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Besides scientific impact, papers can also achieve a technological impact that remains less known in the scientific community. Cited papers in the patents are considered as the index to measure the technological impact. This study aimed to analyze the technological impact of Iranian publications using co-authorship and coword map, their evolution, the journals, and the subject areas of these publications. This applied research focuses on the quantitative study and visualization with a scientometric approach. The research population was all studies (4554 records) that were published during 2011-2020 in one of the Iranian institutions and had been cited by one of the international patents. The data collection tool was the SciVal database. CiteSpace and Excel spreadsheets were used to analyze the data. Of the 4,554 papers cited by the scholarly outputs that have been cited in patents e patents, most of them were published in collaboration with the USA (9%). Islamic Azad University and Tehran University of Medical Sciences (13% each) were the most active Iranian universities. The number of Iranian papers cited in patents had a downward trend from 686 in 2011 to 57 in 2020. RSC Advances journal was the first top journal to publish these papers. Of 27 subject areas, engineering (24.1%) was the first popular subject that patents cite in their publications. The cluster analysis of keywords identified 8 clusters, including "x-ray diffraction," "animal," "adult," "escherichia coli," "tissue engineering," "coronavirus infection," "neural network," and "methane." The technological impact of Iranian research has declined in recent years. It is suggested that research policymakers should consider scholarly outputs that have been cited in patents, which, in a way, shows the flow of knowledge to the industry and encourages researchers to produce such papers. [ABSTRACT FROM AUTHOR]

In this paper, we have collected the data about the 507 high-cited (citations ≥ 25) papers on Covid 19 published by India using Scopus database. Within small span of time many papers have received considerable number of citations. Further we have analyzed in terms of types of papers, major funding agencies, major collaborating countries, prolific authors, productive organizations and cluster analysis of author supplied keywords. Most of the papers were research articles. Department of Science and Technology, Govt. of India has funded considerable numbers of papers on Covid 19 and K. Dhama form Indian Veterinary Research Institute has contributed highest numbers papers and USA was major collaborating country and PGIMER-Chandigarh was most contributing organization. This study will be useful to the scientist/researchers to know the characteristics of high cited papers on Covid-19 from India. [ABSTRACT FROM AUTHOR]

This article analyses high-consistency pulp refining using a disk refiner. During the experiment, the size of the gap between the rotor and stator cutters (0.5 to 1.5 mm), rotor speed (2,000 to 2,500 rpm), pulp consistency (10 to 20%), and freeness value (15 to 60 °SR) of the pulp were varied. The refining results were characterised by changes in 10 output parameters: morphological properties of cellulose fibres (average length, width, fibrillation index, water retention value, average kink angle, and coarseness) and the physical and mechanical characteristics of handsheets (breaking length, bursting strength, tearing resistance, and folding endurance). A total of 56 observations were made on the samples. Factor and cluster analysis methods were used to study the structure of correlations between the output parameters. More than 96% of the total dispersion of all output parameters was due to a change in two latent (hidden) factors: the first one was responsible for 79.6% of the dispersion and is presumably identified as the degree of external fibre fibrillation and the second one (16.6% of the dispersion) as fibre flexibility (including coarseness and average kink angle). [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With growing internationalism, there is a shift in research patterns in developing countries, especially China and India, generating vital and contemporary research areas that are beginning to challenge the existing Western‐dominated research literature in social sciences. Yet, many of the new ideas within conceptual papers by the social sciences are not empirically validated, let alone operationalized. This is where the group concept mapping method can play a role in bridging the gap between phenomenal conceptualization and having an empirically valid model that can then be operationalized. The group concept mapping process involves five steps: create statements, sort statements, run multidimensional scaling (MDS) of sorted units, run cluster analysis, and label the clusters. This approach allows for the collective thoughts of a pre‐defined group to be collected and organized into a tangible output with academic rigor. This paper offers an overview of the group concept mapping methodology, discussing the processes of the method, how the method can be utilized fully within the business and broader social science context, and the strengths, weaknesses, and practical implications of group concept mapping. [ABSTRACT FROM AUTHOR]

For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document's paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging (HSI) is gaining popularity in the field of forensic document analysis. HSI returns more information compared to conventional three channel imaging systems due to the vast number of narrowband images recorded across the electromagnetic spectrum. As a result, HSI can provide better classification results. In this publication, we present results of an approach known as the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which we have applied to HSI paper data analysis. Even though t-SNE has been widely accepted as a method for dimensionality reduction and visualization of high dimensional data, its usefulness has not yet been evaluated for the classification of paper data. In this research, we present a hyperspectral dataset of paper samples, and evaluate the clustering quality of the proposed method both visually and quantitatively. The t-SNE algorithm shows exceptional discrimination power when compared to traditional PCA with k-means clustering, in both visual and quantitative evaluations. [ABSTRACT FROM AUTHOR]

Paper spray-mass spectrometry (PS-MS) is a rapid, solvent-efficient, and high-throughput analytical method for analyzing complex samples. In this study, a PS-MS method was developed to obtain MS profiles of Aurantii Fructus Immaturus ( aka Zhishi in Chinese) in positive and negative ion modes. In combination with multivariate analyses, including principal component analysis and cluster analysis, the PS-MS profiles of 25 batches of Zhishi were discriminated in 25 batches of Citri Reticulatae Pericarpium Viride ( aka Qingpi in Chinese; an adulterant of Zhishi). Moreover, a rapid quantitative analysis of synephrine, a prescriptive quality control component of Zhishi listed in the Chinese Pharmacopoeia, was conducted with PS-MS using synephrine-d2 as an internal standard (IS). The linearity range was 1.68–16.8 μg/mL (R 2 = 0.9985), the limit of quantitation was 0.5 μg/mL. Relative standard deviations in the intra- and inter-day precision of the MS were 4.87 and 4.90%, respectively. Compared with HPLC results, there was no significant difference in the quantitation of synephrine. This study demonstrated that the PS-MS method is useful for the rapid discrimination and quality control of Zhishi samples. [ABSTRACT FROM AUTHOR]

One of the leading national and international objectives is to achieve more egalitarian societies. Avoiding gender or digital gaps are priorities generally assumed as concerns of governments and international organizations. This paper evaluates the digital gender divide in its three stages: access, use and results, relating it to gender and salary gaps in the context of the European Union. Cluster analysis was conducted to classify the countries according to their gender digital divide. The influence of age and studies level of males and females was revised. Based on the OECD and EUROSTAT data, an empirical analysis was conducted. By comparison of means, the significant variables influencing the gender digital divide were identified, probing that the educational level significantly influences it, especially in what refers to the third stage. Finally, through a regression analysis, it was proved that the influence of the insecurity and the gender wage gap goes beyond the women and affects society. [ABSTRACT FROM AUTHOR]

This research aims to analyze the corporate social responsibility (CSR) political interaction strategy used by PT Riau Andalan Pulp and Paper (RAPP) in Riau Province, Indonesia. This is qualitative research with data collected through interviews and the annual reports on the stock exchange of Indonesian companies. This research using cluster analysis to investigate the degree of correlation between political actors and companies. Furthermore, the N Vivo 12 Software was used to strengthen the actors built in the CSR political interaction strategy. The result showed that the constituency development strategy is the strongest of the four other political interactions. A constituency-building strategy means that companies seek to build relationships with multiple stakeholders in addressing political issues. The company's political interaction strategy for constituency development is building relationships with the community, policymakers, and Key Opinion Leaders while engaging with the media. It was also found that of four constituency development strategies in this company, the interaction with the media was the strongest relationship built to support political interaction and CSR. [ABSTRACT FROM AUTHOR]

Background: The Pontifical Academy for Life (PAV) is an academic institution of the Holy See (Vatican), which aims to develop and promote Catholic teachings on questions of biomedical ethics. Palliative care (PC) experts from around the world professing different faiths were invited by the PAV to develop strategic recommendations for the global development of PC ("PAL-LIFE group"). Design: Thirteen experts in PC advocacy participated in an online Delphi process. In four iterative rounds, participants were asked to identify the most significant stakeholder groups and then propose for each, strategic recommendations to advance PC. Each round incorporated the feedback from previous rounds until consensus was achieved on the most important recommendations. In a last step, the ad hoc group was asked to rank the stakeholders' groups by order of importance on a 13-point scale and to propose suggestions for implementation. A cluster analysis provided a classification of the stakeholders in different levels of importance for PC development. Results: Thirteen stakeholder groups and 43 recommendations resulted from the first round, and, of those, 13 recommendations were chosen as the most important (1 for each stakeholder group). Five groups had higher scores. The recommendation chosen for these top 5 groups were as follows: (1) Policy makers: Ensure universal access to PC; (2) Academia: Offer mandatory PC courses to undergraduates; (3) Healthcare workers: PC professionals should receive adequate certification; (4) Hospitals and healthcare centers: Every healthcare center should ensure access to PC medicines; and (5) PC associations: National Associations should be effective advocates and work with their governments in the process of implementing international policy framework. A recommendation for each of the remaining eight groups is also presented. Discussion: This white paper represents a position statement of the PAV developed through a consensus process in regard to advocacy strategies for the advancement of PC in the world. [ABSTRACT FROM AUTHOR]

In order to advance civil aircraft manufacturing to higher levels, there is an urgent need to identify technological innovation opportunities to help new technology development. This paper first analyses the current state of the research field and determines the topic. It preprocesses papers and patents within the research topic to obtain a base database. Then, the database is analyzed using the LDA (Latent Dirichlet Analysis) cluster analysis method. The TF-IDF (Term Frequency-Inverse Document Frequency) algorithm processes the data to obtain critical technical words. The abstracts of patents and papers are processed to construct a binary-based vector of technical keywords. The papers and patents are visualized in a two-dimensional space technology map by generative topographic mapping (GTM) to create a technology map to identify technology blank dots. The combination of technologies characterized by each technology blank dot is obtained by GTM inverse mapping. Finally, technology opportunities with a high probability of development are identified to achieve innovation opportunity identification. It also provides countermeasures for the research institution, enterprise, sector, and industry. After research and analysis, the future in the mechanical connection technology of civil aircraft is necessary to strengthen basic technology development and improve the study of intelligence, integration, and flexibility. Technology such as sensors and lasers can improve the precision and efficiency of mechanical connections. [ABSTRACT FROM AUTHOR]

Background: Deep Brain Stimulation (DBS) has been widely applied and accepted in the treatment of neurological and psychiatric disorders. Despite numerous studies exploring the effects of DBS on the progression of neurodegenerative diseases and the treatment of advanced Parkinson's disease (PD), there is a limited number of articles summarizing this research. The purpose of this study is to investigate the current trends, hot topics, and potential in research surrounding DBS therapy for PD, as well as to anticipate the challenges of such research. Methods: We searched the Web of Science Core Collection database (WoSCC) for DBS research literature related to PD published from January 2014 to January 2024, utilized CiteSpace, VOS viewer, the bibliometric online analysis platform, Scimago Graphica, Microsoft Excel 2021, and R software version 4.2.3 for data analysis. And we conducted quantitative research on publications, citations, journals, authors, countries, institutions, keywords, and references, visualized the results in network graphs. Results: From 2014 to 2024, papers from 39 journals from 11 countries were among the top 100 cited. Most papers were published in Neurology, with the highest average citations per paper in Nature Neuroscience. The United States (US) contributed the most publications, followed by the United Kingdom (UK) and Germany. In terms of total publications, University College London (UCL) contributed the most papers. The primary classifications of articles were Clinical Neurology, Neurosciences, and Surgery. The top five keywords were subthalamic nucleus, DBS, PD, medical therapy, and basal ganglia. Cluster analysis indicates that DBS research focus on improving quality of life and applying computational models. Conclusion: Through bibliometric analysis, researchers could quickly and clearly understand the hotspots and boundaries of their research field, thus guiding their research direction and scope to improve research efficiency and the quality of outcomes. Although studies indicate that DBS is currently a crucial method for treating advanced PD, in the long run, creating a personalized, lowcost treatment regimen with precise targeting and long-term efficacy poses a challenge. [ABSTRACT FROM AUTHOR]

Background: Premenstrual syndrome/premenstrual dysphoric disorder is a serious condition affecting women worldwide, causing clinically significant distress or interference. Therefore, solving these diseases has become the utmost concern worldwide, culminating in numerous studies. In this study, we performed bibliometric analysis on the 100 most cited papers with the aim of identifying research hot spots and trends in this field. Methods: We screened the Science Citation Index Expanded (SCIE) of Web of Science (WOS) to identify the top 100 cited studies on PMS/PMDD. Next, we analyzed relevant literature from various journals, countries/regions, institutions, authors, and keywords. Finally, we used VOSviewer and Citespace software to generate knowledge maps and identify hot spots and trends. Results: The top 100 highly cited studies were published in 55 journals, between 1999 and 2017, across 24 countries/regions around the world. Most articles were published in Obstetrics and Gynecology, whereas Psych neuroendocrinology had the largest average number of citations per paper. The United States had the highest number of publications, followed by England, Canada, and Sweden. The top three institutions that published the highly cited literature were the University of Pennsylvania, Yale University, and National Institute of Mental Health (NIMH). Obstetrics, Gynecology, Psychiatry, and Reproductive Biology were the main research directions, whereas the top 10 Co-occurrence of Keywords included double-blind, fluoxetine, efficacy, prevalence, epidemiology, phase sertraline treatment, depression, progesterone, placebo, and placebo-controlled trial. Results from cluster analysis indicated that more comprehensive epidemiology and steroid pathogenesis have gradually become the hot spots and trends. Conclusion: These findings demonstrated that bibliometric analysis can intuitively and rapidly reveal the frontiers and hot spots of research in PMS/PMDD. Notably, epidemiology, steroid pathogenesis, GABAA receptor delta subunits, and double-blind placebo-controlled trials are potential areas of focus for future research. [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In citation networks, the content relativity of papers is a precondition of engendering citations, which is hard to model by a topological graph. A geometric graph is proposed to predict some features of the citation networks with exponentially growing papers, which addresses the precondition by using coordinates of nodes to model the research contents of papers, and geometric distances between nodes to diversities of research contents between papers. Citations between modeled papers are drawn according to a geometric rule, which addresses the precondition as well as some other factors engendering citations, namely academic influences of papers, aging of those influences, and incomplete copying of references. Instead of cumulative advantage of degree, the model illustrates that the scale-free property of modeled networks arises from the inhomogeneous academic influences of modeled papers. The model can also reproduce some other statistical features of citation networks, e.g. in- and out-assortativities, which show the model provides a suitable tool to understand some aspects of citation networks by geometry. [ABSTRACT FROM AUTHOR]

The time of concentration (Tc) is the main hydrological parameter used to characterize the response of a given Hydrological Response Unit (HRU) to a precipitation event. Because of its importance, the determining Tc is an integral step in several studies involving runoff. Thus, this work presents an unprecedented review of the application of Tc in different lines of research involving water resources around the world. In this article, 1252 publications were listed, obtained from seven different databases, published by 2020, that presented the expressions "time of concentration," "runoff," and "watershed." The articles and conference papers obtained in this research were classified into 12 topics. The number of publications per topic and per country was measured and a cluster analysis was developed to verify the similarity of the distribution of topics per country. In addition, 125 equations applied in related publications for the estimation of Tc are also listed. [ABSTRACT FROM AUTHOR]

Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies. [ABSTRACT FROM AUTHOR]

Fruits and vegetables form the basis of a nutritional diet for the population. In addition to the basic determinants, such as market prices and consumer income, other factors influence consumer demand. This paper aims to investigate how the age and gender of consumers influence the demand for fruit and vegetables in different European countries. In this paper, the demand for fruit and vegetables is understood through the population's preferences for fruit and vegetable consumption, i.e., the proportion of the population that prefers to consume fruit and vegetables five or more times a day. The paper uses data from Eurostat. Factor, cluster and correlation analyses were performed using the SPSS software. The results of the analyses showed that the gender significantly determines the demand for fruit and vegetables in favour of women. Regarding the effect of age, this determinant was positively correlated with the preference for 5FV consumption in most countries. Potential interventions to promote fruit and vegetable consumption should mainly target men aged 15-19 and 75+ in Bulgaria, the Czech Republic, Latvia, Austria, Slovenia and Sweden, men aged 35-44 and 75+ in Croatia, Italy, Poland and Romania and women aged 15-19 and 55+ in Bulgaria and Romania, where their preference for 5FV was the lowest. [ABSTRACT FROM AUTHOR]

The accurate characterization of typhoon wind profile properties is of great importance in the field of wind engineering and wind design of high-rise structures. In this paper, the average typhoon wind profile characteristics are investigated using the 930 m height measurement data of Typhoon Lekima 2019 obtained from the observations of the mobile acoustic profiling radar deployed in the coastal area. Specifically, this paper adopts a cohesive hierarchical cluster analysis method to classify the mean wind profiles of Super Typhoon Lekima 2019, and the optimal number of clusters is obtained as two classes by the profile coefficient with the sum of squares of clustering errors, the Calinski–Harabasz index, and the Davies–Bouldin index, and the two classes of typical wind profiles are named as cluster 1 type and cluster 2 type. The model fitting analysis of the two types of typical wind profiles was carried out in the height range of 0~300 m after classification, and the effects of fitting the cluster 1-type mean wind profiles with the Vickery model and the Snaiki and Wu model and the cluster 2-type mean wind profiles with the Power-law model, the Log-law model, and the Deaves–Harris and Gryning models were discussed. The results show that the cohesive hierarchical cluster analysis method used in this paper can effectively categorize the mean typhoon profiles. In addition, this paper has some reference significance for future research on the characteristics of measured typhoon wind profiles and engineering applications such as the wind-resistant design of high-rise structures. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to identify some specificities of production planning and control (PPC) activities in the one-of-a-kind-production (OKP) process through an extensive literature review. Relevant aspects related to systems and PPC activities in the context of OKP environment are discussed, and six opportunities for future research are highlighted. Design/methodology/approach: The following research is based on a review of 53 articles published in peer-reviewed journals over the past three decades. After an initial descriptive analysis based on bibliometric indicators, a cluster analysis of 15 most cited articles was carried out using multivariate data analysis techniques and in-depth analysis. Findings: The results reveal some specificities inherent to the clusters featured in the research, including aspects of planning, control and systems for OKP process. This cluster addresses information regarding next-generation manufacturing systems, scheduling and design science, computer simulation and project approach. On the other hand, the authors point out six topics for future research regarding contemporary issues associated with PPC in the context of OKP. Originality/value: This paper fills an important gap regarding OKP production planning and control practices. The results provide a theoretical overview of different PPC practices suitable for the OKP environment. Furthermore, it can provide insights for scientific developments in order to manage the complexity inherent in the OKP process. [ABSTRACT FROM AUTHOR]

Transportation infrastructures and operations can be severely impacted during flood events, leading to significant disruptions to the flow of goods and services. Although numerous studies have evaluated the direct impacts of flood events on the performance of transportation infrastructures, the indirect impacts or cascading effects have been rarely assessed. Hence, this paper examines the cascading effects of floods on transportation infrastructure using data mining algorithms. First, 33 effects of flood events on transportation infrastructure have been identified based on data collected for multiple flood events in New York and New Jersey. Second, association rule mining analysis was implemented to identify the key co-occurrences between flooding and the different events. Third, network analysis was conducted to quantify the co-occurrences or key combinations among the events. Fourth, cluster analysis was used to group or prioritize the cascading effects and co-occurring events into highly connected clusters to identify the most critical ones based on two scenarios: (1) without consideration of co-occurrences (Scenario 1); and (2) with consideration of co-occurrences (Scenario 2). The findings provided insights that while some cascading impacts could be individually critical/frequent (under Scenario 1), other cascading impacts could also result due to a combination of different effects that might not be perceived to be critical on the individual level but rather become critical when combined with other cascading events (under Scenario 2). The outcomes of this paper demonstrate the importance of considering the co-occurrences between the events and cascading effects, rather than analyzing them in isolation. This study adds to the body of knowledge by offering an analytical approach that could be used to identify and prioritize critical cascading effects of flood events on the operations and performance of transportation infrastructures. [ABSTRACT FROM AUTHOR]

Because of the environmental consequences of manufacturing activities, the general public, industry, and academia are becoming more aware of sustainable manufacturing (SM), which incorporates environmentally friendly manufacturing processes while emphasizing overall triple bottom line (TBL) performance in manufacturing. This article employs various text mining techniques and bibliometric analysis including cluster analysis, Pearson coefficient and research landscape to conduct an extensive investigation on SM with a focus on the TBL, in which the research content of SM with the TBL is reviewed and discussed systematically from a wide angle and with reduced bias. In this study, three new indicators about the ratios of the number of scientific papers between social, environmental, and economic dimensions of SM are devised to show the weight and level of importance of dimensions in SM, covering scientific papers from 30 years. The findings from this study indicate that the influential power of SM varies across the three dimensions, with a particular emphasis on the social dimension of SM from various countries, implying a current state of imbalance status in TBL for SM, at the same time, the economic and environmental dimensions share similar research topics and academic emphasis in SM. Based on these findings, recommendations based on sustainable development goals (SDGs) of the United Nations (UN) are made to increase the social influence of SM. This article firstly reveals the individual status of the social dimension and the situation of unbalanced TBL in SM, providing sustainable suggestions for enhancing the effectiveness of SM and achieving balanced TBL regarding the SDGs. [ABSTRACT FROM AUTHOR]

Background This analysis estimates the association between smoking-related knowledge and smoking behaviour in a Chinese context. To identify the specific knowledge most directly related to smoking status, we used a novel latent variable analysis approach to adjust for the high correlations between different measures of knowledge about tobacco smoking. Method Data are from the Global Adult Tobacco China Survey, a nationally representative sample of 13 354 household-dwelling individuals 15 years of age or older. Multinomial logistic regressions estimated the association between smoking status (ie, never smoked, current smoker or past smoker) and four smoking-related beliefs: whether or not smoking causes lung cancer, heart attack and stroke, and whether or not low-tar cigarettes are less harmful. A latent variable approach reassessed these associations while taking into account the general level of knowledge about smoking. Results After demographic variables and general knowledge about smoking had been controlled for, the belief that low-tar cigarettes are not less harmful was more prevalent in persons who had never smoked than in current smokers (OR=1.3 (95% CI 1.0 to 1.7) in men and OR=2.8 (95% CI 1.3 to 5.9) in women); this association was even stronger when past smokers and current smokers were compared (OR=2.1 (95% CI 1.5 to 3.0) in men and OR=5.0 (95% CI 1.3 to 20.1) in women). Conclusions Compared with those who have never smoked and those who have ceased smoking, current smokers in China are more likely to believe that low-tar cigarettes are less harmful than regular cigarettes. [ABSTRACT FROM AUTHOR]

Groups of sensor nodes are called as sensor networks. They transmit sensed data to base station in cooperation with one another. An efficient use of power is important for increased network lifespan. Wireless sensor networks are tiny, light in weight, battery powered sensor nodes. Various parameters like clustering overhead, lifetime of network, energy hole, FND (first node dies) LND (last end dies) are optimized in WSN with the help of various proposed hybrid unequal clustering protocol. Another issue for data gathering in WSN is the hot spot or energy hole that are formed near sink node are also addressed. Various new emerging strategies laid stress on the best optimal usage of constrain resources like energy, memory and various other processing abilities. This paper addresses a review of different research works done in this area. [ABSTRACT FROM AUTHOR]

The advancement of Augmented Reality (AR) technology has been remarkable, enabling the augmentation of user perception with timely information. This progress holds great promise in the field of interaction design. However, the mere advancement of technology is not enough to ensure widespread adoption. The user dimension has been somewhat overlooked in AR research due to a lack of attention to user motivations, needs, usability, and perceived value. The critical aspects of AR technology tend to be overshadowed by the technology itself. To ensure appropriate future assessments, it is necessary to thoroughly examine and categorize all the methods used for AR technology validation. By identifying and classifying these evaluation methods, researchers and practitioners will be better equipped to develop and validate new AR techniques and applications. Therefore, comprehensive and systematic evaluations are critical to the advancement and sustainability of AR technology. This paper presents a theoretical framework derived from a cluster analysis of the most efficient evaluation methods for AR extracted from 399 papers. Evaluation methods were clustered according to the application domains and the human–computer interaction aspects to be investigated. This framework should facilitate rapid development cycles prioritizing user requirements, ultimately leading to groundbreaking interaction methods accessible to a broader audience beyond research and development centers. [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper aims at examining the global trends in digital library research based on data extracted from the Scopus database at the global, national, institutional and individual levels. Using performance indicators like total citations, the number of publications, h-index, total link strength the study aims to evaluate the prolific authors, organizations, countries, chronological growth of literature, document forms, most prolific journals, application of Lotkas's law to evaluate the author productivity and analysis of keywords to know the trending topics of research in Digital Libraries. The study uses VOSviewer to prepare the network map of organizational and country co-authorship analysis with clustering technique and MS-Excel for graphical and statistical analysis. The core findings are like the most productive year is 2019 and the number of publications increased rapidly from the starting year of data collection 2016. The results reveal that the USA is the most productive country in terms of the number of publications and impact with the University of Illinois the first rank in the list of prolific institutions. B Gipp from the University of Wuppertal is the most productive author. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of a bibliometric analysis of papers published in six SDI-SOR special issues that have been published since 2011. We analyze and visualize the yearly dynamics of the number of published papers, highlight the most prolific authors and countries, and present the papers with the highest impact in terms of the number of citations they have received. A topic analysis is also performed by examining the keyword co-occurrence network based on the union of DE and ID keywords, and how they occur in the papers' titles and abstracts. This network is decomposed into eight clusters that are characterized in the last part of the paper. They outline the main research topics, which we characterize as motor topics, niche topics, emerging or declining topics, and basic topics. [ABSTRACT FROM AUTHOR]

Purpose Institutional bibliometric analyses compare as a rule the performance of different institutions. The purpose of this paper is to use a statistical approach which not only allows a comparison of the citation impact of papers from selected institutions, but also a comparison of the citation impact of the papers of these institutions with all other papers published in a particular time frame.Design/methodology/approach The study is based on a randomly selected cluster sample (n=4,327,013 articles and reviews from 2000 to 2004), which is drawn from a bibliometric in-house database including Web of Science data. Regression models are used to analyze citation impact scores. Subsequent to the models, average predictions at specific interesting values are calculated to analyze which factors could have an effect on the impact scores-the journal impact factor (JIF), of the journals which published the papers and the number of affiliations given in a paper.Findings Three anonymous German institutions are compared with one another and with the set of all other papers in the time frame. As an indicator of institutional performance, fractionally counted PPtop 50% on the level of individual papers are used. This indicator is a normalized impact score whereas each paper is fractionally assigned to the 50 percent most frequently cited papers within its subject category and publication year. The results show that the JIF and the number of affiliations have a statistically significant effect on the institutional performance.Originality/value Fractional regression models are introduced to analyze the fractionally counted PPtop 50% on the level of individual papers. [ABSTRACT FROM AUTHOR]

In recent years, the custom wardrobe market has been steadily developing. While meeting the functional needs of users, it is gradually shifting towards aesthetic preferences. Rapidly grasping users' preferences for the appearance of custom wardrobes is a key focus of current research. This study collected a large number of decorative surface images of custom wardrobes and objectively analyzed the design features based on color moments and Tamura texture feature data in computer image analysis methods. K-means cluster analysis was performed on the feature data. Collected images of the points closest to the cluster centers were further screened to select representative finish images, and finally a questionnaire survey was conducted at Nanjing Forestry University, with the help of semantic differential method and factor analysis. The characteristics of the samples were comprehensively summarized to infer design elements. The study found that warm-toned, medium-low saturation, and medium brightness surfaces were preferred by the panel. Different colors, contrasts, saturations, brightness, element features, and arrangements have significantly different effects on visual perception. These conclusions can provide a reference for subsequent custom wardrobe design. [ABSTRACT FROM AUTHOR]

Studies have indicated that higher numbers of nurses regarding staffing ensure patient safety and a better practice environment. Using citation analysis, this study visualizes the landscape of nurse staffing research over the last two decades to show the overall publication trends, major contributors, and main research topics. We extracted bibliometric information from PubMed from January 2000 to September 2022. After clustering the network, we analyzed each cluster's characteristics by keyword. A total of 2167 papers were considered for analysis, and 14 clusters were created. The analysis showed that the number of papers published per year has been increasing. Researchers from the US, the UK, Canada, Australia, and Belgium have led this field. As the main clusters in nurse staffing research during the past two decades, the following five research settings were identified: nurse outcome and patient outcome research in acute care hospitals, nurse staffing mandate evaluation research, nursing home research, and school nurse research. The first three clusters accounted for more than 80% of the total number of published papers, and this ratio has not changed in the past 20 years. To further develop nurse staffing research globally, evidence from other geographic areas, such as African and Asian countries, and from long-term care or community settings is necessary. [ABSTRACT FROM AUTHOR]

With an increasing number of new scientific papers being released, it becomes harder for researchers to be aware of recent articles in their field of study. Accurately classifying papers is a first step in the direction of personalized catering and easy access to research of interest. The field of Density Functional Theory (DFT) in particular is a good example of a methodology used in very different studies, and interconnected disciplines, which has a very strong community publishing many research articles. We devise a new unsupervised method for classifying publications, based on topic modeling, and use a DFT-related selection of documents as a use case. We first create topics from word analysis and clustering of the abstracts from the publications, then attribute each publication/paper to a topic based on word similarity. We then make interesting observations by analyzing connections between the topics and publishers, journals, country or year of publication. The proposed approach is general, and can be applied to analyze publication and citation trends in other areas of study, beyond the field of Density Function Theory. [ABSTRACT FROM AUTHOR]

Purpose: This study analyzed research trends in infant and toddler rearing behavior among family caregivers over a 10-year period (2010-2021). Methods: Text network analysis and topic modeling were employed on data collected from relevant papers, following the extraction and refinement of semantic morphemes. A semantic-centered network was constructed by extracting words from 2,613 English-language abstracts. Data analysis was performed using NetMiner 4.5.0. Results: Frequency analysis, degree centrality, and eigenvector centrality all revealed the terms ''scale," ''program," and ''education" among the top 10 keywords associated with infant and toddler rearing behaviors among family caregivers. The keywords extracted from the analysis were divided into two clusters through cohesion analysis. Additionally, they were classified into two topic groups using topic modeling: "program and evaluation" (64.37%) and "caregivers' role and competency in child development" (35.63%). Conclusion: The roles and competencies of family caregivers are essential for the development of infants and toddlers. Intervention programs and evaluations are necessary to improve rearing behaviors. Future research should determine the role of nurses in supporting family caregivers. Additionally, it should facilitate the development of nursing strategies and intervention programs to promote positive rearing practices. [ABSTRACT FROM AUTHOR]

The rapid growth of the aviation industry highlights the need for strong safety management. Analyzing data on unsafe aviation events is crucial for preventing risks. This paper presents a new method that integrates the Transformer network model, clustering analysis, and feature network modeling to analyze Chinese text data on unsafe aviation events. Initially, the Transformer model is used to generate summaries of event texts, and the performance of three pre-trained Chinese models is evaluated and compared. Next, the Jieba tool is applied to segment both summarized and original texts to extract key features of unsafe events and prove the effectiveness of the pre-trained Transformer model in simplifying lengthy and redundant original texts. Then, cluster analysis based on text similarity categorizes the extracted features. By solving the correlation matrix of these features, this paper constructs a feature network for unsafe aviation events. The network's global and individual metrics are calculated and then used to identify key feature nodes, which alert aviation professionals to focus more on the decision-making process for safety management. Based on the established network and these metrics, a data-driven hidden danger warning strategy is proposed and illustrated. Overall, the proposed method can effectively analyze Chinese texts of unsafe aviation events and provide a basis for improving aviation safety management. [ABSTRACT FROM AUTHOR]

Cluster randomized experiments and estimator comparisons are well-documented topics. In this paper, using the datasets of the popular vote in the presidential elections of the United States of America (2012, 2016, 2020), we evaluate the properties (SE, MSE) of three cluster sampling estimators: Ratio estimator, Horvitz–Thompson estimator and the linear regression estimator. While both the Ratio and Horvitz–Thompson estimators are widely used in cluster analysis, we propose a linear regression estimator defined for unequal cluster sizes, which, in many scenarios, performs better than the other two. The main objective of this paper is twofold. Firstly, to indicate which estimator is most suited for predicting the outcome of the popular vote in the United States of America. We do so by applying the single-stage cluster sampling technique to our data. In the first partition, we use the 50 states plus the District of Columbia as primary sampling units, whereas in the second one, we use 3112 counties instead. Secondly, based on the results of the aforementioned procedure, we estimate the number of clusters in a sample for a set standard error while also considering the diminishing returns from increasing the number of clusters in the sample. The linear regression estimator is best in the majority of the examined cases. This type of comparison can also be used for the estimation of any other country's elections if prior voting results are available. [ABSTRACT FROM AUTHOR]

BACKGROUND: Health care workers face a wide range of chemical, physical, and biological occupational hazards in their jobs. OBJECTIVE: The present study aimed to investigate research trends on post-exposure prophylaxis (PEP) against blood-borne viral infections among health care workers. METHOD: Keywords related to health care workers, PEP, and blood-borne viruses were entered in the Scopus database for the period from 1950 to 27 January 2022. RESULTS: The search query returned 271 papers. The earliest publication was in 1984. The ranked first (n = 8, 3.0%), followed by the and with 6 (2.2%) papers for each. One hundred ninety-one journals took part in publishing the retrieved papers. Authors from 63 different countries took part in publishing the retrieved papers. The United States (US) ranked first (n = 53, 19.6%) followed by India (n = 26, 9.6%). The United States Centers for Disease Control and Prevention contributed the most (n = 9, 3.3%) and ranked first in the top active institutions. The mean number of authors per paper was 4.4 and the mean number of citations per paper was 17.0. The most frequent author keywords focused on PEP, health care workers, occupational exposure, HIV, hepatitis B, anti-retroviral and needle-stick injuries. Research themes in the retrieved papers focused on knowledge/attitude/practice and management and epidemiology of occupational exposure and PEP. There was a limited number of research publications in this field. CONCLUSION: Research activity in this field needs to be strengthened in low- and middle-income countries through reporting and training of HCWs. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to evaluate the past contributions of Multinational Business Review (MBR), identify research gaps and opportunities and provide a research agenda that addresses several sustainability-related and other contemporary challenges. Design/methodology/approach: This study analyzes 400 papers published between 2003 and 2021 to map the MBR's intellectual and conceptual structure using advanced bibliometric techniques. Findings: The bibliographic coupling technique identifies core clusters in MBR papers, and subsequent content analysis of these clusters reveals the following five research fronts: internalization theory and the future of international business (IB) research; internationalization and firm performance; regionalization versus globalization debate; internationalization by emerging market firms; and global dynamic capabilities and firm internationalization. Originality/value: To the best of the authors' knowledge, this is the first comprehensive analysis of past contributions of MBR to research on IB and suggests a way for MBR to play a seminal role in addressing contemporary challenges in IB. [ABSTRACT FROM AUTHOR]

In order to secure supply chains (SCs), researchers and policy makers need to be abreast of developments in supply chain risk management (SCRM). This study selected frequently cited papers of WoS from the last 10 years, performing quantitative visualization analysis to establish the current trends within the field. Further attention was paid to those studies that focused on the impact of COVID-19. This study used a keyword timeline and clustering analysis map to establish the main research directions between 2012 to 2018, as well as the perspectives from which SC optimization was studied from 2018 to 2021. The key journals and research institutions for SCRM are established, as well as the key categories that the published literature falls under. Cluster analysis shows which areas in the published literature have the most references. Finally, the study establishes the direction of current trends within SCRM, as well as its understudied areas. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to propose an approach for identifying landmark papers in the long run. These publications reach a very high level of citation impact and are able to remain on this level across many citing years. In recent years, several studies have been published which deal with the citation history of publications and try to identify landmark publications.Design/methodology/approach In contrast to other studies published hitherto, this study is based on a broad data set with papers published between 1980 and 1990 for identifying the landmark papers. The authors analyzed the citation histories of about five million papers across 25 years.Findings The results of this study reveal that 1,013 papers (less than 0.02 percent) are “outstandingly cited” in the long run. The cluster analyses of the papers show that they received the high impact level very soon after publication and remained on this level over decades. Only a slight impact decline is visible over the years.Originality/value For practical reasons, approaches for identifying landmark papers should be as simple as possible. The approach proposed in this study is based on standard methods in bibliometrics. [ABSTRACT FROM AUTHOR]

Background: In a cluster randomised controlled trial (CRCT), randomisation units are "clusters" such as schools or GP practices. This has methodological implications for study design and statistical analysis, since clustering often leads to correlation between observations which, if not accounted for, can lead to spurious conclusions of efficacy/effectiveness. Bayesian methodology offers a flexible, intuitive framework to deal with such issues, but its use within CRCT design and analysis appears limited. This review aims to explore and quantify the use of Bayesian methodology in the design and analysis of CRCTs, and appraise the quality of reporting against CONSORT guidelines. Methods: We sought to identify all reported/published CRCTs that incorporated Bayesian methodology and papers reporting development of new Bayesian methodology in this context, without restriction on publication date or location. We searched Medline and Embase and the Cochrane Central Register of Controlled Trials (CENTRAL). Reporting quality metrics according to the CONSORT extension for CRCTs were collected, as well as demographic data, type and nature of Bayesian methodology used, journal endorsement of CONSORT guidelines, and statistician involvement. Results: Twenty-seven publications were included, six from an additional hand search. Eleven (40.7%) were reports of CRCT results: seven (25.9%) were primary results papers and four (14.8%) reported secondary results. Thirteen papers (48.1%) reported Bayesian methodological developments, the remaining three (11.1%) compared different methods. Four (57.1%) of the primary results papers described the method of sample size calculation; none clearly accounted for clustering. Six (85.7%) clearly accounted for clustering in the analysis. All results papers reported use of Bayesian methods in the analysis but none in the design or sample size calculation. Conclusions: The popularity of the CRCT design has increased rapidly in the last twenty years but this has not been mirrored by an uptake of Bayesian methodology in this context. Of studies using Bayesian methodology, there were some differences in reporting quality compared to CRCTs in general, but this study provided insufficient data to draw firm conclusions. There is an opportunity to further develop Bayesian methodology for the design and analysis of CRCTs in order to expand the accessibility, availability, and, ultimately, use of this approach. [ABSTRACT FROM AUTHOR]

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers. [ABSTRACT FROM AUTHOR]

ith the rapid development of technology, processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming, which cannot meet the demands of scientific research and business. Therefore, this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search (CLARANS) clustering algorithm on the Spark cloud computing platform to cluster China’s climate regions using meteorological data from 1988 to 2018. The aim is to address the challenge of applying clustering algorithms to large datasets. In this paper, the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance, which improves clustering accuracy. Furthermore, the issue of local optima caused by an improper selection of initial clustering centers is addressed by utilizing the max-distance criterion. Compared to the k-means clustering algorithm already implemented in the Spark platform, the proposed algorithm has strong robustness, can reduce the interference of outliers in the dataset on clustering results, and has higher parallel performance than the frequently used serial algorithms, thus improving the efficiency of big data analysis. This experiment compares the clustered centroid data with the annual average meteorological data of representative cities in the five typical meteorological regions that exist in China, and the results show that the clustering results are in good agreement with the meteorological data obtained from the National Meteorological Science Data Center. This algorithm has a positive effect on the clustering analysis of massive meteorological data and deserves attention in scientific research activities. [ABSTRACT FROM AUTHOR]

Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn't reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size. The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data. With this in mind, we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy. [ABSTRACT FROM AUTHOR]

Chemometrics/informatics and data analysis, in general, are increasingly important topics in x-ray photoelectron spectroscopy (XPS) because of the large amount of information (data/spectra) that are often collected in degradation, depth profiling, operando, and imaging studies. In this guide, we discuss vital, theoretical aspects and considerations for chemometrics/informatics analyses of XPS data with a focus on exploratory data analysis tools that can be used to probe XPS datasets. These tools include a summary statistic [pattern recognition entropy (PRE)], principal component analysis (PCA), multivariate curve resolution (MCR), and cluster analysis. The use of these tools is explained through the following steps: (A) Gather/use all the available information about one's samples, (B) examine (plot) the raw data, (C) developing a general strategy for the chemometrics/informatics analysis, (D) preprocess the data, (E) where to start a chemometrics/informatics analysis, including identifying outliers or unexpected features in datasets, (F) determine the number of abstract factors to keep in a model, (G) return to the original data after a chemometrics/informatics analysis to confirm findings, (H) perform MCR, (I) peak fit the MCR factors, (J) identify intermediates in MCR analyses, (K) perform cluster analysis, and (L) how to start doing chemometrics/informatics in one's work. This guide has Paper II [Avval et al., J. Vac. Sci. Technol. A 40, 063205 (2022)] that illustrates these steps/principles by applying them to two fairly large XPS datasets. In these papers, special emphasis is placed on MCR. Indeed, in this paper and Paper II, we believe that, for the first time, it is suggested and shown that (1) MCR components/factors can be peak fit as though they were XPS narrow scans and (2) MCR can reveal intermediates in the degradation of a material. The other chemometrics/informatics methods are also useful in demonstrating the presence of outliers, a break (irregularity) in one of the datasets, and the general trajectory/evolution of the datasets. Cluster analysis generated a series of average spectra that describe the evolution of one of the datasets. [ABSTRACT FROM AUTHOR]

Can government environmental policy harmonize environmental protection with economic output? We explore this issue from the perspective of forestry eco-efficiency, using China's National Ecological Civilization Pilot Zone Policy (NECP), an environmental policy promulgated by the government of China, as the subject of this study. The study introduces forestry eco-efficiency as an indicator to assess the balance between economic development in the forestry sector and environmental conservation. The indicator, grounded in sustainable development theory, employs a super-efficiency SBM model that includes undesirable outputs to evaluate efficiency. Additionally, we empirically analyze the impact of NECP on forestry eco-efficiency by using the difference-in-difference (DID) model with provincial panel data from 2011 to 2020. Ultimately, we analyze the effects of spatial spillover by employing the spatial Durbin model (SDM). Our study yields the following conclusions. (1) In this paper, through hotspot clustering analysis, forestry eco-efficiency in each province is categorized into three categories: effective, semi-effective and ineffective. Our findings suggest that China's average forestry eco-efficiency falls into the ineffective category, highlighting the need to optimize resource allocation within the sector. (2) NECP significantly enhances forestry eco-efficiency, with robust findings across various stability tests. Thus, implementing government environmental policies can have a multiplier effect on forestry, i.e., it can synergize its economic development with environmental protection. (3) In provinces with a strong ecological foundation, the NECP significantly enhances forestry eco-efficiency. However, in other provinces, the improvement is only moderate. Furthermore, while the NECP has a substantial positive impact in the eastern region, it has yet to show a discernible effect in other regions. (4) The positive impacts of NECP implementation on forestry eco-efficiency have spatial spillover effects due to demonstration effects and comparative advantages. [ABSTRACT FROM AUTHOR]