bluuebunny/crossref_metadata_2025_split huggingface dataset:
Verifying the sustainability factors of mega transportation infrastructure: Sydney Metro’s commissioning through the “significance matrix” methodology
Authors: Gharehbaghi Koorosh , McManus Kerry , Georgy Maged , Farnes Ken , Pagliara Francesca , Myers Matt 
Published: December 2021

Purpose
Through the significance matrix, this paper aims to investigate and explore the main sustainability factors of mega transportation infrastructure projects. Sydney’s Metro mega transportation infrastructure is used as a case study. Sydney’s Metro was selected because of its sustainability challenges faced because of the areas’ diverse ecological zones. Sydney’s Metro is thus examined as the basis of best practice for the determination of the sustainability factors of transportation infrastructures.

Design/methodology/approach
Using the significance matrix as a methodology, this research evaluates the environmental impact assessment and environmental assessment processes, to alleviate the problems of the mega transportation infrastructure.

Findings
This research found that a more comprehensive determination is needed to further analyse the sustainability factors of mega transportation infrastructures, use of a significance matrix would further assess the environmental complexities of mega transportation infrastructures and the sustainability factors of mega transportation infrastructures should include a nonlinear and asymmetrical scheme highlighting its components and carefully outlining its integration and consolidation.

Originality/value
Although there is concurrent research into sustainability factors of mega transportation, this paper undertakes a new methodology for such infrastructure. While the significance matrix is not a new concept, it has never been used specifically for mega transportation infrastructure. Subsequently, using the significance matrix as a methodology, this research undertakes such environmental analysis and assessment and thus produces a qualitative risk analysis matrix. The findings from this research will ultimately assist the key stakeholders of mega transportation infrastructures to better plan, monitor and support similar projects.
Digital transformation in the construction industry: a bibliometric review
Authors: Adekunle Samuel Adeniyi , Aigbavboa Clinton Ohis , Ejohwomu Obuks , Adekunle Emmanuel Abiodun , Thwala Wellington Didibhuku 
Published: December 2021

Purpose
The construction industry has been traditionally referred to as slow when it comes to technological transformation. This study aims to investigate and present a scorecard of the construction industry in the past decade, the paper adopted Bibliometrics. The study identified the various digital transformation (DT) aspects in the construction industry and future research directions are also identified.

Design/methodology/approach
To achieve the aim of this research, an inductive approach was adopted through a grounded theory strategy. Secondary data was retrieved from the Scopus database and analysed using Biblioshiny and VOSviewer. The data was retrieved through specific keywords related to the study focus.

Findings
The study also proposed a balanced flow model for DT discussion in the construction industry. DT in the construction industry disrupts every aspect of the industry, albeit at different rates due to the existing barriers; hence, the study identified areas that require further research. It, thus, provides a theoretical and practical basis for researchers and practitioners alike.

Originality/value
The study reviewed the DT research discuss in the construction industry. It is worthy of note that this is the first study that analyses the DT of the construction industry in the past decade.
Entrepreneurial bricolage: crowdfunding for female entrepreneurs during COVID-19 pandemic
Authors: Alva Edgar , Vivas Vanina , Urcia María 
Published: December 2021

Purpose
The crisis generated by the COVID-19 pandemic has had unprecedented effects around the world and particularly on vulnerable populations, such as female entrepreneurs in developing countries. They are facing a new normal, characterized by high uncertainty and resource constraints. This paper aims to use the experiential learning theory applied to entrepreneurship to propose the entrepreneurial bricolage approach to study how female entrepreneurs can overcome this type of crisis.

Design/methodology/approach
Within the research designs of conceptual papers, the study is situated within the model approach because the authors propose novel relationships between constructs to answer the research questions through the development of theoretical propositions.

Findings
The analysis suggests that the entrepreneurial bricolage process represents a type of strategy that allows these women to adapt to this new normal. Based on this approach, the authors suggest that crowdfunding represents a financial resource at hand for both pre-existing female entrepreneurs and new female entrepreneurs.

Practical implications
The theoretical implications suggest an alternative conceptual framework of the entrepreneurial bricolage process to analyse the female entrepreneurial activity in developing countries and during crisis’ contexts. The managerial implications suggest that crowdfunding is a financial resource at hand for female entrepreneurs that allow them to perform well, innovate or implement growth strategies and have more chances of surviving and growing during the crisis. Finally, the policy implications are oriented to the promotion of this kind of financial alternative and the generation of trust for users, along with the strengthening of a regulatory framework to attract more investors and entrepreneurs.

Originality/value
This study provides useful information on how the crisis generated by the COVID-19 pandemic has been affecting female entrepreneurs in developing countries. It also expands on the literature on crowdfunding in terms of its advantages as a non-traditional funding source for women.
Understanding the effect of recycled concrete aggregate and cementitious materials on concrete's fire resistance
Authors: Abed Mohammed Ahmed , Lubloy Eva 
Published: December 2021

PurposeFire can severely affect concrete structures and with knowledge of the properties of materials, the damage can be assessed. Aggregate, cement matrix and their interaction are the most important components that affect concrete behaviour at high temperatures. The effect of incorporating recycled concrete aggregate or cementitious materials, namely, cement type and pulverized fly ash, are reviewed to provide a better understanding of their involvement in fire resistance.Design/methodology/approachMore investigation research is needed to understand the fire resistance of such sustainable concrete that was already constructed. The present study illustrates the effect of using recycled concrete aggregate and cementitious materials on the fire resistance of concrete. To do so, a literature review was conducted and relevant data were collected and presented in a simple form. The author's selected research findings, which are related to the presents study, are also presented and discussed.FindingsRecycled concrete aggregate enhances the concrete behaviour at high temperatures when it substitutes the natural aggregate by reasonable substitution (more than 25–30%). It also almost eliminates the possibility of spalling. Moreover, utilizing both supplementary cementitious materials with recycled concrete aggregate can improve the fire resistance of concrete. The incorporation of pulverized fly ash and slag in Portland cement or blended cement can generally keep the mechanical properties of concrete at a higher level after heating to a high temperature.Originality/valueRecycled concrete aggregate enhances the concrete behaviour at high temperatures when it substitutes the natural aggregate by reasonable substitution (more than 25–30%). It also almost eliminates the possibility of spalling. Moreover, utilizing both supplementary cementitious materials with recycled concrete aggregate can improve the fire resistance of concrete. The incorporation of pulverized fly ash and slag in Portland cement or blended cement can generally keep the mechanical properties of concrete at a higher level after heating to a high temperature.
COVID-19 and tourist mobility at destinations: a literature review and emerging research agenda
Authors: Viana-Lora Alba , Domènech Antoni , Gutiérrez Aaron 
Published: December 2021

PurposeThis paper aims to review conceptual and empirical studies that analyse the impact of the pandemic on mobility and tourism behaviour at destinations in order to identify proposals, forecasts and recommendations to guide the future research agenda on the subject.Design/methodology/approachThis study used a systematic literature review to synthesise information from scientific articles published in journals indexed in the Web of Science database related to tourism mobility at destinations during the COVID-19 pandemic.FindingsThis article found that, according to the existing literature, the COVID-19 pandemic is acting as a catalyst for the sustainable transition of tourism. Although the findings reveal a lack of empirical research on the impact of the pandemic on tourism mobility at destinations, the article synthesizes the short- and long-term impacts of the pandemic and sets out the future research agenda on tourist mobility at destinations.Originality/valueTo the best of the authors' knowledge, this is the first systematic review of the impact of the pandemic on mobility and tourism behaviour at destinations that attempts to describe the emerging challenges and the agenda for future research.
Changing the business narrative: a guide to successful experience offerings
Authors: Rossman J. Robert , Duerden Mat 
Published: December 2021

Purpose
In this article, we aim to increase understanding of the unique nature of experiences in comparison to services and explain how narrative change can enable organizations to fully participate in the experience economy.

Design/methodology/approach
Drawing upon relevant experience design and experience economy literatures the paper outlines key differences between experiences and services. The narrative change process is then employed to provide managers specific guidelines for strategically reframing their value production paradigm to become more experience-centric.

Findings
rticulating key elements of an organization’s narrative such as characters and intended outcomes allows an organization to more intentionally change their narrative to align with the experience economy.

Practical/implications
The essential outcome of an experience is that participants do something for themselves ? for example, learn a new skill. Participation is integral to every experience transaction.

Originality/value
Although Pine, Gilmore, and others have detailed the differences between experiences and services, definitional disagreements and oversimplifications of these concepts still exist. This paper further differentiates these economic offerings. Additionally, this paper is the first to integrate the narrative change process into a discussion about experience strategy.
Rhizosphere impacts bacterial community structure in the tea (Camellia sinensis (L.) O. Kuntze.) estates of Darjeeling, India
Authors: Bhattacharyya Chandrima , Imchen Madangchanok , Mukherjee Triparna , Haldar Shyamalina , Mondal Sangita , Mukherji Shayantan , Haldar Anwesha , Kumavath Ranjith , Ghosh Abhrajyoti 
Published: December 2021

SummaryIndia contributes 28% of the world's tea production, and the Darjeeling tea of India is a world‐famous tea variety known for its unique quality, flavour and aroma. This study analyzed the spatial distribution of bacterial communities in the tea rhizosphere of six different tea estates at different altitudes. The organic carbon, total nitrogen and available phosphate were higher in the rhizosphere soils than the bulk soils, irrespective of the sites. Alpha and beta diversities were significantly (p < 0.05) higher in the bulk soil than in the rhizosphere. Among the identified phyla, the predominant ones were Proteobacteria, Actinobacteria and Acidobacteria. At the genus level, only four out of 23 predominant genera (>1% relative abundance) could be classified, viz., Candidatus Solibacter (5.36 ± 0.36%), Rhodoplanes (4.87 ± 0.3%), Candidatus Koribacter (2.3 ± 0.67%), Prevotella (1.49 ± 0.26%). The rhizosphere effect was prominent from the significant depletion of more ASVs (n = 39) compared to enrichment (n = 11). The functional genes also exhibit a similar trend with the enrichment of N2 fixation genes, disease suppression and Acetoin synthesis. Our study reports that the rhizobiome of tea is highly selective by reducing the alpha and beta diversity while enriching the significant functional genes.
E2F4 regulates the cell cycle and DNA replication in the silkworm, Bombyx mori
Authors: Chen Peng , Wang Ling , Long Yan‐Bi , Liang Guang‐Yan , Yang Xiu , Dong Zhan‐Qi , Jiang Xia , Zhu Yan , Pan Min‐Hui , Lu Cheng 
Published: December 2021

AbstractThe E2F family of transcription factors is crucial for cell cycle progression and cell fate decisions. Although E2Fs have been widely studied in mammals, there have been few studies performed in insects. Here, we determined the function of E2F4 in the silkworm, Bombyx mori. We demonstrate that E2F proteins are highly conserved among species from lower animals to higher mammals. Overexpression of the BmE2F4 gene led to cell cycle arrest in the G1 phase, whereas interfering with the BmE2F4 mRNA led to accumulation of cells in the S phase. These results indicate that BmE2F4 is important in cell cycle regulation. We also demonstrate that the BmE2F4 gene is involved in DNA replication of BmN‐SWU1 cells and DNA synthesis in the silk gland. Furthermore, we identified a protein called Bm14‐3‐3ζ that can interact with BmE2F4 and allow it to localize in the nucleus. Overexpression of the Bm14‐3‐3ζ gene led to cell cycle arrest in the G1 phase, while knocking down the gene increased the proportion of cells in S phase. These findings provide important insights into the function of E2F transcription factors and increase our understanding of their involvement in cell cycle regulation.
Machine learning based prediction models in male reproductive health: Development of a proof‐of‐concept model for Klinefelter Syndrome in azoospermic patients
Authors: Krenz Henrike , Sansone Andrea , Fujarski Michael , Krallmann Claudia , Zitzmann Michael , Dugas Martin , Kliesch Sabine , Varghese Julian , Tüttelmann Frank , Gromoll Jörg 
Published: December 2021

AbstractBackgroundDue to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed.ObjectiveAssessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation.Materials and methodsRetrospective patient data (karyotype, age, height, weight, testis volume, follicle‐stimulating hormone, luteinizing hormone, testosterone, estradiol, prolactin, semen pH and semen volume) collected between January 2005 and June 2019 were retrieved from a patient data bank of a University Centre. Models were trained, validated and benchmarked based on different supervised machine learning algorithms. Models were then tested on an independent, prospectively acquired set of patient data (between July 2019 and July 2020). Benchmarking against physicians was performed in addition.ResultsBased on average performance, support vector machines and CatBoost were particularly well‐suited models, with 100% sensitivity and >93% specificity on the test dataset. Compared to a group of 18 expert clinicians, the machine learning models had significantly better median sensitivity (100% vs. 87.5%, p = 0.0455) and fared comparably with regards to specificity (90% vs. 89.9%, p = 0.4795), thereby possibly improving diagnosis rate. A Klinefelter Syndrome Score Calculator based on the prediction models is available on http://klinefelter‐score‐calculator.uni‐muenster.de.DiscussionDifferentiating Klinefelter Syndrome patients from azoospermic patients with normal karyotype (46,XY) is a problem that can be solved with supervised machine learning techniques, improving patient care.ConclusionsMachine learning could improve the diagnostic rate of Klinefelter Syndrome among azoospermic patients, even more for less‐experienced physicians.
Diagnostic values of different ECG durations in paroxysmal AF diagnosis
Authors: Li Qian , Su Bing , Liu Juan 
Published: December 2021

AbstractObjectiveTo evaluate the application value of different‐duration electrocardiograms (ECGs) in paroxysmal atrial fibrillation (AF) diagnosis.MethodsA total of 220 patients with paroxysmal atrial fibrillation diagnosed by 24‐h dynamic electrocardiogram in our hospital from January 2019 to December 2020 were selected as the study subjects. The patients were monitored for 10 s, 5 min, and 24 hr, respectively. The results of the three different‐duration ECGs were compared and analyzed in order to evaluate their paroxysmal AF diagnosis value.ResultsParoxysmal AF was detected in 18 patients (8.2%) with the 10‐s ECG; in 89 patients (40.5%) with the 5‐min ECG; and in 199 patients (90.5%) with the 24‐h dynamic ECG.ConclusionIn patients with paroxysmal AF, ECGs with longer detection times had higher detection rates.
Risk models for recurrence and survival after kidney cancer: a systematic review
Authors: Usher‐Smith Juliet A. , Li Lanxin , Roberts Lydia , Harrison Hannah , Rossi Sabrina H. , Sharp Stephen J. , Coupland Carol , Hippisley‐Cox Julia , Griffin Simon J. , Klatte Tobias , Stewart Grant D. 
Published: December 2021

ObjectiveTo systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent.Materials and MethodsWe performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence‐free survival (RFS), cancer‐specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C‐statistic and performed multivariate random‐effects meta‐analysis of the logit transformed C‐statistic to rank the models.ResultsOf a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C‐statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C‐statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C‐statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival.ConclusionSeveral models had good discriminative ability, with there being no single ‘best’ model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates.
Automatization through Practice: The Opportunistic‐Stopping Phenomenon Called into Question
Authors: Dewi Jasinta D. M. , Bagnoud Jeanne , Thevenot Catherine 
Published: December 2021

AbstractAs a theory of skill acquisition, the instance theory of automatization posits that, after a period of training, algorithm‐based performance is replaced by retrieval‐based performance. This theory has been tested using alphabet‐arithmetic verification tasks (e.g., is A + 4 = E?), in which the equations are necessarily solved by counting at the beginning of practice but can be solved by memory retrieval after practice. A way to infer individuals’ strategies in this task was supposedly provided by the opportunistic‐stopping phenomenon, according to which, if individuals use counting, they can take the opportunity to stop counting when a false equation associated with a letter preceding the true answer has to be verified (e.g., A + 4 = D). In this case, such within‐count equations would be rejected faster than false equations associated with letters following the true answers (e.g., A + 4 = F, i.e., outside‐of‐count equations). Conversely, the absence of opportunistic stopping would be the sign of retrieval. However, through a training experiment involving 19 adults, we show that opportunistic stopping is not a phenomenon that can be observed in the context of an alphabet‐arithmetic verification task. Moreover, we provide an explanation of how and why it was wrongly inferred in the past. These results and conclusions have important implications for learning theories because they demonstrate that a shift from counting to retrieval over training cannot be deduced from verification time differences between outside and within‐count equations in an alphabet‐arithmetic task.
Application of artificial intelligence techniques in textile wastewater decolorisation fields: A systematic and citation network analysis review
Authors: Liu Senbiao , Lo Chris K. Y. , Kan Chi‐wai 
Published: December 2021

AbstractThis study reviewed 155 journal articles to examine how artificial intelligence techniques are being applied in textile coloration and related fields. Distribution of the reviewed articles was assessed in terms of the type of journals, year of publication, methods, and research background. Based on the citation network analysis method, an objective approach, CitNetExplorer and VOSviewer are used to identify the clusters. It is found that artificial intelligence techniques are mainly three‐layer artificial neural networks with different designs, which are mainly used in textile wastewater decolorisation fields, including wastewater treatment and colour fading, such as colour prediction and real‐time monitoring of textile wastewater, dye removal efficiency, and dye degradation. Finally, the future research direction and limitations of this article are put forward.