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A PM2.5 concentration prediction framework with vehicle tracking system: From cause to effect
(2023)
Representing 3D surfaces as level sets of continuous functions over R3 is the common denominator of neural implicit representations, which recently enabled remarkable progress in geometric deep learning and computer vision tasks. In order to represent 3D motion within this framework, it is often assumed (either explicitly or implicitly) that the transformations which a surface may undergo are homeomorphic: this is not necessarily true, for instance, in the case of fluid dynamics. In order to represent more general classes of deformations, we propose to apply this theoretical framework as regularizers for the optimization of simple 4D implicit functions (such as signed distance fields). We show that our representation is capable of capturing both homeomorphic and topology-changing deformations, while also defining correspondences over the continuously-reconstructed surfaces.
Spektroskopische Qualifizierung und Quantifizierung von Hyaluronsäure in Nahrungsergänzungsmitteln
(2023)
The paper investigates the nature of Kenya's entrepreneurship education ecosystem (EEE) through a comparative analysis of three entrepreneurship education programs and an examination of how the institutions foster a favourable entrepreneurial environment. This study looks at the entrepreneurship education ecosystem through the lens of universities, NGO's and private institutes in Kenya.
A systemic analysis of EEE is provided by utilizing the Actiotope Model as a conceptual framework. The exploratory research adopts a pragmatic mixed-method methodological approach best suited to understand the research problem.
The results reveal that entrepreneurship education at higher education institutions was primarily theoretical and relied on traditional forms of entrepreneurship education. Recurring rigid patterns show minimal personalization of content and learning styles within the University, with more personalization reported in the Mully Model of education and the more specialized entrepreneurship program of the Identity Projects.
The adaptation of the Actiotope Model provided a new and unique approach to analyzing entrepreneurship ecosystems. The person-centred approach of the model provides valuable insights to learners and to entrepreneurship education institutions and researchers.
Enhanced collaboration between the different entrepreneurial education stakeholders could be a more effective short to medium-term solution to addressing the gaps in entrepreneurial education at tertiary institutions.
In the long term, the study recommends adopting practical-based and goal-oriented entrepreneurship teaching models.
Social businesses have a great positive impact on communities and are a sustainable way to do business today and in the future. This impact can be amplified through the means of digitalization. In the past, traditional for-profit business models have been used to understand the structures of business operations. However, the underlying business model of digital social businesses has not yet been explored. This study presents a building block analysis of business models and a subsequent typology. Digital and social business models are identified via a literature review. The building block analysis encompasses an assessment of the individual business activities contained in the business models. The typology is developed from existing literature utilizing a matrix for the evaluation of digital social businesses. Additionally, five semi-structured expert interviews are conducted to inform, extend, or content the findings of this study. To this end, an inductive coding procedure is applied to the transcribed interviews for the detection of themes within the text. This study contributes to social business model research by providing a first insight into the unique building blocks of digital social business models. It also creates a typology tool based on two parameters, which enables the comparison of digital social businesses.
Mobile technologies have evolved into the means of gaining access to information for learning. Its application in higher education is still a novel concept, particularly in underdeveloped countries. This study is aimed at exploring the views of doctoral students regarding their learning experiences with mobile technologies. Student focus group interviews of 24 doctoral students from 3 different academic institutions were interviewed. The participants’ responses were recorded, transcribed, and analyzed to make conclusions. According to the findings of this study, mobile devices play an important part in the learning experiences of doctoral students. The participating students engaged in collaborative learning using mobile technologies. Given the benefits of adopting mobile technologies for learning activities, academic institutions should focus on teaching faculty members to use this to involve students in their learning process. The implications of this study call for the continued advancement of mobile technologies to facilitate effective learning experience for the multitude of mobile learners in developing countries. Another implication is that academic institutions with collaboration with libraries should see the need to develop user friendly mobile app that is linked to the library management system. Such an application would allow the students to optimally use their smartphones and tablets to search the library’s resources from their mobile devices. Training should be offered to the teaching faculty members to come to terms with the benefits of mobile technologies for learning activities.
TREE Jahresbericht 2021/2022
(2023)
Das Institut TREE freut sich, ihnen den Jahresbericht der Jahre 2021 und 2022 präsentieren zu können. Blicken sie mit uns zurück auf zwei herausfordernde Jahre.
Unser neuer Doppel-Jahresbericht 2021/2022 enthält viele, interessante, Beiträgen unserer spannenden, interdisziplinären Forschungprojekte der Bereiche Energie, Modellbildung Simulation, Drohnenforschung, Materialien und Prozesse und Technikkommunikation.
The UN Declaration on the Right to Development (UNDRTD) adopted in 1986 and the 2030 Agenda for Sustainable Development adopted in 2015 share a universal concept of development that refers both to individual and collective dimensions of prosperity and thus includes the rights of future generations.2 They thus offer a definition of the relationship between development and human rights that is very relevant for the 21st century. The core norm of the UNDRTD has been defined later as “the right of peoples and individuals to the constant improvement of their wellbeing and to a national and global enabling environment conducive to just, equitable, participatory and human-centred development respectful of all human rights”3.
The continuously increasing number of biomedical scholarly publications makes it challenging to construct document recommendation algorithms that can efficiently navigate through literature. Such algorithms would help researchers in finding similar, relevant, and related publications that align with their research interests. Natural Language Processing offers various alternatives to compare publications, ranging from entity recognition to document embeddings. In this paper, we present the results of a comparative analysis of vector-based approaches to assess document similarity in the RELISH corpus. We aim to determine the best approach that resembles relevance without the need for further training. Specifically, we employ five different techniques to generate vectors representing the text in the documents. These techniques employ a combination of various Natural Language Processing frameworks such as Word2Vec, Doc2Vec, dictionary-based Named Entity Recognition, and state-of-the-art models based on BERT. To evaluate the document similarity obtained by these approaches, we utilize different evaluation metrics that account for relevance judgment, relevance search, and re-ranking of the relevance search. Our results demonstrate that the most promising approach is an in-house version of document embeddings, starting with word embeddings and using centroids to aggregate them by document.
The continuous increase of biomedical scholarly publications makes it challenging to construct document recommendation algorithms to navigate through literature, an important feature for researchers to keep up with relevant publications. Understanding semantic relatedness and similarity between two documents could improve document recommendations. The objective of this study is performing a comparative analysis of vector-based approaches to assess document similarity in the RELISH corpus. Here we present our approach to compare five different techniques to generate vectors representing the text in the documents. These techniques employ a combination of various Natural Language Processing frameworks such as Word2Vec, Doc2Vec, dictionary-based Named Entity Recognition as well as state-of-the-art models based on BERT.
Tourism in Rwanda is challenging. Since the country is small and hilly, it is difficult to tap the potential. As the country is blessed with diverse nature, the Rwandan government decided to combine ecotourism with high-end tourism, to exploit the full potential. This study aims to assess the extent to which these two types of tourism fit together, as well as if sustainability is a decisive argument in this upscale segment. In this context, ecotourism is characterized by its 3 core criteria: education, nature and sustainability. To evaluate the main question: to what extent can ecotourism projects help to promote the perception of Rwanda as a high-end tourist destination on the German market? As well as if sustainability is a decisive argument, interviews with stakeholder from the Rwandan tourism industry as well as German tour operators were conducted, to gain an understanding of both sites and then evaluate them according to the 3 ecotourism core criteria and the demands of high-end tourists. The results showed that there is a difference in the perception of the needs of high-end tourists. While the 3 core criteria seem to be too relevant while they are in booking decision with the tour operator. The high-end lodges in Rwanda state an interest in these three criteria. It is evident from the results that there is a limited active demand for sustainable tourist products, while nature and education are more relevant, but not yet fully exploited. However, all interviewees indicated that ecotourism, and in particular sustainability, is experiencing an increase in demand and will continue to grow in importance in the future. Accordingly, the results suggest the driving markets approach is relevant to further drive demand in that segment.
As a developing economy, Rwanda has been exploring transitioning to being a technologically driven and sustainable economy. Moreover, research on economic growth have focused on the need to improve human capacity potential within increasing demands of climate change activists but there remains a theoretic and practical lacuna in including renewable energy resources in economic growth and expansion of electricity access. Therefore, it is necessary to study the impact of competent skill acquisition and graduate employment market on the interaction mix between economic growth and the expansion of energy access in Rwanda, particularly finding out the problems advancing the non-inclusiveness of engineering graduates, which result to high rate of unemployment and diversions, especially for the graduates specializing in energy fields. As a result, the following open questions were raised with variations 1; how did employees penetrate energy-sector labour market opportunity in Rwanda? 2; what influenced employee’s decision in pursuing a career in Rwanda’s labour market, 3; what were the specific employee competent skills that enabled smooth transition in energy-sector employment after graduation and the ones required to maintain their current positions? 4; what specific competent skills are required for inclusivity of today's engineering graduates in energy sector employment market? The study is qualitative and it uses the exploratory research design. It is based on the growth pole theory employing snowball/chain purposeful sampling technique, whereby key informants in Rwanda energy sector were located. Data was specifically collected from these primary sources through semi-structured interviews and documentary method. Interview data and text from documents were inductively analysed. The study generally recommended institution or program for connecting learning institutions, industry and employment market in the distributed and renewable energy resources to promote competent skills acquisition, competition and improve graduates’ inclusiveness in the expansion of electricity access, thereby leading to economic growth in Rwanda.
Rapid and sustained innovation in developed markets triggers the generation of innovative start-ups, some with disruptive innovations. However, when their offering faces a saturated market with satisfactory and widely available established traditional solutions, many innovative start-ups from these markets may fail. The literature on some start-ups that successfully brought their innovation to emerging markets shows how using leapfrogging traditional solutions to innovative solutions can offer survival and growth opportunities to these start-ups. However, a wide exploitation of leapfrogging processes in emerging markets for survival or business growth of innovative start-ups from developed markets is not yet theorized. To contribute to closing this gap, we propose a conceptual framework to assess the readiness of an emerging market to leapfrog to innovative solutions.
The design of the conceptual framework uses a scenario-planning like approach with two key factors, namely Context Readiness and Value Network Integration. To test and refine the proposed framework and show its relevance for coming to an informed expansion decision making, we used PAR (Participatory Action Research). For the illustration of the application of the proposed conceptual framework, the case of telehealth in Morocco is used.
While 14 % of the world's working-age population currently lives in sub-Saharan Africa (SSA), this figure will predictably be higher than the rest of the world combined by 2036. If this demographic group finds meaningful employment, Africa experiences an economic and social upswing. To tap this potential, the paper intends to answer the research question, "What are the prerequisites and how are they defined for the successful implementation of sustainable business model ideas in SSA?", by developing a top ten ranking consisting of previously identified sustainable business model ideas best suited for productive use. This achieves a novel approach to implementing future-oriented business models and contributes to current research on sustainable models. Since the geographical scope of SSA is pervasive, this paper focuses on Namibia, Rwanda, Senegal, and Uganda. An extensive literature review on these countries was conducted to gain a broader understanding of the situation in SSA. Additionally, research was carried out on the agricultural, energy, and information and communications technology (ICT) sectors to identify the most promising ideas. To contribute to current knowledge, experts were interviewed, and panel discussions were analyzed. Furthermore, the Business Model Canvas (BMC) was combined with the circular economy concept, which served as a framework for the business model ideas. Experts evaluated these ideas, which were subsequently ranked using fuzzy logic with artificial intelligence, based on the system for exploring country risks (CRISK-Explorer). The paper shows that skipping individual development processes opens up promising opportunities, such as the ICT-based business model e-crowd logistics or the renewable energy-based model e-Boda-Boda. Seven prerequisites for the successful implementation of these ideas were identified and defined: value delivery, promising customers, sufficient capital, presence of key resources, possibility to perform the key activities, sustainability, and profitability. The paper concludes by identifying limitations and suggesting avenues for future research.
Channels of distribution are important factors in the connection between goods and services produced for the final consumer and, therefore, determine the effectiveness with which they are delivered and ultimately availed to the final consumers. Globally, studies show that channels of distribution and sales play an essential role in building bonds between manufacturers, retailers, wholesalers and their consumers. The main purpose of this study is to examine the influence of distribution channels and networks on customer choice of fast-moving consumer goods (FCMG) in the Upper East Region of Ghana. The study adopted a quantitative approach and questionnaires were used to collect primary data from 110 customers of Unilever Ghana Limited in the Upper East Region of Ghana. The findings reveal that product-related factors, such as the price of products, perishability of products, size and weight of products, promote the effective distribution of Unilever goods and services, whilst consumer-related factors, such as the number of customers and increased consumer base, promote effective distribution channels. The study also established a positive influence of factors, such as incentives, receiving feedback and sales performance, on customer choice of fast-moving consumer goods (FMCG). Managers and producers in the FMCGs industry should implement reward and incentive programmes and policies to boost the sale and distribution of fast-moving consumer goods and services in the retail industry in Ghana.
Entrepreneurship is labelled as the panacea for graduate unemployment in Ghana. In the training process, students are mandatorily required to read a course in entrepreneurship, so as to be able to start their own businesses in the face of job adversities caused by the inadequacy of job opportunities created by government and lack of government drive to diversify the economy for more jobs to be created. This study, therefore aimed at investigating the critical precursors of entrepreneurial intentions among higher education students in Ghana. Using the analytical cross-sectional survey design, 250 respondents were recruited from public universities using probability sampling techniques (stratified-disproportionate and simple random) to participate in the survey. Respondents were required to respond to three constructs (entrepreneurial scaffolding, psychological capital, and entrepreneurial intentions). The data analyses were performed using multivariate regression. The study findings showed that entrepreneurial scaffolding and psychological capital were significant predictors of entrepreneurial intentions. The researchers concluded that students' convictions in succeeding or otherwise and planning to engage in entrepreneurial behaviours depended on proper entrepreneurial guidance and a positive mind-set. Therefore, it was recommended that higher education institutions in Ghana strengthened and included practical guides to entrepreneurial training. This will encourage higher education students to consider entrepreneurship, hence, reducing graduate unemployment in Ghana.
The dawn of the 21st Century has witnessed a tremendous increase in trade pacts among nations, resulting in renewed hopes for sustainable enterprise development in emerging economies worldwide. Ghana and other sub- Saharan African (SSA) countries have signed onto several North-South and South-South free trade agreements with the hope of strengthening their presence in the international trade arena, and to promote economic growth in SSA. For over two decades, however, very little has changed, and many have dashed their high hopes as enterprises continue to struggle in SSA. Not even the African Continental Free Trade Agreement (AfCFTA) could renew the hopes of sceptics. Several studies opined that enterprises in SSA could improve their domestic and international competitiveness by establishing mutually beneficial partnerships with their counterparts from the Global North and South. This study delved into the issues that affect North-South and South-South business collaborations and recommends key success factors that could help promote mutually beneficial cross-border business partnerships. The research includes both literature and empirical information on the key success factors of business partnerships between African enterprises as well as between African enterprises and firms from the Global North. We approached the study qualitatively using a phenomenological research design. Research participants included important stakeholders in Africa and Europe's international trade and sustainable enterprise development ecosystem. The study identified several challenges with the current business collaborations and recommended new ways of making such partnerships more beneficial.
The differentiation of the higher education sector in Ethiopia has created a new sector of Higher Education Institutions: Universities of Applied Sciences (UAS). Its focus is on educating academically trained experts for regional industries. Close cooperation between industries and UAS is set as a key requirement. However, Ethiopian industries in many regions are not developed enough that those could be considered as active partners for UASs and able to accommodate interns or to provide expert teachers to UAS classes. European UAS structures serve as benchmarks for the Ethiopian Ministry of Education (MoE). Therefore, UAS curricula of study programmes in building-construction, electro-engineering and economic/business/tourism from different European countries build a common ground for Ethiopian UASs. But, due to the lack of industries in the regions, Ethiopian UAS are not able to mirror the European counterparts, where study programmes at bachelor level comprise 70 credits out or 210 credits as practical works, internships and bachelor thesis. - The question is, how can Ethiopian UASs in the absence of companies offer practice-oriented education in their study programmes? This paper refers to the ongoing research, on how to integrate UAS (academic and non-academic) departments at UAS campuses to create internship placements for students in the absence of internship placements in the private sector. Kotebe University of Education (KUE) - as one of the newly founded UAS in Ethiopia - has agreed to act as subject of this try-out.
Here we present a doc-2-doc relevance assessment performed on a subset of the TREC Genomics Track 2005 collection. Our approach includes an experimental set up to manually assess doc-2-doc relevance and the corresponding analysis done on the results obtained from this experiment. The experiment takes one document as a reference and assesses a second document regarding its relevance to the reference one. The consistency of the assessments done by 4 domain experts was evaluated. The lack of agreement between annotators may be due to: i) The abstract lacks key information and/or ii) Lack of experience of the annotators in the evaluation of some topics.
Neuromorphic computing aims to mimic the computational principles of the brain in silico and has motivated research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) capture local, independent changes in brightness, and offer superior power consumption, response latencies, and dynamic ranges compared to frame-based cameras. SNNs replicate neuronal dynamics observed in biological neurons and propagate information in sparse sequences of ”spikes”. Apart from biological fidelity, SNNs have demonstrated potential as an alternative to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Although potentially beneficial for robotics, the novel event-driven and spike-based paradigms remain scarcely explored outside the domain of aerial robots.
To investigate the utility of brain-inspired sensing and data processing in a robotics application, we developed a neuromorphic approach to real-time, online obstacle avoidance on a manipulator with an onboard camera. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans in a dynamic motion primitive formulation. We conducted simulated and real experiments with a Kinova Gen3 arm performing simple reaching tasks involving static and dynamic obstacles. Our implementation was systematically tuned, validated, and tested in sets of distinct task scenarios, and compared to a non-adaptive baseline through formalized quantitative metrics and qualitative criteria.
The neuromorphic implementation facilitated reliable avoidance of imminent collisions in most scenarios, with 84% and 92% median success rates in simulated and real experiments, where the baseline consistently failed. Adapted trajectories were qualitatively similar to baseline trajectories, indicating low impacts on safety, predictability and smoothness criteria. Among notable properties of the SNN were the correlation of processing time with the magnitude of perceived motions (captured in events) and robustness to different event emulation methods. Preliminary tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation method. These results motivate future efforts to incorporate SNN learning, utilize neuromorphic processors, and target other robot tasks to further explore this approach.
Trueness and precision of milled and 3D printed root-analogue implants: A comparative in vitro study
(2023)
This thesis investigates the benefit of rubrics for grading short answers using an active learning mechanism. Automating short answer grading using Natural Language Processing (NLP) is one of the active research areas in the education domain. This could save time for the evaluator and invest more time in preparing for the lecture. Most of the research on short answer grading was treated as a similarity task between reference and student answers. However, grading based on reference answers does not account for partial grades and does not provide feedback. Also, the grading is automatic that tries to replace the evaluator. Hence, using rubrics for short answer grading with active learning eliminates the drawbacks mentioned earlier.
Initially, the proposed approach is evaluated on the Mohler dataset, popularly used to benchmark the methodology. This phase is used to determine the parameters for the proposed approach. Therefore, the approach with the selected parameter exceeds the performance of current State-Of-The-Art (SOTA) methods resulting in the Pearson correlation value of 0.63 and Root Mean Square Error (RMSE) of 0.85. The proposed approach has surpassed the SOTA methods by almost 4%.
Finally, the benchmarked approach is used to grade the short answer based on rubrics instead of reference answers. The proposed approach evaluates short answers from Autonomous Mobile Robot (AMR) dataset to provide scores and feedback (formative assessment) based on the rubrics. The average performance of the dataset results in the Pearson correlation value of 0.61 and RMSE of 0.83. Thus, this research has proven that rubrics-based grading achieves formative assessment without compromising performance. In addition, the rubrics have the advantage of generalizability to all answers.
Das Interesse an Virtual Reality (VR) für die Hochschullehre steigt aktuell vermehrt durch die Möglichkeit, logistisch schwierige Aufgaben abzubilden sowie aufgrund positiver Ergebnisse aus Wirksamkeitsstudien. Gleichzeitig fehlt es jedoch an Studien, die immersive VR-Umgebungen, nicht-immersive Desktop-Umgebungen und konventionelle Lernmaterialien gegenüberstellen und lehr-lernmethodische Aspekte evaluieren. Aus diesem Grund beschäftigt sich dieser Beitrag mit der Konzeption und Realisierung einer Lernumgebung für die Hochschullehre, die sowohl mit einem Head Mounted Display (HMD) als auch mittels Desktops genutzt werden kann, sowie deren Evaluation anhand eines experimentellen Gruppendesigns. Die Lernumgebung wurde auf Basis einer eigens entwickelten Softwareplattform erstellt und die Wirksamkeit mithilfe von zwei Experimentalgruppen – VR vs. Desktop-Umgebung – und einer Kontrollgruppe evaluiert und verglichen. In einer Pilotstudie konnten sowohl qualitativ als auch quantitativ positive Einschätzungen der Usability der Lernumgebung in beiden Experimentalgruppen herausgestellt werden. Darüber hinaus zeigten sich positive Effekte auf die kognitive und affektive Wirkung der Lernumgebung im Vergleich zu konventionellen Lernmaterialien. Unterschiede zwischen der Nutzung als VR- oder Desktop-Umgebung zeigen sich auf kognitiver und affektiver Ebene jedoch kaum. Die Analyse von Log-Daten deutet allerdings auf Unterschiede im Lern- und Explorationsverhalten hin.
Hydrogen as a versatile, greenhouse gas-free energy carrier will play an important role in our future economy. Yet sustainable, competitive production and distribution of hydrogen remains a challenge. Highly integrated solar water splitting systems aim to combine solar energy harvesting and electrolysis in a single device, similar to a photovoltaic module.[1] Such a system can produce hydrogen locally without the requirement to be connected to the electricity grid. Unlike large electrolysis that draws power from the grid, the power density of such a device is reduced so far that it does not require active cooling, but its operating temperature will closely follow outdoor conditions.
Here, we present our work on high-efficiency integrated solar water splitting devices based on multi-junction solar absorbers. The light-absorbing component is sensitive to the shape of the solar spectrum and generally becomes more efficient at lower temperatures. Catalysis, on the other hand, benefits from higher temperatures. These conflicting trends wih respect to the temperature impact the design of the solar hydrogen production system. We analyse how the local climate affects production efficiency[2] and show in a lab study that adequate system design allows efficient operation at temperatures as low as -20°C.[3] These insights can help to design small-scale distributed solar hydrogen production for both temperate regions, but also more extreme climatic conditions.
Dieses Buch beleuchtet den Online-Lebensmittelhandel in Deutschland aus Anbieter- und Kundenperspektive, leitet Zukunftsprognosen ab und zeigt Konsequenzen für Handel und Hersteller. Trotz des Aufwinds während der Corona-Pandemie bewegen sich die Umsätze im Online-Handel mit Lebensmitteln noch auf relativ niedrigem Niveau; die Entwicklung verläuft jedoch turbulent und wird kontrovers diskutiert. Dieses Buch beschreibt den Status quo und regt zu Diskussionen an. Es bietet eine systematische Analyse einschlägiger Studien sowie aktuelle Erkenntnisse auf Basis qualitativer Interviews mit Experten aus Handel, Industrie und Wissenschaft. (Verlagsangaben)
There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT's is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.
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.
Background: Bloodstream infections (BSIs) remain a significant cause of mortality worldwide. Causative pathogens are routinely identified and susceptibility tested but only very rarely investigated for their resistance genes, virulence factors, and clonality. Our aim was to gain insight into the clonality patterns of different species causing BSI and the clinical relevance of distinct virulence genes.
Methods: For this study, we whole-genome-sequenced over 400 randomly selected important pathogens isolated from blood cultures in our diagnostic department between 2016 and 2021. Genomic data on virulence factors, resistance genes, and clonality were cross-linked with in-vitro data and demographic and clinical information.
Results: The investigation yielded extensive and informative data on the distribution of genes implicated in BSI as well as on the clonality of isolates across various species.
Conclusion: Associations between survival outcomes and the presence of specific genes must be interpreted with caution, and conducting replication studies with larger sample sizes for each species appears mandatory. Likewise, a deeper knowledge of virulence and host factors will aid in the interpretation of results and might lead to more targeted therapeutic and preventive measures. Monitoring transmission dynamics more efficiently holds promise to serve as a valuable tool in preventing in particular BSI caused by nosocomial pathogens.
Background: the potency of drugs that interfere with glucose metabolism, i.e., glucose transporters (GLUT) and nicotinamide phosphoribosyltransferase (NAMPT) was analyzed in neuroendocrine tumor (NET, BON-1, and QPG-1 cells) and small cell lung cancer (SCLC, GLC-2, and GLC-36 cells) tumor cell lines. (2) Methods: the proliferation and survival rate of tumor cells was significantly affected by the GLUT-inhibitors fasentin and WZB1127, as well as by the NAMPT inhibitors GMX1778 and STF-31. (3) Results: none of the NET cell lines that were treated with NAMPT inhibitors could be rescued with nicotinic acid (usage of the Preiss–Handler salvage pathway), although NAPRT expression could be detected in two NET cell lines. We finally analyzed the specificity of GMX1778 and STF-31 in NET cells in glucose uptake experiments. As previously shown for STF-31 in a panel NET-excluding tumor cell lines, both drugs specifically inhibited glucose uptake at higher (50 μM), but not at lower (5 μM) concentrations. (4) Conclusions: our data suggest that GLUT and especially NAMPT inhibitors are potential candidates for the treatment of NET tumors.
Climate change is increasingly affecting vulnerable groups and resulting in dire social and economic consequences, especially for those in the Global South. Managing current and emerging climate-related risks will require increasing individual’s and communities’ resilience, including enhancing absorptive, adaptive, and transformative capacities. Policymakers are now considering the role that social protection policies and programmes can play in building climate resilience by contributing to these capacities. However, there is a limited understanding of the extent to which social protection instruments can influence these three resilience-related capacities. Lack of assessment tools or frameworks might contribute to limited evidence of social protection’s ability to increase climate resilience. In particular, there appear to be no frameworks or tools that help assess the role of social cash transfers (SCT) in building adaptive capacity. Based on a multi-staged literature review, we develop an adaptive capacity outcomes framework (ACOF) that can help assess SCT’s contribution to building adaptive capacity, and, consequently, resilience. The framework is then tested using impact evaluation and assessment reports from SCT programmes in Indonesia, Zambia, Ethiopia, Bangladesh, and Tanzania. The exercise finds that SCTs alone have a limited contribution to adaptive capacity outcomes, but interventions that combine cash transfers with other components such as nutrition or livelihood training show positive impacts. We find that the ACOF can support assessments of SCT’s contribution towards adaptive capacity. It can help build evidence, evaluate impacts, and through further research, can facilitate learning on SCTs' role in increasing climate resilience.
Forschungsdatenmanagement (FDM) nimmt in Wissenschaftsinstitutionen und Scientific Communities einen immer größeren Stellenwert ein. Hochschulen für angewandte Wissenschaften (HAW) sehen sich mithin der Aufgabe gegenübergestellt, Rahmenbedingungen für ein gelingendes FDM in Forschungsvorhaben zu schaffen. Die vorliegende Grafik hat zum Ziel, die Ausgestaltung dieser Rahmenbedingungen zu befördern, indem sie – mit Blick auf die operative Ebene – die Bedarfe der Forschenden mit FDM-Dienstleistungen zusammenbringt sowie Wechselwirkungen visualisiert. Auf diese Weise soll sie die Komplexität des Handlungsfelds FDM veranschaulichen und zugleich als Handreichung zur Ausgestaltung adäquater FDM-Ressourcen und -Prozesse für die Zielgruppe der Forschenden dienen.
Die Zusammenstellung basiert auf den generalisierten Erfahrungen, die zwischen 2020 und 2023 von den in der Förderlinie „FDMScouts.nrw“ finanzierten Projektverbünden in vier gemeinsamen Handlungsfeldern (Netzwerkarbeit, Information und Sensibilisierung, Koordination, Beratung) gesammelt wurden. Die zehn beteiligten Hochschulen arbeiteten in fünf Verbünden an Strukturen und Prozessen für eine nachhaltige Etablierung des Forschungsdatenmanagements vor Ort. Dabei berücksichtigt die Grafik überregionale Serviceangebote und Institutionen für den Bereich FDM, wie sie bis zum Zeitpunkt der Veröffentlichung zur Verfügung standen.
Die Projektverbünde in der Förderlinie FDMScouts.nrw haben am 28.03.2023 die Online-Veranstaltung "#datendienstag: Datenmanagementpläne und Forschungsdatenmanagement in Forschungsanträgen" angeboten. Der Vortrag richtete sich an Forschende und Infrastrukturangehörige – vor allem aus der Forschungsförderung, welche die Antragsstellung begleiten.
Viele Drittmittelgeber erwarten als Teil eines Förderantrags Informationen zum Umgang mit Forschungsdaten. Ein formeller Datenmanagementplan (DMP) wird nur in den seltensten Fällen verlangt. Dennoch ist ein DMP für die Arbeit in einem Forschungsprojekt von Vorteil. Welche Vorteile dies sind und welche Anforderungen Forschende bei der Antragstellung bezüglich des FDMs zu erwarten haben, waren – neben Tipps und Tricks – Gegenstand dieser Veranstaltung.
Zertifizierungsnormen
(2023)
Dieses Video aus der Videoreihe „Normen-ABC“ gibt eine Übersicht über Zertifizierungsnormen von A-Z. Die Zertifizierung von „Produkten“, „Prozessen“, „Systemen“ und „Personen“ wird erklärt. Am Beispiel der FFP2-Masken mit richtiger CE-Kennzeichnung wird begründet, wie wichtig die Einhaltung von Normen für Gesundheit und Leben ist.
Dieses Video aus der Videoreihe „Normen-ABC“ erklärt die DIN-Norm, die alle kennen sollten: DIN 5008 „Schreib- und Gestaltungsregeln für die Text- und Informationsbearbeitung“ Beuth-Verlag, Berlin: 2020. Es werden nützliche Hinweise, wie z. B. für Abschlussarbeiten, Bewerbungsschreiben oder Geschäftsbriefe gegeben.
Gesundheitsnormen
(2023)
Dieses Video aus der Videoreihe „Normen-ABC“ gibt eine Übersicht zu Gesundheitsnormen von A bis Z. Es wird veranschaulicht, wie Normen durch regionale, europäische und weltweite Vereinheitlichung Leben retten und Gesundheit schützen. Als Praxisbeispiel wird der Aufbau der Zertifizierungsnorm DIN ISO 45001 „Sicherheit und Gesundheit bei der Arbeit“ kurz erläutert.
Formatnormen
(2023)
Dieses Video aus der Videoreihe „Normen-ABC“ zeigt verschiedene Formatnormen, wie Audio-, Bild- und Medienformate. Am Beispiel des weltweit einheitlichen Papierformats nach DIN EN ISO 216 wird der Aufbau durch die drei Normsätze „Halbierung und Verdoppelung“, „Ähnlichkeit“ und „Proportionalität“ erklärt.
CE-Kennzeichnung
(2023)
Skill generalisation and experience acquisition for predicting and avoiding execution failures
(2023)
For performing tasks in their target environments, autonomous robots usually execute and combine skills. Robot skills in general and learning-based skills in particular are usually designed so that flexible skill acquisition is possible, but without an explicit consideration of execution failures, the impact that failure analysis can have on the skill learning process, or the benefits of introspection for effective coexistence with humans. Particularly in human-centered environments, the ability to understand, explain, and appropriately react to failures can affect a robot's trustworthiness and, consequently, its overall acceptability. Thus, in this dissertation, we study the questions of how parameterised skills can be designed so that execution-level decisions are associated with semantic knowledge about the execution process, and how such knowledge can be utilised for avoiding and analysing execution failures. The first major segment of this work is dedicated to developing a representation for skill parameterisation whose objective is to improve the transparency of the skill parameterisation process and enable a semantic analysis of execution failures. We particularly develop a hybrid learning-based representation for parameterising skills, called an execution model, which combines qualitative success preconditions with a function that maps parameters to predicted execution success. The second major part of this work focuses on applications of the execution model representation to address different types of execution failures. We first present a diagnosis algorithm that, given parameters that have resulted in a failure, finds a failure hypothesis by searching for violations of the qualitative model, as well as an experience correction algorithm that uses the found hypothesis to identify parameters that are likely to correct the failure. Furthermore, we present an extension of execution models that allows multiple qualitative execution contexts to be considered so that context-specific execution failures can be avoided. Finally, to enable the avoidance of model generalisation failures, we propose an adaptive ontology-assisted strategy for execution model generalisation between object categories that aims to combine the benefits of model-based and data-driven methods; for this, information about category similarities as encoded in an ontology is integrated with outcomes of model generalisation attempts performed by a robot. The proposed methods are exemplified in terms of various use cases - object and handle grasping, object stowing, pulling, and hand-over - and evaluated in multiple experiments performed with a physical robot. The main contributions of this work include a formalisation of the skill parameterisation problem by considering execution failures as an integral part of the skill design and learning process, a demonstration of how a hybrid representation for parameterising skills can contribute towards improving the introspective properties of robot skills, as well as an extensive evaluation of the proposed methods in various experiments. We believe that this work constitutes a small first step towards more failure-aware robots that are suitable to be used in human-centered environments.
Loading of shipping containers for dairy products often includes a press-fit task, which involves manually stacking milk cartons in a container without using pallets or packaging. Automating this task with a mobile manipulator can reduce worker strain, and also enhance the efficiency and safety of the container loading process. This paper proposes an approach called Adaptive Compliant Control with Integrated Failure Recovery (ACCIFR), which enables a mobile manipulator to reliably perform the press-fit task. We base the approach on a demonstration learning-based compliant control framework, such that we integrate a monitoring and failure recovery mechanism for successful task execution. Concretely, we monitor the execution through distance and force feedback, detect collisions while the robot is performing the press-fit task, and use wrench measurements to classify the direction of collision; this information informs the subsequent recovery process. We evaluate the method on a miniature container setup, considering variations in the (i) starting position of the end effector, (ii) goal configuration, and (iii) object grasping position. The results demonstrate that the proposed approach outperforms the baseline demonstration-based learning framework regarding adaptability to environmental variations and the ability to recover from collision failures, making it a promising solution for practical press-fit applications.
In the design of robot skills, the focus generally lies on increasing the flexibility and reliability of the robot execution process; however, typical skill representations are not designed for analysing execution failures if they occur or for explicitly learning from failures. In this paper, we describe a learning-based hybrid representation for skill parameterisation called an execution model, which considers execution failures to be a natural part of the execution process. We then (i) demonstrate how execution contexts can be included in execution models, (ii) introduce a technique for generalising models between object categories by combining generalisation attempts performed by a robot with knowledge about object similarities represented in an ontology, and (iii) describe a procedure that uses an execution model for identifying a likely hypothesis of a parameterisation failure. The feasibility of the proposed methods is evaluated in multiple experiments performed with a physical robot in the context of handle grasping, object grasping, and object pulling. The experimental results suggest that execution models contribute towards avoiding execution failures, but also represent a first step towards more introspective robots that are able to analyse some of their execution failures in an explicit manner.