Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrocardiographic changes among beta-thalassemic major patients in ibn al-baladi thalassemia center-Baghdad

Al – Kindy Col Med J 2013; Vol. 9 No. 1 P: 36 Abstract Background :Thalassemia is an autosomal disease of the haemoglobin. Two types of thalassemia are recognized: thalassemia major and thalassemia intermedia. The most serious cardiac complication in thalassemia major is due to multiple blood transfusions rather than the disease itself, which is due to iron overload. Cardiomyopathy is the most common cardiac defect that occurs with iron overload. Pricarditis, congestive heart failure and arrhythmias are due to hemosidrosis and chronic aneamia. Aim of the study: to demonstrate the prevalence and types of electrocardiographic changes among thalassemic patients with aged over ten years old. Methods :The study is a cross sectional study conducted in Thalassemic center in Ibn Al-Baladi Hospital/Resafa Directorate of Health/ Baghdad during the period of December/ 1/2010-May/ 3
Financial development and ecological footprint: A global panel data analysis

This study examines the environmental effects of financial development using a comprehensive indicator of ecological footprint for a panel of 131 countries from 1971 to 2017. For the empirical analysis, pooled ordinary least squares (OLS), fixed effects model, random effects model, Driscoll-Kraay (DK) standard errors, and system generalized method of moments (GMM) are employed. The findings reveal that all indicators of financial development namely domestic credit to private sector, domestic credit to private sector by banks, and domestic credit provided by financial sector significantly help to improve the environmental quality by reducing the ecological footprint. Comparatively, the effect of domestic credit to private sector is stronger than other measures of financial development. Similarly, urbanization has been accompanied by the significant reduction in ecological footprint. In contrast, energy consumption, foreign direct investment (FDI), and GDP per capita worsen the environmental quality by increasing the ecological footprint. The study also validates the existence of “pollution haven hypothesis” for the global economy. Findings of the study have global implications. In general, financial sector has the potential to support the global efforts towards environmental protection. However, regional or country specific experiences can differ depending upon the financial sector priority towards environmental protection.
odelling of blood pressure and total cardiovascular isk outcomes after second-line valsartan therapy : he BSCORE study odélisation

a University of Antwerpen, Middelheimlaan 1, 2020 Antwerpen, Belgium b SGS Life Science Services, Generaal De Wittelaan 19A Bus 5, 2800 Mechelen, Belgium c Novartis Pharma, Medialaan 40, 1800 Vilvoorde, Belgium d Matrix45, 620, Frays Ridge Road, Earlysville, VA 22936, Virginia, USA e Center for Health Outcomes and Pharmacoeconomic Research, and Department of Pharmacy Practice and Science, College of Pharmacy, The University of Arizona, 1295 N. Martin, Tucson, AZ 85721, Arizona, USA
Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries - PERSIANN-cGAN

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.
Preconscious effects of temporary goals on attention q

Two experiments examined whether accessible goals preconsciously direct selective attention. Goal activation was manipulated by one’s goal pursuit being either undermined or affirmed (resulting in an experienced state of either incompleteness or completeness). Attention responses were assessed through a Stroop-like task (Experiment 1) and a response-time task in which participants indicated a stimulus’ direction of motion (Experiment 2). Results showed that when goals were accessible, attention was drawn toward goal-relevant items—even when these items were to be ignored and when responses occurred too fast for conscious control. Accessible goals directed implicit cognition (i.e., attention toward stimuli associated with the goal), despite the fact that these goals were not chronically held, but manipulated in the lab. 2002 Elsevier Science (USA). All rights reserved.
RELIGIOSITY AND LIFESTYLE OF AN ADULT POPULATION

Objective: to characterize the profile of religiosity and lifestyle of an adult population and to analyze the association between these variables. Methods: Quantitative descriptive cross-sectional research conducted with participants (n=206) of two Health Fairs held in the city of São Paulo on October, 10th 2013 and May, 4th 2014. Two questionnaires were applied, The Duke University Religion Index (DUREL, subdivided into the dimensions Organizational, Non-organizational and Intrinsic Religiosity) and the FANTASTIC Lifestyle Assessment. Spearman’s Rho test was performed to associate the variables “lifestyle” and “religiosity”, and the chi-squared test was used for the bivariate analysis between sex and religiosity. Results: We found a mostly good religiosity profile in the three dimensions, particularly the Intrinsic Religiosity, which reached 83.5% (n=167) in the three questions. As to lifestyle (FANTASTIC), the following rating was obtained: 26.6% (n=34) “Good”, 48.4% (n=62) “Very Good”, and 12.5% (n=16) “Excellent”. Of the 125 bivariate analysis, 13 (11.3%) presented a weak (ρ<0.30), however significant (p< 0.05), correlation. In the chi-squared associations, religiosity was related to lifestyle dimensions regarding the use of alcohol and drugs, balanced diet and mental health. Conclusion: The study population’s religiosity, considering the three dimensions analyzed, was characterized as “good” and “very good”; and the lifestyle, according to the total score of the FANTASTIC assessment, could be considered healthy. Although weak, there was an association between religiosity and lifestyle. Descriptors: Religion; Spirituality; Life Style; Health Promotion.
PHYTOTOXIC POTENTIAL OF THE CRUDE EXTRACT AND LEAF FRACTIONS OF Machaerium hirtum ON THE INITIAL GROWTH OF Euphorbia heterophylla AND Ipomoea grandifolia

Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are credited. PHYTOTOXIC POTENTIAL OF THE CRUDE EXTRACT AND LEAF FRACTIONS OF Machaerium hirtum ON THE INITIAL GROWTH OF Euphorbia heterophylla AND Ipomoea grandifolia
Taphonomy and sequence stratigraphy of spectacular shell accumulations from the type stratum of the Central Paratethys stage Eggenburgian ( Lower Miocene , NE Austria ) With 12 fi gs , 2 tabs

The mollusc faunas of the Lower Miocene Eggenburgian stage are known to science for nearly 200 years. However, the fi rst detailed taphonomic and paleoecologic analysis of the famous coquinas from the stratotype area of that stage is presented in the current paper. An extremely accurate documentation of the shell accumulations at Nonndorf in Lower Austria allowed the separation of 7 coquina types which are discussed in terms of taphonomy, paleoecology and sedimentology. One of these types represents a typical storm wave concentration which later acted as barrier for subsequent populations of burrowing glycymerids. These bivalves are preserved with articulated valves within the slightly older death-assemblage forming a very conspicuous coquina. Hence, the term “forced within-habitat concentration” is proposed for this rare type of multi-phased coquina formation. Based on the paleoecology of the pectinid Oopecten gigas and granulometric data, a two-stepped transgression can be documented for the investigation area. This new approach allows a further division of the 3rd order Bur-1 cycle which comprises the Lower Eggenburgian deposits.
Pengukuran Kesadaran Keamanan Informasi Dan Privasi Pada Pengguna Smartphone Android Di Indonesia

Based on statistical data, it is known that Android is the most popular smartphone with the largest number of users in the world, which is about 1.8 billion users. The high number of users also invite the many cases of information security and privacy caused by the lack of awareness of the user such as : spam, spoofing/phising, network incident, malware, uploading something personal data such as photos, phone numbers, addresses or having no antivirus. This study aims to find out about the awareness of the security of information and privacy of Android smartphone users by doing measurement of problem. The awareness has some dimensions such as attitude, knowledge and behavior with the seven focus areas of information security namely trust in app repository, misconception about app testing, security and agreement message, pirated application, adoption Security control, spam sms and report of security incidents and three focus areas of privacy are perceived surveillance, perceived intrusion, secondary use of information. This research uses analytical hierarchy process (AHP) to measure the level of awareness of information security and privacy of smartphone users. Overall, the results of the research show that information security has an average level of awareness (71%) but the focus area of report for security incidents has a poor level of awareness (37%) this occur because users prefer to solve their own information security issues experienced and privacy has an average level of awareness (76%). However, for secondary use of information in attitude dimension has low awareness level (66%). Based on the results of this study, it can be concluded that smartphone users in Indonesia have a poor awareness level in maintaining security and privacy of their information.
The role of predictability in shaping phonological patterns

Abstract A diverse set of empirical findings indicate that word predictability in context influences the fine-grained details of both speech production and comprehension. In particular, lower predictability relative to similar competitors tends to be associated with phonetic enhancement, while higher predictability is associated with phonetic reduction. We review evidence that these in-the-moment biases can shift the prototypical pronunciations of individual lexical items, and that over time, these shifts can promote larger-scale phonological changes such as phoneme mergers. We argue that predictability-associated enhancement and reduction effects are based on predictability at the level of meaning-bearing units (such as words) rather than at sublexical levels (such as segments) and present preliminary typological evidence in support of this view. Based on these arguments, we introduce a Bayesian framework that helps generate testable predictions about the type of enhancement and reduction patterns that are more probable in a given language.
Hybrid digital-analog transmission systems : design and evaluation

During the last 25 years, most of the communication systems have been converted to purely digital technology, although the transmitted content mostly is analog by nature. The principal advantages of digital communication are compression by source encoding, bit error protection by channel coding and robust transmission over noisy channels by appropriate modulation. Digital systems are usually designed for worst case channel conditions. However, often the channel quality is much better, which is not reflected in an improved end-to-end transmission quality due to inevitable quantization noise produced by the source encoder. In this thesis, the focus is set on systems which • are not purely digital anymore, • benefit from increasing channel qualities, and • avoid the saturation effect using discrete-time, continuous-amplitude techniques. In the first part, purely analog, i.e., discrete-time and continuous-amplitude transmission systems are considered – with linear or nonlinear components. Theoretical performance limits are discussed and a new rate-distortion upper bound is derived which can be evaluated semi-analytically. The performance of linear transmission systems is derived analytically while simulations assess several nonlinear systems including the famous Archimedes spiral. A new class of nonlinear discrete-time analog coding systems, i.e., the Analog Modulo Block Codes (AMBCs) is developed. One important observation is that nonlinear discrete-time, continuous-amplitude systems can be decomposed into a discrete and a continuous-amplitudes part. The considered systems exhibit a considerable gap to capacity but they all circumvent the saturation effect due to the continuous-amplitude components. In the main part of the thesis, these findings are turned into a design principle. Hybrid Digital-Analog (HDA) transmission systems consist of separate digital and analog branches while each is constructed independently. By combining both worlds – digital and continuous-amplitude transmission – new concepts emerge which fuse their advantages: capacity achieving performance in the digital branch with a huge variety of conventional digital codes plus avoiding the saturation effect in the analog branch. The performance of HDA systems is assessed theoretically as well as by simulations. These Hybrid Digital-Analog (HDA) transmission systems outperform both purely digital and continuous-amplitude concepts. HDA transmission is an attractive solution for wireless systems such as microphones, loudspeakers or distributed sensors.
Evidence of Encoding Specificity for the Effects of Synopses on the Recognition of Believable and Unbelievable News Headlines

Gibbons, Lukowski, and Walker (2005) argued that elaborate processing explained the finding that unbelievable news headlines were recognized better than believable news headlines, and they suggested that headline synopses may further enhance processing. In Experiment 1, we tested whether elaborative synopses at presentation (i.e., learning) enhanced recognition, and we found that they decreased recognition. Instead of elaborate processing, these results were congruent with encoding specificity. In Experiments 2-5, we used reiterative synopses to create matching and mismatching synopsis conditions at presentation and test to assess encoding specificity. The results for Experiments 2-4 demonstrated encoding specificity for believable headlines, and the results for Experiment 5 showed encoding specificity for believable and unbelievable headlines. These results provide some support for encoding specificity in the realm of media headlines.
Optimizing the recovery of disrupted single-sourced multi-echelon assembly supply chain networks

Abstract We consider optimization problems related to the scheduling of Multi-Echelon Assembly Supply Chain (MEASC) networks that find application in the recovery from large-scale disruptive events. Each manufacturer within this network assembles a component from a series of sub-components received from other manufacturers and, due to high qualification standards, each sub-component of the manufacturer is single-sourced. Our motivating industries for this problem are defense aircraft and biopharmaceutical manufacturing. We develop scheduling decision rules that are applied locally at each manufacturer and are proven to optimize two industry-relevant global recovery metrics: (i) minimizing the maximum tardiness of any order of the final product of the MEASC network, and (ii) and minimizing the time to recover from the disruptive event. Our approaches are applied to a data set based on an industrial partner’s supply chain to show their applicability as well as their advantages over Integer Programming (IP) models. The developed decision rules are proven to be optimal, faster, and more robust than the equivalent IP formulations. In addition, they provide conditions under which local manufacturer decisions will lead to globally optimal recovery efforts. These decision rules can help managers to make better production and shipping decisions to optimize the recovery after disruptions and quantitatively test the impact of different pre-event mitigation strategies against potential disruptions. They can be further useful in MEASCs with or expecting a large amount of backorders.
Formation Lithology Classification: Insights into Machine Learning Methods

With the recent tremendous development in algorithms, computations power and availability of the enormous amount of data, the implementation of machine learning approach has spurred the interest in oil and gas industry and brings the data science and analytics into the forefront of our future energy. The idea of using automated algorithms to determine the rock facies is not new. However, the recent advancement in machine learning methods encourages to further research and revisit the supervised classification tasks, discuss the methodological limits and further improve machine learning approach and classification algorithms in rock facies classification from well-logging measurements. This paper demonstrates training different machine learning algorithms to classify and predict the geological facies using well logs data. Previous and recent research was done using supervised learning to predict the geological facies. This paper compares the results from the supervised learning algorithms, unsupervised learning algorithms as well as a neural network machine learning algorithm. We further propose an integrated approach to dataset processing and feature selection. The well logs data used in this paper are for wells in the Anadarko Basin, Kansas. The dataset is divided into training, testing and evaluating wells used for testing the model. The objective is to evaluate the algorithms and limitations of each algorithm. We speculate that a simple supervised learning algorithm can yield score higher than neural network algorithm depending on the model parameter selected. Analysis for the parameter selection was done for all the models, and the optimum parameter was used for the corresponding classifier. Our proposed neural network algorithm results score slightly higher than the supervised learning classifiers when evaluated with the cross-validation test data. It is concluded that it is important to calculate the accuracy within the adjacent layers as there are no definite boundaries between the layers. Our results indicate that calculating the accuracy of prediction with taking account the adjacent layers, yield higher accuracy than calculating accuracy within each point. The proposed feed-forward neural network classifier trains using backpropagation (gradient descent) provides accuracy within adjacent layers of 88%. Our integrated approach of data processing along with the neural network classifier provides more satisfactory results for the classification and prediction problem. Our finding indicates that utilizing simple supervised learning with an optimum model parameter yield comparable scores as a complex neural network classifier.
Computation offloading for algorithms in absence of the Cloud

Mobile cloud computing is a way of delegating complex algorithms from a mobile device to the cloud to complete the tasks quickly and save energy on the mobile device. However, the cloud may not be available or suitable for helping all the time. For example, in a battlefield scenario, the cloud may not be reachable. This work considers neighbouring devices as alternatives to the cloud for offloading computation and presents three key contributions, namely a comprehensive investigation of the trade-off between computation and communication, Multi-Objective Optimisation based approach to offloading, and Queuing Theory based algorithms that present the benefits of offloading to neighbours. Initially, the states of neighbouring devices are considered to be known and the decision of computation offloading is proposed as a multi-objective optimisation problem. Novel Pareto optimal solutions are proposed. The results on a simulated dataset show up to 30% increment in performance even when cloud computing is not available. However, information about the environment is seldom known completely. In Chapter 5, a realistic environment is considered such as delayed node state information and partially connected sensors. The network of sensors is modelled as a network of queues (Open Jackson network). The offloading problem is posed as minimum cost problem and solved using Linear solvers. In addition to the simulated dataset, the proposed solution is tested on a real computer vision dataset. The experiments on the random waypoint dataset showed up to 33% boost on performance whereas in the real dataset, exploiting the temporal
Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

MetaboAnalyst (https://www.metaboanalyst.ca) is an easy‐to‐use web‐based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever‐expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS‐DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta‐analysis, and network‐based multi‐omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web‐based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc.