Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation of deep UV transparent AlN substrates with high structural perfection for optoelectronic devices

In this paper, the optimal growth conditions during the physical vapour transport of bulk AlN crystals are evaluated with regard to significantly increased deep UV transparency, while maintaining the high structural quality of the AlN crystals which are grown on N-polar c-facets. We show that carbon concentration [C], oxygen concentration [O], and the ratio between both concentrations [C]/[O] have a significant influence on the deep UV transparency. At 3[C] < [O] with [C] + [O] < 1019 cm−3, deep UV transparent AlN single crystals with absorption coefficients at around 265 nm (α265nm) smaller than 15 cm−1 can be prepared. These conditions can be achieved in the N-polar grown volume parts of the AlN crystals using growth temperatures in the range of TG = 2030–2050 °C and tungsten and tantalum carbide as getter materials for carbon and oxygen, respectively. Deep UV transparent AlN substrates (α265nm < 30 cm−1) ≥10 mm in diameter and of high crystalline perfection (rocking curve FWHM < 15 arcsec) are shown for the first time.
Primary Adrenal Malignant Rhabdoid Tumor in a 14-Year-Old Female: A Case Report and Literature Review

Malignant rhabdoid tumor (MRT) is a rare, SWItch/sucrose nonfermentable-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 (SMARCB1)-deficient, aggressive tumor, occurring predominantly in children below 3 years of age. Primary adrenal MRT is extremely rare, with only 3 cases reported in the literature. A previously healthy 14-year-old female presented with left upper quadrant/epigastric abdominal pain. Imaging studies revealed an 8.0 × 8.0 × 6.5 cm, heterogeneous, partially enhancing mass along the superior margin of the left kidney encasing the adrenal gland. Surgical resection of the tumor revealed a hypercellular heterogeneous neoplasm arising from the adrenal gland. It was composed predominantly of primitive small round blue cells with focal true rosettes and areas of vague glandular epithelial differentiation and chondroid differentiation. Classic rhabdoid-type cytoplasmic inclusions were focally present. Mitoses, tumor necrosis, and hemorrhage were readily seen. Tumor cells showed complete loss of SMARCB1 (INI1) nuclear staining, demonstrated strong, and diffuse positivity for glypican 3, patchy positivity for CD99, cytokeratin, Sal-like protein 4, Lin-28 homolog A, epithelial membrane antigen, and S100. Molecular studies revealed biallelic frameshift mutations in the SMARCB1 gene (c.673delG and c.683dupT) without pathogenic copy number aberrations. The histologic, immunohistochemical, and molecular findings support a diagnosis of MRT. The unusual age, location, and mutations of this case expand the clinicopathologic and molecular spectrum of MRT.
Towards More Meaningful Evaluation of Cultural Heritage Programmes: Engaging Stakeholders Through Programme Modelling

Recently, cultural heritage scholars and practitioners have questioned how the field evaluates heritage initiatives. This article considers how a collaborative approach to evaluation may enhance heritage initiatives, particularly at critical developmental stages in their design and implementation. We explore how recent developments in evaluation theory and practice serve interests shared by heritage programme managers, funders and other stakeholders. In particular, programme modelling techniques have proven valuable for engaging a wide array of heritage stakeholders (including staff, funders and site owners). A programme model provides a graphic representation of how a programme is intended to achieve its goals. Programme models have become prevalent in other disciplines, and many government and philanthropic funders require their inclusion in funding proposals and progress reports. However, such models—and the collaborative, stakeholder-driven processes used to develop them—are largely absent from heritage studies. Examples from the US National Park Service (https://www.nps.gov/aboutus/index.htm) and Cultural Heritage without Borders-Albania (http://chwb.org/) illustrate how these techniques serve important learning and evaluation needs identified by heritage programmes. These contrasting examples demonstrate how programme modelling can enhance programme design, communications and reflective practice among a cross-section of programmatic actors. The concluding discussion explores the implications of programme modelling for the future of heritage management.
Book Review: Paul Gulino and Connie Shears. The Science of Screenwriting: The Neuroscience Behind Storytelling Strategies

By joining forces, a cognitive psychologist and a professor of screenwriting aimed to shed some much needed light on the science behind storytelling. In recent years, scientists have learned a lot about the psychology and physiology that make humans particularly attracted to film and television. It must be noted that our species did not specifically evolve to try and make sense of static images displayed in rapid succession before our eyes. But then we also didn’t evolve to eat puffed up kernels of corn drowning in butter and/or salt. On both counts, we have certainly adapted well to our environment. Indeed, this ability to confuse the brain (and entice it with the perfect combination of sweet and salty) is precisely why films work so well on us. We can be transported beyond the mundane, to other worlds and times, and it would seem that we’re very much built to enjoy such things. This book embraces the overlap between science and screenwriting, with the goal of applying what we know about psychology to the process of making a better film. For example, how can screenwriters make sure that viewers connect with the main character (Chapter 2) and don’t lose interest along the way (Chapter 6)? To this end, the book is successful in a number of ways. First, it is surprisingly short, and perhaps as a result, is very readable. The light-hearted writing style emphasises the authors’ enjoyment of the subject matter, and readers might easily consume the material in one sitting. Second, the book is filled with plenty of examples to illustrate each point, taken from both television and film. While One Flew Over the Cuckoo’s Nest and Breaking Bad suitably epitomise the difficulties with presenting main characters who make questionable decisions (to put it mildly), Silver Linings Playbook is presented as a study in conflict. Third, a simple summary, along with some thought experiments and exercises, round off most chapters. These support readers in their understanding of the concepts presented and provide “homework” aimed at developing some of the techniques discussed.
Detecting and Quantifying Geometric Features in Large Series of Cluster Structures

Abstract Detecting and quantifying geometric features in large series of cluster structures is in the focus of the present paper. Three so-called similarity functions that have been presented earlier are compared and their ability to point to highly symmetric clusters is shown. These functions quantify the similarity between different cluster structures or between a cluster and bulk structures. As an example, we have chosen a continuous series of Lennard–Jones cluster structures with 350 to 1000 atoms. The similarity functions of these systems are compared to other descriptions of relative stability, cluster shape and shell building and are found to be very useful and reliable.
Reform and Practice of Modern Electrical Control Technology Based on OBE

There are many problems in the traditional teaching of modern electrical control technology, such as teacher-centered, not paying attention to the process examination and ability training. OBE is the abbreviation of output-oriented education, its core concept is student-centered and focuses on what students can produce after receiving education. Based on the concept of OBE education and the requirements of engineering education certification, aiming at the practical characteristics of modern electrical control technology, taking the actual occupation demand as the guidance and focusing on the cultivation of practical engineering application ability, the targeted reform is carried out from the four aspects of teaching content, practical teaching, assessment method and curriculum design. With students as the center and results as the guidance, teachers can raise their expectations of students' learning, set challenging goals, and cultivate outstanding professionals serving the society and enterprises.
Target Classification Algorithms Based on Multispectral Imaging: A Review

Multispectral imaging extracts rich spectral information from targets, which greatly expands the function of traditional imaging technology. Multispectral imaging is widely used in agriculture, military, medicine, industry, and meteorology. Because of the information redundancy in multispectral images, it is necessary to reduce the dimension by pre-processing. In recent years, most of the researchers have adopted the methods of pre-processing before classification. Based on the principles of feature selection, feature transformation, and feature extraction, common dimensionality reduction methods are introduced, and the advantages and disadvantages of them are discussed. Afterwards, classification methods are divided into traditional methods and deep learning methods, and their characteristics and application prospect are discussed. Through comparison, the former are cost-effective and have the mature theories, while the latter have strong adaptability and high classification accuracy. At present, methods could be optimized from the perspective of saving computing resources and using spectral information efficiently. In the future, traditional methods will be improved and comprehensively used, while new methods with stronger adaptability and precision will be developed.
Automated prediction of sensory scores for color and appearance in canned black beans (Phaseolus vulgaris L.) using machine vision

ABSTRACT Evaluation of canning quality of beans is commonly carried out by simple visual inspection that is time-consuming, resource intensive, and biased by the experience of the panelist. Moreover, there is not a standard scale to rate visual quality traits of canned beans. In this research, a machine vision system was implemented and tested for automatic inspection of color (COL) and appearance (APP) in canned black beans. Various color and textural image features (average, standard deviation, contrast, correlation, energy, and homogeneity from red, green, blue, lightness, red/green, yellow/blue, hue, saturation and value color scales) were extracted from beans and brine images, and evaluated to predict the quality rates for COL and APP of a group of bean panelists using multivariate statistics. Sixty-nine commercial canned black bean samples from different brands and markets were used for analysis. In spite of the “fair” agreement among the sensory panelists for COL and APP, as determined by multi-rater Kappa analysis, machine vision data based on partial least squares regression model showed high predictive performance for both COL and APP with correlation coefficients of 0.937 and 0.871, and standard errors of 0.26 and 0.38, respectively. When a classification was performed based on both COL and APP traits, a support vector machine model was able to sort the samples into two sensory quality categories of “acceptable” and “unacceptable” with an accuracy of 89.7%. Using simple color and texture image data, a machine vision system showed potential for the automatic evaluation of canned black beans by COL and/or appearance as a professional visual inspection.
Tensor Voting Based 3-D Point Cloud Processing for Downsampling and Registration

Point cloud registration is related with many significant and compelling 3D perception problems including simultaneous localization and mapping (SLAM), 3D object reconstruction, dense 3D environment generation, pose estimation, and object tracking. A point cloud can be defined as a data format that consists of a combination of multiple points used to identify an object or environment. The aim of this study is to propose a point cloud registration method, which ensures that the point clouds obtained with 3D LiDAR are sampled while preserving their geometric features and the point clouds are registered with high success rate. For this process, it is inspired from the method known in the literature as Tensor Voting, which is originally used to extract geometric features in N-dimensional space. In point cloud registration process, a coarse registration step has been proposed, which focusses on feature registration instead of point registration.
The Effectiveness of Peer Assessment in EFL Blended Learning Environments

There is an increasingly application of peer assessment (PA) in blended learning environments to motivate and evaluate learners. In spite of its popularity, there are few studies to be conducted to analyze and corroborate its impact on students’ learning in this context. This study aims to evaluate effectiveness of peer assessment in blended learning settings, investigating what effects contribute to learning and teaching. The results suggest that peer assessment in blended learning context creates positive learning outcomes and more interesting attitudes for students. The findings as well as the implications for practice and research are discussed.
Autonomous Decision-Making of Path Re-planning for UGV

A novel approach of autonomous decision-making of path re-planning for UGV is proposed when encountering the blocked road. The system includes a global route planner and a local path planner. Based on prior information in known environment, a topological map is built first to describe connectivity of roads. The global route planner does path planning or re-planning based on the topological map to generate the global route. According to the route, A* search algorithm combined with model predictive control is used for local path planning and judgment on the blocked road. The complete autonomous decision-making process includes: judgment of the local blocked road, reversing to the fork/intersection road node, the global route re-planning and detour through the blocked road. Experiments show that in known dynamic environment, the proposed approach can effectively solve the problem of path re-planning in order to improve the autonomous traffic performance for UGV.
Digital Government Measurement and Perception – Reality Check in Poland: Measurement and Perception – Reality Check

Can the digital government strategy in middle-income countries be driven by their position in e-government indexes and their components? Based on the case of Poland, the paper explores the applicability of three indexes – Network Readiness Index (NRI), Electronic Government Development Index (EGDI) and Digital Economy and Society Index (DESI) – by confronting them with the perception of the country's decision-makers in digitalization. The research objective was to find out the presence and the nature of the measurement-perception gap. To this end, a survey was carried and its results were compared to the structure of the indexes. The comparison uncovered the presence of a gap but the nature and width of this gap varies between indexes, DESI being most aligned with the past and future priorities of the country.
ICT Governance in Brazilian Smart Cities:: An Integrative Approach in the Context of Digital Transformation

A smart city can be described as an organizational environment that seeks to provide a better quality of life for citizens by applying digital technologies to public services. In general, ICT governance involves managing investments and ICT resources to guide organizations to achieve their strategic goals. Thus, ICT governance in smart cities should evaluate, direct, and monitor Information Technology to achieve public value for the local community by providing digital services. Most research investigated ICT governance in private and public organizations and smart governance in smart city environments, but few focused on ICT governance in smart cities, especially in developing countries. Thus, this research investigated ICT governance in the context of Brazilian smart cities by developing a conceptual and practical integrative approach for the cities. To achieve this objective, a literature review and a survey with Brazilian professionals were conducted to identify relevant ICT governance practices applied to this context. As a result, this work introduces a set of related practices that municipalities can track when designing smart city projects in a digital transformation environment.
Research on Suggestions of Improving Chinese Open Government Data in Innovation of Public Governance

This paper collects a large number of cases and makes a comparative analysis of the typical application of Chinese and American open government data for public governance. Through comparison, this paper finds the gap between China's open government data and the United States, and then analyzes the reasons. On this basis, through the investigation of advanced experience, this paper puts forward the suggestions of open government data to innovate public governance, including data catalogue compilation, data standard formulating, data quality assessment and open government data sharing cooperation, in order to improve Chinese open government data to innovate the public governance level.
A Study on the Modeling of Major Factors for the Principles of AI Ethics

The fourth industrial revolution, centered on artificial intelligence (AI), signals a significant transformation in human society. For this social transformation to ultimately be for humankind's prosperity and happiness, serious consideration of AI ethics is needed. As a result, many countries have begun to establish AI ethics principles, and the international community is pushing for standardization on AI ethics principles. This study aims to derive general factors of ethical principles that should be considered to establish and standardize AI ethics principles. To this end, we present a "general AI ethics principles model," including 12 major factors, based on the recently published AI ethics principles in 15 countries. Furthermore, the major factors for AI ethics principles that we derived through this study ultimately confirmed that AI should be useful to all humans and is oriented toward the value of building a "Trustworthy" AI society. Based on these fundamental ideologies, we confirmed that each factor interconnects with each other. It is hoped that the AI ethics principles model that reflects this will be referred to national and international communities that have yet to develop Principles of AI Ethics. However, Factors for AI ethics principles should be constructed following the principles' intent and goal orientation, ensuring its feasibility, rather than merely duplicating the principles model.
A neural network-cellular automata based model for predicting vertical urban growth

This research aims to support the urban planning decisions in Mexico through a model that predicts the vertical urban growth using main factors with artificial neural networks and cellular automata techniques. We use raster image data from the Landsat 8 and vectorial maps from the open data of the Mexican Institute of Geography and Statistics (INEGI) from 2015 to 2020, the results are validated using Kappa and Jaccard indexes. Analysing different vertical urban growth scenarios, by modifying growth factors or cellular automata rules, allow better-informed decisions for urban planning and anticipating new infrastructure needs, projections, and regulations.
The Later Life Audio and Radio Co-operative:: considering radio as a technology to promote citizen dialogue in later life

The Later Life Audio and Radio Co-operative (LLARC) is an England-wide network of older content creators, age-inclusive radio stations, third sector organisations, local authorities and academics working on ageing issues. The co-operative was established in 2019 as an outcome from a participatory action research project. Aiming to promote talk-based content created by older adults, the LLARC advocates for greater representation of older adults in broadcasting. This paper considers radio production as an approach to support civic participation in later life. Firstly, we present findings from a two-day radio festival for older adults at which the LLARC was launched. Drawing on discussions at the radio festival, we highlight three themes that underpin the LLARC's mission: 1) challenging ageist stereotypes; 2) advocating for intergenerational cohesion; and 3) considering radio as a technology for community engagement. Secondly, based on observations, we report on the organisation's ongoing digital development and adaptation to remote digital production workflows in response to the COVID-19 pandemic. We discuss the potential of radio as a technology to involve older adults in citizen dialogue. Due to its combination of digital and non-digital elements, we conclude that radio production can be an accessible technology for older adults to engage with in order to have their voices heard more widely. Our work contributes to key debates at the intersection of technology use and community engagement in later life, suggesting a central role for audio and radio as accessible routes to support public debates and promote citizen dialogue in later life.
Dance Basic Training Methods Based on Motion Capture Technology

The traditional dance teaching mode makes the learning efficiency of students seriously decline. This research mainly discusses basic dance training methods based on motion capture technology. The 60 elective ballet students were divided into a regular group and an experimental group by lottery. The students in the regular group adopt an outdoor learning model for ballet learning. The teacher explains, demonstrates, finds problems and corrects the actions. The students follow the sequence of actions taught by the teacher. Students in the experimental group go to the designated laboratory to watch the 3D dance animation video generated by motion capture first, and then learn ballet under the motion capture system. Using capture equipment and computer to process learning data, it can acquire and replay students' learning in real time, and provide timely corrections to students' irregular actions. Using SPSS software for t-test analysis, P=0.002<0.05 and P<0.01, reflecting that motion capture technology can improve students' learning efficiency, stimulate learning interest, better understand teaching materials, and increase the sense of interaction.
Efficient Mini-batch Training for Echo State Networks

Echo state networks (ESNs) are generally optimized by the ordinary recursive least squares (ORLS) algorithm. Although ORLS has fast convergence, it can process only one sample per iteration, which makes ESNs difficult to scale to large datasets. To tackle this problem, a novel mini-batch RLS (MRLS) algorithm is proposed in this paper. On this basis, to prevent overfitting in the ESN training, an L 1 regularization method is suggested for MRLS. In addition, to make ESNs more suitable for time-varying tasks, an adaptive method of the forgetting factor is also introduced for MRLS. Experimental results of two time-series problems show that ESNs have faster processing speed and better convergence performance with MRLS than with ORLS.