bluuebunny/crossref_metadata_2025 huggingface dataset:
Screening Clinical Changes for the Diagnosis of Early Knee Osteoarthritis: A Cross-Sectional Observational Study
Authors: Alabajos-Cea Ana , Herrero-Manley Luz , Suso-Martí Luis , Sempere-Rubio Núria , Cuenca-Martínez Ferran , Muñoz-Alarcos Vicente , Pérez-Barquero Juan Alonso , Viosca-Herrero Enrique , Vázquez-Arce Isabel
Published: October 2022

Background: The main objective was to evaluate differences in the clinical, motor, or functional variables in patients with Early Osteoarthritis (EOA) and individuals at risk of developing osteoarthritis (OA). Methods: A cross-sectional study was performed. All the participants were divided into two groups: EOA patients and healthy subjects (HS) at risk of developing OA. The main outcomes were clinical tests, such as those of knee morphology, instability, or proprioception; motor and functional variables, such as knee strength, range of motion, walking speed, and the sit-to-stand test; pain and disability, assessed through the Western Ontario McMaster Universities Osteoarthritis Index (WOMAC) and Knee injury and Osteoarthritis Outcome Score (KOOS) scales; and knee alignment and leg length inequality, assessed via X-ray images. Results: A total of 97 participants were included (54 EOA and 43 HS). Patients with EOA showed a greater presence of knee pain (p < 0.01). In addition, more EOA patients showed instability both in the left (p < 0.01) and right legs (p < 0.05). Regarding the knee alignment variable, significant differences were found (p < 0.04), with more patients with EOA diagnosed as possessing a varus alignment. In addition, EOA patients showed lower knee strength, since statistically significant differences were found regarding flexion and extension strength in the left leg (Mean Difference (MD): 12.92; p = 0.03; d = −0.46 and MD:7.81; p = 0.04; d = −0.39). Differences were found for the sit-to-stand test scores, showing lower results for the EOA group (MD: −1.91; p < 0.01; d = 0.54). Conclusions: The results of this research show statistically significant differences between patients with EOA and HS at risk of developing OA with respect to pain, disability, instability, knee strength, and the sit-to-stand test. Our results suggest that the evaluation of clinical, motor, and functional features could contribute to an early management of knee OA.
An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease
Authors: Wang Xinlei , Xin Junchang , Wang Zhongyang , Li Chuangang , Wang Zhiqiong
Published: October 2022

In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods.
Platelet and Red Blood Cell Volume Indices in Patients with Rheumatoid Arthritis: A Systematic Review and Meta-Analysis
Authors: Zinellu Angelo , Mangoni Arduino A.
Published: October 2022

Alterations in the volume of platelets (mean platelet volume, MPV; platelet distribution width, PDW) and erythrocytes (red blood cell distribution width, RDW) have been reported in rheumatoid arthritis (RA) and might serve as diagnostic biomarkers. We conducted a systematic review and meta-analysis of the MPV, PDW, and RDW in RA patients and healthy controls. Relevant articles were searched in PubMed, Web of Science, Scopus, and Google Scholar from inception to June 2022. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist and certainty of evidence was assessed using GRADE. In 23 studies (2194 RA patients and 1565 healthy controls), the RDW, but not MPV or PDW, was significantly higher in RA patients (standardized mean difference, SMD = 0.96, 95% CI 0.78 to 1.15, p < 0.001; moderate certainty of evidence). The substantial heterogeneity observed (I2 = 75.1%, p < 0.001) was virtually removed in a subgroup of prospective studies. In sensitivity analysis, the magnitude of the effect size was not substantially modified by sequentially removing individual studies. There was no significant publication bias. No significant associations were observed between the effect size and pre-defined study or patient characteristics. The results of our study suggest that the RDW might be a useful biomarker for the diagnosis of RA, and complement the clinical information provided by other patient characteristics and laboratory parameters (PROSPERO registration number: CRD42022349432).
Impact of Undergraduate Research as a Compulsory Course in the Dentistry Study Program Universitas Indonesia
Authors: Amir Lisa R. , Soekanto Sri Angky , Julia Vera , Wahono Nieka A. , Maharani Diah Ayu
Published: October 2022

Undergraduate (UG) research is regarded as a fundamental component in dental education. The present study was designed to examine the perception of the clinical students and the graduates of dentistry profession programs in the past 10 years on UG research as a compulsory course at the Faculty of Dentistry Universitas Indonesia. A total of 310 respondents, consisting of clinical students (64.8%) and alumni (35.2%), participated in this study. The majority of respondents (81.3%) agreed to UG research as part of compulsory courses in the curriculum of dentistry study programs. The positive impact of UG research on their professional careers was perceived by 78.3% of participants. Only 11.6% of participants responded that UG research experiments were not important in dental education, and 18.7% preferred UG research as an elective course. UG research as a compulsory course in the dental curriculum was well received by the majority of participants. Recommendations included student autonomy to select research topics of interest, longer duration to complete UG research, and more opportunities to present the research results in scientific conferences and to publish in scientific journals. Dental schools and their faculties play essential roles in improving the research environment for undergraduate dental students.
Channel Estimation for UAV Communication Systems Using Deep Neural Networks
Authors: Al-Gburi Ahmed , Abdullah Osamah , Sarhan Akram Y. , Al-Hraishawi Hayder
Published: October 2022

Channel modeling of unmanned aerial vehicles (UAVs) from wireless communications has gained great interest for rapid deployment in wireless communication. The UAV channel has its own distinctive characteristics compared to satellite and cellular networks. Many proposed techniques consider and formulate the channel modeling of UAVs as a classification problem, where the key is to extract the discriminative features of the UAV wireless signal. For this issue, we propose a framework of multiple Gaussian–Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural network. The developed system used UAV measurements of a town’s already existing commercial cellular network for training and validation. To evaluate the proposed approach, we run ray-tracing simulations in the program Remcom Wireless InSite at a distinct frequency of 28 GHz and used them for training and validation. The results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs.
Drones and Blockchain Integration to Manage Forest Fires in Remote Regions
Authors: Mahmudnia Dena , Arashpour Mehrdad , Bai Yu , Feng Haibo
Published: October 2022

Central management of fire stations and traditional optimization strategies are vulnerable to response time, a single point of failure, workload balancing, and cost problems. This is further intensified by the absence of modern communication systems and a comprehensive management framework for firefighting operations. These problems motivate the use of new technologies such as unmanned aerial vehicles (UAVs) with the capability to transport extinguishing materials and reach remote zones. Forest fire management in remote regions can also benefit from blockchain technology (BC) due to the facilitation of decentralization, tamper-proofing, immutability, and mission recording in distributed ledgers. This study proposed an integrated drone-based blockchain framework in which the network users or nodes include drones, drone controllers, firefighters, and managers. In this distributed network, all nodes can have access to data; therefore, the flow of data exchange is smooth and challenges on spatial distance are minimized. The research concluded with a discussion on constraints and opportunities in integrating blockchain with other new technologies to manage forest fires in remote regions.
Guidance, Navigation and Control System for Multi-Robot Network in Monitoring and Inspection Operations
Authors: Hayajneh Mohammad , Al Mahasneh Ahmad
Published: October 2022

This work focuses on the challenges associated with autonomous robot guidance, navigation, and control in multi-robot systems. This study provides an affordable solution by utilizing a group of small unmanned ground vehicles and quadrotors that collaborate on monitoring and inspection missions. The proposed system utilizes a potential fields path planning algorithm to allow a robot to track a moving target while avoiding obstacles in a dynamic environment. To achieve the required performance and provide robust tracking against wind disturbances, a backstepping controller is used to solve the essential stability problem and ensure that each robot follows the specified path asymptotically. Furthermore, the performance is also compared with a proportional-integral-derivative (PID) controller to ensure the superiority of the control system. The system combines a low-cost inertial measurement unit (IMU), a GNSS receiver, and a barometer for UAVs to generate a navigation solution (position, velocity, and attitude estimations), which is then used in the guidance and control algorithms. A similar solution is used for UGVs by integrating the IMU, a GNSS receiver, and encoders. Non-linear complementary filters integrate the measurements in the navigation system to produce high bandwidth estimates of the state of each robotic platform. Experimental results of several scenarios are discussed to prove the effectiveness of the approach.
A Simplified Quantum Walk Model for Predicting Missing Links of Complex Networks
Authors: Liang Wen , Yan Fei , Iliyasu Abdullah M. , Salama Ahmed S. , Hirota Kaoru
Published: October 2022

Prediction of missing links is an important part of many applications, such as friends’ recommendations on social media, reduction of economic cost of protein functional modular mining, and implementation of accurate recommendations in the shopping platform. However, the existing algorithms for predicting missing links fall short in the accuracy and the efficiency. To ameliorate these, we propose a simplified quantum walk model whose Hilbert space dimension is only twice the number of nodes in a complex network. This property facilitates simultaneous consideration of the self-loop of each node and the common neighbour information between arbitrary pair of nodes. These effects decrease the negative effect generated by the interference effect in quantum walks while also recording the similarity between nodes and its neighbours. Consequently, the observed probability after the two-step walk is utilised to represent the score of each link as a missing link, by which extensive computations are omitted. Using the AUC index as a performance metric, the proposed model records the highest average accuracy in the prediction of missing links compared to 14 competing algorithms in nine real complex networks. Furthermore, experiments using the precision index show that our proposed model ranks in the first echelon in predicting missing links. These performances indicate the potential of our simplified quantum walk model for applications in network alignment and functional modular mining of protein–protein networks.
A Linguistic Analysis of News Coverage of E-Healthcare in China with a Heterogeneous Graphical Model
Authors: Liu Mengque , Fan Xinyan , Ma Shuangge
Published: October 2022

E-healthcare has been envisaged as a major component of the infrastructure of modern healthcare, and has been developing rapidly in China. For healthcare, news media can play an important role in raising public interest and utilization of a particular service and complicating (and, perhaps clouding) debate on public health policy issues. We conducted a linguistic analysis of news reports from January 2015 to June 2021 related to E-healthcare in mainland China, using a heterogeneous graphical modeling approach. This approach can simultaneously cluster the datasets and estimate the conditional dependence relationships of keywords. It was found that there were eight phases of media coverage. The focuses and main topics of media coverage were extracted based on the network hub and module detection. The temporal patterns of media reports were found to be mostly consistent with the policy trend. Specifically, in the policy embryonic period (2015–2016), two phases were obtained, industry management was the main topic, and policy and regulation were the focuses of media coverage. In the policy development period (2017–2019), four phases were discovered. All the four main topics, namely industry development, health care, financial market, and industry management, were present. In 2017 Q3–2017 Q4, the major focuses of media coverage included social security, healthcare and reform, and others. In 2018 Q1, industry regulation and finance became the focuses. In the policy outbreak period (2020–), two phases were discovered. Financial market and industry management were the main topics. Medical insurance and healthcare for the elderly became the focuses. This analysis can offer insights into how the media responds to public policy for E-healthcare, which can be valuable for the government, public health practitioners, health care industry investors, and others.
Optimized LightGBM Power Fingerprint Identification Based on Entropy Features
Authors: Lin Lin , Zhang Jie , Zhang Na , Shi Jiancheng , Chen Cheng
Published: October 2022

The huge amount of power fingerprint data often has the problem of unbalanced categories and is difficult to upload by the limited data transmission rate for IoT communications. An optimized LightGBM power fingerprint extraction and identification method based on entropy features is proposed. First, the voltage and current signals were extracted on the basis of the time-domain features and V-I trajectory features, and a 56-dimensional original feature set containing six entropy features was constructed. Then, the Boruta algorithm with a light gradient boosting machine (LightGBM) as the base learner was used for feature selection of the original feature set, and a 23-dimensional optimal feature subset containing five entropy features was determined. Finally, the Optuna algorithm was used to optimize the hyperparameters of the LightGBM classifier. The classification performance of the power fingerprint identification model on imbalanced datasets was further improved by improving the loss function of the LightGBM model. The experimental results prove that the method can effectively reduce the computational complexity of feature extraction and reduce the amount of power fingerprint data transmission. It meets the recognition accuracy and efficiency requirements of a massive power fingerprint identification system.
The Analysis of the Power Law Feature in Complex Networks
Authors: Zhang Xiaojun , He Zheng , Zhang Liwei , Rayman-Bacchus Lez , Shen Shuhui , Xiao Yue
Published: October 2022

Consensus about the universality of the power law feature in complex networks is experiencing widespread challenges. In this paper, we propose a generic theoretical framework in order to examine the power law property. First, we study a class of birth-and-death networks that are more common than BA networks in the real world, and then we calculate their degree distributions; the results show that the tails of their degree distributions exhibit a distinct power law feature. Second, we suggest that in the real world two important factors—network size and node disappearance probability—will affect the analysis of power law characteristics in observation networks. Finally, we suggest that an effective way of detecting the power law property is to observe the asymptotic (limiting) behavior of the degree distribution within its effective intervals.
Group Testing with Blocks of Positives and Inhibitors
Authors: Bui Thach V. , Echizen Isao , Kuribayashi Minoru , Kojima Tetsuya , Nguyen Thuc D.
Published: October 2022

The main goal of group testing is to identify a small number of specific items among a large population of items. In this paper, we consider specific items as positives and inhibitors and non-specific items as negatives. In particular, we consider a novel model called group testing with blocks of positives and inhibitors. A test on a subset of items is positive if the subset contains at least one positive and does not contain any inhibitors, and it is negative otherwise. In this model, the input items are linearly ordered, and the positives and inhibitors are subsets of small blocks (at unknown locations) of consecutive items over that order. We also consider two specific instantiations of this model. The first instantiation is that model that contains a single block of consecutive items consisting of exactly known numbers of positives and inhibitors. The second instantiation is the model that contains a single block of consecutive items containing known numbers of positives and inhibitors. Our contribution is to propose efficient encoding and decoding schemes such that the numbers of tests used to identify only positives or both positives and inhibitors are less than the ones in the state-of-the-art schemes. Moreover, the decoding times mostly scale to the numbers of tests that are significantly smaller than the state-of-the-art ones, which scale to both the number of tests and the number of items.
Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function
Authors: Cao Yuan , Liu Wanyi , Qin Lan , Liu Bingqiang , Chen Shuai , Ye Jing , Xia Xianzhao , Wang Chao
Published: October 2022

Entropy is a measure of uncertainty or randomness. It is the foundation for almost all cryptographic systems. True random number generators (TRNGs) and physical unclonable functions (PUFs) are the silicon primitives to respectively harvest dynamic and static entropy to generate random bit streams. In this survey paper, we present a systematic and comprehensive review of different state-of-the-art methods to harvest entropy from silicon-based devices, including the implementations, applications, and the security of the designs. Furthermore, we conclude the trends of the entropy source design to point out the current spots of entropy harvesting.
The Effects of the Transmigration Programme on Poverty Reduction in Indonesia’s Gorontalo Province: A Multidimensional Approach
Authors: Murtisari Amelia , Irham Irham , Mulyo Jangkung Handoyo , Waluyati Lestari Rahayu
Published: October 2022

The transmigration program in Gorontalo Province plays an important role in poverty reduction. The districts of Gorontalo and Boalemo, as a part of Gorontalo Province, were purposefully selected as research locations. A total of 240 respondents of transmigrant and local households were interviewed using a questionnaire containing a list of questions with a 5-Likert scale. The objectives of this research are: (1) to measure the multidimensional poverty level of transmigrant and local households, (2) to determine the impact of the transmigration program on poverty reduction and (3) to identify the factors that influence poverty status of transmigrant and local households. The results show that the poverty level of transmigrant households tends to be lower than local households. The longer the placement of transmigration, the more likely it is to reduce regional poverty levels. The health dimension has a high contribution to the cause of poverty of transmigrants, while the education dimension contributes to the highest cause of poverty of local households. The results of the analysis also show that farmers who are more educated, participate in skills training and have a side business have more opportunity to reduce poverty. The study confirms that the transmigration program has a significant impact on poverty reduction in the region.
Corruption and Inflation in Agricultural Production: The Problem of the Chicken and the Egg
Authors: Peixoto Paulo , Martinho Vítor João Pereira Domingues , Mourao Paulo
Published: October 2022

Corruption and inflation are two economic problems with serious social consequences. This paper analyzes the link between these two problems, focusing on the case of 19 prices observed for agricultural products in 90 countries since 2000. Using ‘panel data cointegration’ techniques, we conclude that, in most cases, there is a long-term relationship between inflation and corruption. The direction of causality favors the hypothesis that the inflation of agricultural products promotes incentives that lead to an increase in corruption levels. These results have important implications in terms of fighting corruption, giving special attention to controlling inefficiencies in agricultural markets that lead to higher prices that are then tapped into corruption mechanisms.
Reflections of the “Export-Led Growth” or “Growth-Led Exports” Hypothesis on the Turkish Economy in the 1999–2021 Period
Authors: Orhan Ayhan , Emikönel Melek , Emikönel Murat , Castanho Rui Alexandre
Published: October 2022

Various factors determine and affect economic growth, one of which is exports. Trade theory also states that exports increase the growth of the domestic economy in various ways. For this reason, the effect of exports on economic growth is a long-term area of research. In addition to the studies examining the effect of foreign trade on economic growth in the literature, some studies investigate the effects of economic growth on export capacity. These studies suggest that the export-based economic growth hypothesis is valid when the causality relationship between exports and growth is from exports to growth, and the growth-led export hypothesis is valid when it is from growth to exports. To this end, the primary purpose of this study is to investigate the validity of the new economic model for Turkey in two different periods. In this context, this study comparatively focuses on the 1999:Q1–2013:Q4 and 2014:Q1–2021:Q4 periods to test the validity of the export-led growth hypothesis and the growth-led export hypothesis. According to the analysis results for the 1999:Q1–2013:Q4 periods, only the growth-led export hypothesis is valid, and a 1% increase in the economic growth rate in this period increases exports by 0.42%. Considering the 2014:Q1–2021:Q4 period, the hypotheses of “Economic growth is not the cause of exports and exports are not the cause of economic growth” are rejected, and according to these test results, it was determined that both the export-led growth hypothesis and the growth-led export hypothesis are valid. In the results of this period, a 1% increase in economic growth rate increases exports by 0.38%, and a 1% increase in exports increases economic growth by 1.36%.
From the Adoption to the Implementation of Online Teaching in a Post-COVID World: Applying Ely’s Conditions of Change Framework
Authors: Al-Freih Maha
Published: October 2022

The pivot to online and distance teaching during the COVID-19 pandemic in 2020 created a strong momentum for the establishment and integration of online learning and teaching opportunities in higher education on a global scale, and the Saudi higher education sector is no different. However, the adoption of online teaching during the pandemic was abrupt and sudden; the goal was to provide continued access to education during lockdowns rather than to ensure the effective and sustained use of online and distance courses and programs. The purpose of this study was to examine faculty members’ perceptions regarding the presence or non-presence of eight conditions of change as suggested by Ely and to examine any differences in their perceptions based on various factors (e.g., college affiliations and number of online courses taught). According to Ely, the presence of these environmental conditions is necessary for the successful and sustained implementation of technological changes in educational institutions. The participants in this survey study were 168 faculty members at one public university in Riyadh, Saudi Arabia. The findings indicated that on average, only four out of the eight conditions were perceived as being present by the current sample. Analysis of variance also indicated significant differences in faculty members’ perceptions regarding the presence of some of the conditions based on their reported knowledge of online teaching as well as the number of online courses taught. A discussion of these findings as well as recommendations for the improvement of institutional conditions to support online teaching is provided.
Guiding Students towards an Understanding of Climate Change through a Teaching–Learning Sequence
Authors: Toffaletti Stefano , Di Mauro Marco , Rosi Tommaso , Malgieri Massimiliano , Onorato Pasquale
Published: October 2022

In this paper, we put forward a proposal for the design and the evaluation of teaching–learning sequences (TLSs) on the greenhouse effect (GHE), relying on the educational reconstruction model (MER). The first design, which starts from a critical analysis of textbook treatments of the GHE, is followed by a cyclic, recursive process, which consists of theoretical reflection, conceptual analysis, design, and test of a sequence. At each iteration, the analysis of the students’ learning progression provided relevant information for addressing the persistent hurdles and misunderstandings that affect it. Our findings show how design choices can support the learning of the GHE, leading to the formulation of design principles that help foster understanding. The iterative approach strongly improved the design and evaluation and allowed for a significant refinement of the TLSs. The implementation and evaluation process, which went on from 2017 to 2021, involved undergraduate students attending a course on “experimental physics laboratory” at the University of Trento in those years. The results indicate that, in the end, students can reach an effective understanding of the physical grounds of the GHE.
Student Adoption and Effectiveness of Flipped Classroom Implementation for Process Simulation Class
Authors: Ruslan Muhammad Syafiq Hazwan , Sapiee Nurul Haiza , Kurnia Kiki Adi , Amran Nurul Aini , Abd Rahman Norazah
Published: October 2022

A flipped classroom (FC) teaching approach offers a personalized learning environment for the learners to study the course using pre-recorded material prior to the in-class session. The synchronous sessions are then dedicated to knowledge confirmation and learning activities. Although this technique is considered promising, the learners’ acceptance of FC for skill-based courses conducted entirely via open distance learning (ODL) has yet to be assessed in Malaysia. Thus, this study aims to evaluate the FC approach's effectiveness and student readiness and acceptance during ODL. A questionnaire was used to assess the students’ adoption and overall implementation of FC. Results show that students who underwent the FC approach gave a better performance in their course. In fact, 39.29% of the students who underwent the FC approach managed to score A and A- compared to 19.82% of students that went for conventional delivery. Regarding the adoption, 100% of the surveyed students agreed that the pre-recorded video assisted them in preparing for the class. Furthermore, 100% of students surveyed agreed that they were able to prepare the simulation before the next class at the end of the semester, compared to 96.154% in the first 4 weeks. It can be concluded that the FC approach has been shown to be effective and easily adopted by students. Furthermore, the FC learning framework has been developed to map the learning activities and the learning domain. Principles such as How People Learn and Bloom taxonomy were embedded in the development of this framework.
Relationship between Students’ Perception of a Rubric for Oral Presentations and Their Academic Characteristics
Authors: Ferrer-Pardo Víctor Raul , Jimenez-Perez Irene , Gil-Calvo Marina , Pérez-Soriano Pedro , Priego-Quesada Jose Ignacio
Published: October 2022

The use of rubrics in the evaluation of oral presentations has been associated with several benefits for students. However, it is unknown whether students with better academic marks and greater self-regulation find the use of rubrics more useful or not. This paper aims to assess the relationship between how students perceive the use of a rubric and their academic characteristics, and to analyze the congruence between the professor’s and students’ evaluations when using the rubric. Eighty-five students studying for a Degree in Sport Sciences participated in this study. A rubric for oral presentations was used to assess the students’ performance. The students then filled out a questionnaire about their perception of the validity of the rubric, an assessment of academic performance, and a self-regulation questionnaire. Inverse correlations were observed between the academic record and two items of the rubric validity perception (r < −0.24). Direct correlations were also found between learning oriented self-regulation and four items of the rubric validity perception (r > 0.22). There was very good congruence between the professor’s and students’ marks when using the rubric (ICC = 0.78). The results suggest that the rubric used is a good instrument to ensure fair and consistent evaluations, despite possible differences between evaluators.
How to Get More People into Teaching? Comparing Undergraduates’ and Teacher Trainees’ Motivation and Perceptions of a Teaching Career
Authors: Gorard Stephen , See Beng Huat , Morris Rebecca
Published: October 2022

Understanding people’s perceptions of teaching as an occupation, and their motivation or otherwise to teach, can help identify which initiatives/policies are most promising in improving teacher recruitment. Previous studies have often sought only the views of teachers/teacher trainees to understand why people choose teaching as a career. Recruitment strategies based on evidence from such research are therefore only appealing to those who are already interested in teaching. It is the views of those who might otherwise have gone into teaching that can provide better clues to what we can do to get more people into teaching. This paper compares the views of 4469 undergraduate students and 788 trainee teachers in England. Our analysis goes beyond the usual approach by comparing young people completely uninterested in teaching, those who considered teaching but rejected it, those intending to be teachers, and those already in training. Our study found little or no difference between prospective teachers and others in terms of generic career drivers and the attraction of financial incentives, although prospective teachers tended to have lower levels of qualifications and to come from less prestigious occupational backgrounds. However, those not planning to be teachers are much less concerned about teachers’ workload, suggesting that this is probably not a great deterrent for those considering teaching. Compared to those who intend to be teachers, those who have considered but rejected teaching are less likely to view teaching as intellectually stimulating. They are least likely to see teaching as a high-status profession, offering good promotion prospects and job security. Policies to improve recruitment need to make teaching more attractive to the second group by addressing these issues. Additionally, these policies should be distinct from policies to retain existing teachers.
Room2Educ8: A Framework for Creating Educational Escape Rooms Based on Design Thinking Principles
Authors: Fotaris Panagiotis , Mastoras Theodoros
Published: October 2022

By immersing learners in a playful, interactive, and engaging experience, Educational Escape Rooms (EERs) have been found to enhance learners’ motivation, help them to develop 21st century skills, and improve knowledge acquisition. As research into EERs is still in a preliminary phase, no unified framework about how to design them has been established yet. Additionally, existing frameworks rarely validate the quality and efficacy of the frameworks themselves in terms of usability and usefulness. Therefore, the present paper proposes Room2Educ8, a learner-centred framework for EER design that follows Design Thinking principles. It provides detailed heuristics for empathising with learners, defining learning objectives and constraints, adding narrative, designing puzzles, briefing and debriefing participants, prototyping and playtesting, documenting the whole process, and evaluating the EER experience. A mixed-methods internal validation study based on Instructional Design model validation was conducted with 104 postgraduate students between 2018–2022 to assess the framework’s integrity and use. The study findings suggest that Room2Educ8 can be proposed as a valid tool for developing a wide range of EER types that cover a variety of topics. Its well-described and practical steps make it appropriate for educators regardless of a lack of prior experience in EER design.
Empirical Findings on Learning Success and Competence Development at Learning Factories: A Scoping Review
Authors: Reining Nine , Kauffeld Simone
Published: October 2022

To meet the changing competence requirements for employees in engineering professions, education and training need to adapt accordingly. Learning factories offer various possibilities to design or integrate practice-oriented learning into training measures. Whether this approach in fact facilitates learning and competence development is rarely investigated. For this reason, the objective of this scoping review is to analyze and summarize the existing empirical findings on learning success and competence development in learning factories regarding their evaluation methods and results. Following standardized guidelines (PRISMA, JBI) for scoping reviews, 12 databases were researched. The literature screening led to the identification of 24 publications included in the final analysis. The results indicate that a variety of evaluation methods are used to assess learning and competences at learning factories and that criteria of all four competence facets (professional, methodological, social, and self-competence) can be enhanced at learning factories in general. As many of the identified studies show potential for improvement regarding the quality of the used methods and analysis of results, further studies on these topics are needed. Evaluations should be integrated into all training measures at learning factories to ensure learning success and competence development and to be able to readjust design, structure, and didactics where necessary.
The Witch of Agnesi: Thematic Fulcrum for a Shared Learning Path in the Classroom
Authors: Canepa Giuseppe , Fenaroli Giuseppina , Florio Emilia
Published: October 2022

In this article, we propose an educational path to learn mathematics in the framework of the European Community Action Scheme for the Mobility of University Students (ERASMUS) project developed in an Italian high school class on the topic of the contribution of women in science. The choice of the educational path fell on Maria Gaetana Agnesi and the particular curve, the “versiera” (witch), studied by her. During the experimentation, the students have been divided into four groups, each one taking care of particular aspects of the human and cultural experience of Maria Gaetana Agnesi, and the construction of her famous curve under different mathematical registers: the geometric, algebraic, analytical, mechanical and computer science, as well as non-mathematical ones such as the gestural and voice registers. The effectiveness of the proposed educational path was evaluated through interviews with the students during the course and at the end of the activities. All the students, even those less skilled in mathematics, have shown enthusiasm and satisfaction by recognizing the mathematical object, the “witch”, presented by using different languages.
Method for Improving the Reliability of SRAM-Based PUF Using Convolution Operation
Authors: Cao Ruihu , Mei Niansong , Lian Qian
Published: October 2022

This paper introduces a novel and efficient physical unclonable function (PUF) extraction method for SRAM. The proposed one-layer convolution scheme is based on a convolution operation, which significantly enhances the reliability of the PUF. To further reduce the hardware resources, a lightweight solution is presented based on a one-layer convolution scheme at the cost of a higher redundancy coefficient and a larger range for the inter-chip Hamming distance (HD). Both the above schemes only use certain hardware resources in the initial stage and the hardware resources are automatically released after PUF verification. The two schemes were verified using SRAM cells in three stm32f407 chips to output a 256-bit PUF response. The experimental results show that the one-layer convolution scheme required 8 KB SRAM, while the lightweight scheme used only 0.5 KB SRAM. The reliability of the one-layer convolution was found to be 100% when the redundancy coefficient was 0.08 and the inter-chip HD was 50.8073%. The reliability of the lightweight scheme was 100% and of the inter-chip HD was 50.195%.
Design of Optical-Wireless IR-UWBoF Systems with Spectral Line Suppression Capabilities
Authors: Perez-Ramos Aldo-Eleazar , Villarreal-Reyes Salvador , Galaviz-Mosqueda Alejandro , Lepers Catherine
Published: October 2022

Impulse-Radio Ultra-Wide Band (IR-UWB) over Fiber (IR-UWBoF) has been proposed to interconnect IR-UWB-based deployments separated by hundreds of meters or even kilometers. IR-UWB transmissions must comply with spectral masks provided by radio spectrum regulatory agencies. The maximum transmit power of an IR-UWB signal is adversely affected by the presence of spectral lines in its Power Spectral Density (PSD). Thus, it is desirable that the PSD of signals generated by IR-UWBoF systems does not show spectral lines. Previous works have shown the feasibility of deploying of optical-wireless IR-UWBoF systems. However, most of these proposals report PSDs showing spectral lines. To the best of our knowledge, spectral line suppression has not been previously studied for optical-wireless IR-UWBoF systems. This work shows the design and implementation of optical-wireless IR-UWBoF systems generating signals with Spectral Line-Free (SLF) PSDs. The proposal considers the use the use of a specifically designed convolutional code combined with Binary Phase Shift Keying (BPSK) or Quaternary Biorthogonal Pulse Position Modulation (Q-BOPPM) to provide a SLF PSD in IR-UWBoF systems. A testbed consisting of 30 km of single-mode optical fiber (SMF) concatenated to a 20 cm wireless link was physically implemented. The results show that a SLF PSD is achieved for both the optical and the wireless transmissions, even when the binary data source feeding the system is not perfectly random.
Fast Self-Attention Deep Detection Network Based on Weakly Differentiated Plant Nematodess
Authors: Zhuang Jiayan , Liu Yangming , Xu Ningyuan , Zhu Yi , Xiao Jiangjian , Gu Jianfeng , Mao Tianyi
Published: October 2022

High-precision, high-speed detection and classification of weakly differentiated targets has always been a difficult problem in the field of image vision. In this paper, the detection of phytopathogenic Bursaphelenchus xylophilus with small size and very weak inter-species differences is taken as an example. Our work is aimed at the current problem of weakly differentiated target detection: We propose a lightweight self attention network. Experiments show that the key feature recognition areas of plant nematodes found by our Self Attention network are in good agreement with the experience and knowledge of customs experts, and the feature areas found by this method can obtain higher detection accuracy than expert knowledge; In order to optimize the computing power brought by the whole image input, we use low resolution images to quickly obtain the location coordinates of key features, and then obtain the information of high resolution feature regions based on the coordinates; The adaptive weighted multi feature joint detection method based on heat map brightness is adopted to further improve the detection accuracy; We have constructed a more complete high-resolution training data set, involving 24 species of Equisetum and other common hybrids, with a total data volume of more than 10,000. The algorithm proposed in this paper replaces the tedious extensive manual labelling in the training process, improves the average training time of the model by more than 50%, reduces the testing time of a single sample by about 27%, optimizes the model storage size by 65%, improves the detection accuracy of the ImageNet pre-trained model by 12.6%, and improves the detection accuracy of the no-ImageNet pre-trained model by more than 48%.
Tohjm-Trained Multiscale Spatial Temporal Graph Convolutional Neural Network for Semi-Supervised Skeletal Action Recognition
Authors: Gou Ruru , Yang Wenzhu , Luo Zifei , Yuan Yunfeng , Li Andong
Published: October 2022

In recent years, spatial-temporal graph convolutional networks have played an increasingly important role in skeleton-based human action recognition. However, there are still three major limitations to most ST-GCN-based approaches: (1) They only use a single joint scale to extract action features, or process joint and skeletal information separately. As a result, action features cannot be extracted dynamically through the mutual directivity between the scales. (2) These models treat the contributions of all joints equally in training, which neglects the problem that some joints with difficult loss-reduction are critical joints in network training. (3) These networks rely heavily on a large amount of labeled data, which remains costly. To address these problems, we propose a Tohjm-trained multiscale spatial-temporal graph convolutional neural network for semi-supervised action recognition, which contains three parts: encoder, decoder and classifier. The encoder’s core is a correlated joint–bone–body-part fusion spatial-temporal graph convolutional network that allows the network to learn more stable action features between coarse and fine scales. The decoder uses a self-supervised training method with a motion prediction head, which enables the network to extract action features using unlabeled data so that the network can achieve semi-supervised learning. In addition, the network is also capable of fully supervised learning with the encoder, decoder and classifier. Our proposed time-level online hard joint mining strategy is also used in the decoder training process, which allows the network to focus on hard training joints and improve the overall network performance. Experimental results on the NTU-RGB + D dataset and the Kinetics-skeleton dataset show that the improved model achieves good performance for action recognition based on semi-supervised training, and is also applicable to the fully supervised approach.
Adaptive Chattering-Free PID Sliding Mode Control for Tracking Problem of Uncertain Dynamical Systems
Authors: Liang Yufei , Zhang Dong , Li Guodong , Wu Tao
Published: October 2022

Aiming at the trajectory tracking problem with unknown uncertainties, a novel controller composed of proportional-integral-differential sliding mode surface (PIDSM) and variable gain hyperbolic reaching law is proposed. A PID-type sliding mode surface with an inverse hyperbolic integral terminal sliding mode term is proposed, which has the advantages of global convergence of integral sliding mode (ISM) and finite time convergence of terminal sliding mode (TSM), and the control effect is significantly improved. Then, a variable gain hyperbolic approach law is proposed to solve the sliding mode approaching velocity problem. The variable gain term can guarantee different approaching velocities at different distances from the sliding mode surface, and the chattering problem is eliminated by using a hyperbolic function instead of the switching function. The redesign of the sliding mode surface and the reaching law ensures the robustness and tracking accuracy of the uncertain system. Adaptive estimation can compensate for uncertain disturbance terms in nonlinear systems, and the combination with sliding mode control further improves the tracking accuracy and robustness of the system. Finally, the Lyapunov stability principle is used for stability analysis, and the simulation study verifies that the proposed control scheme has the advantages of fast response, strong robustness, and high tracking accuracy.
The Systems Approach and Design Path of Electronic Bidding Systems Based on Blockchain Technology
Authors: Xu De , Yang Qing
Published: October 2022

The electronic tendering and bidding system has realized the digitalization, networking, and high integration of the whole process of tendering, bidding, bid evaluation, and contract, which has a wide range of applications. However, the trust degree, cooperation, and transaction efficiency of the parties involved in electronic bidding are low, and bidding fraud and collusion are forbidden repeatedly. Blockchain technology has the characteristics of decentralization, transparent transactions, traceability, non-tampering and forgery detection, and data security. This paper proposes a design path of an electronic bidding system based on blockchain technology, which aims to solve the efficiency, trust, and security of the electronic trading process. By building the underlying architecture platform of blockchain and embedding the business process of electronic bidding, this realizes the transparency, openness, and traceability during the whole process of electronic bidding. This paper uses qualitative and quantitative methods to prove the effectiveness of the system.