bluuebunny/crossref_metadata_2025_split huggingface dataset:
Local 5G services on campus premises: scenarios for a make 5G or buy 5G decision
Authors: Kulkarni Venkatesh , Walia Jaspreet , Hämmäinen Heikki , Yrjölä Seppo , Matinmikko-Blue Marja , Jurva Risto 
Published: August 2021

Purpose
The purpose of this paper is to build possible future scenarios for indoor connectivity in a venue such as a university campus and build alternative value network configurations (VNCs) defining different local network deployment options, focused on the Finnish telecom market.

Design/methodology/approach
In this paper, Schoemaker’s scenario planning method (Schoemaker, 1995) is used to construct future scenarios and the VNC method of Casey et al. (2010) is used to build alternative VNCs. The paper studies the Aalto University campus network for current end-user data usage demand and the existing technology used in meeting the end-user needs and forecasts the demand for the next five years to understand the need for 5G.

Findings
This research concludes that with the provision of local spectrum licenses, there is an opportunity for venue owners to take the role of 5G local operator on the venue premises. Furthermore, it enables venue owners to collaborate with the incumbent mobile network operators (MNOs) in a neutral host model and provide venue-specific connectivity services.

Research limitations/implications
A detailed economic assessment for the network deployment in the campus is considered for future study.

Originality/value
Considering the provision for local spectrum licenses, this paper has taken a unique attempt in identifying the future scenarios for local 5G network operations. It provides a strategic direction for the venue owners in adopting 5G technology and whether to make 5G or buy 5G from MNOs.
An implicit-explicit local method for parabolic partial differential equations
Authors: Tunc Huseyin , Sari Murat 
Published: August 2021

PurposeThe purpose of this article is to derive an implicit-explicit local differential transform method (IELDTM) in dealing with the spatial approximation of the stiff advection-diffusion-reaction (ADR) equations.Design/methodology/approachA direction-free numerical approach based on local Taylor series representations is designed for the ADR equations. The differential equations are directly used for determining the local Taylor coefficients and the required degrees of freedom is minimized. The complete system of algebraic equations is constructed with explicit/implicit continuity relations with respect to direction parameter. Time integration of the ADR equations is continuously utilized with the Chebyshev spectral collocation method.FindingsThe IELDTM is proven to be a robust, high order, stability preserved and versatile numerical technique for spatial discretization of the stiff partial differential equations (PDEs). It is here theoretically and numerically shown that the order refinement (p-refinement) procedure of the IELDTM does not affect the degrees of freedom, and thus the IELDTM is an optimum numerical method. A priori error analysis of the proposed algorithm is done, and the order conditions are determined with respect to the direction parameter.Originality/valueThe IELDTM overcomes the known disadvantages of the differential transform-based methods by providing reliable convergence properties. The IELDTM is not only improving the existing Taylor series-based formulations but also provides several advantages over the finite element method (FEM) and finite difference method (FDM). The IELDTM offers better accuracy, even when using far less degrees of freedom, than the FEM and FDM. It is proven that the IELDTM produces solutions for the advection-dominated cases with the optimum degrees of freedom without producing an undesirable oscillation.
Three decades of interventions for the unemployed – review of practices between 1990 and 2020 and their effects on (re) employment competencies
Authors: Celume Macarena-Paz , Korda Helene 
Published: August 2021

PurposeThis paper provides evidence of the outcomes proposed by the reviewed programmes and their level of effectiveness.Design/methodology/approachArticles were screened by title and abstract to ensure correspondence with exclusion/inclusion criteria. Themes were analysed through collective coding and scoring. Size effects were calculated.FindingsThree expected outcomes: psychological, technical and (re) employment. The most frequent and significant results are found in psychological. The review could not find consistent results in effectiveness of the type of training, because of the diversity of propositions and socio-cultural origins of interventions.Research limitations/implicationsSocio-cultural context acts as variable; thus, deep interdisciplinary analysis on context where trainings were developed is suggested in order to understand the impact of trainings according to their population defining effectiveness.Originality/valueThere is no previous review of type of interventions for the unemployed focusing on last 30 years.
A bibliometric review on the development in e-tourism research
Authors: Singh Shalini , Bashar Abu 
Published: August 2021

PurposeE-tourism is instilling in the tourism industry with the advancement in the technological infrastructure all over the world and fetching tremendous tourists’ attention. The dynamic changes in the technological aspects unveil varied developments in the tourism industry. This paper attempts to reveal the developments in the field of e-tourism by a systematic review of the literature using bibliometric analysis.Design/methodology/approachIn total,146 research articles were retrieved from the Web of Science data during the period of 2004 – 2020, for further analysis using VOSviewer and Biblioshiny package of R Studio.FindingsUseful insights resulted in the form of most cited papers, contribution in e-tourism research by different authors, countries, institutions, journals and so on, co-occurrence analysis and cluster analysis for major trends or themes of e-tourism. This study solicits an elaborated review of e-tourism research and unveils the future directions for the researchers.Originality/valueThis study adds substantial value to the research of e-tourism by analysing the bibliometric data of the last 16 years, that is, from 2004 – 2020, procured from the Scopus by analysing the significant trends developed in the e-tourism research. It also adds value by indicating the emerging areas of e-tourism.
Enablers of knowledge management: practical research-based in the construction industry
Authors: Boamah Fredrick Ahenkora , Zhang Jianhua , Wen Dandan , Sherani Mrs , Hayat Adil , Horbanenko Oleksandra 
Published: August 2021

Purpose
To effectively implement knowledge management, one prerequisite is to understand and implement the knowledge management enablers in the sense of optimal institutional efficiency. This paper aims to analyze and measure the significant enablers in overseeing knowledge management in the construction sector.

Design/methodology/approach
The mixed-method technique was used to achieve the objective of this study and involved the application of detailed questions to project engineers and project managers within leading construction engineering companies, provided by the Institution of Engineering and Technology Ghana. In total, 150 questionnaires were collected and analyzed using the Statistical Package for Social Sciences v. 26.

Findings
The study confirms that the knowledge management enablers such as employees knowledge, motivations, effective decisions and strategic planning are some of the important ways in which construction professionals can achieve different strategic goals in many processes and concluded that the progress of the next generation knowledge management strategy will be based on content sharing, decision-making and by promoting the free flow of ideas.

Originality/value
The study offers perspectives into knowledge management enablers and recommends key performance factors, championed by Ghanaian construction contractors for institutional knowledge capture.
Insights gained from early modelling of COVID-19 to inform the management of outbreaks in UK prisons
Authors: Bays Declan , Williams Hannah , Pellis Lorenzo , Curran-Sebastian Jacob , O'Mara Oscar , Team PHE Joint Modelling , Finnie Thomas 
Published: August 2021

Purpose
In this work, the authors present some of the key results found during early efforts to model the COVID-19 outbreak inside a UK prison. In particular, this study describes outputs from an idealised disease model that simulates the dynamics of a COVID-19 outbreak in a prison setting when varying levels of social interventions are in place, and a Monte Carlo-based model that assesses the reduction in risk of case importation, resulting from a process that requires incoming prisoners to undergo a period of self-isolation prior to admission into the general prison population.

Design/methodology/approach
Prisons, typically containing large populations confined in a small space with high degrees of mixing, have long been known to be especially susceptible to disease outbreaks. In an attempt to meet rising pressures from the emerging COVID-19 situation in early 2020, modellers for Public Health England’s Joint Modelling Cell were asked to produce some rapid response work that sought to inform the approaches that Her Majesty’s Prison and Probation Service (HMPPS) might take to reduce the risk of case importation and sustained transmission in prison environments.

Findings
Key results show that deploying social interventions has the potential to considerably reduce the total number of infections, while such actions could also reduce the probability that an initial infection will propagate into a prison-wide outbreak. For example, modelling showed that a 50% reduction in the risk of transmission (compared to an unmitigated outbreak) could deliver a 98% decrease in total number of cases, while this reduction could also result in 86.8% of outbreaks subsiding before more than five persons have become infected. Furthermore, this study also found that requiring new arrivals to self-isolate for 10 and 14 days prior to admission could detect up to 98% and 99% of incoming infections, respectively.

Research limitations/implications
In this paper we have presented models which allow for the studying of COVID-19 in a prison scenario, while also allowing for the assessment of proposed social interventions. By publishing these works, the authors hope these methods might aid in the management of prisoners across additional scenarios and even during subsequent disease outbreaks. Such methods as described may also be readily applied use in other closed community settings.

Originality/value
These works went towards informing HMPPS on the impacts that the described strategies might have during COVID-19 outbreaks inside UK prisons. The works described herein are readily amendable to the study of a range of addition outbreak scenarios. There is also room for these methods to be further developed and built upon which the timeliness of the original project did not permit.
Comprehensive evaluation of Iranian regional innovation system (RIS) performance using analytic hierarchy process (AHP)
Authors: Sadabadi Ali Asghar , Rahimi Rad Zohreh , Fartash Kiarash 
Published: August 2021

PurposeThis study aims to to provide a model based on the context and conditions of the regions in Iran for monitoring regional innovation systems (RISs).Design/methodology/approachIn the first stage, after reviewing the literature, interviewing the experts, and then thematic analyzing the content of the obtained data, initial Dimensions and indicators were identified. Then, a fuzzy Delphi technique and a seven-scale questionnaire were used to test the results and expert opinion. Finally, after the extraction and correction of the indicators to evaluate the RISs, a comprehensive model was identified in 5 dimensions, 16 indicators and 176 sub-indicators. In the next step, the importance weights of the main 16 indicators were determined using the analytic hierarchy process method.FindingsThe proposed RIS assessment model consists of firms’ behavior and regional interactions, economy, human capital, innovative outputs, locational features, that each dimension has its own indicators and sub-indicators, each one is discussed in the paper.Originality/valueThe research contribution is that the proposed RIS model of this research is the first model that is appropriate to the context of Iran.
A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction
Authors: Liu Yitong , Yang Yang , Xue Dingyu , Pan Feng 
Published: August 2021

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.
Smart system to generate the optimal authorized bounding volume
Authors: Belkaïd Alia , Ben Saci Abdelkader , Hassoumi Ines 
Published: August 2021

PurposeThe overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine, and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the artificial intelligence (AI) in accomplishing this task and solving the problem.Design/methodology/approachThis paper provides a method of computational design in generating the optimal authorized bounding volume which uses the best target values of morphological urban rules. It examines an intelligent system, adopting the multi-agent approach, which aims to control and increase urban densification by optimizing morphological urban rules. The process of the system is interactive and iterative. It allows collaboration and exchange between the machine and the designer. This paper is adopting and developing a new approach to resolve the distributed constraint optimization problem in generating the authorized bounding volume. The resolution is not limited to an automatic volume generation from urban rules, but also involves the production of multiple optimal-solutions conditioned both by urban constraints and relevance chosen by the designer. The overall functioning of this system is based on two approaches: construction and supervision. The first is conducted entirely by the machine and the second requires the intervention of the designer to collaborate with the machine. The morphological translation of urban rules is sometimes contradictory and may require additional external relevance to urban rules. Designer arbitration assists the AI in accomplishing this task and solving the problem. The human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction. This paper shows and analyses interactions with the machine during the building generation process. It presents different cases of application and discusses the relationship between relevance and constraints satisfaction. This topic can inform a chosen urban densification strategy by assisting a typology of the optimal authorized bounding volume.FindingsThe human-computer collaboration is achieved at the appropriate time and relies on the degree of constraint satisfaction with fitness function.Originality/valueThe resolution of the distributed constraint optimization problem is not limited to an automatic generation of urban rules, but involves also the production of multiple optimal ABV conditioned both by urban constraints as well as relevance, chosen by the designer.
Users’ perception toward Bitcoin Green with big data analytics
Authors: Mnif Emna , Lacombe Isabelle , Jarboui Anis 
Published: August 2021

Purpose
Nowadays, Bitcoin is facing many environmental problems arising from the proof of work based on blockchain. For this reason, Bitcoin Green (BITG) has been created and would solve these issues. The purpose of this paper is to visualize the users’ perception toward BITG through Twitter text analysis.

Design/methodology/approach
The big data used in this study includes two sources. The first data were extracted from the “Google Trends” engine during the period between 20 September 2015 and 15 September 2020. The second data were extracted from the Twitter application. This research explores the perceived ease of use, the perceived usefulness, the social influence, the perceived control and the user attitudes toward BITG. Therefore, lexicon-based sentiment analysis techniques combined with different dictionaries are built to visualize the drivers of investor attitudes toward the BITG using Twitter text messages. Besides, this study has checked the validity of two main assumptions using the normality (Jarque-Bera) and Kruskal-Wallis rank sum tests capable to conclude whether users mostly perceive BITG as a sustainable technology.

Findings
This empirical work affords insights into users’ intentions by exploring the drivers of BITG perception. The results show that users positively perceive the use of BITG as a sustainable blockchain. Besides, its usefulness is more appreciated from its ethical and technological characteristics, and its perceived application is mainly based on investment and coin offering use. Similarly, users are mostly showing positive emotions toward BITG.

Research limitations/implications
Tweets related to “BITG” are not as voluminous as the other cryptocurrencies like Bitcoin and Ethereum, which make it difficult to extract all the characteristics and use cases.

Originality/value
To the best of the authors’ knowledge, this work is the first one that uses the theory of planned behavior and the theory of acceptance model to explore cognitive factors in understanding investor intentions in adopting BITG.
Bitcoin’s value proposition: shorting expansionary monetary policies
Authors: Morillon Thibaut 
Published: August 2021

Purpose
Professionals and academics alike hold polarized opinions about Bitcoin’s purpose and its fundamental value. This paper aims to describe Bitcoin’s unique features that make it such an intriguing asset and proposes a new way to consider Bitcoin and its underlying value.

Design/methodology/approach
In this paper the author discusses Bitcoin’s defining features that make it a unique asset. The author argues that Bitcoin should not be considered as a single purpose asset only, but rather as a new digital financial asset serving several functions, at least partially. The author discusses the role of Bitcoin in the traditional financial system, contrasts Bitcoin to gold, considers the implications of the continuance of expansionary policies on Bitcoin and discusses the impact of the emergence of cryptocurrencies as a new asset class on public policies.

Findings
In addition to functioning as a means of payment (at least partially) and a diversification tool, part of Bitcoin’s value proposition stems from its worth as a short position on modern expansionary monetary policies. Indeed, Bitcoin’s value should rise if expansionary monetary policies are maintained, amounting to a tool to short these policies, which should be considered in future attempts to value Bitcoin.

Originality/value
The author adds a new layer to the ongoing thought process by arguing of a function played by Bitcoin unaccounted for thus far by the literature. Additionally, the author describes the features and mechanisms, allowing Bitcoin to play that role.
Defect patterns study of pick-and-place machine using automated optical inspection data
Authors: Cen Yuqiao , He Jingxi , Won Daehan 
Published: August 2021

Purpose
This paper aims to study the component pick-and-place (P&P) defect patterns for different root causes based on automated optical inspection data and develop a root cause identification model using machine learning.

Design/methodology/approach
This study conducts experiments to simulate the P&P machine errors including nozzle size and nozzle pick-up position. The component placement qualities with different errors are inspected. This study uses various machine learning methods to develop a root cause identification model based on the inspection result.

Findings
The experimental results revealed that the wrong nozzle size could increase the mean and the standard deviation of component placement offset and the probability of component drop during the transfer process. Moreover, nozzle pick-up position can affect the rotated component placement offset. These root causes of defects can be traced back using machine learning methods.

Practical implications
This study provides operators in surface mount technology assembly lines to understand the P&P machine error symptoms. The developed model can trace back the root causes of defects automatically in real line production.

Originality/value
The findings are expected to lead the regular preventive maintenance to data-driven predictive and reactive maintenance.