Recent decades have witnessed increased number of studies focusing on digitalization and related capabilities. Across disciplines digitalization capability is viewed as a sources of sustained competiveness. Nonetheless, several issues related to conceptualizing digitalization capabilities remain ambivalent. The present study, uses co-citation analysis to clarify concept of digitalization capability and identify three underlining capabilities, namely digital integration capabilities, digital platform capabilities, and digital innovation capabilities, that represents micro-foundation of digitalization capabilities. Further, a capability-based model is developed which includes antecedents and consequences of digitalization capabilities in an integrated conceptual model. Suggestions for future research, theoretical contributions and managerial contributions are also presented.
The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.
The power system transition to smart grids brings challenges to electricity distribution network development since it involves several stakeholders and actors whose needs must be met to be successful for the electricity network upgrade. The technological challenges arise mainly from the various distributed energy resources (DERs) integration and use and network optimization and security. End-customers play a central role in future network operations. Understanding the network's evolution through possible network operational scenarios could create a dedicated and reliable roadmap for the various stakeholders' use. This paper presents a method to develop the evolving operational scenarios and related management schemes, including microgrid control functionalities, and analyzes the evolution of electricity distribution networks considering medium and low voltage grids. The analysis consists of the dynamic descriptions of network operations and the static illustrations of the relationships among classified actors. The method and analysis use an object-oriented and standardized software modeling language, the unified modeling language (UML). Operational descriptions for the four evolution phases of electricity distribution networks are defined and analyzed by Enterprise Architect, a UML tool. This analysis is followed by the active management architecture schemes with the microgrid control functionalities. The graphical models and analysis generated can be used for scenario building in roadmap development, real-time simulations, and management system development. The developed method, presented with high-level use cases (HL-UCs), can be further used to develop and analyze several parallel running control algorithms for DERs providing ancillary services (ASs) in the evolving electricity distribution networks.
Our study provides new evidence on asymmetric dependencies in international government bond markets, by examining bonds from developed, emerging, and frontier countries, using a quantile regression methodology. We find that the dependence structure for emerging and frontier markets significantly changes during financial crisis periods, which we show has important implications for international diversification of investment strategies. Moreover, we also examine in detail stock-bond correlations and uncover several instances of decoupling. In contrast, developed markets exhibit a more stable dependence pattern. In addition, we document that the degree and structure of dependence vary when foreign currencies are hedged or unhedged, and across bond maturity segments.
Background Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. Objectives This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. Data sources We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. Eligibility criteria Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. Data extraction Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. Results A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. Conclusion Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.
This paper examines whether foreign shareholders, foreign board members, and cross-listing, are related to corporate social responsibility (CSR) disclosure in Russia. A sample of 223 Russian listed companies is analyzed for the period 2012–2015. In line with legitimacy theory and agency theory, our empirical results demonstrate that foreign board members and cross-listing help companies to raise their accountability through increased CSR disclosure. At the same time we report that foreign ownership does not enhance CSR disclosure, as the majority of foreign shareholders of Russian companies are registered in offshore domiciles that are used for more efficient tax allocation.
Objectives. This study examines the effects of including acoustic research-based elements of the vocal expression of emotions in the singing lessons of acting students during a seven-week teaching period. This information may be useful in improving the training of interpretation in singing. Study design. Experimental comparative study. Methods. Six acting students participated in seven weeks of extra training concerning voice quality in the expression of emotions in singing. Song samples were recorded before and after the training. A control group of six acting students were recorded twice within a seven-week period, during which they participated in ordinary training. All participants sang on the vowel [a:] and on a longer phrase expressing anger, sadness, joy, tenderness, and neutral states. The vowel and phrase samples were evaluated by 34 listeners for the perceived emotion. Addi-tionally, the vowel samples were analyzed for formant frequencies (F1-F4), sound pressure level (SPL), spectral structure (Alpha ratio = SPL 1500-5000 Hz -SPL 50-1500 Hz), harmonic-to-noise ratio (HNR), and perturba-tion (jitter, shimmer). Results. The number of correctly perceived expressions improved in the test group's vowel samples, while no significant change was observed in the control group. The overall recognition was higher for the phrases than for the vowel samples. Of the acoustic parameters, F1 and SPL significantly differentiated emotions in both groups, and HNR specifically differentiated emotions in the test group. The Alpha ratio was found to statistically signifi-cantly differentiate emotion expression after training. Conclusions. The expression of emotion in the singing voice improved after seven weeks of voice quality train-ing. The F1, SPL, Alpha ratio, and HNR differentiated emotional expression. The variation in acoustic parame-ters became wider after training. Similar changes were not observed after seven weeks of ordinary voice training.
Recent advances in AI algorithms and computational power have led to opportunities for new methods and tools. Particularly when it comes to detecting the current status of inter-industry technologies, the new tools can be of great assistance. This is important because the research focus has been on how firms generate value through managing their business models. However, further attention needs to be given to the external technological opportunities that also contribute to value creation in firms. We applied unsupervised machine learning techniques, particularly DBSCAN, in an attempt to generate a macro-level technological map. Our results show that AI and machine learning tools can indeed be used for these purposes, and DBSCAN is a potential algorithm. Further research is needed to improve the maps and to use the generated data to study related phenomena including entrepreneurship.
This paper presents an effective hybrid supercapacitor-battery energy storage system (SC-BESS) for the active power management in a wind-diesel system using a fuzzy type distributed control system (DCS) to optimally regulate the system transient. It addresses a new online intelligent approach by using a combination of the fuzzy logic and DCS based on the particle swarm optimization techniques for optimal tuning and reduce the design effort of the control system. This mechanism combines the features of online fuzzy theory and distributed control system (DOFCS), which has a flexible structure. The proposed energy management algorithm for the hybrid SC-BESS is well able to repel the peak-impact of the battery storage system during the wind speed and load changes. The high performance of the suggested methodology is represented on a typical wind-diesel test system.
One of the most significant current topics in the energy market is the decentralized operation of the power system wherein the entire discipline is constructed based on the consensus concept. In such a framework, an agreement among all effective participants in the market should be brought without the presence of an independent arbitrator. This paper proposes a secured energy market architecture based on peer-to-peer (P2P) idea to provide an appropriate platform based on the decentralized network and key aspects of energy market, including network architecture, interface communication, and network security. It is apparent that communication interfaces connecting the market participants is a fundamental property for achieving this purpose and that must be secured against the malicious attacks. In this regard, reaching the secured consensus is guaranteed by providing a new blockchain platform coincided by P2P energy market. The energy market is conducted by an effective Relaxed Consensus-Innovation (RCI) based algorithm with the aim of bringing the power/price exchanged among connecting participants in the form of P2P structure. In the proposed model, a microgrid and a smart grid are considered as the market participants, who tend to negotiate with each other in a way that follow their own benefits in the secured environment. The microgrid includes the wind turbine (WT), photovoltaic (PV), tidal turbine and storage unit to satisfy its demand and the smart grid is composed of distributed generations (DGs) and lines in the form of the IEEE 24-bus test system. In order to handle the uncertainty effects in the problem, a stochastic framework based on unscented transform (UT) is proposed in the P2P energy market. In order to assess and verify the fault-tolerant system ability against the cyber-attack, the fault data injection attack (FDIA) is modeled and applied to the P2P energy market in a blockchain platform. The simulation results approve the appropriate performance and applicable nature of the proposed concepts in this paper.
The electric vehicle (EV), when aggregated by an agent (Aggregator), is a suitable candidate for participating in demand response in power system operation. As the interface between distribution network and EV users, as well as an independent party at the same time, an optimal scheduling algorithm is necessary with consideration of benefits of three parties, which in return will affect aggregators’ sustainable development. The benefits of distribution system from demand response, aggregator and EV users are defined in this paper. EV users’ benefit is described by their satisfaction on SOCs reached after a given period of time and overall costs/revenues for charging/discharging and policy award/penalty, while the benefit of distribution network for the integration of large amount EV loads through aggregator is evaluated by aggregator’s load shifting capability through a price-based demand response (DR) program under real time electricity price. The optimal scheduling of the aggregator is with an objective of maximizing its own benefit under constraints of EV users’ minimum satisfaction and minimum load-shifting capability required by distribution network. The optimization scheduling is tested by a test system, and further analysis is given on the effect of aggregator’s facility level and technology (Vehicle to Vehicle) and the operation mode of aggregator group on the benefits of three parties.
American Journal of Industrial MedicineVolume 62, Issue 12 p. 1076-1078 HEAT SPECIAL ISSUE ARTICLES Workplace Heat: An increasing threat to occupational health and productivity Tord Kjellstrom PhDMed, HonDrEng, Corresponding Author Tord Kjellstrom PhDMed, HonDrEng kjellstromt@yahoo.com orcid.org/0000-0002-7951-8245 National Center for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia HEAT-SHIELD project, Center for Technology Research and Innovation Ltd, (CETRI), Limassol, Cyprus Health and Environment International Trust, Mapua, New Zealand Correspondence Tord Kjellstrom, Health and Environment International Trust, 168 Stafford Drive, Mapua, New Zealand. Email: kjellstromt@yahoo.comSearch for more papers by this authorBruno Lemke PhD, Bruno Lemke PhD Health and Environment International Trust, Mapua, New Zealand School of Business, Nelson-Marlborough Institute of Technology, Nelson, New ZealandSearch for more papers by this authorJason Lee PhD, Jason Lee PhD Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore Global Asia Institute, National University of Singapore, Singapore, Singapore N.1 Institute for Health, National University of Singapore, Singapore, SingaporeSearch for more papers by this author Tord Kjellstrom PhDMed, HonDrEng, Corresponding Author Tord Kjellstrom PhDMed, HonDrEng kjellstromt@yahoo.com orcid.org/0000-0002-7951-8245 National Center for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia HEAT-SHIELD project, Center for Technology Research and Innovation Ltd, (CETRI), Limassol, Cyprus Health and Environment International Trust, Mapua, New Zealand Correspondence Tord Kjellstrom, Health and Environment International Trust, 168 Stafford Drive, Mapua, New Zealand. Email: kjellstromt@yahoo.comSearch for more papers by this authorBruno Lemke PhD, Bruno Lemke PhD Health and Environment International Trust, Mapua, New Zealand School of Business, Nelson-Marlborough Institute of Technology, Nelson, New ZealandSearch for more papers by this authorJason Lee PhD, Jason Lee PhD Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore Global Asia Institute, National University of Singapore, Singapore, Singapore N.1 Institute for Health, National University of Singapore, Singapore, SingaporeSearch for more papers by this author First published: 09 October 2019 https://doi.org/10.1002/ajim.23051Citations: 5 [Correction updated after online publication dated 18 September 2019: Author's affiliations have been revised and replaced with new affiliations.] Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume62, Issue12Special Issue on HeatDecember 2019Pages 1076-1078 RelatedInformation
This paper explores whether asset market equilibria in cryptocurrency markets exist. In doing so, it distinguishes between privacy and non-privacy coins. Most recently, privacy coins have attracted increasing attention in the public debate as non-privacy cryptocurrencies, such as Bitcoin, do not satisfy some users’ demands for anonymity. Analyzing ten cryptocurrencies with the highest market capitalization in each sub-market in the 2016–2018 period, we find that privacy coins and non-privacy coins exhibit two distinct market equilibria. Contributing to the current debate on the market efficiency of cryptocurrency markets, our findings provide evidence of market inefficiency. Moreover, the asset market equilibrium of privacy coins appears to be unrelated to the market equilibrium in the non-privacy coin markets, implying that the privacy coin market is emerging as a distinct asset market among cryptocurrency markets.
The existing energy system is heavily centralized, where production and distribution systems are largely controlled by big utility companies. The issue of global warming, the need to reduce emissions, sustainable use of conventional hydrocarbons, and improvements in renewable energy technologies – both in terms of cost and performance – has highlighted the need to transform the energy market. The trend is further exacerbated by the advancement in technology. The transformation is said to have implications for the stakeholders involved in the process, including customers, utility companies, and regulatory bodies. The objective of this study is to explore the effect this transformation would have on the local energy system and the changes needed to be made to ensure the transition provides desired outcomes. Based on the findings, the study proposes changes companies need to make in their business models to adapt to the changing needs of the market as well as of customers.
The issue of climate change, greenhouse gas emissions, global warming, and their effect on nature and the ecosystem has raised serious concerns. The desire to sustain economic growth and development while keeping a check on the environmental footprints is one of the leading challenges the contemporary world is currently facing. To ensure sustained growth, there is a need for technologies and solutions that has the potential to meet industrial needs without compromising the environment. Cleantech offers a possibility to address these needs in a sustainable and environmentally friendly manner. Cleantech, being an umbrella term, is often confused and misunderstood, in terms of its definition and scope. This study seeks to explore what cleantech actually is, how this sector came into prominence, what are the driving factors behind its surge, and what kind of socio-economic, technical, and regulatory prerequisites are necessary for the advancement of this sector.
Although the production of the built environment is increasingly globalized, and architecture and urban planning (AUP) professionals are known to be the key carriers of mobile ideas, the internationalization of small owner-centred architectural offices has gained little academic attention, compared to the large consultancies that dominate the global market. We bring together the literature on the circuits of urban planning ideas, the international movement of AUP firms and professional ethoses to explore the internationalization of small AUP offices. Using interview data from Finnish offices, we investigate the 'what, where, why and how' and find that they have entered specific geographically delimited AUP circuits demarcated by type of project. We contribute to the literature by identifying motives characteristic of small offices guided by professional ethoses suited to the circuits where they internationalize. Their ethoses may evolve in time and space, as they operate in new circuits. We propose that ethos-circuit coherence may contribute to the successful internationalization of small architectural offices. The findings open avenues for further research on professional ethos not only of architects but also other internationally operating professionals, as it may guide their decisions by other than a narrowly conceived profit motive.
IMPACTComplexity concepts help to open the black box of social innovation for public sector managers and policy-makers and to understand why social innovation can simultaneously be both a solution and problem. Complexity thinking guides formulating essential questions and helps to imagine the desired future and, in so doing, it also provides heuristic tools to address the paradox of social innovation.
This paper contributes by showing simultaneously the interlinked challenges of sustainability-based (based on long-term economic, social and environmental targets) executive remuneration and the problems of transparency in remuneration reporting. Our empirical data, analyzed using qualitative content analysis, consists of the published remuneration statements and sustainability reports of 43 Finnish companies reporting according to the Global Reporting Initiative (GRI) framework. Our results indicate that comprehensive sustainability remuneration is still rare in Finnish large companies: long-term financial targets are implemented at most companies, but social or environmental targets were only reported by 7 companies (16%). We conclude that executive remuneration policies are still mainly concerned with financial targets and aligning the interests of executives and shareholders and ignoring other stakeholders. The dominance of the remuneration reporting by the local Finnish Corporate Governance Code (FCGC) over GRI reporting on remuneration highlights sustainability accounting as local practice and is a reason for the lack of fully transparent reporting about the criteria of sustainability remuneration. We conclude that sustainable executive remuneration and simultaneously lacking transparency of reporting on it do not support implementation of sustainability strategies in Finnish companies and may hinder the development towards genuine sustainability and shows that there is an important (missing) link between incentives and sustainability in business.
The industrial sector is considered one of the fastest-growing sources of greenhouse gases, due to the excessive consumption of energy required to cope with the growing production of energy exhaustive products. The sta-tistical process monitoring (SPM) can be an effective tool for monitoring and controlling carbon emissions from industries. This article presents an economic-statistical design of the combined Shewhart X and exponentially weighted moving average (EWMA) scheme (X&EWMA scheme) for monitoring carbon emissions from industries to allow prompt action for controlling excessive emissions. The parameters of the proposed SPM scheme have been optimized for minimizing the expected total cost, including cost from carbon emissions and operational costs of the SPM scheme. The design of the X&EWMA scheme has been optimized considering a wide range of shifts in the mean of the emission process, and ensuring that the constraints on inspection rate, sample size, and false alarm rate are all satisfied. Comparative studies showed that the optimal X&EWMA scheme reduced the expected total cost by about 40%, 77%, and 28% compared with the basic X, EWMA, and X&EWMA schemes, respectively. The impact of the design parameters on the effectiveness of the proposed SPM scheme has also been investigated by sensitivity analysis. Finally, the application of the proposed SPM scheme is demonstrated by using real data for carbon emissions from different industrial facilities. This study is expected to considerably reduce the cost owing to excessive carbon emissions from industries and widen the literature on the utilization of SPM tools in managing the quality of the environment.
The social contributions of research activities have become more and more important in the rapidly changing innovation environment. Despite the fact that industrial commercialization of research results constitutes one of the most essential drivers for innovation and competitiveness, most generally used social impact evaluation criteria do not include clear metrics involving research commercialization possibilities. In a similar manner, principles regarding sustainable development have been largely omitted from the impact criteria. This paper considers the "broader impacts criteria" (BIC) model developed for social impact evaluation in the National Science Foundation in United States. We propose extensions to the BIC criteria related to commercialization and sustainable development viewpoints on impact evaluation. This paper also considers a newly introduced extension to BIC, called "inclusionimmediacy criteria" (IIC). Based on it, we propose an extended version of the model that aims to additionally evaluate the impact of research from commercialization point of view.
