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The strong demand for a transformation of the textile and fashion industry towards sustainability requires a continuous implementation of the guiding principle of Education for Sustainable Development (ESD) in education and industry [1, 2]. In a first step of the European research project "Sustainable fashion curriculum at textile Universities in Europe - Development, Implementation and Evaluation of a Teaching Module for Educators" (Fashion DIET) a continuing education module shall be created to implement ESD as a guiding principle in university teaching. The research-based teaching and learning materials are delivered through an e-learning portal.
We address the problem of 3D face recognition based on either 3D sensor data, or on a 3D face reconstructed from a 2D face image. We focus on 3D shape representation in terms of a mesh of surface normal vectors. The first contribution of this work is an evaluation of eight different 3D face representations and their multiple combinations. An important contribution of the study is the proposed implementation, which allows these representations to be computed directly from 3D meshes, instead of point clouds. This enhances their computational efficiency. Motivated by the results of the comparative evaluation, we propose a 3D face shape descriptor, named Evolutional Normal Maps, that assimilates and optimises a subset of six of these approaches. The proposed shape descriptor can be modified and tuned to suit different tasks. It is used as input for a deep convolutional network for 3D face recognition. An extensive experimental evaluation using the Bosphorus 3D Face, CASIA 3D Face and JNU-3D Face datasets shows that, compared to the state of the art methods, the proposed approach is better in terms of both computational cost and recognition accuracy.
What might the attendee be able to do after being in your session?
Our work shows how to connect intra-operative devices via IEEE 11073 Service-oriented Device Connectivity (SDC).
Description of the Problem or Gap
Standardized device communication is essential for interoperability, availability of device data, and therefore for the intelligent operating room (OR) and arising solutions. The SDC standard was developed to make information from medical devices available in a uniform manner and enable interoperability. Existing devices are rarely SDC-capable and need interfaces to be interoperable via SDC.
Methods: What did you do to address the problem or gap?
We conceived an SDC-based architecture consisting of a service provider and service consumer. In our concept, the service provider is connected to the medical device and capable to translate the proprietary protocol of the device into SDC and vice versa. The service consumer is used to request or send information via the SDC protocol to the service provider and can function as a uniform bidirectional interface (e.g. for displaying or controlling). This concept was exemplarily demonstrated with the patient monitor MX800 of Philips to retrieve the device data (e.g. vital parameters) via SDC and partly for the operating light marLED X of KLS Martin Group.
Results: What was the outcome(s) of what you did to address the problem or gap?
The patient monitor MX800 was connected to a Raspberry Pi (RPi) via LAN, on which the service provider is running. The python script on the RPi establishes a connection to the monitor and translates incoming and outgoing messages from the proprietary protocol to SDC and vice versa to/from the service consumer. The service consumer is running on a laptop and acts as a simulation for different kinds of systems that want to get vital parameters or other information from the patient monitor. The operating light marLED X was connected to an RPi via USB-to-RS232. A python script on the RPi establishes a connection to the light and makes it possible via proprietary commands to get information of the light (e.g. status) and to control it (e.g. toggle the light, increment the intensity). A translation to SDC is not integrated yet.
Discussion of Results
Our practical implementation shows that medical devices can be accessed via external connections to get device data and control the device via commands. The example SDC implementation of the patient monitor MX800 makes it possible to request its data via the standardized communication protocol SDC. This is also possible for the operating light marLED X if its proprietary protocol is analyzed to be translatable to/from SDC. This would allow to control the device from an external system, or automatically depending on the status of the ongoing procedure. The advantage is, that existing intra-operative devices can be extended by a service provider which is capable of translating the proprietary protocol of the device in SDC and vice versa. This enables interoperability and an intelligent OR that, for example, is aware of all devices, their status, and data and can use this information to optimally support the surgeons and their team (e.g. provision of information, automated documentation). This interoperability allows that future innovations merely need to understand the SDC protocol instead of all vendor-dependent communication protocols.
Conclusion
Standardized device communication is essential to reach interoperability, and therefore intelligent ORs. Our contribution addresses the possibility of subsequently making medical devices SDC-capable. This may eliminate the need of understanding all the different proprietary protocols when developing new innovative solutions for the OR.
The Fourteenth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2022), held between May 22 – 26, 2022, continued a series of international events covering a large spectrum of topics related to advances in fundamentals on databases, evolution of relation between databases and other domains, data base technologies and content processing, as well as specifics in applications domains databases.
Advances in different technologies and domains related to databases triggered substantial improvements for content processing, information indexing, and data, process and knowledge mining. The push came from Web services, artificial intelligence, and agent technologies, as well as from the generalization of the XML adoption.
High-speed communications and computations, large storage capacities, and load-balancing for distributed databases access allow new approaches for content processing with incomplete patterns, advanced ranking algorithms and advanced indexing methods.
Evolution on e-business, ehealth and telemedicine, bioinformatics, finance and marketing, geographical positioning systems put pressure on database communities to push the ‘de facto’ methods to support new requirements in terms of scalability, privacy, performance, indexing, and heterogeneity of both content and technology.
Purpose
Artificial intelligence (AI), in particular deep learning (DL), has achieved remarkable results for medical image analysis in several applications. Yet the lack of human-like explanations of such systems is considered the principal restriction before utilizing these methods in clinical practice (Yang, Ye, & Xia, 2022).
Methods
Explainable Artificial Intelligence (XAI) provides a human-explainable and interpretable description of the “black-box” nature of DL (Gulum, Trombley, & Kantardzic, 2021). An effective XAI diagnosis generator, namely NeuroXAI (refer to Fig. 1), has been developed to extract 3D explanations from convolutional neural networks (CNN) models of brain gliomas (Zeineldin et al., 2022). By providing visual justification maps, NeuroXAI can help make DL models transparent and thus increase the trust of medical experts.
Results
NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e. image classification and segmentation using magnetic resonance imaging (MRI). Visual attention maps of multiple XAI methods have been generated and compared for both applications, which could help to provide transparency about the performance of DL systems.
Conclusion
NeuroXAI helps to understand the prediction process of 3D CNN networks for brain glioma using human-understandable explanations. Results revealed that the investigated DL models behave in a logical human-like manner and can improve the analytical process of the MRI images systematically. Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist medical professionals in the detection and diagnosis of brain tumors. NeuroXAI code is publicly accessible at https://github.com/razeineldin/NeuroXAI
This paper reviews suggestions for changes to database technology coming from the work of many researchers, particularly those working with evolving big data. We discuss new approaches to remote data access and standards that better provide for durability and auditability in settings including business and scientific computing. We propose ways in which the language standards could evolve, with proof-of-concept implementations on Github.
The general conclusion of climate change studies is the necessity of eliminating net CO2 emissions in general and from the electric power systems in particular by 2050. The share of renewable energy is increasing worldwide, but due to the intermittent nature of wind and solar power, a lack of system flexibility is already hampering the further integration of renewable energy in some countries. In this study, we analyze if and how combinations of carbon pricing and power-to-gas (PtG) generation in the form of green power-to-hydrogen followed by methanation (which we refer to as PtG throughout) using captured CO2 emissions can provide transitions to deep decarbonization of energy systems. To this end, we focus on the economics of deep decarbonization of the European electricity system with the help of an energy system model. In different scenario analyses, we find that a CO2 price of 160 €/t (by 2050) is on its own not sufficient to decarbonize the electricity sector, but that a CO2 price path of 125 (by 2040) up to 160 €/t (by 2050), combined with PtG technologies, can lead to an economically feasible decarbonization of the European electricity system by 2050. These results are robust to higher than anticipated PtG costs.
We study whether compulsory religious education in schools affects students' religiosity as adults. We exploit the staggered termination of compulsory religious education across German states in models with state and cohort fixed effects. Using three different datasets, we find that abolishing compulsory religious education significantly reduced religiosity of affected students in adulthood. It also reduced the religious actions of personal prayer, church-going, and church membership. Beyond religious attitudes, the reform led to more equalized gender roles, fewer marriages and children, and higher labor-market participation and earnings. The reform did not affect ethical and political values or non-religious school outcomes.
We study whether compulsory religious education in schools affects students' religiosity as adults. We exploit the staggered termination of compulsory religious education across German states in models with state and cohort fixed effects. Using three different datasets, we find that abolishing compulsory religious education significantly reduced religiosity of affected students in adulthood. It also reduced the religious actions of personal prayer, church-going, and church membership. Beyond religious attitudes, the reform led to more equalized gender roles, fewer marriages and children, and higher labor-market participation and earnings. The reform did not affect ethical and political values or non-religious school outcomes.
Early exposure makes the entrepreneur: how economics education in school influences entrepreneurship
(2022)
Many countries that seek to boost their economy share the goal of promoting entrepreneurship. Whereas there is ample research on the predictors of entrepreneurship during adulthood, we know little about how pre-adulthood experience influences entrepreneurship later in life. Using a natural experiment, this paper examines whether introducing economics classes in school enhances entrepreneurial behavior in adulthood. Our difference-in-differences approach exploits curricula reforms across German states that introduced compulsory economics education classes in secondary schools. Using information on school and labor market careers for more than 10,000 individuals from 1984 to 2019, we find that the reform increases students’ entrepreneurial activities by three percentage points. Examining gender differences, we find that economics classes equally benefit female and male students. Our results advance our understanding of how pre-adulthood experiences shape individuals’ entrepreneurial behavior.
Being exposed to compulsory religious education in school can have long-run consequences for students’ lives. At different points in time since the 1970s, German states terminated compulsory religious education in public schools and replaced it by a choice between ethics classes and religious education. This article shows that the reform not only led to reduced religiosity in students’ later life, but also eroded traditional attitudes towards gender roles and increased labor-market participation and earnings.
Gender pay gaps are commonly studied in populations with already completed educational careers. We focus on an earlier stage by investigating the gender pay gap among university students working alongside their studies. With data from five cohorts of a large-scale student survey from Germany, we use regression and wage decomposition techniques to describe gender pay gaps and potential explanations. We find that female students earn about 6% less on average than male students, which reduces to 4.1% when accounting for a rich set of explanatory variables. The largest explanatory factor is the type of jobs male and female students pursue.
With the digital transformation, companies will experience a change that focuses on shaping the organization into an agile organizational form. In today's competitive and fast-moving business environment, it is necessary to react quickly to changing market conditions. Agility represents a promising option for overcoming these challenges. The path to an agile organization represents a development process that requires consideration of countless levels of the enterprise. This paper examines the impact of digital transformation on agile working practices and the benefits that can be achieved through technology. To enable a solution for today's so-called VUCA (Volatility, Uncertainty, Complexity und Ambiguity) world, agile ways of working can be applied project management requires adaptation. In the qualitative study, expert interviews were conducted and analyzed using the grounded theory method. As a result, a model can be presented that shows the influencing factors and potentials of agile management in the context of the digital transformation of medium-sized companies.
Uncontrolled movement of instruments in laparoscopic surgery can lead to inadvertent tissue damage, particularly when the dissecting or electrosurgical instrument is located outside the field of view of the laparoscopic camera. The incidence and relevance of such events are currently unknown. The present work aims to identify and quantify potentially dangerous situations using the example of laparoscopic cholecystectomy (LC). Twenty-four final year medical students were prompted to each perform four consecutive LC attempts on a well-established box trainer in a surgical training environment following a standardized protocol in a porcine model. The following situation was defined as a critical event (CE): the dissecting instrument was inadvertently located outside the laparoscopic camera’s field of view. Simultaneous activation of the electrosurgical unit was defined as a highly critical event (hCE). Primary endpoint was the incidence of CEs. While performing 96 LCs, 2895 CEs were observed. Of these, 1059 (36.6%) were hCEs. The median number of CEs per LC was 20.5 (range: 1–125; IQR: 33) and the median number of hCEs per LC was 8.0 (range: 0–54, IQR: 10). Mean total operation time was 34.7 min (range: 15.6–62.5 min, IQR: 14.3 min). Our study demonstrates the significance of CEs as a potential risk factor for collateral damage during LC. Further studies are needed to investigate the occurrence of CE in clinical practice, not just for laparoscopic cholecystectomy but also for other procedures. Systematic training of future surgeons as well as technical solutions address this safety issue.
User innovators follow multiple diffusion and adoption pathways for their self-developed innovations. Users may choose to commercialize their self-developed products on the marketplace by becoming entrepreneurs. Few studies exist that focus on understanding personal and interpersonal factors that affect some user innovators’ entrepreneurial decision-making. Hence, this paper focuses on how user innovators make key decisions relating to opportunity recognition and evaluation and when opportunity evaluation leads to subsequent entrepreneurial action in the entrepreneurial process. We conducted an exploratory study using a multi-grounded theory methodology as the user entrepreneurship phenomenon embodies complex social processes. We collected data through the netnography approach that targeted 18 entrepreneurs with potentially relevant differences through crowdfunding platforms. We integrated self-determination, human capital, and social capital theory to address the phenomena under study. This study’s significant findings posit that users’ motives are dissatisfaction with existing goods, interest in innovation, altruism, social recognition, desire for independence, and economic benefits. Besides, use-related experience, product-related knowledge, product diffusion, and iterative feedback positively impact innovative users’ entrepreneurial decision-making.
Industrial practice is characterized by random events, also referred to as internal and external turbulences, which disturb the target-oriented planning and execution of production and logistics processes. Methods of probabilistic forecasting, in contrast to single value predictions, allow an estimation of the probability of various future outcomes of a random variable in the form of a probability density function instead of predicting the probability of a specific single outcome. Probabilistic forecasting methods, which are embedded into the analytics process to gain insights for the future based on historical data, therefore offer great potential for incorporating uncertainty into planning and control in industrial environments. In order to familiarize students with these potentials, a training module on the application of probabilistic forecasting methods in production and intralogistics was developed in the learning factory 'Werk150' of the ESB Business School (Reutlingen University). The theoretical introduction to the topic of analytics, probabilistic forecasting methods and the transition to the application domain of intralogistics is done based on examples from other disciplines such as weather forecasting and energy consumption forecasting. In addition, data sets of the learning factory are used to familiarize the students with the steps of the analytics process in a practice-oriented manner. After this, the students are given the task of identifying the influencing factors and required information to capture intralogistics turbulences based on defined turbulence scenarios (e.g. failure of a logistical resource) in the learning factory. Within practical production scenario runs, the students apply probabilistic forecasting using and comparing different probabilistic forecasting methods. The graduate training module allows the students to experience the potentials of using probabilistic forecasting methods to improve production and intralogistics processes in context with turbulences and to build up corresponding professional and methodological competencies.
Bioactive cations, including calcium, copper and magnesium, have shown the potential to become the alternative to protein growth factor-based therapeutics for bone healing. Ion substitutions are less costly, more stable, and more effective at low concentrations. Although they have been shown to be effective in providing bone grafts with more biological functions, the precise control of ion release kinetics is still a challenge. Moreover, the synergistic effect of three or more metal ions on bone regeneration has rarely been studied. In this study, vaterite-calcite CaCO3 particles were loaded with copper (Cu2+) and magnesium (Mg2+). The polyelectrolyte multilayer (PEM) was deposited on CaCuMg-CO3 particles via layer-by-layer technique to further improve the stability and biocompatibility of the particles and to enable controlled release of multiple metal ions. The PEM coated microcapsules were successfully combined with collagen at the outmost layer, providing a further stimulating microenvironment for bone regeneration. The in vitro release studies showed remarkably stable release of Cu2+ in 2 months without initial burst release. Mg2+ was released in relatively low concentration in the first 7 days. Cell culture studies showed that CaCuMg-PEM-Col microcapsules stimulated cell proliferation, extracellular maturation and mineralization more effectively than blank control and other microcapsules without collagen adsorption (Ca-PEM, CaCu-PEM, CaMg-PEM, CaCuMg-PEM). In addition, the CaCuMg-PEM-Col microcapsules showed positive effects on osteogenesis and angiogenesis in gene expression studies. The results indicate that such a functional and controllable delivery system of multiple bioactive ions might be a safer, simpler and more efficient alternative of protein growth factor-based therapeutics for bone regeneration. It also provides an effective method for functionalizing bone grafts for bone tissue engineering.
Context: Companies that operate in the software-intensive business are confronted with high market dynamics, rapidly evolving technologies as well as fast-changing customer behavior. Traditional product roadmapping practices, such as fixed-time-based charts including detailed planned features, products, or services typically fail in such environments. Until now, the underlying reasons for the failure of product roadmaps in a dynamic and uncertain market environment are not widely analyzed and understood.
Objective: This paper aims to identify current challenges and pitfalls practitioners face when developing and handling product roadmaps in a dynamic and uncertain market environment.
Method: To reach our objective we conducted a grey literature review (GLR).
Results: Overall, we identified 40 relevant papers, from which we could extract 11 challenges of the application of product roadmapping in a dynamic and uncertain market environment. The analysis of the articles showed that the major challenges for practitioners originate from overcoming a feature-driven mindset, not including a lot of details in the product roadmap, and ensuring that the content of the roadmap is not driven by management or expert opinion.
Providing a digital infrastructure, platform technologies foster interfirm collaboration between loosely coupled companies, enabling the formation of ecosystems and building the organizational structure for value co-creation. Despite the known potential, the development of platform ecosystems creates new sources of complexity and uncertainty due to the involvement of various independent actors. For a platform ecosystem to succeed, it is essential that the platform ecosystem participants are aligned, coordinated, and given a common direction. Traditionally, product roadmaps have served these purposes during product development. A systematic mapping study was conducted to better understand how product roadmapping could be used in the dynamic environment of platform ecosystems. One result of the study is that there are hardly any concrete approaches for product roadmapping in platform ecosystems so far. However, many challenges on the topic are described in the literature from different perspectives. Based on the results of the systematic mapping study, a research agenda for product roadmapping in platform ecosystems is derived and presented.
Cell migration plays an essential role in wound healing and inflammatory processes inside the human body. Peripheral blood neutrophils, a type of polymorphonuclear leukocyte (PMN), are the first cells to be activated during inflammation and subsequently migrate toward an injured tissue or infection site. This response is dependent on both biochemical signaling and the extracellular environment, one aspect of which includes increased temperature in the tissues surrounding the inflammation site. In our study, we analyzed temperature-dependent neutrophil migration using differentiated HL-60 cells. The migration speed of differentiated HL-60 cells was found to correlate positively with temperature from 30 to 42 °C, with higher temperatures inducing a concomitant increase in cell detachment. The migration persistence time of differentiated HL-60 cells was higher at lower temperatures (30–33 °C), while the migration persistence length stayed constant throughout the temperature range. Coupled with the increased speed observed at high temperatures, this suggests that neutrophils are primed to migrate more effectively at the elevated temperatures characteristic of inflammation. Temperature gradients exist on both cell and tissue scales. Taking this into consideration, we also investigated the ability of differentiated HL-60 cells to sense and react to the presence of temperature gradients, a process known as thermotaxis. Using a two-dimensional temperature gradient chamber with a range of 27–43 °C, we observed a migration bias parallel to the gradient, resulting in both positive and negative thermotaxis. To better mimic the extracellular matrix (ECM) environment in vivo, a three-dimensional collagen temperature gradient chamber was constructed, allowing observation of biased neutrophil-like differentiated HL-60 migration toward the heat source.