Reviewerly/exHarmony huggingface dataset:
COVID-19 smart surveillance: Examination of Knowledge of Apps and mobile thermometer detectors (MTDs) in a high-risk society

Technological innovations gained momentum and supported COVID-19 intelligence surveillance among high-risk populations globally. We examined technology surveillance using mobile thermometer detectors (MTDs), knowledge of App, and self-efficacy as a means of sensing body temperature as a measure of COVID-19 risk mitigation. In a cross-sectional survey, we explored COVID-19 risk mitigation, mobile temperature detectable by network syndromic surveillance mobility, detachable from clinicians, and laboratory diagnoses to elucidate the magnitude of community monitoring.In a cross-sectional survey, we create in-depth comprehension of risk mitigation, mobile temperature Thermometer detector, and other variables for surveillance and monitoring among 850 university students and healthcare workers. An applied structural equation model was adopted for analysis with Amos v.24. We established that mobile usability knowledge of APP could effectively aid in COVID-19 intelligence risk mitigation. Moreover, both self-efficacy and mobile temperature positively strengthened data visualization for public health decision-making.The algorithms utilize a validated point-of-center test to ascertain the HealthCode scanning system for a positive or negative COVID-19 notification. The MTD is an alternative personal self-testing procedure used to verify temperature rates based on previous SARS-CoV-2 and future mobility digital health. Personal self-care of MTD mobility and knowledge of mHealth apps can specifically manage COVID-19 mitigation in high or low terrestrial areas. We found mobile usability, mobile self-efficacy, and app knowledge were statistically significant to COVID-19 mitigation. Additionally, interaction strengthened the positive relationship between self-efficacy and COVID-19. Data aggregation is entrusted with government database agencies, using natural language processing and machine learning mechanisms to validate and analyze.The study shows that temperature thermometer detectors, mobile usability, and knowledge of App enhanced COVID-19 risk mitigation in a high or low-risk environment. The standardizing dataset is necessary to ensure privacy and security preservation of data ethics.
How Well Are Pre-Service Teachers Prepared to Impart Digital Skills in Secondary-Level Education?

This research full paper is concerned with the question, in how far advanced pre-service teachers at the University of Vienna feel ‘fit’ to mediate digital skills as part of their instruction at secondary levels K5–K8. While the digital transformation is progressing rapidly, digital skills have become indispensable for all stakeholders in the educational field. To address this issue, we composed an online questionnaire consisting of closed- and open-ended questions. It was distributed among 4,054 students of the teacher education degree program and generated 322 full responses. In the survey, the students were asked to state their opinion on the integration of digital competencies in their attended teacher education degree program and how to improve it. With the use of qualitative content analysis (QCA), we analyzed which competencies and tools they missed the most in their studies or had to acquire on their own.The results of this mixed methods study indicate a lack of essential digital skills in the studies of most students, and that they feel a lack of preparation for the challenges to come as a teacher in the modern world. Moreover, most students agree that they had to acquire more than 75% of the required digital competencies on their own and pointed out the skills and tools they required the most throughout their study program or during their work as a teacher. Yet, some students report good practices in use in elective courses and in courses of various subject didactics. The paper provides a short overview of which digital competencies students wish most to be covered in their studies, including a truly professional handling of MS Teams and other tools such as learning apps, response systems, and tools for issuing learning materials and quizzes. As much as we value students’ capacities for self-learning in the digital sphere, we conclude with implications for a thoughtful integration of digital skills in teacher education programs to optimally prepare students for tomorrow’s challenges.
Domain-specific language to design educational programs with the use of X-matrix approach

This paper introduces a prototype of a domain-specific language for quick and efficient preparation of educational programs. Rapid changes in technology lead to constant modification process of the current program especially in scientific-intensive areas. As a result, teachers may need a tool representing a convenient, structural and formalized approach for learning program construction. The domain-specific language is devoted to serve as such an instrument and is based on concepts of X-matrix and problem areas map. In this paper functions of the language prototype and its syntax are represented. Key entities which comprise X-matrix and problem area matrix are explained. The implementation of operations on the entities in the markup language are described.
Research on multi-dimensional monitoring of “point, line, plane, body” in hydropower engineering

With the global low-carbon awareness and the rapid improvement of China’s social economy, the development of China’s hydropower resources has gradually shifted to southeastern Tibet, which is located in high mountains and valleys. The complicated geological conditions make the deformation problem of the main structure of hydropower engineering such as tunnel and dam become increasingly prominent, so it is particularly important to monitor the deformation of hydraulic structures. This paper studies various safety monitoring methods currently used in hydropower projects and summarizes the disadvantages of single safety monitoring method. the mechanism of “point, line, plane and body” multi-dimensional monitoring is proposed based on conventional displacement monitoring, distributed fiber bragg grating monitoring, 3D laser scanning and microseismic monitoring. In order to achieve the best monitoring effect of coordinated deformation of dam-foundation-slopes coupling system, a multi-dimensional monitoring scheme is developed for dam of Jinchuan hydropower station. The research can promote the innovation and development of monitoring and construction technology of large-scale hydropower projects in Southwest China, and has important scientific significance and application value for the analysis of deformation failure mechanism and stability evaluation of hydraulic structures.
SMGNN: an entity alignment method based on subgraph matching and graph neural network

With the wide application of knowledge graph such as recommendation system and text analysis, it is particularly important to create high-quality knowledge graph, it requires precise knowledge graph fusion. As a key part of knowledge graph fusion, entity alignment can provide more prior knowledge for knowledge graph and improve its usability. In order to obtain a more global graph structure feature, this paper designs a subgraph matching based method for entity alignment, named SMGNN. It based on the two features of map structure information and local relation semantics, and captures relationships between entities through GNN capture. Firstly, the entity is encoded by the subgraph information of the target node through the GNN based on two entity-aligned knowledge graphs. Secondly, the subgraph is the graph composed of the target node and all neighboring nodes connected to the node. Then, the alignment between the two graphs is regarded as a mapping on the hyperplane, and TransH model is used for alignment. Finally, we do experiments on DBP15K, a crosslanguage entity alignment dataset, the results show that SMGNN can effectively improve the alignment accuracy of knowledge graphs.
Secure medical image encryption algorithm for e-healthcare applications

In recent years, with the constant evolution of the e-healthcare and in particular cloud services, medical images have taken a major part in data transmission. This medical data must be compressed and protected against illegal access and fraudulent usage. To ensure data confidentiality, a secure medical image encryption algorithm for e-healthcare applications is proposed in this chapter. In the first stage, precision limited logistic map (LM) and skew tent map (STM) is adopted to encrypt plain medical image, obtaining the cipher messages with a relationship to the plain image. Here, the method uses secure hash algorithm (SHA-512) to generate the initial sequences and parameters of the LM and STM. Then, Lempel-Ziv-Welch (LZW) compression scheme is utilized to compress the cipher image, which is used to reduce communication costs and storage space. According to our obtained results, the proposed encryption before compression algorithm is effective and resist to the many attacks on image and Kaggle dataset. Furthermore, extensive experimental results on real-world datasets demonstrate that the proposed algorithm outperforms the state-of-the-art approaches.
Building a hybridised meta-heuristic optimisation algorithm for efficient cluster analysis

Nature-inspired algorithms are a relatively recent field of meta-heuristics introduced to optimise the process of clustering unlabelled data. In recent years, hybridisation of these algorithms has been pursued to combine the best of multiple algorithms for more efficient clustering and overcoming their drawbacks. In this paper, we discuss a novel hybridisation concept where we combine the exploration and exploitation processes of the vanilla bat and vanilla whale algorithm to develop a hybrid meta-heuristic algorithm. We test this algorithm against the existing vanilla meta-heuristic algorithms, including the vanilla bat and whale algorithm. These tests are performed on several single objective CEC functions to compare convergence speed to the minima coordinates. Additional tests are performed on several real-life and artificial clustering datasets to compare convergence speeds and clustering quality. Finally, we test the hybrid on real-world cases with unlabelled clustering data, namely a credit card fraud detection dataset, and a COVID-19 diagnosis dataset, and end with a discussion on the significance of the work, its limitations and future scope.
An Audit on Transfusion error in 16 years of blood transfusion practice at a Teaching Hospital in North Eastern Malaysia

Clinical demand for blood transfusion is endless, and patients are exposed to the risk of transfusion error at any point along the transfusion chain which lead to incorrect blood component transfused (IBCT) if the standard transfusion practices have not complied. This study aimed to determine the prevalence, sources, and causes of error among IBCT in Hospital USM. This retrospective study involved all packed red cell (PC) transfusion and reported IBCT in Transfusion Medicine Units, Hospital USM from January 2005 to December 2020. The recipient and transfusion data of IBCT were collected from the medical record and laboratory information system (MyTransfusi). A total of 193 697 PC transfusions were documented, and 14 IBCT were reported within 16 years of transfusion services. The incidence of IBCT was 1 in 13 836 of PC transfusion. Most of the IBCT contributed by ward error (64.3%). The major cause of error was patient miss identification (85.7%) either in patient sampling, blood component issuing and administration. Most ABO incompatibility IBCT (3 out of 5) end up with severe morbidity or mortality related to an acute haemolytic transfusion reaction. Most of the errors occurred in the wards. Patient misidentification is a major cause of IBCT, and it is preventable. Thus, preventive measures should emphasize on positive patient identification at every step of transfusion chain.
The Necessity of Artificial Intelligence Universal Education Course in Liberal Arts

At present, artificial intelligence education has had a wide impact on society. The development of new liberal arts needs to rely on artificial intelligence technology, so it is necessary to set up artificial intelligence general education courses for new liberal arts. In the course setting, general education of artificial intelligence knowledge is carried out according to the nature of humanities. Since the introduction of artificial intelligence major and its related courses, the traditional liberal arts have presented a new development trend driven by The Times: the combination of intelligent information technology and humanities. Not only science and engineering students need to learn artificial intelligence, but also liberal arts students need to learn the necessary knowledge of artificial intelligence. By defining the connotation and basic characteristics of the general education course of artificial intelligence, we affirm the necessity of its opening, and envision the development trend of the new liberal arts construction in the future.
Codehound: Helping Instructors Track Pedagogical Code Dependencies in Course Materials

Instructors of programming courses must manage a variety of pedagogical dependencies in their teaching materials. For instance, updating the code used in a single lesson can require cascading changes to other lessons in the course. Currently, they must manually maintain these dependencies across many files, which is tedious and error-prone. To help instructors track pedagogical code dependencies, we created a system called Codehound that uses static analysis to automatically detect where functions are introduced and reused through an entire course. To show how Codehound can be used, we present three usage scenarios inspired by our own experiences teaching large data science courses. These scenarios demonstrate how Codehound can help instructors create new content, collaborate with staff to refactor existing content, and estimate the cost of future course changes.
Ascending the Ladder to Self-Sustainability: Achieving Open Evolution in an Interactive Graphical System

Programming is usually based on an inconvenient separation between an implementation level and a user level. Self-sustaining systems expose their implementation at their user level so that they can be modified and improved from within. However, the few examples that exist are tightly linked to textual language-based accounts of compiler bootstrapping. If we want systems to be truly open for modification, we need to step beyond programming languages and support more structured, visual ways of programming as well. How the bootstrapping process can work in such an interactive context is important yet unexplored territory.
Influence of Special Learning Technology on the Effectiveness of Pedagogical Ethics Formation in Future Teachers

This study aims to identify the effectiveness of special training technology in the formation of the pedagogical ethics of future teachers, the problem of a regional university in the formation of pedagogical ethics among students and how the entire formation pedagogical ethics process takes place. The study included 102 students (aged 18–22) with pedagogical profiles who were all present. Expert assessment method, a scaling method for studying the level of formedness of the future teacher’s professional ethics, a survey of students and teachers (questions with attached assessment characteristics), questioning, including the ranking of value orientations and observation of the process of pedagogical ethics formation among future teachers were used. The revealed results have shown that the propensity and willingness of students for future pedagogical activity, their desire for continuous self-education and self-improvement as well as a creative and responsible attitude towards their profession have not been formed. It was necessary to involve future teachers in a specially designed programme aimed at developing pedagogical ethics as a method of self-regulation in the educational process and within the framework of the organisation of students’ extracurricular activities.
Applications of blockchain technology for sustainable education

Distributed ledgers, particularly blockchains, are expected to change how current centralised systems work. The properties of blockchains like immutability, distributed consensus, traceability, and smart contracts give a new perspective to building industry 4.0. While most of the applications in blockchain revolve around Non-Fungible Tokens (NFTs) and Decentralized Finance (DeFi), attempts are made to leverage the unique properties of blockchain in other sectors. The sustainable development goals proposed by the United Nations consider quality education as one of its focus areas. The idea of accessible and affordable education for everyone globally aligns with the principles of public blockchains. This chapter attempts to identify the role of blockchain technology and distributed ledgers in education by comparing existing research and developments in this field. A comprehensive review of the existing literature on the subject of blockchain use in education is covered in this chapter. This work discusses sustainable education through the implementation of blockchain-based solutions. A case study of existing blockchain-based solutions digitising the issuance and verification of certificates is done. This work proposes a framework for document signature verification, which helps to prevent fake certificates and signatures. A futuristic perspective on the sustainability of education through the lens of blockchain technology is envisioned in this chapter.
X-parameter based design and simulation of Josephson traveling-wave parametric amplifiers for quantum computing applications

We present an efficient, accurate, and comprehensive analysis framework for generic, multi-port nonlinear parametric circuits, in the presence of dissipation from lossy circuit components, based on "quantum-adapted" X-parameters. We apply this method to Josephson traveling-wave parametric amplifiers (JTWPAs) - a key component in superconducting and spin qubit quantum computing architectures - which are challenging to model accurately due to their thousands of linear and nonlinear circuit components. X-parameters are generated from a harmonic balance solution of the classical nonlinear circuit and then mapped to the field ladder operator basis, so that the energy associated with each of the multiple interacting modes corresponds to photon occupancy, rather than classical power waves. Explicit relations for the quantum efficiency of a generic, multi-port, multi-frequency parametric circuit are presented and evaluated for two distinct JTWPA designs. The gain and quantum efficiency are consistent with those obtained from Fourier analysis of time-domain solutions, but with enhanced accuracy, speed, and the ability to include real-world impairments, statistical variations, parasitic effects, and impedance mismatches (in- and out-of-band) seamlessly. The unified flow is implemented in Keysight's PathWave Advanced Design System (ADS) and independently in an open-source simulation code, JosephsonCircuits.jl, from the MIT authors.
EAST coil power supply analysis tool based on Python

The coil power supply is the core system of the EAST (Experimental Advanced Super-conducting Tokamak) device. In the field of nuclear fusion, the MDSplus database is often used to store experimental data and the built-in GUI tool jScope to display the experimental data. jScope can not meet the debugging needs. Based on the MDSplus database and the Python programming language, this paper develops the data analysis module of the power system of the EAST device, establishes the Python and MDSplus database interfaces, reads the data generated by the power system from the model tree, and calls the matplotlib package for waveform drawing. Visualize the power system data through Python, view the curve of voltage/current over time, obtain the difference, maximum, average, etc. of the voltage/current in any time period, and at the same time, you can read the relevant parameter values at any time point. The operation of the system plays a more precise and targeted debugging.
Explicit teaching of searching and sourcing strategies in engineering: insights from an inclusive dyslexia-friendly online EAP course

This paper describes a small-scaled qualitative study exploring the perceptions of four first-year EFL engineering students with dyslexia regarding an online dyslexia-friendly English for Academic Purposes course at a Greek technical university. The course aimed at familiarizing students with popular academic genres and training them to use effective academic online searching and sourcing strategies. The theories underpinning the course design and materials' development included the MSL approach, metacognitive-genre awareness and cognitive apprenticeship. Data were gathered via semi-structured individual interviews. Findings indicate participants appreciated dyslexia-friendly affordances and perceived the course as relevant to their immediate academic needs.
Corporate Social Responsibility and Financial Performance of Macro, Small, and Medium-Scale Enterprises (MSMEs) in Ekiti State, Nigeria

The study examined the effect of CSR on the financial performance of MSMEs in Nigeria using Ekiti State as a case. Specifically, the study examined the effect of employees’ health and safety, community development, and environmental remediation on the financial performance of MSMEs. A survey research design was adopted. The population of the study comprises 967,468 registered MSMEs in Ekiti State as of 31 st December 2021 (SMEDAN,2021). A sample size of 400 respondents was selected using Taro Yamane’s formula. Data was obtained from primary sources using a well-structured questionnaire. The Cronbach Alpha test result of an average of 80% confirmed the reliability of the research instrument. The data collected were analysed using descriptive statistics and OLS regression. The results revealed that employees’ health and safety, community development, and environmental remediation exhibited a significant positive relationship to the financial performance of MSMEs. The study concluded that, the financial performance of MSMEs in Nigeria will increase at the instant of their social responsibility. It is therefore recommended that MSMEs in Nigeria should embrace the philosophy of CSR as a bait to improve their financial performance. Keywords: Corporate Social Responsibility, Financial Performance, Employees’ Health and Safety, Community Development, Environmental Remediation, MSMEs. JEL Classification Codes: L25; M14; I13; R11; P18. DOI: 10.7176/RJFA/13-22-06 Publication date: November 30 th 2022