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Individual Stress Burden and Mental Health in Health Care Workers during the COVID-19 Pandemic: Moderating and Mediating Effects of Resilience
No full textIntroduction: The COVID-19 pandemic generated a significant burden on the German health care system, affecting the mental health of health care workers (HCW) in particular. Resilience may serve as an essential protective factor for individuals' well-being. Objective: Our objective was to identify demographic and work-related correlates of individual resilience and to investigate the association between pandemic-related stress, resilience and mental health using different resilience models. Methods: Our sample comprised 1034 German HCW in different medical professions who completed an online survey from 20 April to 1 July 2020. Resilience was assessed using the Resilience Scale-5 (RS-5). The pandemic-related self-reported stress burden was captured by a single item, while depression and anxiety symptoms were measured with the PHQ-2 and GAD-2, respectively. Additionally, various sociodemographic and work-related factors were assessed. Results: Overall, we found high levels of resilience in the sample compared to a German sample before the pandemic, which were significantly associated only with the older age of participants and having children in both univariate and multivariate analyses. Regarding mechanisms of resilience, moderation analysis revealed that low individual resilience and high pandemic-related stress burden independently contributed to both anxiety and depression symptoms while resilience additionally moderated the relationship between stress burden and anxiety symptoms. The link between self-reported stress burden and mental health symptoms was also partially mediated by individual resilience. Conclusion: Taken together, the findings based on the present sample during the COVID-19 pandemic suggest that resilience plays a central role in the mental health of healthcare workers and that resilience-building interventions should be expanded, especially with a focus on younger employees
Wie KI Innere Führung lernt: Wertebasierte Technik mit IEEE7000(TM)-2021
No full textDass sich künstliche Intelligenz (KI) weltweit ausgebreitet hat, ist eine Binsenwahrheit. Die rasche und unaufhaltsame Proliferation von KI der letzten zehn Jahre spricht für sich, und längst ziehen auch Gesetzgeber und Regulierungsbehörden nach, um KI und ihre Technikfolgen einzuhegen. Für Deutschland relevante Gestaltungsanforderungen haben die High-Level Expert Group on Artificial Intelligence der Europäischen Kommission (HLEG AI) und auf nationaler Ebene die Datenethikkommission der Bundesregierung (DEK) und die Enquetekommission Künstliche Intelligenz des Deutschen Bundestags (EKKI) geäußert
Digitalisierung in zentralen Feldern der Sozialpolitik: Entwicklungstendenzen, Chancen und Risiken
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A New Setup for the Measurement of Total Organic Carbon in Ultrapure Water Systems
No full textWith the increasing demand for ultrapure water in the pharmaceutical and semiconductor industry, the need for precise measuring instruments for those applications is also growing. One critical parameter of water quality is the amount of total organic carbon (TOC). This work presents a system that uses the advantage of the increased oxidation power achieved with UV/O3 advanced oxidation process (AOP) for TOC measurement in combination with a significant miniaturization compared to the state of the art. The miniaturization is achieved by using polymer-electrolyte membrane (PEM) electrolysis cells for ozone generation in combination with UV-LEDs for irradiation of the measuring solution, as both components are significantly smaller than standard equipment. Conductivity measurement after oxidation is the measuring principle and measurements were carried out in the TOC range between 10 and 1000 ppb TOC. The suitability of the system for TOC measurement is demonstrated using the oxidation by ozonation combined with UV irradiation of defined concentrations of isopropyl alcohol (IPA)
Employees’ privacy perceptions: exploring the dimensionality and antecedents of personal data sensitivity and willingness to disclose
No full textThe processing of employees’ personal data is dramatically increasing, yet there is a lack of tools that allow employees to manage their privacy. In order to develop these tools, one needs to understand what sensitive personal data are and what factors influence employees’ willingness to disclose. Current privacy research, however, lacks such insights, as it has focused on other contexts in recent decades. To fill this research gap, we conducted a cross-sectional survey with 553 employees from Germany. Our survey provides multiple insights into the relationships between perceived data sensitivity and willingness to disclose in the employment context. Among other things, we show that the perceived sensitivity of certain types of data differs substantially from existing studies in other contexts. Moreover, currently used legal and contextual distinctions between different types of data do not accurately reflect the subtleties of employees’ perceptions. Instead, using 62 different data elements, we identified four groups of personal data that better reflect the multi-dimensionality of perceptions. However, previously found common disclosure antecedents in the context of online privacy do not seem to affect them. We further identified three groups of employees that differ in their perceived data sensitivity and willingness to disclose, but neither in their privacy beliefs nor in their demographics. Our findings thus provide employers, policy makers, and researchers with a better understanding of employees’ privacy perceptions and serve as a basis for future targeted research
on specific types of personal data and employees
Composite nanoparticles derived by self-assembling of hydrophobic polysaccharide derivatives and lignin
No full textComposite nanoparticles (NPs) consisting of lignin and different polysaccharide (PS) derivatives were prepared. In this synergistic approach, the PS derivative acts as biocompatible matrix that forms spherical NPs while lignin is a functional compound with therapeutic potential (e.g., antioxidative, antimicrobial, antiviral). Organosolv lignin and three different PS derivatives (cellulose acetate/CA, cellulose acetate phthalate/CAPh, xylan phenyl carbonate/XPC) were used in this study. Nanocomposites with particle sizes in the range of about 200–550 nm containing both types of biopolymers are accessible by dialysis of organic PS/lignin solutions against water. In particular, XPC and CAPh, which both contain aromatic substituents, were found to be suitable for incorporation of lignin within the PS nanomatrix. The present work paves the way for future studies in which the pharmaceutical potential and biocompatibility of composite NPs of lignin and PS derivatives with tailored properties are investigated
Highly potent inhibitors of cathepsin K with a differently positioned cyanohydrazide warhead: structural analysis of binding mode to mature and zymogen-like enzymes
No full textCathepsin K (CatK) is a target for the treatment of osteoporosis, arthritis, and bone metastasis. Peptidomimetics with a cyanohydrazide warhead represent a new class of highly potent CatK inhibitors; however, their binding mechanism is unknown. We investigated two model cyanohydrazide inhibitors with differently positioned warheads: an azadipeptide nitrile Gü1303 and a 3-cyano-3-aza-β-amino acid Gü2602. Crystal structures of their covalent complexes were determined with mature CatK as well as a zymogen-like activation intermediate of CatK. Binding mode analysis, together with quantum chemical calculations, revealed that the extraordinary picomolar potency of Gü2602 is entropically favoured by its conformational flexibility at the nonprimed-primed subsites boundary. Furthermore, we demonstrated by live cell imaging that cyanohydrazides effectively target mature CatK in osteosarcoma cells. Cyanohydrazides also suppressed the maturation of CatK by inhibiting the autoactivation of the CatK zymogen. Our results provide structural insights for the rational design of cyanohydrazide inhibitors of CatK as potential drugs
Identification, characterisation, and evaluation of bioactive plant-based compounds for the sustainable stabilisation of food packaging materials
No full textTypically, plastic packaging materials are produced using additives, like e.g. stabilisers, to introduce specific desired properties into the material or, in case of stabilisers, to prolong the shelf life of such packaging materials. However, those stabilisers are typically fossil-based and can pose risks to both environmental and human health. Therefore, the present study presents more sustainable alternatives based on regional renewable resources which show the relevant antioxidant, antimicrobial and UV absorbing properties to successfully serve as a plastic stabiliser. In the study, all plants are extracted and characterised with regard to not only antioxidant, antimicrobial and UV absorbing effects, but also with regard to additional relevant information like chemical constituents, molar mass distribution, absorbance in the visible range et cetera. The extraction process is furthermore optimised and, where applicable, reasonable opportunities for waste valorisation are explored and analysed. Furthermore, interactions between analysed plant extracts are described and model films based on Poly-Lactic Acid are prepared, incorporating analysed plant extracts. Based on those model films, formulation tests and migration analysis according to EU legislation is conducted.
The well-known aromatic and medicinal plant thyme (Thymus vulgaris L.) includes phenolic terpenoids like thymol and carvacrol which have strong antioxidant, antimicrobial and UV absorbing effects. Analyses show that those effects can be used in both lipophilic and hydrophilic surroundings, that the variant Varico 3 is a more potent cultivar than other analysed thyme variants, and that a passive extraction setup can be used for extract preparation while distillation of the Essential Oils can be a more efficient approach.
Macromolecular antioxidant polyphenols, particularly proanthocyanidins, have been found in the seed coats of the European horse chestnut (Aesculus hippocastanum L.) which are regularly discarded in phytopharmaceutical industry. In this study, such effects and compounds have been reported for the first time while a valorisation of waste materials has been analysed successfully. Furthermore, a passive extraction setup for waste materials and whole seeds has been developed. In extracts of snowdrops, precisely Galanthus elwesii HOOK.F., high concentrations of tocopherol have been found which promote a particularly high antioxidant capacity in lipophilic surroundings. Different coniferous woods (Abies div., Picea div.) which are in use as Christmas trees are extracted after separating the biomass in leafs and wood parts before being analysed regarding extraction optimisation and drought resistance of active substances. Antioxidant and UV absorbing proanthocyanidins are found even in dried biomasses, allowing the circular use of already used Christmas trees as bio-based stabilisers and the production of sustainable paper as a byproduct
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series
No full textDeep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called posthoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data, namely model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality