Areas Of Current Research Interest Research Articles

Spatiotemporal characteristics and driving factors of chemical oxygen demand emissions in China’s wastewater: An analysis based on spatial autocorrelation and geodetector

The advancement of industrialization and urbanization has exacerbated global water pollution. Chemical Oxygen Demand (COD) serves as a crucial metric for assessing organic contamination in aquatic environments. Consequently, the spatiotemporal characteristics and driving factors of COD emissions have emerged as a significant area of current research interest. This study employs spatial autocorrelation and geodetector methods to rigorously analyze the spatial–temporal patterns and principal drivers of COD emissions in China’s wastewater, drawing on eleven geographic and socio-economic datasets spanning from 2011 to 2019. This study indicates that COD emissions in China’s wastewater have consistently decreased annually between 2011 and 2019, with a marked reduction observed post-2016. Regarding spatial distribution, COD emissions in the eastern region significantly surpass those in the western region, highlighting pronounced regional disparities. The analysis of the global Moran’s Index indicates that the COD emissions across China’s provincial regions demonstrate spatial clustering of similar values, with this clustering intensifying annually. Furthermore, the local Moran’s Index identifies the precise locations of these spatial clusters. Geodetector analysis identified that the consumption of nitrogen fertilizers and the consumption of chemical fertilizers had substantial impacts on COD emissions, whereas the proportion of urban population at year-end and the number of granted inventions exerted lesser effects. Furthermore, the interaction between two factors notably enhances the explanatory power regarding COD emissions. This research underpins the formulation of efficacious pollution control and environmental protection strategies, contributing vital insights for the stewardship of water quality.

Visualising Current Research Trends in Class Imbalance using Clustering Approach: A Bibliometrics Analysis

In recent years, extensive research has been carried out on big data class imbalance problems using the clustering approach. The bibliometric analysis employs statistical techniques to map and assess trends in a specific research domain based on keywords, author affiliations, and citations. Bibliographic analysis assists us in comprehending unstructured big data. This study aims to present a comprehensive literature review on class imbalance problems using the clustering approach and identify gaps in the research domain using bibliometric analytical techniques. The Scopus and Web of Science databases were used to extract 663 articles on class imbalance data using a clustering approach published between 2010 and 2021. We used the VOS (Visualisation of Similarities) viewer to visualise the bibliometric analytical outcomes. Co-citation and co-word analysis were used to visualise the publication trend and identify areas of current research interest. The study's key findings evidenced a growing interest in the research domain. Herrera, f., and Chawla N. V. are dominant authors in this field, and China is leading the publication in the clustering approach for the big data imbalance problem. The top three affiliations are from China: Tsinghua University, the Chinese Academy of Sciences, and Beihang University. Conducting an in-depth bibliometric analysis using other databases such as Science Direct, IEEE, and Emerald is recommended. This study may assist researchers in understanding the nature of the big data imbalance problem using a clustering approach and providing insights for future research derived from these worldwide databases.

Worldwide research history and trends on Blockchain applications in education: A bibliometric analysis, 2017-2022

Academic scholars have been studying Blockchain technology since 2017. As a result, the progression of this technology has drawn interest from researchers in various research fields, including education. Because of this tendency, it is imperative that the existing literature be thoroughly reviewed to assist researchers in identifying gaps for future studies. Thus, this study used bibliometric analysis to assess the current research trend on blockchain applications in education. This study compiled all published works on Blockchain in education as of January 8th, 2023, from the Scopus database. The data was integrated using Publish or Perish software, and data visualization was done using VOSviewer. Additionally, Microsoft Excel was used to analyze the data. Fifty-one documents were retrieved in total based on the search results. In particular, from 2018 to 2019, the growth rate of publications in educational Blockchain increased dramatically. Most of the publications were written in English and published in journals and conference proceedings. Computer science has dominated research on educational Blockchain, and most research was done in India. The analysis also revealed that the most commonly used terms, "blockchain" and "blockchain in education," correspond to the main areas of study that have been done on the educational Blockchain. Unfortunately, the assessment component has received very little attention from previous educational blockchain studies. In addition to outlining the development of the scientific literature on the subject, this study also highlights areas of current research interest and possible future research prospects.

Application and development of Deuterium Metabolic Imaging in tumor glucose metabolism: visualization of different metabolic pathways

Cancer metabolism has emerged as a pivotal area of research recently. The ability to visualize and comprehend the metabolic processes of cancer holds immense clinical value, particularly in the diagnosis of malignant tumors and the assessment of treatment responses. Deuterium Metabolic Imaging (DMI), as a robust, simple, and versatile MR spectroscopic imaging tool, demonstrates promise in tumor diagnosis and treatment efficacy assessment. This review explored the latest developments and applications of DMI in oncology across various tumor metabolic axes, with a specific emphasis on its potential for clinical translation. DMI offers invaluable insights into tumor biology, treatment responses, and prognostic outcomes. Notably, DMI can identify early responses to immunotherapy, a prominent area of current research interest. In conclusion, DMI harbors the potential to evolve into a convenient and efficient imaging technique in clinical practice, thereby advancing precision medicine and improving the diagnosis and evaluation of cancer treatments.

An overview of mindfulness theories applied to tourism: systematic review update and bibliometric analysis

This paper presents a systematic review update regarding mindfulness theories applied to tourism, complemented with a bibliometric analysis. Scientific studies listed in the WOS, SCOPUS, PubMed and PsycInfo databases were analysed using PRISMA guidelines. Twenty-five studies were selected for review from 670 records identified. In recent years, mindfulness has proved to be a promising area of research in tourism, and the number of published studies is increasing. The findings reveal a positive association between mindfulness and tourism experiences, suggesting that tourists who adopt states of mindfulness during their travels benefit from increased pro-environmental behaviours, satisfaction levels, and memorable experiences, leading to destination loyalty. This study provides an in-depth overview of the literature, and helps redress the lack of bibliometric studies in this field. It also identifies common areas of current research interest, offers insights into commonly studied themes, and seeks to raise awareness about the relevance of the concept of mindfulness in tourist experiences and well-being by suggesting implications for research and tourism management.

Generative adversarial network based image inpainting

Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are listed. After that, the evaluation metrics, and common datasets of deep learning-based image inpainting are listed. Finally, the existing image inpainting methods are summarized and summarized, and the ideas for future key research directions are presented and prospected.

Online Learning Models during the COVID-19 Pandemic: A Bibliometric Analysis

Online learning as a method of avoiding the spread of COVID-19 adds a new dimension to the fight against this virus. The first publication with online learning as a topic can be traced back to 1986, An Algorithm for Learning Without External Supervision and Its Application to Learning Control Systems (Nikolić & Fu, 1986). Online learning in the education sector has become increasingly prominent in recent years. The study aims to discover and illustrate the direction of trend development of research on online learning during COVID-19. The study used the bibliometric analysis method. It examined 1,594 scientific articles from 159 authors across 110 countries and 160 affiliations. The information gathered between 2019 and 2021 was relevant to the topic. To visualize the result of the study, the researcher used the VOSViewer application. The study shows that research on online learning during the COVID-19 pandemic increased consistently from 2019 to 2021. This study found 1249 documents in that period. Yang H is the most co-authored author. While the most prolific writer is Hong J.C, with 8 articles. This systematic field mapping helps in graphically depicting the evolution of publications as well as identifying areas of current research interest and future research potential. These findings provide a solid basis for future research in this area.

The more things changed, the more they stayed the same: Trends in conservation focus 2010–2019

The conservation literature provides an important resource for conservation practice and reflects key areas of current research interest, but not necessarily those of highest priority, in the field of conservation science. In one of the most comprehensive studies of its kind, this paper evaluates 4388 articles from three leading conservation journals with different impact factors (Biological Conservation, Conservation Letters, and Oryx) for the period 2010–2019. The analysis shows that previously identified trends still hold true in many instances, including a continuing taxonomic bias towards vertebrates and particularly in favour of mammals. Geographical biases also persist, favouring Europe and North America with respect to where research is undertaken, who writes about it, and how it is funded, although publication of research conducted in Africa is increasing. It is difficult to evaluate changes in author gender outside the decade studied as this topic has not been widely considered in previous studies, but parity in authorship has not yet been achieved in any of the journals examined. To overcome the biases identified, researchers, editors, and funding organisations collectively still need to do more to redirect conservation efforts where they are most urgently needed and encourage more women and more researchers from lower income countries to participate in research by helping to remove the barriers that currently limit them.

Effect of Geometric Arrangement on Mechanical Properties of 2D Woven Auxetic Fabrics

Textiles-fibres, yarns and fabrics are omnipresent in our daily lives, with unique mechanical properties that fit the design specifications for the tasks for which they are designed. The development of yarns and fabrics with negative Poisson’s ratio (NPR) is an area of current research interest due to their potential for use in high performance textiles (e.g., military, sports, etc.). The unique braiding technology of interlacement for preparation of braided helically wrapped yarns with NPR effect with later development of auxetic woven fabric made it possible to avoid the slippage of the wrapped component from the core. The applied geometrical configuration and NPR behaviour of the braided helical yarn structure with seven different angles comprising of monofilament elastomeric polyurethane (PU) core with two wrap materials that include multifilament ultra-high molecular weight polyethylene (UHMWPE) and polyethylene terephthalate (PET) fibres were investigated and analysed. The mechanically stable 2D woven textile auxetic fabrics (AF) with various weave patterns such as 2/2 matt and 3/1 twill were developed from the auxetic yarn with PU elastomer core having maximum NPR effect of −1.70 using lower wrapped angle of 9° to study and compare their mechanical responses. The auxetic yarn was used in weft direction and multifilament UHMWPE yarn in warp direction, using semi-automatic loom. Auxeticity of AF was analysed and its various mechanical properties such tensile strength, impact energy absorption, in-plane, and out-of-plane auxeticity, and puncture resistance were studied. Higher energy absorption of 84 Nm for matt fabric was seen compared to twill fabric having an energy of 65 Nm. The puncture resistance capability of matt fabric was better than twill fabric. While twill fabric exhibited better auxetic effect in both in-plane and out-of-plane mode compared to matt fabric. In short, both the twill and matt design AF’s showed unique characteristics which are beneficial in making various protective textiles such as protective helmets, bullet proof shields, cut resistance gloves, blast resistant curtains, and puncture tolerant elastomeric composites.

The Translational Potential of Microglia and Monocyte-Derived Macrophages in Ischemic Stroke.

The immune response to ischemic stroke is an area of study that is at the forefront of stroke research and presents promising new avenues for treatment development. Upon cerebral vessel occlusion, the innate immune system is activated by danger-associated molecular signals from stressed and dying neurons. Microglia, an immune cell population within the central nervous system which phagocytose cell debris and modulate the immune response via cytokine signaling, are the first cell population to become activated. Soon after, monocytes arrive from the peripheral immune system, differentiate into macrophages, and further aid in the immune response. Upon activation, both microglia and monocyte-derived macrophages are capable of polarizing into phenotypes which can either promote or attenuate the inflammatory response. Phenotypes which promote the inflammatory response are hypothesized to increase neuronal damage and impair recovery of neuronal function during the later phases of ischemic stroke. Therefore, modulating neuroimmune cells to adopt an anti-inflammatory response post ischemic stroke is an area of current research interest and potential treatment development. In this review, we outline the biology of microglia and monocyte-derived macrophages, further explain their roles in the acute, subacute, and chronic stages of ischemic stroke, and highlight current treatment development efforts which target these cells in the context of ischemic stroke.

The Impact of College Students’ Creativity on Their Innovation Behavior in the “Internet +” Era: The Mediating Role of Creative Self-Efficacy

Social progress is driven by innovation; especially, in the context of “Internet +” innovation and entrepreneurship, college students serve as a frontier group that brings new ideas and new technologies into society, and as such college students’ creativity and innovation behavior are important for their country’s future social and economic development. Therefore, China vigorously advocates the cultivation of college students’ innovative ability to meet the needs of the development of the times. Determining the key factors currently influencing innovation behavior by college students is an area of current research interest. Building on the literature, this study uses the questionnaire survey method to analyze data from 226 college students relating to creativity, innovation behavior, and creative self-efficacy, exploring the mechanisms by which the students’ creativity affects their innovation behavior in the context of “Internet +” innovation and entrepreneurship. The results show that creativity has a significant positive effect on innovation behavior among college students and that creative self-efficacy plays a mediating role in this relationship.

Agritourism: structured literature review and bibliometric analysis

ABSTRACT This paper performs a systematic review of the research literature in the field of agritourism. In the study, using a structured literature review methodology, established as well as emerging research clusters have been identified for epistemological analysis. Such analysis helps in the identification of key research topics, interrelations, and collaboration patterns in the field of agritourism and other research areas involving similar concepts. Graphical mapping of the agritourism research field is done using visualization of similarities (VOS). VOS software analyzed bibliographic data of 459 research publications obtained from Web of Science and Scopus databases from a period of 1980–2019. This graphical mapping illustrated the authors’ and countries’ contributions, their collaboration, and publication pattern over the period. This paper gives insights about majorly studied research themes and also identified common areas of current research interest and potential directions for future research.

Computational Schemes in the Design of Novel Materials for Energy Savings

The current demand for lightweight energy efficient materials in industries to help in addressing the current challenges faced in reducing green house gas emissions is one of the motivations behind innovative material design. This paper has tried to review in particular the importance of the hierarchical multiscale modelling strategy in the design of novel lightweight materials. It also highlights on one of the hierarchical multiscale modelling methodologies from ab-initio level to macro level in predicting macroscopic material behaviour and the impact this simulation strategy will have on the development of innovative materials. It highlights on the limitations of this modelling strategy such as: the unreasonable computational time associated with the relaxation of polymeric chains or entanglements as well as scale bridging approaches between low level and high level models which are areas of current research interest.
 Keywords: Multiscale modelling, lightweight materials, energy-efficiency, scale bridging, length scale.

Geometric perspective on quantum parameter estimation

Quantum metrology holds the promise of an early practical application of quantum technologies, in which measurements of physical quantities can be made with much greater precision than what is achievable with classical technologies. In this Review, the authors collect some of the key theoretical results in quantum parameter estimation by presenting the theory for the quantum estimation of a single parameter, multiple parameters, and optical estimation using Gaussian states. The authors give an overview of results in areas of current research interest, such as Bayesian quantum estimation, noisy quantum metrology, and distributed quantum sensing. The authors address the question of how minimum measurement errors can be achieved using entanglement as well as more general quantum states. This review is presented from a geometric perspective. This has the advantage that it unifies a wide variety of estimation procedures and strategies, thus providing a more intuitive big picture of quantum parameter estimation.

What Is so Positive about Positive Animal Welfare?-A Critical Review of the Literature.

Simple SummaryPositive animal welfare (PAW) is thought to have come about as a response to there being too much of a focus on avoiding negatives in animal welfare science. However, despite its development over the last 10 years, it is not clear what it adds to the study of animal welfare. To clarify this, we conduct a review of the literature on PAW. We aim to identify the characteristic features of PAW and to show how PAW connects to the wider literature on animal welfare. We find that the PAW literature is characterised by four features: (1) positive emotions which highlights the capacity of animals to experience positive emotions; (2) positive affective engagement which seeks to create a link between positive emotions and behaviours animals are motivated to engage in; (3) quality of life which acts to give PAW a role in defining an appropriate balance of positives over negatives and; (4) happiness which brings a full life perspective to PAW. While the first two are already well situated in animal welfare studies the two last points open research agendas about aggregation of different aspects of PAW and how earlier experiences affect animals’ ability to have well-rounded lives.It is claimed that positive animal welfare (PAW) developed over the last decade in reaction to animal welfare focusing too much on avoiding negatives. However, it remains unclear what PAW adds to the animal welfare literature and to what extent its ideas are new. Through a critical review of the PAW literature, we aim to separate different aspects of PAW and situate it in relation to the traditional animal welfare literature. We find that the core PAW literature is small (n = 10 papers) but links to wider areas of current research interest. The PAW literature is defined by four features: (1) positive emotions which is arguably the most widely acknowledged; (2) positive affective engagement which serves to functionally link positive emotions to goal-directed behavior; (3) quality of life which serves to situate PAW within the context of finding the right balance of positives over negatives; (4) happiness which brings a full life perspective to PAW. While the two first points are already part of welfare research going back decades, the two latter points could be linked to more recent research agendas concerning aggregation and how specific events may affect the ability of animals to make the best of their lives.

Bone morphogenetic protein-2 and bone therapy: successes and pitfalls

Bone morphogenetic proteins (BMPs), more specifically BMP-2, are being increasingly used in orthopaedic surgery due to advanced research into osteoinductive factors that may enhance and improve bone therapy. There are many areas in therapy that BMP-2 is being applied to, including dental treatment, open tibial fractures, cancer and spinal surgery. Within these areas of treatment, there are many reports of successes and pitfalls. This review explores the use of BMP-2 and its successes, pitfalls and future prospects in bone therapy. The PubMed database was consulted to compile this review. With successes in therapy, there were descriptions of a more rapid healing time with no signs of rejection or infection attributed to BMP-2 treatment. Pitfalls included BMP-2 'off-label' use, which lead to various adverse effects. Our search highlighted that optimising treatment with BMP-2 is a direction that many researchers are exploring, with areas of current research interest including concentration and dose of BMP-2, carrier type and delivery.

The Use of Social Media by State Health Departments in the US: Analyzing Health Communication Through Facebook.

The use of social media as a powerful health communication tool is an area of current research interest. Our objective was to describe use of Facebook by State Health Departments (SHDs) in US, and their relationship with CDC's Behavioral Risk Factor Surveillance System (BRFSS) data. Facebook pages of 34 SHDs were studied over a 200 day period, coding 2597 posts into 19 broad health communication categories. Mean number of Facebook posts per SHD was 76.4 (range 34-133); most frequent topic areas included healthy living (12%), communicable diseases (9%), vaccines and immunization (7%), emergency preparedness and response (7%), infant and child health (5%), smoking and tobacco use (5%), and miscellaneous (32%). Through web-based interactive graphics (Google motion charts), we contrasted Facebook posts with CDC's BRFSS data on adult nutrition and physical activity, vaccination, smoking, adolescent health and road traffic accidents. Our research finds an apparent disconnect between content provided on Facebook by SHDs and the health conditions that affect their populations. Acknowledging the severe limitations in funding and human resources faced by the SHDs, our research attempts to present the factual situation in embracing a vastly popular social media platform for health communication. We believe there is a need for research exploring methods to balance the demands and resources.

Antiviral Potential of Cathelicidins

The global burden of morbidity and mortality arising from viral infections is high; however, the development of effective therapeutics has been slow. As our understanding of innate immunity has expanded over recent years, knowledge of natural host defenses against viral infections has started to offer potential for novel therapeutic strategies. An area of current research interest is in understanding the roles played by naturally occurring cationic host defense peptides, such as the cathelicidins, in these innate antiviral host defenses across different species. This research also has the potential to inform the design of novel synthetic antiviral peptide analogs and/or provide rationale for therapies aimed at boosting the natural production of these peptides. In this review, we will discuss our knowledge of the antiviral activities of cathelicidins, an important family of cationic host defense peptides, and consider the implications for novel antiviral therapeutic approaches.

Coinage metals (Cu, Ag and Au) in the synthesis of natural products

The chemistry of natural products has emerged as an extremely important area of contemporary research owing to their profound biological activities, complex molecular architecture with challenging stereochemistry and potential for use as medicine. Searches for efficient synthetic procedures for natural products, drugs, and materials with economical and ecological advantages are important areas of current research interest. This article describes the isolation, bioactivity and synthesis of several natural products and drugs by employing coinage metal-catalyzed/mediated reaction/strategy either in a sequential or a domino fashion. We have made an attempt to summarize the progress made in the area from 2008 onwards.

Bimodal fusion of low-level visual features and high-level semantic features for near-duplicate video clip detection

The detection of near-duplicate video clips (NDVCs) is an area of current research interest and intense development. Most NDVC detection methods represent video clips with a unique set of low-level visual features, typically describing color or texture information. However, low-level visual features are sensitive to transformations of the video content. Given the observation that transformations tend to preserve the semantic information conveyed by the video content, we propose a novel approach for identifying NDVCs, making use of both low-level visual features (this is, MPEG-7 visual features) and high-level semantic features (this is, 32 semantic concepts detected using trained classifiers). Experimental results obtained for the publicly available MUSCLE-VCD-2007 and TRECVID 2008 video sets show that bimodal fusion of visual and semantic features facilitates robust NDVC detection. In particular, the proposed method is able to identify NDVCs with a low missed detection rate (3% on average) and a low false alarm rate (2% on average). In addition, the combined use of visual and semantic features outperforms the separate use of either of them in terms of NDVC detection effectiveness. Further, we demonstrate that the effectiveness of the proposed method is on par with or better than the effectiveness of three state-of-the-art NDVC detection methods either making use of temporal ordinal measurement, features computed using the Scale-Invariant Feature Transform (SIFT), or bag-of-visual-words (BoVW). We also show that the influence of the effectiveness of semantic concept detection on the effectiveness of NDVC detection is limited, as long as the mean average precision (MAP) of the semantic concept detectors used is higher than 0.3. Finally, we illustrate that the computational complexity of our NDVC detection method is competitive with the computational complexity of the three aforementioned NDVC detection methods.