How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications

In this paper an analysis of the presence and possibilities of altmetrics for bibliometric and performance analysis is carried out. Using the web based tool Impact Story, we collected metrics for 20,000 random publications from the Web of Science. We studied both the presence and distribution of altmetrics in the set of publications, across fields, document types and over publication years, as well as the extent to which altmetrics correlate with citation indicators. The main result of the study is that the altmetrics source that provides the most metrics is Mendeley, with metrics on readerships for 62.6 % of all the publications studied, other sources only provide marginal information. In terms of relation with citations, a moderate spearman correlation (r = 0.49) has been found between Mendeley readership counts and citation indicators. Other possibilities and limitations of these indicators are discussed and future research lines are outlined.     

An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data

This study empirically analyzes the effects of artificial intelligence (AI) on electric vehicle technology innovation by employing a machine learning-based text mining model and the international patent classification (IPC) co-occurrence network analysis, using patent data filed from 1980 to 2017. Based on artificial intelligence algorithms classified, the study demonstrates the dynamic changing pattern of the convergence of artificial intelligence and electric vehicle technology and reveals how artificial intelligence has affected electric vehicle technology innovation over time. This study reveals that artificial intelligence accelerates the automation of electric vehicle driving, and that artificial intelligence algorithms that are widely used in electric vehicles have changed over time, and that technology areas of electric vehicles that AI affects also have been changed.     

An analysis of safety control effectiveness

The cost of injuries and “accidents” to an organisation is very important in establishing how much it should spend on safety control. Despite the usefulness of information about the cost of a company's accidents, it is not customary accounting practice to make these data available. Of the two kinds of costs incurred by a company through occupational injuries and accidents, direct costs and indirect costs; the direct costs are much easier to estimate. However, the uninsured costs are usually more critical and should be estimated by each company. The authors investigate a general model to estimate the above costs and hence to establish efficient safety control. One construction company has been a pilot for this study. By analysing actual company data for three years, it is found that the efficient safety control cost should be 1.2–1.3% of total contract costs.     

Topical review of artificial intelligence national policies: A mixed method analysis

A number of countries have adopted national policies and directives to balance the advantages and disadvantages of innovative technologies. The purpose of this paper is to identify the most prominent topics addressed by national AI policies, as well as their relative importance across nations. This paper integrates the results of a topic modeling analysis of 30 national AI policies with a qualitative content analysis of the policies. Based on this analysis, fourteen main common themes have been identified among national AI policies, which predominantly relate to educational, technological, government, ethical/legal, and social good concerns. Following this, we conducted a co-occurrence analysis of topics across countries to determine the extent of topic prioritization in each country. In this investigation, several marginalized AI policy topics were also identified. In general, the challenges and concerns of the majority of policies pertain to education, technology, and the government. Governments refer to real-world projects and investments in AI technologies without developing shared digital governance platforms that promote responsible and sustainable AI among technology titans and mitigate the negative effects of surveillance capitalism. Although governments acknowledge the ethical and legal aspects of AI development and frequently cite the GDPR, they limit their discussion to the data level, particularly data sharing, and marginalize ethical algorithms and other phases of data and AI management and design. In addition, government policies marginalize AI startups and the API economy, even though they play a crucial role in fostering the AI ecosystem. The paper contributes to the existing literature on AI policy and will serve as a guide for AI policymakers to help them better understand the topical similarities across countries and the neglected or marginalized challenges that require further attention.     

Environmental science research in India: An analysis of publications

Investigates the trends in environmental science research in India with regard to its various branches, channels of communication used, authorship pattern of the papers, institution-wise output, rank of journals, extent of collaboration and scholarship of papers basing the entries noticed in theParyavaran Abstracts. Major areas of interest of Indian environmentalists are given and prolific investigators have been listed. Journals used by Indian workers for publication of their work are studied. Subject areas with number of papers, number of authors, and average authorship are tabulated.     

Contribution to development of risk analysis methods by application of artificial intelligence techniques

During the life cycle of technical systems, precise and detail failure risk analysis gives suitable input elements for taking appropriate actions, which allows reducing of unwanted uncertainty and occurrences. Traditional method for risk analysis, which is applied for many years, especially in analysis of functionality of technical systems, is Failure Mode and Effects Analysis (FMEA) method. However, in many cases, this method shows weaknesses related to the inconsistency, which is a result of insecure subjectivity during the determination of values for parameters that gives Risk Priority Number (RPN), as well as other weaknesses. This paper shows contribution to the development of failure risk analysis based on FMEA method. Contribution of the development of risk analysis methods is given through modification of traditional FMEA method by integration of artificial intelligence techniques, in this case, by integration of fuzzy logic and by including a few principles based on special classification of recognized failures. Thus, it is minimized effect of methodological inconsistency and some of other identified weaknesses of traditional FMEA method. FMEA method is improved, which provides more precise failure risk evaluation and thus better prediction and minimizing of unwanted occurrences (failures of elements, subsystems, components, etc., of technical systems). It was proved by comparative analysis of applied traditional FMEA method as well as modified FMEA method, hereinafter called “intelligent” FMEA method (IFMEA) on system of tires for city busses.     

Author-level altmetrics for the evaluation of Chinese scholars

Scientometrics - Altmetrics has been widely recognized in the evaluation of articles, books, and journals. However, only a few of scholars have focused on author-level metrics which limits the wide...     

Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals

As they are used to evaluate the importance of research at different levels by funding agencies and promotion committees, bibliometric indices have received a lot of attention from the scientific community over the last few years. Many bibliometric indices have been developed in order to take into account aspects not previously covered. The result is that, nowadays, the scientific community faces the challenge of selecting which of this pool of indices meets the required quality standards. In view of the vast number of bibliometric indices, it is necessary to analyze how they relate to each other (irrelevant, dependent and so on). Our main purpose is to learn a Bayesian network model from data to analyze the relationships among bibliometric indices. The induced Bayesian network is then used to discover probabilistic conditional (in)dependencies among the indices and, also for probabilistic reasoning. We also run a case study of 14 well-known bibliometric indices on computer science and artificial intelligence journals.     

Towards a deliberative framework for responsible innovation in artificial intelligence

The rapid innovation in artificial intelligence (AI) is raising concerns regarding human autonomy, agency, fairness, and justice. While responsible stewardship of innovation calls for public engagement, inclusiveness, and informed discourse, AI seemingly challenges such informed discourse by way of its opacity (poor transparency, explainability, and accountability). We apply a deliberative approach to propose a framework for responsible innovation in AI. This framework foregrounds discourse principles geared to help offset these opacity challenges. To support better public governance, we consider the mutual roles and dependencies of organizations that develop and apply AI, as well as civil society actors, and investigative media in exploring pathways for responsible AI innovation.     

An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal

In this study, five different machine learning (ML) and artificial intelligence (AI) models: random forest (RF), artificial neural networks (ANN), the adaptive neuro-fuzzy inference system (ANFIS), Mamdani fuzzy logic (MFL) and a hybrid neural fuzzy inference system (HyFIS) were employed to predict the flotation behavior of fine high ash coal in the presence of a novel “hybrid” ash depressant consisting of polyacrylamide chains grafted onto aluminium hydroxide nanoparticles: Al(OH)₃-PAM (Al-PAM). A total of 51 flotation tests were conducted on coal samples with 38% ash-content and a P₈₀ of approximately 49?μm. Different influencing variables of coal flotation including polymer dosage, pH, polymer conditioning time, sodium metasilicate dosage (commercial dispersant), and the impeller speed were used as inputs for the models. The combustible recovery and ash content of coal reported to the concentrate were used as response variables (outputs). For AI model development, 80% of the total data was used for training phase and 20% was used for testing phase. Coefficient of determination (R²) and root-mean-square error (RMSE) were used as performance indicators of the models. The MFL model showed the best accuracy for the prediction of the combustible recoveries and the froth ash contents for this specific feed. However, in case of any significant change in the characteristics of the feed, these models would have to be re-trained using the data obtained through further physical experimentation and/or process model simulations. Moreover as these models are trained on laboratory scale data, these are only good for the predictions at laboratory scale.     

Citation regression analysis of computer science publications in different ranking categories and subfields

A number of bibliometric studies point out that the role of conference publications in computer science differs from that in other traditional fields. Thus, it is interesting to identify the relative status of journal and conference publications in different subfields of computer science based on the citation rates categorised by the China Computer Federation (CCF) classifications and venue types. In this research, we construct a dataset containing over 100,000 papers recommended by the CCF catalogue and their citation information. We also investigate some other factors that often influence a paper’s citation rate. An experimental study shows that the relative status of journals and conferences varies greatly in different subfields of computer science, and the impact of different publication levels varies according to the citation rate. We also verify that the classification of a publication, number of authors, maximum h-index of all authors of a paper, and average number of papers published by a publication have different effects on the citation rate, although the citation rate may have a different degree of correlation with these factors.     

Simulation embedded artificial intelligence search method for supplier trading portfolio decision

An electric power supplier in the deregulated environment needs to allocate its generation capacities to participate in contract and spot markets. Different trading portfolios will provide suppliers with different future revenue streams of various distributions. The classical mean?variance (MV) method is inappropriate to deal with the trading portfolios whose return distribution is non-normal. In order to consider the non-normal characteristics in electricity trading, this study proposes a new model based on expected utility theory (EUT) and employs a hybrid genetic algorithm (GA) -- Monte-Carlo simulation technique as solution approach. In the real market data-based numerical studies, the performances of the proposed method and the standard MV method are compared. It was found that the proposed method is able to obtain better portfolios than MV method when non-normal asset exists for trading. The simulation results also reveal the accumulation effect along trading period, which will improve the normality of the supplier trading portfolios. The authors believe the proposed method is a useful complement for the MV method and conditional value at risk (CVaR)-based methods in the supplier trading portfolio decision and evaluation.     

Embrace artificial intelligence technologies for advanced analytics and management of intellectual properties

This short communication describes the background, objectives, and publications of World Patent Information's special issue on Artificial Intelligence for Intellectual Property (AI for IP). The report serves as the editorial for the WPI's special issue on AI for IP. We look forward to receiving future contributions in research articles, literature/book reviews, conference reports and short communications in the subject areas.     

An analysis and design of implantable blood heat exchangers for artificial hearts

The use of radioisotopic fueled power conversion systems in long lived artificial hearts are currently being studied by a number of AEC and NHLI contracts. All of these systems require a heat rejection subsystem to remove waste heat. A parametric analysis of an implantable blood heat exchanger was carried out to determine the problem areas and to arrive at a blood heat exchanger design.Biological and physiological design constraints were first established. The parametric analyses showed that the thoracic and abdominal aortic regions are needed for the transfer of relatively large amounts of heat (up to 50 W). Blood heat exchanger designs are presented for the rejection of 25 and 50 W of waste heat.     

An observation framework for retracted publications in multiple dimensions

Scientometrics - To describe the impact of the retracted publications on the scientific community more comprehensively, this paper introduces the impact of retracted publications on technology. It...