Recent Research Trends Research Articles

Preface

This volume of Journal of Physics: Conference Series is dedicated to 2023 3rd International Conference on Power System and Energy Internet (PoSEI 2023) supported by Murdoch University, Australia, held during 28th–30th July 2023 in Qingdao, China (virtual conference). The Conference, focused exclusively on recent trends of research in power system and energy Internet, attracted about 80 participants around the world.The immediate purpose of this Conference was to bring together experienced as well as young scholars who are interested in working actively on various aspects of power system and energy Internet, in order to share and discuss recent related researches. And the ultimate plan is to make it a regular event every year based on the very positive response we received which was beyond our expectation.The keynote speeches addressed major theoretical issues, current and forthcoming observational data as well as upcoming ideas in both theoretical and observational sectors. Keeping in mind the principle academic exchange first, the lectures were arranged in such a way that the young researchers had ample scope to interact with the stalwarts who are internationally leading experts in their respective fields of research.Besides the keynote speeches, a good proportion of the participants also presented their work through contributory talks and posters on this big platform. This was particularly encouraging and of benefit to the young participants, given that there were a good number of scientists of international repute among the participants, the feedback from whom could guide them in the right direction. All the contributions were refereed by experts rigorously.The major topics covered in the Conference are: Cogeneration and Distributed Generation, Power Quality and Electromagnetic Compatibility, Energy Efficiency and Energy Management, Information and Communication for Energy Internet, Sensor and Micro-machines, etc.We are indebted to the Murdoch University, Australia for providing us with generous support to organize such a huge conference. We thank all the members of the Conference Committee who contributed their hard labour to make the Conference a great success. We gratefully acknowledge the experts and reviewers for their valuable suggestions on the review process of the papers submitted.We sincerely thank the staff of Journal of Physics: Conference Series for their efforts in the publication of this issue. Last but not the least, we thank all the speakers and participants without whom the Program would not have been such a success. We hope we will have your active participation in the future conference versions of PoSEI as well.The Committee of PoSEI 2023List of Committee Member is available in this Pdf.

A Review of Stock Price Prediction Based on LSTM and TCN Methods

In financial analysis, stock price prediction is a difficult and important problem that has received a lot of attention from researchers and practitioners in recent years. The application of machine learning and artificial intelligence algorithms to stock price forecasting has demonstrated significant potential for increasing forecasting accuracy. Long momentary memory (LSTM) and transient convolutional networks (TCN) are two famous profound learning calculations that have been generally utilized for stock cost expectation. The most recent approaches to stock price prediction using LSTM and TCN methods are reviewed in this paper. We highlight the most recent research trends in this field and talk about these methods' benefits and drawbacks. Additionally, we discuss potential future research directions in this field. The survey is expected to give a knowledge into the present status of exploration on stock value forecast and guide specialists and experts in working on the exactness of expectation.

유아교사의 놀이지원 관련 연구동향 : 한국학술지인용색인(KCI)에 등재된 논문을 대상으로

The purpose of this study is to understand how teachers' interest in and approach to play support has changed by analyzing recent domestic research trends on early childhood teachers' play support. For this purpose, 114 studies related to early childhood teachers' play support published in domestic academic journals from 1991 to 2023 were selected. For the selected papers, frequencies and percentages were calculated according to the research period, research subjects, research methods, and research topics according to the analysis criteria established by the researcher based on related literature. The analysis results are as follows. First, research on teachers' play support continues to increase, and in particular, it has rapidly increased since 2018, with research on teachers' play support being most actively conducted between 2021 and 2023. The research topics were 'implementation and reality', followed by 'related variables and others', 'play support competency', 'awareness and understanding', 'needs and methods search' and 'development'. The most common research topic was ‘implementation and reality.’ The results of this study were discussed for each analysis category and ways to revitalize teachers' play support research for young children were discussed.

Can L2 Pronunciation Be Evaluated without Reference to a Native Model? Pillai Scores for the Intrinsic Evaluation of L2 Vowels

In this article, we explore the possibility of evaluating L2 pronunciation, and, more specifically, L2 vowels, without referring to a native model, i.e., intrinsically. Instead of comparing L2 vowel productions to native speakers’ productions, we use Pillai scores to measure the overlap between target vowel categories in L2 English (/iː/ — /ɪ/, /ɑː/ — /æ/, /ɜː/ — /ʌ/, /uː/ — /ʊ/) for L1 French, L1 Spanish, and L1 Italian learners (n = 40); and in L2 French (/y/ — /u/, /ø/ — /o/, /ø/ — /e/, /ɛ˜/ — /e /, /ɑ˜/ — /a/, /ɔ˜/ — /o/) for L1 English, L1 Spanish, and L1 Italian learners (n = 48). We assume that a greater amount of overlap within a contrast indicates assimilated categories in a learner’s production, whereas a smaller amount of overlap indicates the establishment of phonological categories and distinct realisations for members of the contrast. Pillai scores were significant predictors of native ratings of comprehensibility and/or nativelikeness for many of the contrasts considered. Despite some limitations and caveats, we argue that Pillai scores and similar methods for the intrinsic evaluation of L2 pronunciation can be used, (i) to avoid direct comparisons of L2 users’ performance with native monolinguals, following recent trends in SLA research; (ii) when comparable L1 data are not available; (iii) within longitudinal studies to track the progressive development of new phonological categories.

Recent Research Progress on All-Solid-State Mg Batteries

Current Li battery technology employs graphite anode and flammable organic liquid electrolytes. Thus, the current Li battery is always facing the problems of low energy density and safety. Additionally, the sustainable supply of Li due to the scarce abundance of Li sources is another problem. An all-solid-state Mg battery is expected to solve the problems owing to non-flammable solid-state electrolytes, high capacity/safety of divalent Mg metal anode and high abundance of Mg sources; therefore, solid-state electrolytes and all-solid-state Mg batteries have been researched intensively last two decades. However, the realization of all-solid-state Mg batteries is still far off. In this article, we review the recent research progress on all-solid-state Mg batteries so that researchers can pursue recent research trends of an all-solid-state Mg battery. At first, the solid-state electrolyte research is described briefly in the categories of inorganic, organic and inorganic/organic composite electrolytes. After that, the recent research progress of all-solid-state Mg batteries is summarized and analyzed. To help readers, we tabulate electrode materials, experimental conditions and performances of an all-solid-state Mg battery so that the readers can find the necessary information at a glance. In the last, challenges to realize the all-solid-state Mg batteries are visited.

A Vector Space Approach for Measuring Relationality and Multidimensionality of Meaning in Large Text Collections

In this article, we develop a methodological approach for organizational research regarding the construction of multidimensional and relational similarity measures by using the vector space model in natural language processing (NLP). Our vector space approach draws on the well-established premise in organizational research that texts provide a window into social reality and allow measuring theory-based constructs ( e.g., organizations’ self-representations). Using a vector space approach allows capturing the multidimensionality of these theory-based constructs and computing relational similarities between organizational entities ( e.g., organizations, their members, and subunits) in social spaces and with their environments, such as the organization itself, industries, or countries. Thus, our methodological approach contributes to the recent trend in organizational research to use the potential inherent in big (textual) data by using NLP. In an example, we provide guidance for organizational scholars by illustrating how they can ensure validity when applying our methodological contribution in concrete research practice.

Heritage and Railways: Sustainable Tourism Opportunities Boosted by Digital Transformation

This Special Issue focuses on the debate and recent trends in research dealing with railways, touristic itineraries, landscape and heritage digital documentation, while keeping in mind the goals of “The European Year of Rail” [...]

Recent Research Trends in Stem Cells Using CRISPR/Cas-Based Genome Editing Methods.

The clustered regularly interspaced short palindromic repeats (CRISPR) system, a rapidly advancing genome editing technology, allows DNA alterations into the genome of organisms. Gene editing using the CRISPR system enables more precise and diverse editing, such as single nucleotide conversion, precise knock-in of target sequences or genes, chromosomal rearrangement, or gene disruption by simple cutting. Moreover, CRISPR systems comprising transcriptional activators/repressors can be used for epigenetic regulation without DNA damage. Stem cell DNA engineering based on gene editing tools has enormous potential to provide clues regarding the pathogenesis of diseases and to study the mechanisms and treatments of incurable diseases. Here, we review the latest trends in stem cell research using various CRISPR/Cas technologies and discuss their future prospects in treating various diseases.

국내 삼킴장애 연구의 최근 동향 문헌 연구

국내 삼킴장애 연구의 최근 동향 문헌 연구

Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network

Studying domain adaptation is a recent research trend. Generally, many generative models that researchers have studied perform well on training data from a specific domain. However, their ability to be generalized to other domains might be limited. Therefore, a growing body of research has utilized domain adaptation techniques to address the problem of generative models being vulnerable to input from other domains. In this paper, we focused on generative models and representation learning. Generative models have received a lot of attention for their ability to generate various types of data such as images, music, and text. In particular, studies utilizing generative adversarial neural networks (GANs) and autoencoder structures have received a lot of attention. In this paper, we solved the domain adaptation problem by reconstructing real image data using an autoencoder structure. In particular, reconstructed image data, considered a type of noisy image data, are used as input data. How to reconstruct data by extracting features and selectively transforming them in order to reduce differences in characteristics between domains entails representative learning. Considering these research trends, this paper proposed a novel methodology combining bidirectional feature learning and generative networks to innovatively approach the domain adaptation problem. It could improve the adaptation ability by accurately simulating the real data distribution. The experimental results show that the proposed model outperforms the traditional DANN and ADDA. This demonstrates that combining bidirectional feature learning and generative networks is an effective solution in the field of domain adaptation. These results break new ground in the field of domain adaptation. They are expected to provide great inspiration for future research and applications. Finally, through various experiments and evaluations, we verify that the proposed approach outperforms the existing works. We conducted experiments for representative generative models and domain adaptation techniques and found that the proposed approach was effective in improving data and domain robustness. We hope to contribute to the development of domain-adaptive models that are robust to the domain.

Recent Trends in Porang Research via Bibliometrics Analysis Approach

Porang is a native Indonesian plant with many benefits and has been widely studied by researchers in Indonesia. However, bibliometrics analysis of porang research is still limited. So, this study aims to establish a bibliometric analysis to know the recent trends in porang research. Data were taken from Scopus using Publish or Perish (PoP) software from 2012 to 2022 and processed further using Microsoft Excel and VOSViewer to determine the bibliometric mapping of porang research. The information on publication trends, authors, keywords, journal sources, numbers of publications, author productivity, and citations was analyzed descriptively. The 164 articles were retrieved from the Scopus database using PoP software from 2012 to 2022. The tendency for the number of publications was increased throughout the observation period, with the highest of annual publications in 2022 with 44 articles. Different trends are shown in the fluctuating number of citations each year, and the highest number of citations was found in 2014, with 91 citations. Food Research, Journal of Tropical Life Science, and IOP Conference Series: Earth and Environmental Science are the journals and conference papers that publish the most articles related to porang research, with 4, 4, and 43 articles, respectively, with Wardhani being the most productive as the first author. The “glucomannan”, “Amorphophallus muelleri" and “Amorphophallus oncophyllus” were the most title keyword in porang research, with 35, 18, and 18 occurrences, respectively.

A systematic review of trends in photobiomodulation in dentistry between 2018 and 2022: advances and investigative agenda

Background Photobiomodulation (PBM) involves laser therapy utilized in medical sciences to modulate biological processes acting as a palliative and immune response-enhancing treatment. This study conducts a comprehensive bibliometric analysis to explore current trends in PBM-related scientific production, encompassing publications, citations, impact, keywords and clusters. Additionally, it aims to predict future research trends in this domain. Methods The data for this quantitative and qualitative bibliometric analysis were obtained from 608 scientific documents retrieved in November 2022, with 123 sourced from Web of Science and 485 from Scopus, Utilizing Excel, the data was processed in Excel to extract essencial information. Productivity and impact were evaluated for eligibility, and VOSviewer aided in determining associativity for the bibliometric analysis. Results The findings of this study demostrate that the scientific production related to PBM adheres to a growth power law, exhibiting characteristics of both exponential and linear phases. Notably, recent research trends emphasize critical concepts such as laser therapy, orthodontics, and dental pulp stem cells. Particularly significant is the burgeoning interest in utilizing PBM within dentistry as a complementary alternative to existing protocols. Conclusions PBM stands as a promising laser therapy within medical applications. Through a detailed bibliometric analysis, this study underscores the increasing significance of PBM, especially within the realm of dental treatments. These insights offer a glimpse into the evolving landscape of PBM research and provide valuable guidance for potential future directions of study.

Thermal conductivity of Portlandite: Molecular dynamics based approach

Energy storage provides a greener path of efficient energy utilization. Recent trends of research suggest concrete as a potential thermal energy storage (TES) material. Its cheap commercial availability makes it one of the most deserving candidates. Cement paste is the key glue of concrete, hence performance of its major components in thermal conduction seeks thorough scientific study. Portlandite or calcium hydroxide is the second major component of cement paste, microscopically visible at a scale length of <10−4 m. Molecular dynamics (MD) simulation has been utilized to investigate the thermal transport mechanism of portlandite at an atomistic scale. The thermal conductivity of this component has been computed using three major force fields which are compared with the experimental outcome using modulated differential scanning calorimetry (MDSC). It has been observed that the simulation outcomes are in order with the experimental value but the results are sensitive to the choice of force fields. Excitation of molecules during thermal transport is predominantly governed by lower-frequency molecular vibration indicating the existence of Boson Peaks. This manuscript presents the full thermal conductivity tensor of portlandite along with the possible mechanism associated with thermal transport.

A review of detecting malware in android devices based on machine learning techniques

AbstractMalware developers install malware on mobile users' devices and steal their personal information without their knowledge. According to recent studies, it has been observed that malware developers are now targeting Android mobile devices. Researchers have examined the issues of detecting malware in these devices and proposed different methods and techniques. This study's main goal is to aid researchers in gaining a basic understanding of Android malware and its numerous detection methods. Earlier experiments that used machine learning to detect Android malware will be carefully reviewed in this paper. This in‐depth review article thoroughly examines the origins, evolution, and sustainability of Android malware detection. It offers an in‐depth literature review that includes the most recent approaches and research trends for detecting malware, from static analysis to dynamic analysis, machine learning, and deep learning. Additionally, we review current approaches' shortcomings and difficulties and suggest possible paths for further investigation. The paper aims to stimulate further innovation in this essential field by providing researchers and practitioners with a comprehensive overview of the current status of Android malware detection.

A Brief Journey in Nanomaterials: Base and Recent Research Trends

Nano – term is a mimicking dimensional philosophy of natural structure. With same context, nanomaterials in research and processing display a mimic philosophy with particular function(s) in human and other living creatures such as DNA, ribosome, antibody, enzyme, protein, glucose, haemoglobin, and bone (hydroxyapatite or collagen). Also, some microorganisms (bacteria or viruses) are in nano-sized that enabling them for easily entrance to their hosts. According to range definition (1-100(nm)), type, application, toxicity beside environmental impact for short – and long term, nanomaterial and nanotechnology have important achievements starting from discovery to production state. Nanomaterials are high surface materials having enormous selectivity – reactivity resulted from high active sites to area ratio. They prepared by physical, chemical and bio -green reactions that applied to formulate micro- and nano- organic(s) and inorganic(s). These significant materials performe amazing levels in research, discovery, development, and processing in industry, medicine, pharmacy, science, technology, and other life fields. In this mini - review, fast journey with more than seventy references beside their cited books or articles demonstrated that nano sized materials are superior and adjustable in their physical, chemical, and / or biological properties compared to bulk counterparts. Their classification varied according to composition, origin, size (or surface area), and shape that lead to safety – application balance. The dramatically innovation in nano science and technology is a result of surface area to volume ratio that promoted multiple uses and toxicological changing in cell, membrane, or organ. Finally, there is a serious need for more research in these smaller particles to demonstrate what, how, where, and when nanomaterials are safe and eco-friendly through type, concentration, time of exposure, and biological target specifications beside motivate them as more suitable for bio-, agro, food, and medical uses.

Recent Research Trends in the Fields of Extrusion, Mixing and Kneading Technologies

Recent Research Trends in the Fields of Extrusion, Mixing and Kneading Technologies

Nano Fertilizers: Revolutionizing Agricultural Nutrient Delivery and Efficiency

Nanotechnology has emerged as a promising frontier in agriculture, offering innovative solutions to enhance crop productivity and sustainability. Nano fertilizers, a key application of nanotechnology in agriculture, hold immense potential to revolutionize nutrient delivery and efficiency in crop production systems. This paper provides a comprehensive overview of nano fertilizers, highlighting their synthesis methods, mechanisms of action, and potential applications in modern agriculture. Nano fertilizers capitalize on nanoscale materials to encapsulate, deliver, and release nutrients to plants in a controlled and targeted manner. By enhancing nutrient solubility, facilitating nutrient uptake, and minimizing nutrient losses, nano fertilizers offer several advantages over traditional fertilizer formulations. Additionally, nano fertilizers can mitigate environmental risks associated with nutrient leaching and runoff, thereby promoting sustainable agricultural practices. Despite their promising benefits, the commercialization and adoption of nano fertilizers face various challenges and considerations. Safety concerns, regulatory frameworks, environmental impact assessments, and scalability issues are among the critical factors that influence the widespread implementation of nano fertilizers in agricultural systems. Addressing these challenges requires interdisciplinary collaboration, robust regulatory oversight, and stakeholder engagement to ensure the responsible development and deployment of nano-enabled agricultural technologies. This paper reviews recent advancements and research trends in nano fertilizer technology, highlighting key achievements, ongoing research endeavors, and future directions in this rapidly evolving field. By elucidating the opportunities and challenges associated with nano fertilizers, this paper aims to inform researchers, policymakers, industry stakeholders, and agricultural practitioners about the transformative potential of nanotechnology in modern agriculture.

Current Nutritional and Pharmacological Approaches for Attenuating Sarcopenia.

Sarcopenia is characterized by a gradual slowing of movement due to loss of muscle mass and quality, decreased power and strength, increased risk of injury from falls, and often weakness. This review will focus on recent research trends in nutritional and pharmacological approaches to controlling sarcopenia. Because nutritional studies in humans are fairly limited, this paper includes many results from nutritional studies in mammals. The combination of resistance training with supplements containing amino acids is the gold standard for preventing sarcopenia. Amino acid (HMB) supplementation alone has no significant effect on muscle strength or muscle mass in sarcopenia, but the combination of HMB and exercise (whole body vibration stimulation) is likely to be effective. Tea catechins, soy isoflavones, and ursolic acid are interesting candidates for reducing sarcopenia, but both more detailed basic research on this treatment and clinical studies in humans are needed. Vitamin D supplementation has been shown not to improve sarcopenia in elderly individuals who are not vitamin D-deficient. Myostatin inhibitory drugs have been tried in many neuromuscular diseases, but increases in muscle mass and strength are less likely to be expected. Validation of myostatin inhibitory antibodies in patients with sarcopenia has been positive, but excessive expectations are not warranted.

Computational methods for in situ structural studies with cryogenic electron tomography.

Cryo-electron tomography (cryo-ET) plays a critical role in imaging microorganisms in situ in terms of further analyzing the working mechanisms of viruses and drug exploitation, among others. A data processing workflow for cryo-ET has been developed to reconstruct three-dimensional density maps and further build atomic models from a tilt series of two-dimensional projections. Low signal-to-noise ratio (SNR) and missing wedge are two major factors that make the reconstruction procedure challenging. Because only few near-atomic resolution structures have been reconstructed in cryo-ET, there is still much room to design new approaches to improve universal reconstruction resolutions. This review summarizes classical mathematical models and deep learning methods among general reconstruction steps. Moreover, we also discuss current limitations and prospects. This review can provide software and methods for each step of the entire procedure from tilt series by cryo-ET to 3D atomic structures. In addition, it can also help more experts in various fields comprehend a recent research trend in cryo-ET. Furthermore, we hope that more researchers can collaborate in developing computational methods and mathematical models for high-resolution three-dimensional structures from cryo-ET datasets.

Bibiliometric Mapping of Local Communities’ Environmental Conservation Behaviour

Before new trends emerge after the COVID pandemic is over, this paper aims to examine the development of recent research trends in the theories and methods applied to studies of local communities' behaviours towards environmental protection in the years 2018–2021. This paper presents a review of more than one thousand local communities' environmental conservation behaviours that was published between 2018 and 2021. The purpose of this research is to achieve its objective. Following that, content analysis was performed on the articles that were pertinent. In order to clarify, categorise, and interpret the current research on rural tourism definitions and applications, a classification scheme and an extensive literature review have been presented in this article. Both of these elements are presented in this paper. A total of 38 academic papers from 25 different journals published between the years 2018 and 2021 are included in this review's classification scheme. These papers are organised according to their use of one of five primary keywords: local, community, environmental, conservation, or behaviour. The fact that the articles under consideration in this research were only found in the Scopus database represents a potential limitation of the bibliometric mapping. This research contributes to the existing body of knowledge by conducting a bibliometric review of studies that have already investigated locals' perspectives on tourism, and this review adds to the tourism literature by analysing the growing trend of looking at how different communities' actions affect environmental protection. Future research on these trends can be guided by identifying where the gaps in knowledge currently exist.