete axially Loaded columns

Eight (120 mm) square and (1 m) long reinforced concrete columns were tested under axial load to large inelastic deformations. The main purpose of this research was to investigate the behavior of column sections confined by rectilinear ties. Major variables considered in this program included: (1) Distribution of longitudinal and lateral steel, including unbalanced section due to the asymmetric distribution of reinforcing steel bars (2) type of concrete; normal concrete and self-compacting concrete. Finite element model was performed to validate the experimental results of this investigation. Test results indicate that a asymmetric distribution longitudinal bars results in desired performance of columns. Unsupported longitudinal bars and its number effective only at large deformations and result in rapid deterioration of column behavior at a later stage. There is 8.75% and 35.65% decrease in ultimate capacity under the control column, asymmetric distribution of reinforcing steel bars appears a more brittle while symmetric bars yields more elastic than brittle, it adds safety when failure happens.
Exploratory Factor Analysis of Distribution Networks Characterization by Metrics of Complex Networks Theory

Distribution networks are responsible for supplying electricity to most consumers, and also are the power system part where the majority of electricity interruptions occurs. These infrastructures are in urban and rural regions and are organized to meet different geographical and operational restrictions and the energy demand. In this study, the use of metrics from complex networks theory to describe the organization of such important systems in topological and electrical perspectives was evaluated. A variety of metrics were extracted from different distribution networks. They were calculated considering topological and active power flow in nominal conditions information. The values obtained were investigated using exploratory factor analysis approach. Results indicated that the metrics can be grouped into three distinct factors, and there is a metric, unrelated to such factors, which describes how the power flow is distributed over the network structure. Considering the importance of such systems and the various possibilities of the operational and topological organization, the knowledge of metrics capable of characterizing, in a systemic perspective, is significant for the analysis of current and future challenges related to energy distribution. This topic and its applications will be further explored in future research.
Multi-sensor analysis of monthly gridded snow precipitation on alpine glaciers

Abstract. Accurate and reliable solid precipitation estimates for high mountain regions are crucial for many research applications. Yet, measuring snowfall at high elevation remains a major challenge. In consequence, observational coverage is typically sparse, and the validation of spatially distributed precipitation products is complicated. This study presents a novel approach using reliable daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glacier sites to assess the performance of various gridded precipitation products. The ground observations are available during two and four winter seasons. The performance of three readily-available precipitation data products based on different data sources (gauge-based, remotely-sensed, and re-analysed) is assessed in terms of their accuracy compared to the ground reference. Furthermore, we include a data set, which corresponds to the remotely-sensed product with a local adjustment to independent SWE measurements. We find a large bias of all precipitation products at a monthly and seasonal resolution, which also shows a seasonal trend. Moreover, the performance of the precipitation products largely depends on in situ wind direction during snowfall events. The varying performance of the three precipitation products can be partly explained with their compilation background and underlying data basis.
Learning Opportunistic Adversarial Model on Global Wildlife Trade

Global illegal wildlife trade threatens biodiversity and acts as a potential crisis of invasive species and disease spread. Despite a wide range of national and international policies and regulations designed to stop illegal wildlife trade, high profit margins and increasing demand drive a vigorous global illicit trade network. In this paper, we aim to build an adversarial model to predict the future wildlife trade based on the historical trade data. We hypothesize that the majority of illegal wildlife trade is opportunistic crime, which is highly correlated to legal wildlife trade. We can therefore leverage the abundant legal wildlife trade data to forecast the future wildlife trade, where a fixed fraction of trade volume will reflect the opportunistic wildlife trade volume. To learn a legal wildlife trade model, we propose to use graph neural networks and meta-learning to handle the network and species dependencies, respectively. Lastly, we suggest to incorporate agent-based models on top of our model to study the evolution from opportunistic to more organized illegal wildlife trade behavior.
Grounded theory research on the factors that lead to doctor moral hazard

Abstract Moral hazard clearly exists among doctors, and it has a dramatic impact on doctor-patient relationships, medical costs and medical risks. This study explored the factors that lead to doctor moral hazard, as well as the interrelationships and internal regularity of these factors. This study takes doctor moral hazard as the research content and the inducing factors as the core theme, conducting grounded theory research on the causes of doctor moral hazard. Scientific understanding of doctor behavior would facilitate the prevention and control of doctor moral hazard behavior. This study used the principles and methodology of Glaser and Strauss’s grounded theory. Theoretical and snowball samplings were used to identify 24 subjects. Semi-structured in-depth interviews were conducted with each subject. Themes were identified through substantial (open) coding and theoretical coding. The factors that lead to doctor moral hazard were categorized into five dimensions, i.e. motivation, opportunity, self-rationalization, exposure and punishment. These five factors influence each other, forming the inducing mechanism of doctor moral hazard. This will provide useful theoretical support and method guidance for the follow-up prevention and control of moral hazard for doctors.
Frailty and risk of mortality and hospitalization in nursing home residents affected by COVID-19

Coronavirus disease 2019 (COVID-19) is a widespread condition in nursing homes (NHs). However, no research was made regarding frailty in NH residents during COVID-19 pandemic. The aim of this study was to assess whether frailty, assessed by the multidimensional prognostic index (MPI), can predict mortality/hospitalization in COVID-19 NH residents. A Cox’s regression analysis was used, reporting the results as hazard ratios (HRs) with 95% confidence intervals (CIs). 1146 NH residents affected by COVID-19 were included (mean age: 86 years; 75.4% females). During the followup period, we observed 286 deaths and 239 hospitalizations. Taking those with MPI <0.41 as reference, an MPI ≥0.50 was associated with a higher risk of death (HR=1.41; 95%CI: 1.07-1.85). Similar results were obtained using the MPI score increase in 0.10 points (HR=1.12; 95%CI: 1.03-1.23). Using hospitalization as outcome, only MPI score as continuous variable was associated with this outcome (HR=1.08; 95%CI: 1.002-1.17). In conclusion, frailty, as assessed by the MPI, was associated with a significant higher risk of mortality and hospitalization in NH residents affected by COVID-19 further indicating the necessity of assessing frailty in NH.
Association of PRDM16 rs12409277 and CtBP2 rs1561589 gene polymorphisms with lipid profile of adolescents

Introduction Positive regulatory domain containing 16 (PRDM16) protein represents the key regulator of brown adipose tissue (BAT) development. It induces brown fat phenotype and represses white adipose tissue specific genes through the association with C-terminal binding co-repressor proteins (CtBP1 and CtBP2). In healthy adults presence of BAT has been associated with lower glucose, total cholesterol and low-density lipoprotein (LDL) cholesterol levels. Our aim was to analyze the association of PRDM16 gene (rs12409277) and CtBP2 gene (rs1561589) polymorphisms with body mass index (BMI), fasting glucose level and lipid profile of adolescents. Material and methods Our study included 295 healthy school children, 145 boys (49.2%) and 150 girls (50.8%), 15 years of age. Genotypes for the selected polymorphisms were detected by the real-time PCR method. Age, gender, height, weight, lipid profile (total cholesterol, high-density lipoprotein (HDL) cholesterol, LDL cholesterol, triglycerides) and fasting glucose levels were recorded. Results We did not find a statistically significant association of rs12409277 and rs1561589 polymorphisms with BMI, fasting glucose and lipid profile of adolescents. We further analyzed the combined effect of the two SNPs and the statistical analysis showed that carriers of CT genotype of rs12409277 polymorphism and GG genotype of rs1561589 polymorphism had significantly lower total cholesterol (p = 0.001) and LDL cholesterol (p = 0.008) levels compared to all other groups of genotypes. Conclusions Our study suggests that rs12409277 and rs1561589 polymorphism might have an influence on total and LDL cholesterol levels in adolescents. Larger studies should be performed in order to confirm our results.
EFFECTS OF "CIRHEP10" ON BIOCHEMICAL PARAMETERS BASED ON SERUM AND HEART IN ALCOHOL INDUCED LIVER CIRRHOSIS

The aim of the study was to investigate the hepatoprotective activity of the polyherbal formulation on alcohol induced liver cirrhosis albino rats. The groups of animals were administered with CIRHEP10 at different doses. The effects of CIRHEP10 on preventing the changes in the metabolism of lipids in alcohol induced liver cirrhosis was investigated. Further, the drug was also studied for its effect of decreasing the concentration of cholesterol and triglycerides in serum and heart. The results demonstrated that CIRHEP10 significantly reduces the values in level of GOT, GPT and alkaline phosphatase. The treatment with drug decreases the level of total cholesterol, free fatty acid, TAG, ALP, AST and ALT activity very significantly. Thus it significantly reduced the deleterious effect caused by the administration of alcohol. The drug also showed significant increase in total protein content and A/G ratio. All our findings suggest that CIRHEP10 could protect the liver cells from alcohol induced liver damages.
Robust Malware Detection using Residual Attention Network

Recent advancements in Cyber Security have amalgamated the strengths of Artificial Intelligence and Human Intelligence for Intrusion Detection. The colossal increase in the volume of new malware generated everyday and the constant risk of zero day attacks demand research for a robust malware detection system. Significant research has gone into exploring the use of Machine Learning and Convolutional Neural Networks (CNNs). There has been a transition from using Malware byte information for Machine Learning and Deep Learning based methods to using an Image based Intrusion Detection system for better assessment of the malware file. Though CNNs have helped in capturing local features, Attention based mechanisms play a vital role in detecting structural changes in malware. In this paper, we have explored the use of Residual Attention for malware detection and have compared this with the existing CNN based methods and conventional Machine Learning algorithms using GIST features. The proposed method could efficiently focus the ’attention’ to precisely those sections of the malware which were significant in being distinguished from benign files, thus reducing the False Positives which is primary from the Accessibility point of view in Cyber Security. The method is robust against structural changes in malware and has outperformed traditional malware detection methods by demonstrating an accuracy of 99.25%.