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Missing value imputation (MVI) has been studied for several decades being the basic solution method for incomplete dataset problems, specifically those where some data samples contain one or more missing attribute values. This paper aims at reviewing and analyzing related studies carried out in recent decades, from the experimental design perspective. Altogether, 111 journal papers published from 2006 to 2017 are reviewed and analyzed. In addition, several technical issues encountered during the MVI process are addressed, such as the choice of datasets, missing rates and missingness mechanisms, and the MVI techniques and evaluation metrics employed, are discussed. The results of analysis of these issues allow limitations in the existing body of literature to be identified based upon which some directions for future research can be gleaned.

COVID-19 took a heavy toll on older adults. In Belgium, by the end of August, 93% of deaths due to COVID-19 were aged 65 or older. Similar trends were observed in other countries. As a consequence, older adults were identified as a group at risk, and strict governmental restrictions were imposed on them. This has caused concerns about their mental health. Using an online survey, this study established the impact of the COVID-19 pandemic on adults aged 65 years or older, and which factors moderate this impact. Participants reported a significant decrease in activity level, sleep quality and wellbeing during the COVID-19 pandemic. Depression was strongly related to reported declines in activity level, sleep quality, wellbeing and cognitive functioning. Our study shows that the COVID-19 pandemic had a severe impact on the mental health of older adults. This implies that this group at risk requires attention of governments and healthcare.

Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June–August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.

Human-robot interaction (HRI) promises to be a means whereby manufacturing companies will be able to address current challenges like a higher demand for customization. However, despite comparably low costs, there are only few applications in practice. To date, it remains unclear which factors facilitate or hinder the successful introduction of industrial collaborative robots (cobots). In a three-step approach, we first developed a comprehensive two-dimensional framework covering three separate phases and four essential components for human-robot working systems. Secondly, we reviewed related literature to identify relevant success factors. Thirdly, in an online survey we asked leading representatives of German manufacturing companies ( n = 81) to assess the importance of these factors from a practical point of view. The results reveal that besides technology-related factors like occupational safety and appropriate cobot configuration, employee-centered factors like the fear of job loss and ensuring an appropriate level of trust in the robot are considered important. However, company representatives seem to underestimate the impact of subtle measures to increase employee acceptance which could be incorporated into internal communication strategies prior to and during the introduction of cobots. Comparative analysis based on three distinct application scenarios suggests that most success factors’ practical importance is independent of the motivation for implementing HRI. Furthermore, answers from practitioners in free-text fields reveal that success factors which intuitively come to their mind such as financial factors are not necessarily perceived most important. Finally, we argue for more application-oriented research that focuses on practically relevant factors to guide HRI research, inform cobot development, and support companies in overcoming apparent barriers.

The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or time-consuming. This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with less training data and in particular less human supervision. In light of this debate, this work investigates the issue of algorithms’ data hungriness. First, it surveys the issue from different perspectives. Then, it presents a comprehensive review of existing data-efficient methods and systematizes them into four categories. Specifically, the survey covers solution strategies that handle data-efficiency by (i) using non-supervised algorithms that are, by nature, more data-efficient, by (ii) creating artificially more data, by (iii) transferring knowledge from rich-data domains into poor-data domains, or by (iv) altering data-hungry algorithms to reduce their dependency upon the amount of samples, in a way they can perform well in small samples regime. Each strategy is extensively reviewed and discussed. In addition, the emphasis is put on how the four strategies interplay with each other in order to motivate exploration of more robust and data-efficient algorithms. Finally, the survey delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning.

Purpose Building on the rhetoric question “ quo vadis? ” (literally “ Where are you going ?”), this article critically investigates the state of the art of normalisation and weighting approaches within life cycle assessment. It aims at identifying purposes, current practises, pros and cons, as well as research gaps in normalisation and weighting. Based on this information, the article wants to provide guidance to developers and practitioners. The underlying work was conducted under the umbrella of the UNEP-SETAC Life Cycle Initiative, Task Force on Cross-Cutting issues in life cycle impact assessment (LCIA). Methods The empirical work consisted in (i) an online survey to investigate the perception of the LCA community regarding the scientific quality and current practice concerning normalisation and weighting; (ii) a classification followed by systematic expert-based assessment of existing methods for normalisation and weighting according to a set of five criteria: scientific robustness, documentation, coverage, uncertainty and complexity. Results and discussion The survey results showed that normalised results and weighting scores are perceived as relevant for decision-making, but further development is needed to improve uncertainty and robustness. The classification and systematic assessment of methods allowed for the identification of specific advantages and limitations. Conclusions Based on the results, recommendations are provided to practitioners that desire to apply normalisation and weighting as well as to developers of the underlying methods.

During the COVID pandemic, many companies, schools, and public organizations all around the world asked their employees to work from home i.e. to adopt what are called “smart working” modalities. This has and will presumably have a serious impact on both employees and employers, which still needs to be clarified and investigated: indeed, if smart working becomes a common working modality, this may have a significant impact on both organizations and employees. This paper reports the results of an online survey of “smart workers” in Italy during the COVID pandemic, when a great number of employees suddenly moved to working from home with no or little preparation. The study offers interesting indications about the involvement and usefulness perception of smart working by the sampled people and makes it possible to single out different categories of employees based on their attitude towards this modality. Also, it points out the potential impact on socialization among colleagues, and the consequent implications for knowledge sharing and knowledge management. From the collected responses, a fully positive or negative conclusion about working from home was not possible, nor a clear indication about the efficiency and effectiveness of this working modality. The analysis, instead, highlighted the presence of different but numerically similar groups of people, i.e. those who were not satisfied at all with the experience, those who were very satisfied, and those who were “undecided”. Furthermore, respondents underlined the importance and the difficulty to maintain working contacts and the intense use of communication systems made for this purpose. Lastly, collected opinions on positive and negative aspects of working from home provided some practical suggestions about how to successfully implement this solution.

In order to describe the prevalence and characteristics of complementary medicine (CM) practice and product use by Australians, we conducted a cross-sectional online survey with Australian adults aged 18 and over. Rates of consultation with CM practitioners, and use of CM products and practices were assessed. The sample (n = 2,019) was broadly representative of the Australian population. Prevalence of any CM use was 63.1%, with 36% consulting a CM practitioner and 52.8% using any CM product or practice. Bodywork therapists were the most commonly consulted CM practitioners (massage therapists 20.7%, chiropractors 12.6%, yoga teachers 8.9%) and homeopaths were the least commonly consulted (3.4%). Almost half of respondents (47.8%) used vitamin/mineral supplements, while relaxation techniques/meditation were the most common practice (15.8%). CM users were more likely to be female, have a chronic disease diagnosis, no private health insurance, a higher education level, and not be looking for work. Prevalence of CM use in Australia has remained consistently high, demonstrating that CM is an established part of contemporary health management practices within the general population. It is critical that health policy makers and health care providers acknowledge CM in their attempts to ensure optimal public health and patient outcomes.

Purpose The Internet of Things (IoT) is expected to have a huge impact on businesses and, especially, the way we think about supply chain management (SCM). However, there is still a paucity of studies on the impact of IoT adoption on supply chains and on different aspects of the business in general. The purpose of this paper is to examine the perception of the academic community of the impact of the IoT adoption in organizational supply chains with a view to verify potential key benefits and challenges existent in the literature. The research presents the impact on an organization along with the impact across its entire supply chain. Design/methodology/approach Data were collected through the use of an online survey and 87 participants completed the survey. Participants were mainly from the academic community and were university scholars based in different countries located in six continents. Participants were authors, or co-authors, of academic papers published in the Decision Science Institute 2015 and 2016 annual conference proceedings, the 21st International Symposium of Sustainable Transport and Supply Chain Innovations, the Supply Chain Management: An International Journal 2016 issues, and the Operations and Supply Chain Management: An International Journal 2016 issues. Findings The authors were able to confirm the significance of some of the examined potential benefits to individual organizations and their entire supply chains. However, the study identified other potential benefits that were not seen as a direct impact of IoT adoption. Most of the examined potential benefits were found to contribute to a number of critical success factors for implementing successful SCM. The authors were also able to confirm that some of the examined potential challenges were still perceived as key hinders to IoT adoption but examined potential challenges were not seen as hurdles to IoT adoption. Originality/value To the best of the authors’ knowledge, this is the first study of its kind. Although some literature attempted to provide an overview about the IoT management, no study has specifically explored potential benefits and challenges related to the adoption of IoT in supply chains and ranked them based on their significance. The results can be beneficial to academic scholars interested in the researched topic, business professionals, organizations within different sectors, and any other party interested in understanding more about the impact of adopting IoT on SCM.