bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of social scientifically relevant topics in an interview repository: a natural language processing experiment
Authors: Gárdos Judit , Egyed-Gergely Julia , Horváth Anna , Pataki Balázs , Vajda Roza , Micsik András 
Published: October 2023

PurposeThe present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.Design/methodology/approachThe authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.FindingsThe study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.Originality/valueInterview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.
Swedish public procurement and the defence industry: obstacles and opportunities
Authors: Hellberg Roland 
Published: October 2023

PurposeA deteriorating security situation and an increased need for defence equipment calls for new forms of collaboration between Armed Forces and the defence industry. This paper aims to investigate the ways in which the accelerating demand for increased security of supply of equipment and supplies to the Armed Forces requires adaptability in the procurement process that is governed by laws on public procurement (PP).Design/methodology/approachThis paper is based on a review of current literature as well as empirical data obtained through interviews with representatives from the Swedish Defence Materiel Administration and the Swedish defence industry.FindingsCollaboration with the globalized defence industry requires new approaches, where the PP rules make procurement of a safe supply of defence equipment difficult.Research limitations/implicationsThe study's empirical data and findings are based on the Swedish context. In order to draw more general conclusions in a defence context, the study should be expanded to cover more nations.Practical implicationsThe findings will enable the defence industry and the procurement authorizations to better understand the requirements of Armed Forces, and how to cooperate under applicable legal and regulatory requirements.Originality/valueThe paper extends the extant body of academic knowledge of the security of supply into the defence sector. It serves as a first step towards articulating a call for new approaches to collaboration in defence supply chains.
Determination of disease risk factors using binary data envelopment analysis and logistic regression analysis (case study: a stroke risk factors)
Authors: Gholamazad Maedeh , Pourmahmoud Jafar , Atashi Alireza , Farhoudi Mehdi , Deljavan Anvari Reza 
Published: October 2023

Purpose
A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

Design/methodology/approach
In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

Findings
The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

Originality/value
The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.
Exploring the influence of service employees' characteristics on their willingness to work with service robots
Authors: Tu Yangjun , Liu Wei , Yang Zhi 
Published: October 2023

PurposeThis research empirically investigates how service employees' ratings of technology readiness (TRI), negative attitudes towards robots (NARS), Big Five personality traits (BFI) and emotional demands (ED) affect their willingness to work with service robots (WTW).Design/methodology/approachOne set of data is collected from 410 service employees expected to work with service robots in Study 1. Another set of field data is collected from 102 employees working with service robots in Study 2. Hierarchical regression is used to test hypotheses about the impact of technology readiness, negative attitudes towards robots and Big Five personality traits on WTW. Additionally, the interactions of emotional demands in the workplace are analysed.FindingsTRI-optimism and TRI-insecurity significantly affect WTW in Study 2 but are nonsignificant in Study 1. The impacts of NARS-emotions in interaction with robots and NARS-interaction with robots situations on WTW are significant in Study 1 but nonsignificant in Study 2. Moreover, BFI-neuroticism negatively affected WTW in Study 1, while these effects were nonsignificant in Study 2. Finally, emotional demands significantly interact with three of eleven dimensions of IVs in Study 1, but all interactions are nonsignificant in Study 2.Practical implicationsThis research provides a guiding framework for service companies to screen employees expected to cowork with service robots, to enhance newly hired employees' WTW and to improve existing employees' WTW.Originality/valueIntegrating the characteristics of service employees, service robots and jobs into a theoretical framework, this research is the first to empirically examine the effects of service employees' several critical characteristics (technology readiness, negative attitudes towards robots and Big Five personality) on WTW and the moderation of job characteristics (emotional demands).
Data-driven optimization for production planning with multiple demand features
Authors: Su Xiaoli , Zeng Lijun , Shao Bo , Lin Binlong 
Published: October 2023

PurposeThe production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.Design/methodology/approachIn this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.FindingsNumerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.Originality/valueCompared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.
Development of protective coating system using natural rubber – acrylic polyol resin for mild steel
Authors: Kerinasamy Naidu Monapriya Naidu , Wonnie Ma Iling Aema , Kumar Sachin Sharma Ashok , Balakrishnan Vengadaesvaran , Subramaniam Ramesh , Kasi Ramesh 
Published: October 2023

Purpose
The purpose of this study is to develop a protective coating system on mild steel panel incorporating epoxidized natural rubber with acrylic polyol resin.

Design/methodology/approach
In this work, a novel attempt is made to develop binder coatings using epoxidized natural rubber-based material and an organic resin (acrylic resin) for corrosion protection on metal substrate. Seven different samples of multifunctional coatings are developed by varying the compositions of epoxidized natural rubber (ENR) and acrylic resin. The properties of the developed coatings have been characterized using analytical methods such as Fourier transform infrared spectroscopy (FTIR) and electrochemical impedance spectroscopy (EIS). EIS has been carried out for 30 days to evaluate the corrosion resistance after immersing into 3.5 wt.% of sodium chloride. Cross hatch cut tester (CHT) has been used to study the adhesive properties. UV–Visible Spectroscopy (UV–Vis) was also used to assess changes in the coating-film transparency of the natural rubber-based coating systems in this study.

Findings
The developed coatings have formed uniform layer on the substrate. CHT results show excellent adhesion of the coatings. Higher concentrations of ENR have higher transparency level, which reduces when the acrylic concentration increases. FTIR analysis confirms the crosslinking that occurred between the components of the coatings. Based on the impedance data from EIS, the incorporation of natural rubber can be an additive for the corrosion protection, which has the coating resistance values well above 108Ω even after 30 days of immersion.

Practical implications
The blending method provides a simple and practical solution to improve the strength and adhesion properties of acrylic polyol resin with epoxidized natural rubber. There is still improvement needed for long-term applications.

Originality/value
The work has been conducted in our laboratory. The combination of natural rubber-based materials and organic resins is a new approach in coating research.
Drivers of the next-minute Bitcoin price using sparse regressions
Authors: Gurrib Ikhlaas , Kamalov Firuz , Starkova Olga , Elshareif Elgilani Eltahir , Contu Davide 
Published: October 2023

Purpose
This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Design/methodology/approach
Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings
Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

Originality/value
To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.
Digital inclusion for all? A gender-disaggregated analysis of e-government service use in Indonesia
Authors: Ariansyah Kasmad , Setiawan Ahmad Budi , Darmanto Darmanto , Nupikso Daru , Budhirianto Syarif , Hidayat Dayat , Hikmaturokhman Alfin 
Published: October 2023

Purpose
This study aims to investigate the factors that influence the use of e-government services among male and female samples, aiming at gaining a deeper understanding of how to promote digital inclusion and maximize the benefits of digitalization.

Design/methodology/approach
This study uses binomial logistic regression modeling on two data sets: a nationwide survey at the individual level of 8,854 usable samples conducted in 2019 and village potential statistics.

Findings
The survey indicates low adoption and use of e-government services among both males and females. The statistical analysis further reveals that although there might be some variations in the influencing factors between the genders, the overall patterns are similar. These findings suggest that the low adoption and use of e-government services among males and females can be attributed to factors such as misperceptions about internet-based services, resulting in low motivation to use the services, inadequate internet connectivity, affordability, limited digital skills and exposure to harmful online content.

Originality/value
This study conducts a gender-disaggregated analysis to determine whether the antecedents of males’ decisions to adopt e-government services differ from those of females. Such a study is underrepresented in the extant literature, particularly in the context of e-government service use.
Does workplace incivility affect travel agency performance through innovation, organizational citizenship behaviors, and organizational commitment?
Authors: Al-Romeedy Bassam Samir , El-Sisi Shaymaa 
Published: October 2023

Purpose
The purpose of this study is to evaluate the impact of workplace incivility on innovation, organizational citizenship behaviors, organizational commitment and performance in travel agencies. The study also aims at exploring the mediating roles of innovation, organizational citizenship behaviors and organizational commitment in the relationship between workplace incivility and performance.

Design/methodology/approach
The questionnaire was used to collect study data from the sample, which consisted of employees in Egyptian travel agencies, category (A), in Cairo Governorate. The questionnaire link was sent to 854 employees in travel agencies, with 644 obtained responses. Only 586 responses were suitable for analysis.

Findings
The results depicted that there is a significant and negative effect of workplace incivility on innovation, organizational citizenship behaviors, organizational commitment and performance. The results also revealed a significant and positive effect of innovation, organizational citizenship behaviors and organizational commitment on performance. Moreover, the results indicated that innovation, organizational citizenship behaviors and organizational commitment play partial mediating roles in the link between workplace incivility and performance.

Originality/value
The current study attempts to measure the mediating role of innovation, organizational citizenship behaviors and organizational commitment in the link between workplace incivility and performance. Managerial implications, limitations and future research are also presented.
Defoliation enhances the positive effects of soil microbial diversity on plant productivity
Authors: Zhang Ruihuan , Zhou Ping , Wei Yuqi , Liu Nan , Zhang Yingjun 
Published: October 2023

AbstractDefoliation, often induced by herbivores, and soil microbiomes are recognized as key drivers of plant diversity and productivity in grasslands. However, lack of experiments simultaneously manipulating defoliation and soil microbial diversity has constrained our understanding of how these two factors interactively influence both above‐ground and below‐ground net primary productivity (ANPP and BNPP).We employed a soil filtering approach to create the gradient of soil microbial diversity and removal of above‐ground biomass by clipping to simulate defoliation effect from herbivores. Our goal was to investigate how soil microbial diversity affected both ANPP and BNPP under the stress of defoliation.We found that defoliation and soil microbial diversity loss decreased the ANPP and BNPP of the plant community, andMedicago falcata(legume) was more responsive to these treatments thanLeymus chinensis(grass) andArtemisia frigida(forb). ANPP was positively correlated with soil microbial richness only when plants suffered defoliation. By contrast, BNPP showed a closer relationship with soil fungal richness than bacteria, and defoliation enhanced this relationship. The structural equation model (SEM) showed that defoliation indirectly regulated ANPP by affecting plant P content, while soil bacterial diversity loss decreased the ANPP through altering plant P and fungal diversity loss directly decreased BNPP. The LEfSe and linear regression analysis revealed that the biomarkers of bacteria (e.g.Actinobacteria,Xanthobacteraceae) and fungi (e.g.Ascomycota,Glomerales) significantly contributed to the variation of ANPP and BNPP under defoliation treatment respectively. Defoliation enhanced phosphorus acquisition by soil microorganisms, further strengthening the microbial regulation of plant productivity.Overall, we provide novel insights on how defoliation enhances the positive effects of soil microbial diversity on above‐ and below‐ground net primary productivity. Our findings highlight that soil microorganisms associated with phosphorus cycling are mobilized to play a more active role in promoting productivity after defoliation. It may provide a new perspective on how to better utilize the role of soil microorganisms in grazed or managed grasslands.Read the freePlain Language Summaryfor this article on the Journal blog.
Anatomical distribution of starch in the stemwood influences carbon dynamics and suggests storage‐growth trade‐offs in some tropical trees
Authors: Herrera‐Ramírez David , Hartmann Henrik , Römermann Christine , Trumbore Susan , Muhr Jan , Maracahipes‐Santos Leonardo , Brando Paulo , Silvério Divino , Huang Jianbei , Kuhlmann Iris , Sierra Carlos A. 
Published: October 2023

Abstract

Trees balance temporal asynchrony in carbon source and sink activity by accumulating and using non‐structural carbon (NSC). Previous work has demonstrated differences in the amount and distribution of NSC stored in stemwood in tropical tree species and related these patterns in NSC distribution to tree growth and mortality rates. However, we still do not know how changes in the amount and location of starch, a major component of NSC in stemwood, influence the seasonal carbon dynamics of mature trees and how this may reflect storage‐growth trade‐offs.

In this work, we hypothesized that combining two life history traits, here leaf habit (evergreen/semi‐deciduous) and the anatomical distribution of starch within the stemwood (parenchyma storage and fibre storage), would allow us to explain differences in the seasonal interplay between carbon sources and sinks and the use and accumulation of starch in the tree stem. We expected semi‐deciduous/fibre‐storing species to have greater seasonal amplitudes of carbon source and sink activity, and therefore greater variation in starch content and stronger storage‐growth trade‐offs than evergreen/parenchyma‐storing species.

We measured monthly increments in stem radial growth, soluble sugars and starch every 3 months during 2019 in Dacryodes microcarpa (semi‐deciduous/fibre‐storing species), Ocotea leucoxylon (evergreen/parenchyma‐storing species) and Sacoglottis guianensis (semi‐deciduous/parenchyma‐storing species).

We found seasonal changes in starch but not sugars in the semi‐deciduous species, with greater amplitude in the fibre‐storing species that also had greater storage capacity and stem respiration rates. The fibre‐storing species further showed a negative relationship between starch consumption/accumulation and growth during the rainy season, suggesting a trade‐off between growth and storage, with starch accumulating in some cases when growth was low.

Synthesis. Our results show the influence of seasonal starch storage on carbon dynamics in three species of tropical trees that differ in leaf phenology and starch storage traits. Semi‐deciduous/fibre‐storing species have greater temporal variation in carbon sink activities and more seasonally dynamic starch content. Since the fibre‐storing species we studied are slower‐growing and longer‐lived trees with lower mortality rates compared to the parenchyma‐storing species, these results may provide clues about how storage traits could influence their survival and life span.