Miracle-dz/UniFAR-FTUs huggingface dataset:
National and Universal Values Specific to Mathematics Education from the Perspective of Prospective Mathematics Teachers

The purpose of this study is to reveal opinions of prospective teachers about national and universal values specific to mathematics education. 230 prospective teachers attending 1st, 2nd, 3rd and 4th year Mathematics Teaching Program, Faculty of Education, Giresun University during fall semester of 2015-2016 academic year. The study utilizes the qualitative research approach. The data were analyzed via the descriptive and content analysis methods. Eleven different themes were obtained as a result of the data analysis: love and value, righteousness, protecting cultural heritage, placing importance on aesthetics, fairness, patriotism, humility, leadership, creativity and success, sharing, independent and free thinking. It was found as a result of the study that prospective teachers sufficiently internalized national and universal values specific to mathematics education. In conclusion, it is expected that prospective teachers, who will be implementers of the curriculum in near future, possess values specific to mathematics education.
LayBack: SDN Management of Multi-Access Edge Computing (MEC) for Network Access Services and Radio Resource Sharing

Existing radio access networks (RANs) allow only for very limited sharing of the communication and computation resources among wireless operators and heterogeneous wireless technologies. We introduce the LayBack architecture to facilitate communication and computation resource sharing among different wireless operators and technologies. LayBack organizes the RAN communication and multi-access edge computing (MEC) resources into layers, including a devices layer, a radio node (enhanced Node B and access point) layer, and a gateway layer. LayBack positions the coordination point between the different operators and technologies just behind the gateways and thus consistently decouples the fronthaul from the backhaul. The coordination point is implemented through a software defined networking (SDN) switching layer that connects the gateways to the backhaul (core) network layer. A unifying SDN orchestrator implements an SDN-based management framework that centrally manages the fronthaul and backhaul communication and computation resources and coordinates the cooperation between different wireless operators and technologies. We illustrate the capabilities of the introduced LayBack architecture and SDN-based management framework through a case study on a novel fluid cloud RAN (CRAN) function split. The fluid CRAN function split partitions the RAN functions into function blocks that are flexibly assigned to MEC nodes, effectively implementing the RAN functions through network function virtualization. We find that for non-uniform call arrivals, the computation of the function blocks with resource sharing among operators increases a revenue rate measure by more than 25% compared to the conventional CRAN where each operator utilizes only its own resources.
Current Knowledge on Exostoses Formation in Hereditary Multiple Exostoses: Where do Exostoses Originate and in What Way is their Growth Regulated

Multiple hereditary exostoses is an autosomal dominant inherited disease causing exostoses: growth on the bones of children. The disease is mainly caused by mutated exostosin (EXT)-1 or EXT-2 genes. These mutations yield non-functional EXT-gene products. Lack of functional proteins cause a defect in heparan sulphate synthesis and therefore in proteoglycan modification and cell signalling. It is assumed that a subset of chondrocytes form an exostoses, through a growth and differentiation process which is only partially understood. The place of origin of these exostoses-forming chondrocytes is still unknown. We also do not know in detail which processes influence the exostoses growth, and what shelters the exostoses from being resorbed by osteoclast activity. In this paper we systematically review the major pathophysiological theories of exostoses, with a focus on the aforementioned knowledge gaps.
Graviton: Trusted Execution Environments on GPUs

We propose Graviton, an architecture for supporting trusted execution environments on GPUs. Graviton enables applications to offload security- and performance-sensitive kernels and data to a GPU, and execute kernels in isolation from other code running on the GPU and all software on the host, including the device driver, the operating system, and the hypervisor. Graviton can be integrated into existing GPUs with relatively low hardware complexity; all changes are restricted to peripheral components, such as the GPU's command processor, with no changes to existing CPUs, GPU cores, or the GPU's MMU and memory controller. We also propose extensions to the CUDA runtime for securely copying data and executing kernels on the GPU. We have implemented Graviton on off-the-shelf NVIDIA GPUs, using emulation for new hardware features. Our evaluation shows that overheads are low (17-33%) with encryption and decryption of traffic to and from the GPU being the main source of overheads.
Efficacy of the combination of crude extracts of Solanum nigrum and Plumbago capensis on Leishmania major

Background:Leishmaniasis is an endemic tropical disease caused byLeishmaniaparasites, transmitted mainly by phlebotomine sandflies, impacting both health and socioeconomic wellbeing. Currently there are inadequate therapeutic measures to manage the disease thus indicating the need for the development of affordable and effective therapeutic interventions from herbal plants as alternative medicine. This study investigated theinvitroantileishmanial effects of blends of crude extracts ofSolanum nigrumandPlumbago capensisagainstLeishmania major.Methods:The promastigote parasites ofLeishmania majorwere cultured and grown for 3 days in different concentrations of the individual extracts to determine minimum inhibitory concentrations (MIC). Thein vitroantileishmanial efficacy was determined by exposing promastigotes and macrophages infected withL. majorto the blends of extracts in ratios of 2000:250, 1000:500, 500:1000 and 250:2000. Finally, nitric oxide released byL. majorinfected macrophages that were treated with the plant extracts at ratio of 125:125 was quantified using a standard nitrite curve.Results:The individual methanol extracts were most effective in inhibiting the growth of promastigotes with MIC values between 0.25 mg/ml and 1.0 mg/ml as compared to aqueous extracts. The most active ratios for the blends were 250:2000 and 2000:250 for methanolic and aqueous blends respectively. The infection rates and multiplication indices associated with all the combined extracts were significantly different (P< 0.05) from those of pentostam and amphotericin B at all the concentrations studied. The OD for the combined test extracts ranged between 0.034 and 0.041 and these corresponded to < 5 µM of NO.Conclusion:Findings from this study demonstrate that combination therapy usingS. nigrumandP. capensisextracts is effective in treatingLeishmania major infection. Based on our findings we recommendin vivostudies to be conducted to determine the efficacy of these combined therapies againstLeishmaniamajor.
White out of mind: Identity suppression as a coping strategy among Whites anticipating racially charged interactions

Discussing racial issues often makes Whites anxious, particularly when their conversation partners are Black. We theorized that Whites seek to avoid anxiety by suppressing thoughts of White identity prior to such interactions. In Study 1, White participants expected to discuss a race-related or nonracial topic with a Black or White partner. An Implicit Association Test (IAT) measured subsequent changes in the activation of participants’ White identities (i.e., self–White associations). The prospect of discussing race-related (vs. nonracial) topics with a Black partner reduced participants’ self–White associations, implying identity suppression. Moreover, participants’ nonverbal responses suggest that identity suppression functioned to mute participants’ anxiety. In Study 2, participants completed the identity activation measure only after learning that they would not interact with a partner. Consistent with “rebound” effects known to follow suppression, participants who previously expected to discuss a race-related topic with a Black partner showed heightened self–White associations.
Bounds Analysis of Competing Risks: A Nonparametric Evaluation of the Effect of Unemployment Benefits on Migration in Germany

"This paper suggests an approach for analyzing a dependent competing risks model in presence of partly identified interval data. We apply our nonparametric bounds framework to empirically evaluate the effect of unemployment benefits on the cumulative incidence of local job finding and inter-regional migration of unemployed workers in Germany. Our findings indicate that reducing the entitlement length for unemployment benefits has heterogenous effects depending on the household composition and the wage replacement ratio in absence of unemployment benefits. " (Author's abstract, IAB-Doku) ((en))
Learning Computer Vision through the development of a Camera-trackable Game Controller

The trade-off between the available classroom time and the complex- ity of the proposed task is central to the design of any Computer Science labora- tory lecture. Special care must be taken to build up an experimental setup that allows the students to get the most significant information from the experience without getting lost in the details. This is especially true when teaching Com- puter Vision concepts to prospective students that own little or no previous background in programming and a strongly diversified knowledge with respect to mathematics. In this paper we describe a setup for a laboratory lecture that has been administered through several years to prospective students of the Computer Science course at the University of Venice. The goal is to teach basic concepts such as color spaces or image transforms through a rewarding task, that is the development of a vision-based game controller similar in spirit to the recent human-machine interfaces adopted by the current generation of game consoles.
Study of the electromyographic signal dynamic behavior in Amyotrophic Lateral Sclerosis (ALS)

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor neurons degeneration, which reduces muscular force, being very difficult to diagnose. Mathematical methods are used in order to analyze the surface electromiographic signal’s dynamic behavior (Fractal Dimension (FD) and Multiscale Entropy (MSE)), evaluate different muscle group’s synchronization (Coherence and Phase Locking Factor (PLF)) and to evaluate the signal’s complexity (Lempel-Ziv (LZ) techniques and Detrended Fluctuation Analysis (DFA)). Surface electromiographic signal acquisitions were performed in upper limb muscles, being the analysis executed for instants of contraction for ipsilateral acquisitions for patients and control groups. Results from LZ, DFA and MSE analysis present capability to distinguish between the patient group and the control group, whereas coherence, PLF and FD algorithms present results very similar for both groups. LZ, DFA and MSE algorithms appear then to be a good measure of corticospinal pathways integrity. A classification algorithm was applied to the results in combination with extracted features from the surface electromiographic signal, with an accuracy percentage higher than 70% for 118 combinations for at least one classifier. The classification results demonstrate capability to distinguish members between patients and control groups. These results can demonstrate a major importance in the disease diagnose, once surface electromyography (sEMG) may be used as an auxiliary diagnose method.
Pearlin Mastery Scale IRT Item Parameter Estimates, Scores and Standard Errors with Custom Weighted Z-Scores and Percentile Ranks

For the NLSY79 1992 survey (R38942.00 – R38948.00) and the NLSY79 Child and Young Adult surveys (1994: Y03343.00 Y3349.00; 1996: Y06347.00 – Y06353.00; 1998: Y09292.00 – Y09298.00; 2000: Y11592.00 – Y11598.00; 2002: Y13943.00 – Y13949.00; 2004: Y16458.00 – Y16464.00; 2006: Y19176.00 – Y19182.00; 2008: Y22328.00 – Y22334.00), respondents were presented with the 7-item PM scale, as it appears in Table 1.
Effects of a New Artificial Reef Complex on Red Snapper and the Associated Fish Community: an Evaluation Using a Before–After Control–Impact Approach

AbstractArtificial reefs are commonly created with the goal of enhancing fish populations. However, many studies evaluating their effects on these populations have been hindered by a lack of preconstruction data from existing natural habitats and temporal comparisons with control areas. Here, we present findings from a before–after control–impact study designed to assess the effects of a new artificial reef on fish populations in the western Gulf of Mexico. Vertical line and fish traps were used to sample the reef site and a paired control site with soft bottom substrates for 1 year before and 2 years after reef construction. Prior to reef construction in October 2013, and over bare substrates in general, infrequent catches of sea catfishes and small coastal sharks were observed. With the exception of rare occurrences of juvenile Gray Triggerfish Balistes capriscus and Red Snapper Lutjanus campechanus, which were observed only during the summer recruitment season, the control site exhibited a lack of reef.
Technology - Empowering the Educational Researcher through Remote Observation

Observing students using computers often occurs through three methods: user-lab, on-site and remote data logging. Whilst each of these have their advantages with the new type of students such as elearners, an alternative method called web-conferencing remote observation is presented for observing students at a distance. This method collects both audio and video data of the observer through webcams and voice/video conversations. Students are able to interact with the software through application sharing facilities. Further, it allows both quantitative and qualitative data to be collected. This proof-of-concept method is presented here where it has been used in two previous studies using Windows Messenger and Netviewer. Although, video quality is not high the quality is sufficient for observational data.
The dynamics of Italian public debt: alternative paths for fiscal consolidation

This article analyses possible targets for the Italian debt-to-GDP ratio with a small macroeconomic model. The role of international macroeconomic variables such as the US GDP growth, prices of raw materials, EUR/USD exchange rate and European Central Bank (ECB) monetary policy stance and domestic policy instruments is analysed in the debt dynamics. We find that external conditions play a fundamental role for the Italian fiscal consolidation. To reach a target of 100% of debt-to-GDP ratio by 2020, a further growth-sustaining policy has to be implemented.
Factors Related to Engaging in Physical Activity: A Mixed Methods Study of Female University Students

Objective: As a needs assessment for intervention, quantitative and qualitative methods were used to examine attitude, subjective norms, perceived behavioral control, intention, knowledge, and weight control status related to physical activity in female university students within the Theory of Planned Behavior (TPB). Methods: A two-phase mixed method design was used. In Phase I, 362 students participated in an online survey, and in Phase II, 33 students participated in five focus group discussions. Ages of participants ranged from 18 to 45 years old, with 18 - 25 year olds making up over 74% of the sample. Results: Attitude, subjective norms, and perceived behavioral control, along with weight control status of trying to lose weight, were found to be significant predictors of intention to follow physical activity recommendations, which in turn were the strongest predictor of physical activity. Knowledge was not found to be significant. Group discussions revealed barriers to meeting physical activity recommendations, which included lack of companionship and social support, lack of motivation, time and cost restrictions, and lack of privacy in the gym. Social norms exerted both positive and negative influences. Conclusion: The mixed method approach provided a deeper insight into the influential factors pertaining to physical activity among female students, and results could be used in further research to develop effective interventions.
De noising and Dimensionality Reduction of Hyperspectral Images Using Framelet Transform with Different Shrinkage Functions

Present study is focussed on providing alternatives beyond discrete wavelet transform that not only reduce the dimensionality of the data and also simultaneously denoised the data cube by combining framelet transform (FRT) with different shrinkage functions and dimensionality reduction methods. Universal shrink (US), Visu shrink (VS), Minimax shrink (MS), Sure shrink (SS), Bayes shrink (BS) and Normal shrink (NS) will be applied to threshold the detail coefficients of framelet transform. Discrete wavelet transform (DWT), wavelet packet transform (WPT) and curvelet transform (CUT) also used for evaluating the performance of the proposed method. Peak signal to noise ratio (PSNR) is calculated for each method and the results are compared. A higher value of the PSNR indicates the good quality of the denoised data cube. Then dimensionality reduction methods such as principal component analysis (PCA), Singular value decomposition (SVD) and Linear discriminant analysis (LDA) are applied on the denoised data cube. The efficiency of the simultaneous denoising and dimensionality reduction of hyperspectral data cube is calculated in terms of entropy with and without denoising. Edge detection also performed, directional properties of framelet transform can able to capture edges and contours which are the main features in images. The framelet transform, Bayes shrink with soft thresholding produce sound results in terms of denoising and edge detection over DWT, WPT and curvelet transform based methods.
Effects of psychiatric nursing Clinical Simulation Experience on Students ' Anxiety , Therapeutic Communication Skills and Psychiatric nursing Assessment

Background: "The simulation strategy was a formative experience designed to introduce students to Psychiatric assessment while concurrently providing a forum to practice therapeutic communication. It replicated common patient diagnoses that students would encounter during their psychiatric clinical rotation". The aim of the study was to evaluate the effects of psychiatric nursing clinical simulation experience on nursing students' anxiety, therapeutic communication skills and psychiatric assessment. Quasi-experimental design (experimental group and control group) was used to achieve the purpose of the study. This study was conducted at Meet Khalaf and Tanta Psychiatric Hospitals and faculty of nursing Menofiya University. A purposive sample of (n = 104) from undergraduate psychiatric nursing student enrolled in psychiatric nursing course second semester academic year 2016.Four tools for data collection as; Structured interview questionnaire, The depression, anxiety and stress scale, Psychiatric Assessment rubric scale and Therapeutic Communication skill were used. Results reveals that there is a highly significant reduction in the mean score of total anxiety among experimental group compared to control group. There is a highly significant increase in the mean score of student’s psychiatric assessment and student’s communication skill among experimental group compared to control group. Based on the findings of the current study, it can be concluded that: simulation has great positive effect on decreasing student's level of anxiety, improving communication skills and competence in psychiatric assessment. Based on the findings of the present study, the following recommendations are suggested: Integrate simulation in psychiatric nursing course to: Decreasing student’s anxiety, Improving student’s communication skills and improving student’s psychiatric assessment
REDCLAN - Relative Density Based Clustering and Anomaly Detection

Cluster analysis and Anomaly Detection are the primary methods for database mining. However, most of the data in today's world, generated from multifarious sources, don’t adhere to the assumption of single or even known distribution hence the problem of finding clusters in the data becomes arduous as clusters are of widely differing sizes, densities and shapes, along with the presence of noise and outliers. Thus, we propose a relative-KNN-kernel density-based clustering algorithm. The un-clustered (noise) points are further classified as anomaly or nonanomaly using a weighted rank-based anomaly detection method. This method works particularly well when the clusters are of varying variability and shape, in these cases our algorithm not only finds the “dense” clusters that other clustering algorithms find, it also finds low-density clusters that these approaches fail to identify. This more accurate clustering in turn helps reduce the noise points and makes the anomaly detection more accurate.
Evidence for multiple navigational sensory capabilities of Chinook salmon

To study the complex coastal migrations patterns exhibited by juvenile Columbia River Chinook salmon as they enter and move through the marine environment, we created an individual-based model in a coupled Eulerian-Lagrangian framework. We modeled 5 distinct migration strategies and compared the resulting spatial distributions to catch data collected dur- ing May and June in 3 years. Two strategies produced fish distributions similar to those observed in May, but only one also produced the observed June distributions. In both strategies, salmon dis- tinguish north from south (i.e. they have a compass sense), and they control their position relative to particular landmarks, such as the river mouth. With these 2 abilities, we posit that salmon follow spatially explicit behavior rules that prevent entrapment in strong southward currents and advec- tion offshore. Additionally, the consistent spatio-temporal distributions observed among years suggest that salmon use a clock sense to adjust their swim speed, within and among years, in response to progress along their migration.
An Integrated View of Complex Landscapes: A Big Data-Model Integration Approach to Transdisciplinary Science

The Earth is a complex system comprising many interacting spatial and temporal scales. We developed a transdisciplinary data-model integration (TDMI) approach to understand, predict, and manage for these complex dynamics that focuses on spatiotemporal modeling and cross-scale interactions. Our approach employs human-centered machine-learning strategies supported by a data science integration system (DSIS). Applied to ecological problems, our approach integrates knowledge and data on (a) biological processes, (b) spatial heterogeneity in the land surface template, and (c) variability in environmental drivers using data and knowledge drawn from multiple lines of evidence (i.e., observations, experimental manipulations, analytical and numerical models, products from imagery, conceptual model reasoning, and theory). We apply this transdisciplinary approach to a suite of increasingly complex ecologically relevant problems and then discuss how information management systems will need to evolve into DSIS to allow other transdisciplinary questions to be addressed in the future.