Research Topics Research Articles

Coal-Hosted Al-Ga-Li-REE Deposits in China: A Review

Investigation of the critical metal elements in coal and coal-bearing strata has become one of the hottest research topics in coal geology and coal industry. Coal-hosted Ga-Al-Li-REE deposits have been discovered in the Jungar and Daqingshan Coalfields of Inner Mongolia, China. Gallium, Al, and Li in the Jungar coals have been successfully extracted and utilized. This paper reviews the discovery history of coal-hosted Ga-Al-Li-REE deposits, including contents, modes of occurrence, and enrichment origin of critical metals in each coal mine, including Heidaigou, Harewusu, and Guanbanwusu Mines in the Jungar Coalfield and the Adaohai Coal Mine in the Daqingshan Coalfield, as well as the recently reported Lao Sangou Mine. Gallium and Al in the coals investigated mainly occur in kaolinite, boehmite, diaspore, and gorceixite; REEs are mainly hosted by gorceixite and kaolinite; and Li is mainly hosted by cholorite. Gallium, Al, and REEs are mainly derived from the sediment-source region, i.e., weathered bauxite in the Benxi Formation. In addition, REE enrichment is also attributed to the intra-seam parting leaching by groundwater. Lithium enrichment in the coals is of hydrothermal fluid input. The content of Al2O3 and Ga in coal combustions (e.g., fly ash) is higher than 50% and ~100 µg/g, respectively; concentrations of Li in these coals also reach the cut-off grade for industrial recovery (for example, Li concentration in the Haerwusu coals is ~116 µg/g). Investigations of the content, distribution, and mineralization of critical elements in coal not only provide important references for the potential discovery of similar deposits but also offer significant coal geochemical and coal mineralogical evidence for revealing the geological genesis of coal seams, coal seam correlation, the formation and post-depositional modification of coal basins, regional geological evolution, and geological events. Meanwhile, such investigation also has an important practical significance for the economic circular development of the coal industry, environmental protection during coal utilization, and the security of critical metal resources.

Automated Academic Supervisor Allocation Using the C4.5 Decision Tree Algorithm: A Scalable Web-Based Solution

Efficient allocation of academic supervisors is a critical yet challenging process in higher education, often hindered by mismatches in expertise and uneven workload distribution. This study introduces a web-based recommendation system leveraging the C4.5 decision tree algorithm to address these issues. By assessing supervisor expertise, workload, and alignment with student research topics, the system generates data-driven, accurate recommendations. Developed using the Laravel framework and the waterfall development model, the system emphasizes scalability and modularity. Functional testing demonstrated a 96.7% accuracy rate for recommendations, while usability testing reflected high user satisfaction, with an average score of 92% for ease of use and relevance. These results underscore the system’s effectiveness in optimizing supervisor assignments, enhancing administrative efficiency, and providing a scalable solution for educational institutions. Future work will focus on integrating diverse data sources and AI-driven features to further improve adaptability and responsiveness to evolving academic demands.

Similar but Different Three Major Traditional Medicines in East Asia: A Bibliometric Analysis.

Traditional medicine (TM) has played a key role in the health care system of East Asian countries, including China, Japan and South Korea. This bibliometric study analyzes the recent research status of these three TMs, including traditional Chinese medicine (TCM), traditional Korean medicine (TKM), and Kampo medicine (KM). Research topics of studies published for recent 10 years (2014 to 2023), through a search on MEDLINE via PubMed, was analyzed. Medical Subject Headings were used to distinguish between the three TMs researches. Bibliographic information was analyzed through VOSViewer version. Total 10,151 documents were included: TCM studies (n=9,630); TKM studies (n=256); and KM studies (n=295). Comparing the three co-occurrence analysis maps, TCM studies generally overwhelm the quantitative scale of TKM and KM studies. In the trend of the latest research of TCM, not only corona virus disease 2019 (COVID-19), but also clinical research topics such as gastrointestinal microbiome and diabetes mellitus have emerged, with in silico research approaches being actively applied. In the case of TKM, obesity and cooperative treatment with Western medicine are gaining attention. In KM, COVID-19 and Scutellaria baicalensis were recent research focuses. Unique features that distinguished from the other two TM research trends included 'gut microbiota', 'diabetes mellitus', 'clinical trials', 'disease models', and 'quality control' in the TCM map; 'prospective studies', 'cell line, tumor', and 'panax' in the TKM map; and 'aged, 80 and over', 'retrospective studies', 'glycyrrhiza', 'panax', and 'paeonia' in the KM map. Also, some quantitative and qualitative differences were found in author co-operation maps in each TM. This analysis revealed that there were clear quantitative and qualitative differences among TCM, TKM, and KM. Although these medicines have a common root, they may have become distinct due to factors such as the size of research funds, cultural differences, and the medical licensing system.

State of the art on biodegradability of bio-based plastics containing polylactic acid

Bio-based plastics represent an opportunity to reduce the impact of petroleum-based plastics on the environment, leading to harmful effects on both terrestrial and marine ecosystems. Nevertheless, the plant origin of bio-based plastics does not necessarily imply better management of their end of life. However, when recycling is impossible, the biological degradation of bio-based plastics would be an effective method to reduce their environmental impact. Polylactic acid (PLA) is one of the most produced biopolymers currently among the bio-based plastics already developed for several years. Thus, the objective of this article is to provide a state of the art on the biodegradation of bio-based plastics based on PLA. In particular, the microorganisms catalyzing the different biochemical reactions and the main biodegradation mechanisms are reviewed according to aerobic and anaerobic conditions. Moreover, different microorganisms involved in the degradation of PLA are summarized. Furthermore, a special attention is paid to the analytical methods to evaluate the biodegradation of polylactic acid and to the different existing biodegradation test methods, because this subject has rarely been reviewed in the literature. In the end, several promising topics for the future research are proposed, such as enzyme engineering technology as a recently emerging method for PLA degradation and a new common testing method to collect as much data as possible on the biodegradability to compare different studies.

Emerging Themes, Leaders, and Collaboration in Library and Information Science Research

Introduction: This study uses bibliometric methods to evaluate research articles within the library and information science (LIS) domain. The focus is to uncover trends and patterns in social network analysis related to LIS, particularly examining research collaborations and content within highly cited articles. By analyzing these aspects, the study seeks to identify influential authors, prominent research themes, and key contributors in the LIS field. Methods: A dataset of 14,517 articles published between 1954 and 2023 was extracted from the Scopus database for bibliometric analysis. The study concentrated on publications in the LIS domain, focusing on the journal Library Philosophy and Practice. Multiple Correspondence Analysis (MCA) was used to identify clusters within the research field, while content analysis was performed to determine prevalent topics and disciplinary influences within the articles. Results: The analysis revealed that China is home to many of the most influential authors in the LIS domain, with the United States, China, and the United Kingdom identified as the top contributing countries to LIS research. Common research themes include information science, bibliometrics, academic libraries, information literacy, and LIS education. Two main clusters emerged from the MCA: one focused on information-related concepts and the other on bibliometrics and scholarly communication. Content analysis indicated a significant presence of topics from physics, computer science, and information technology within LIS research. Conclusions: This study highlights key trends and patterns in LIS research, with academic libraries, information literacy, LIS education, and librarians' roles identified as critical areas for future exploration. Expanding databases and refining keyword searches are recommended to enhance knowledge dissemination and educational adaptability in the LIS field. The findings aim to support LIS researchers, facilitate research planning, and promote global interinstitutional cooperation

Computer-based assessment research trends and future directions: A bibliometric analysis

Computer-based assessment (CBA) is a top-rated tool for conducting assessments, mapping learning outcomes, and selecting new candidates. Research that examines the development and use of CBA is also increasing from year to year, so without bibliometric analysis, it would be quite challenging to keep up with all of these studies. This study aims to clarify the status and world trends in CBA research around the world. Bibliometric analysis was performed using bibliometrix based on 2,020 articles published in Scopus from 1982 to 2023 (data update time on 5 January 2024). This bibliometric analysis identified the state of research publications, scientific productivity, the network state of research publications, and CBA research priorities and directions going forward. Results show that research on CBA has grown rapidly, with the highest number of publications in 2022, with the USA being the most productive country. Greiff is the most prolific and Wise is the most impactful author. The journal with the highest impact is “Computers and Education”. These findings also indicate that research themes that show research potential due to their high relevance to CBA but are still little studied are those related to “regression analysis”, “automation”, “information technology”, “assessment”, “motivation”, “quality control”, “problem solving”, “online systems”, “decision making”, and “teaching”.

Blockchain in resilient and leagile supply chains: Research themes and opportunities

Blockchain in resilient and leagile supply chains: Research themes and opportunities

SoK: (Un)usable Privacy: the Lack of Overlap between Privacy-Aware Sensing and Usable Privacy Research

As the number of smart devices increases in our lives, the data they collect to perform valuable tasks, such as voice assistant requests, comes at the cost of user privacy. To mitigate their privacy impact, emerging usable privacy-aware sensing (UPAS) research has relied on cross-disciplinary approaches that extend past the core focus of broader academic research communities, such as Security & Privacy or Human-Computer Interaction. These works incorporate privacy design principles, whereby systems include safeguards by combining usable privacy (UP) with privacy-aware sensing (PAS) design to protect users' privacy. To better understand this emerging area of research, we conducted a mixed qualitative and quantitative Systematization of Knowledge (SoK). With a thorough review of pertinent literature, resulting in 114 selected works (reduced from 10,122 across 12 venues), we found that, despite the similarity of these works, many are dispersed across multiple communities, utilize community-specific jargon and keywords, and minimally overlap in design and evaluation approaches, potentially hindering cross-pollination across communities and thereby slowing the growth of this emerging research area. Thus, these factors helped reveal a research gap in this space. We use these findings to present four research themes and provide community and design recommendations to encourage cross-disciplinary UPAS research.

Mapping the research landscape on temporomandibular disorders and orthodontics treatment: A bibliometric analysis.

Temporomandibular disorders (TMD) and orthodontic treatment are significant areas within dental and maxillofacial research. However, comprehensive bibliometric analyses highlighting global trends and collaboration networks are lacking. This study aims to systematically map the publication landscape, identify prevailing research trends, highlight influential authors and institutions, and illuminate emerging topics in TMD and orthodontic research. Publications concerning TMD and orthodontic treatment from 2000 to 2024 were retrieved from the Web of Science Core Collection. A bibliometric analysis was conducted using VOSviewer, CiteSpace, and R to evaluate co-authorship networks, institutional collaborations, and keyword co-occurrence patterns. A total of 871 publications were identified, with an average annual growth rate of 4.4 %. Contributions came from 47 countries and involved 3,463 authors, with the USA and Japan leading in citations. Notable institutions included Kyung Hee University and Baylor University. Key journals included the American Journal of Orthodontics and Dentofacial Orthopedics and the Journal of Oral and Maxillofacial Surgery. Frequently occurring keywords were "temporomandibular-joint," "orthognathic surgery," and "orthodontic treatment," indicating essential research foci. Emerging trends highlighted advancements in surgical techniques and technological innovations in orthodontics. This bibliometric analysis offers valuable insights into global research trends and hotspots in TMD and orthodontic treatment, emphasizing the collaborative nature of the field and illuminating emerging areas for future investigation.

Bibliometric Analysis of Alzheimer's Disease and Depression.

The link between Alzheimer's disease and depression has been confirmed by clinical and epidemiological research. Therefore, our study examined the literary landscape and prevalent themes in depression-related research works on Alzheimer's disease through bibliometric analysis. Relevant literature was identified from the Web of Science core collection. Bibliometric parameters were extracted, and the major contributors were defined in terms of countries, institutions, authors, and articles using Microsoft Excel 2019 and VOSviewer. VOSviewer and CiteSpace were employed to visualize the scientific networks and seminal topics. The analysis of literature utilised 10,553 articles published from 1991 until 2023. The three countries or regions with the most publications were spread across the United States, China, and England. The University of Toronto and the University of Pittsburgh were the major contributors to the institutions. Lyketsos, Constantine G., Cummings, JL were found to make outstanding contributions. Journal of Alzheimer's Disease was identified as the most productive journal. Furthermore, "Alzheimer's", "depression", "dementia", and "mild cognitive decline" were the main topics of discussion during this period. Data were searched from a single database to become compatible with VOSviewer and CiteSpace, leading to a selection bias. Manuscripts in English were considered, leading to a language bias. Articles on "Alzheimer's" and "depression" displayed an upward trend. The prevalent themes addressed were the mechanisms of depression-associated Alzheimer's disease, the identification of depression and cognitive decline in the early stages of Alzheimer's, alleviating depression and improving life quality in Alzheimer's patients and their caregivers, and diagnosing and treating neuropsychiatric symptoms in Alzheimer. Future research on these hot topics would promote understanding in this field.

Research focus and theme evolution on enhanced external counterpulsation: A bibliometric analysis

Research focus and theme evolution on enhanced external counterpulsation: A bibliometric analysis

Quantum-inspired metaheuristic algorithms for Industry 4.0: A scientometric analysis

Quantum-inspired Metaheuristic algorithms have redefined non-deterministic polynomial time hard optimization challenges by leveraging quantum mechanics principles. These algorithms herald a broad range of application scenarios in Industry 4.0 and offer feasible time solutions for complex, large-scale industrial landscapes. The potential benefits provided by the quantum-inspired metaheuristic algorithms have accelerated the scientific advancements in this domain. Consequently, the present research contributes to the existing knowledge base by presenting the intellectual landscape through scientometric and systematic literature analysis. The study is conducted on the dataset derived from the Scopus and Web of Science databases, covering 2001 to 2023. The study employs co-citation and co-occurrence analyses to discern prominent research topics, emerging research frontiers, significant authors, and the most collaborating countries. The research findings underscore that electric vehicles, energy efficiency, and combinatorial optimization are prominent research topics, while carbon emission, resource management, and path planning are burgeoning areas of exploration in this knowledge domain. The intricate and entangled network linkage determines that the research community in this domain fosters a dynamic and synergistic relationship. Overall, the pivotal insights and the research challenges articulated in this article offer valuable insights to researchers and the academic community, aiding in discerning the intellectual terrain and emerging research patterns in quantum-inspired metaheuristic algorithms. This, in turn, fosters the advancement of innovation and facilitates well-informed decision-making within this evolving research paradigm.

Achieving ecological sustainability in European countries: Does low carbon energy lead to a carbon neutrality pathway?

The European Union (EU) recently intensified its focus on Sustainable Development Goals (SDGs) to achieve its net-zero emissions target. Thus, climate action and access to clean energy have become prominent research topics. Previous studies have predominantly explored the individual impacts of nuclear and renewable energy on EU economies within the environmental Kuznets curve (EKC) hypothesis. Departing from this conventional approach, the present study examines the effect of low-carbon energy consumption (LEC) on environmental quality while controlling for ecological influences of natural resource rents (NAT) and foreign direct investment (FDI). The study adopts the CS-ARDL approach to analyze annual data from 1996 to 2022. This analysis is framed within a novel framework: the load capacity curve (LCC) hypothesis. The empirical assessment reveals the following findings: i) The LCC hypothesis is validated for EU economies, indicating that these countries are on a path towards achieving their net-zero emissions target. ii) LEC positively impacts the ecosystem. iii) FDI and NAT are found to degrade environmental quality. Given these findings, EU economies should continue to increase their LEC within the total energy mix. Environmental sustainability can be further supported by implementing green policies aimed at regulating NAT and FDI. In conclusion, this study offers policies that will put the EU on the zero-emission path within the framework of SDGs.

Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends

Numerous studies have highlighted the significance of artificial intelligence (AI) in breast cancer diagnosis. However, systematic reviews of AI applications in this field often lack cohesion, with each study adopting a unique approach. The aim of this study is to provide a detailed examination of AI's role in breast cancer diagnosis through citation analysis, helping to categorize the key areas that attract academic attention. It also includes a thematic analysis to identify the specific research topics within each category. A total of 30,200 studies related to breast cancer and AI, published between 2015 and 2024, were sourced from databases such as IEEE, Scopus, PubMed, Springer, and Google Scholar. After applying inclusion and exclusion criteria, 32 relevant studies were identified. Most of these studies utilized classification models for breast cancer prediction, with high accuracy being the most commonly reported performance metric. Convolutional Neural Networks (CNN) emerged as the preferred model in many studies. The findings indicate that both the quantity and quality of AI-based algorithms in breast cancer diagnosis are increases in the given years. AI is increasingly seen as a complement to healthcare sector and clinical expertise, with the target of enhancing the accessibility and affordability of quality healthcare worldwide.

The vehicle routing problem as applied to residential solid waste collection operations: Systematic literature review

The accelerated growth of cities, population increase and economic development have leveraged waste generation globally. This trend is expected to continue, with a significant increase projected in the coming years. Therefore, efficient waste management has become a crucial concern for local, national and international authorities. Transportation plays a key role in waste collection and disposal, being directly related to traffic congestion, fuel consumption and environmental pollution. Despite the existing studies on household waste collection, there is a gap in the literature regarding routing for residential waste collection in medium-sized cities, especially in emerging and frontier developing countries. Therefore, this study seeks through the science tree metaphor and PRISMA methodology, to find studies focused on the vehicle routing problem in waste collection operations, considering aspects such as Modeling approaches and solution techniques, applied Vehicle Routing Problems variants, objective functions, decision variables and constraints, applications in real environments, applied algorithms, and studies considering uncertainty and real conditions. A methodological outline of Vehicle Routing Problems in waste collection operations is presented, where central research topics are identified such as processes developed with Geographic Information System and their integration with exact methods, time windows, multi-objective capacitated vehicle routing problems, the application of stochastic models consider the uncertainty in waste collection, which has allowed including future prediction and optimization as prediction models, based on neural networks, to foresee uncertain conditions of the operations. This article analyzes the evolution in the optimization of municipal solid waste collection routes since 1964, highlighting the transition from iterative models to advanced technologies and multi-objective approaches. The importance of tools such as 3D Geographic Information System and heuristic/metaheuristic algorithms in improving planning and efficiency, despite limitations in the face of uncertainty, is emphasized. The systematic review shows a trend towards sustainable and efficient solutions, indicating future directions for research in urban waste management.

The Role of Algal Symbiont Growth in Driving the Thermal Responses of a Widespread Photosymbiotic Ciliate

Understanding the impacts of climate change on ecosystems remains a major contemporary research theme in the biosciences. However, the role of mixotrophs, organisms that combine autotrophic and heterotrophic processes to grow and reproduce, as important determinants of the thermal responses of ecosystems and their services is largely unresolved. In particular, photosymbioses, ubiquitous mixotrophic associations in which autotrophs reside within heterotrophic hosts, are known to play key roles in global biodiversity and carbon cycling. While specific impacts, particularly in the coral-zooxanthellae interaction, are thoroughly documented, ecologists lack a general theoretical framework describing the impacts of temperature change on photosymbiotic interactions. Here, I apply principles of the metabolic theory of ecology to assess the metabolic basis of the temperature-induced disruption of photosymbiosis in the microbial <i>Paramecium bursaria - Chlorella</i> spp. association. In contrast to the general prediction that net autotrophy should decrease with temperature, this microbial photosymbiosis harboured larger algal symbiont populations and consumed fewer prey with warming, suggestive of increased net autotrophy with warming - a pattern that held across strains isolated from three different continents. This observation appeared to be a simple consequence of the response of symbiont growth rate. I conclude that a metabolic framework for photosymbiosis may prove insightful, but the ecological dynamics of the associations (e.g. mixotrophic strategy) must be considered in tandem.

Transition-Metal Free Approaches to Access N-Heterocycles and Valuable Intermediates from Aryldiazonium Salts

Efforts for the discovery of novel and efficient synthetic methods to achieve valuable N-fused heterocycles is an important research theme of organic chemistry for several decades. Owing to the importance...

The development of multifunctional biochar with NiFe2O4 for the adsorption of Cd (II) from water systems: The kinetics, thermodynamics, and regeneration.

High concentrations of Cd (II) in wastewater have been reported several times which attracted top research attention to mitigate the pollution impacts of the contaminant. Therefore, this study aimed to develop a Zn-doped NiFe2O4- pinecone biochar composite (ZNiF@PB) for the adsorption of Cd (II) from wastewater. FTIR confirmed immobilization of PB on the surface of ZNiF by the presence of C = O at 1638cm-1, COOH at 1385cm-1, C-O at 1009cm-1 and Fe-O at 756cm-1. Similarly, XRD determined the crystallite structure of the adsorbents where the ZNiF crystallite size of 43nm was obtained while the particle size of ZNiF@PB was found to be 38nm. These XRD results agreed with those values obtained from TEM images showing ZNiF and ZNiF@PB had a spherical shape with similar particle sizes. On the other hand, the surface areas of ZNiF, PB, and ZNiF@PB were found to be 78.4m2/g, 125m2/g, and 104m2/g, respectively. These high surface areas have a huge potential to enhance Cd removal. With these adsorbents, the maximum Cd (II) adsorption of 96% was recorded at the optimum experimental condition of adsorbent dosage 0.5g/50mL, solution pH 6, initial Cd (II) concentration 100mg/L, and contact time 120min. Practical adsorption kinetics data were well described by the pseudo-second order model whereas the adsorption isotherm was a perfect fit to the Langmuir isothermal model implying the adsorption process to be a monolayer with mainly a chemically bonded mechanism. In conclusion, this adsorbent is efficient for the adsorption of Cd (II) from wastewater and has also a huge potential to be applied for industrial-scale water purification.

Clues for improvement of research in objective structured clinical examination

ABSTRACT While objective clinical structured examination (OSCE) is a worldwide recognized and effective method to assess clinical skills of undergraduate medical students, the latest Ottawa conference on the assessment of competences raised vigorous debates regarding the future and innovations of OSCE. This study aimed to provide a comprehensive view of the global research activity on OSCE over the past decades and to identify clues for its improvement. We performed a bibliometric and scientometric analysis of OSCE papers published until March 2024. We included a description of the overall scientific productivity, as well as an unsupervised analysis of the main topics and the international scientific collaborations. A total of 3,224 items were identified from the Scopus database. There was a sudden spike in publications, especially related to virtual/remote OSCE, from 2020 to 2024. We identified leading journals and countries in terms of number of publications and citations. A co-occurrence term network identified three main clusters corresponding to different topics of research in OSCE. Two connected clusters related to OSCE performance and reliability, and a third cluster on student’s experience, mental health (anxiety), and perception with few connections to the two previous clusters. Finally, the United States, the United Kingdom, and Canada were identified as leading countries in terms of scientific publications and collaborations in an international scientific network involving other European countries (the Netherlands, Belgium, Italy) as well as Saudi Arabia and Australia, and revealed the lack of important collaboration with Asian countries. Various avenues for improving OSCE research have been identified: i) developing remote OSCE with comparative studies between live and remote OSCE and issuing international recommendations for sharing remote OSCE between universities and countries; ii) fostering international collaborative studies with the support of key collaborating countries; iii) investigating the relationships between student performance and anxiety.

BIBLIOMETRIC ANALYSIS OF INNOVATION IN THE SOCIAL ECONOMY. ITS THEORETICAL IMPLICATIONS

The present research develops a bibliometric analysis of innovation in the social economy, the study provides a comprehensive vision of the current state of the field and highlights the most relevant publications, authors, sources and research topics, taking as its general objective to analyze the behavior and advances in the literature about innovation in the social economy through a bibliometric analysis, in this way Scopus and Web of Science (WoS) were considered as data sources, forming a database of 304 registered articles, of which 111 belong to Scopus and 193 Web of Science (WoS). In the processing and representation of data, the Bibliometrix and VoSviewer programs were used, which highlights information on trends, citation analysis, H index, analysis of co-occurrences, keywords, affiliations on innovation research in the social economy. The growing scientific production underlines the growing importance of the social economy as a driver of sustainable economic development. Geographic distribution, thematic trends, and identification of influential contributors contribute to future research and practical efforts within this field.