Reviewerly/exHarmony huggingface dataset:
A Predictable Obstacle Avoidance Model Based on Geometric Configuration of Redundant Manipulators for Motion Planning

When a manipulator works in dynamic environments, it may be affected by obstacles and may cause danger to people around. This requires the manipulator to be able to plan the obstacle avoidance motion in real time. Therefore, the problem solved in this paper is dynamic obstacle avoidance with the whole body of the redundant manipulator. The difficulty of this problem is how to model the manipulator to reflect the motion relationship between the manipulator and the obstacle. In order to describe accurately the occurrence conditions of the collision, we propose the triangular collision plane, a predictable obstacle avoidance model based on the geometric configuration of the manipulator. Based on this model, three cost functions, including the cost of the motion state, the cost of a head-on collision, and the cost of the approach time, are established and regarded as optimization objectives in the inverse kinematics solution of the redundant manipulator combined with the gradient projection method. The simulations and experiments on the redundant manipulator and the comparison with the distance-based obstacle avoidance point method show that our method improves the response speed of the manipulator and the safety of the system.
Blockchain-Assisted Reputation Management Scheme for Internet of Vehicles

With the rapid development of Internet of Vehicles (IoV), particularly the introduction of Mobile Edge Computing (MEC), vehicles can efficiently share data with one another. However, edge computing nodes are vulnerable to various network attacks, posing security risks to data storage and sharing. Moreover, the presence of abnormal vehicles during the sharing process poses significant security threats to the entire network. To address these issues, this paper proposes a novel reputation management scheme, which proposes an improved multi-source multi-weight subjective logic algorithm. This algorithm fuses the direct and indirect opinion feedback of nodes through the subjective logic trust model while considering factors such as event validity, familiarity, timeliness, and trajectory similarity. Vehicle reputation values are periodically updated, and abnormal vehicles are identified through reputation thresholds. Finally, blockchain technology is employed to ensure the security of data storage and sharing. By analyzing real vehicle trajectory datasets, the algorithm is proven to effectively improve the differentiation and detection rate of abnormal vehicles.
Quantum algorithms: applications, criteria and metrics

Abstract In the field of data processing and IoT communication it is possible to develop more robust solutions by combining quantum algorithms with metaheuristics. Said solutions can be applied in the industry and be measured using metrics associated with complexity, efficiency, processing, and accuracy. An extensive bibliographical review is carried out to determine which is the most efficient and effective hybrid algorithm that can be applied to a real experimental case, which aims to improve communication to reduce occupational risks. Criteria, metrics, and experimental results were obtained, in which it is shown that the quantum genetic algorithm is better than the genetic algorithm. A detailed discussion on the objective function, the convergence to the global optimum, and the need to improve the obtained solutions is given. The conclusions raise new aspects that need investigation.
Multi‐scale feature fusion pyramid attention network for single image dehazing

Abstract Texture and color distortion are common in existing learning‐based dehazing algorithms, and it is argued that one of the major reasons is that the shallow features of fog images are underutilized, and the deep features of fog images are insufficient for single image dehazing. In order to provide more texture and color information for image restoration, more shallow features need to be added in the process of image decoding. Therefore, a multi‐scale feature fusion pyramid attention network (PAN) for single image dehazing is proposed. In PAN, combined with the attention mechanism, a shallow and deep feature fusion (SDF) strategy is designed. SDF considers multi‐scale as well as channel‐level fusion to provide feature information under different receptive fields while also highlighting important channels, such as texture and color information. DC is designed as a latent space mapping module to learn a mapping relationship between the latent space representation of the hazy image at low resolution and the corresponding latent space representation of the haze‐free image. Additionally, network deconvolution (ND) and deformed convolution network (DCN) are introduced into PAN. The ND module can remove pixel‐wise and channel‐wise correlation of features, reduce data redundancy to obtain sparse representation of features, and speed up network convergence. The DCN module can use its adaptive receptive field to focus on the area of interest for calculation and play a role in texture feature enhancement. Finally, the perceptual loss is chosen as the regularization item of the loss function, which makes style features of the restored image closer to the real fog‐free image. Extensive experiments reveal that the proposed PAN outperforms other existing dehazing methods on real‐world and synthetic datasets.
Educational supply chain sustainability

Purpose The purpose of the paper is to determine the importance of digital literacy (DL) in educational supply chain (EduSC) sustainability. Design/methodology/approach A semi-systematic review technique was used with Google Scholar and Scopus databases. Findings The growing use of technology and digital resources revealed a significant digital divide between people, educational processes and access to technologies and suitable infrastructures, as well as the need for DL, particularly in the EduSC in the 21st century. According to the results, just 58.6% of EduSC researchers emphasize economic sustainability, while 72.4% emphasize environmental sustainability. A total of 93.1% of scholars examine the impact of DL on society, demonstrating the significance of DL in the EduSC. Originality/value DL was discovered to have the greatest impact on society, with students and educators being related to all processes in an EduSC.
Study on centroid type-reduction of general type-2 fuzzy logic systems with sensible beginning weighted enhanced Karnik–Mendel algorithms

General type-2 fuzzy logic systems have received wide concerns in current academic subject. Type-reduction is the kernel module for the systems. This paper shows the interpretations for the beginning of Karnik–Mendel (KM) algorithms. According to the famous numerical integration technique, the weighting approaches of enhanced Karnik–Mendel (EKM) algorithms are put forward. Then, the sensible beginning weighted enhanced Karnik–Mendel (SBWEKM) algorithms are put forward to perform the centroid type-reduction. Compared with the EKM algorithms, WEKM algorithms and SBEKM algorithms, this approach helps to improve both the absolute errors and convergence speeds as shown in four computer simulation experiments.
Step into a new dimension with augmented reality. Can Augmented Reality (AR) replicate the tactile experience in a virtual mathematics classroom and what is the impact on engagement and deeper understanding?

ABSTRACTThe global pandemic has changed the mode of delivering lessons. Adult learners have missed and continue to miss the hands-on learning activity, which is a form of experiential learning. When it comes to teaching three-dimensional, geometric shapes (3D), learners often benefit from handling and manipulating concrete shapes. This study examined the use of an AR mobile application, as a learning tool for teaching geometric shapes in virtual mathematics classrooms. Data was collected from thirty learners at Functional Skills and General Certificate of Secondary Education (GCSE) levels in a Further Education college in Birmingham, UK. Learners were divided into Control and Experimental groups. The Control group used traditional learning materials, while in the Experimental group, an AR application was utilised during lessons. Qualitative data was gathered using mixed methods of one-to-one interviews and questionnaires to evaluate the learners’ experience. The data was triangulated with observation and note-taking by the teacher. In this study, the content analysis method has been used to analyse the data. Results showed an improved activity and interactivity within the session, when compared to the traditional, passive, teaching and learning strategies used in the learning process. From the results of this study, we can deduce that AR-based tools are a beneficial resource in teaching mathematics and improving learner experience in virtual classrooms. Further studies are required to evaluate quantitative improvements in student performance, as well as to implement AR as a regular teaching strategy.KEYWORDS: Augmented Realitymathematics3D shapesexperiential AcknowledgmentI thank Dr. Ayad Harb for proofreading my work and Dr. John Cooper for regular feedback and support.Disclosure statementNo potential conflict of interest was reported by the author(s).
Development of speech recognition system for remote vocal music teaching based on Markov model

With the popularization of smart homes, car audio systems and various speech recognition software, speech recognition systems have gradually entered people’s sights and are favored by most users because of their practicability and accuracy. Cognition is an important interface for human–computer interaction. It will become a research focus in the field of artificial intelligence. It plays an important role in cultivating the basic characteristics of music and cultivating students’ interest in music, and vocal music teaching. Teaching traditional vocal music education to students is in the form of classrooms, such as vocal music, arrangement, and bel canto. The disadvantage is the lack of communication between the classroom and teachers and students. On the other hand, the development of Internet technology provides a new teaching method for traditional vocal music teaching and provides a network infrastructure for building a vocal teaching system platform. Therefore, this article provides a preliminary construction of a remote vocal music education platform by combining vocal music education with Internet technology. The remote audio and video training system is a complex and relatively large project with multiple functions and is to introduce important functions in this system. At the same time, register and log in to the remote voice and video implementation requirements and system functions, respectively, to realize functions such as video training and video-on-demand training.
Research on the sustainable development of new media physical teaching: big data analysis of the relevance of language expression ability

ABSTRACTABSTRACTThrough the sports group teaching of school students, this paper uses the evaluation method to construct the ability evaluation model, collects the changes of language ability, and tests the students’ understanding ability, expression ability, action language, and group cooperation. Following the teaching for three months, the average value of the group which processes experimental activities has increased to 0.55, and the average value of the control group has increased to about 0.4. In terms of teamwork, the experiment should lead the control group by 35 percentage points. It is found that physical education teachers encounter some problems when using new media teaching, such as lack of teacherâ student interaction, blind pursuit of teaching progress, blind use of courseware, etc. This study has improved the understanding of furthermost students who are pursuing physical education in college on new media teaching, and is conducive to the sustainable development of physical education in the new media environment. There is a guarantee of big data analytics capability that it can investigate the data and can turn it into useful information for a variety of purposes.KEYWORDS: Big datanew mediaphysical teaching modelanguage expression abilitybig data analytics capability Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analyzed in this study.Ethics approvalThis article does not contain any studies with human participants.Additional informationFundingThe study was supported by “Philosophy and Social Sciences Planning Projects in Zhejiang Province in 2023: Research on Big Data Promoting Public Fitness Service Innovation from Zhejiang Governance Practice(23NDJC416YBM); 14th Five-Year Plan & Double First-Class Discipline Construction of Mudanjiang Normal University: Research on the Curriculum Development for Snow Sports Outside School (No. msysyl2022026); Open Fund Project of Hubei Leisure Sports Development Research Center in 2021: Research on the Mode of Three Low Development from the Perspective of Rural Revitalization in the Snow and Ice Sports Resources of Heilongjiang Province (No.202120B016); Project of China University Sports Association in 2021: Guided Management University in the E-sports Event Organization (No.L202103009).”Notes on contributorsLiquan ChenLiquan Chen (July, 1976), associate professor and doctor, born in Harbin, Heilongjiang Province, and doctor of Beijing University of Physical Education, now works in the School of Culture and Tourism of Quzhou Vocational and Technical College, and his research interests include college sports teaching and management, sports economy and health industry, media sports, etc.
Cascading information on best practice: Cyber security risk management in UK micro and small businesses and the role of IT companies

Micro and small businesses are increasingly reliant on digital and online technology. They have, though, very limited resources and expertise to devote to cyber security. There is, thus, a pressing economic and social challenge of how to improve cyber security in small businesses. We look at the potential role of IT companies as a conduit through which to cascade information on best practice, focusing on the United Kingdom. We first present an analysis of the UK's Cyber Security Breaches Survey (2018–2021) distinguishing different channels through which micro and small businesses access information on cyber security. We find that the main channel, by far, is through IT companies. Very few businesses directly access information from the government or law enforcement. To further explore the role of IT companies we conducted a series of focus groups and interviews with experts in IT and cyber security for small businesses in the UK. One theme to emerge is that IT companies, while they can be part of the solution, can also be part of the problem and so a number of interventions are needed if IT companies are to effectively disseminate best practice. These include advice and guidance for micro and small businesses on how to distinguish 'good' IT companies, as well as appropriate support for IT companies, who themselves are typically micro and small businesses that lack expertise on cyber security.
Natural Sciences Teachers’ Experiences using Blended Teaching in Township Smart Schools: Perceived Benefits and Challenges

This study explored the perceived benefits and challenges that Natural Sciences teachers experience when using blended teaching in Smart Schools. This qualitative research focused on public township schools with challenging socio-economic contexts in the Gauteng Province, South Africa. A non-probability purposive sampling method was followed. The data collection technique used involved individual, face-to-face, semi-structured interviews. Interviews conducted with 10 Natural Sciences teachers were analysed through inductive analysis. The key findings of this study portrayed that teachers perceived that the incorporation of blended teaching and learning presented major pedagogical benefits, such as increased learner enjoyment and improved engagement as visual enhancements of the scientific processes and concepts aid learning, save teaching and reduce learning time since complex scientific concepts are simplified and use virtual experiments instead of traditional hands-on practicals. Efficiency in long-term running costs was seen as another benefit. According to these Natural Science teachers, one disadvantage in particular was an increased workload owing to double lesson planning for classroom activities and particularly for science practicals with and without the use of smart equipment, where there were external challenges such as load shedding, inoperable devices, connectivity challenges and/or smart equipment thefts. It is recommended that Smart School teachers are encouraged to form communities of practice to share strategies to minimise the perceived or experienced challenges in blended teaching.
Estimating the thermal conductivity of plutonic rocks from major oxide composition using machine learning

SUMMARY The accurate estimation of temperature distribution in the earth's crust and modelling of heat-related processes in geodynamics requires knowledge of the thermal conductivity of plutonic rocks. This study compiled an extensive data set of 530 representative plutonic rock samples, including thermal conductivity, major oxide composition and (for two subsets of data) modal mineralogy. For the first time, three machine learning algorithms (ML; i.e. support vector regression, random forest and extreme gradient boosting) were employed to estimate the thermal conductivity of plutonic rocks using the major oxide composition feature as input variables. The performance of these ML-based models was evaluated against a geochemically compositional model and eight mineral-driven physically based empirical mixing models. Results show that the means of predicted thermal conductivity by the ML-based models and the geochemically compositional model are not significantly different from the measured thermal conductivity at a significance level of 5 per cent. However, the ML-based models outperformed the best-performing non-ML model, the geochemically compositional model. The highest prediction accuracy was achieved by extreme gradient boosting, which reduced the mean absolute percentage error and root mean square error by more than 50 per cent. Furthermore, SiO2 is confirmed as the most important independent variable, followed by Al2O3, TiO2, CaO and K2O. The turning point observed in the thermal conductivity trend with SiO2 wt per cent may be primarily attributed to variations in mineral composition within the subgroup of igneous rock types classified based on SiO2 wt per cent. This study explores, for the first time, the use of ML algorithms to estimate the thermal conductivity of plutonic rocks from their major oxide composition.
Framing blockchain-integrated digital twins for emergent healthcare: a proof of concept

It has been witnessed that digital technology has the potential to improve the efficiency of emergent healthcare management of Covid-19; however, it has not been widely adopted due to unclear definition and configuration. This research aims to propose a proof of concept of digital twins for emergent healthcare management by configuring the cyber and functional interdependencies of healthcare systems at local and city levels. Critical interdependencies of healthcare systems were firstly identified at both levels, and then the information and associated cyber and functional interdependencies embedded in seven critical hospital information systems (HISs) were identified and mapped. The proposed conceptual digital-twin-based approach was then developed for information coordination among these critical HISs at both local and city levels based on permissioned blockchain to (a) integrate and manage information from the seven critical HISs and (b) further predict the demands for medical resources according to patient trajectories. A case study was finally conducted at three hospitals in London during the Covid-19 period, and the results showed that the developed framework of blockchain-integrated digital twins is a promising way to provide more accurate and timely procurement information to decision makers and can effectively support evidence-based decisions on medical resource allocation in the pandemic.
Intercomparison of the parameterized Linke turbidity factor in deriving global horizontal irradiance

This study presents a comparative assessment of nine parameterized methods to estimate Linke turbidity (TL) in the five Köppen–Geiger climate zones. The evaluation is intensified by simulating all TL values into a global horizontal irradiance (GHI) model, called the Hammer model. The results found that atmospheric turbidity on the Korean Peninsula varies per season. The atmosphere tends to be more transparent in fall and winter, with lower turbidity. Meanwhile, the turbidity consistently rises in spring and peaks and summer, indicating a significant decline in atmospheric transparency. All nine TL methods generated varied degrees of accuracy according to their Köppen–Geiger climate zones. In terms of relative root mean square error (rRMSE) and relative mean bias error (rMBE), TLValko1, TLValko2, and TLCapdrou consistently perform poorly. Further, these three methods can only classify the atmospheric turbidity into two types: least and fully turbid. This study recommends the following methods for evaluating atmospheric turbidity in various climate types: TLRemund, TLDogniaux1, TLDogniaux2, TLIneichen, TLMolineaux, and TLGreiner. This study concludes that none of the six methods can be considered "the most superior" due to minor differences in rRMSE and rMBE values.
Impacts of Virtual Reality on Tourism Experience and Behavioral Intentions: Moderating Role of Novelty Seeking

Creating and delivering memorable experiences for tourists constitute the essence of tourism and tourism management. Technological developments, such as virtual reality (VR), enable tourists to achieve a more unique tourism experience by intensifying engagement and extending experience co-creation in an interactive and simulated environment. This study examines how VR influences the tourism experience and behavioral intentions associated with boundary conditions. Based on a survey in China, the results indicate significant positive relationships among VR presence, flow, tourism experience, and behavioral intentions. Novelty seeking moderates the relationship between VR enjoyment and flow. The findings of this study suggest that VR is a promising tool for tourism in the pre-travel stage.