Taking Responsibility for Industrial Digitalization: Navigating Organizational Challenges

In this article, an employee perspective has been applied in aiming to explore how organizations face challenges and take responsibility for industrial digitalization, thus extending the research on the human-centric perspective in relation to Industry 4.0 technologies. To give emphasis to the human-centric perspective, the co-workership wheel was applied to identify and analyze data. The findings of an explorative longitudinal qualitative case study consisting of 35 in-depth interviews with informants from a manufacturing company were used. Additional data collection consisted of documents and project meetings. By applying a human-centric perspective, llessons learned from this case study show that taking responsibility for industrial digitalization is challenging and the importance of an adaptive organizational culture and a focus on learning and competence are crucial. We argue that the findings give useful implications for manufacturing organizations navigating the challenges of industrial digitalization to sense and seize the benefits of Industry 4.0 technologies.

An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial

Background Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial’s follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. Methods We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. Results There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily included any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. Conclusions We present a straightforward regression-based method for computing the EVSI of extending an existing trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. Highlights Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we have developed new methods for computing the EVSI of extending a trial’s follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations. The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context.

Risk Factors for Increased Online Gambling during COVID-19 Lockdowns in New Zealand: A Longitudinal Study

Recent research investigating changes in gambling behaviors during periods of COVID-19 social restrictions, such as enforced lockdowns, are somewhat limited by methodology, being generally cross-sectional in nature and with participant samples recruited via online panels. The present study overcame these limitations via a secondary analysis of data collected in 2012 and 2015 from a New Zealand (NZ) longitudinal gambling study, with questions related to gambling behaviors due to COVID-19 lockdown periods included in an additional data collection, of participants who had previously scored as a risky gambler, during 2020/21. Almost one-quarter of online gamblers increased their gambling during lockdown with this most likely to be on overseas gambling sites, instant scratch card gambling and Lotto. The only sociodemographic risk factor for increased online gambling was higher education. Behavioral risk factors included being a current low risk/moderate risk/problem gambler, a previously hazardous alcohol drinker or past participation in free-to-play gambling-type games. These past behaviors could act as trigger points for health services or family and friends to monitor a person’s gambling behaviors during lockdown, or future stressful periods when usual terrestrial gambling opportunities are curtailed or unavailable, and to support safer gambling practices.

Grandmothers Raising Grandchildren: Predictors of Healthcare Access and Financial Security During COVID-19

Grandmothers living with or raising grandchildren who had just completed the final data point of an NIH-funded, national, behavioral RCT were asked to complete an additional data collection point to capture the effects of the Covid-19 pandemic on their families’ access to healthcare and financial security. In Spring 2020, 258 grandmothers completed measures of access to healthcare and financial security (3 and 4 item composite scales), family strain, family functioning, and psychosocial and demographic variables. Financial security (Adj. R2=.52) was explained by knowing other grandfamilies; better family functioning; and fewer financial worries, unmet service needs, and depressive symptoms. Access to healthcare (Adj. R2=.24) was explained by being married, employed and having fewer financial worries and unmet service needs. Findings that family functioning, knowing other grandfamilies and depressive symptoms contributed to financial security, and that marital and employment status affect access to healthcare show the importance of support.

Mission Engineering and Design using Real-Time Strategy Games: An Explainable-AI Approach

In this paper, we adapt computational design approaches, widely used by the engineering design community, to address the unique challenges associated with mission design using RTS games. Specifically, we present a modeling approach that combines experimental design techniques, meta-modeling using convolutional neural networks (CNNs), uncertainty quantification, and explainable AI (XAI). We illustrate the approach using an open-source real-time strategy (RTS) game called microRTS. The modeling approach consists of microRTS player agents (bots), design of experiments that arranges games between identical agents with asymmetric initial conditions, and an AI infused layer comprising CNNs, XAI, and uncertainty analysis through Monte Carlo Dropout Network analysis that allows analysis of game balance. A sample balanced game and corresponding predictions and SHapley Additive exPlanations (SHAP) are presented in this study. Three additional perturbations were introduced to this balanced gameplay and the observations about important features of the game using SHAP are presented. Our results show that this analysis can successfully predict probability of win for self-play microRTS games, as well as capture uncertainty in predictions that can be used to guide additional data collection to improve the model, or refine the game balance measure.

Who makes it all the way? Participants vs. decliners, and completers vs. drop-outs, in a 6-month exercise trial during cancer treatment. Results from the Phys-Can RCT

Purpose To compare sociodemographic, health- and exercise-related characteristics of participants vs. decliners, and completers vs. drop-outs, in an exercise intervention trial during cancer treatment. Methods Patients with newly diagnosed breast, prostate, or colorectal cancer were invited to participate in a 6-month exercise intervention. Background data for all respondents (n = 2051) were collected at baseline by questionnaire and medical records. Additional data were collected using an extended questionnaire, physical activity monitors, and fitness testing for trial participants (n = 577). Moreover, a sub-group of decliners (n = 436) consented to additional data collection by an extended questionnaire . Data were analyzed for between-group differences using independent t-tests and chi2-tests. Results Trial participants were younger (59 ± 12yrs vs. 64 ± 11yrs, p < .001), more likely to be women (80% vs. 75%, p = .012), and scheduled for chemotherapy treatment (54% vs. 34%, p < .001), compared to decliners (n = 1391). A greater proportion had university education (60% vs 40%, p < .001), reported higher anxiety and fatigue, higher exercise self-efficacy and outcome expectations, and less kinesiophobia at baseline compared to decliners. A greater proportion of trial participants were classified as ‘not physically active’ at baseline; however, within the group who participated, being “physically active” at baseline was associated with trial completion. Completers (n = 410) also reported less kinesiophobia than drop-outs (n = 167). Conclusion The recruitment procedures used in comprehensive oncology exercise trials should specifically address barriers for participation among men, patients without university education and older patients. Individualized efforts should be made to enroll patients with low exercise self-efficacy and low outcome expectations of exercise. To retain participants in an ongoing exercise intervention, extra support may be needed for patients with kinesiophobia and those lacking health-enhancing exercise habits at baseline.

The value of additional data for public transport origin–destination matrix estimation

Passenger origin–destination data is an important input for public transport planning. In recent years, new data sources have become increasingly common through the use of the automatic collection of entry counts, exit counts and link flows. However, collecting such data can be sometimes costly. The value of additional data collection hence has to be weighed against its costs. We study the value of additional data for estimating time-dependent origin–destination matrices, using a case study from the London Piccadilly underground line. Our focus is on how the precision of the estimated matrix increases when additional data on link flow, destination count and/or average travel distance is added, starting from origin counts only. We concentrate on the precision of the most policy-relevant estimation outputs, namely, link flows and station exit flows. Our results suggest that link flows are harder to estimate than exit flows, and only using entry and exit data is far from enough to estimate link flows with any precision. Information about the average trip distance adds greatly to the estimation precision. The marginal value of additional destination counts decreases only slowly, so a relatively large number of exit station measurement points seem warranted. Link flow data for a subset of links hardly add to the precision, especially if other data have already been added.