Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study

Abstract Aims Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms. Methods and results In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years. Conclusion ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease.
Rietveld Refinement with Spallation Neutron Powder Diffraction Data

The mathematical functions necessary for Rietveld refinement of time-of-flight neutron powder diffraction patterns from spallation sources are developed and a computer program for least-squares analysis is described. The results of Rietveld refinements of nickel and a low-carbon steel are described and discussed. The method fully exploits the high resolution (Δd/d ~ 0.3 ~ 0.5%) available with powder diffractometers currently in operation on these sources and examples are given of precise determination of atom coordinates, thermal parameters, lattice parameters and the detection of small strains.
Deep Learning for Equity Time Series Prediction

We examine the performance of Deep Learning methods applied to equity financial time series. Predicting equity time series is a crucial topic in Finance. To form equity portfolios and do asset allocation, we need to predict returns, compute their risk, and optimize market impact. One of the modeling benefits of Deep Learning architectures is the ability to model non-linear highly dimensional problems. The lack of transparency and a rigorous mathematical theory could be considered less positive sides. The fact that most progress in Deep Learning has been made by trial and error is also cumbersome. Equity financial time series is a challenging domain with some stylized facts: weak stationarity, fat tails in return distributions, small data sets compared to other areas of Artificial Intelligence (AI), slow decay of autocorrelation in returns, and volatility clustering, to name the most important ones. We perform a comparative study between Long ShortTerm Memory Networks (LSTM), Recurrent Neural Networks (RNN), Deep Feed-Forward neural networks (DNN), and Gated Recurrent Unit Networks (GRU). We perform two types of studies. The first focused on a univariate test, and the second a multivariate test. Our tests show that the LSTM performs the best compared to other Deep Learning and classical machine learning models. In terms of performance metrics, the LSTM is better than the baseline model. We also show that the predictions are better than chance. There is enough evidence thatRNN and LSTM can deal with stationary time series and learn the data generating process. Nevertheless, predicting equity non-stationary time series, with market developments like the one caused by the COVID-19 pandemic in 2020, is challenging.
Excimer laser‐assisted metalorganic vapor phase epitaxy of CdTe on GaAs

We have successfully grown CdTe (111) on GaAs (100) at 165 °C using a 248 nm excimer laser to photodissociate dimethylcadmium and diethyltellurium in the gas phase. Good crystalline quality of the layers is confirmed by x‐ray diffractometry. Growth rates up to 2 μm/h have been recorded in real time using time‐resolved reflectivity. Auger analysis reveals that the films are stoichiometric throughout the thickness of the layer, and that carbon and oxygen contaminants are below the level of detectability. We have used laser‐induced fluorescence spectroscopy to examine the photodissociation mechanism of diethyltellurium and have observed a linear dependence of Te atom production on excimer laser power.
Acoustic Investigation of Textile Fabrics

Why is it possible to distinguish between different textile fabrics by just touching them and moving your hand over them and listening to the sound? Particularly for high-quality woven fabrics, e.g. used for tailor-made suits, it is quite common that the dressmaker listens to the sound as their hand rubs the fabric. Can this approach be translated into a technical measurement? What could a sound analysis tell us about the fabric properties? As a first simple approach, we used a record player to rotate different cotton fabrics, and tested fine tips from diverse materials, such as plastic pipettes, pens, glass tips, etc. Our results show clear differences between the textile fabrics, which can be attributed to different yarn, knitted or woven structures. While the rotational mode of investigation impedes fully automated fast Fourier transform (FFT) evaluations, our first results suggest developing this promising method further.
A A Systematic Review on Impact of Edentulism on Nutritional Status of Eldrly Adults as Compared to Dentulous Adults

ABSTRACT Background: Age related changes in oral structures and nutritional deficiencies cause difficulties in wearing complete dentures amongst elderly adults Objective: To identify the impact of edentulism on nutritional status of eldrly adults. Methodology: To conduct the present study, information of existing literature are collected from textbooks, printed journals, and electronic databases such as pubmed, scopus, and science direct. Result: Prevalence of malnutrition rises with an increase in age. Edentulous subjects more likely to report trouble in chewing their food as compared to dentulous subjects. Edentulous subjects had significantly lower intake of calories, protein, carbohydrate, fibres, vitamins A, C,B1, and B6 . Intake of calcium and protein was lower in women with dentures than in dentate women. Compromised oral functional status was associated with lower serum albumin levels and lower BMI in functionally dependent elderly people. Conclusion: There is good quality evidence that edentulism is associated with poor diet and compromised nutrition.Although the majority of the studies cited here have not established a cause and effect relationship, results from Sheiham and others. However, more longitudinal studies on this topic are required to further understand the potential role of nutrition in the prevention of age related changes and reduce the failure of complete denture treatment in elderly adults. Keywords:Aging, Complete Denture, Elderly adults, Nutritional Assessment, Nutrition.
Social capital in retirement villages: a literature review

Abstract Retirement villages are a model of extra-care housing, offering purpose-designed housing that incorporates both care services and a range of non-care-related facilities and activities. These generate opportunities for formal and informal social activity, and promote community engagement, solidarity between residents, and active and independent ageing. Providers suggest that retirement villages are able to foster an environment rich in social capital. This study's purpose is to review and summarise key findings on the topic of social capital in retirement villages in the gerontological literature. Social capital is defined as both an individual attribute of single actors and a feature of communities as a whole. A clear conceptualisation of social capital is used to organise the reviewed studies along different dimensions: on an individual level, social networks, trustworthiness and obligations are differentiated, while the collective level distinguishes between system control, system trust and system morality. Thirty-four studies are reviewed. While retirement villages are generally described as friendly places with widespread helping behaviour where new friends are made, research has also highlighted the difficulty of socially integrating the frail and very old. While, in particular, social networks and system morality have received much attention, there is a clear need for future research into the other domains of social capital.
Impact of VOCs and NOx on Ozone Formation in Moscow

Volatile organic compounds (VOCs), ozone (O3), nitrogen oxides (NOx), carbon monoxide (CO), meteorological parameters, and total non-methane hydrocarbons (NMHC) were analyzed from simultaneous measurements at the MSU-IAP (Moscow State University—Institute of Atmospheric Physics) observational site in Moscow from 2011–2013. Seasonal and diurnal variability of the compounds was studied. The highest O3 concentration in Moscow was observed in the summer daytime periods in anticyclonic meteorological conditions under poor ventilation of the atmospheric boundary layer and high temperatures (up to 105 ppbv or 210 μg/m3). In contrast, NOx, CO, and benzene decreased from 8 a.m. to 5–6 p.m. local time (LT). The high positive correlation of daytime O3 with secondary VOCs affirmed an important role of photochemical O3 production in Moscow during the summers of 2011–2013. The summertime average concentrations of the biogenic VOCs isoprene and monoterpenes were observed to be 0.73 ppbv and 0.53 ppbv, respectively. The principal source of anthropogenic VOCs in Moscow was established to be local vehicle emissions. Yet, only about 5% of the observed isoprene was safely attributed to anthropogenic sources, suggesting significant contribution of biogenic sources into the total levels of ozone precursors. The non-linear O3–NOx dependence shows a decrease in ground-level O3 with an increase in NOx during the summers of 2011–2013, which is typical for the VOC-sensitive photochemical regime of O3 formation. Nevertheless, during the elevated ozone episodes in July 2011, the photochemical regime of ozone production was either transitional or NOx-sensitive. Contribution of various anthropogenic and biogenic VOCs into the measured ozone values was evaluated. The ozone-forming potential (OFP) of total VOCs was 31–67 μg/m3 on average and exceeded 100 μg/m3 in the top 10% of high ozone events, reaching 136 μg/m3. Acetaldehyde, 1.3-butadiene, and isoprene have the highest ozone production potential in Moscow compared to that of other measured VOCs.
Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behavior

Location recommending frameworks plays a very significant role in suggesting the users with new places to visit especially when users are visiting unfamiliar areas. Most of the existing recommender systems do not consider the fact that different users have different behavior while checking in. Some approaches do not consider the essential factors while providing recommendations. These systems lack adaptability and hence they provide poor recommendations. An adaptive approach to provide users with a personalized recommendation has been proposed in this paper. We have considered three features namely, user activeness feature, user similarity feature, and the spatial feature. In addition to this, we have also considered the location popularity for a given timeslot. We have divided the users into inactive and active based on the degree of activeness on social networks using fuzzy c‐means clustering. We have provided two strategies based on the activeness of the user. A two‐dimensional Gaussian kernel density estimation strategy is used for the active user. A one‐dimensional power‐law function strategy is used for inactive users. Moreover, we have integrated the time‐based popularity of the location and probability estimation based on the similarity between the users. To evaluate the proposed model, we have used a large‐scale Foursquare dataset.
Towards A Model to Improve English Language Standards in Schools: Impact of Socio-Economic Factors of Stakeholders

There is a dire need to enhance the English language standards in schools of developing countries using English as a second or Foreign language, due to its importance in employability and high-earning ability in the job market. Enhancing English language standards in schools is vital to improving the English language competence of school leavers and undergraduates to achieve an English language quality level as a nation, to produce diversified graduates of global quality, to address the unemployability problem in developing countries. Sri Lanka, with a rich history of a colonial era, is no exception. Studies on the influence of Socio-Economic factors of stakeholders on improving the English language standards in schools had received poor attention from past researchers, especially in the Sri Lankan education context. Hence, the overall purpose of this study is to develop a theoretical model, to explore the impact of socio-economic factors of stakeholders on English Language Standards in Sri Lankan schools. The study reviewed reliable secondary data published in scholarly extant literature, government Policy Documents, Research Reports of reputed institutions, etc., relevant to the above primary relationship and key concepts of the study. Six main stakeholders in the socio-economic context of the school English Language education were identified: (i) Education Policy Makers, (ii) School Management, (iii) School Principals, (iv) English Language Teachers, (v) Students, and (vi) Parents. An integrated, seven-construct conceptual model, labeled ‘ELS Model’ (English Language Standard Model), was developed, to examine the impact of socio-economic factors of the six stakeholders on improving English Language Standards in schools. This ELS Model presents original insights and future directions to scholars/researchers and significant implications for policymakers.
THE ROLE OF BUSINESS INTELLIGENT SYSTEMS IN MONITORING AND ANALYSIS OF UNIVERSITY DATA

Nowadays, it has become clear that the success of different sized organizations depends on the speed at which they adapt to the dynamic changes and challenges of competitive market structures. At the same time, it is considered that information is a key element for identifying the strengths and weaknesses of the business activities of organizations and the trends for their future development and market consolidation. Modern universities face major challenges related to the processing of large amounts of data, which are continuously generated by different systems and units, but in most cases, the information flow is not analysed effectively enough. Namely the efficient extraction of educational data is an important aspect for the analysis of the state of the university as well as the effective planning of its future development. Therefore, the main purpose of this study is to consider the capabilities of intelligent business analysis information systems to monitor and control the large volumes of data generated at the University of Forestry, Bulgaria. Implementing such a system will help transform data into valuable information and knowledge that will assist academic leadership in taking timely, informed, reasoned managerial decisions and actions, taking into account the dynamic and competitive educational environment and rapidly changing educational needs in higher education. UDC Classification: 004.02, DOI: https://doi.org/10.12955/pns.v1. 121
Bridge Vibration Virtual Sensor Based on Eulerian Video Magnification and Gray Mean Difference

A non-contact measurement method is proposed to overcome the shortcomings of traditional contact sensors in this paper. The camera is used as a non-contact virtual sensor of the whole field vibration. The motion in raw video was amplified by Eulerian Video Magnification technology and the gray mean of the selected region from processed video is obtained. The gray value difference between frames represents the dynamic response signal of the local area of the bridge structure under the excitation. The actual displacement amplitude in the time domain of the target structure can be obtained by conversion. A model bridge experiment was carried on, the results show that good coincidence in time domain and good accuracy in frequency domain, the error of the proposed method is 2.2% compared with the traditional measurement, indicating that the method without manual mark has the advantages of accurate, easy to obtain, low cost and high efficiency.
Angular analysis of the decay B$^+$ $\to$ K$^*$(892)$^+\mu^+\mu^-$ in proton-proton collisions at $\sqrt{s} =$ 8 TeV

Angular distributions of the decay B$^+$ $\to$ K$^*$(892)$^+\mu^+\mu^-$ are studied using events collected with the CMS detector in $\sqrt{s}=$ 8 TeV proton-proton collisions at the LHC, corresponding to an integrated luminosity of 20.0 fb$^{-1}$. The forward-backward asymmetry of the muons and the longitudinal polarization of the K$^*$(892)$^+$ meson are determined as a function of the square of the dimuon invariant mass. These are the first results from this exclusive decay mode and are in agreement with a standard model prediction.
Fracture Sustainability in Enhanced Geothermal Systems: Experimental and Modeling Constraints

Enhanced geothermal systems (EGS) offer the potential for a much larger energy source than conventional hydrothermal systems. Hot, low-permeability rocks are prevalent at depth around the world, but the challenge of extracting thermal energy depends on the ability to create and sustain open fracture networks. Laboratory experiments were conducted using a suite of selected rock cores (granite, metasediment, rhyolite ash-flow tuff, and silicified rhyolitic tuff) at relevant pressures (uniaxial loading up to 20.7 MPa and fluid pressures up to 10.3 MPa) and temperatures (150–250 °C) to evaluate the potential impacts of circulating fluids through fractured rock by monitoring changes in fracture aperture, mineralogy, permeability, and fluid chemistry. Because a fluid in disequilibrium with the rocks (deionized water) was used for these experiments, there was net dissolution of the rock sample: this increased with increasing temperature and experiment duration. Thermal-hydrological-mechanical-chemical (THMC) modeling simulations were performed for the rhyolite ash-flow tuff experiment to test the ability to predict the observed changes. These simulations were performed in two steps: a thermal-hydrological-mechanical (THM) simulation to evaluate the effects of compression of the fracture, and a thermal-hydrological-chemical (THC) simulation to evaluate the effects of hydrothermal reactions on the fracture mineralogy, porosity, and permeability. These experiments and simulations point out how differences in rock mineralogy, fluid chemistry, and geomechanical properties influence how long asperity-propped fracture apertures may be sustained. Such core-scale experiments and simulations can be used to predict EGS reservoir behavior on the field scale.
Effects of Fuel Oxidation and Dissolution on Volatile Fission Product Release under Severe Accident Conditions

The release of volatile fission products from high-burnup UO2 fuel was examined in a steam atmosphere under severe accident conditions as a part of the VEGA program. The effects of fuel oxidation and dissolution were totally evaluated, by comparing the results with those from previous inert, hydrogen and steam atmosphere tests. It was shown that the oxidation of UO2 to UO2+x by steam generally enhances Cs and Kr release. However, the enhancement becomes smaller above the melting temperature of Zircaloy, about 2030 K, likely due to reduction of UO2+x by molten Zircaloy. The burst release of Cs occurs above about 2300K in the hydrogen atmosphere, while the release rate does not increase so significantly for the examined temperature range (<2800 K) in the steam atmosphere. Analysis of the hydrogen atmosphere test showed that fuel dissolution is apparently connected with the burst release and that a large fraction of Cs is quickly released from the dissolved fuel above 2300 K. It is considered that the fuel dissolution rate in the steam atmosphere is about 1/1000 of that in the hydrogen atmosphere.
Strontium Removal by New Alkyl Phenylphosphonic Acids in Supported Liquid Membranes with Strip Dispersion

We have synthesized a family of new extractants, alkyl phenylphosphonic acids, for strontium removal by supported liquid membranes (SLMs) with strip dispersion. The stability of the SLMs has been ensured by the improved SLM with strip dispersion technique. The SLMs removed radioactive Sr-90 to the target of 8 pCi/L or lower from feed solutions of 300−1000 pCi/L. Overall mass transfer coefficient data indicate that the mass transfer resistance for the membrane phase is negligible. An analysis of mass transfer resistances shows that the interfacial resistances due to extraction and stripping reactions are dominant. Stripping with HCl was nearly quantitative and more effective than that with H2SO4. The extractant was more effective than di(2-ethylhexyl) phosphoric acid.
Estimation of effort in agile software development: Study of the current state in Bogotá

Contexto: A lo largo de los anos se han desarrollado numerosos estudios relacionados con la estimacion de esfuerzo en el desarrollo de software agil los cuales permiten comprender los avances que han tenido y la necesidad de su uso en las empresas. Objetivo: Conocer la perspectiva de empresarios de distintas organizaciones de Bogota que trabajan en el sector o esten relacionados con el desarrollo de software agil y aquellos que han tenido proyectos relacionados con el tema. Metodo: Para la elaboracion del articulo se realizar una investigacion a nivel bibliografico. Resultados: Los datos obtenidos son de 314 encuestados en la cuidad de Bogota, los principales hallazgos son: 1) Para las companias es sumamente importante con (34%) la adopcion de una metodologia de estimacion de esfuerzo. 2) La tecnica de estimacion mas usada es el punto de caso de uso con (43,31%). 3) El predictor de esfuerzo mas utilizado es la Linea de codigo (38,85%) y seguida de este STORY POINT con (33,76%). 4) Como metodologia agil adoptada en las empresas se encuentra SCRUM con (43,95%). 5) Como factor mas determinante para estimar el esfuerzo es considerado la experiencia de equipo con (53,50%). 6) En el porcentaje de error entre estimacion de esfuerzo proyectada y esfuerzo real el (26,75%) de los encuestados hacen determina que esta 5%-25% subestimada. 7) Finalmente, como dificultad en su empresa para estimar el esfuerzo de proyectos mediante desarrollo de software agil los encuestados reconocer con (26,11%) que los recursos como el mayor. Conclusiones: La estimacion de esfuerzo es relativamente variable, para muchas companias esto depende tambien de las necesidades de cada una, por otro lado, el uso de metodologia agil y tecnicas de estimacion varia en su uso dependiendo del cruce que se de entre ellas y como se complementan entre si.