Refine
Departments, institutes and facilities
- Fachbereich Informatik (68)
- Fachbereich Angewandte Naturwissenschaften (59)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (43)
- Fachbereich Ingenieurwissenschaften und Kommunikation (38)
- Fachbereich Wirtschaftswissenschaften (37)
- Institut für funktionale Gen-Analytik (IFGA) (36)
- Fachbereich Sozialpolitik und Soziale Sicherung (22)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (20)
- Institut für Cyber Security & Privacy (ICSP) (16)
- Institut für Verbraucherinformatik (IVI) (16)
Document Type
- Article (118)
- Conference Object (89)
- Part of a Book (27)
- Preprint (15)
- Book (monograph, edited volume) (7)
- Research Data (6)
- Doctoral Thesis (5)
- Working Paper (4)
- Conference Proceedings (2)
- Part of Periodical (1)
Year of publication
- 2021 (276) (remove)
Language
- English (276) (remove)
Keywords
- Augmented Reality (4)
- Machine Learning (4)
- Big Data Analysis (3)
- Kenya (3)
- Machine learning (3)
- Usable Security (3)
- recovery (3)
- sustainability (3)
- AML (2)
- Africa (2)
It has been well proved that deep networks are efficient at extracting features from a given (source) labeled dataset. However, it is not always the case that they can generalize well to other (target) datasets which very often have a different underlying distribution. In this report, we evaluate four different domain adaptation techniques for image classification tasks: DeepCORAL, DeepDomainConfusion, CDAN and CDAN+E. These techniques are unsupervised given that the target dataset dopes not carry any labels during training phase. We evaluate model performance on the office-31 dataset. A link to the github repository of this report can be found here: https://github.com/agrija9/Deep-Unsupervised-Domain-Adaptation.
This paper gives an overview of how we can benefit from using container technology in our academic work. It aims to be a starting point for fellow researchers which also think about applying these technologies. Hence, we focus on decribing our own experiences and motivations instead of proving hard scientific facts.
Ice accumulation in the blades of wind turbines can cause them to describe anomalous rotations or no rotations at all, thus affecting the generation of electricity and power output. In this work, we investigate the problem of ice accumulation in wind turbines by framing it as anomaly detection of multi-variate time series. Our approach focuses on two main parts: first, learning low-dimensional representations of time series using a Variational Recurrent Autoencoder (VRAE), and second, using unsupervised clustering algorithms to classify the learned representations as normal (no ice accumulated) or abnormal (ice accumulated). We have evaluated our approach on a custom wind turbine time series dataset, for the two-classes problem (one normal versus one abnormal class), we obtained a classification accuracy of up to 96$\%$ on test data. For the multiple-class problem (one normal versus multiple abnormal classes), we present a qualitative analysis of the low-dimensional learned latent space, providing insights into the capacities of our approach to tackle such problem. The code to reproduce this work can be found here https://github.com/agrija9/Wind-Turbines-VRAE-Paper.
Experience made with free and open source software (FOSS) in the public research is shared with the community. The motivation for using and publishing FOSS is to increase visibility, transparancy and feedback quality while at the same time lowering software licensing costs. Also, the idea of giving back and returning a value plays a role. The most frequently given counter arguments are discussed. In the end, it’s important to embed FOSS publishing into the company’s strategy for the exploitation of scientific research results. To help with this, a checklist of criteria to indicate FOSS publishing is suggested. On the backround of wireless sensor networks, some case studies of FOSS contribution are detailed. The emphasis is on checking the original motivation and the spirit of FOSS back with the reality. Finally, further potential of publishing FOSS in the context of scientific research is identified.
Cancer is one of the leading causes of death worldwide [183], with lung tumors being the most frequent cause of cancer deaths in men as well as one of the most common cancers diagnosed in woman [40]. As symptoms often arise in advanced stages, an early diagnosis is especially important to ensure the best and earliest possible treatment. In order to achieve this, Computed Tomography (CT) scans are frequently used for tumor detection and diagnosis. We will present examples of publicly available CT image data of lung cancer patients and discuss possible methods to realize an automatic system for automated cancer diagnosis. We will also look at the recent SPIE-AAPM Lung CT Challenge [10] data set in detail and describe possible methods and challenges for image segmentation and classification based on this data set.
Neurodevelopmental disorder with dysmorphic facies and distal limb anomalies (NEDDFL), defined primarily by developmental delay/intellectual disability, speech delay, postnatal microcephaly, and dysmorphic features, is a syndrome resulting from heterozygous variants in the dosage-sensitive bromodomain PHD finger chromatin remodeler transcription factor BPTF gene. To date, only 11 individuals with NEDDFL due to de novo BPTF variants have been described. To expand the NEDDFL phenotypic spectrum, we describe the clinical features in 25 novel individuals with 20 distinct, clinically relevant variants in BPTF, including four individuals with inherited changes in BPTF. In addition to the previously described features, individuals in this cohort exhibited mild brain abnormalities, seizures, scoliosis, and a variety of ophthalmologic complications. These results further support the broad and multi-faceted complications due to haploinsufficiency of BPTF.
BACKGROUND
Biallelic loss-of-function variants in NCF1 lead to reactive oxygen species deficiency and chronic granulomatous disease (CGD). Heterozygosity for the p.Arg90His variant in NCF1 has been associated with susceptibility to systemic lupus erythematosus, rheumatoid arthritis, and Sjögren's syndrome in adult patients. This study demonstrates the association of the homozygous p.Arg90His variant with interferonopathy with features of autoinflammation and autoimmunity in a pediatric patient.
CASE PRESENTATION
A 5-year old female of Indian ancestry with early-onset recurrent fever and headache, and persistently elevated antinuclear, anti-Ro, and anti-La antibodies was found to carry the homozygous p.Arg90His variant in NCF1 through exome sequencing. Her unaffected parents and three other siblings were carriers for the mutant allele. Because the presence of two NCF1 pseudogenes, this variant was confirmed by independent genotyping methods. Her intracellular neutrophil oxidative burst and NCF1 expression levels were normal, and no clinical features of CGD were apparent. Gene expression analysis in peripheral blood detected an interferon gene expression signature, which was further supported by cytokine analyses of supernatants of cultured patient's cells. These findings suggested that her inflammatory disease is at least in part mediated by type I interferons. While her fever episodes responded well to systemic steroids, treatment with the JAK inhibitor tofacitinib resulted in decreased serum ferritin levels and reduced frequency of fevers.
CONCLUSION
Homozygosity for p.Arg90His in NCF1 should be considered contributory in young patients with an atypical systemic inflammatory antecedent phenotype that may evolve into autoimmunity later in life. The complex genomic organization of NCF1 poses a difficulty for high-throughput genotyping techniques and variants in this gene should be carefully evaluated when using the next generation and Sanger sequencing technologies. The p.Arg90His variant is found at a variable allele frequency in different populations, and is higher in people of South East Asian ancestry. In complex genetic diseases such as SLE, other rare and common susceptibility alleles might be necessary for the full disease expressivity.
Somatic Mutations in UBA1 Define a Distinct Subset of Relapsing Polychondritis Patients With VEXAS
(2021)
The deficiency of adenosine deaminase 2 (DADA2) is an autosomal recessively inherited disease that has undergone extensive phenotypic expansion since being first described in patients with fevers, recurrent strokes, livedo racemosa, and polyarteritis nodosa in 2014. It is now recognized that patients may develop multisystem disease that spans multiple medical subspecialties. Here, we describe the findings from a large single center longitudinal cohort of 60 patients, the broad phenotypic presentation, as well as highlight the cohort's experience with hematopoietic cell transplantation and COVID-19. Disease manifestations could be separated into three major phenotypes: inflammatory/vascular, immune dysregulatory, and hematologic, however, most patients presented with significant overlap between these three phenotype groups. The cardinal features of the inflammatory/vascular group included cutaneous manifestations and stroke. Evidence of immune dysregulation was commonly observed, including hypogammaglobulinemia, absent to low class-switched memory B cells, and inadequate response to vaccination. Despite these findings, infectious complications were exceedingly rare in this cohort. Hematologic findings including pure red cell aplasia (PRCA), immune-mediated neutropenia, and pancytopenia were observed in half of patients. We significantly extended our experience using anti-TNF agents, with no strokes observed in 2026 patient months on TNF inhibitors. Meanwhile, hematologic and immune features had a more varied response to anti-TNF therapy. Six patients received a total of 10 allogeneic hematopoietic cell transplant (HCT) procedures, with secondary graft failure necessitating repeat HCTs in three patients, as well as unplanned donor cell infusions to avoid graft rejection. All transplanted patients had been on anti-TNF agents prior to HCT and received varying degrees of reduced-intensity or non-myeloablative conditioning. All transplanted patients are still alive and have discontinued anti-TNF therapy. The long-term follow up afforded by this large single-center study underscores the clinical heterogeneity of DADA2 and the potential for phenotypes to evolve in any individual patient.
What is Design Theory?
(2021)
One of the biggest challenges faced by many tech start-ups from developed markets is to have validated market-fit products/services and to see their solutions implemented. In several sectors, stringent regulations, and the law of handicap of head start at home can be hurdles that limit the development and even the survival potential of theses start-ups. Tech start-ups seeking implementation, learning, and legitimacy may have a solution in expanding into emerging markets. Emerging markets offer both business opportunities in sectors in need of new technologies as they are “fertile grounds” for developing and testing internationalisation business models. We present here a process designed to help tech start-ups to identify, access, shape and seize these opportunities and to overcome their own specificities and emerging markets specificities. The three phases of the proposed process cover entry node concept, partnership, and business, operating and revenue joint models’ development. DesignScience Research Paradigm is used for the design and evaluation of the process. To show the relevance of this process, a case study on the expansion in Morocco of a Dutch start-up active in e-health is used. The study shows the importance of the process for the embeddedness in a local relevant value network with a relevant adopter’s system, a key enabler to achieve time and cost-effective expansion in that specific business and institutional contexts. A pilot to assess the proposed models and evidence of benefits is under development. To boost their chances of growth tech start-ups from developed markets should consider expansion into emerging markets in their strategy. It would be beneficial that policy makers adopt a strategy by which to assist tech start-ups in accessing value networks in emerging markets. It is also important for policy makers from emerging markets to consider developing schemes to attract tech start-ups from developed markets.
Research on entrepreneurial eco-systems is evolving with exhortations for empirical studies at regional and local levels to augment national surveys. The study, therefore, sought to explore the entrepreneurial eco-system of the Central Region, which is relatively well-endowed with natural resources but lags behind in economic advancement in Ghana. Through descriptive research design, quantitative data were collected using self-administered questionnaires from a convenience sample of 44 entrepreneurs under the presidential business support programme in the Central Region of Ghana, in 2019. Data were analysed, by conducting descriptive analysis such as means (M) and percentages and by exploratory factor analysis, with the IBM SPSS Version 25. Descriptive results of 37 valid responses showed that the respondents were satisfied, in varying degrees (M = 4.19-5.65), with 11 factors within the eco-system; the top three factors were demand, security and availability of raw materials. Respondents were, however, not satisfied with access to business development services, access to finance, rent charges and access to repairers of equipment and thus, pose as challenges to their entrepreneurial pursuits. Principal component analysis revealed inter-connectedness among the factors in the eco-system with strong loadings of measures of institutions and resource endowment under the two components of the solution. Based on the findings, it is concluded that the entrepreneurs surveyed were satisfied with more factors in the EES of the Central Region while they were dissatisfied with relatively few but critical factors in the EES, thereby posing as major challenges to their entrepreneurial activities. As an exploratory study, the findings suggest that the entrepreneurial eco-system of the Central Region of Ghana is, to some extent, supportive of entrepreneurial activities but has key challenges. To achieve maximum outcomes, policy interventions should collectively address, at a time, factors that interact strongly to influence entrepreneurship within the system.
Developing the Circular Economy in Uganda: Prospects for Academia-Public-Private-Partnerships
(2021)
Issues: Circular economy is a production system that optimizes the reusability of by-products/waste as raw materials. As the global population threatens to reach 9 billion by 2050, consumption levels grow proportionally, raising food, material, and energy demands. In Uganda, soil nutrient depletion and energy poverty are key challenges faced by urban and rural communities. Rampart depletion of natural resources calls for transit from the linear economic models towards sustainable production/consumption technologies. This study investigated prospects for APPP to optimize the reusability of by-products/waste as raw materials. Approach: Quantitative and qualitative tools were used to collect data via document analysis, interviews, and participant observations. The tools were administered to municipal authorities, private waste-collecting agencies in cities and municipalities; officials in Ministries of energy and Agriculture; officials in universities research units and entrepreneurs that deal in agricultural and energy products; officials from civil society organizations. Findings: there are a number of sustainability projects being undertaken by Universities and High schools, Government agencies, companies, and civil society organization isolation. Singlehandedly, individual agencies lack the requisite capacity to develop closed-loop production/consumption models. Analysis of a few successful RRR projects suggests that APPP is positioned to promote CE. Transiting towards a circular economy requires joint ventures to optimize human, technological, and financial resources and develop policy and institutional frameworks. In Uganda, recycling biotic by-products can promote environmental sustainability; reduce stress on natural resources; enable cost savings; promote green entrepreneurship, and create jobs/livelihoods. Conclusion: working jointly, CE could be enhanced via technical and business models by the academia, private capital investment by companies, community engagement by CSOs, and development of supportive policy and institutional frameworks to facilitate decision-making processes. The APPPs are positioned to use interactive platforms for creating awareness and promote sensitization about green values through education and multimedia communication platforms.
This study sought to apply the Structure Conduct Performance paradigm to Africa´s air transport landscape in general. To do that, it examines the past, present, and future expectations of four of Sub-Saharan Africa’s biggest aviation economies, namely South Africa, Kenya, Ethiopia, and Nigeria. Secondary data containing historical passenger traffic was analysed, and predictions for growth in the next ten years were proposed. The findings suggest that the experience of the existing liberalization initiatives, such as the Yamoussoukro Declaration (YD), has produced less than expected benefits. However, the future of aviation in Africa is somewhat positive, with a growth trajectory expected to follow a linear and gradual path supported by various initiatives, including the Single African Air Transport Market (SAATM) and the African Continental Free Trade Area (AFCTA). The study’s contribution is to illuminate the current discourse on the aviation sector in Africa through the Structure-Conduct-Performance theory paradigm and suggests a conceptual model that could be applied to future studies relating to aviation in Africa.
Personal values and electronic waste disposal behaviours among households in Cape Coast Metropolis
(2021)
The study examined social values that accounted for electronic waste recycling and reuse behaviours. Via a crosscommunity survey of 193 of households in the Cape Coast Metropolis, a correlational design was employed in the study. Partial Least Squares-Structural equation modelling was used to analyse the data. Results from the analysis showed the influence of altruistic values (β = 0.275, p < 0.05) on reuse behaviour. Similarly, environmental awareness (β = 0.213, p<0.05) also showed significant influence on participation in recycling, whereas psychological ownership significantly influenced both reuse (β = 0.319, p < 0.05), and participation in recycling (β = 0.339, p < 0.05), The joint significance of altruistic values, environmental awareness and psychological ownership to explaining recycling participation was 21.3% (R2 = 0.213, p < 0.05) and that of reuse was 24.6% (R2 = 0.246, p < 0.05). The results of the study showed that individuals who are knowledgeable about the state of their environment were more likely to participate in recycling. On the other hand, individuals with altruistic values preferred giving unwanted electronic equipment to others for reuse. Altruistic values are particularly true of collectivist cultural orientation. Psychological ownership was significant in predicting both behaviours, however, the effect size on reuse was moderate. Psychological ownership due to waste aversion and frugality lead consumers to keep, and subsequently give to close relatives in their social network. It was recommended that individuals should be encouraged to patronize formal recycling services. as a way to show concern for the well-being of others by reducing pollution due to improper waste treatment. Again, like in developed economies, second-hand collection systems for unwanted electronic products can be developed, and made convenient for individuals with reusable items, who may be willing to donate or even resell.
There is severe clinical vitamin A deficiency (VAD) prevalence among Ghanaians and many African countries. Foodbased diets has been suggested as a more sustainable approach to solving the VAD situation in Africa. In this study, A participatory action research between orange flesh sweet potato farmers, gari processors within central region and academia was adopted to develop gari containing provitamin A beta-carotene. Gari is a major staple for Ghanaians and people in the West African subregion due to its affordability and swelling capacity. It is mainly eaten raw with water, sugar, groundnut and milk as gari-soakings or with hot water to prepare gelatinized food called gari-kai in Ghana or “eba” among Nigerians. However, gari is limited in provitamin A carotenoids. Orange fleshed sweet potato (OFSP) is known to contain large amount of vitamin A precursor. Therefore, addition of OFSP to gari would have the potential to fight the high prevalence rate of vitamin A deficiency amongst less developed regions of Africa. To develop this, different proportions of orange fleshed sweet potatoes (OFSP) was used to substitute cassava mash and fermented spontaneously to produce composite gari - a gritty-crispy ready-to-eat food product. Both the amount of OFSP and the fermentation duration caused significant increases in the β-carotene content of the composite gari. OFSP addition reduced the luminance while roasting made the composite gari yellower when compared with the cake used. Addition of OFSP negatively affected the swelling capacity of the gari although not significant. The taste, texture, flavour and the overall preferences for the composite gari decreased due to the addition of the OFSP but fermentation duration (FD) improved them. The sample with 10% OFSP and FD of 1.81 days was found to produce the optimal gari. One-portion of the optimal gari would contribute to 34.75, 23.2, 23.2, 27, 17 and 16% of vitamin A requirements amongst children, adolescent, adult males, adult females, pregnant women and lactating mothers respectively. The study demonstrated that partial substitution of cassava with OFSP for gari production would have the potential to fight the high prevalence rate of vitamin A deficiency amongst less developed regions of Africa while involvement of farmers and processors prior to the design of research phase enhanced the adoption of intervention strategies.
In times of climatic or political grievances that affect not only human life worldwide, but also the environment and the economic situation of a country, a change in the way of thinking about tourism is beginning and the sector of ecotourism is also becoming increasingly important in Germany. The applicability of this form of tourism in the East African destination Kenya in the form of a travel package that is both partly unique and can be designed individually describes the subject matter of this elaboration and is illustrated using the example of the charitable organization Mully Children's Family and the related registered tourism company, MCF Africa Safaris. The underlying research aims to determine how to transform the organisation's own tree planting initiative into a niche tourist market and how this must be geared to gain the interest of the German eco-tourist. Based on the evaluation of the research results, there is high potential, which is dedicated to the implementation of a form of travel consisting of the active support of the named charity and its initiative as well as individually selectable holiday activities in the target market Kenya. As a result, there are basic prerequisites, the consideration of which is essential for the successful integration of the so-called niche market tree planting and the branch-specific nature of ecotourism in the Kenyan travel market.
Most economies across the globe rely on entrepreneurship for growth. There is evidence to suggest that entrepreneurship creates job opportunities and spurs economic growth and development (Pacheco, Dean, & Payne, 2010; Mojica, Gebremedhin, & Schaeffer, 2010, and Solomon, 2007). Even though entrepreneurship is one of the fastest growing education disciplines globally, researchers are still divided on what should be taught and how it should be taught in institutions of higher learning. Entrepreneurial decision-making is laced with uncertainty and drawbacks. Hence, entrepreneurship learners must be taught using practical and conceptual methodologies to equip them with the requisite knowledge and skill that will enable them to confront such challenges in their entrepreneurial activities. This calls for entrepreneurship teachers to be innovative and to also encourage their learners to be innovative as entrepreneurship involves the generation of new business ideas. This paper sought to examine teaching methodologies for entrepreneurship education in institutions of higher learning in Kenya. A mixed-method approach that involved triangulation as the main data collection technique was used. Interviews were administered with teachers and learners of entrepreneurial education in Kenya, with a view to identifying the most commonly used teaching methodologies of entrepreneurial education and their shortcomings. Course outlines and curricula borrowed from twenty (20) institutions of higher learning in Kenya were reviewed. Results indicate that entrepreneurial education in Kenya is largely theoretical and does not meet the needs of the modern entrepreneur. The paper therefore recommends innovative teaching methodologies of entrepreneurial education that can be utilised by the teacher to prepare students adequately to generate entrepreneurial ideas and to identify entrepreneurial opportunities. For this reason, the paper recommends the use of such methodologies as business plan generation, idea generation, innovation, creativity, networking, opportunity recognition, expecting and embracing failure, and adapting to change.
Rural Social Entrepreneurship (RSE) is considered an essential factor for achieving Sustainable Rural Development (SRD) and improving rural people's socio-economic status through increasing production, productivity, reducing unemployment, and accelerating the progress in achieving SDGs. The paper aims at examining the role of social entrepreneurship in achieving (SRD) in Sudan with reference to Wad Balal Project for investment and rural development in Gezira State, which established in 2005 in small villages in Gezira State through mobilizing of local savings and resources for creating job opportunities, sponsoring poor households, improving the infrastructures, and reducing poverty. The study depends on cross-sectional data collected through a questionnaire and focus group discussion from 100 head of households in the village under research. A questionnaire is internally consistent, and its questions are stable. Frequencies and percentages have been used for describing the basic characteristics of the respondents. Statistical t-test was adopted to test the opinions of respondents about the role of the project based on the Likert scale. The results revealed that the project has significantly increased the opportunities of job and training as well as household income, the results also confirmed that the project has improved the status of education and health services in the village. The project has extended and established many branches; the project also diversified its investment to cover more kinds of investments, the project reinvested 50% of its profits and directed the rest to charity, and social services in the village, many lessons can be learned from the project story. The research recommended that a similar social entrepreneurship project can be generalized to more villages in Sudan and other developing countries to accelerate sustainable rural development. Local communities have to support similar initiatives for developing their villages.
A school leader’s achievement is not what they study in learning institutions but the way they organize themselves into problem solving and realistic decision making. While this includes some taught hard skills, the bulk of school activities rely on soft skills. Soft skills, however, are frequently neglected, although they play an important role in school principals’ daily operations as an instructional supervisor. This study aimed to examine the relationship between soft skills training and Principals' performance. The study adopted a cross-sectional mixed survey design. Using Yamane formulae, the sample comprised of 167 principals from 286 public secondary schools in Kiambu County. These were spread proportionally across all the 12 sub-counties in the County. The principal research instrument was primarily a questionnaire. The reliability of the instrument using the Cronbach Alpha coefficient was deemed reasonable at.73. The findings showed that a substantial relationship exists between the training of the principal on soft skills and their good performance of the duties. The study suggests routine in-service training should be undertaken in the county to improve the development of soft skills. It is also advisable that undergraduate, postgraduate, or in-service training include soft skills as a unit, to build knowledge of the value of soft skills.
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.
Machine learning and neural networks are now ubiquitous in sonar perception, but it lags behind the computer vision field due to the lack of data and pre-trained models specifically for sonar images. In this paper we present the Marine Debris Turntable dataset and produce pre-trained neural networks trained on this dataset, meant to fill the gap of missing pre-trained models for sonar images. We train Resnet 20, MobileNets, DenseNet121, SqueezeNet, MiniXception, and an Autoencoder, over several input image sizes, from 32 x 32 to 96 x 96, on the Marine Debris turntable dataset. We evaluate these models using transfer learning for low-shot classification in the Marine Debris Watertank and another dataset captured using a Gemini 720i sonar. Our results show that in both datasets the pre-trained models produce good features that allow good classification accuracy with low samples (10-30 samples per class). The Gemini dataset validates that the features transfer to other kinds of sonar sensors. We expect that the community benefits from the public release of our pre-trained models and the turntable dataset.
The role of tourism entrepreneurship in rural development continues to be a subject of interest and debate among academia and practitioners. Theoretically, it is anticipated that tourism entrepreneurship will lead to livelihood diversification, enhancement and ultimately a revitalization of the rural economy. While tourism is posited as an accessible entrepreneurship pathway, there is a dearth of information regarding rural dwellers’ actual experiences with it, especially within the Ghanaian context. Using a case study approach and qualitative data from Wli; a rural tourism destination in Ghana, this paper delves into the opportunities and concerns associated with tourism entrepreneurship in rural areas. Data was obtained between November and December 2016 from 27 persons who were either tourism enterprise owners or employees. Findings from the study showed that entrepreneurial activities centred on the provision of accommodation, food and beverage, souvenir and guiding services. The nature of the activities enabled easy transfer of existing skills and knowledge. Further, entry into tourism entrepreneurship was perceived to be easy by the majority of study participants. These findings confirm the potential for tourism to be employed in boosting entrepreneurial activities in rural areas. Nevertheless, there were concerns regarding access to credit, institutional support, unhealthy competitions, low incomes, unguaranteed pensions, and seasonality and skewness of demand. These concerns threatened the growth and sustainability of tourism entrepreneurship within the community. From a policy perspective, there is a need for institutional recognition and support for tourism entrepreneurial intentions and activities in rural areas. Practice-wise, credit facilities need to be designed specifically for tourism-related rural enterprises. Further, periodic skills and knowledge augmentation programmes must be initiated to help expand the skill sets for the rural entrepreneurs. Finally, there is a need for the formation of traderelated networks to provide a platform for knowledge and experience sharing among the entrepreneurs.
Kenya as a touristic destination is well known as an exotic country offering many different landscapes as well as the diversity of wildlife; this is typical for several African countries. To ensure a sustainable tourism development, different forms of tourism have to be considered. One of these forms could be eco-mountain bike cycling tours, as these tours are gaining in popularity, for example in Germany. The aim of this study was to obtain results regarding the market potential for mountain bike eco-tourism in Kenya. The up-and-coming tourism branch of mountain biking was examined in connection with the increasing demand for long-distance travel. The results of this study showed that mountain biking in exotic countries like Kenya has market potential in principle. However, it was also found that mountain biking alone is not a sufficient pull factor for tourists. The combination with other activities turned out to be promising. It was found that tourist packages that include mountain biking as an activity are perceived as attractive. Moreover, it was obtained that not only tourists who ride a mountain bike regularly are addressed as a target group. Even "regular" tourists find mountain biking an attractive (touristic) activity, especially in combination with game drives. Experts also assess the market potential for eco-mountain bike tourism as positive and find existing routes and accommodation attractive. The findings are giving indications for the possibilities to develop eco-mountain bike tourism as a touristic alternative and addition to existing touristic products.
Object-Based Trace Model for Automatic Indicator Computation in the Human Learning Environments
(2021)
This paper proposes a traces model in the form of an object or class model (in the UML sense) which allows the automatic calculation of indicators of various kinds and independently of the computer environment for human learning (CEHL). The model is based on the establishment of a trace-based system that encompasses all the logic of traces collecting and indicators calculation. It is im-plemented in the form of a trace database. It is an important contribution in the field of the exploitation of the traces of apprenticeship in a CEHL because it pro-vides a general formalism for modeling the traces and allowing the calculation of several indicators at the same time. Also, with the inclusion of calculated indica-tors as potential learning traces, our model provides a formalism for classifying the various indicators in the form of inheritance relationships, which promotes the reuse of indicators already calculated. Economically, the model can allow organi-zations with different learning platforms to invest only in one traces Management System. At the social level, it can allow a better sharing of trace databases be-tween the various research institutions in the field of CEHL.
The universal basic income grant (UBIG): A comparative review of the characteristics and impact
(2021)
In recent years, public debates, pilot projects and academic research have internationally boosted the prominence of the universal basic income grant (UBIG) as a policy option. Despite this prominence, the arguments and evidence of the UBIG discussion have not been systematically put forward and discussed in light of the different UBIG conceptual understandings and applications. This paper adds value to the debate by systematic presenting the social, economical and political arguments in support of and against a UBIG. It furthermore discusses the UBIG dimensions/characteristics and variations, and also pose questions about whether all the UBIG experiments can really be classified as a UBIG. Antagonist of a UBIG often raise concerns about the negative effect of the lack of conditions and targeting in a UBIG. Since evidence on the impact of UBIG is limited, this paper turns to the evidence base on unconditional cash transfers and conditional cash transfers. The results show that it is the cash transfer rather than the conditionality and targeting that produce positive outcomes in areas of personal wellbeing.
This study investigated the application potential of Black Soldier Fly Larva Hermetia illucens Stratiomyidae: Diptera (L.1758) for wastewater treatment and the removal potential of chemical oxygen demand, ammonia, and phosphorus of and liquid manure residue and municipal waste water containing 1% solids content. Black Soldier Fly Larva were found to reduce the concentration of chemical oxygen demand, but unfortunately, increase the concentration of ammonia and phosphorus. The ability of Black Soldier Fly Larva to feed on organic waste of Liquid manure residue showed that Black Soldier Fly Larva increase their weight by 365% in a solution with 12% solids content and by 595% in a solution having 6% solids content. The study also showed that Black Soldier Fly Larva have the ability to survive in a solution of 1% solids content and have the ability to reduce chemical oxygen demand by up to 86.4% for liquid manure residue and 46.9% for municipal wastewater after 24 hours. Generally, ammonia increased by 43.9% for Liquid manure residue and 98.6% for municipal wastewater. Total phosphorus showed an increase of 11.0% and 88.6% increase for liquid manure residue and municipal wastewater respectively over the 8-day study. Transparent environments tend to reduce the COD content more than the dark environment, both for the liquid manure residue (55.8% and 65.4%) and municipal wastewater (71.5% and 66.4%).
The idea of a basic income grant (BIG) is not new and there are ongoing debates internationally as well as nationally in low- and middle-income countries just like in high-income countries of a BIG as a social protection policy option. The challenge is that there are different conceptualisations, which conflates and muddles the understanding. In the context of social assistance provision, a universal basic income grant (UBIG) is often compared and contrasted against targeted cash transfers (CTs). This case study systematically presents the arguments for targeted CTs and UBIGs. The value of the case study is that it systematically brings together these arguments, highlighting the variations in UBIG applications, including the evidence and actual impact of UBIG experiments. The structure of the case study is as follows: Section 2 simultaneously contrasts and compares the arguments for targeted CTs and UBIG. Section 3 discusses UBIG experiments, as well as presenting the evidence on the application of the UBIG idea, and Section 4 concludes.
The Global Compact for Safe, Orderly and Regular Migration defines Global Skill Partnerships (GSP) as an innovative means of strengthen skills development among origin countries and countries of destination in mutually beneficial manner. However, GSPs are very limited in number and scope, and empirical analyses of them are, to date, relatively rare. This study helps fill this gap in data by presenting and examining existing GSPs or GSP-like approaches (e.g., transnational training partnerships). The aim of the study is to take stock of the various conceptual discourses on and practical experience with transnational skill partnerships. Using Kosovo as a case study, the study details the structure of such partnerships and the processes they entail. It documents the experience of those involved and catalogues the factors contributing to success. On this basis, the authors propose a means of categorizing the various practices that will help structure the empirical diversity of such approaches and render them conceptually feasible: Transnational Skills and Mobility Partnerships (TSMP).
Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models
(2021)
When faced with an execution failure, an intelligent robot should be able to identify the likely reasons for the failure and adapt its execution policy accordingly. This paper addresses the question of how to utilise knowledge about the execution process, expressed in terms of learned constraints, in order to direct the diagnosis and experience acquisition process. In particular, we present two methods for creating a synergy between failure diagnosis and execution model learning. We first propose a method for diagnosing execution failures of parameterised action execution models, which searches for action parameters that violate a learned precondition model. We then develop a strategy that uses the results of the diagnosis process for generating synthetic data that are more likely to lead to successful execution, thereby increasing the set of available experiences to learn from. The diagnosis and experience correction methods are evaluated for the problem of handle grasping, such that we experimentally demonstrate the effectiveness of the diagnosis algorithm and show that corrected failed experiences can contribute towards improving the execution success of a robot.
The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited. To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs. This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against two baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.083. Additionally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. Finally, the source code and pre-trained STonKGs models are available at https://github.com/stonkgs/stonkgs and https://huggingface.co/stonkgs/stonkgs-150k.
A qualitative study of Machine Learning practices and engineering challenges in Earth Observation
(2021)
Machine Learning (ML) is ubiquitously on the advance. Like many domains, Earth Observation (EO) also increasingly relies on ML applications, where ML methods are applied to process vast amounts of heterogeneous and continuous data streams to answer socially and environmentally relevant questions. However, developing such ML- based EO systems remains challenging: Development processes and employed workflows are often barely structured and poorly reported. The application of ML methods and techniques is considered to be opaque and the lack of transparency is contradictory to the responsible development of ML-based EO applications. To improve this situation a better understanding of the current practices and engineering-related challenges in developing ML-based EO applications is required. In this paper, we report observations from an exploratory study where five experts shared their view on ML engineering in semi-structured interviews. We analysed these interviews with coding techniques as often applied in the domain of empirical software engineering. The interviews provide informative insights into the practical development of ML applications and reveal several engineering challenges. In addition, interviewees participated in a novel workflow sketching task, which provided a tangible reflection of implicit processes. Overall, the results confirm a gap between theoretical conceptions and real practices in ML development even though workflows were sketched abstractly as textbook-like. The results pave the way for a large-scale investigation on requirements for ML engineering in EO.
When an autonomous robot learns how to execute actions, it is of interest to know if and when the execution policy can be generalised to variations of the learning scenarios. This can inform the robot about the necessity of additional learning, as using incomplete or unsuitable policies can lead to execution failures. Generalisation is particularly relevant when a robot has to deal with a large variety of objects and in different contexts. In this paper, we propose and analyse a strategy for generalising parameterised execution models of manipulation actions over different objects based on an object ontology. In particular, a robot transfers a known execution model to objects of related classes according to the ontology, but only if there is no other evidence that the model may be unsuitable. This allows using ontological knowledge as prior information that is then refined by the robot’s own experiences. We verify our algorithm for two actions – grasping and stowing everyday objects – such that we show that the robot can deduce cases in which an existing policy can generalise to other objects and when additional execution knowledge has to be acquired.
Target meaning representations for semantic parsing tasks are often based on programming or query languages, such as SQL, and can be formalized by a context-free grammar. Assuming a priori knowledge of the target domain, such grammars can be exploited to enforce syntactical constraints when predicting logical forms. To that end, we assess how syntactical parsers can be integrated into modern encoder-decoder frameworks. Specifically, we implement an attentional SEQ2SEQ model that uses an LR parser to maintain syntactically valid sequences throughout the decoding procedure. Compared to other approaches to grammar-guided decoding that modify the underlying neural network architecture or attempt to derive full parse trees, our approach is conceptually simpler, adds less computational overhead during inference and integrates seamlessly with current SEQ2SEQ frameworks. We present preliminary evaluation results against a recurrent SEQ2SEQ baseline on GEOQUERY and ATIS and demonstrate improved performance while enforcing grammatical constraints.
Simultaneous determination of selected catechins and pyrogallol in deer intoxications by HPLC-MS/MS
(2021)
Aim: To understand how transcriptional factors Pdr1 and Pdr3, belonging to the pleiotropic drug resistance system, are activated, and regulated after introducing chemical toxins to the cell in the model organism Saccharomyces cerevisiae.
Methods: Series of molecular methods were applied using different strains of S. cerevisiae over-expressing proteins of interest as a eukaryotic cell model. The chemical stress introduced to the cell is represented by menadione. Results were obtained performing protein detection and analysis. Additionally, the regulation of the DNA binding of the transcriptional activators after stimulation is quantified using chromatin immunoprecipitation, employing epitope-tagged factors and real-time qPCR.
Results: Our results indicated higher expression levels of the Pdr1 transcriptional factor, compared to its homologous Pdr3 after treatment with menadione. The yeast-cell defence system was tested against various organic solvents to exclude the possibility of their presence potentially affecting the results. The results indicate that Pdr1 is most abundant after 30 minutes from the beginning of the treatment, compared with 240 minutes after the treatment when the function of the transcription factor is faded. It appears that Pdr1 binding to the PDR5 and SNQ2 promoters, which are both activated by Pdr1, peaks around the same time, or more precisely after 40 minutes from the start of the treatment.
Conclusion: The tendency of Pdr1 reduction after its activation by menadione is detected. One possibility is that Pdr1, after recognizing the xenobiotic menadione, is removed by a degradation mechanism. Given the fact that Pdr1 directly binds the xenobiotic molecule, its destruction might help the cells to remove toxic levels of menadione. It is possible that overexpressing the part of Pdr1 which recognizes menadione alone was sufficient to detoxify and hence produce a tolerance towards menadione.
Our study shows ZP2 to be a new biomarker for diagnosis, best used in combination with other low abundant genes in colon cancer. Furthermore, ZP2 promotes cell proliferation via the ERK1/2-cyclinD1-signaling pathway. We demonstrate that ZP2 mRNA is expressed in a low-abundant manner with high specificity in subsets of cancer cell lines representing different cancer subtypes and also in a significant proportion of primary colon cancers. The potential benefit of ZP2 as a biomarker is discussed. In the second part of our study, the function of ZP2 in cancerogenesis has been analyzed. Since ZP2 shows an enhanced transcript level in colon cancer cells, siRNA experiments have been performed to verify the potential role of ZP2 in cell proliferation. Based on these data, ZP2 might serve as a new target molecule for cancer diagnosis and treatment in respective cancer types such as colon cancer.
In thyroid carcinoma cells, the soluble βgalactosidespecific lectin, galectin3, is extra and intracellularly expressed and plays a significant role in thyroid cancer diagnosis. The functional relevance of this molecule, particularly in its extracellular environment however, warrants further elucidation. To gain insight into this topic, the present study characterized principal functional properties of galectin3 in 3 commonly used thyroid carcinoma cell lines (BCPAP, Cal62 and FTC133) that express the molecule intra and extracellulary. Cellintrinsic galectin3 harbors a functional carbohydrate recognition domain as determined by affinity purification. Moreover, cell surface expressed galectin3 can be partially removed by treatment with lactose or asialofetuin, but not with sucrose. Thyroid carcinoma cells adhere to substratebound galectin3 in a βgalactosidespecific manner, whereby only cell adhesion, but not cell migration is promoted. Thus, thyroid tumor cells harbor functional active galectin3 that, inter alia, specifically interacts with cell surfaceexpressed molecular ligands in a βgalactosidedependent manner, whereby the molecule can at least interfere with cell adhesion. The modulation of galectin3 expression level or its ligands in such tumor cells could be of therapeutic interest and needs further experimental clarification.
Background: Staurosporine-dependent single and collective cell migration patterns of breast carcinoma cells MDA-MB-231, MCF-7, and SK-BR-3 were analysed to characterise the presence of drug-dependent migration promoting and inhibiting yin-yang effects. Methods: Migration patterns of various breast cancer cells after staurosporine treatment were investigated using Western blot, cell toxicity assays, single and collective cell migration assays, and video time-lapse. Statistical analyses were performed with Kruskal–Wallis and Fligner–Killeen tests. Results: Application of staurosporine induced the migration of single MCF-7 cells but inhibited collective cell migration. With the exception of low-density SK-BR-3 cells, staurosporine induced the generation of immobile flattened giant cells. Video time-lapse analysis revealed that within the borderline of cell collectives, staurosporine reduced the velocity of individual MDA-MB-231 and SK-BR-3, but not of MCF-7 cells. In individual MCF-7 cells, mainly the directionality of migration became disturbed, which led to an increased migration rate parallel to the borderline, and hereby to an inhibition of the migration of the cell collective as a total. Moreover, the application of staurosporine led to a transient activation of ERK1/2 in all cell lines. Conclusion: Dependent on the context (single versus collective cells), a drug may induce opposite effects in the same cell line.
In recent years, the basic income grant (BIG) discourse has gained attention worldwide as a potential policy option in social protection as testified by recent public debates, ongoing pilot projects, campaigning efforts,1 policy measures during Covid-19 and the surge in academic research. A BIG refers to regular cash transfers paid to all members of society irrespective of their socio-economic status, their capacity or willingness to participate in the labour market or having to meet pre-determined conditions (Offe 2008; Van Parijs 1995, 2003; Wright 2004, 2006). Despite the recent hype around BIG, Iran is the only country worldwide with a scaled-up BIG (Tabatabai 2011, 2012). Other programmes have never gone beyond pilot programmes. This raises the question why this is the case.
Most people use disaster apps infrequently, primarily only in situations of turmoil, when they are physically or emotionally vulnerable. Personal data may be necessary to help them, data protections may be waived. In some circumstances, free movement and liberties may be curtailed for public protection, as was seen in the current COVID pandemic. Consuming and producing disaster data can deepen problems arising at the confluence of surveillance and disaster capitalism, where data has become a tool for solutionist instrumentarian power (Zuboff 2019, Klein 2008) and part of a destructive mode of one world worlding (Law 2015, Escobar 2020). The special use of disaster apps prompts us to ask what role consumer protection could play in safeguarding democratic liberties. Within this work, a set of current approaches are briefly reviewed and two case studies are presented of what we call appropriation or design against datafication. These combine document analysis and literature research with several months of online and field ethnographic observation. The first case study examines disaster app use in response to the 2010 Haiti earthquake, the second explores COVID Contact Tracing in Taiwan in 2020/21. Against this backdrop we ask, ‘how could and how should consumer protection respond to problems of surveillance disaster capitalism?’ Drawing on our work with the is IT ethical? Exchange, a co-designed community platform and knowledge exchange for disaster information sharing, and a Societal Readiness Assessment Framework that we are developing alongside it, we explore how co-design methodologies could help define answers.
Policy analysis is the cornerstone of evidence-based policy making.1 It identifies the problems, informs programme design, supports the monitoring of policy implementation and is needed to evaluate programme impacts (Scott 2005). Rigorous and credible policy evidence is necessary to ensure the transparency and accountability of policy decisions, to secure political and public support and, hence, the allocation of financial resources. Sound policy analysis helps design effective and efficient programmes, thereby maximizing programme impact.
The future of work
(2021)
Driven by the exponential increase in the computational power of machines, data digitalization and scientific advancement in robotics and automation, the current wave of technological change is seemingly unprecedented in speed and scale. It transforms manufacturing and businesses making them more flexible, decentralized and efficient (Lasi et al. 2014). Even though technological change is nothing new, some argue that it is different this time. The new technologies have not only the potential to substitute labor (Nomaler and Verspagen 2018), they also change the way people work. The trend towards new forms of employment is no longer a marginal phenomenon.
Application of underwater robots are on the rise, most of them are dependent on sonar for underwater vision, but the lack of strong perception capabilities limits them in this task. An important issue in sonar perception is matching image patches, which can enable other techniques like localization, change detection, and mapping. There is a rich literature for this problem in color images, but for acoustic images, it is lacking, due to the physics that produce these images. In this paper we improve on our previous results for this problem (Valdenegro-Toro et al, 2017), instead of modeling features manually, a Convolutional Neural Network (CNN) learns a similarity function and predicts if two input sonar images are similar or not. With the objective of improving the sonar image matching problem further, three state of the art CNN architectures are evaluated on the Marine Debris dataset, namely DenseNet, and VGG, with a siamese or two-channel architecture, and contrastive loss. To ensure a fair evaluation of each network, thorough hyper-parameter optimization is executed. We find that the best performing models are DenseNet Two-Channel network with 0.955 AUC, VGG-Siamese with contrastive loss at 0.949 AUC and DenseNet Siamese with 0.921 AUC. By ensembling the top performing DenseNet two-channel and DenseNet-Siamese models overall highest prediction accuracy obtained is 0.978 AUC, showing a large improvement over the 0.91 AUC in the state of the art.
The dataset contains the following data from successful and failed executions of the Toyota HSR robot placing a book on a shelf.
RGB images from the robot's head camera
Depth images from the robot's head camera
Rendered images of the robot's 3D model from the point of view of the robot's head camera
Force-torque readings from a wrist-mounted force-torque sensor
Joint efforts, velocities and positions
extrinsic and intrinsic camera calibration parameters
frame-level anomaly annotations
The anomalies that occur during execution include:
the manipulated book falling down
books on the shelf being disturbed significantly
camera occlusions
robot being disturbed by an external collision
The dataset is split into a train, validation and test set with the following number of trials:
Train: 48 successful trials
Validation: 6 successful trials
Test: 60 anomalous trials and 7 successful trials
Due to ongoing digitalization, more and more cloud services are finding their way into companies. In this context, data integration from the various software solutions, which are provided both on-premise (local use or licensing for local use of software) and as a service, is of great importance. In this regard, Integration Platform as a Service (IPaaS) models aim to support companies as well as software providers in the context of data integration by providing connectors to enable data flow between different applications and systems and other integration services. Since previous research has mostly focused on technical or legal aspects of IPaaS, this article focuses on deriving integration practices and design-related barriers and drivers regarding the adoption of IPaaS. Therefore, we conducted 10 interviews with experts from different software as a services vendors. Our results show that the main factors regarding the adoption of IPaaS are the standardization of data models, the usability and variety of connectors provided, and the issues regarding data privacy, security, and transparency.
Property-Based Testing in Simulation for Verifying Robot Action Execution in Tabletop Manipulation
(2021)
An important prerequisite for the reliability and robustness of a service robot is ensuring the robot’s correct behavior when it performs various tasks of interest. Extensive testing is one established approach for ensuring behavioural correctness; this becomes even more important with the integration of learning-based methods into robot software architectures, as there are often no theoretical guarantees about the performance of such methods in varying scenarios. In this paper, we aim towards evaluating the correctness of robot behaviors in tabletop manipulation through automatic generation of simulated test scenarios in which a robot assesses its performance using property-based testing. In particular, key properties of interest for various robot actions are encoded in an action ontology and are then verified and validated within a simulated environment. We evaluate our framework with a Toyota Human Support Robot (HSR) which is tested in a Gazebo simulation. We show that our framework can correctly and consistently identify various failed actions in a variety of randomised tabletop manipulation scenarios, in addition to providing deeper insights into the type and location of failures for each designed property.
Execution monitoring is essential for robots to detect and respond to failures. Since it is impossible to enumerate all failures for a given task, we learn from successful executions of the task to detect visual anomalies during runtime. Our method learns to predict the motions that occur during the nominal execution of a task, including camera and robot body motion. A probabilistic U-Net architecture is used to learn to predict optical flow, and the robot's kinematics and 3D model are used to model camera and body motion. The errors between the observed and predicted motion are used to calculate an anomaly score. We evaluate our method on a dataset of a robot placing a book on a shelf, which includes anomalies such as falling books, camera occlusions, and robot disturbances. We find that modeling camera and body motion, in addition to the learning-based optical flow prediction, results in an improvement of the area under the receiver operating characteristic curve from 0.752 to 0.804, and the area under the precision-recall curve from 0.467 to 0.549.
In the field of service robots, dealing with faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment due to insufficient knowledge for action execution. In our previous work [1], we have shown that such missing knowledge can be obtained through learning by experimentation. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. However we did not propose a suitable representation of the symbolic model. In this work we investigate such symbolic representation and evaluate its learning capability. The experimental analysis is performed on four use cases using four different learning paradigms. As a result, the symbolic representation together with the most suitable learning paradigm are identified.
Short summary
Accompanying dataset for our paper
A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.
Contents
The dataset includes a single zip archive, containing data from the experiment described in the paper (conducted with a Toyota HSR). The zip archive contains three subdirectories:
handle_grasping_failure_database: A dump of a MongoDB database containing data from the handle grasping experiment, including ground-truth grasping failure annotations
pre_arm_motion_images: Images collected from the robot's hand camera before moving the robot's hand towards the handle
pregrasp_images: Images collected from the robot's hand camera just before closing the gripper for grasping
The image names include the time stamp at which the images were taken; this allows matching each image with the execution data in the database.
Database usage
After unzipping the archive, the database can be restored with the command
mongorestore handle_grasping_failure_database
This will create a MongoDB database with the name drawer_handle_grasping_failures.
Code for processing the data and failure analysis can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository.
Contents
There are two zip archives included (grasping.zip and throwing.zip), corresponding to two experiments (grasping objects and throwing them in a drawer), both performed with a Toyota HSR. Each archive contains two directories - learning and generalisation - with object-specific learning and generalisation data. For each object, we provide a dump of a MongoDB database, which contains data sufficient for learning the models used in our experiments.
Usage
After unzipping the archives, each database can be restored with the command
mongorestore [data_directory_name]
This will create a MongoDB database with the name of the directory. Code for processing the data and model learning can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository.
The solvent exchange as one of the most important steps during the manufacturing process of organic aerogels was investigated. This step is crucial as a preparatory step for the supercritical drying, since the pore solvent must be soluble in supercritical carbon dioxide to enable solvent extraction. The development and subsequent optimization of a suitable system with a peristaltic pump for automatic solvent exchange proved to be a suitable approach. In addition, the influence of zeolites on the acceleration of the process was found to be beneficial. To investigate the process, the water content in acetone was determined at different times using Karl Fischer titration. The shrinkage, densities, as well as the surface areas of the aerogels were analyzed. Based on these, the influence of various process parameters on the final structure of the obtained aerogels was investigated and evaluated. Modeling on diffusion in porous materials completes this study.
Over the last decades, different kinds of design guides have been created to maintain consistency and usability in interactive system development. However, in the case of spatial applications, practitioners from research and industry either have difficulty finding them or perceive such guides as lacking relevance, practicability, and applicability. This paper presents the current state of scientific research and industry practice by investigating currently used design recommendations for mixed reality (MR) system development. We analyzed and compared 875 design recommendations for MR applications elicited from 89 scientific papers and documentation from six industry practitioners in a literature review. In doing so, we identified differences regarding four key topics: Focus on unique MR design challenges, abstraction regarding devices and ecosystems, level of detail and abstraction of content, and covered topics. Based on that,we contribute to the MR design research by providing three factors for perceived irrelevance and six main implications for design recommendations that are applicable in scientific and industry practice.
Augmented/Virtual Reality (AR/VR) is still a fragmented space to design for due to the rapidly evolving hardware, the interdisciplinarity of teams, and a lack of standards and best practices. We interviewed 26 professional AR/VR designers and developers to shed light on their tasks, approaches, tools, and challenges. Based on their work and the artifacts they generated, we found that AR/VR application creators fulfill four roles: concept developers, interaction designers, content authors, and technical developers. One person often incorporates multiple roles and faces a variety of challenges during the design process from the initial contextual analysis to the deployment. From analysis of their tool sets, methods, and artifacts, we describe critical key challenges. Finally, we discuss the importance of prototyping for the communication in AR/VR development teams and highlight design implications for future tools to create a more usable AR/VR tool chain.
The purpose of this study is to research the antecedents of the sustainable travel decision-making of European travelers and thereby identify important lessons for the transition towards sustainable travel and tourism. The study is based on data collected through a representative survey, conducted in five European countries, with a sample of n = 5024 respondents. The results of descriptive statistics, EFA (Exploratory Factor Analysis) and FA (Factor Analysis) are presented in order to explore sustainable travel decision-making through environmental (policy-related and personal) attitudes and travel mode decision priorities in the European context. Furthermore, the study provides new evidence regarding the under-researched phenomenon of the attitude–behavior gap by presenting a model for the sustainability-oriented decision-making of travelers, including attitudes and travel mode priorities as antecedents. The results confirm the existence of moral licensing in travel decision-making, thereby extending the relevance of this theory into travel and tourism, which has not been done before. The denial of environmental issues is also being researched as regards its interaction with positive environmental attitudes, environmental travel mode priorities and non-environmental travel priorities, thereby advancing our understanding of the interplay between these categories. The interplay between the four categories furthers our understanding of the perplexity of travelers in terms of sustainable travel decision-making.
The article improves understanding on leveraging new technology for DT (digital transformation) of grape harvest in SME wineries. It provides evidence on technologies used and workplace types deployed in grape harvesting, as well as strategic paths in deploying new technology, thereby contributing to the literature on networked sensing and seizing capabilities in the wine industry 4.0. The research approach is explorative and qualitative drawing on 31 interviews with wine industry 4.0 experts and managers, mostly owners of SMEs: wineries, wine software and wine machinery enterprises. Resulting findings serve as a roadmap for digital transformation of grape harvest process in SME wineries explaining technologies and work roles necessary for DWT (digital workplace transformation), as well as strategic paths of deployment of novel grape harvest technology. Previous research on the wine industry 4.0 has focused on BMI, while this research expands the focus to include a wider concept of technology adoption strategy as well as DWT. The research identifies two types of factors impacting the strategic deployment of grape harvest technology: pull factors, also termed servitization factors, as well as push factors, termed also digital transformation factors.
This study set out to uncover brand positioning configurations by presenting state-of-the-art brand management literature and applying a novel, mixed-methods approach to examine the under-researched wine industry transformation towards open innovation in branding. German winery brands were analyzed using a multimethod approach leaning on a novel netnographic methodology and multiple sources. The sample included 572 wineries from all 13 German wine regions with website text data and online review text data from each winery. The study identified nine prime words used to describe both brand identity as well as wine brand image. It revealed word–price clusters of brand identity and image. The results offer insights into communication and pricing opportunities for wine brand identity as well as image, thereby contributing to open brand innovation.
The purpose of this paper is to examine the impact that various types of business model extensions (hospitality and tourism, online sales platforms, and sustainability) have on the winery business. The research is based on company data and online observations of N = 886 German wineries and deploys a content analysis, netnography, and structural equation modeling (SEM) in order to test the hypothesis on business model extensions of wineries, which have been set forth in the previous literature. The findings indicate that business model extensions related to online sales platforms have a positive impact on winery business size. These results mean that developing online sales platforms enlarges the winery BM (business model) size and type (manager-run, state-owned, or cooperatives). The paper presents in detail the impact of winery BM extensions on winery BM model type and size, thereby contributing to the literature on business model innovation.
The article explores SME (Small and Medium Sized Enterprises) brand strategies as a means to position and successfully engage in competitive markets. A derived typology of brand strategy types deals with social profiling and sheds light on brand strategy internalization of two current managerial paradigms—sustainability and co-creation. N = 895 German SME wineries were examined, leaning on a netnographic analysis of predominantly websites and social media interactions. A two-step clustering method thereby identified eight winery SME brand strategy types. The importance of sustainability across the identified eight brand strategy types is significant. Co-creation turned out to be a key profiling trait characterizing one brand strategy type. The typology illustrates strategic richness, with brand strategies leaning predominantly on traditional values, on sustainability, on external reputation, or on more innovative customer centric concepts such as co-creation. Hereby, the typology and the identified brand levers invite to strategically design brand management, governance, and sustainability. Wineries which focus on traditional positioning and legitimacy were found to be cautious in deploying co-creation through social media. Winery brands that are characterized by engagement in digital co-creation apparently either tend to expand their scope or partially combine it with traditional values, making them the most diverse type identified. Sustainability obviously needs to be addressed by all brand strategies. Despite industry and country focus, the analyses illustrate the relevance of socially-oriented profiling and highlights that sustainability has reached a status of a fundamental business approach still allowing to differentiate thereon. Furthermore, the business models of the SMEs need to deliver communicated values.
This article provides insights into the modalities of business-model change and innovation. On the basis of an analysis of empirical data of small and medium enterprises, a transition from wine production centrism to its expanded use in hospitality and tourism is explored. Previous research on wine tourism and hospitality predominantly focuses on a destination perspective, neglecting the organizational winery perspective. The article deploys a mixed methods approach, combining netnography and a content analysis for data collection with grounded research and clustering for theory building. The sample size included 885 German wineries. Data stemmed from two distinct sources (websites and a secondary publication in form of a wine guide) and has been analyzed through a two-step clustering algorithm as well as a Principal Component Analysis (PCA). The two-step clustering algorithm resulted in nine different business models while the PCA analysis grouped the variables into the following two categories: basic winery business model (BM) and BM extension into hospitality and tourism, thereby validating the difference between the two constructs. The results point to the diverse nature of business model extensions of wineries in tourism and hospitality, depending on their organizational type and size. This study offers a classification of small and medium sized enterprise’s strategic business model expansion, and explores the expansion of the wine industry through wine hospitality and tourism services, starting with the winery organizational perspective, which has not been done before.
In tree-based adaptive mesh refinement (AMR) we store refinement trees in the cells of an unstructured coarse mesh. This lets us combine the speed and simpler management of structured refinement trees with the more flexible mesh generation of the unstructured coarse mesh. But this creates a conflict between performance and geometrical accuracy. If we favor speed we reduce the cells in our coarse mesh and hence reduce the accuracy of our geometrical representation. If we want more accurate results we generate a finer coarse mesh and lose performance by managing more cells in our unstructured coarse mesh. To mitigate this conflict we present the prototype of an geometry description which we implement in an already existing library. With this description we build geometry adapted hexahedral refinement trees, which also support high-order curved boundary cells. We also present examples on how to use this description. Moreover, we test the speedup of this new algorithm compared with coarse meshes with different geometrical errors.
Off-lattice Boltzmann methods increase the flexibility and applicability of lattice Boltzmann methods by decoupling the discretizations of time, space, and particle velocities. However, the velocity sets that are mostly used in off-lattice Boltzmann simulations were originally tailored to on-lattice Boltzmann methods. In this contribution, we show how the accuracy and efficiency of weakly and fully compressible semi-Lagrangian off-lattice Boltzmann simulations is increased by velocity sets derived from cubature rules, i.e. multivariate quadratures, which have not been produced by the Gauss-product rule. In particular, simulations of 2D shock-vortex interactions indicate that the cubature-derived degree-nine D2Q19 velocity set is capable to replace the Gauss-product rule-derived D2Q25. Likewise, the degree-five velocity sets D3Q13 and D3Q21, as well as a degree-seven D3V27 velocity set were successfully tested for 3D Taylor-Green vortex flows to challenge and surpass the quality of the customary D3Q27 velocity set. In compressible 3D Taylor-Green vortex flows with Mach numbers Ma={0.5;1.0;1.5;2.0} on-lattice simulations with velocity sets D3Q103 and D3V107 showed only limited stability, while the off-lattice degree-nine D3Q45 velocity set accurately reproduced the kinetic energy provided by literature.
This thesis explores novel haptic user interfaces for touchscreens, virtual and remote environments (VE and RE). All feedback modalities have been designed to study performance and perception while focusing on integrating an additional sensory channel - the sense of touch. Related work has shown that tactile stimuli can increase performance and usability when interacting with a touchscreen. It was also shown that perceptual aspects in virtual environments could be improved by haptic feedback. Motivated by previous findings, this thesis examines the versatility of haptic feedback approaches. For this purpose, five haptic interfaces from two application areas are presented. Research methods from prototyping and experimental design are discussed and applied. These methods are used to create and evaluate the interfaces; therefore, seven experiments have been performed. All five prototypes use a unique feedback approach. While three haptic user interfaces designed for touchscreen interaction address the fingers, two interfaces developed for VE and RE target the feet. Within touchscreen interaction, an actuated touchscreen is presented, and study shows the limits and perceptibility of geometric shapes. The combination of elastic materials and a touchscreen is examined with the second interface. A psychophysical study has been conducted to highlight the potentials of the interface. The back of a smartphone is used for haptic feedback in the third prototype. Besides a psychophysical study, it is found that the touch accuracy could be increased. Interfaces presented in the second application area also highlight the versatility of haptic feedback. The sides of the feet are stimulated in the first prototype. They are used to provide proximity information of remote environments sensed by a telepresence robot. In a study, it was found that spatial awareness could be increased. Finally, the soles of the feet are stimulated. A designed foot platform that provides several feedback modalities shows that self-motion perception can be increased.
Mebendazole Mediates Proteasomal Degradation of GLI Transcription Factors in Acute Myeloid Leukemia
(2021)
The prognosis of elderly AML patients is still poor due to chemotherapy resistance. The Hedgehog (HH) pathway is important for leukemic transformation because of aberrant activation of GLI transcription factors. MBZ is a well-tolerated anthelmintic that exhibits strong antitumor effects. Herein, we show that MBZ induced strong, dose-dependent anti-leukemic effects on AML cells, including the sensitization of AML cells to chemotherapy with cytarabine. MBZ strongly reduced intracellular protein levels of GLI1/GLI2 transcription factors. Consequently, MBZ reduced the GLI promoter activity as observed in luciferase-based reporter assays in AML cell lines. Further analysis revealed that MBZ mediates its anti-leukemic effects by promoting the proteasomal degradation of GLI transcription factors via inhibition of HSP70/90 chaperone activity. Extensive molecular dynamics simulations were performed on the MBZ-HSP90 complex, showing a stable binding interaction at the ATP binding site. Importantly, two patients with refractory AML were treated with MBZ in an off-label setting and MBZ effectively reduced the GLI signaling activity in a modified plasma inhibitory assay, resulting in a decrease in peripheral blood blast counts in one patient. Our data prove that MBZ is an effective GLI inhibitor that should be evaluated in combination to conventional chemotherapy in the clinical setting.
In this contribution, we perform computer simulations to expedite the development of hydrogen storages based on metal hydride. These simulations enable in-depth analysis of the processes within the systems which otherwise could not be achieved. That is, because the determination of crucial process properties require measurement instruments in the setup which are currently not available. Therefore, we investigate the reliability of reaction values that are determined by a design of experiments.
Specifically, we first explain our model setup in detail. We define the mathematical terms to obtain insights into the thermal processes and reaction kinetics. We then compare the simulated results to measurements of a 5-gram sample consisting of iron-titanium-manganese (FeTiMn) to obtain the values with the highest agreement with the experimental data. In addition, we improve the model by replacing the commonly used Van’t-Hoff equation by a mathematical expression of the pressure-composition-isotherms (PCI) to calculate the equilibrium pressure.
Finally, the parameters’ accuracy is checked in yet another with an existing metal hydride system. The simulated results demonstrate high concordance with experimental data, which advocate the usage of approximated kinetic reaction properties by a design of experiments for further design studies. Furthermore, we are able to determine process parameters like the entropy and enthalpy.
Using Visual and Auditory Cues to Locate Out-of-View Objects in Head-Mounted Augmented Reality
(2021)
The Covid-19 pandemic has challenged educators across the world to move their teaching and mentoring from in-person to remote. During nonpandemic semesters at their institutes (e.g. universities), educators can directly provide students the software environment needed to support their learning - either in specialized computer laboratories (e.g. computational chemistry labs) or shared computer spaces. These labs are often supported by staff that maintains the operating systems (OS) and software. But how does one provide a specialized software environment for remote teaching? One solution is to provide students a customized operating system (e.g., Linux) that includes open-source software for supporting your teaching goals. However, such a solution should not require students to install the OS alongside their existing one (i.e. dual/multi-booting) or be used as a complete replacement. Such approaches are risky because of a) the students' possible lack of software expertise, b) the possible disruption of an existing software workflow that is needed in other classes or by other family members, and c) the importance of maintaining a working computer when isolated (e.g. societal restrictions). To illustrate possible solutions, we discuss our approach that used a customized Linux OS and a Docker container in a course that teaches computational chemistry and Python3.
The identification of energetic materials in containments is an important challenge for analytical methods in the field of safety and security. Opening a package without knowledge of its contents and the resulting hazards is highly involved with risks and should be avoided whenever possible. Therefore, preferable methods work non-destructive with minimal interaction and are capable of identifying target substances in a containment quickly and reliably. Most spectroscopic methods find their limits, if the target substance is shielded by a covering material. To solve this problem, a combined laser drilling method with subsequent identification of the target substance by means of Raman spectroscopic measurements through microscopic bore holes of the covering material is presented. A pulsed laser beam is used for both the drilling process and as an excitation source for Raman measurements in the same optical setup. Results show the ability of this new method to gain high-quality spectra even when performed through microscopic small bore channels. With the laser parameters chosen right, the method can even be performed on highly sensitive explosives like triacetone triperoxide (TATP). Another advantageous effect arises in an observed reduction in unwanted fluorescence signal in the spectral data, resulting from the confocal-like measurement setup with the bore hole acting as aperture.
This textbook contains and explains essential mathematical formulas within an economic context. A broad range of aids and supportive examples will help readers to understand the formulas and their practical applications. This mathematical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice.