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Introduction: Recovery experiences have thus far been portrayed as experiences that simply “happen” to people. However, recovery can also be understood from a crafting perspective; that is, individuals may proactively shape their work and non-work activities to recover from stress, satisfy their psychological needs, and achieve optimal functioning.
Materials and Methods: In my talk, I will present the theoretical basis of needs-based crafting based on a conceptual review of the literature. Moreover, I will present empirical findings on the validation of a newly developed off-job crafting scale.
Results: In five sub studies, we found that off-job crafting was related to optimal functioning over time. Moreover, the newly developed off-job crafting scale had good convergent and discriminant validity, internal consistency, and test-retest reliability.
Conclusions: Theoretical and empirical evidence suggests that needs-based crafting can enhance optimal functioning in different life domains and support people in performing their work duties sustainably. Proactive attempts to achieve better recovery through needs satisfaction may be beneficial in an intensified and continually changing and challenging working life. Our line of research provides important avenues for organizational research and practices regarding recovery and needs satisfaction occurring at work and outside work.
Intact Transition Epitope Mapping - Targeted High-Energy Rupture of Extracted Epitopes (ITEM-THREE)
(2019)
Epitope mapping, which is the identification of antigenic determinants, is essential for the design of novel antibody-based therapeutics and diagnostic tools. ITEM-THREE is a mass spectrometry-based epitope mapping method that can identify epitopes on antigens upon generating an immune complex in electrospray-compatible solutions by adding an antibody of interest to a mixture of peptides from which at least one holds the antibody's epitope. This mixture is nano-electrosprayed without purification. Identification of the epitope peptide is performed within a mass spectrometer that provides an ion mobility cell sandwiched in-between two collision cells and where this ion manipulation setup is flanked by a quadrupole mass analyzer on one side and a time-of-flight mass analyzer on the other side. In a stepwise fashion, immune-complex ions are separated from unbound peptide ions and dissociated to release epitope peptide ions. Immune complex-released peptide ions are separated from antibody ions and fragmented by collision induced dissociation. Epitope-containing peptide fragment ions are recorded, and mass lists are submitted to unsupervised data base search thereby retrieving both, the amino acid sequence of the epitope peptide and the originating antigen. ITEM-THREE was developed with antiTRIM21 and antiRA33 antibodies for which the epitopes were known, subjecting them to mixtures of synthetic peptides of which one contained the respective epitope. ITEM-THREE was then successfully tested with an enzymatic digest of His-tagged recombinant human β-actin and an antiHis-tag antibody, as well as with an enzymatic digest of recombinant human TNFα and an antiTNFα antibody whose epitope was previously unknown.
The Fragile X Syndrome (FXS) is one of the most common forms of inherited intellectual disability in all human societies. Caused by the transcriptional silencing of a single gene, the fragile x mental retardation gene FMR1, FXS is characterized by a variety of symptoms, which range from mental disabilities to autism and epilepsy. More than 20 years ago, a first animal model was described, the Fmr1 knock-out mouse. Several other models have been developed since then, including conditional knock-out mice, knock-out rats, a zebrafish and a drosophila model. Using these model systems, various targets for potential pharmaceutical treatments have been identified and many treatments have been shown to be efficient in preclinical studies. However, all attempts to turn these findings into a therapy for patients have failed thus far. In this review, I will discuss underlying difficulties and address potential alternatives for our future research.
Sexuelle Belästigung am Arbeitsplatz ist ein tiefgreifendes Thema, welches den (Arbeits-)Alltag vieler Menschen massiv beeinträchtigt. Trotz vieler Studien und der juristischen Grundlage, die die Relevanz der Thematik hervorheben, findet es in Unternehmen und im öffentlichen Diskurs noch zu wenig Aufmerksamkeit. In der vorliegenden Studie wird deshalb untersucht, inwiefern das Allgemeine Gleichbehandlungsgesetz (AGG) Anwendung in der Praxis findet und was konkrete Verbesserungspotenziale von Unternehmen hinsichtlich des Umgangs mit sexueller Belästigung sind. Im Rahmen einer qualitativen Untersuchung werden Expert*inneninterviews geführt, die anschließend nach der Inhaltsanalyse nach Mayring, in Form einer Zusammenfassung, ausgewertet werden. Der Themenschwerpunkt wird hierbei auf die subjektiven Erfahrungen der Befragten gelegt. Die Ergebnisse dieser Arbeit unterstreichen, dass es sich bei sexueller Belästigung nach wie vor um ein Tabuthema handelt, obwohl es nachweislich sowohl auf die Beschäftigten als auch auf das Unternehmen negative Auswirkungen hat. Da das AGG in den wenigsten Unternehmen Anwendung findet, wird es von einem Großteil der Arbeitnehmenden nicht als Schutz vor sexueller Belästigung wahrgenommen. Maßnahmen, die existieren sind entweder dysfunktional oder werden nicht ausreichend bekannt gemacht. Die Auswertung zeigt vor allem, dass Unternehmen in Zukunft ein Unternehmensklima etablieren müssen, in dem eine Nulltoleranz-Haltung gegenüber Diskriminierung herrscht. Ganzheitliche Präventions- und Interventionskonzepte sollten unter anderem die Schaffung von transparenten Anlaufstellen, klare Richtlinien und Konzepte zur präventiven Aufklärungsarbeit enthalten. Hierbei sollten vor allem marginalisierte Gruppen berücksichtigt werden. Wenn Unternehmen ihre im AGG festgehaltenen Pflichten in Zukunft wahrnehmen und entsprechende Maßnahmen ergreifen, kann eine Enttabuisierung des Themas angestoßen werden. Neben der Stärkung der Betroffenen, kann dies letztendlich zu einem Rückgang der Übergriffe führen.
Climate change is transforming the risks individuals and households face, with potentially profound socioeconomic consequences such as increased poverty, inequality, and social instability. Social protection is a policy tool that governments use to help individuals and households manage risks linked to income and livelihoods, and to achieve societal outcomes such as reducing poverty and inequality. Despite its potential as a policy response to climate change, the integration of social protection within the climate policy agenda is currently limited. While the concept of risk is key to both sectors, different understandings of the nature and scope of climate change impacts and their implications, as well as of the adequacy of social protection instruments to address them, contribute to the lack of policy and practice integration.
Our goal is to bridge this cognitive gap by highlighting the potential of social protection as a policy response to climate change. Using a comprehensive climate risk lens, we first explore how climate change drives risks that are within the realm of social protection, and their implications, including likely future trends in demand for social protection. Based on this analysis, we critically review existing arguments for what social protection can do and evidence of what it currently does to manage risks arising from climate change. From the analysis, a set of reconceptualised roles emerge for social protection to strategically contribute to climate-resilient development.
It has become increasingly clear that caspases, far from being merely cell death effectors, have a much wider range of functions within the cell. These functions are as diverse as signal transduction and cytoskeletal remodeling, and caspases are now known to have an essential role in cell proliferation, migration, and differentiation. There is also evidence that apoptotic cells themselves can direct the behavior of nearby cells through the caspase-dependent secretion of paracrine signaling factors. In some processes, including the differentiation of skeletal muscle myoblasts, both caspase activation in differentiating cells as well as signaling from apoptotic cells has been reported. Here, we review the non-apoptotic outcomes of caspase activity in a range of different model systems and attempt to integrate this knowledge.
PURPOSE
Cervical cancer (CC) is caused by a persistent high-risk human papillomavirus (hrHPV) infection. The cervico-vaginal microbiome may influence the development of (pre)cancer lesions. Aim of the study was (i) to evaluate the new CC screening program in Germany for the detection of high-grade CC precursor lesions, and (ii) to elucidate the role of the cervico-vaginal microbiome and its potential impact on cervical dysplasia.
METHODS
The microbiome of 310 patients referred to colposcopy was determined by amplicon sequencing and correlated with clinicopathological parameters.
RESULTS
Most patients were referred for colposcopy due to a positive hrHPV result in two consecutive years combined with a normal PAP smear. In 2.1% of these cases, a CIN III lesion was detected. There was a significant positive association between the PAP stage and Lactobacillus vaginalis colonization and between the severity of CC precursor lesions and Ureaplasma parvum.
CONCLUSION
In our cohort, the new cervical cancer screening program resulted in a low rate of additional CIN III detected. It is questionable whether these cases were only identified earlier with additional HPV testing before the appearance of cytological abnormalities, or the new screening program will truly increase the detection rate of CIN III in the long run. Colonization with U. parvum was associated with histological dysplastic lesions. Whether targeted therapy of this pathogen or optimization of the microbiome prevents dysplasia remains speculative.
Studies in entrepreneurship education in hospitality and tourism has indicated that further attention could be given toward helping students to develop creativity and critical thinking skills, engage in deeper self-discovery experiences, and understand tourism more fully to help prepare them for entrepreneurial roles. This study aims at evaluating Hospitality entrepreneurial modules offered in Tourism programs in Ghanaian institutions. The curriculum of Tourism in two tertiary institutions in Ghana offering Tourism is studied. The research highlights on the need to integrate hospitality technical skills into Tourism education to create a culture that will enhance the growth of entrepreneurial hospitality into Tourism as culinary Tourism is becoming common. Some of the challenges faced by tourism students and entrepreneurship educators are highlighted. Structured interview technique was used to collect data from 20 purposive sampled students of the selected institutions. The results revealed that the level of importance and attention given to hospitality skills in tourism and the perception of students on acquisition of the required competencies is minimal. It is therefore recommended that more skills and competences in hospitality operation, food and beverage production and service be introduced in tourism education in a more holistic manner with emphasis on skill acquisition in order to make the tourism graduate more creative and critical thinker in today’s global competitive environment.
Destination Development for Entrepreneurial Tourism in Lake Bosomtwe and Kintampo falls (Ghana)
(2019)
The tourism industry is one of the world’s largest industries (direct, indirect and induced Africa has the potential with its cultural and natural resources to outpace other regions in attracting valuable tourism dollars. The main aim of the study is to improve visitor experience on the two tourist sites. To do this it is necessary to explore the elements and success factors of Tourism Destination Development and using these as a checklist to identify the strength and weaknesses of the selected Tourist Destinations in Ghana West Africa. The rationale behind the study is to outline the crucial Destination Management (DM) criteria of all aspect that contribute to boost ultimate visitor experience, articulating the roles of the different stakeholders and identifying clear actions for effective Tourism Development in Ghana. The interview technique was employed to collect data from staff and management of the selected destinations. Data was analyzed for themes related to elements, success factors and challenges of destination development and new ideas for development was also solicited. It was revealed that some of the elements that feature for tourists’ attraction are good hotels, high hygiene and sanitation standards, good food and activities of amusements. Competency gaps identified suggest collaboration with academia to secure a high level of knowledge through research in this present world of dynamism. Some of the critical success factors found are: systematic provision of cultural events, advance knowledge of agents and tour operators and quality leisure and recreation. It is recommended that product and service development should be a joint idea of all stakeholders. The research team therefore, have plans underway to proceed on the second phase of the project: that is to gather resources together to make lake Bosomtwe and Kintampo falls sites attractive to tourists.
In the context of the Franco-German research project Re(h)strain, this work focuses on a global system analysis integrating both safety and security analysis of international and/or urban railway stations. The Re(h)strain project focuses on terrorist attacks on high speed train systems and investigates prevention and mitigation measures to reduce the overall vulnerability and strengthen the system resilience. One main criterion regarding public transport issues is the number of passengers. For example, the railway station of Paris “Gare du Nord” deals with a bigger number of passengers than the biggest airport in the world (SNCF open Data 2014), the Atlanta airport, but in terms of passengers, it is only around the 23rd rank railway station in the world. Due to the enormous mass of people, this leads to the system approach of breaking out the station into several classes of zones, e.g. entrance, main hall, quays, trains, etc. All classes are analysed considering state-of-the-art parameters, like targets attractiveness, feasibility of attack, possible damage, possible mitigation and defences. Then, safety incidence of security defence is discussed in order to refine security requirement with regard to the considered zone. Finally, global requirements of security defence correlated to the corresponding class of zones are proposed.
Novel methods for contingency analysis of gas transport networks are presented. They are motivated by the transition of our energy system where hydrogen plays a growing role. The novel methods are based on a specific method for topological reduction and so-called supernodes. Stationary Euler equations with advanced compressor thermodynamics and a gas law allowing for gas compositions with up to 100% hydrogen are used. Several measures and plots support an intuitive comparison and analysis of the results. In particular, it is shown that the newly developed methods can estimate locations and magnitudes of additional capacities (injection, buffering, storage etc.) with a reasonable performance for networks of relevant composition and size.
The white ground crater by the Phiale Painter (450–440 BC) exhibited in the “Pietro Griffo” Archaeological Museum in Agrigento (Italy) depicts two scenes from Perseus myth. The vase is of utmost importance to archaeologists because the figures are drawn on a white background with remarkable daintiness and attention to detail. Notwithstanding the white ground ceramics being well documented from an archaeological and historical point of view, doubts concerning the compositions of pigments and binders and the production technique are still unsolved. This kind of vase is a valuable rarity, the use of which is documented in elitist funeral rituals. The study aims to investigate the constituent materials and the execution technique of this magnificent crater. The investigation was carried out using non-destructive and non-invasive techniques in situ. Portable X-ray fluorescence and Fourier-transform total reflection infrared spectroscopy complemented the use of visible and ultraviolet light photography to get an overview and specific information on the vase. The XRF data were used to produce false colour maps showing the location of the various elements detected, using the program SmART_scan. The use of gypsum as the material for the white ground is an important result that deserves to be further investigated in similar vases.
In recent years, there has been a growing interest in the start-up scene in sub-Saharan Africa. "Silicon Savannah" is today widely used to describe the thriving IT industry in and around Nairobi. Kenya's geographical advantage, its favorable economic reforms, and mature start-up ecosystem makes it stands out positively. Since a lot of hype exists around the start-up scene many investors are drawn to it, but in reality very few start-ups are investment-ready. The increasing start-up requirements and needs force incubators to diversify their offer. In contrast, to traditional incubators, an Innovation Hub (Hub) is characterized based on the concept of open innovation and collaboration. A Hub nurtures an enabling environment where a community of entrepreneurs can grow. At the same time, it serves as a nexus point for the local start-up community, investors, academia, technology companies and the wider private sector. It aims to create a structure where people serendipitously interact with others that they would not typically meet. Considering the great interest for and the large amounts of money invested in Hubs by governments, universities, private companies and other interested parties, not only researchers have been raising the question of the actual benefit of Hubs. This research study aims to investigate to what extent the support offered by the Hubs is tackling the challenges faced by start-ups in Nairobi, Kenya. The analysis can serve as a basis for identifying strength and weaknesses in the Hub models.
A firm link between endoplasmic reticulum (ER) stress and tumors has been wildly reported. Endoplasmic reticulum oxidoreductase 1 alpha (ERO1α), an ER-resident thiol oxidoreductase, is confirmed to be highly upregulated in various cancer types and associated with a significantly worse prognosis. Of importance, under ER stress, the functional interplay of ERO1α/PDI axis plays a pivotal role to orchestrate proper protein folding and other key processes. Multiple lines of evidence propose ERO1α as an attractive potential target for cancer treatment. However, the unavailability of specific inhibitor for ERO1α, its molecular inter-relatedness with closely related paralog ERO1β and the tightly regulated processes with other members of flavoenzyme family of enzymes, raises several concerns about its clinical translation. Herein, we have provided a detailed description of ERO1α in human cancers and its vulnerability towards the aforementioned concerns. Besides, we have discussed a few key considerations that may improve our understanding about ERO1α in tumors.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-power. However, cloud formation processes and movements are subject to ongoing research. For now-casting applications, all-sky-imagers (ASI) are used to offer an appropriate forecasting for aforementioned application. Recent research aims to achieve these forecasts via deep learning approaches, either as an image segmentation task to generate a DNI forecast through a cloud vectoring approach to translate the DNI to a GHI with ground-based measurement (Fabel et al., 2022; Nouri et al., 2021), or as an end-to-end regression task to generate a GHI forecast directly from the images (Paletta et al., 2021; Yang et al., 2021). While end-to-end regression might be the more attractive approach for off-grid scenarios, literature reports increased performance compared to smart-persistence but do not show satisfactory forecasting patterns (Paletta et al., 2021). This work takes a step back and investigates the possibility to translate ASI-images to current GHI to deploy the neural network as a feature extractor. An ImageNet pre-trained deep learning model is used to achieve such translation on an openly available dataset by the University of California San Diego (Pedro et al., 2019). The images and measurements were collected in Folsom, California. Results show that the neural network can successfully translate ASI-images to GHI for a variety of cloud situations without the need of any external variables. Extending the neural network to a forecasting task also shows promising forecasting patterns, which shows that the neural network extracts both temporal and momentarily features within the images to generate GHI forecasts.
Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.
Multiwalled carbon nanotubes (MWCNTs) were easily and efficiently functionalised with highly cross-linked polyamines. The radical polymerisation of two bis-vinylimidazolium salts in the presence of pristine MWCNTs and azobisisobutyronitrile (AIBN) as a radical initiator led to the formation of materials with a high functionalisation degree. The subsequent treatment with sodium borohydride gave rise to the reduction of imidazolium moieties with the concomitant formation of secondary and tertiary amino groups. The obtained materials were characterised by thermogravimetric analysis (TGA), elemental analysis, solid state 13C-NMR, Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), potentiometric titration, and temperature programmed desorption of carbon dioxide (CO2-TPD). One of the prepared materials was tested as a heterogeneous base catalyst in C–C bond forming reactions such as the Knoevenagel condensation and Henry reaction. Furthermore, two examples concerning a sequential one-pot approach involving two consecutive reactions, namely Knoevenagel and Michael reactions, were reported.
Less is Often More: Header Whitelisting as Semantic Gap Mitigation in HTTP-Based Software Systems
(2021)
The web is the most wide-spread digital system in the world and is used for many crucial applications. This makes web application security extremely important and, although there are already many security measures, new vulnerabilities are constantly being discovered. One reason for some of the recent discoveries lies in the presence of intermediate systems—e.g. caches, message routers, and load balancers—on the way between a client and a web application server. The implementations of such intermediaries may interpret HTTP messages differently, which leads to a semantically different understanding of the same message. This so-called semantic gap can cause weaknesses in the entire HTTP message processing chain.
In this paper we introduce the header whitelisting (HWL) approach to address the semantic gap in HTTP message processing pipelines. The basic idea is to normalize and reduce an HTTP request header to the minimum required fields using a whitelist before processing it in an intermediary or on the server, and then restore the original request for the next hop. Our results show that HWL can avoid misinterpretations of HTTP messages in the different components and thus prevent many attacks rooted in a semantic gap including request smuggling, cache poisoning, and authentication bypass.
Das Cutting sticks-Problem ist in seiner allgemeinen Formulierung ein NP-vollständiges Problem mit Anwendungspotenzialen im Bereich der Logistik. Unter der Annahme, dass P ungleich NP (P != NP) ist, existieren keine effizienten, d.h. polynomiellen Algorithmen zur Lösung des allgemeinen Problems.
In diesem Papier werden Ansätze aufgezeigt, mit denen bestimmte Instanzen des Problems effizient berechnet werden können. Für die Berechnung wichtige Parameter werden charakterisiert und deren Beziehung untereinander analysiert.