Mainstream Research Research Articles

The output improvement of droplet-based nanogenerators through enhanced coupling displacement and conducting currents

The advent of triboelectric nanogenerator (TENG) preludes the high entropy energy harvesting and is gradually applied to droplet energy harvesting. However, the mainstream research concentrates on exploring the solid-liquid contact electrification (CE) and electrostatic induction (EI) processes, ignoring the possibility of charge extraction from water. As a result, the droplet-based nanogenerator (DNG) is limited to small alternating-current (AC) output, which also asks for complex power management. This study reveals that the displacement current is not the sole driving force during the power generation of DNG, and the contribution of the conducting current is equally important. Here, the existence of conducting charge is confirmed by a top-electrode-only DNG, and the charge transfer processes in traditional DNGs are re-examined. Finally, a three-electrode total current DNG is introduced to enhance the coupling of displacement current and conducting current. The charge extraction efficiency is then improved by more than one order of magnitude (charge extraction from single droplet is increased from 1 to 27.3 nC) and retains the direct-current (DC) and high voltage (up to 2315 V) output characteristics.

DegreEmbed: Incorporating entity embedding into logic rule learning for knowledge graph reasoning

Knowledge graphs (KGs), as structured representations of real world facts, are intelligent databases incorporating human knowledge that can help machine imitate the way of human problem solving. However, KGs are usually huge and there are inevitably missing facts in KGs, thus undermining applications such as question answering and recommender systems that are based on knowledge graph reasoning. Link prediction for knowledge graphs is the task aiming to complete missing facts by reasoning based on the existing knowledge. Two main streams of research are widely studied: one learns low-dimensional embeddings for entities and relations that can explore latent patterns, and the other gains good interpretability by mining logical rules. Unfortunately, the heterogeneity of modern KGs that involve entities and relations of various types is not well considered in the previous studies. In this paper, we propose DegreEmbed, a model that combines embedding-based learning and logic rule mining for inferring on KGs. Specifically, we study the problem of predicting missing links in heterogeneous KGs from the perspective of the degree of nodes. Experimentally, we demonstrate that our DegreEmbed model outperforms the state-of-the-art methods on real world datasets and the rules mined by our model are of high quality and interpretability.

Addressing the stagnation issue in accounting education research while incorporating mainstream publications and professional practice into the accounting curriculum

The accounting practice and academic research-oriented sides of the profession have had difficulties concerning the integration of their outputs. The practice side has maintained that very little of the “mainstream” research produced by academics has been of value in their day to day activities, and the research side has complained about the lack of cooperation as to the availability of data from the practice segment that is needed to conduct academic research. Add to this situation the teaching focused faculty of the profession, who contend that the research conducted in the “mainstream” is of little value to their efforts in teaching undergraduate and graduate students. Additionally, faculty in the classroom need input from the practice side to determine what information the students need to be successful after graduation. The problems were noted in 2012 by the AICPA and AAA supported Pathways Commission in their major report, with questionable efforts to date addressing the concerns. Take all of these components together and you have a view of our current “situation” in accounting.We attempt, in this paper, to further address the previously identified stagnation in accounting education research (Rebele and St. Pierre, 2015) and, at the same time, to bridge the gaps between these different accounting groups noted by the Pathways Commission. We do this by offering a potentially unique solution to address the current accounting environment. First, we analyze the findings from a mainstream research study that both examines the practice side of the equation and directly affects our classroom coverage of topics in several of the required undergraduate and graduate courses typically found in accounting programs around the country. Second, this paper breaks from many of the current education articles in that we examine what should be taught in the classroom based on actual real - world practice issues rather than hypothetical academic approaches to the topics covered. Third, our efforts in tying together the three components - mainstream research, practice, and classroom content - will hopefully move our colleagues in a direction that benefits our students and opens new accounting education research opportunities.

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed.

Efficient Synthesis of Pyrrole Disulfides Catalyzed by Lipase in Ethanol

Disulfides, as fundamental scaffolds, are widely present in peptides, natural products, and pharmaceutical molecules. However, traditional synthesis of disulfides often involves the utilization of toxic reagents or environmentally unfriendly reaction conditions. In this work, a green and efficient method was developed for synthesizing pyrrole disulfides using β-ketothioamides and ethyl cyanoacetate as substrates, with lipase serving as a catalyst. Under the optimal conditions (β-Ketothioamides (1 mmol), ethyl cyanoacetate (1 mmol), PPL (200 U), and EtOH (5 mL)), lipase leads to the formation of pyrrole disulfides in yields of up to 88% at 40 °C. The related mechanism is also speculated in this paper. This approach not only presents a new application of lipase in enzyme catalytic promiscuity, but also offers a significant advancement in the synthetic pathway for pyrrole disulfides and aligns with the current mainstream research direction of green chemistry, contributing to the further development of environmentally friendly biocatalytic processes.

A Literature Review of Virtual Reality in Interpreting Teaching (2012-2023)

As an important content of metaverse, the integration of virtual reality technology with interpreting teaching has attracted increasingly attention in the field of interpreting research. By screening major journals and databases, based on the application of 19 virtual reality technologies in interpreting teaching in recent 11 years, by a combination of quantitative and qualitative research methods, from five aspects: publication trends, research topics, researchers and levels, research areas, and the design and development of the virtual reality platform, this paper formed an coding analytical framework for a systematic literature review of the application of virtual reality technology in interpreting teaching. Through content analysis to conduct a systematic literature review, it aims to systematically analyze the development trend, hot research topics and mainstream research methods of virtual reality technology applied in interpreting teaching in the past 11 years, and then summarize its research direction and the use of the platform, finally give the problems and suggestions.

An Empirical Test Based on the Validity of the Capital Asset Pricing Model of American Firms

Nowadays, quantitative finance has become a more and more mainstream research direction, and the origin model of quantitative finance is the capital asset pricing model (CAPM)created by William Sharp and others. This paper mainly studies the feasibility of CAPM model for the American market, in order to prove the universality and accuracy of CAPM model, and analyzes the relevant errors. The research object of this paper is 59 randomly selected listed companies in 11 industries in S&P500 index, and the regression is carried out by the ordinary least square method, and the goodness of fit is obtained, so as to prove its universality. The data comes from Yahoo Finance. Through data analysis, this paper believes that CAPM model is universal, but it has low adaptability for some special industries, so it is necessary to introduce more variable factors or carry out non-linear regression to improve the accuracy of its prediction.

A Lightweight and Accurate Method for Detecting Traffic Flow in Real Time

Traffic flow detection provides significant information for intelligent transportation systems. However, as the mainstream research direction, vision-based traffic flow detection methods currently face the challenges of a trade-off between accuracy and speed. Furthermore, it is crucial that modularization be incorporated into the system design process to enhance the maintainability and flexibility of the system. To achieve this, we propose a modular design method that divides this task into three parts: vehicle detecting, vehicle tracking, and vehicle counting. As an important link of the system, vehicle detection greatly influences the accuracy and speed of the system. We therefore introduce a lightweight network called feature adaptive fusion-YOLOX, which is based on YOLOX. Specifically, in order to eliminate redundant information brought by bilinear interpolation, we propose a feature-level upsampling method called channel to spatial, which enables upsampling without additional calculations. Based on this module, we design a lightweight, multi-scale feature fusion module, feature adaptive fusion pyramid network (FAFPN). Compared with PA-FPN, FAFPN reduces FLOPs by 61% and parameters of the neck by 50% while maintaining comparable or even slightly improved performance. Through experimental tests, the traffic flow detection method proposed in this paper achieves high accuracy and adaptability in a series of traffic surveillance videos in different types of weather and perspectives and can realize traffic flow detection in real time.

Mātauranga and science

Mātauranga – Māori knowledge and Māori ways of knowing – developed in Aotearoa along with the cultural and epistemological identities of iwi Māori, thus is rooted in these islands and holds a well of human understanding of Aotearoa ecosystems. Mātauranga encompasses traditional and contemporary knowledge and is becoming increasingly sought after and integrated into mainstream education, research, and environmental policy in Aotearoa New Zealand. With ‘Vision Mātauranga’ a critical point of engagement for researchers, what does the scientist working in this space need to know about mātauranga? I have highlighted what I see to be key aspects of the discussion on how scientists work with those who hold and access mātauranga, and with mātauranga itself. The scholarly literature on mātauranga is extensive, so I focus on that which is relevant toscience education and science research. Throughout I describe and explore the potential of research in which mātauranga and science connect and relate.

A Survey on Recommendation Methods Based on Social Relationships

With the rapid development of online social networks recently, more and more online users have participated in social network activities and rich social relationships are formed accordingly. These social relationships provide a rich data source and research basis for in-depth study on recommender systems (RSs), while also promoting the development of RSs based on social networks. To solve the problems of cold start and sparsity in RSs, many recommendation algorithms are constantly being proposed. Motivated by the availability of rich social connections in today’s RSs, a large number of recommendation techniques based on social relationships have been proposed recently, achieving good recommendation results, and have become the mainstream research direction in the field of RSs, attracting more and more researchers to engage in this research. In this study, we mainly review and summarize the social relationship-based recommendation methods and techniques in RSs, and study some recent deep social relationship recommendation methods and techniques based on deep learning (DL), including the latest social matrix factorization (MF)-based recommendation methods and graph neural network (GNN)-based recommendation methods. Finally, we discuss the potential impact that may improve the RS and future direction. In this article, we aim to introduce the recent recommendation techniques integrating social relationships to solve data sparsity and cold start, and provide a new perspective for improving the performance of RSs, thereby providing useful resources in the state-of-the-art research results for future researchers.

A deep learning aided differential distinguisher improvement framework with more lightweight and universality

In CRYPTO 2019, Gohr opens up a new direction for cryptanalysis. He successfully applied deep learning to differential cryptanalysis against the NSA block cipher SPECK32/64, achieving higher accuracy than traditional differential distinguishers. Until now, one of the mainstream research directions is increasing the training sample size and utilizing different neural networks to improve the accuracy of neural distinguishers. This conversion mindset may lead to a huge number of parameters, heavy computing load, and a large number of memory in the distinguishers training process. However, in the practical application of cryptanalysis, the applicability of the attacks method in a resource-constrained environment is very important. Therefore, we focus on the cost optimization and aim to reduce network parameters for differential neural cryptanalysis.In this paper, we propose two cost-optimized neural distinguisher improvement methods from the aspect of data format and network structure, respectively. Firstly, we obtain a partial output difference neural distinguisher using only 4-bits training data format which is constructed with a new advantage bits search algorithm based on two key improvement conditions. In addition, we perform an interpretability analysis of the new neural distinguishers whose results are mainly reflected in the relationship between the neural distinguishers, truncated differential, and advantage bits. Secondly, we replace the traditional convolution with the depthwise separable convolution to reduce the training cost without affecting the accuracy as much as possible. Overall, the number of training parameters can be reduced by less than 50% by using our new network structure for training neural distinguishers. Finally, we apply the network structure to the partial output difference neural distinguishers. The combinatorial approach have led to a further reduction in the number of parameters (approximately 30% of Gohr’s distinguishers for SPECK).

THE IMPLEMENTATION OF POSITIVISTIC PRINCIPLE IN QUALITATIVE RESEARCH

Nowadays, mainstream scientific research activities have been carried out using two methods or approaches, the first is quantitative (positivistic) by testing hypotheses as evidence and the second is qualitative (post-positivistic) by analyzing case studies, phenomena to measure the depth of formal aspects in assessing subjective events that occur in the field. This research provides a thorough understanding to identify the differences between positivistic and post-positivistic lines of thought and review the mutual combination of positivistic thinking with qualitative-based research activities which tend to be at odds. This research was conducted through library research method. Reference sources are obtained from books, journals and other relevant references. The results of this research indicate that positivistic thinking of thought does not tend to be an obstacle to conduct a qualitative research. This is proven by the emergence of several theories that can be used as analytical tools to provide additional references to support the validity of data collection in the field.

DJAN: Deep Joint Adaptation Network for Wildlife Image Recognition.

Wildlife recognition is of utmost importance for monitoring and preserving biodiversity. In recent years, deep-learning-based methods for wildlife image recognition have exhibited remarkable performance on specific datasets and are becoming a mainstream research direction. However, wildlife image recognition tasks face the challenge of weak generalization in open environments. In this paper, a Deep Joint Adaptation Network (DJAN) for wildlife image recognition is proposed to deal with the above issue by taking a transfer learning paradigm into consideration. To alleviate the distribution discrepancy between the known dataset and the target task dataset while enhancing the transferability of the model's generated features, we introduce a correlation alignment constraint and a strategy of conditional adversarial training, which enhance the capability of individual domain adaptation modules. In addition, a transformer unit is utilized to capture the long-range relationships between the local and global feature representations, which facilitates better understanding of the overall structure and relationships within the image. The proposed approach is evaluated on a wildlife dataset; a series of experimental results testify that the DJAN model yields state-of-the-art results, and, compared to the best results obtained by the baseline methods, the average accuracy of identifying the eleven wildlife species improves by 3.6 percentage points.

Serious games research streams for social change: Critical review and framing

The number of scientific publications about serious games has exponentially increased, often surpassing human limitations in processing such a large volume of information. Consequently, the importance of frameworks for summarising such fast‐expanding literature has also grown. This paper draws a panorama of serious game research streams, focusing on higher education in engineering and management. The research design involves a systematic review using PRISMA guidelines, along with bibliometric and content analyses. The sample comprises 701 documents collected from both Scopus and Web of Science databases. For supporting bibliometric analyses, Bibliometrix and Biblioshiny tools are employed. In addition, a coding schema is developed for in‐depth analysis of 701 documents selected according to the inclusion criteria. In short, the literature on serious games for engineering and management education grows more rapidly than modern science, following a globalised, collaborative and context‐based trajectory. The results reveal five main research streams: game design guidelines, game design cases, game experiment guidelines, game experiment cases and generalists. These streams are summarised in a proposed framework. Cross‐tabulation and statistical analyses conducted in SPSS Statistics identify the key relationships amongst the research streams. Finally, opportunities to investigate serious games for sustainable development education arise, and there is a need for future efforts to formalise the framework classification algorithm. Practitioner notesWhat is already known about this topic Scientific production related to Serious Games (SGs) has grown exponentially in a globalised manner. It reflects the interest from various domains, particularly the field of sustainability in management. Literature reviews on SGs have emphasised various topics, including the acceptance of simulations and games as effective methods of teaching and learning. The role of technology‐enhanced games and simulations in the context of digital transformation in education and the emergence of sustainability as a promising field for future SGs research are also highlighted. The literature has introduced three categories for empirical research on SGs: (1) game presentation, (2) game evaluation and (3) game effectiveness. It has also highlighted methodological rigour as a common challenge across studies. What this paper adds Scientific research on SGs in Engineering and Management Higher Education (EMHE) experiences exponential and significantly faster growth compared to modern science in a globalised and collaborative manner. This growth reveals the scarcity of experts in this area and attracts the attention of various fields, particularly in the realm of sustainability. Thematic trajectories indicate a decline in discussions regarding users' perceptions of SGs and their validity as educational tools. They also demonstrate consistency in discussions about SGs design, and the potential of sustainability emerges as a promising area for future SGs in EMHE. A comprehensive framework composed by five primary research streams connects game design cases and guidelines, game experiment cases and guidelines and generalists. This framework can serve as a lens for future context‐based literature reviews, and the relationships amongst its streams reinforce the idea that the field can benefit from increased methodological rigour in experiments. Implications for practice and/or policy This review offers categorised supplementary material in which educators can discover a variety of artefacts for application in their specific educational contexts. Designers can access guidelines for enhancing the design of innovative games. Researchers can access guidelines for more effective evaluation of their artefacts across various contexts. Finally, policymakers can explore numerous experiments to inform decisions related to technology‐enhanced innovations in the classroom.

텍스트마이닝과 네트워크 분석을 활용한 웹툰 연구 동향 분석

This study conducted text mining and network analysis for the purpose of analyzing webtoon research trends. Analysis data were collected from articles and research assignments in RISS and NTIS. As a result of the analysis, keywords such as storytelling, cultural contents, OSMU, and transmedia appeared with high frequency in articles. And it was classified into a total of 6 topics, including IP development. This was interpreted as subdividing the scope and methodology within the mainstream research for the purpose of creating added value, but there is a bias in the research topic. In the task, keywords such as artificial intelligence, content, platform, and data appeared with high frequency. And it was classified into a total of 4 topics including image computing. Application technology development using generative artificial intelligence occupied the main project, and policy supplementation, such as copyright protection, should be established together.

Epistemic Inclusion as the Key to Benefiting from Cognitive Diversity in Science

ABSTRACT Throughout scientific history, there have been cases of mainstream science dismissing novel ideas of less prominent researchers. Nowadays, many researchers with different social and academic backgrounds, origins and gender identities work together on topics of crucial importance. Still, it is questionable whether the privileged groups consider the views of underprivileged colleagues with sufficient attention. To profit from the diversity of thoughts, the scientific community first has to be open to minority viewpoints and epistemically include them in mainstream research. Moreover, the idea of inclusive science poses stronger requirements than the paradigm of open science. We argue that the concept of integration of different opinions is insufficient because the process of integration assumes adjusting oneself to the majority view and fitting into the dominant paradigm while contributing only with smaller amendments. Epistemic inclusion, on the other hand, means dynamically changing the research paradigm during the interaction with diverse methods and hypotheses. The process of inclusion preserves marginalized views and increases epistemic justice.

Mapping the evolution path of citizen science in education: a bibliometric analysis

ABSTRACT For over two decades now, the application of Citizen Science to Education has been evolving, and fundamental topics, such as the drivers of motivation to participate in Citizen Science projects, are still under discussion. Some recent developments, though, like the use of Artificial Intelligence to support data collection and validation, seem to point to a clear-cut divergence from the mainstream research path. The objective of this paper is to summarise the development trajectory of research on Citizen Science in Education so far, and then shed light on its future development, to help researchers direct their efforts towards the most promising open questions in this field. We achieved these objectives by using the lens of the Affordance-Actualisation theory and the Main Path Analysis method.

Written Corrective Feedback Scientometric Review (1987–2022)

Written corrective feedback is widely used in language learning and teaching, and related studies have attracted significant attention in recent years. Through analysis and visualization of 497 articles indexed in the Web of Science core collection over the past 35 years using CiteSpace, this scientometric review aims to explore the developmental stages, involved disciplines, geospatial fingerprint, main research streams, and emerging research trends of written corrective feedback. The following stages were identified based on the timeline of publications and citations: the sprouting phase (1987–2005), the fluctuating phase (2006–2016), and the explosive phase (2017–2022). The interdisciplinary trend toward written corrective feedback becomes increasingly prominent throughout each stage, and its primary disciplines range from linguistics and cognitive science to psychology and computer science. The primary research contexts for written corrective feedback research are ESL and EFL; countries with multiple coexisting language variants have prioritized research in this field. Existing studies have witnessed a shift from quantitative to qualitative research, and case studies focusing on individual differences are emerging as a newer research frontier. As one of the first few scientometric reviews of written corrective feedback since the phrase first appeared as a combined index term, this study is significant as a reference for comprehensively understanding the intellectual background, dynamics, and evolution of this research field.

Research Status and Development Prospect of Wellbore Integrity in China

Summary As the exploration and development of oil and gas move into increasingly challenging locations and harsher environments, well integrity becomes more difficult to maintain. High temperatures, pressures, and corrosion can all contribute to wellbore integrity failure. Such failures can have significant financial and environmental consequences, including gas leakage and fluid spills. In this paper, we review the development and technical advancements of wellbore integrity research both in China and abroad and look forward to the development direction of wellbore integrity in China. We provide basic background knowledge for those interested in wellbore integrity and also share the progress and development direction of integrity research for wellbore integrity researchers. Through research and analysis, some conclusions can be drawn. Countries around the world are actively studying wellbore integrity and have developed a large number of standards, especially the United States and Norway, which have the most standards. The most common way to analyze wellbore integrity is to first divide the entire wellbore into different wellbore barrier units according to different standards, such as ISO 16530-1, and then study risk factors and integrity management measures in different units. Mainstream research is mostly carried out around the integrity of casing, cement, and tubing, and many achievements have been made, but the study of packer and downhole safety valve is still on the way. Wellbore integrity risk assessment aims to quantify potential risks and establish risk levels to support decision-making for on-site wellbore integrity control. This is achieved by identifying factors affecting wellbore integrity, establishing an evaluation index system and processing evaluation indicators to determine failure probability and impact consequences. The resulting risk value can be divided into different areas using the “as low as reasonably practicable principle” or a risk matrix graph. However, due to the complexity of the factors involved and the subjectivity of risk classification rules, there are still challenges in promoting the evaluation model and reducing errors in the evaluation results. China should actively promote interdisciplinary integration and respond to the call for “dual carbon goals” to break through the current bottleneck in wellbore integrity research. This can be achieved by promoting the development of quantitative wellbore integrity risk assessment methods, developing supporting evaluation software based on big data, and by tackling the integrity challenges faced by different types of wells and promoting the development of wellbore integrity discipline.

Three-Dimensional Point Cloud Reconstruction Method of Cardiac Soft Tissue Based on Binocular Endoscopic Images

Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This study introduces a new paradigm to address existing challenges. An innovative stereoscopic endoscopic image correction algorithm is proposed, exploiting intrinsic insights into stereoscopic calibration parameters. The synergy between the stereoscopic endoscope parameters and the disparity map derived from the cardiac soft tissue images ultimately leads to the acquisition of precise 3D points. Guided by deliberate filtering and optimization methods, the triangulation process subsequently facilitates the reconstruction of the complex surface of the cardiac soft tissue. The experimental results strongly emphasize the accuracy of the calibration algorithm, confirming its utility in stereoscopic endoscopy. Furthermore, the image rectification algorithm exhibits a significant reduction in vertical parallax, which effectively enhances the stereo matching process. The resulting 3D reconstruction technique enables the targeted surface reconstruction of different regions of interest in the cardiac soft tissue landscape. This study demonstrates the potential of binocular stereo vision-based 3D reconstruction techniques for integration into clinical settings. The combination of joint calibration algorithms, image correction innovations, and precise tissue reconstruction enhances the promise of improved surgical precision and outcomes in the field of cardiac interventions.