Miracle-dz/UniFAR-FTUs huggingface dataset:
Genuine Faculty-Mentored Research Experiences for In-Service Science Teachers: Increases in Science Knowledge, Perception, and Confidence Levels

ABSTRACT The overall purpose of this multifocused study was to explore how participation in genuine mentored scientific research experiences impacts in-service science teachers and the knowledge and skills needed for their own science teaching. The research experiences resulted from a partnership between the University of Nebraska at Omaha and the Omaha Public School District. This Teacher-Researcher Partnership Program facilitated opportunities in inquiry, science content, interaction with laboratory instrumentation and technologies, critical discussion of literature, and dissemination of findings for participating in-service science teacher professional development utilizing an inquiry-based theoretical framework wherein we examined science teacher preparation via inquiry-based methods in the research laboratory. A mixed-methods approach with a convergent typology (i.e., qualitative and quantitative analyses conducted separately and integrated) was used to investigate the impact of the program on teachers. Our research question was as follows: How do teachers define and approach scientific research before and after a genuine research experience? We observed 3 emergent nodes or themes by which teachers indicated significant gains: science content knowledge, confidence, and perception. Moreover, we determined that participation by science teachers in a mentored research experience using current scientific technologies and tools improved teacher confidence in science and inquiry as well as an ongoing commitment to provide similar types of experiences to their students. These data support the need for the participation of in-service science teachers in genuine research experiences to boost technological and pedagogical content knowledge, con fidence in process and content, and the perception of translatability to the classroom.
A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer.

BACKGROUND Clinical trials have indicated a benefit of adjuvant chemotherapy for patients with stage IB, II, or IIIA--but not stage IA--non-small-cell lung cancer (NSCLC). This classification scheme is probably an imprecise predictor of the prognosis of an individual patient. Indeed, approximately 25 percent of patients with stage IA disease have a recurrence after surgery, suggesting the need to identify patients in this subgroup for more effective therapy. METHODS We identified gene-expression profiles that predicted the risk of recurrence in a cohort of 89 patients with early-stage NSCLC (the lung metagene model). We evaluated the predictor in two independent groups of 25 patients from the American College of Surgeons Oncology Group (ACOSOG) Z0030 study and 84 patients from the Cancer and Leukemia Group B (CALGB) 9761 study. RESULTS The lung metagene model predicted recurrence for individual patients significantly better than did clinical prognostic factors and was consistent across all early stages of NSCLC. Applied to the cohorts from the ACOSOG Z0030 trial and the CALGB 9761 trial, the lung metagene model had an overall predictive accuracy of 72 percent and 79 percent, respectively. The predictor also identified a subgroup of patients with stage IA disease who were at high risk for recurrence and who might be best treated by adjuvant chemotherapy. CONCLUSIONS The lung metagene model provides a potential mechanism to refine the estimation of a patient's risk of disease recurrence and, in principle, to alter decisions regarding the use of adjuvant chemotherapy in early-stage NSCLC.
Essential role of the czc determinant for cadmium, cobalt and zinc resistance in Gluconacetobacter diazotrophicus PAl 5.

The mechanisms of cadmium, cobalt and zinc resistance were characterized in the plant-growth-promoting bacterium Gluconacetobacter diazotrophicus PAl 5. The resistance level of the wild-type strain was evaluated through the establishment of minimum inhibitory concentrations (MIC) of the soluble compounds CdCl2·H2O, CoCl2·6H2O and ZnCl2. Gluconacetobacter diazotrophicus PAl 5 was resistant to high concentrations of Cd, Co and Zn, with MICs of 1.2, 20 and 20 mM, respectively. Screening of an insertion library from transposon EZ-Tn5 in the presence of ZnO revealed that the mutant GDP30H3 was unable to grow in the presence of the compound. This mutant was also highly sensitive to CdCl2·H2O, CoCl2·6H2O and ZnCl2. Molecular characterization established that the mutation affected the czcA gene, which encodes a protein involved in metal efflux. In silico analysis showed that czcA is a component of the czcCBARS operon together with four other genes. This work provides evidence of the high tolerance of G. diazotrophicus PAl 5 to heavy metals and that czc is a determinant for metal resistance in this bacterium.
ESTIMATING THE FAILURE INTENSITIES OF MULTI-COMPONENT SYSTEM WITH MULTI - STATE COMPONENTS

Under consideration of this paper are unrecoverable multi-state systems with independent multi state components. It is assumed that each component can fail by one level down in time, and that transitions are homogenous Markov transitions. In order to calculate the reliability of such a system, we need to estimate the one level transition intensities of each component. Here we give an estimators for this parameters, under assumption that all one level transition of the components of the system are known.
A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs

The paper proposes a specific algorithm for the pre-estimation filtering of bad data (BD) in PMU-based power systems linear State Estimators (SEs). The approach is framed in the context of the so-called real-time SEs that take advantage of the high measurement frame rate made available by PMUs (i.e., 50-60 frames per second). In particular, the proposed algorithm examines PMU measurement innovations for each new received set of data in order to locate anomalies and apply countermeasures. The detection and identification scheme is based on: (i) the forecasted state of the network obtained by means of a linear Kalman filter, (ii) the current network topology, (iii) the accuracy of the measurement devices and (iv) their location. The incoming measurement from each PMU is considered reliable, or not, according to a dynamic threshold defined as a function of the confidence of the predicted state estimated by using an AutoRegressive Integrated Moving Average (ARIMA) process. The performances of the proposed algorithm are validated with respect to single and multiple bad data of different nature and magnitudes. Furthermore, the algorithm is also tested against faults occurring in the power system to show its robustness during these unexpected operating conditions.
Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution

It is often difficult to predict the optimal neural network size for a particular application. Constructive or destructive methods that add or subtract neurons, layers, connections, etc. might offer a solution to this problem. We prove that one method, recurrent cascade correlation, due to its topology, has fundamental limitations in representation and thus in its learning capabilities. It cannot represent with monotone (i.e., sigmoid) and hard-threshold activation functions certain finite state automata. We give a "preliminary" approach on how to get around these limitations by devising a simple constructive training method that adds neurons during training while still preserving the powerful fully-recurrent structure. We illustrate this approach by simulations which learn many examples of regular grammars that the recurrent cascade correlation method is unable to learn.
A feature selection method based on minimum redundancy maximum relevance for learning to rank

Learning to rank has considered as a promising approach for ranking in information retrieval. In recent years feature selection for learning to rank introduced as a crucial issue. Reducing the feature set by removing irrelevant and redundant features can improve the prediction performance. In this paper we address the problem of filter feature selection for ranking. We propose to apply minimum redundancy maximum relevance (mRMR) method that select feature subset based on importance of features and similarity between them. We reweight the component of mRMR to balance between importance and similarity. We apply two methods for measuring the similarity between features and two methods for evaluating importance. Experimental results on two standard datasets from Letor demonstrate that the proposed algorithm 1)outperform two stateof- the-art learning to rank algorithms in term of accuracy, 2) learn a more spars model compared to a feature selection model for ranking.
Solving the core theoretical issues in consumer behavior in tourism

Abstract Consumer behavior in tourism (CBT) is an interdisciplinary field of study encompassing the basic behavioral and economic sciences (e.g., psychology, sociology, and economics) and applied fields of study (e.g., management, marketing, tourism, and hospitality) focusing on all aspects of discretionary travel. This chapter describes major issues and findings in the literature relating to CBT. The chapter directs the reader’s attention to some of the highly-cited studies in this literature – these studies provide a foundation of knowledge on the central topics, issues, methods, findings, and theoretical/practical contributions in research on CBT. Research studies in CBT focus on one-to-all five core theoretical issues in basic and applied fields of study: describe who is doing what, when, where, how, and the consequences of the activities; explain the meanings of activities and motivations of the actors; predict (model) what actions and outcomes will occur and the impacts of influence attempts before, during, and after engaging in tourist actions; control (influence) the beliefs, attitudes, behaviors, and assessments of tourists, local community members, and additional stakeholders; evaluate tourism service/product delivery, tourism management performance, and customer satisfaction. Survey research using verbal (written) responses to questions is pervasive and the most frequent method for data collection in CBT. Additional research genres in CBT include direct observations of tourism behavior with or without some oral questioning (unobtrusive studies, the long interview method (McCracken, 1988), use of “consumer culture theory”), participant observation including semester abroad and unpaid internships away from home, formal field experiments, and the study of secondary sources (e.g., photographs and writings in blogs and social media (e.g., TripAdvisor) reviews).
Why is the support for extreme right higher in more open societies

We investigate the support for extreme right across societies of different levels of openness in Europe. Societal openness is defined as a greater tendency to accept universal vis-avis traditional values, and is expected to catalyze and filter the socioeconomic factors that affect the vote shares of extreme right (i.e., neofascist and populist parties). We establish that, in more open societies the direct effect of openness on neofascist votes is, as expected, negative. Paradoxically, however, openness increases the neofascist support indirectly through immigration and unemployment. We explain this with socioeconomic dynamics whereby vulnerable native segments of a more open society, i.e., a society manifesting higher welcoming sentiments towards immigrants, turn to neofascists as immigration and unemployment start threatening their material welfare. Moreover, we find that openness has no direct effect on the support for populist parties, but has indirect positive effects through unemployment. Our results establish strong links among openness, immigration and unemployment in determining the sources of neofascist and populist support in Europe
Identifying Unvaccinated Individuals in Canada: A Predictive Model

Recently, the media and public health officials have become increasingly aware of the rise in anti-vaccine sentiment. Vaccinations have numerous health benefits for immunized individuals as well as for the general public through herd immunity. Given the rise in immunization-preventable diseases, a consequence of people opting out of their routine vaccinations, we determined that Canadian health data can identify individuals over the age of 60 who chose not to get vaccinated (80.1% negative predictive value) and individuals under the age of 60 who have recently been vaccinated (96.4% positive predictive value). Using the 2009-2014 Canadian Community Health Surveys (CCHS), a probit model identified the variables that were most commonly associated with flu vaccination outcomes. Of 1,381 variables, 47 with the most significant marginal effects were selected, including the presence of diseases (e.g. diabetes and cancer), behavioral characteristics (e.g. smoking and exercise), exposure to the medical system (e.g. whether the individual gets a regular check-up), and a person's living situation (e.g. having young children in the household). These variables were then used to generate a Random Forest classification model, trained on the 2009-2013 dataset, and tested on the 2014 dataset. We achieved an overall accuracy of 87.8% between the two final models, each using 25 classification trees with bounded depth of 20 nodes, randomly selecting from all 47 variables. With the two proposed policies, this model can be leveraged to efficiently allocate vaccination promotion efforts. Additionally, it can be applied to future surveys, only requiring 3.6% of the variables in the CCHS for successful prediction.
A preliminary research on rhetorical structural and prosodic features in Chinese reading texts

This paper investigates the interface of rhetorical and rhythmic aspects of several Chinese reading discourses within the RST framework. Distributions of rhetorical relations, stress degree and nuclearity in pre-pausal and post-pausal positions are examined in this current research. The RSTTool is used in diagramming the rhetorical structures of three reading texts, Narrative, Expositive and Descriptive respectively. The recordings, extracted from the Annotated Speech Corpus of Chinese Discourse (ASCCD), are further analyzed acoustically and statistically by applying Praat and R. The statistical analysis results show unbalanced distributions of rhetorical relations in different contexts, stress combinations and hierarchies. The examination of nuclearity shows nucleus-satellite combination, compared with satellite-nucleus combination, is inclined to extend the pause duration significantly. Based on the results, the study further proposes that distribution of relations, hierarchies, stress degrees and nuclearity are significantly related to pause duration and the intra-distribution of one and another.
Genetic Background Modifies Inner Ear and Eye Phenotypes of Jag1 Heterozygous Mice

Mice heterozygous for missense mutations of the Notch ligand Jagged1 (Jag1) exhibit head-shaking behavior indicative of an inner ear vestibular defect. In contrast, mice heterozygous for a targeted deletion of the Jag1 gene (Jag1del1) do not demonstrate obvious head-shaking behavior. To determine whether the differences in inner ear phenotypes were due to the types of Jag1 mutations or to differences in genetic background, we crossed Jag1del1 heterozygous mice onto the same genetic background as the missense mutants. This analysis revealed that variation of the Jag1 mutant inner ear phenotype is caused by genetic background differences and is not due to the type of Jag1 mutation. Genome scans of N2 backcross mice identified a significant modifier locus on chromosome 7, as well as a suggestive locus on chromosome 14. We also analyzed modifiers of an eye defect in Jag1del1 heterozygous mice from this same cross.
Image Distribution Mechanisms in Large Scale Cloud Providers

This paper presents the various mechanisms for virtual machine image distribution within a large batch farm and between sites that offer cloud computing services. The work is presented within the context of the Large Hadron Collider Computing Grid (LCG), it has two main goals. First it aims at presenting the CERN specific mechanisms that have been put in place to test the pre-staging of virtual machine images within a large cloud infrastructure of several hundred physical hosts. Second it introduces the basis of a policy for trusting and distributing virtual machine images between sites of the LCG. Finally experimental results are shown for the distribution of a 10 GB virtual machine image distributed to over 400 physical nodes using a binary tree and a Bit Torrent algorithm. Results show that images can be pre-staged within 30 minutes.
Teacher victimization, turnover, and contextual factors promoting resilience

ABSTRACT Teacher victimization is a relatively understudied phenomenon that may contribute to teacher turnover. The purpose of this study is to explore the relationship between teacher reports of victimization and teachers leaving their school and the profession. Using nationally representative data (n = 104,840) from the Schools and Staffing Survey, we examine the extent to which being threatened or attacked by students predicts higher rates of teacher turnover and whether this relationship differs due to factors that may promote teacher resilience. We utilize conditional multinomial logistic regression, implicitly controlling for school-by-year fixed effects. Findings suggest that perceived victimization predicts an increased probability of leaving the school and profession. School-level promoters of resilience are found to lessen this relationship. We discuss ways schools can mitigate the impact of victimization. This work contributes to a nascent body of literature on teacher victimization and informs a policy lever by which turnover may be reduced.
How effectively can we form opinions?

People make decisions and express their opinions according to their communities. An appropriate idea for controlling the diffusion of an opinion is to find influential people, and employ them to spread the desired opinion. We investigate an influencing problem when individuals' opinions are affected by their friends due to the model of Friedkin and Johnsen [4]. Our goal is to design efficient algorithms for finding opinion leaders such that changing their opinions has great impact on the overall opinion of the society. We define a set of problems like maximizing the sum of individual opinions or maximizing the number of individuals whose opinions are above a threshold. We discuss the complexity of the defined problems and design optimum algorithms for the non NP-hard variants of the problems. Furthermore, we run simulations on real-world social network data and show our proposed algorithm outperforms the classical algorithms such as degree-based, closeness-based, and pagerank-based algorithms.
The Classical Exchange Algebra of AdS5 × S 5 String Theory

The classical exchange algebra satisfied by the monodromy matrix of AdS5× S 5 string theory in the Green-Schwarz formulation is determined by using a first-order Hamiltonian formulation and by adding to the Bena-PolchinskiRoiban Lax connection terms proportional to constraints. This enables in particular to show that the conserved charges of this theory are in involution. This result is obtained for a general world-sheet metric. The same exchange algebra is obtained within the pure spinor description of AdS5 × S 5 string theory. These results are compared to the one obtained
Towards Understanding Optimal Load-Balancing of Heterogeneous Short-Range Molecular Dynamics

For heterogeneous dynamic short-range molecular dynamics simulations it is critical to employ suitable load-balancing methods to minimize the time to solution. However, designing and parametrizing the optimal load-balancing method is a complex task, depending on detailed properties of the simulation scenario.The main challenge in balancing the load of molecular dynamics simulations is the extreme difference in load density for scenarios with heterogeneous particle density, which can easily reach 4-6 orders of magnitude. Therefore, heterogeneity is deemed to be a relevant property and a suitable metric to reliably quantifying heterogeneity is formulated. This metric, which is based on binning particles and evaluating statistical moments, is then applied to example scenarios and correlated to the performance of five load balancing methodologies. Furthermore, how rapidly the load varies over time will determine how long the benefits of a specific partitioning are expected to last. We deem this to be another relevant property, the dynamics, and introduce corresponding metrics. The results indicate that these metric are useful to differentiate between scenarios and facilitate reasoning over the complex relationship between particle simulation scenarios and optimal load balancing methods. This work is a first step towards understanding this relationship, while introducing key concepts we regard as a crucial for this understanding.
What’s technology got to do with it? Exploring the impact of mobile phones on female sex workers’ lives and livelihood in India

Abstract Technology has brought about substantial changes to the landscape of sex work globally. However, few studies have captured the holistic impact of mobile phones on the lives of female sex workers (FSWs) in India. In this exploratory study, 67 FSWs and 18 staff and leaders from local non-governmental organizations participated in in-depth interviews and focus group discussions. Respondents strongly emphasized the innumerable ways in which mobile phones had changed the sex work environment in terms of client solicitation and managing long-term relationships with clients. Results revealed a complex picture of significant risks as well as gains resulting from the use of mobile phones. Participant narratives also highlighted the challenges posed by technology in terms of invasion of privacy, incidents of blackmail, and violence. FSWs’ personal agency was evidenced in innovative strategies to connect to social networks, stay safe, and maintain privacy. Results point to the need to adopt a nuanced stance regarding understanding the role of mobile phone technology in FSWs’ lives. There is an urgent need to develop on-going supportive phone-based networks in addition to examining the broader implications for the development of technology-based interventions for vulnerable populations.
The Effect of Shoe Outsole Containing Nanoclay Particles on Knee Joint Power during the Stance Phase of Running

The popularity of running and consequently of running injuries has increased tends to develop shoe sole constructions aiming at preventing different injuries. The purpose of this study was to investigate the effect of shoe outsole containing nanoclay particles on knee joint power during stance phase of running. Fourteen healthy male shod runners run 3 times at 3.5 m.s-1 under three shoe conditions manufactured by polyurethane (PU), 1% Polyurethanes-clay nano-composites (NPU1) and 2% Polyurethanes-clay nano-composites (NPU2). The joint power (Pj) was calculated by multiplying the joint moment (Mj) and the joint angular velocity (ωj). Knee joint peak power in the three dimensional planes were compared between the shod conditions during the stance phase of running using repeated measurement ANOVA (P< 0.05). The results showed that the negative peak of knee joint power in sagittal plane was significantly different between the PU and NPU2 conditions. No significant differences were noted in other positive and negative peaks. It is concluded that using Polyurethanesclay nano-composites can change shock absorbing ability of knee joint.