Hochschule Emden/Leer
  • Emden, Germany
Recent publications
Gesundheitskompetenz ist im vergangenen Jahrzehnt zu einem zentralen Thema in der Gesundheitsforschung und Gesundheitspolitik aufgestiegen, und die Corona-Krise hat die Brisanz des Themas nochmal erhöht. Gängige Surveys bescheinigen der Mehrheit der deutschen Bevölkerung eine „inadäquate“ oder gar „problematische“ Gesundheitskompetenz. Daher gehen Expert:innen in der Regel davon aus, dass die meisten Menschen gar nicht in der Lage sind, kompetent mit ihrer Gesundheit sowie mit Gesundheitsinformationen umzugehen, sondern erst dazu befähigt werden müssen. Sie übersehen allerdings die Tatsache, dass das Konzept der Gesundheitskompetenz sowohl in den gängigen Definitionen als auch in den Surveys die vielschichtigen sozialen und kulturellen Dimensionen von Gesundheitswissen ausklammert. Gesundheitskompetenz wird einseitig als individuelle und kognitive Fähigkeit verstanden, abstrakte Information zu verarbeiten und rationale Entscheidungen zu treffen – und zwar so, dass sie den Anforderungen des Gesundheitssystems entsprechen. Dieser Definition liegt ein eindimensionales Konzept von Wissen zugrunde, das u. a. Erfahrung, körperliches Wissen und Wissen um Machtverhältnisse ausblendet. Patientenorientierung im Gesundheitswesen müsste stattdessen heißen, diese Erfahrungen und Wissenspraktiken im Hinblick auf körperliches, geistiges und soziales Wohlbefinden zu verstehen und sowohl in der Theorie als auch in der Praxis ernst zu nehmen.
Die Innovationskraft von Unternehmen hängt von ihrer Beharrlichkeit beim Problemlösen ab. Zentral für die Entwicklung erfolgreicher Innovationen ist es zudem, die Probleme der Kund:innen zu kennen, zu verstehen und zu lösen. Konsumentenpsychologisches Wissen hilft dabei, ihre Perspektive einzunehmen, ihre Reaktionen auf neue Angebote zu verstehen und entsprechende Anpassungen zu entwickeln.
Clostridioides difficile (C. difficile) is the most common pathogen causing antibiotic-associated intestinal diseases in humans and some animal species, but it can also be present in various environments outside hospitals. Thus, the objective of this study was to investigate the presence and the characteristics of toxin-encoding genes and antimicrobial resistance of C. difficile isolates from different environmental sources. C. difficile was found in 32 out of 81 samples (39.50%) after selective enrichment of spore-forming bacteria and in 45 samples (55.56%) using a TaqMan-based qPCR assay. A total of 169 C. difficile isolates were recovered from those 32 C. difficile-positive environmental samples. The majority of environmental C. difficile isolates were toxigenic, with many (88.75%) positive for tcdA and tcdB. Seventy-four isolates (43.78%) were positive for binary toxins, cdtA and cdtB, and 19 isolates were non-toxigenic. All the environmental C. difficile isolates were susceptible to vancomycin and metronidazole, and most isolates were resistant to ciprofloxacin (66.86%) and clindamycin (46.15%), followed by moxifloxacin (13.02%) and tetracycline (4.73%). Seventy-five isolates (44.38%) showed resistance to at least two of the tested antimicrobials. C. difficile strains are commonly present in various environmental sources contaminated by feces and could be a potential source of community-associated C. difficile infections.
Purpose Microsatellite instability (MSI) due to mismatch repair (MMR) defects accounts for 15-20% of colon cancers (CC). MSI testing is currently standard of care in CC with immunohistochemistry of the four MMR-proteins representing the gold standard. Instead, label-free quantum cascade laser (QCL) based infrared (IR) imaging combined with artificial intelligence (AI) may classify MSI/microsatellite stability (MSS) in unstained tissue sections user-independently and tissue preserving. Methods Paraffin-embedded unstained tissue sections of early CC from patients participating in the multicentre AIO ColoPredict Plus (CPP) 2.0 registry were analysed after dividing into three groups (training, test, validation). IR images of tissue sections using QCL-IR microscopes were classified by AI (convolutional neural networks, CNN) using a two-step approach. The first CNN (modified U-Net) detected areas of cancer while the second CNN (VGG-Net) classified MSI/MSS. Endpoints were area under receiver operating characteristic (AUROC) and area under precision recall characteristic (AUPRC). Results The cancer detection in the first step was based on 629 patients (train n=273, test n=138, validation n=218). Resulting classification AUROC was 1.0 for the validation dataset. The second step classifying MSI/MSS was performed on 547 patients (train n=331, test n=69, validation n=147) reaching AUROC and AUPRC of 0.9 and 0.74, respectively, for the validation cohort. Conclusion Our novel label-free digital pathology approach accurately and rapidly classifies MSI vs. MSS. The tissue sections analysed were not processed leaving the sample unmodified for subsequent analyses. Our approach demonstrates an AI-based decision support tool potentially driving improved patient stratification and precision oncology in the future.
With the rapid development of information technologies, the computing, networking, and physical elements in industrial environments are becoming tightly amalgamated with each other, resulting in the formation of the so-called Industrial Cyber-Physical Systems (ICPS). These systems forge the core of current real-world networked industrial infrastructures, having a cyber-representation of physical assets through digitalization of data across the enterprise, along the value stream and process engineering life cycle, along the digital thread, and along the supply chain. Typical applications of ICPS include smart grids, digital factory, cognitive and collaborative robots, freight transportation, process control, plant-wide systems, medical monitoring, etc. ICPS often operate in an unpredictable and challenging environment, where various disturbances, such as unplanned natural events, human faults or malicious behaviors, software and hardware failures, etc., may occur during the automation process at runtime. Moreover, ICPS can exhibit strong reconfigurability and evolve structurally for many purposes. During this evolution, new and unforeseen possibilities in the service-oriented business process may appear among various ICPS components. In particular, new “emergent” behaviors may arise that need to be monitored, understood, managed and controlled. When there are significant uncertainties, such emergent behaviors could make the evolved ICPS unstable and unable to meet the quality/performance targets, even resulting in hazards. Well-designed machine-learning techniques have the potential to effectively address the uncertainties and disturbances in the automation of ICPS. They can also facilitate the automated discovery of valuable underlying rules and patterns to improve the performance of ICPS in all phases of their life cycles.
The present study focused on the influence of different aging conditions on the strain-dependent damping of the high-strength aluminum alloy AA7075. For this purpose, different artificial aging strategies were carried out after solution heat treatment with subsequent water quenching to identify correlations between microstructural evolution, hardness development, and individual material damping. The resulting material damping was measured using an experimental setup based on the principle of electromagnetic feedback. Scanning transmission electron microscopy (STEM) investigations were carried out using a scanning electron microscope (SEM) to characterize the material’s microstructure. Depending on the aging conditions, the damping investigations revealed specific characteristic behaviors in the strain-dependent range from 1 × 10−7 to 0.002. Peak aging conditions showed lower damping than the overaged conditions but resulted in the highest hardness. The hardness decreased with increasing aging time or temperature.
Background: The coronavirus disease 2019 (COVID-19) pandemic increased multiple risk factors for mental health. Evidence-based, intersectoral public mental health responses are therefore critical. The primary aim of this study was to collate public mental health responses from across Europe. Methods: We conducted a cross-sectional survey in March 2021. Participants were public and mental health professionals from across Europe. We developed an online instrument exploring five domains: changes in mental health supports during the pandemic; mental health support for vulnerable groups; multi-sectoral and service-user involvement; published mental health response plans; and perceived quality of overall country response. Results: Fifty-two individuals from 20 European nations responded. Reported changes in mental health supports included an increase in online mental health supports (n = 18); but no change in long-term mental health funding (n = 13); and a decrease in access to early interventions (n = 9). Responses indicated mental health support for vulnerable groups was limited, as was multi-sectoral and service-user involvement. Few national mental health response plans existed (n = 9) and 48% of respondents felt their countries mental health response had been 'poor' or 'very poor'. Conclusions: Our results give insights into the changes in mental health support at a country level across Europe during the COVID-19 pandemic. They indicate countries were not prepared to respond and people with existing vulnerabilities were often neglected in response planning. To be prepared for future pandemics and environmental disasters Public Mental Health preparedness plans are highly needed. These must be developed cross-departmentally, and through the meaningful inclusion of vulnerable groups.
In times of crises, such as the refugee crisis or the corona pandemic, the workload in public administrations increases because of the demands of citizens or short-term legal changes. In addition, there is an increasing need for digitalization or to be able to react flexibly to changes. Agile process models and agile practices are appropriate to overcome these challenges. The objective of this paper is to investigate how public administrations can measure their degree of agility to identify potential for improving it. The authors conducted a descriptive single-case study which included multiple units of analysis in a public administration in Germany. The case study was supported by their questionnaire for measuring the degree of agility. One outcome of this study is a conceptual framework that can be used to drive agile transformation in public administrations by continuously measuring agility. Therefore, a questionnaire for measuring agility at team level in public administrations has been developed. The application of the questionnaire in three teams provide insights into dysfunctionality in the interdisciplinary teams as well as optimization potential in terms of affinity to change. The adoption of agility in public administration is a challenge, given that resistance to change is still prevalent. A transformational change is a constant journey and therefore the measurement of progress plays an important role in the continuous improvement of an organization. The applied approach delivers high potential for improvement in terms of agility and provides interesting insights for both practitioners and academics.
The replacement of existing technology or the introduction of novel technology into the day-today routines of higher education institutions is not a trivial task. Currently , many higher education institutions are faced with the challenge of replacing existing procedures for administering written exams with e-exams. To guide this process, this paper proposes the novel technology-based exams acceptance model (TEAM) and empirically evaluates its model structure and usefulness from the perspective of higher education teachers. The model can be used to guide the transition from paper-based exams to e-exams and the implementation of innovative (e.g., adaptive) e-exam formats. The model includes perceived usefulness, computer self-efficacy, computer anxiety, prior experience, facilitating conditions, and subjective norm as predictors of the behavioral intention to use e-exams. To test the model empirically, the responses of 992 teachers at 63 German universities to a standardized online questionnaire were analyzed using structural equation modeling. The model fit was acceptable. With 77% (conventional e-exams) and 82% (adap-tive e-exams), a large proportion of the variance of the intention to use these types of exams was explained. With TEAM, a highly predictive model for explaining the behavioral intention to use e-exams is now available. It offers a theoretical basis that can be used for the successful implementation of e-exams in higher education.
Gamification is widely known and implemented for various purposes. But it is also criticized for recurring lack of quality. Many researchers developed gamification frameworks and tools to ensure a purposeful gamification, but these theoretical frameworks are used by less than half of gamification research. There are numerous gamification frameworks and it is difficult to find a specific one. Our research aims to tackle this problem by providing a fast and easy process, that allows finding a gamification framework for a specific use case. We want to achieve this, by identifying selection and quality criteria and developing a method to match these criteria to a gamification framework. Succeeding this we will develop a tool, that allows the user to identify the most suited gamification frameworks for any combination of the selection criteria.
Hierarchical Bayesian modeling is beneficial when complex models with many parameters of the same type, such as item response theory (IRT) models, are to be estimated with sparse data. Recently, Koenig et al. (Applied Psychological Measurement, 44, 311–326, 2020) illustrated in an optimized hierarchical Bayesian two-parameter logistic model (OH2PL) how to avoid bias due to unintended shrinkage or degeneracies of the posterior, and how to benefit from this approach in small samples. The generalizability of their findings, however, is limited because they investigated only a single specification of the hyperprior structure. Consequently, in a comprehensive simulation study, we investigated the robustness of the performance of the novel OH2PL in several specifications of their hyperpriors under a broad range of data conditions. We show that the novel OH2PL in the half-Cauchy or Exponential configuration yields unbiased (in terms of bias) model parameter estimates in small samples of N = 50. Moreover, it outperforms (especially in terms of the RMSE of the item discrimination parameters) marginal maximum likelihood (MML) estimation and its nonhierarchical counterpart. This further corroborates the possibility that hierarchical Bayesian IRT models behave differently than general hierarchical Bayesian models. We discuss these results regarding the applicability of complex IRT models in small-scale situations typical in psychological research, and illustrate the extended applicability of the 2PL IRT model with an empirical example.
Circular economy is a system solution framework replacing the traditional linear approach and aims at creating value from waste. Bladderwrack, a brown sea weed is often cast away in huge volumes on the North Sea coast and is perceived as a waste product. Removal of this algae incurs heavy costs for the municipal authorities since deposits of this sea wrack weakens and poses threat to the flood protective dikes and also to the tourism industry due to its unpleasant sight and smell. This study investigated the biogas potential of the nutrient-rich bladderwrack, Fucus vesiculosus, and co-digestion experiments were performed with maize silage. Mathematical simulations were performed with the Anaerobic Digestion Model No. 1 (ADM1) in order to assess optimal operational conditions and understand the reaction kinetics. While bladderwrack and maize silage on an average yielded 178 ml/g FM together during mono-fermentation, the co-digestion experiment provided 236 ml/g FM, clearly demonstrating the synergistic effect with nearly 33% more biogas. This means reduced dependence on a forage crop like maize by substituting it with a waste product like the brown sea weed along with attaining enhanced biogas production. This positive effect is reasoned to be due to the balanced C/N ratio in the digester during co-digestion. Furthermore, bladderwrack supplements trace metals and vitamins to the nutrient deficient maize silage. The ADM1 model indicated active microbial growth and activities and proved its competence in predicting the synergistic impacts with high accuracy.
Voice User Interfaces (VUIs) are becoming increasingly available while users raise, e.g., concerns about privacy issues. User Experience (UX) helps in the design and evaluation of VUIs with focus on the user. Knowledge of the relevant UX aspects for VUIs is needed to understand the user’s point of view when developing such systems. Known UX aspects are derived, e.g., from graphical user interfaces or expert-driven research. The user’s opinion on UX aspects for VUIs, however, has thus far been missing. Hence, we conducted a qualitative and quantitative user study to determine which aspects users take into account when evaluating VUIs. We generated a list of 32 UX aspects that intensive users consider for VUIs. These overlap with, but are not limited to, aspects from established literature. For example, while Efficiency and Effectivity are already well known, Simplicity and Politeness are inherent to known VUI UX aspects but are not necessarily focused. Furthermore, Independency and Context-sensitivity are some new UX aspects for VUIs.
In order to be able to meaningfully classify the user experience and thus the popularity of products, UX questionnaires such as the UEQ, SUS or UMUX are frequently used in practice to measure the UX. This makes it possible to specifically evaluate the ratings of pragmatic and hedonic UX factors. However, it is conceivable that, in addition to users' own perceptions, external factors also have an influence on the evaluation of the UX of products. These include, for example, time or duration of use. It can be assumed that users who rate the UX of a product as good also use this product more frequently and vice versa. Such a consideration of influencing factors is particularly interesting for products that have been used frequently in recent years and thus also during the pandemic. For this reason, Netflix, Microsoft PowerPoint, Zoom and BigBlueButton were selected, which cover the range from primarily hedonic to primarily pragmatic quality. These are examined for their UX ratings as well as influencing factors.
Background A relationship between green space and health has been shown in several epidemiological studies. The impact of different types of green space is still relatively unknown. To start filling this gap, we looked at associations between different green space types and health outcomes (depression and mental health). Methods Data are obtained from a cross-sectional study (n = 479). Depression (assessed with PHQ-9) and mental health (assessed with GHQ-28) are dependent variables. Availability of green space in the surrounding neighborhood was assessed as independent variable by the percentage of green space ( > =1ha) within a 250m radius participants residence. Survey data were analyzed using IBM SPSS 26 and Geo data using QGIS 3.18.0. Results N = 479 participants of a cross-sectional study in 2018 provided data (49.4%, n = 240 women; 49.6%, n = 239 men). Participants had a mean age of 57.55 years (SD: 18.80, min-max:18-95), majority (75.2%, n = 360) were married or partnered, had a lower educational qualification than A-levels equivalent (56.8%, n = 272), were not employed (53%, n = 254), had a net household income of at least 3. 000€ per month (40.1%, n = 192) and at least sometimes financial worries (51.4%, n = 246). Green areas without agricultural areas show an association with frequency of depression (B(SE)=0.056(0.024), p = 0.018). This contrasts with green spaces including agricultural areas, where there is no statistically significant association (B(SE)=0.007(0.012), p = 0.564). Discussion We found an association between type of green space and depression. Further studies are needed to establish a grid for assessing characteristics and quality criteria of green spaces. However, it can already be assumed that there is an association between quality of green spaces and psychosocial outcomes.
Background Family functioning can have positive and negative mental health consequences. Positive relationships can boost mental health, the opposite is true for negative relationships. 1 in 4 individuals are affected by at least one mental health condition in their life. Family-based interventions can help prevent the onset of mental health conditions and mitigate its consequences. Methods Following databases were systematically searched: Medline; PsychInfo, Web of Sciences and Cochrane, resulting in 3719 hits. After removing 12 duplicates, 3707 studies were screened. After exclusion of irrelevant studies, 362 studies were assessed for eligibility and 40 studies were included. Inclusion criteria were original studies with ≥100 participants, ≥18 years, general population, and family members. Exposure had to be family social cohesion or conflict, or social capital. The outcome had to be a mental health condition. Results Most studies (n = 37) used a cross-sectional design. 37 studies included a measure of family functioning and 3 studies used one of family structure. Most used was the Family Adaptability and Cohesion Evaluation Scale (n = 17), followed by the Family Functioning Scale (n = 5). Family relationship quality was related to depression, anxiety, and substance use. All aspects family cohesion were related to mental health outcomes. Family conflicts are associated with an increase in mental health conditions. Conclusions Family cohesion shows an association with positive mental health while conflict is associated with negative mental health. This is an indication, that interventions at the family level are useful to help prevent/mitigate mental health conditions over the life course. Main message: As mental health conditions are a big public health issue affecting at least 1 in 4 individuals, family-based interventions for mental health condition prevention could not only help individuals but the whole family to strengthen and maintain positive mental health.
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Thies Pfeiffer
  • Electrical Engineering and Information Technology
Thorsten Schmidt
  • Faculty of Technology
Frank Uhlenhut
  • Fachbereich Technik, Emder Institut für Umwelttechnik - EUTEC
Emden, Germany