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Abstract This paper aims to explore the response of a floating icesheet to a load moving in a curved path. We investigate the effect of turning on the wave patterns and strain distribution, and explore scenarios where turning increases the wave amplitude and strain in the ice, possibly leading to crack formation, fracturing and eventual ice failure. The mathematical model used here is the linearized system of differential equations introduced in Dinvay et al. (J. Fluid Mech. 876:122–149, 2019). The equations are solved using the Fourier transform in space, and the Laplace transform in time. The model is tested against existing results for comparison, and several cases of load trajectories involving turning and decelerating are tested.

Abstract Metal-free chemical vapor deposition (CVD) of single-layer graphene (SLG) on c-plane sapphire has recently been demonstrated for wafer diameters of up to 300 mm, and the high quality of the SLG layers is generally characterized by integral methods. By applying a comprehensive analysis approach, distinct interactions at the graphene-sapphire interface and local variations caused by the substrate topography are revealed. Regions near the sapphire step edges show tiny wrinkles with a height of about 0.2 nm, framed by delaminated graphene as identified by the typical Dirac cone of free graphene. In contrast, adsorption of CVD SLG on the hydroxyl-terminated α-Al2O3 (0001) terraces results in a superstructure with a periodicity of (2.66 ± 0.03) nm. Weak hydrogen bonds formed between the hydroxylated sapphire surface and the π-electron system of SLG result in a clean interface. The charge injection induces a band gap in the adsorbed graphene layer of about (73 ± 3) meV at the Dirac point. The good agreement with the predictions of a theoretical analysis underlines the potential of this hybrid system for emerging electronic applications.

Solar photovoltaic (PV) is a key technology for any renewable energy system. As subsidy-free PV becomes more and more economically feasible, region-specific planning tools that define areas suitable for ground-mounted PV are needed. While many top-down studies have assessed suitable areas at a national scale, an accurate scalable bottom-up assessment of regional ground-mounted PV potentials in high spatial and temporal resolution that goes further than a mere identification of appropriate land areas is missing. This work introduces such a method based on digital landscape models that consider terrain slope, orientation, location-specific irradiation, and land use type, and combines this geoinformatical information with a PV yield model that allows to assess hourly PV generation potential on suitable areas. The method is validated with three existing ground-mounted PV plants in Germany, where a comparison of real and simulated annual electricity yields shows average deviations of 5%. Subsequently, ground-mounted PV potentials in three German counties with varying settlement structures as well as topographic and weather patterns are assessed and a comparison of yearly and hourly simulated generation potentials with regional electricity demand is performed. While the yearly analysis demonstrates the substantial overall potentials of local ground mounted-PV in all regions, with demand coverages ranging from 80% to hypothetically more than 40 times of current electricity demand according to current regulations, the hourly autarky ratio, defined as the share of hours of a year where ground-mounted PV can satisfy demand, ranges from 25% to 40%, without consideration of storage or demand side management. A subsequent investigation of the ability to export excess electricity generation from ground-mounted PV shows that the two regions with highest ground-mounted PV potentials have less-developed grid infrastructures, thus restricting excess electricity generation export potentials.

Abstract The COVID-19 pandemic is a global stressor with inter-individually differing influences on mental health trajectories. Polygenic Risk Scores (PRSs) for psychiatric phenotypes are associated with individual mental health predispositions. Elevated hair cortisol concentrations (HCC) and high PRSs are related to negative mental health outcomes. We analyzed whether PRSs and HCC are related to different mental health trajectories during the first COVID lockdown in Germany. Among 523 participants selected from the longitudinal resilience assessment study (LORA), we previously reported three subgroups (acute dysfunction, delayed dysfunction, resilient) based on weekly mental health (GHQ-28) assessment during COVID lockdown. DNA from blood was collected at the baseline of the original LORA study (n = 364) and used to calculate the PRSs of 12 different psychopathological phenotypes. An explorative bifactor model with Schmid-Leiman transformation was calculated to extract a general genetic factor for psychiatric disorders. Hair samples were collected quarterly prior to the pandemic for determining HCC (n = 192). Bivariate logistic regressions were performed to test the associations of HCC and the PRS factors with the reported trajectories. The bifactor model revealed 1 general factor and 4 sub-factors. Results indicate a significant association between increased values on the general risk factor and the allocation to the acute dysfunction class. The same was found for elevated HCC and the exploratorily tested sub-factor “childhood-onset neurodevelopmental disorders”. Genetic risk and long-term cortisol secretion as a potential indicator of stress, indicated by PRSs and HCC, respectively, predicted different mental health trajectories. Results indicate a potential for future studies on risk prediction.

BackgroundStress-related mental disorders are highly prevalent and pose a substantial burden on individuals and society. Improving strategies for the prevention and treatment of mental disorders requires a better understanding of their risk and resilience factors. This multicenter study aims to contribute to this endeavor by investigating psychological resilience in healthy but susceptible young adults over 9 months. Resilience is conceptualized in this study as the maintenance of mental health or quick recovery from mental health perturbations upon exposure to stressors, assessed longitudinally via frequent monitoring of stressors and mental health. ObjectiveThis study aims to investigate the factors predicting mental resilience and adaptive processes and mechanisms contributing to mental resilience and to provide a methodological and evidence-based framework for later intervention studies. MethodsIn a multicenter setting, across 5 research sites, a sample with a total target size of 250 young male and female adults was assessed longitudinally over 9 months. Participants were included if they reported at least 3 past stressful life events and an elevated level of (internalizing) mental health problems but were not presently affected by any mental disorder other than mild depression. At baseline, sociodemographic, psychological, neuropsychological, structural, and functional brain imaging; salivary cortisol and α-amylase levels; and cardiovascular data were acquired. In a 6-month longitudinal phase 1, stressor exposure, mental health problems, and perceived positive appraisal were monitored biweekly in a web-based environment, while ecological momentary assessments and ecological physiological assessments took place once per month for 1 week, using mobile phones and wristbands. In a subsequent 3-month longitudinal phase 2, web-based monitoring was reduced to once a month, and psychological resilience and risk factors were assessed again at the end of the 9-month period. In addition, samples for genetic, epigenetic, and microbiome analyses were collected at baseline and at months 3 and 6. As an approximation of resilience, an individual stressor reactivity score will be calculated. Using regularized regression methods, network modeling, ordinary differential equations, landmarking methods, and neural net–based methods for imputation and dimension reduction, we will identify the predictors and mechanisms of stressor reactivity and thus be able to identify resilience factors and mechanisms that facilitate adaptation to stressors. ResultsParticipant inclusion began in October 2020, and data acquisition was completed in June 2022. A total of 249 participants were assessed at baseline, 209 finished longitudinal phase 1, and 153 finished longitudinal phase 2. ConclusionsThe Dynamic Modelling of Resilience–Observational Study provides a methodological framework and data set to identify predictors and mechanisms of mental resilience, which are intended to serve as an empirical foundation for future intervention studies. International Registered Report Identifier (IRRID)DERR1-10.2196/39817

Abstract Background There is increasing interest in the development and use of clinical prediction models, but a lack of evidence-supported guidance on the merits of different modelling approaches. This is especially true for time-to-event outcomes, where limited studies have compared the vast number of modelling approaches available. This study compares prediction accuracy and variable importance measures for four modelling approaches in prediction of time-to-revision surgery following total knee arthroplasty (TKA) and total hip arthroplasty (THA). Methods The study included 321,945 TKA and 151,113 THA procedures performed between 1 January 2003 and 31 December 2017. Accuracy of the Cox model, Weibull parametric model, flexible parametric model, and random survival forest were compared, with patient age, sex, comorbidities, and prosthesis characteristics considered as predictors. Prediction accuracy was assessed using the Index of Prediction Accuracy (IPA), c-index, and smoothed calibration curves. Variable importance rankings from the Cox model and random survival forest were also compared. Results Overall, the Cox and flexible parametric survival models performed best for prediction of both TKA (integrated IPA 0.056 (95% CI [0.054, 0.057]) compared to 0.054 (95% CI [0.053, 0.056]) for the Weibull parametric model), and THA revision. (0.029 95% CI [0.027, 0.030] compared to 0.027 (95% CI [0.025, 0.028]) for the random survival forest). The c-index showed broadly similar discrimination between all modelling approaches. Models were generally well calibrated, but random survival forest underfitted the predicted risk of TKA revision compared to regression approaches. The most important predictors of revision were similar in the Cox model and random survival forest for TKA (age, opioid use, and patella resurfacing) and THA (femoral cement, depression, and opioid use). Conclusion The Cox and flexible parametric models had superior overall performance, although all approaches performed similarly. Notably, this study showed no benefit of a tuned random survival forest over regression models in this setting.

Pharmacovigilance is defined by the World Health Organization as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine related problem". Pharmacovigilance studies are critical for detecting and assessing adverse events of medicines that may not have been observed in clinical trials. This activity is especially important in older people who are often excluded from clinical trials as they have multiple chronic conditions and use multiple medicines for longer durations than the clinical trials. In this narrative review we describe innovative methods in pharmacovigilance studies of medicines in older people that leverage the increasing availability of digital health technologies, electronic health records and real‐world health data to identify and quantify medication related harms in older people. [ABSTRACT FROM AUTHOR]

Background: Uncemented total hip arthroplasty (THA) is a successful treatment for advanced hip joint diseases. More recently, short stems became increasingly popular, but stem subsidence remains a concern. This study investigates early short stem subsidence in a large patient cohort using a simple measurement approach for everyday practice. Methods: This retrospective, single center, single implant design study included 1000 patients with primary THA. Subsidence was evaluated using standardized weight-bearing radiographs taken 3–5 days and 2–3 weeks postoperatively with full weight-bearing (FWB). A novel Subsidence Index (SID) was introduced to quantify stem subsidence in a simple and reproducible manner. The SID is calculated by averaging four distinct linear measurements between defined anatomical landmarks on the femur and the implant, captured on standard radiographs without additional software. Results: Out of all analyzed patients 6% (60/1000) had subsidence of more than 3 mm. The mean subsidence was 1.3 mm (range, 0 to 16.25 mm). There were 0.6% (6) who underwent stem revision for symptomatic subsidence. Men and obese patients had greater subsidence. However, patient age, BMI, stems without lateral bone contact and other demographic factors were not associated with subsidence. Conclusion: Early subsidence is relatively frequent with this uncemented short stem, however revisions are rare. Patients with risk factors should be counseled regarding FWB and radiographic controls should be performed. The SID provides an easy, non-invasive and inexpensive tool for early subsidence assessment; however, its simplicity limits its accuracy. Further research is needed in comparison to more elaborate methods. [ABSTRACT FROM AUTHOR]

Abstract Deep learning approaches can uncover complex patterns in data. In particular, variational autoencoders achieve this by a non-linear mapping of data into a low-dimensional latent space. Motivated by an application to psychological resilience in the Mainz Resilience Project, which features intermittent longitudinal measurements of stressors and mental health, we propose an approach for individualized, dynamic modeling in this latent space. Specifically, we utilize ordinary differential equations (ODEs) and develop a novel technique for obtaining person-specific ODE parameters even in settings with a rather small number of individuals and observations, incomplete data, and a differing number of observations per individual. This technique allows us to subsequently investigate individual reactions to stimuli, such as the mental health impact of stressors. A potentially large number of baseline characteristics can then be linked to this individual response by regularized regression, e.g., for identifying resilience factors. Thus, our new method provides a way of connecting different kinds of complex longitudinal and baseline measures via individualized, dynamic models. The promising results obtained in the exemplary resilience application indicate that our proposal for dynamic deep learning might also be more generally useful for other application domains.

Introduction Teamwork is identified as a key contributor to patient safety and good teamwork is recognized as one of the presumptions of healthy work environment in nursing. The importance of job satisfaction in nursing has repeatedly been confirmed, but only recently has the association of job satisfaction and nursing teamwork been identified. Objective To identify the level of nursing teamwork in hospitals in Iceland and its relationship with job satisfaction. Methods This was a quantitative descriptive cross-sectional study. Data were collected with the Nursing Teamwork Survey administered to nursing staff in medical, surgical, and intensive care units in hospitals in Iceland. This study is based on data from 567 participants. Results A logistic regression analysis indicated that work experience on current unit and perceived staffing adequacy contributes to job satisfaction and when controlling for unit type, role, experience on current unit and staffing adequacy, those reporting better teamwork are significantly more likely to be satisfied with their current position. With an additional unit for overall nursing teamwork, participants are almost five times likelier to be satisfied with their current position. Conclusion Study findings show that there is a significant relationship between nursing teamwork and job satisfaction. The findings of this study confirm the importance of adequate staffing and good teamwork for nurses’ job satisfaction. Staffing however, will remain the most challenging part of the equation as lack of nursing staff is foreseen globally in the coming decades turning the spotlight to teamwork. All stakeholders, including clinical nurse leaders, administrators, and instructors, need to emphasize on strengthening nursing teamwork. Good teamwork with increased job satisfaction may prevent turnover and shortage of nurses, an issue expected to grow during and following the COVID-19 pandemic. Facilitating good teamwork should be one of the priorities of every nurse leader.

Introduction European researchers are collaborating in the EU Horizon 2020-funded project “RESPOND” to address the psychological and psychosocial effects of the Covid 19 pandemic in order to prepare health systems for future crises. In the Co-RESPOND subproject, several longitudinal cohorts are contributing to an individual participant data (IPD) meta-analysis. Objectives Co-RESPOND aims to assess trajectories of mental health and resilience, and to identify relevant moderators using a meta-analysis of individual participant data (“IPD”) approach. Moreover, a research network of European cohorts is being established alongside a sustainable shared IT infrastructure. Co-RESPOND aims to publish the results of the collaboration in a findable, accessible, interoperable and reusable way according to the “FAIR publication” principles. Methods To achieve these aims, a federated network for remote data analysis is being built. In this talk we describe the steps necessary to join existing cohorts into one network, and which challenges need to be met: First, existing data sets need to be harmonized retrospectively, second, data sharing and processing needs to be done in accordance with the GDPR requirements, and third, a technical solution needs to be found to facilitate joint analyses and publication of the network and its products. Results We identified the Maelstroem guidelines for retrospective data harmonisation of epidemiologic studies as appropriate guidance to carry out and document the transformation of individual data sets. The OBiBa software suite is used to build the IT infrastructure of the project by connecting local data servers of the study sites and making them available for remote analyses by other partners. As of autumn 2022, data transformation is finalized and data sets uploaded on the local servers. A platform on the internet is created where the main characteristics of all participating cohorts (“meta-data”) are catalogued to help them gain visibility and make them findable for future joint projects. The Co-RESPOND network will be open for more partner cohorts to join. Conclusions The Covid pandemic has stimulated lots of international remote collaborations, and federated networks for data analyses are increasingly used as a means of enhancing the value of existing data sets. User-friendly and cost-free software solutions are already available (e.g., OBiBa) to facilitate such endeavours. However, researchers intiating cohort studies should be aware of such technology and methods and consider the use of their data in overarching collaborations from the start. We conclude with concrete recommendations how to optimize the design of epidemiologic data collections to enhance their interoperability with other cohorts, e.g., by using international coding standards. Disclosure of Interest None Declared

IntroductionMigrant populations, including workers, undocumented migrants, asylum seekers, refugees, internationally displaced persons, and other populations on the move, are exposed to a variety of stressors and potentially traumatic events before, during, and after the migration process. In recent years, the COVID-19 pandemic has represented an additional stressor, especially for migrants on the move. As a consequence, migration may increase vulnerability of individuals toward a worsening of subjective wellbeing, quality of life, and mental health, which, in turn, may increase the risk of developing mental health conditions. Against this background, we designed a stepped-care programme consisting of two scalable psychological interventions developed by the World Health Organization and locally adapted for migrant populations. The effectiveness and cost-effectiveness of this stepped-care programme will be assessed in terms of mental health outcomes, resilience, wellbeing, and costs to healthcare systems.Methods and analysisWe present the study protocol for a pragmatic randomized study with a parallel-group design that will enroll participants with a migrant background and elevated level of psychological distress. Participants will be randomized to care as usual only or to care a usual plus a guided self-help stress management guide (Doing What Matters in Times of Stress, DWM) and a five-session cognitive behavioral intervention (Problem Management Plus, PM+). Participants will self-report all measures at baseline before random allocation, 2 weeks after DWM delivery, 1 week after PM+ delivery and 2 months after PM+ delivery. All participants will receive a single-session of a support intervention, namely Psychological First Aid. We will include 212 participants. An intention-to-treat analysis using linear mixed models will be conducted to explore the programme's effect on anxiety and depression symptoms, as measured by the Patient Health Questionnaire—Anxiety and Depression Scale summary score 2 months after PM+ delivery. Secondary outcomes include post-traumatic stress disorder symptoms, resilience, quality of life, resource utilization, cost, and cost-effectiveness.DiscussionThis study is the first randomized controlled trial that combines two World Health Organization psychological interventions tailored for migrant populations with an elevated level of psychological distress. The present study will make available DWM/PM+ packages adapted for remote delivery following a task-shifting approach, and will generate evidence to inform policy responses based on a more efficient use of resources for improving resilience, wellbeing and mental health.Clinical trial registrationClinicalTrials.gov, identifier: NCT04993534.

Science asserts that resilience at work can be developed, with evidence pinpointing to multiple resources that can be built through deliberate coaching, training and interventions. This paper presents a mixed-methods study exploring the effectiveness of group coaching using SPARK Resilience training, a model and a structured coaching protocol that have been administered in educational and workplace settings in face-to-face format and remotely. The study used a non-randomised controlled design with a pre-test and a post-test in a sample of French adults (N = 101 in the intervention group and N = 86 in the waitlist control group). The SPARK Resilience programme was administered online with 8 sessions spanning 4 weeks in April 2020, during the very early stage of the pandemic and lockdown in France. The results indicate beneficial effects of the intervention on meaning, resilience, positive affect, and perceived stress outcomes (d in the .40-.56 range), as well as weaker effects on negative affect (d = .35) and work engagement (d = .21). Moderator analyses suggest that the effects of the intervention on perceived stress and negative affect tended to be stronger for older adults. Participants reported high levels of satisfaction with the intervention and provided 151 responses to three open-ended questions that were coded using thematic analysis, revealing specific benefits of the intervention. The findings are interpreted within the pandemic context, showing the way resilience interventions can help people overcome unprecedented challenges.

Extensive research into two-dimensional transition metal dichalcogenides (2D-TMDCs) over the past decade has paved the way for the development of (opto)electronic devices with enhanced performance and novel capabilities. To realize devices based on 2D-TMDC layers, compatible and optimized technologies such as layer transfer and photolithography are required. Challenges arise due to the ultrathin, surface-only nature of 2D layers with weak van der Waals adhesion to their substrate. This might potentially compromise their integrity during transfer and photolithography processes, in which prolonged exposure at usually high temperature to reactive chemicals and strong solvents are conventionally used. In this paper, we show that employing a dry-transfer technique based on thermal release tape (TRT) as an alternative to wet processes based on KOH solution better preserves layer quality. In the succeeding device fabrication process, an optimized photolithography as a cost-effective and widely available method for device patterning is utilized. The introduced photolithography protocol presents a near-perfect yield and reproducibility. To validate our optimized techniques, we fabricated field-effect transistors (FETs) using 2D-MoS2 layers from metal–organic chemical vapor deposition (MOCVD), wet- and dry-transferred onto SiO2/Si substrates. Our findings mark a significant stride towards the efficient and industry-compatible utilization of 2D van der Waals materials in device fabrication.