Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Media Production of Refugee-Background Youth: A Scoping Review

Reviews of research have provided insights into the digital media production practices of youth in and out of school. Although such practices hold promise for the language and literacy education of refugee-background youth, no review has yet integrated findings across studies and different digital media production practices to explore this promise. This scoping review summarizes and discusses the key findings from research on varied types of digital media produced specifically by refugee-background youth in and out of school. It situates digital media production practices in the context of this diverse population, which experiences forced migration, and highlights 5 main themes from findings in 42 reviewed articles. Digital media production afforded refugee-background youth: (1) Ownership of representations across time and space; (2) opportunity to expand, strengthen, or maintain social networks; (3) identity work; (4) visibility and engagement with audiences; and (5) communication and embodied learning through multimodal literacies.
Continuance intention to use e-campus: The role of service quality in Malaysian higher education context

This article examines the role of service quality on continuance intention to use e-campus among private higher education students. This research uses only one predictor taken out from well-established information system success model, namely, ‘De Lone & Mclean Success Model. A quantitative research method is used and paper-and-pencil self-administered questionnaire survey as method of data collection. Data was analysed using simple linear regression analysis. The results indicate that service quality were positively related to continuance intention to use e-campus explaining a total of 24.2% variance. This paper provides an extension of service quality in the post adoption stage at private higher learning context. Implications for service provider are highlighted.
LAC-UFF STATUS REPORT: CURRENT PROTOCOLS AND RECENT DEVELOPMENTS

ABSTRACT The Radiocarbon Laboratory of the Fluminense Federal University was installed in 2009, and its NEC Single Stage Accelerator Mass Spectrometry system has been operational since 2012. As the first 14C-AMS facility in Latin America, the LAC-UFF became a reference center for radiocarbon (14C) dating in Brazil. Over the years we have implemented pretreatment protocols for several kinds of materials, such as cellulose, soil, bone, and biofuels. In the present paper we describe our current protocols for the preparation of these types of samples. Moreover, after 10 years of operation, with the aim of expanding the range of materials we are able to analyze, we report the results of several tests to improve accuracy, precision and background levels. For example, here we discuss how isotopic fractionation during the graphitization and measurement steps has been controlled. Concerning results interpretation, our research group has been using OxCal chronological models to analyze different contexts such as stromatolite growth, tree rings, soil deposition and marine reservoir effect (MRE) determination.
Optoelectronic and Nanosensors Detection Systems: A Review

The detection is the main feature of a sensor, and, in particular, nanosensor. This latter may detain high capability of detection, given its dimensions. Optoelectronics comes to help the field of nanosensors with innovations regarding new solutions. This paper provides an overview of the most important significant milestones with clear applications, namely in the fields of pressure nanosensors, nanomaterials to be characterized in an optical viewpoint, using artificial intelligence in the design of nanosensors, optical communications with dedicated circuits, and nanosensors for extreme radiation. These cases illustrate the importance of detection, especially the efficiency of the sensor/nanosensor.
Conflict Resolution Strategies and Marital Adjustment of Heterosexual Couples: Assessment of Actor–Partner Interaction

This study tested a dyadic theoretical model in which conflict resolution strategies have impact on couples’ marital adjustment. The study features a quantitative and transversal approach and a correlational and explanatory design. Participants included 231 heterosexual couples from different regions of Brazil, contacted after indication. The instrument composed of the Conflict Resolution Behavior Questionnaire (CRBQ) and the Revised Dyadic Adjustment Scale (R-DAS) was filled out in the residence of the participants. Data were submitted to dyadic analysis by the latent trait model. The results indicated that the strategies have a strong impact on the individual’s adjustment. Assessing the impact of one of the spouse’s strategies on the other, only the marital adjustment of the men was affected. The evidence suggests differences between husbands and wives regarding the impact of strategies on adjustment. The data are discussed in light of the literature and considering their applicability in the clinical area.
Fast and Slow Nonlinearities in Epsilon‐Near‐Zero Materials

Novel materials, with enhanced light–matter interaction capabilities, play an essential role in achieving the lofty goals of nonlinear optics. Recently, epsilon‐near‐zero (ENZ) media have emerged as a promising candidate to enable the enhancement of several nonlinear processes including refractive index modulation and harmonic generation. Here, the optical nonlinearity of ENZ media is analyzed to clarify the commonalities with other nonlinear media and its unique properties. Transparent conducting oxides as the family of ENZ media with near‐zero permittivity in the near‐infrared (telecom) band are focused on. The instantaneous and delayed nonlinearities are investigated. By identifying their common origin from the band nonparabolicity, it is shown that their relative strength is entirely determined by a ratio of the energy and momentum relaxation (or dephasing) times. Using this framework, ENZ materials are compared against the many promising nonlinear media that are investigated in literature and show that while ENZ materials do not radically outpace the strength of traditional materials in either the fast or slow nonlinearity, they pack key advantages such as an ideal response time, intrinsic slow light enhancement, and broadband nature in a compact platform making them a valuable tool for ultrafast photonics applications for decades to come.
Remaining Useful Life Prediction of High-Frequency Swing Self-Lubricating Liner

The remaining useful life (RUL) prediction of self-lubricating spherical plain bearings is essential for replacement decision-making and the reliability of high-end equipment. The high-frequency swing self-lubricating liner (HSLL) is the key component of self-lubricating spherical plain bearings under high-frequency oscillation conditions. In this study, a RUL prediction method was proposed based on the Wiener process and grey system theory. First, the predictive processing of the wear depth was carried out using the grey model GM(1,1) to reduce the randomness and enhance the inherent regularity of the life test data. A degradation process model was established and the RUL was predicted online with the model parameter estimates based on the Bayesian updating strategy. Finally, examples were provided to elaborate the RUL prediction of the HSLL. The results show that the prediction accuracy of the proposed RUL prediction model is higher than that of the simple Wiener process during the entire residual life cycle of the HSLL. Based on the original wear data, the prediction accuracy of the RUL exhibited a strong dependence on prior samples and was relatively low owing to the larger deviation of the wear rate between the test sample and prior samples.
Forecasts of the July 2020 Kyushu Heavy Rain Using a 1000-Member Ensemble Kalman Filter

Forecast performances of the July 2020 Kyushu heavy rain have been revisited with the aim of improving the forecasts for this event. While the Japan Meteorological Agency’s (JMA) deterministic forecasts were relatively good, the JMA’s ensemble forecasts somehow missed this event. Our approach is to introduce flow-dependence into assimilation by running a 1000-member local ensemble transform Kalman filter (LETKF1000) to extract more information from observations and to better quantify forecast uncertainties. To save computational costs, vertical localization is removed in running LETKF1000. Qualitative and quantitative verifications show that the LETKF1000 forecasts outperform the operational forecasts both in deterministic and probabilistic forecasts. Rather than a trick to save computational costs, removal of vertical localization is shown to be the main contribution to the outperformance of LETKF1000. If vertical localization is removed, forecasts with similar performances can be obtained with 100 ensemble members. We hypothesize that running ensemble Kalman filters with around 1000 ensemble members is more effective if vertical localization is removed at the same time. Since this study examines only one case, to assess benefit of removing vertical localization rigorously when the number of ensemble members is around 1000, a larger set of cases needs to be considered in future. (Citation: Duc, L., T. Kawabata, K. Saito, and T. Oizumi, 2021: Forecasts of the July 2020 Kyushu heavy rain using a 1000member ensemble Kalman filter. SOLA, 17, 41−47, doi:10.2151/ sola.2021-007.)
Exact Acceleration of K-Means++ and K-Means∥

K-Means++ and its distributed variant K-Means|| have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation,and theoretical grounding of the K-means++ and || methods have made them difficult to "best" from a holistic perspective. We focus on using triangle inequality based pruning methods to accelerate both of these algorithms to yield comparable or better run-time without sacrificing any of the benefits of these approaches. For both algorithms we are able to reduce distance computations by over 500×. For K-means++ this results in up to a 17×speedup in run-time and a551×speedup for K-means||. We achieve this with simple, but carefully chosen, modifications to known techniques which makes it easy to integrate our approach into existing implementations of these algorithms.
Schwinger pair production in inhomogeneous electric fields with symmetrical frequency chirp

symmetrical frequency chirp Melike Mohamedsedik,1 Lie-Juan Li,1 and B. S. Xie ∗1, 2 1Key Laboratory of Beam Technology of the Ministry of Education, and College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875, China 2Beijing Radiation Center, Beijing 100875, China (Dated: May 10, 2021) Abstract Pair production in inhomogeneous electric fields with symmetrical frequency chirp is studied numerically using the Dirac-Heisenberg-Wigner formalism. We investigate highand low-frequency modes and consider two carrier envelope phases. Momentum spectrum is sensitive to chirp causing different interference effect for different spatial scales as well as the carrier phase of the external field. The reduced particle number is in general enhanced with increasing chirp. The effect of spatial scale of the field on the reduced particle number is also examined. It is found that it is enhanced at small spatial scale but is almost unchangeable at large spatial scales for the considered field parameters. On the other hand, at small spatial scale, the reduced particle number is enhanced by one or two orders when chirp is applied with the exception of cosine lowfrequency field which is only a few times larger. Moreover it is found that the reduced particle number is further increased by symmetrical chirp at about two times by comparing to the usual asymmetrical chirp in high frequency field.
Molecular characterization of Cotton leaf curl Multan virus and DNA-satellites complex associated enation leaf curl and yellow vein mosaic disease of hollyhock

Hollyhock is one important decorative plant grown in garden beds in different region of the the world. The ornamental plant is susceptible to many diseases caused by diverse pathogens. Among these viral pathogens can cause enormous damage to the ornamental plant. The aim of the present study was identification of begomovirus and DNA sateelites is associated with the yellow vein mosaic and enation leaf curl disease complex of hollyhock. The hollyhock plants showing the typical begomovirus-like symptoms were collected from Pusa campus, New Delhi (India). To know the status of the begomovirus, the total DNA isolated from the infected hollyhock was subjected to PCR amplification using primers specific to the begomovirus. The partial (1.2 kb) genome sequencing of ten hollyhock samples indicates the associated of begomovirus (nucleotide identities is more 95% among themselves). Therefore three representative samples (H1, H2, H3) full-length genome (DNA-A, betasatellite and alphasatellite) was amplified through RCA method. The pairwise comparision of complete genome of the begomoviruses, betasatellites and alphasatellites using Sequence Demarcation Tool (SDT) showed highest nucleotide (nt) identity of 88.0 to 92.7% (DNA-A) with Cotton leaf curl Multan virus, 92.5–96.7% with Ludwigia leaf distortion betasatellite and 90.4 to 93. 2% % with Ageratum enation alphasatellite. Further recombinantion analysis showed that the begomoviruses and DNA satellites under study was recombinants of previously reported begomoviruses and DNA sattelites. This is the first report of Cotton leaf curl Multan virus and DNA satellites associated complex disease of hollyhock in India.
Download time analysis for distributed storage systems with node failures

We consider a distributed storage system which stores several hot (popular) and cold (less popular) data files across multiple nodes or servers. Hot files are stored using repetition codes while cold files are stored using erasure codes. The nodes are prone to failure and hence at any given time, we assume that only a fraction of the nodes are available. Using a cavity process based mean field framework, we analyze the download time for users accessing hot or cold data in the presence of failed nodes. Our work also illustrates the impact of the choice of the storage code on the download time performance of users in the system.
A Multi-Size Neural Network with Attention Mechanism for Answer Selection

Semantic matching is of central significance to the answer selection task which aims to select correct answers for a given question from a candidate answer pool. A useful method is to employ neural networks with attention to generate sentences representations in a way that information from pair sentences can mutually influence the computation of representations. In this work, an effective architecture -multi-size neural network with attention mechanism (AM-MSNN) – is introduced into the answer selection task. This architecture captures more levels of language granularities in parallel, because of the various sizes of filters comparing with single-layer CNN and multi-layer CNNs. Meanwhile it extends the sentence representations by attention mechanism, thus containing more information for different types of questions. The empirical study on three various benchmark tasks of answer selection demonstrates the efficacy of the proposed model in all the benchmarks and its superiority over competitors. The experimental results show that (1) multi-size neural network (MSNN) is a more useful method to capture abstract features on different levels of granularities than single/multi-layer CNNs; (2) the attention mechanism (AM) is a better strategy to derive more informative representations; (3) AM-MSNN is a better architecture for the answer selection task for the moment.
Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections

Model ensembles are becoming one of the most effective approaches for improving object detection performance already optimized for a single detector. Conventional methods directly fuse bounding boxes but typically fail to consider proposal qualities when combining detectors. This leads to a new problem of confidence discrepancy for the detector ensembles. The confidence has little effect on single detectors but significantly affects detector ensembles. To address this issue, we propose a novel ensemble called the Probabilistic Ranking Aware Ensemble (PRAE) that refines the confidence of bounding boxes from detectors. By simultaneously considering the category and the location on the same validation set, we obtain a more reliable confidence based on statistical probability. We can then rank the detected bounding boxes for assembly. We also introduce a bandit approach to address the confidence imbalance problem caused by the need to deal with different numbers of boxes at different confidence levels. We use our PRAE-based non-maximum suppression (P-NMS) to replace the conventional NMS method in ensemble learning. Experiments on the PASCAL VOC and COCO2017 datasets demonstrate that our PRAE method consistently outperforms state-of-the-art methods by significant margins.
The Relation between Karyotype Findings and Gonadotropin Levels in Pediatric Turner’s syndrome Patients

Background Abnormal pubertal development and fertility are among the frequent complications in Turner’s syndrome. Although elevated level of gonadotropins in Turner’s syndrome patients is well documented, the possible correlation with the karyotype findings and anthropometric features of patients is not clearly addressed. The present report aimed to evaluate the relation between the follicle-stimulating hormone (FSH) serum level and cytogenetic findings in Iranian Turner’s syndrome patients. Materials and Methods: Abnormal pubertal development and fertility are among the frequent complications in Turner’s syndrome. Although elevated level of gonadotropins in Turner’s syndrome patients is well documented, the possible correlation with the karyotype findings and anthropometric features of patients is not clearly addressed. The present report aimed to evaluate the relation between the follicle-stimulating hormone (FSH) serum level and cytogenetic findings in Iranian Turner’s syndrome patients. Results: The mean age of the Turner’s syndrome patients was 9.78 years and most of the patients were mosaic for Turner syndrome (54.3%). Although the FSH level increased, there was not any significant different between the FSH level in the initial evaluation and the second evaluation within the entire study population (P=0.605). Among those who were mosaic for Turner’s syndrome and those with 45,XO karyotype, the FSH level increased during the follow up (P=0.476, and P=0.357, respectively). Conclusion The present study demonstrated that although Turner’s syndrome patients face abnormal FSH levels, there is not any significant relation between the cytogenetic findings as well as anthropometric characteristics including height, weight and BMI with the serum FSH levels.
Adapting to Teaching During a Pandemic: Pedagogical Adjustments for the Next Semester of Teaching During COVID-19 and Future Online Learning

COVID-19 has altered public health higher education and its impact on pedagogy will be felt long into the future. In response to social distancing measures, teaching academics implemented a number of changes to curricula. It is important to better understand and begin to evaluate these changes, as well as set a course for future changes to public health curricula both during and after the pandemic to best enable transformative learning. Teaching academics have an understanding of academic hierarchies and student perceptions and are well placed to provide insights into current and future changes to pedagogy in response to the pandemic. A survey was developed to examine changes that academics had made to their teaching in response to COVID-19. Responses were received from 63 public health teaching academics from five universities in Australia, the United States, and Canada. Public health teaching academics rapidly implemented a number of changes to their teaching, including alterations that enabled online teaching. The great majority of changes to teaching were related to tools or techniques, such as synchronous tutorials delivered in a video meeting room. There remains further work for the public health pedagogy community in reevaluating teaching aims and teaching philosophies in light of the COVID-19 pandemic. This could include examination of the weighting of different topics, including communicable diseases, in curricula. A series of questions to assist academics reformulating their curricula is provided. Public health teaching evolved rapidly to meet the challenges of COVID-19; however, ongoing adaptation is necessary to further enhance pedagogy.
Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification

Although existing person re-identification (Re-ID) methods have shown impressive accuracy, most of them usually suffer from poor generalization on unseen target domain. Thus, generalizable person Re-ID has recently drawn increasing attention, which trains a model on source domains that generalizes well on unseen target domain without model updating. In this work, we propose a novel adaptive domain-specific normalization approach (AdsNorm) for generalizable person Re-ID. It describes unseen target domain as a combination of the known source ones, and explicitly learns domain-specific representation with target distribution to improve the model's generalization by a meta-learning pipeline. Specifically, AdsNorm utilizes batch normalization layers to collect individual source domains' characteristics, and maps source domains into a shared latent space by using these characteristics, where the domain relevance is measured by a distance function of different domain-specific normalization statistics and features. At the testing stage, AdsNorm projects images from unseen target domain into the same latent space, and adaptively integrates the domain-specific features carrying the source distributions by domain relevance for learning more generalizable aggregated representation on unseen target domain. Considering that target domain is unavailable during training, a meta-learning algorithm combined with a customized relation loss is proposed to optimize an effective and efficient ensemble model. Extensive experiments demonstrate that AdsNorm outperforms the state-of-the-art methods. The code is available at: https://github.com/hzphzp/AdsNorm.
Power to the People: The Impact of Student Personal Power on Performance in Teams

This article examines teammates’ perceptions of individual expert and referent power (personal power) in student teams working on a semester-long project. In our study, we found a positive relationship between being perceived as high in personal power (expert and referent power) by teammates and faculty advisor-rated performance, measured by quality and quantity of work, efficiency, meeting team goals, meeting deadlines, and overall performance. To examine the mechanism behind this relationship, we also examined and found that expressed humility mediated the effect between teammates’ power perceptions and individual performance. These findings suggest that power plays an important role in teammates’ perceptions of individuals, in the individual’s own performance, and in how humility functions in team settings. We further discuss these results in terms of practical implications as well as implications for management educators.