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Bacterial cellulose (BC) is a versatile and sustainable biomaterial with applications across various industries, including fashion and medicine. This study presents a bibliometric and thematic analysis of the research landscape surrounding BC production with the use of alternative substrates. The bibliometric review analyzed 198 papers selected via Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), identifying 62 distinct academic disciplines, 906 keywords categorized into 6 clusters, and 98 journals contributing to the field. Of the dataset, approximately 57.6% of the papers were published in Q1 journals, 18.7% in Q2, 12.1% in Q3, 4.0% in Q4, 3.0% were not Q-ranked, and 4.5% were conference papers. The top 12 journals were all Q1 and held 42.9% of the dataset. Citation records, author networks, and keyword trends were examined using Publish or Perish 8 for citation analysis and VOSViewer for Author Network Analysis and keyword clustering. Thematic analysis identified key applications of BC in health and medicine, industry and materials, food and nutrition, electronics and energy, cosmetics and personal care, and environmental and green technology. Advantages such as high mechanical strength, biocompatibility, and biodegradability were frequently highlighted, making BC a preferred material in sustainability-driven industries. However, high production costs and yield/time limitations remain barriers to commercial adoption. The findings underscore the need for innovative solutions and interdisciplinary collaboration to enhance BC's commercial viability and sustainability. [ABSTRACT FROM AUTHOR]

This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into the journal’s evolution at the intersection of neuroscience and computational science. Using advanced tools such as VOS viewer and methodologies like co-citation analysis, bibliographic coupling, and keyword co-occurrence, we examine trends in publication, citation patterns, and the journal’s influence. Our analysis reveals enduring research themes like neuroimaging, data sharing, machine learning, and functional connectivity, which form the core of Neuroinformatics. These themes highlight the journal’s role in addressing key challenges in neuroscience through computational methods. Emerging topics like deep learning, neuron reconstruction, and reproducibility further showcase the journal’s responsiveness to technological advances. We also track the journal’s rising impact, marked by a substantial growth in publications and citations, especially over the last decade. This growth underscores the relevance of computational approaches in neuroscience and the high-quality research the journal attracts. Key bibliometric indicators, such as publication counts, citation analysis, and the h-index, spotlight contributions from leading authors, papers, and institutions worldwide, particularly from the USA, China, and Europe. These metrics provide a clear view of the scientific landscape and collaboration patterns driving progress. This analysis not only celebrates Neuroinformatics’s rich history but also offers strategic insights for future research, ensuring the journal remains a leader in innovation and advances both neuroscience and computational science. [ABSTRACT FROM AUTHOR]

Mathematical Methods of Operations Research is a scholarly journal that publishes high-quality contributions related to mathematics, statistics, and computer science, focusing specifically on operations research. It was established in 1956 and reached Volume 100 in 2024. To mark this significant milestone, this study aims to identify and elucidate the main patterns in the journal's publication and citation structure, highly cited documents, prolific authors, universities and countries, and popular keywords and topics. To achieve this, the paper collects all publications from 1956 to 2023 from the Scopus database, and Web of Science (WoS) Core Collection from 1997 to 2023. Their bibliographic information is analysed using various bibliometric indicators. Additionally, the study creates a visual representation of the bibliographic data using the Visualization of Similarities (VOS) viewer software. This approach encompasses different bibliometric techniques, such as bibliographic coupling, co-citation, and co-occurrence of keywords. The findings reveal a significant growth in the journal over the past few years and its international impact with contributions from various parts of the world. The most productive researchers are based in Germany and Netherlands. [ABSTRACT FROM AUTHOR]

The spread of antibiotic resistance is a global threat, causing elevated death rates and economic costs. A growing number of studies have focused on the removal of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in environmental settings. However, summaries and reviews of removal techniques are limited. This study examined publications on ARB and ARGs removal from 1998 to 2023 through a bibliometric approach based on the Web of Science database. Research progress during the past 26 years was analyzed by collecting annual publications, countries, journals and keywords. The number of articles related to the removal of ARB and ARGs has increased annually. The main types of ARB and ARGs, their environmental milieus and the most commonly studied removal techniques were summarized by keyword clustering. The results revealed that tetracycline- and sulfonamide-resistant bacteria are the ARB of greatest concern; that sul1, sul2, and tetA are the most frequently studied ARGs; and that municipal sewage and drinking water are the most studied ARB and ARGs transmission sites. For treatment techniques, adsorption technology is the most widely studied, and the selection of adsorption materials is particularly important, with nanomaterials and biomodified materials having great prospects for development. The combination of membrane filtration with advanced oxidation treatment or biodegradation technology is the most promising technology in this field. Our findings can inform future efforts to further reduce the distribution risks of antibiotic resistance and improve removal techniques. [ABSTRACT FROM AUTHOR]

The project aimed to conduct an up-to-date and comprehensive bibliometric analysis of robotic surgery to provide a detailed and holistic understanding of the field. Three strategies were employed in the data analysis i.e. search terms were explored in (A) the title, abstract, and keywords and (B) only in the title of the documents. In 3rd part we analyzed the top 100 most cited papers. Vosviewer and R Studio were utilized for detailed bibliometric and network analyses. Strategy one identified 38,469 publications, and strategy two identified 6451 publications from 2001 to 2023. The top authors, universities, countries, sponsors, and sources based on the number of publications were identified for both strategies. The top 100 most cited papers were analyzed, providing the annual number of publications and various citation metrics. Top authors (by number of publications, total citations, h-index, g-index, and m-index), universities, and countries within these highly cited papers, along with their co-authorship networks and dynamics, were examined. Co-words analysis of the top 100 most cited papers revealed the primary focus of these documents across 25 categories. This comprehensive bibliometric analysis of robotic surgery highlighted significant contributions and collaborations in the field, emphasizing the importance of global and collaborative efforts in advancing robotic surgery research. [ABSTRACT FROM AUTHOR]

The recent advancements in geospatial technologies together with the availability of geographic information system (GIS) software and tools with enhanced capabilities in the recent decades have shifted the use of this technology from an early adoption phase to a more mature phase. The application of GIS technology has found new avenues in solving complex socio-economic engineering problems from disaster management and mitigation planning to environmental modelling for sustainable development of cities. In order to reveal the recent developments in use of this technology in different scientific disciplines, a new methodology is developed by considering the semantics to formalize the engineering knowledge in GIS applications. This formalized knowledge is then used to reveal emerging macrotrends, challenges and directions of future research in two different semantic aspects, the "the application" aspect and "the domain" aspect. The review of "the application" aspect shows that a significant change is seen in the applications of GIS, a shift towards complexity in analyses for engineering and building systems for management and decision-making. Further, a systematic keyword analysis on "the domain" aspect is used to develop sub-domains ontologies and a review of literature on each of these sub-domains details the core developments in GIS and some emerging domains of application. Semantic inferences are drawn based on domain-specific requirements, problems and challenges, and outcomes to improve the domain knowledge. The GIS domain knowledge can further be used in developing computational methods using ontologies, formalization of logic and engineering the future GIS systems. The review interestingly finds a growing trend in using GIS as a Decision Support System (DSS), to model domain-specific contexts, semantically integrate data and develop expert intelligence. [ABSTRACT FROM AUTHOR]

There is an absent systematic analysis or review that has been conducted to clarify the topic of nomenclature history and terms misuse about Chiari malformations (CMs). We reviewed all reports on terms coined for CMs for rational use and provided their etymology and future development. All literature on the nomenclature of CMs was retrieved and extracted into core terms. Subsequently, keyword analysis, preceding and predicting (2023–2025) compound annual growth rate (CAGR) of each core term, was calculated using a mathematical formula and autoregressive integrated moving average model in Python. Totally 64,527 CM term usage was identified. Of these, 57 original terms were collected and then extracted into 24 core-terms. Seventeen terms have their own featured author keywords, while seven terms are homologous. The preceding CAGR of 24 terms showed significant growth in use for 18 terms, while 13, three, three, and five terms may show sustained growth, remain stable, decline, and rare in usage, respectively, in the future. Previously, owing to intricate nomenclature, Chiari terms were frequently misused, and numerous seemingly novel but worthless even improper terms have emerged. For a very basic neuropathological phenomenon tonsillar herniation by multiple etiology, a mechanism-based nosology seems to be more conducive to future communication than an umbrella eponym. However, a good nomenclature also should encapsulate all characteristics of this condition, but this is lacking in current CM research, as the pathophysiological mechanisms are not elucidated for the majority of CMs. [ABSTRACT FROM AUTHOR]

In the context of climate change, the decline in biodiversity and current socio-economic trends, which support the processes of industrialization or abandonment of agricultural land, have generated numerous concepts and trends to encourage sustainable agriculture and a better connection between agricultural activities and nature. Concepts such as traditional agricultural landscapes (TALs), high nature value farmlands (HNV farmlands), organic farming, and agricultural heritage systems are associated with the maintenance or sustainable development of ecological agricultural systems, with an emphasis on providing ecosystem services, biodiversity conservation, and heritage protection. This study aimed to review the concept associated with traditional agricultural landscapes and identify the evolution and trends in research on traditional agricultural landscapes. We hypothesize that our findings will advance our understanding of potential alternative methods of sustainable agricultural development associated with traditional agricultural landscapes. We analyzed articles published in international journals, linked to the Web of Science database, related to traditional agricultural landscapes between 1992 and 2021 (n = 230). Keyword analysis was used to identify trends and developments in traditional agricultural landscape research. We chose a non-metric multidimensional scaling analysis (NMDS) because we intended to examine how the relationships between different concepts are established. Our results illustrate the most common research topics related to traditional agricultural landscapes and alternative development pathways associated with sustainable agriculture. The terms used are promoted by the journal Biodiversity and Conservation, which contains articles addressing the top topics of interest and relevance to the research topic of this paper. The NMDS analysis captures the complexity of the topic addressed through the resulting areas of intersection. Our findings suggest that several topics of current interest are addressed in the research on traditional agricultural landscapes. The interest of the scientific community in traditional agricultural landscape research shows a growing trend. The adaptation of traditional agriculture to the current context is a particularly important subject to be addressed in future research. [ABSTRACT FROM AUTHOR]

Vladimir V. Putin has banned the use of the word 'war' to refer to the conflict in Ukraine. While one's choice of words is deliberate and conscious, grammatical categories are obligatory and pivotal to signaling the roles notions have in a discourse. Over- and underrepresentation of grammatical cases can be identified by Keymorph Analysis, which measures deviations from corpus norms analogously to Keyword Analysis. This first application of Keymorph Analysis to Russian data compares the use of grammatical case for the nouns meaning 'Russia', 'Ukraine', and 'NATO' in Putin's speeches in the period leading up to and immediately after the invasion of Ukraine. Our analysis reveals a narrative in which Putin depicts Russia as a dynamic, agentive, foregrounded actor, a reliable partner for collaboration, but also the victim of unfair geopolitical maneuvers. Ukraine, by contrast, is dehumanized as relatively static and backgrounded, often merely a territorial location rather than a state. NATO appears primarily as the label for an untrustworthy organization and a destination for Ukraine. [ABSTRACT FROM AUTHOR]

This study examines the evolution of digital transformation (DT) in the service industry through real-world application cases and articles published in journals that are related to DT. This research collected 2,897,024 papers published in Scopus journals from 1991 to March 4, 2022. From this population of papers, 2,683 were reclassified and finally, 1831 were chosen for examination. Among these papers, 437 DT-related articles that focused on service fields were analyzed through word cloud analysis of keywords. This paper delineated evolutionary processes of DT in service sectors by reviewing and comparing both the commonalities and differences between the practice (actual cases) and research (published journal articles) of DT. In addition, this research explored the key contributors of DT in the three stages of DT evolution in the service sector. The results of this study provide theoretical and practical implications that offer strategic insights for planning and implementing DT in the service industry. [ABSTRACT FROM AUTHOR]

The adoption of artificial intelligence (AI) systems is on the rise owing to their many benefits. This study conducted a bibliometric analysis to identify (1) how the literature on AI adoption has evolved over the past few years, (2) key themes associated with AI adoption in the literature, and (3) the gaps in the literature. To achieve these objectives, we utilised the Biblioshiny of R-package bibliometric analysis tool to analyse the AI adoption literature. A total of 91 articles were reviewed and analysed in this study. Four major themes were identified: AI, machine learning, the unified theory of acceptance and use of technology (UTAUT) model and the technology acceptance model (TAM). Using a content analysis of the identified themes, the study gained additional insight into the studies on AI adoption. Previous studies have been limited to specific industries and systems, and adoption theories like the UTAUT and TAM have also been utilised to a limited extent. Directions for future studies were provided. [ABSTRACT FROM AUTHOR]

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Biochar has caught great attention over the last decade, yielding a large number of publications in a broad range of disciplines. This scientometric study produces a combined qualitative–quantitative assessment of 10,000 publications recorded in the period 2005–2019 in the Web of Science (WoS) database, based on innovative methods and indicators, and focusing in particular on biochar production and valorization pathways. The cumulated number of publications was analyzed with power and logistic models, and the economic indicator CAGR (Compound Annual Growth Rate) for the estimation of future academic output. Mapping of the evolution of academic output revealed the worldwide diffusion of academic production toward many countries and continents. According to the analysis of collaboration networks of most productive countries, the development of academic output may be linked to high levels of international collaboration, as well as the diversity of these networks. Furthermore, the rise of academic output in the African continent may preclude an evolution toward a multipolar academic world. The average number of citations per paper at both author level (number of citations/number of papers) and journal level (total citations/total papers) was found to be a useful indicator of scientific productivity. In particular, academic journals with impact factors as low as 2.5–3 still ranked high on this criterion, revealing their high impact on academic research in the biochar sector. Finally, the analysis of targeted keywords co-occurrences emphasized the diversification of research on biochar applications, from its initial use as soil improver toward engineered biochar for versatile applications. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the topical areas and collaboration patterns of North Korean researchers. To conduct this study, bibliographic records of researchers affiliated with institutions in the city of Pyongyang for the years from 2002 to 2018 were retrieved from the Scopus database. The results showed disproportional levels of collaboration with China and the marginal level of research papers. North Korean researchers seemed to have worked on labor-intensive subject areas that have both civilian and military applications. The results implied that North Korea is only making modest levels of contributions to the global academic community. The increase in the number of international journal papers authored by North Korean researchers can be considered as a side benefit of maintaining a close relationship with China. The collaboration patterns of North Korea and China seemed to reflect China's engagement policy toward North Korea and the rapid rise of China's scientific research production in recent years. The results of this study should be taken into consideration in the design of national policies that tolerate or inhibit collaboration with North Korean researchers. [ABSTRACT FROM AUTHOR]

This paper aims to provide a comprehensive analysis of publication patterns on computational thinking (CT) over two recent periods (period I: 2006–2012; period II: 2013–2018). Based on keyword analysis, a total of 3798 (period I) and 7175 (period II) keywords were found. Derived from the content analysis, a research typology of two-period keywords was consolidated and framed according to its attributes, including background settings, domain-specific factors, and learning outcomes. Main findings show as follows: (1) Regarding the research background, students from secondary and higher education are the main participants; and computer science, mathematics, and engineering are the major subjects. (2) As the domain-specific factors, game and peer collaboration were found to be the main pedagogies, while web-based and face-to-face learning environments were almost equally referred to in CT research settings. However, compared with traditional command-based tools, Scratch, Lego, and Python were identified as the emerging visual-based programming languages. (3) Finally, the keywords related to learning outcomes were classified based on the Bloom's framework of three learning domains. First, knowledge and mental understanding are the main goals in the cognitive domain; motivation and attitude are the main tasks in the affective domain; and social and communication skills are the central outcomes in the training of psychomotor ability. Further discussions and research directions are provided. [ABSTRACT FROM AUTHOR]

Keyword analysis has been an important research theme in bibliometrics. The deduction of new valuable bibliometric indicators/approaches through keyword analysis is important for prompting the further development of this subject area. In this study, the following three bibliometric indicators/approaches were thus derived. Indicator K was derived using the ratio between the average unique keyword number and average keyword frequency of a discipline for quantitatively describing the discipline’s development stages highlighted by scientific-philosopher Kuhn. Next, the correlation matrix analysis was used after k-core filtration to quantitatively expose the detailed correlations between topics for a large network. Thirdly, indicators I (node betweenness divided by node degree) and C (clustering coefficient) were collectively introduced to predict potential growth keywords. Diverse topical evolutions were categorized into a strategic diagram according to the tendencies of I and C. With sustainable development as a case study, we verified that the three new bibliometric indicators/approaches work well and can realize many new concepts beyond the scope of available indicators or approaches. In summary, the present paper makes a renewed effort to promote the development of bibliometrics. We hope our work could catalyze the further studies from the communities in the scientometric fields. [ABSTRACT FROM AUTHOR]

Recent literature on smart cities stresses the role of digitization in tackling urban issues such as environmental degradation and poverty. The wicked nature of these issues gives rise to the need to understand the diverse perspectives of relevant stakeholder groups on smart cities. However, existing research that compares these perspectives tends to exclude the beliefs of those living in smart cities. Integrating these beliefs in smart city discourses is paramount to increase the likelihood that these systems will be accepted. With the view that the literature consumed by an audience will influence that audience’s perspectives, the main aim of this study is to compare and contrast the pertinent topics found in various types of literature on smart cities. Using an innovative approach of literature comparison, based on a semantic entity annotator and keyword analysis, this article extracts and compares topics in news media (for citizens), trade publications (for businesses), academic articles (for research organizations) and government reports (for governments). The findings suggest that citizens tend to be under-represented in discussions on smart cities and highlight those topics considered relevant only by smart city citizens. Increased understanding in this area can help guide discussions and policies that are relevant for all stakeholders. [ABSTRACT FROM AUTHOR]

As an increasing number of scientific literature dataset are open access, more attention has gravitated to keyword analysis in many scientific fields. Traditional keyword analyses include the frequency based and the network based methods, both providing efficient mining techniques for identifying the representative keywords. The semantic meanings behind the keywords are important for understanding the research content. However, traditional keyword analysis methods pay scant attention to semantic meanings; the network based or frequency based methods as traditionally used, present limited semantic associations among the keywords. Moreover, the ways in which the semantic meanings behind the keywords are associated to the citations are not clear. Thus, we use the Google Word2Vec model to build word vectors and reduce them to a two-dimensional plane in a Voronoi diagram using the t-SNE algorithm, to link meanings with citations. The distance between semantic meanings of keywords in two-dimensional plane are similar to distances in geographical space, thus we introduce a geographic metaphor, “Ghost City” to describe the relationship between semantics and citations for hot topics that have recently become not so hot. Along with “Ghost City” zones, “Always Hot”, “Newly Emerging Hot”, and “Always Silent” areas are classified and mapped, describing the spatial heterogeneity and homogeneity of the semantic distribution of keywords cited in a domain database. Using a collection of “geographical natural hazard” literature datasets, we demonstrate that the proposed method and classification scheme can efficiently provide a unique viewpoint for interpreting the interaction between semantics and the citations, as “Ghost City”, “Always Hot”, “Newly Emerging Hot”, and “Always Silent” areas. [ABSTRACT FROM AUTHOR]

Over the last decades, CSCW research has undergone significant structural changes and has grown steadily with manifested differences from other fields in terms of theory building, methodology, and socio-technicality. This paper provides a quantitative assessment of the scientific literature for mapping the intellectual structure of CSCW research and its scientific development over a 15-year period (2001-2015). A total of 1713 publications were subjected to examination in order to draw statistics and depict dynamic changes to shed new light upon the growth, spread, and collaboration of CSCW devoted outlets. Overall, our study characterizes top (cited and downloaded) papers, citation patterns, prominent authors and institutions, demographics, collaboration patterns, most frequent topic clusters and keywords, and social mentions by country, discipline, and professional status. The results highlight some areas of improvement for the field and a lot of well-established topics which are changing gradually with impact on citations and downloads. Statistical models reveal that the field is predominantly influenced by fundamental and highly recognized scientists and papers. A small number of papers without citations, the growth of the number of papers by year, and an average number of more than 39 citations per paper in all venues ensure the field a healthy and evolving nature. We discuss the implications of these findings in terms of the influence of CSCW on the larger field of HCI. [ABSTRACT FROM AUTHOR]

This study aims to gain insights into emerging research fields in the area of marketing and tourism. It provides support for the use of quantitative techniques to facilitate content analysis. The authors present a longitudinal latent semantic analysis of keywords. The proposed method is illustrated by two different examples: a scholarly journal (International Marketing Review) and conference proceedings (ENTER eTourism Conference). The methodology reveals an understanding of the current state of the art of marketing research and e-tourism by identifying neglected, popular or upcoming thematic research foci. The outcomes are compared with former results generated by traditional content analysis techniques. Findings confirm that the proposed methodology has the potential to complement qualitative content analysis, as the semantic analysis produces similar outcomes to qualitative content analysis to some extent. This paper reviews a journal's content over a period of nearly three decades. The authors argue that the suggested methodology facilitates the analysis dramatically and can thus be simply applied on a regular basis in order to monitor topic development within a specific research domain. [ABSTRACT FROM AUTHOR]

Today, governments tackle the science of science policy with quantitative analysis, especially in the USA. This trend promotes the use of objective data by decision-makers in the fields of innovation policy and technology management. These decision-makers seek more reliable research and development at an earlier stage to contribute to the national economy. Under this worldwide trend, there is a high demand for quantitative analysis accompanying complex IT skills from governmental officers and non-IT researchers. The purpose of this research is to extract the destinations of currently growing fields of science by means of tracking citations and to capture signs of state of the art studies. The analysis data are bibliographic data of academic papers retrieved from Web of Science. I provide results for 16 growing topics as of July 2016. I deepen the understanding of the 'convolutional neural network' among a lot of topics, and found future application candidates. I provide a methodology to discover future seeds of research and development. Additionally, the analysis system used in this research automatically publishes its results, and always provides the results. This research will contribute to improving strategies around research and development. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze the characteristics of traffic information propagation via Twitter using a keyword analysis and a network analysis. For the keyword analysis, the main contents of Twitter messages were identified using a TF-IDF (Term Frequency - Inverse Document Frequency) model. For the network analysis, the network connectivity among Twitter users, including Traffic Information Producers (TIPs), Opinion Leaders (OLs), and their followers were measured by estimating the densities and mean distances in their follow networks. Based on the keyword analysis result, the words representing traffic conditions were revealed as the most influential keywords. In addition, the information regarding traffic accident occurrences was found to be most frequently retweeted. As a result of the network analysis, MBC news which is one of the biggest newscasts in Korea showed the greatest connectivity among TIPs. OLs proved more powerful in information propagation than TIPs. Conclusively, there is an apparent demand for establishing strategies to propagate traffic information based on the characteristics of Twitter in a more efficient manner. [ABSTRACT FROM AUTHOR]

This study involved using three methods, namely keyword, bibliographic coupling, and co-citation analyses, for tracking the changes of research subjects in library and information science (LIS) during 4 periods (5 years each) between 1995 and 2014. We examined 580 highly cited LIS articles, and the results revealed that the two subjects 'information seeking (IS) and information retrieval (IR)' and 'bibliometrics' appeared in all 4 phases. However, a decreasing trend was observed in the percentage of articles related to IS and IR, whereas an increasing trend was identified in the percentage of articles focusing on bibliometrics. Particularly, in the 3rd phase (2005-2009), the proportion of articles on bibliometrics exceeded 80 %, indicating that bibliometrics became predominant. Combining various methods to explore research trends in certain disciplines facilitates a deeper understanding for researchers of the development of disciplines. [ABSTRACT FROM AUTHOR]

By employing the social network analysis technique, this study decomposed author and title keyword networks of the information technology management domain formed by 351 outlets, 914 institutions, 64 countries, 1913 authors, and thousands of keywords. The network and ego level properties-such as, degree centralities, density, components, and degree distribution-suggest that the keyword network exhibits power law distribution: a few popular keywords or themes are frequently used by follow-on studies. The study sheds light on the emerging and fading themes in the domain. In light of the analysis some important implications are discussed. [ABSTRACT FROM AUTHOR]

In order to distinguish the research focus between different Library and Information Science (LIS) research institutions in China, we use the Keyword Activity Index (KAI) to identify their institution-specific keywords. The KAI, whose idea is borrowed from the Activity Index, measures whether an institution has alternatively comparative advantage in a particular topic according to its share in publications. In this study, a total of 65,653 papers from 19 core LIS journals in China during the period of 2000-2013 are collected. The top 8 most prolific LIS research institutions in China are selected for further investigation of the utility of KAI. Their institution-specific keywords are extracted based on the KAI values to represent their research focus and then clustered using co-word analysis; the research advantages of each institution are analyzed and compared according to these clusters. The reasons of their research advantages are analyzed based on their research function and research background. [ABSTRACT FROM AUTHOR]

This study describes the structure and trends of the family business literature. We analyze the content of the papers focused on family firms published in any journal of the categories 'business', 'business finance', 'economics' and 'management' of the Social Science Citation Index during the 1961-2008 period. Bibliometric methods are used to describe the evolution of publication activity, the most representative contributors, the methodologies applied, and the content of the articles in order to explore the main themes researched. These analyses enable the identification of potential avenues for future research that could be meaningful to advance in the consolidation of the discipline. [ABSTRACT FROM AUTHOR]

Using the public survey 'The Top 100 Japanese Rural Landscapes' conducted by Asahi Shimbun Newspaper Company in 2008, this study attempted to analyze public perception of rural landscapes and their cultural value. A total of 3,024 nominated sites were given coordinates and combined with land use and topographic datasets using a GIS, and classified into several landscape types using cluster analysis. Keywords that appeared frequently in the nominations were extracted using a text-mining tool and used to interpret the cultural value of each type. As a result, the nominated sites were divided into six types. The majority of nominated sites were classified as Forest Type (forest 88%) and associated with the keywords traditional rural lifestyle, and sites to visit. Mixed Type (forest 50%, paddy field 20%, and other agricultural fields 10%) were associated with biodiversity and conservation activities, and Paddy Field Type (paddy field 60%) was significantly associated with Furusato (Home). Urban and Suburban Type sites (built-up land 50%) were concentrated in the Kanto Region and nominated mainly by local citizens for their nature activities. There was also the Other Agricultural Type, which made up 10% each of the nominations, and the Coastal Type, which was mostly nominated by ocean-related organizations and rarely nominated by the general public. Additionally, the concept of biodiversity seemed to be difficult for the public to understand. The results indicated that future studies should consider public perception of the variety of Satoyama landscapes and how it should be incorporated into future Satoyama management strategy. [ABSTRACT FROM AUTHOR]

The pluralist narrative of language policies suggests that language policies are influenced by public perceptions of immigrants (Darder ; González ; Pavlenko ; Valdés ). This paper investigates the relationship between newspaper discourse about language policies and newspaper discourse about immigration. It asks how much key, lexical overlap exists between the discourses. This study compared Arizona newspaper corpora representing discourse about language policies and discourse about immigration to evaluate the degree of similarity in discourses. Keyword analysis, identifying unusually frequent words, was used to assess the degree of important semantic overlap across topic-based corpora. Four topic-based corpora were constructed from the newspapers published between January 1999 and October 2007. Surprisingly, little key, lexical overlap was found between language policy corpora and immigration corpora. In light of the findings, advantages and limitations of various methodologies for language policy questions are discussed—corpus-based methods in particular. [ABSTRACT FROM AUTHOR]

Search engine optimization (SEO) pertains to the activity of optimizing individual websites and webpages to get higher page rank in the search results. Websites are ordinarily optimized through back links, while individual webpages are optimized through specific keywords. In this paper, we propose a framework based on set of guidelines for keyword research analysis and back links generation. The proposed framework suggests that webpage content should be based on keywords and the site traffic should be monitored through referrals. We argue that if a website has some prevalent and pertinent keywords in its content and title along with a reasonable amount of back links that help watch the website traffic, then we can get better rank for the website in the search results. The framework also emphasizes that proper keyword selection and link building should be taken into account when developers and designers work on a software development project. The results of the study are reported here. [ABSTRACT FROM AUTHOR]