Miracle-dz/UniFAR-FTUs huggingface dataset:
Teachers’ Perceptions of Supporting Young Carers in Schools: Identifying Support Needs and the Importance of Home–School Relationships

Recognition and support for young carers has improved steadily in the past two decades; with stronger legislation and more visibility and awareness of the challenges that many of the YC face, especially with respect to their education. Recent UK-based initiatives providing toolkits and guidance for school staff have provided much needed direction for schools, to support the loosely defined statutory requirements. The aim of the current research was to hear from school staff about their experiences in identifying and supporting young carers, to better understand any enablers and barriers. The thematic analysis of the interview data from 18 school staff was organized into two main themes: perceptions regarding the characteristics of young carers; and perceptions regarding the importance of home–school communication. Each superordinate theme contained several sub-themes. Overall, the teachers perceived many difficulties identifying young carers who did not volunteer this information and felt that the main enabler of identification was the trust relationships between the school and the pupil and parents. Once identified, the schools perceived the main areas of need that they could provide support for were the emotional wellbeing of the pupils and additional academic opportunities. They spoke too of the difficulties balancing the provision of this extra support within the constraints of the school context, both in terms of the school day, and the competing priorities relating to academic and social–emotional needs. School staff recognized that extra time outside of school was difficult for young carers to attend. Other subthemes are discussed with consideration to enablers and barriers. The implications for the dissemination of good practice, and addressing policy are considered.
Illumination invariance at a single pixel

This paper addresses the question of what can be said about the colors in images that is independent of illumination. We make two main assumptions: Firstly, the illumination can be characterized as Planckian (a realistic assumption for most real scenes). Secondly, the camera behaves as if it were equipped with narrow band sensors (true for a large number of cameras). An alternative set of assumptions (with broad-band sensors) leading to the same invariant expression is also presented to show the robustness of the invariant.The resulting physics-based method results in a transformation of the original color image to a grey-scale one which does not vary with illumination. Experiments demonstrate that the distribution of grey-scale invariants in an image is a reliable cue for illumination independent object recognition.
Mapping sustainability of professional development in four Norwegian lower-secondary schools

Abstract The aim of this study was to investigate how principals (n = 4) and teachers (n = 20) from four lower-secondary schools perceived and evaluated sustainable professional development processes in their schools. To understand their experiences and perceptions, interviews and focus group conversations were conducted at the end of a one-year research and development project and again two years later. All teachers had in common that they taught 9th graders (14–15-year-old students) in Mathematics. Findings indicate that at the end of the common project period, all the participating teachers state that they had experienced professional development. They had made changes to their teaching practice and were collaborating more with colleagues. After two years the teachers still state that the research and development project had contributed to their learning, but that their schools have not been able to maintain a context of sustainable professional development processes. The core finding from this study suggests that if professional development in schools is to be sustainable, teachers should have ownership, work in teams over time and gain necessary support by their leaders and external experts for facilitating the learning processes and to develop new knowledge. Both the principals and teachers in these four schools claimed that to maintain sustainability of a continuing professional development there is a need to find time and structures to reflect on teaching practices and student learning.
High Velocity Impact Phenomena and Coating Quality in Cold Spraying

In Cold Spraying, bonding occurs when the impact velocities of particles exceed a critical value. This critical velocity depends not only on the type of spray material, but also on the powder quality, particle size and the particle impact temperature. For metallic materials, the critical velocity is in the range of 200 – 1200 m/s. In analogy with explosive welding, bonding in Cold Spraying is associated with adiabatic shear instabilities caused by high strain rate deformation during impact. Numerical and experimental methods are developed to investigate the influence of impact conditions and related phenomena on the coating quality. For a deeper understanding of impact phenomena and coating formation, the particle impact was modelled by using the finite element software ABAQUS/Explicit. The numerical analyses indicate shear instabilities localized to the particle surfaces, and thus provide a basis for the calculation of critical velocity in terms of materials properties and process parameters. In addition, modelling is used to obtain information about the effect of process parameters on the bonding quality. For most materials, high-strain-rate data are not available. For a quantitative analysis, therefore, the respective materials behaviour was investigated through individual spraying experiments, which were complemented by additional relevant experiments such as impact tests or explosive powder compaction. In this way, impact dynamics, bonding mechanism and critical velocities could be linked. This type of analysis was proved as a powerful tool to reduce the number of experiments for the optimisation of coating quality in Cold Spraying and also to provide a broader overview of the process.
The Epidemiology of Lung Cancer- How Much Have We Discovered?

Lung Cancer has become a global health burden in the past years due to being the second largest contributor to new cases and the high mortality rates worldwide which is around 1.8 million patients. There are two main histological groups of Lung Cancer, however non-small cell lung cancer is more prevalent and is composed of three histologic subtypes of which adenocarcinoma represents the highest prevalence. It is calculated that the incidence in male is higher than female. Cigarette smoking is the main risk factor for developing lung cancer, however, other risk factors contribute to the increasing cases. Studying the epidemiology of lung cancer is essential to establish robust surveillance system and establish policies to reduce this global concern. In this article we will review the epidemiological data, the history of the disease, and the associated risk factors.
The influence of camera-trap flash type on the behavioural response, detection rate and individual recognition of Eld’s deer

Abstract Context. Camera traps are available with infrared or white flash, with the former being more commonly used. However, white flash produces colour night-time photographs that can be critically useful for both species and individual identification. White flash was thought to cause more disturbance to wildlife than was infrared and this may lead to camera avoidance. Evaluating the extent of this response, and differences between the flash types, is useful to develop improved survey designs. Aims. This research aimed to quantify the behavioural responses of Eld’s deer to white and infrared flash, to determine whether white-flash cameras were suitable for use in population surveys of this species. Methods. A behavioural ethogram was used to quantify the responses of the deer to the two flash types, as well as the responses of different sex-age classes and group sizes when encountering a camera trap. Additionally, the detection rate for white flash and infrared flash cameras was compared through time, to determine any pattern of avoidance. Key results. While deer were more likely to observe and be startled by white flash than infrared, this did not adversely affect the detection of the deer, with no significant change in the detection rate between the two different flash types over time. Group size was found not to influence behavioural response when encountering camera traps, whereas different age–sex classes of deer showed very few differences in response to camera traps. Conclusions. White flash cameras were found to be suitable for Eld’s deer population surveys and were beneficial in providing colour night-time photos that allow for spotted female deer to be individually identified. Implications. Practitioners should not be concerned about the influence of white flash when using camera traps to monitor populations of Eld’s deer, and using white flash is recommended when individual identification is required.
Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica

ABSTRACT Objective. To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods. Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results. The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions. Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.
A Guide for the Differential Diagnosis of Multiple System Atrophy in Clinical Practice

Multiple system atrophy (MSA) is a sporadic and progressive neurodegenerative disorder with a complex differential diagnosis. A range of disorders— also of nondegenerative etiology— can mimic MSA, expanding its differential diagnosis. Both misdiagnosis and diagnostic delays are relatively common in clinical practice. A correct diagnosis is vital for daily clinical practice, in order to facilitate proper counselling and to timely install therapies in treatable disorders that mimic MSA. A correct diagnosis is also essential for including properly classified individuals into research studies that aim to better understand the pathophysiology of MSA, to develop specific biomarkers or to evaluate novel symptomatic or disease-modifying therapies. Here, we offer some practical guidance to support the diagnostic process, by highlighting conditions that may be considered as MSA lookalikes, by emphasizing some key clinical aspects of these mimics, and by discussing several useful ancillary diagnostic tests.
CAiTST: Conv-Attentional Image Time Sequence Transformer for Ionospheric TEC Maps Forecast

In recent years, transformer has been widely used in natural language processing (NLP) and computer vision (CV). Comparatively, forecasting image time sequences using transformer has received less attention. In this paper, we propose the conv-attentional image time sequence transformer (CAiTST), a transformer-based image time sequences prediction model equipped with convolutional networks and an attentional mechanism. Specifically, we employ CAiTST to forecast the International GNSS Service (IGS) global total electron content (TEC) maps. The IGS TEC maps from 2005 to 2017 (except 2014) are divided into the training dataset (90% of total) and validation dataset (10% of total), and TEC maps in 2014 (high solar activity year) and 2018 (low solar activity year) are used to test the performance of CAiTST. The input of CAiTST is presented as one day’s 12 TEC maps (time resolution is 2 h), and the output is the next day’s 12 TEC maps. We compare the results of CAiTST with those of the 1-day Center for Orbit Determination in Europe (CODE) prediction model. The root mean square errors (RMSEs) from CAiTST with respect to the IGS TEC maps are 4.29 and 1.41 TECU in 2014 and 2018, respectively, while the RMSEs of the 1-day CODE prediction model are 4.71 and 1.57 TECU. The results illustrate CAiTST performs better than the 1-day CODE prediction model both in high and low solar activity years. The CAiTST model has less accuracy in the equatorial ionization anomaly (EIA) region but can roughly predict the features and locations of EIA. Additionally, due to the input only including past TEC maps, CAiTST performs poorly during magnetic storms. Our study shows that the transformer model and its unique attention mechanism are very suitable for images of a time sequence forecast, such as the prediction of ionospheric TEC map sequences.
Some Key Advices for the Modeling of Plasma Jets using FLUENT

Many CFD software packages are commonly used for the modeling of thermal plasma jets. Unfortunately, according to some recent results reported in the literature, one may notice that two different software products do not always provide similar results for a similar case. For example, PHOENICS and FLUENT were recently used in a single study and some abnormal differences were observed in the numerical predictions [1]. After some intensive search, the reasons were identified and are explained in the present paper: it appears that all Fluent built-in k-å type turbulence models have to be modified especially for high temperature flows. Since Fluent is now extensively used for the modeling of plasma jets, it was considered useful to report the required correction to the scientific thermal spray community and this is the purpose of the present paper.
A Hierarchical Information Extraction Method for Large-Scale Centralized Photovoltaic Power Plants Based on Multi-Source Remote Sensing Images

In the context of global sustainable development, solar energy is very widely used. The installed capacity of photovoltaic panels in countries around the world, especially in China, is increasing steadily and rapidly. In order to obtain accurate information about photovoltaic panels and provide data support for the macro-control of the photovoltaic industry, this paper proposed a hierarchical information extraction method, including positioning information and shape information, and carried out photovoltaic panel distribution mapping. This method is suitable for large-scale centralized photovoltaic power plants based on multi-source satellite remote sensing images. This experiment takes the three northwest provinces of China as the research area. First, a deep learning scene classification model, the EfficientNet-B5 model, is used to locate the photovoltaic power plants on 16-m spatial resolution images. This step obtains the area that contains or may contain photovoltaic panels, greatly reducing the study area with an accuracy of 99.97%. Second, a deep learning semantic segmentation model, the U2-Net model, is used to precisely locate photovoltaic panels on 2-m spatial resolution images. This step achieves the exact extraction results of the photovoltaic panels from the area obtained in the previous step, with an accuracy of 97.686%. This paper verifies the superiority of a hierarchical information extraction method in terms of accuracy and efficiency through comparative experiments with DeepLabV3+, U-Net, SegNet, and FCN8s. This meaningful work identified 180 centralized photovoltaic power plants in the study area. Additionally, this method makes full use of the characteristics of different remote sensing data sources. This method can be applied to the rapid extraction of global photovoltaic panels.
High-Speed and Time-Interleaved ADCs Using Additive-Neural-Network-Based Calibration for Nonlinear Amplitude and Phase Distortion

This paper presents a neural network-based digital calibration algorithm for high-speed and time-interleaved (TI) ADCs. In contrast with prior methods, the proposed work features joint amplitude-dependent and phase-dependent nonlinear distortion correction without prior-knowledge of ADC architecture feature. A dynamic calibration is first used to compensate for phase-dependent distortion. Two training optimizations, including a sub-range-sample-based batch schemes and a recursive foreground co-calibration flow are proposed to reduce the error and overfitting and further save hardware resources. A practical calibration engine is also investigated for interleaved ADCs with distributed weight and shared weight methods. To demonstrate the effectiveness of the method, the calibration engine is verified by two fabricated ADC prototypes, a 5 GS/s 16-way interleaved ADC and a 625 MS/s interleaving-SAR assisted pipeline ADC. Measurement results show that SFDR is improved between 16.9dB and 36.4dB before and after calibration for different frequency inputs. To trade-off between accuracy and power consumption, a quantized and pruned engine is implemented on both FPGA and 28nm CMOS technology. Experimental results show that the dedicated calibration on silicon consumes 8.64mW with 0.9V power supply at 333MHz clock rate. Measurement results show that the quantized hardware implementation has only 0.4-4 dB loss in SFDR.
Spatial Distribution of Soil Moisture Content and Tree Volume Estimation in International Institute of Tropical Agriculture Forest, Ibadan, Nigeria

The role of soil moisture in the survival and growth of trees cannot be over-emphasized and it contributes to the net productivity of the forest. However, information on the spatial distribution of the soil moisture content regarding the tree volume in forest ecosystems especially in Nigeria is limited. Therefore, this study combined spatial and ground data to determine soil moisture distribution and tree volume in the International Institute of Tropical Agriculture (IITA) forest, Ibadan. Satellite images of 1989, 1999, 2009 and 2019 were obtained and processed using topographic and vegetation-based models to examine the soil moisture status of the forest. Satellite-based soil moisture obtained was validated with ground soil moisture data collected in 2019. Tree growth variables were obtained for tree volume computation using Newton’s formular. Forest soil moisture models employed in this study include Topographic Wetness Index (TWI), Temperature Dryness Vegetation Index (TDVI) and Modified Normalized Difference Wetness Index (MNDWI). Relationships between index-based and ground base Soil Moisture Content (SMC), as well as the correlation between soil moisture and tree volume, were examined. The study revealed strong relationships between tree volume and TDVI, SMC, TWI with R 2 values of 0.91, 0.85, and 0.75, respectively. The regression values of 0.89 between in-situ tionship (R 2 = 0.03) with ground data. The strong relationships between soil moisture and tree volume suggest tree volume can be predicted based on available soil moisture content. Any slight undesirable change in soil moisture could lead to severe forest conditions.
Development and Initial Validation of the Betrayal Blindness Questionnaires (BBQs)

ABSTRACT Emerging adults of Color are at increased risk for interpersonal trauma. Rotating betrayal blindness (RBB) is when awareness of trauma shifts across contexts and time. With no known measures assessing RBB, the purpose of this study is to provide an initial validation of the Betrayal Blindness Questionnaires (BBQs) among a sample of emerging adults of Color with trauma histories. The BBQs assess past memory (PM-BBQ), current memory (CM-BBQ), and rotating betrayal blindness (R-BBQ). Participants (N = 155) were college students of Color who completed the one-hour online survey at a location of their choice. Using exploratory factor analysis (EFA), the PM-BBQ retained 22 items, the CM-BBQ retained 18 items, and the R-BBQ retained 19 items. With each questionnaire having four factors, the BBQs can be used in basic and applied research to better examine nonlinear changes to autobiographical memory for trauma.
Magnetism and Magneto-Optics in DFT

Magneto-optical effects provide a broad variety of tools for investigation of magnetic and electronic properties of materials. The basics of magneto-optics are discussed in these lecture notes with emphasis on ab initio calculations. After a general introduction the basic ingredients for calculation of frequency dependent conductivity tensor are discussed: treatment of spin magnetism within density functional theory, spin-orbit coupling and interaction of matter with external electro-magnetic field at optical and X-ray frequencies. Special attention is paid to the X-ray spectral range and its special features. A qualitative understanding of X-ray MO effects is presented on the example of L2,3 edges of d-electron systems. The main aim of these lecture notes is to provide understanding of the role of interplay between spin-orbit coupling and magnetic order for the existence of magneto-optical effects.
The gender order in action: consistent evidence from two distinct workplace settings

ABSTRACT In this article, we elaborate an integrative framework of the gender order that considers gender as something simultaneously structurally outside of individual action and as constantly done through interaction. Combining a structural perspective with micro-interactionist accounts makes it possible to show how these mechanisms manifest themselves and how individuals engage with and concretely ‘do gender’ in situ. We focus on three mechanisms through which the gender order emphasizes difference and creates inequality: androcentrism, agentic masculinity, and female devaluation. We illustrate our elaboration of the gender order with empirical evidence from two dramatically different male-dominated employment settings, meat-processing and higher education, in Switzerland and Germany, respectively.
The Interplay between Social Media and Cultural Adjustment: Analysis of the Subjective Well-Being, Social Support, and Social Media Use of Asian International Students in the U.S.

This research focuses on social media use by Asian international students in the U.S. and its impacts on their subjective well-being, social support, and cultural adjustment. In-depth interviews were conducted with 17 international students from six Asian countries who are studying in a Midwestern University in the U.S. The research finds that social media helps to compensate Asian international students for the loss of social support and offline social capital from their home countries. However, the function of social media, which provides bridging social capital and expands their social networks in the host country, is limited. This research extends the existing literature on social media’s moderating effect on sociocultural and psychological adjustment.