Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Volume during Human Development: A Comparison of Imagej and Linear Measures on MRI

Brain volumes of 73 infants, children, and adolescents were determined in three planes (sagittal, axial, coronal) using ImageJ, and these were then compared separately and collectively to those obtained using simple linear measurements. The “R” package was used for statistical analyses. Correlations were strong for all measured and measured to calculated comparisons, with r values of 0.87 - 0.93 (p <0.001). Correlation of measured right and left cerebral hemispheric volume (means = 450 and 458 cm 3 , respectively) was r = 0.94 (p <0.001), and measured compared to calculated right or left cerebral hemispheric volumes were r = 0.90 and 0.86, respectively (p <0.001). The left cerebral hemisphere was greater in 43/73 (58%) brains. There was no correlation between the extent of the hemispheric volume percent differences or side to side asymmetries and age (r = -0.11; p = 0.34). The results indicate that calculated measurements of brain and cerebral hemispheric volumes are near identical to respective measurements obtained with ImageJ. The findings justify the use of linear measurements as a means of calculating regional and global brain volumes
Studying user behavior through a participatory sensing framework in an urban context

espanolEsta tesis presenta el diseno e implementacion de una plataforma participativa multi proposito (Citizense) creada a partir de los requerimientos obtenidos de la literatura existente. Esta plataforma le permite a los usuarios crear campanas para medir y recolectar informacion de multiples tipos. Por lo tanto Citizense fue desplegada en diferentes escenarios para verificar sus diferentes funcionalidades. A traves de dicho despliegues, se indentificaron multiples patrones de comportamiento. Primero, los usuarios reaccionario positivamente a los incentivos monetarios, siendo los micro pagos fijos preferidos sobre los micro pagos variables y los aleatorios. Los usuarios usualmente contribuyeron en su tiempo libre durante dias laborales, sin embargo esta decision estuvo correlacionada con el contexto geografico de las campanas o las actividades mas recientes. Fue posible clasificar los participantes entre dos grupos, un grupo pequeno de participantes activos que constantemente usaron la plataforma y un grupo mas grande de participantes regulares con un comportamiento mas intermitente. EnglishThe thesis details the design and implementation of a multi-purpose participatory sensing framework (Citizense) based on a list of requirements extracted from the literature. This framework enables ordinary users to create sensing campaigns and collect various types of data. Subsequently, Citizense is deployed in several real-world scenarios in which its designed features are verified. Through these deployments, several user behavior patterns have been identified. First, users react positively to all the three deployed monetary incentives. Among them, fixed micro-payment is preferred over lottery payout and variable micro-payment. They tend to contribute in their free time during the working week, although this decision seems to be correlated to the campaign’s geographical context and/or the recency of the participants’ activities. Participants can be divided into two groups: a smaller group of active participants who frequently perform sensing activities and a larger group of regular participants who exhibit more intermittent behaviors.
Shared

Forms – Shared Facility Request Adopted 07/15 Shared Facility Request See OSAA Handbook, Executive Board Policies, “Shared Facility” for additional information. All Sports: No practice or competition to or between groups approved to share a facility. It is recommended that each team have their own coaching staff and that staggered practice times be utilized when possible. Sport specific guidelines: SPORT GUIDELINES
Investigating and Supporting Sensemaking within Online Health Communities

Investigating and Supporting Sensemaking within Online Health Communities Drashko Nakikj This dissertation focuses on understanding and supporting individual and collective sensemaking within online health communities (OHCs). This major goal was achieved in three aims. In Aim 1, this dissertation contributes a rich descriptive account of collective sensemaking in OHCs forums by describing how it occurs and develops, what triggers it, what elements constitute collective construction of meaning, and what conversational moves positively contribute to this process. Further, it describes how collective sensemaking in OHCs is impacted by the interplay between informational and socio-emotional needs of OHCs members. Moreover, it examines how design of different social computing platforms influences OHCs members’ ability to meet their informational and socio-emotional needs and engage in collective sensemaking. In Aim 2, this dissertation explores the design space of tools for supporting individual sensemaking through optimized information access. Through the design and evaluation of a prototype DisVis it examines the impact of such tools on OHCs members’ ability to understand information within discussion threads. In the final Aim 3, this dissertation proposes a novel approach for meeting the three main needs identified in Aims 1 and 2: promoting individual sensemaking, while at the same time encouraging collective sensemaking, and facilitating development of social awareness and ties among community members. The design and evaluation of the novel solution for visualizing discussion threads that synergistically addresses these three needs—dSense— provides insights for future research and design of interactive solutions for supporting individual and collective sensemaking within OHCs.
A review of feature extraction and classification algorithms for image RSVP based BCI

In this chapter, we introduce an architecture for rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) systems that use electroencephalography (EEG). Hereafter, we will refer to the coupling of the RSVP protocol with EEG to support a target-search BCI as RSVP-EEG. Our focus in this chapter is on a review of feature extraction and classification algorithms applied in RSVP-EEG development. We briefly present the commonly deployed algorithms and describe their properties based on the literature. We conclude with a discussion on the future trajectory of this exciting branch of BCI research.
An Empirical Study of Human Mobility Patterns

This paper documents our efforts towards understanding which factors are more relevant in human mobility prediction. Our work is divided into two phases. First, we characterize a dataset consisting of more than 200,000 user check-ins in the Foursquare social network, inferring important patterns in human mobility. Second, we use factorial design to quantify the importance of several types of contextual information in human mobility prediction. Our results show that the proximity of the users possible next check-in to his or her home and work location are the most important factors (among the ones we analyzed) to be used by mobility prediction models.
Effect of Designed Teaching Protocol Regarding Patients' Safety after Cardiac Catheterization on Nurses' Performance and Patients’ Incidence of Vascular Complications

Background: Vascular access-site complications are an important cause of morbidity and mortality, which is related mainly to vascular percutaneous intervention procedures and requires safe and standardized care policies, as well as qualified and skilled health provider to obtain good outcome of management. Aim of the study: to investigate the effect of designed teaching protocol regarding patients’ safety after cardiac catheterization on nurses’ performance and patients’ incidence of vascular complications. Aquasi-experimental design was used to conduct the current study in cardiac catheterization and coronary care units at Benha University Hospital during the period from beginning of April 2018 till beginning of October 2018. Subjects: A sample of convenience of 50 nurses who are willing to participate in the study, and patients who admitted for performing cardiac catheterization were 170 patients recruited according to the study formula based on the total number admitted to the study settings during 2017. Tools: Two tools were utilized for data collection, (Ι) Structured interviewing questionnaire for nurses, (II) Patients’ assessment sheet. Results: Showed that nurses’ level of knowledge and practice post implementing the designed teaching protocol was higher than pre implementation, with a significant correlation between nurses’ knowledge and practice(p= <0.001**), besides a lower incidence of complications among patients in intervention group than in control group . Conclusion: The designed teaching protocol was effective in improving nurses’ knowledge and practice, which were positively associated with each other and lower incidence of vascular complications among patients in the intervention group. Recommendation: The study suggested equipping cardiac catheterization unit with simple illustrated guidelines protocol covering percutaneous coronary intervention procedure practices knowledge pre/post cardiac catheterization.
Development of Smoothed Particle Hydrodynamics approach for modelling of multiphase flows with interfaces

Smoothed Particle Hydrodynamics (SPH) is a fully Lagrangian, particle based approach for fluid-flow simulations. One of its advantages over Eulerian techniques is no need of a numerical grid. Therefore, there is no necessity to handle the interface shape as it is done in Volume-of-Fluid, Lavel-Set or Front-Tracking methods. Thus, the SPH approach is increasingly used for hydro-engineering and geophysical applications involving free-surface flows where the natural treatment of evolving interfaces makes it an attractive method. However, for real-life multi-phase simulations this method has only started to be considered and many problems like a proper formulation or a spurious fragmentation of the interface remain to be solved. One of the aims of this work is to critically analyse the existing SPH variants and assess their suitability for complex multi-phase problems. For modelling the surface-tension phenomena the Continuum Surface Force (CSF) methods are validated and used. The natural convection phenomena are modeled using a new, more general formulation, beyond the Boussinesq approximation. A substantial part of the work is devoted to the problem of a spurious fragmentation of the interface (the micro-mixing of SPH particles). Its negative effects and possible remedies are extensively discussed and a new variant is proposed. Contrary to general opinion, it is proven that the micro-mixing is not only the problem of flows with neglegible surface tension. A significant part of this work is devoted to the modelling of bubbles rising through liquids, including bubble-bubble interactions. The SPH simulations were performed for several flow regimes corresponding to different relative importance of surface tension, viscosity and buoyancy effects. The predicted topological changes, bubble terminal velocity and drag coefficients were validated with respect to reference experimental data and compared to other numerical methods. In the work, fundamental concepts of assuring the incompressibility constraint in SPH are also recalled. An important part of work is a thorough comparison of two different incompressibility treatments: the weakly compressible approach, where a suitably chosen equation of state is used, and truly incompressible method (in two basic variants), where the velocity field is projected onto a divergence-free space. Their usefulness for multi-phase modelling is discussed. Problems associated with the numerical setup are investigated, and an optimal choice of the computational parameters is proposed and verified. For these purposes the study is supported by many two- and three-dimensional validation cases. In addition, the present work opens new perspectives to future simulations of boiling phenomena using the SPH method. First ideas and sketches for the implementation of the liquid-vapour phase change are presented
Nanoparticles applications in poultry production: an updated review

SUMMARY Nanotechnology is an innovative promising technology that has a great scope of applications as well as a socio-economic potential in poultry industry sector. Nanoparticles (NPs) show advantages of high absorption and bioavailability with more effective delivering to the target tissue compared to their bulk particles. Nanomaterials show variable forms, sizes, shapes, applications, surface modifications, charges and natures. Top-down and bottom-up methods are the common types of NPs preparation. There are different mechanisms through which NPs could exert their action. In the poultry field, NPs have been considered in diagnosis of many diseases using different techniques, vaccines preparations and immuno-stimulation, disinfection, production enhancement, detection of food adulteration and antimicrobial activities (antiviral, antibacterial, antiparasitic, antifungal) and antimycotoxins. As a result of enhancement of poultry production and reducing the bacterial load after using NPs, the antibiotics drug resistance obstacle can be overcome. Despite the wide benefits of using NPs in poultry production, some concerns about their safety and hazardous effects should be regarded. Therefore, this review article is focused on NPs’ types, manufacture, mode of actions, and different applications with reference to safety and hazards impact.
Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.
Multicriteria recommendation based on bacterial foraging optimization

Recommender systems assist users to make decisions among a huge volume of options. Accuracy‐oriented recommender systems focus on the prediction power of algorithms and neglect that users may appreciate diverse and novel recommendations in real‐world scenarios. Thus, this paper proposed a multicriteria recommendation model that can optimize the recommendation accuracy, diversity, novelty, and individual tendency simultaneously. Additionally, a new multiobjective bacterial foraging optimization method is proposed to improve its searching capability and the performance of recommendation model. The proposed optimization‐based multicriteria recommendation algorithm is compared with existing methods on both benchmark functions and real‐world data sets. The results demonstrate that the proposed algorithm is superior to other recommendation algorithms in most cases. This study provides insights in recommendation system design and draws scholarly attention to the optimization‐based recommendation strategy.
Scalability and some optimization of the Finite-volumE Sea ice-Ocean Model, Version 2.0 (FESOM2)

Abstract. A study of the scalability of the Finite-volumE Sea ice-Ocean circulation Model, Version 2.0 (FESOM2), the first mature global model of its kind formulated on unstructured meshes, is presented. This study includes an analysis of main computational kernels with a special focus on bottlenecks in parallel scalability. Several model enhancements, improving this scalability for large numbers of processes, are described and tested. Model grids at different resolutions are used on four HPC systems with differing computation and communication hardware to demonstrate model's scalability and throughput. Furthermore, strategies for improvements in parallel performance are presented and assessed. We show that in terms of throughput FESOM2.0 is on par with the state-of-the-art structured ocean models and in realistic eddy resolving configuration (1/10° resolution) can produce about 16 years per day on 14 000 cores. This suggests that unstructured-mesh models are becoming extremely competitive tools in high-resolution climate modelling. It is shown that main bottlenecks of FESOM parallel scalability are the two-dimensional components of the model, namely the computations of external (barotropic) mode and the sea-ice model. It is argued that these bottlenecks are shared with other general ocean circulation models.
Vision-Based Skin Disease Identification Using Deep Learning

Skin disease is the most common health problems worldwide.Human skin is one of the difficult areas topredict. The difficulty is due to rough areas, irregular skin tones, various factors like burns, moles. We have to identify the diseases excluding these factors.In a developing country like India, it is expensive for a large number of people to go to the dermatologist for their skin disease problem.Every year a large number of population in developing countries like India suffer due to different types of skin diseases. So the need for automatic skin disease prediction is increasing for the patients and as well as the dermatologist. In this paper, a method is proposed that uses computer vision-based techniques to detectvariouskinds of dermatological skin diseases. Inception_v3, Mobilenet, Resnetare three deep learning algorithms used for feature extraction in a medical image and machine learning algorithm namely Logistic Regression is used for training and testing the medical images.Using the combined architecture of the three convolutional neural networks considerable efficiency can be achieved.
Wound Healing Gel Based on Chlorella vulgaris Extract Characterization

Chlorella vulgaris cell extract was used to develop wound-healing products by our research group. However, it is required to characterize this formulated for improved patient compliance and to reduce the force required to spread the product on the injured, painful and sensitive areas. Therefore, C. vulgaris extract was characterized by the presence of phytochemicals and proteins, the formulation was characterized in relation to organoleptic properties, pH, and rheological profiles, while both cell extract and excipients of the formulation were analyzed by infrared spectroscopy and thermogravimetric (TGA). In the cell, extract contain 1.35 ± 0.18 mg/g AG of total phenol content and 65.57 μg mL-1 of one lectin of 36 kDa with the hemagglutinating activity of ≥ 23 for erythrocyte type B. The formulation showed a green color, smooth texture, pH of 7.09 ± 0.07, suitable for skin injury and pseudoplastic behavior. The infrared showed the presence of amine, carbohydrates, and terpenes in the cell extract and the absence of chemical interaction between the cell extract and carbopol hydrogel. The formulation is very stable near room temperature. The results obtained will provide parameters for the production of inputs in the pharmaceutical industry, as well as for the quality control of intermediates and the finished product based on C. vulgaris.
An Efficient Approach on Constant Human Motion Detection in Survileicne

Video handling is a standout amongst the most difficult zones in picture preparing. It manages distinguishing a protest of intrigue. Movement identification has been utilized as a part of numerous fields either straightforwardly or by implication. In this paper a proficient way to deal with continuous movement recognition in video observation utilizing shading highlight extraction administrator. Utilizing this approach, we enhance the foundation subtraction and recognizing the moving item with more noteworthy precision. In this paper, foundation demonstrating is done keeping in mind the end goal to make the refresh of foundation because of light brightening and change in the climate condition. Forefront identification is done before refreshing the foundation demonstrate. Shading highlight extraction is done with a specific end goal to keep away from the dynamic foundation, for example, moving leaves, rain, snow, undulating water
A Review of Purchasing Preferences for Margarine among Hungarian and International Students

This paper assesses consumer preferences for margarine among Hungarian and foreign university students (studying in Hungary) by using the discrete choice experiment. The questionnaire-based survey was preceded by a focus group interview which, supplemented with knowledge gained from literature, established product attributes involved in the examination (such as price, fat, salt and sunflower oil content). Results suggest that the increase in fat and salt content result in reduced con sumer utility and willingness to pay for margarine products. Sunflower oil content, however, was not found to play a significant role in consumer choices. When comparing the two groups, we found that international students tended to be more health conscious than their Hungarian counterparts.
Customers’ Intention in Internet Banking Adoption: The Moderating Effect of Demographic Characteristics

The growing number of internet banking (i-banking) users in Bangladesh requires examining their behavioral intention (BI) to adopt i-banking services. This study aims to investigate the factors that influence the customers’ intention in i-banking adoption encompassing the moderating effect of demographic characteristics. This study uses an expansive approach of the Unified Theory of Acceptance and Use of Technology (UTAUT) model with customer satisfaction. The structural equation modeling (SEM) has been used to analyze the data collected from the internet banking users of Bangladesh. Findings of the study conclude that the higher the level of performance expectancy (PE), effort expectancy (EE) and social influence (SI), the greater the level of customers’ inclination to adopt i-banking. Whereas, facilitating condition negatively affects their BI in i-banking adoption. Moreover, this study finds a positive and significant impact of system, service and information quality and security, privacy and trust (SPT) on customers’ satisfaction, which, in turn, substantially influence their intention toward i-banking adoption. While investigating the moderating effect of demographic characteristics, this study finds that all categories of customers, regardless of their age, gender, experience and education, are influenced by the customers’ satisfaction of services qualities and trust perception in i-banking adoption. Therefore, it is suggested to the policymakers that they should take such initiatives in designing the i-banking system that satisfy the customers’ expectations.