Miracle-dz/UniFAR-FTUs huggingface dataset:
Echocardiographic Characteristics of Subjects With COVID-19: A Case Series

Although coronavirus disease 2019 (COVID-19) manifests in most cases with respiratory symptoms, other presentations can occur. Direct damage to the cardiovascular system has been reported and recently, acute myocardial injury has been identified as a risk factor for mortality. Transthoracic echocardiography is a non-invasive tool that allows the detection of myocardial damage with validated markers (left ventricular ejection fraction and global longitudinal strain). Herein, we present the echocardiographic findings in four patients with COVID-19. All cases had acute respiratory distress syndrome (100%). Three out of four had elevated levels of creatine kinase and creatine kinase myocardial band. One case had ventricular concentric remodeling (25%). All cases (100%) had altered ventricular function: two had a reduced ejection fraction (50%) and, of those available for global longitudinal strain analysis, all had abnormal global longitudinal strain (100%). One case was found to have a tricuspid vegetation of 12 × 10 mm with no other manifestation of endocarditis. All of our cases had left ventricular dysfunction as assessed by echocardiography. One of our patients had a vegetation in the tricuspid valve. Two of our cases had a reduced ejection fraction. The importance of acute cardiac injury in COVID-19 has recently been established. A recent study found it to be an independent risk factor for mortality in patients with this disease. Information regarding echocardiographic characteristics of this population is scarce. Further research to elucidate the impact of these characteristics on morbidity and mortality is urgently needed.
Exploring the Tourism-Poverty Alleviation Nexus in the Brics Groupof Nations

The tourism-poverty alleviation nexus is becoming an increasingly significant subject of academic inquiry within the tourism economics discourse. Using time series data from the World Bank (1995–2017) in a P-ARDL model, the present study explores the relationship between tourism (receipts from exports, the travel subsector, hospitality and accommodation subsector) and poverty alleviation (final household consumption) with tourism arrivals as the control variable within the context of the BRICS group. The results suggest that receipts from the travel subsector and exports met the a priori expectation – positively influencing poverty alleviation within BRICS nations in the long run. Contrastingly, receipts from the hospitality and accommodation subsector did not meet the a priori expectation of a positive sign, with the results indicating statistical insignificance in the long run. However, receipts from the hospitality and accommodation were found to only influence poverty alleviation in the short run. Relatedly, the results suggest that increases in consumption associated with growth in tourism arrivals did not influence poverty in the BRICS. The results point to the heterogeneity of the influence of tourism on poverty alleviation, whereby certain dimensions of tourism contribute to poverty alleviation in the long run and others do so in the short run. Based on these findings it is recommended that BRICS countries harness their tourism potential and promote intra-BRICS tourism to maximise the positive impact of travel and tourism export receipts on household consumption, which catalyses poverty alleviation.
Concrete Damage Detection Based on Machine Learning Classification of Terrestrial Laser Scanner Point Clouds

The detection of surface damage on concrete structures is an important requirement to assess the integrity and safety of the structure. This paper develops a machine learning-based model for high-precision detection of damage on concrete surfaces using terrestrial laser scanner point clouds (PCs). The developed damage detection model relies on a support vector machine (SVM) algorithm to train a point-wise defect classifier for locating the concrete damage. It employs an unsupervised defect clustering approach to accurately annotate the training data without the need for exhaustive manual interventions. The distinctive features used in this classifier include local point density and a variety of eigenvalue-based features such as change of curvature, eigenentropy, and dimensionality features. The statistical redundancy and correlation of features are assessed through a classifier-independent statistical measure. The influence of the selected features and the model parameters are investigated. The performance of the proposed approach was evaluated extensively on three real datasets: a 12 m long span comprising the first five flumes of a concrete aqueduct with over 250 million points as well as two civil pedestrian concrete structures with 150 million points and 10 million points, respectively. A small part of the aqueduct site with 9000 points on concrete surfaces, balanced between points with and without damage, was used for training the system. The use of machine learning with a relatively heterogeneous dataset enables the development of a concrete damage detection system that can account for limiting conditions of PC processing, e.g., irregular and varying point density and optimal neighbourhood size, and enables detection of various types of concrete damage. The results obtained from these three datasets demonstrate the validity of the proposed supervised model for reliable prediction of the location of damage of any type which makes roughness as small as 1 cm or smaller on the surface of concrete structures captured with any laser-scanning PC with a minimum spatial resolution of 5 mm. This yields an average classification precision and F1-score of 97.33% showing the potential of using machine learning for concrete damage detection.
The Impact of Social Media Marketing on Customer Purchase Intention among the Fashion-Wear Customers in Sri Lanka

Social media provide new varieties of communication between marketers and consumers. The amount of information available to consumers’ increases day-by-day, resulting in being expose to different types of information via social media. Most of the marketers want to find some way to follow their target audience with a small period. Customers make use of information published on different social media applications such as Facebook, Twitter and, Instagram as a guide for their future purchases. When considering about the purpose of the study, mainly this study target to give information and knowledge for future researchers and people who are interested in fashion. Moreover, how the Facebook impact on purchase intention among customers is also concentrate in this study. Furthermore, this study important for academic purpose and which use for business purpose also. This research also has some limitations. The main limitation is less number of availability of past researchers. Moreover, less concentrate on how the Facebook impact on fashion wears customers is also considering as a limitation of this study.
Comment on EFSA’ s draft on Synthetic Biology developments in micro-organisms, environmental risk assessment aspects (ERA)

General comments Applications of synthetic biology to micro-organisms (SynBio-MO), including, for example, bacteria, viruses, fungi and yeast, may become a threat to the global biosphere to greater extent compared to many other applications of genetic engineering of more complex species. Microorganisms (MO) are the basis of life on earth and the common network of all existing species. They are essential for mobilising and exchanging abiotic and biotic resources. Further, they transmit biologically active molecules and information across boundaries between species. The emergence of diseases further adds to the complex interactions between MO and other species. MO reproduce fast compared to more highly developed species and are thus adept at spreading rapidly in their environments. Therefore, in dealing with SynBio MO, the precautionary principle deserves to be given the highest priority.
DISCLOSING TWEET POLARITY USING FEATURE REPRESENTATION PRACTICE

-With the establishment of Internet and the World Wide Web, one has experienced a substantial escalation of data on the web. But in the past decade, novel variety of communication, such as Twitter, Facebook, Blogs, have emerged and have become a medium through which public not only convey messages but also expresses their sentiments or feelings regarding activities happening all around the globe. The concept of cataloguing these views of people as positive, negative or neutral will provide us with the insights which can prove to be a cutting edge for various organizations. In this paper, we are focusing on performing classification of twitter messages with the help of Feature Selection (Bag of Words) and Feature Weighting (Term Frequency-Inverse Document Frequency) practices. The two different datasets has been fetched which will consist of tweets on two topics i.e. iphone and samsung mobile phones respectively and hence, prediction will be made on the basis of the results of experiments which will be portrayed in the form of histograms and a word cloud.
DCE-CT Perfusion Parametric Coloured Image Using Steepest Gradient Approach Based on Time Frame

Technological advances in medical imaging modality, counting higher special and temporal resolution, have been utilized to aid radiologist in diagnosing, prognostic and, hence, management of brain tumour. After tracer injection, CT scanning along with pharmacokinetic models are applied to measure several hemodynamic parameters used to describe microvasculature within a targeted region of interest (ROI). These parameters are a choice for diagnostic and prognostic and the likelihood of preferable treatment for brain tumour. The perfusion pixel-wise perfusion map image is a fingerprint of abnormality of ROI within brains that aid radiologist to separate between necrotic and neoplastic of tumour territory and to avoid arterial partial-volume averaging. This work aims to generate pixel-wise perfusion map colour image of brain by using the steepest gradient approach based on the time frame of tracer passage.
CROSS-LANGUAGE TEXT ALIGNMENT FOR PLAGIARISM DETECTION BASED ON CONTEXTUAL AND CONTEXT-FREE MODELS

In this paper, we present a dataset for cross-language (Russian-English) text alignment subtask of plagiarism detection. We compare different models for detecting translated plagiarism. One is based on different textual similarity scores, which exploit word embeddings. Another model extends the previous one with the features obtained via neural machine translation. The last model is built on top of pre-trained language representation (Bert) via fine-tuning for our task. The Bert model shows great performance and outperforms other models. However, it requires much more computation resources than simpler models. Therefore, it seems reasonable to use both context-free models and contextual models together in modern plagiarism detection systems.
RANSOMWARE DETECTION USING CLASSIFICATION METHOD AGAINST REGISTRY DATA

An intrusion detection system (IDS) is used to detect numerous kinds of malware attacks, and many classification methods have been introduced by the researcher to detect malware behavior. However, even though various classification method has been proposed, the detection of malware behavior remains a challenging task as the detection method focusing more on traffic data classification. Consequently, there is a lack of classification approach employed to classify Windows Registry data for malware detection. Such a situation could cause more damages if the ransomware activity intended to affect registry besides traffic. Henceforward, the objective of this paper is to study the malware behavior which targeted registry and analyzing a series of machine learning algorithm as well as identify the most accurate algorithm in the detection of malware. Thus, this paper proposes a framework for ransomware detection by using registry data as features through a number of a machine learning algorithm. Based on conducted literature, Support Vector Machine, Decision Tree, Random Forest, Jrip, and Naïve widely applied as a classification method for malware detection. The experiments have been carried out via the algorithm mentioned above against registry data that been affected by ransomware. The algorithm is capable of classifying registry data to detect ransomware activity precisely. The main contribution of this research illustrates that registry data could be examined via the proposed framework ‘Malware Registry Detection Framework (MRDF)’ specifically for malware detection. The findings of this experiment is the capability of the proposed method to identify ransomware activity and classify which machine learning algorithm come with the highest detection rate.
Can we directly survey adherence to non-pharmaceutical interventions? : Evidence from a list experiment conducted in Germany during the early Corona pandemic

Self-reports of adherence to non-pharmaceutical interventions in surveys may be subject to social desirability bias. Existing questioning techniques to reduce bias are rarely used to monitor adherence. We conducted a list experiment to elicit truthful answers to the question whether respondents met friends or acquaintances and thus disregarded the social distancing norm. Our empirical findings are mixed. Using the list experiment, we estimate the prevalence of non-compliant behavior at 28%, whereas the estimate from a direct question is 22%. However, a more permissively phrased direct question included later in the survey yields an estimate of 47%. All three estimates vary consistently across social groups. Interestingly, only the list experiment reveals somewhat higher non-compliance rates among the highly educated compared to those with lower education, yet the variance of the list estimates is considerably higher. We conclude that the list experiment compared unfavorably to simpler direct measurements in our case.
Readiness and Understanding of Technical Vocational Education and Training (TVET) Lecturers in the Integration of Industrial Revolution 4.0

This study aims to determine the readiness and understanding of Technical Vocational Education and Training (TVET) lecturers in the implementation of Industrial Revolution 4.0 (IR4.0). A total of 203 TVET lecturers in Malaysia were involved in this study, which also used descriptive and inferential statistics. Findings reveal a high level of readiness, understanding and implementation of IR 4.0 amongst the TVET lecturers. A significant relationship likewise exists amongst readiness, understanding and implementation of IR4.0. Findings also show that the TVET lecturers’ understanding is a mediator of the relationship between readiness and implementation of IR4.0. Lastly, results imply that the lecturers can further enhance their effort in implementing IR4.0 to achieve the Mission of 2020.
Study on hypergolic ignition of ionic liquid solutions

In recent investigations at DLR Lampoldshausen a highly active catalytically promoted ionic liquid fuel with hydrogen peroxide was found. This fuel is based on an ionic liquid and a dissolved metal salt as catalytic promoter. But due to high viscosity of this fuel and bad mixing behaviour between hydrogen peroxide and fuel reliable ignition was not achieved. This investigation introduced different amounts of an organic solvent to the fuel and drop test were conducted to evaluate hypergolic performance. Repeatable ignition was achieved and the ignition delay times were around 28 ms.
EXTRACTION AND PROCESSING OF SITUATION SPATIOTEMPORAL TRAFFIC USING SVM ALGORITHM WITH BIG DATA

With the wide variety of motion sensors that traffic information can come from many research has been reserved for the development of traffic forecast, which in turn increases the shipping routes, traffic management, urban planning, etc. The most important challenge is to predict how traffic based on predictive models based on historical data traffic in real time, which may differ from historical data and change over time. In this system can learn new context of the current online traffic situation (or context) in real time, most effectively formed using a predictive historical data traffic model is intended to predict the future of the current situation. If traffic in real time, distributed environment enters the bloodstream space efficiently adapt to assess the effectiveness of each significant predictor different situations. We can show you the way, and short-term and long-term performance guarantees (STEP), our algorithm is designed in accordance with the algorithm works well in situations where there are no real signs (for ex. Traffic Ready) or later. We proposed an algorithm called “ Extraction and Processing of situation Spatiotemporal traffic using SVM algorithm with Big data” By using the proposed framework, a context in which the most important is to predict the traffic by monitoring the movement of vehicles, which can further reduce the complexity of the request and inform the trade-policy. Our experience with real data in real-time circumstances indicates that the proposed approach is superior to existing solutions.
Portable, Low Cost, Low Power Cardiac Interpreter

Electrocardiogram (ECG) is one of the most important tools for the diagnosis of disorders related to the cardiovascular system. The ECG signal is produced by the heart in form time varying potentials during the cardiac cycle. The signal contains some characteristic peaks, polarities and time periods which are used for detecting underlying abnormalities in the functioning of the heart. The aim of the project is to design an automatic, easy to read system as opposed to the manual techniques employed by physicians. The purpose is to acquire ECG signals from a patient’s body, extract the parameters required and suggest a diagnosis with respect to the extracted parameters. This is low cost, consumes low power and is portable. The output is represented in a suitable way such that any individual with a very basic knowledge of computers, irrespective of their background can operate this and derive a basic understanding of one’s heart condition. In this way one can approach close to a medical opinion by detecting the probable diseases before consultation with a doctor. Also, the outputs received after processing can be communicated between PCs via email.
Short-Term Forecast of Ukrainian Economy Including Shadow Sector Using Causal Simulation Model

Abstract A new causal simulation model of economic development was created, which comprehensively in detail and fully reflects various types of legal and shadow economic activities and their interrelations. The model is used to forecast the whole (legal and shadow) country’s economy up to 2022. The dynamics of shadow and legal indicators are different. The biggest and most important difference is about exports and imports. Official statistics give a negative balance of the Ukrainian foreign trade of Ukraine in 2019-22. However, total export, determined by the model, considerably exceeds imports, so actually we expect a surplus. This is very important for the National Bank: its policy based on the official (legal) negative balance of Ukraine foreign trade should be one (throw foreign currency reserves into the market or to devalue the hryvnia), but with the actual balance that includes shadow flows and is positive, - contrary one (to buy currency on the market or to revalue the national currency). Our model calculates how the production volumes of all types of goods and services should change to ensure that supply and demand are balanced. These numbers can serve a reference for manufacturers. We suggested that the relevant Ukrainian authorities take an active position in the implementation of the developed forecast for the economic development of Ukraine: measuring actual rates of changes in the production of these types of goods during the year, they provide recommendations to producers to increase or decrease their production.
Electrophoretic Study of Taxonomic Relationships in the Saussurea nipponica Complex (Compositae)

The Saussurea nipponica complex was examined with enzyme electrophoresis. Earlier morphological evidence suggested that the complex comprises S. sugimurai and seven subspecies of S. nipponica. Electrophoretic evidence supports the separation of the two species and the subspecies classification of S. nipponica. Nei's genetic distance (D= 0.19) between S. sugimurai and S. nipponica exceeds the estimated infraspecific level. Saussurea nipponica is newly defined as a species composed of seven infraspecific taxa which have their own distribution ranges and habitats. They are in a very early stage of differentiation. The extensive morphological diversity in S. nipponica is presumed to be a result of geographical isolation under strong environmental selection.
A new numerical model for coral distribution

A critical review of previous numerical work on coral distribution has shown that all previous workers have used models which assume the probability of occurrence of a coral to be spatially uniform over the study area. This is not necessarily true. Here we define a new 'constrained occupancy model' which permits spatial variation in probability. A case study involving extensive data (46 transects) on the distribution and abundance of 8 coral species and 2 octocorals throughout the Caribbean was used to test the new model against the commonly used Poisson model. The Poisson model demonstrated that 4 of these species were highly significantly aggregated in distribution and six were random. The new constrained occupancy model, allowing for variation in probability of occurrence, demonstrated that, in fact, 1 tended towards aggregation, most were random, and 2 species were significantly uniform in distribution. We believe that accounting for variation in probability of occurrence in sessile organisms is important when analyzing their distribution, and we consider these findings warrant the attention of field ecologists. While no analytic solution to the new model is known, computational procedures are presented, some of which can be used with a Monte Carlo simulation.
Investigating Ways to Assess ICT4D’s Impact on the Larger Community

As the field of ICT4D is starting to mature, its research is increasingly concerned with evaluating the contribution of ICT projects towards development. However, impact evaluation remains too focused on easily measurable, direct and predominantly economic benefits that are not appropriate for measuring human development. ICT4D evaluation needs to look at intangible benefits as well, at an individual as well as collective level. Very little has been done to date in terms of qualitative impact evaluation at collective or community level. This study investigates impact assessment frameworks that could be used to pay attention to ICT’s impact on the broader community. A literature search is used to identify suitable frameworks, which are then described and analysed. Evidence is presented of these frameworks’ prior successful application in ICT4D. The frameworks presented are Sen’s Capability Approach, the Sustainable Livelihoods Framework as well as a recently developed framework using Social Autopoiesis. The findings of the analysis are as follows: All the frameworks assessed meet the criteria for being able to consider intangible benefits, evaluate human development, and being applied to assess impact at the community level. However, they are all difficult to apply, requiring effort to ensure that qualitative framework concepts are interpreted correctly, and requiring more time and effort to collect and interpret data. Since the need to do more thorough impact assessment remains, it means that the ICT4D community needs to take impact assessment more seriously and be willing to invest more in it. The contribution of this paper is to alert ICT4D researchers and practitioners of the need to do community level impact assessment, to provide guidance by means of a discussion of evaluation frameworks that could be applied at community level, and to point to the commitment required to perform appropriate impact assessment.