Miracle-dz/UniFAR-FTUs huggingface dataset:
EDUCATIONAL ROBOTICS AS A MEANS OF DEVELOPING PUPILS’ AND STUDENTS’ COMPUTATIONAL THINKING

The accelerating development of technology over the past decades has brought many radical changes in all aspects of life and has unquestionably affected the functioning of our society. The expansion of the digital space and the technological innovation in industry, commerce and household have given rise to a large number of new concepts relating to digital and information technology and their use. In the context of supporting these activities and needs, the FEP document has been updated for the educational area Information and Communication Technology in elementary schools but also in general and technical secondary schools. This innovation is built primarily on the development of computational thinking, which is based on two pillars—algorithmization/programming and educational robotics. The present paper uses educational research to analyse the area of educational robotics as one of the important tools for promoting the concept of developing computational thinking including a description of the specific tools for the implementation of this type of teaching. The aim of the research study was to analyse the current level of pupils’ awareness of and practical experience with educational robotics, both in school and in extracurricular activities. The research tool for data collection was an online questionnaire designed by the authors.
Multifactor Productivity Measurement at Statistics Canada

This paper describes the evolution of the Multifactor Productivity Program launched at Statistics Canada in 1987 and the improvements made in multifactor productivity measurement since then. The improvements were made in response to developments in the economic literature, better data sources, and the needs of the user community. The paper also summarizes research that uses alternate data and methodologies to assess the accuracy of the Multifactor Productivity Program and to provide insights into areas that traditional international multifactor productivity programs omit. Finally, the paper outlines future directions that are being contemplated to further improve the measurement of productivity at Statistics Canada.
Foreign interference and digital democracy: is digital era governance putting Australia at risk?

ABSTRACT As liberal democracies intensify their efforts to digitise democracy, more governance services and processes are shifting online. Malign foreign entities (MFEs) are exploiting this phenomenon of digital era governance (DEG) to weaken democracies through information warfare operations. Australia is not immune to this, yet there is limited research exploring the relationship between digital democracy and foreign interference in the Australian context. Addressing this lacuna, this paper identifies the ways in which DEG might inadvertently produce opportunities for MFEs to target the Nation’s core democratic infrastructure. Through the implicit application of a tri-theoretical framework of DEG, democratic theory, and institutional theory, I argue that DEG has induced a series of new vulnerabilities in Australia’s political processes and institutions that challenge the legitimacy of decision-making inputs and outputs. MFEs may exploit these potential vulnerabilities by tapping into key digitally-amplified problems such as inauthenticity, data insecurity, and disinformation, thereby threatening Australia’s democratic sovereignty.
Deep Cross-Attention Network for Crowdfunding Success Prediction

Crowdfunding creates opportunities for entrepre- neurs. It allows startup companies to reach a large audience for fundraising and bring their creative ideas to life. In this work, we are concerned with crowdfunding project success prediction problem, i.e., to predict whether a project will successfully reach its funding goal by using its project profiles. This is important for startup companies to refine their project profiles and achieve their goals. Crowdfunding project success prediction is a typical classification problem but with a few critical challenges. On the one hand, with only coarse-grained project status as weak supervision, it is hard for a deep learning network to learn the relationship between project profiles and explain why it makes this prediction. On the other hand, on the project homepage, there are various modalities of description, including metadata, textual description, images, and videos. Among those, videos play an important role in the success of a crowdfunding project, however, were ignored in previous works, due to the difficulty in extracting useful semantic and authentic information from videos, especially for the crowdfunding project where information in different modalities are unaligned. To this end, we propose a novel framework called Deep Cross-Attention Network to learn and fuse information from introduction videos and textual descriptions of project profiles. More specifically, we develop a cross-attention block to align and represent mismatched textual description and untrimmed introduction videos and fuse the information from these two modalities, which effectively remedies the lack of supervised information caused by project status as weak supervision. More importantly, with our cross-attention mechanism, the model is able to interpret how it makes such predictions and show which keywords and keyframes it depends on. We conduct extensive experiments on two crowdfunding datasets (collected from Kickstarter and Indiegogo) and show that our method achieves superior performance over existing state-of-the-art baselines.
Impact of materials and design changes on the recycling of automobiles

Current and prospective changes in automotive materials and design will in time affect the recycling of discarded automobiles. These changes are already altering the pattern of materials consumption and process-scrap generation in terms of size reduction, the use of ferrous metals, aluminum, plastics and composites, as well as of some other materials that will be used in future automobiles. Likely effects of materials and design changes on the technology and economics of the automobile-recycling industry are also considered. 46 references.
School-based health promotion to improve mental health literacy: a comparative study of peer- versus adult-led delivery

Abstract Background School-based youth mental health promotion is increasingly recognised as a useful tool to improve mental health knowledge and help-seeking among adolescents. Peer-led initiatives are emerging as a potentially viable mode of delivering this material. Yet, there is limited evidence on the effectiveness of these initiatives compared to more traditional, adult-led, forms of mental health promotion. Aims This study aimed to compare improvements in students’ mental health knowledge and help-seeking after attending either a peer- or adult-led youth mental health promotion workshop. It also sought to examine differential effects by gender. Method A within- and between-group comparative design was used to examine secondary school students’ (N = 536) mental health knowledge and help-seeking intentions before and after attending a school-based mental health promotion workshop. Results Students’ mental health knowledge and help-seeking intentions significantly improved in both peer- and adult-led groups. Outcomes did not differ across modes of delivery, although the impact of adult-led workshops on mental health knowledge was moderated by gender. Conclusions Peer-led youth mental health promotion appears to be as effective as traditional adult-led delivery, and seems to be particularly beneficial for male students who respond more favourably to content communicated through their peers.
Primary Founder Mutations in the PRKDC Gene Increase Tumor Mutation Load in Colorectal Cancer

The clonal composition of a malignant tumor strongly depends on cellular dynamics influenced by the asynchronized loss of DNA repair mechanisms. Here, our aim was to identify founder mutations leading to subsequent boosts in mutation load. The overall mutation burden in 591 colorectal cancer tumors was analyzed, including the mutation status of DNA-repair genes. The number of mutations was first determined across all patients and the proportion of genes having mutation in each percentile was ranked. Early mutations in DNA repair genes preceding a mutational expansion were designated as founder mutations. Survival analysis for gene expression was performed using microarray data with available relapse-free survival. Of the 180 genes involved in DNA repair, the top five founder mutations were in PRKDC (n = 31), ATM (n = 26), POLE (n = 18), SRCAP (n = 18), and BRCA2 (n = 15). PRKDC expression was 6.4-fold higher in tumors compared to normal samples, and higher expression led to longer relapse-free survival in 1211 patients (HR = 0.72, p = 4.4 × 10−3). In an experimental setting, the mutational load resulting from UV radiation combined with inhibition of PRKDC was analyzed. Upon treatments, the mutational load exposed a significant two-fold increase. Our results suggest PRKDC as a new key gene driving tumor heterogeneity.
Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response

It has been demonstrated that the inflammatory response influences cancer development and can be used as a prognostic biomarker in various tumors. However, the relevance of genes associated with inflammatory responses in hepatocellular carcinoma (HCC) remains unknown. The Cancer Genome Atlas (TCGA) database was analyzed using weighted gene coexpression network analysis (WGCNA) and differential analysis to discover essential inflammatory response-related genes (IFRGs). Cox regression studies, both univariate and multivariate, were employed to develop a prognostic IFRGs signature. Additionally, Gene Set Enrichment Analysis (GSEA) was used to deduce the biological function of the IFRGs signature. Finally, we estimated immune cell infiltration using a single sample GSEA (ssGSEA) and x-cell. Our results revealed that, among the major HCC IFRGs, two (DNASE1L3 and KLKB1) were employed to create a predictive IFRG signature. The IFRG signature could correctly predict overall survival (O.S) as per Kaplan-Meier time-dependent roc curves analysis. It was also linked to pathological tumor stage and T stage and might be used as a prognostic predictor in HCC. GSEA analysis concluded that the IFRG signature might influence the immune response in HCC. Immunological cell infiltration and immune checkpoint molecule expression differed in the high-risk and low-risk groups. As a result of our findings, DNASILE may play a role in the tumor microenvironment. However, more research is necessary to confirm the role of DNASE1L3 and KLKB1.
Veterinary anatomy teaching from real to virtual reality: An unprecedented shift during COVID‐19 in socially distant era

Teaching Veterinary Anatomy in a virtual world is an utmost requirement of the ongoing era of COVID‐19 pandemic. Due to sudden lockdown, all the educational institutes, professional medical and veterinary colleges were closed (except emergency services) and academic activities were suspended altogether. This situation was challenging as well as thought‐provoking for various academicians and professors to rethink about the means to keep the conduit of imparting education unceasing. This review focuses on the virtual reality aids that are being adopted worldwide and the initiatives taken by academicians during this era to impart best practical anatomy lessons yet being in a socially distant world. However, during the lockdown and social distancing, face‐to face interactions were not feasible, and therefore, number of distant learning methods were created on virtual platforms for continuing the process of teaching veterinary anatomy closer to reality available at their doorsteps where academic staff was actively involved to reconstruct and renovate resources, upgraded their digital competencies to develop new ways to shift from one on one and merged to remote online teaching, and anatomy teaching was transformed to an abrupt ‘virtual mode’. Adopting virtual reality (VR) aids such as 3D animations, software packages, virtual dissections and E‐museum serves as a great saviour throughout this unprecedented pandemic in a socially distant world to continue the teaching connexion.
Millimetre-wave FMCW radar for remote sensing and security applications

is thesis presents a body of work on the theme ofmillimetre-wave FMCWradar, for the purposes of security screening and remote sensing. First, the development of an optimised soware radar signal processor will be outlined. rough use of threading and GPU acceleration, high data processing rates were achieved using standard PC hardware. e flexibility of this approach, compared to specialised hardware (e.g. DSP, FPGA etc.), allowed the processor to be rapidly adapted and has produced a significant performance increase in a number of advanced real-time radar systems. An efficient tracker was developed and was successfully deployed in live trials for the purpose of real-time wave detection in an autonomous boat control system. Automated radar operation and remote data telemetry functions were implemented in a terrain mapping radar to allow continuous monitoring of the Soufrière Hills volcano on the Caribbean island of Montserrat. is work concluded with the installation of the system 3 km from the volcano. Hardware modifications were made to enable coherent measurement in a number of existing radar systems, allowing phase sensitive measurements, including range-Doppler, to be performed. Sensitivity to displacements of less than 200 nmwas demonstrated, which is limited by the phase noise of the system. Efficient compensation techniques are presented which correct for quadrature mixer imbalance, FMCW chirp non-linearity, and scanner drive distortions. In collaboration with the Home Office, two radar systems were evaluated for the stand-off detection of concealed objects. Automatic detection capability, based on polarimetric signatures, was developed using data gathered under controlled conditions. Algorithm performance was assessed through blind testing across a statistically significant number of subjects. A detailed analysis is presented, which evaluates the effect of clothing and object type on detection efficiency.
Sufficient sleep duration in autistic children and the role of physical activity

Emerging evidence suggests that physical activity may be associated with improved sleep in autistic children. We aimed to determine whether physical activity associated with sufficient sleep duration in children and whether this association was modified by reported autism spectrum disorder (ASD) status. We analyzed existing data of children 6–17 years old whose caregivers completed the 2018 National Survey of Children’s Health (nonautistic N = 20,845; ASD N = 681). Logistic regression determined whether physical activity (days active in the past week) associated with sufficient sleep duration. Physical activity-by-ASD interactions were included to determine whether the association differed for autistic children. Physical activity-by-ASD-by-sex and physical activity-by-ASD-by-age-group interactions were also modeled. Physical activity was associated with increased odds of sufficient sleep duration (e.g. 0 days vs 4–6 days: odds ratio (OR) = 1.85; 95% confidence interval (CI): 1.48–2.32). We did not observe an overall statistically significant interaction between physical activity and reported ASD status; however, the positive association between physical activity and sufficient sleep duration was weaker in autistic children, especially those with more severe ASD, female autistic children, and autistic children ages 6–12 years old. In conclusion, physical activity is a promising approach to improve sufficient sleep duration but with nuanced findings in autistic children. Lay abstract Higher levels of physical activity may be associated with improved sleep in children, but this relationship is still being determined, especially in autistic children. In this study, we used existing data from the 2018 National Survey of Children’s Health. Caregivers of children 6–17 years old, including caregivers of autistic children, completed a questionnaire that included questions about physical activity (days active in the past week) and sleep duration. We then determined if children were obtaining the recommended hours of sleep for their age (i.e. sufficient sleep). We found that higher physical activity levels were associated with sufficient sleep duration, but this finding was weaker in autistic children. In particular, this association was not observed in autistic children with more severe autism spectrum disorder, female autistic children, and autistic children 6–12 years old. In conclusion, physical activity is a promising approach to help children obtain sufficient sleep duration. However, more personalized approaches to improving sleep may be needed for certain groups of autistic children.
Metaheuristic Algorithms Applied to Color Image Segmentation on HSV Space

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes’ rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.
Factors affecting use of massive open online courses by Ghanaian students

Abstract The study examined how the perceived level of students’ knowledge on Massive Open Online Courses (MOOCs) influence its use. The study utilizes the use of Innovation Diffusion Theory and Technology Acceptance Model to examine factors that influence students use of MOOCs within the Ghanaian university. The data was analyzed using the Statistical Package for the Social Sciences. The data was analyzed using Structural Equation Modeling (SEM-Amos). The findings showed perceived usefulness, perceived ease of use MOOCs as well as perceived compatibility and observability positively affect students use of MOOCs systems. However, the perceived complexity of use negatively affects students use of MOOCs systems. The findings further validate students’ motive to use the MOOCs systems for learning. Students’ attitudes toward using MOOCs systems and behavioral intentions to use MOOCs systems are heavily influenced by technology and resource availability. When technology tools are available, students’ intents to use them in their studies.
In Silico Analysis of Honeybee Venom Protein Interaction with Wild Type and Mutant (A82V + P375S) Ebola Virus Spike Protein

Venom from different organisms was used in ancient times to treat a wide range of diseases, and to combat a variety of enveloped and non-enveloped viruses. The aim of this in silico research was to investigate the impact of honeybee venom proteins and peptides against Ebola virus. In the current in silico study, different online and offline tools were used. RaptorX (protein 3D modeling) and PatchDock (protein–protein docking) were used as online tools, while Chimera and LigPlot + v2.1 were used for visualizing protein–protein interactions. We screened nine venom proteins and peptides against the normal Ebola virus spike protein and found that melittin, MCD and phospholipase A2 showed a strong interaction. We then screened these peptides and proteins against mutated strains of Ebola virus and found that the enzyme phospholipase A2 showed a strong interaction. According to the findings, phospholipase A2 found in honeybee venom may be an effective source of antiviral therapy against the deadly Ebola virus. Although the antiviral potency of phospholipase A2 has been recorded previously, this is the first in silico analysis of honeybee phospholipase A2 against the Ebola viral spike protein and its more lethal mutant strain.
Improving the brief through information and process modelling

Due to the vast amount of information and knowledge involved in construction projects, clients often have difficulties in identifying and communicating their actual requirements. In order to address this information effectively during the briefing process, such information is modelled into structured data models using EXPRESS-G technique. The processes involved during the development of the brief, however, are modelled using IDEF0 technique.The developed models establish the foundation for the development of a computerised system, which utilise an object oriented environment. The implemented object oriented data models then form the framework for the presentation of the client's brief. These models, when implemented into the computerised environment, will significantly enhance the communication channel between the various parties involved from a very early stage of a construction project. The outcome is an improved brief, which fulfils the client's budgetary constraints.
Application of ammonium sulfate affects greenhouse gases and microbial diversity of an oil palm plantation on tropical peat

ABSTRACT Nitrogen fertilizer application is crucial in maintaining soil fertility and crop productivity in oil palm (Elaeis guineensis Jacq.) plantations. However, the effects of N addition on soil’s chemical and biological properties, particularly tropical peat are still unclear. This study investigated the impact of N fertilizer addition on peat properties, greenhouse gas (GHG) fluxes, soil bacterial and fungal diversity in an oil palm plantation on tropical peat. A long-term field experiment (from 2012 to 2013 and 2016 to 2017) was conducted to compare the application of 124.3 kg N ha −1 y −1 of ammonium sulfate (the treatment) to no N application (the control). Baseline data were collected from a primary peat swamp. Overall, N fertilizer input slightly decreased soil pH, total carbon (C), total N, pyrophosphate solubility index (PSI) but increased nitrate (NO3 −), ammonium (NH4 +) concentration, methane (CH4), and nitrous oxide (N2O) fluxes. The result suggested a reduction in soil bacterial diversity but an increase in soil fungal diversity. Forest soil showed naturally low NO3 − concentration but a NH4 + concentration comparable to that in the oil palm plantation. These findings provide evidence of changes in soil chemical and biological properties from long-term ammonium sulfate application on tropical peat.