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Interdependency matrix to evaluate influence factors in circular value creation systems
Get PDFThe circular economy is one of the emerging trends in value creation systems, which emphasises the efficient use of resources, minimising waste and the loss of value added as well as replacing the concept of “end of life” for products and services in value creation systems. Since many of products and complementary services available on the market are not designed for circularity, it is challenging to convert the linear product life to a circular life cycle. However, conceptualise value creation systems that are suitable for a circular economy is a challenge, as a large number of influence factors are interrelated. Thereby not all influence factors reinforce each other, but can also have no or even a negative influence. Thus, in order to make the mutual influence transparent and create a holistic understanding of how the circularity can be implemented in value creation systems, this paper proposes an interdependency matrix which is enhancing the decision-making in the conception of circular value creation systems. The research design follows the mixed method approach. First, a literature review is carried out to review the state of the art. The research is extended by structuring relevant influence factors in the design of circular value creation. The findings from the literature research are supplemented by expert knowledge from industry and research. Finally, the findings are then incorporated into the development of the interdependency matrix. The assessment of the correlations between the individual influence factors is based on unique morphologies. A case study serves as a reference and framework for the application of the developed interdependency matrix. In order to validate and further develop the interdependency matrix, a verification process is carried out by creating application examples for the developed case study. The application examples serve as practical instances to test the applicability and resilience of the interdependency matrix. The proposed interdependency matrix shows which influence factors have correlations to each other – a distinction is made between whether the influence has a positive or negative effect. It also shows which influence factors are to be considered independently and for which influence factors a statement about their correlation is only possible depending on the specific situation in the value creation system. The core of this study is the determination and evaluation of the correlations, which the influence factors of a circular value creation system exhibit. The developed interdependency matrix aims to ensure that decision-makers in value creation systems are increasingly able to make decisions that promote a circular value creation in future. However, therefore it is insufficient to consider only individual influence factors or measures without their correlations. The approach serves to take a holistic view of a circular value creation system and is intended to help accelerate the transformation towards a circular economy
Governance of interconnected value creation systems: rules of engagement for effective cooperation among companies in a competitive context
Get PDFIn the context of the digital transformation of the 21st century, this paper explores the evolution from traditional, hierarchically organized value chains to dynamic, networked multi-actor constellations. The focus is on identifying and analyzing suitable governance mechanisms that are crucial for the success of both individual partners and the value creation system as a whole. Through a combination of comprehensive literature research and qualitative interviews with various leading experts from business, science, and politics the relationships between value creation, networking, and governance were analyzed as part of this study. This emphasizes the importance of transparency, motivation, and trust in these networked systems and discusses the use of technology to ensure effective governance. Technologies can be used to create data security through the traceability and immutability of data. The literature shows that there are no universal solutions, but that individual, situation-specific approaches are required that combine legal expertise and the use of technology. The results of the study indicate that customers are increasingly becoming integral parts of networked value creation systems, with data protection, laws and guidelines, exit plans, exploitation rights, and the challenges of international contract negotiations playing key roles. The work provides important insights into the need for continuous adaptability to constantly changing framework conditions
Deuterium isotope effect in single molecule photophysics and photochemistry of hypericin
Get PDFThe peripherical protons of the dye molecule hypericin can undergo structural interconversion (tautomerization) between different isomers separated by a low energy barrier with rates that depends sensitively on the interaction with local chemical environment defined by the nature of host material. We investigate the deuterium (D) isotope effect of hypericin tautomerism at the single‐molecule level to avoid ensemble averaging in different polymer matrices by a combined spectroscopic and computational approach. In the ′innocent′ PMMA matrix only intramolecular isotope effects on the internal conversion channel and tautomerization are observed; while PVA specifically interacts with the probe via H‐ and D‐bonding. This establishes a single molecular picture on intra‐ and intermolecular nano‐environment effects to control chromophore photophysics and ‐chemistry
Heart rate estimation based on in-bed accelerometer sensor measurement
No full textAccurate monitoring of a patient's heart rate is a key element in the medical observation and health monitoring. In particular, its importance extends to the identification of sleep-related disorders. Various methods have been established that involve sensor-based recording of physiological signals followed by automated examination and analysis. This study attempts to evaluate the efficacy of a non-invasive HR monitoring framework based on an accelerometer sensor specifically during sleep. To achieve this goal, the motion induced by thoracic movements during cardiac contractions is captured by a device installed under the mattress. Signal filtering techniques and heart rate estimation using the symlets6 wavelet are part of the implemented computational framework described in this article. Subsequent analysis indicates the potential applicability of this system in the prognostic domain, with an average error margin of approximately 3 beats per minute. The results obtained represent a promising advancement in non-invasive heart rate monitoring during sleep, with potential implications for improved diagnosis and management of cardiovascular and sleep-related disorders
Using solar PV and stationary storage to buffer the impact of electric minibus charging in grid-constrained sub-Saharan Africa
Get PDFDespite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa’s ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kWpk/taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country’s dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%
Oral health phenotype of postmenopausal women using AI
No full textMenopause is the permanent cessation of menstruation occurring naturally in women's aging. The most frequent symptoms associated with menopausal phases are mucosal dryness, increased weight and body fat, and changes in sleep patterns. Oral symptoms in menopause derived from saliva flow reduction can lead to dry mouth, ulcers, and alterations of taste and swallowing patterns. However, the oral health phenotype of postmenopausal women has not been characterized. The aim of the study was to determine postmenopausal women's oral phenotype, including medical history, lifestyle, and oral assessment through artificial intelligence algorithms. We enrolled 100 postmenopausal women attending the Dental School of the University of Seville were included in the study. We collected an extensive questionnaire, including lifestyle, medication, and medical history. We used an unsupervised k-means algorithm to cluster the data following standard features for data analysis. Our results showed the main oral symptoms in our postmenopausal cohort were reduced salivary flow and periodontal disease. Relying on the classical assessment of the collected data, we might have a biased evaluation of postmenopausal women. Then, we used artificial intelligence analysis to evaluate our data obtaining the main features and providing a reduced feature defining the oral health phenotype. We found 6 clusters with similar features, including medication affecting salivation or smoking as essential features to obtain different phenotypes. Thus, we could obtain main features considering differential oral health phenotypes of postmenopausal women with an integrative approach providing new tools to assess the women in the dental clinic
Automatic content creation system for augmented reality maintenance applications for legacy machines
Get PDFAugmented reality (AR) applications have great potential to assist maintenance workers in their operations. However, creating AR solutions is time-consuming and laborious, which limits its widespread adoption in the industry. It therefore often happens that even with the latest generation machines, instead of an AR solution, the user only receives an electronic manual for the equipment operation and maintenance. This is commonplace with legacy machines. For this reason, solutions are required that simplify the creation of such AR solutions. This paper presents an approach using an electronic manual as a basis to create fast and cost-effective AR solutions for maintenance. As part of the approach, an application was developed to automatically identify and subdivide the chapters of electronic manuals via the bookmarks in the table of contents. The contents are then automatically uploaded to a central server and indexed with a suitable marker to make the data retrievable. The prepared content can then be accessed for creating context-related AR instructions via the marker. The application is characterized by the fact that no developers or experts are required to prepare the information. In addition to complying with common design criteria, the clear presentation of the contents and the intuitive use of the system offer added value for the performance of maintenance tasks. Together, these two elements form a novel way to retrofit legacy machines with AR maintenance instructions. The practical validation of the system took place in a factory environment. For this purpose, the content was created for a filter change on a CNC milling machine. The results show that inexperienced users can extract appropriate content with the software application. Furthermore, it is shown that maintenance workers, can access the content with an AR application developed for the Microsoft HoloLens 2 and complete simple tasks provided in the manufacturer's electronic manual
XTSC-bench: quantitative benchmarking for explainers on time series classification
No full textDespite the growing body of work on explainable machine learning in time series classification (TSC), it remains unclear how to evaluate different explainability methods. Resorting to qualitative assessment and user studies to evaluate explainers for TSC is difficult since humans have difficulties understanding the underlying information contained in time series data. Therefore, a systematic review and quantitative comparison of explanation methods to confirm their correctness becomes crucial. While steps to standardized evaluations were taken for tabular, image, and textual data, benchmarking explainability methods on time series is challenging due to a) traditional metrics not being directly applicable, b) implementation and adaption of traditional metrics for time series in the literature vary, and c) varying baseline implementations. This paper proposes XTSC-Bench, a benchmarking tool providing standardized datasets, models, and metrics for evaluating explanation methods on TSC. We analyze 3 perturbation-, 6 gradient- and 2 example-based explanation methods to TSC showing that improvements in the explainers' robustness and reliability are necessary, especially for multivariate data
Experiment on extraversion distribution in groups through a group formation algorithm
Get PDFAdvances in technology have sparked a surge of interest in systematic group formation in educational contexts. The experimental study investigates group formation by extraversion distributions on group work outcomes, expected to influence group hierarchy. As an initial step in the experimental randomization process, an algorithmic group formation tool ensured an equal partitioning and aligned students into two experimental conditions with either consistent, homogeneous, or varied, heterogeneous, levels of extraversion. Over the course of one semester, a total of 114 students enrolled in several paralleled seminars, were surveyed on both subjective data (satisfaction with group work) and objective data (group performance) to evaluate the effect of the experimental intervention. The formation of extraversion at the group level contributed to the respective outcomes, emphasizing the value of collective social capital for both individuals and groups. Specifically, a homogeneous distribution of extraversion had a positive impact on group performance, as evident in improved grades on course group assignments and increased active participation in group meetings. Findings emphasize considering personality traits at group-level to enhance the success of groups
In-home, smart sleep monitoring system for cardiorespiratory estimation and sleep apnea detection: proof of concept
No full textApnea is a sleep disorder characterized by breathing interruptions during sleep, impacting cardiorespiratory function and overall health. Traditional diagnostic methods, like polysomnography (PSG), are unobtrusive, leading to noninvasive monitoring. This study aims to develop and validate a novel sleep monitoring system using noninvasive sensor technology to estimate cardiorespiratory parameters and detect sleep apnea. We designed a seamless monitoring system integrating noncontact force-sensitive resistor sensors to collect ballistocardiogram signals associated with cardiorespiratory activity. We enhanced the sensor’s sensitivity and reduced the noise by designing a new concept of edge-measuring sensor using a hemisphere dome and mechanical hanger to distribute the force and mechanically amplify the micromovement caused by cardiac and respiration activities. In total, we deployed three edgemeasuring sensors, two deployed under the thoracic and one under the abdominal regions. The system is supported with onboard signal preprocessing in multiple physical layers deployed under the mattress. We collected the data in four sleeping positions from 16 subjects and analyzed them using ensemble empirical mode decomposition (EMD) to avoid frequency mixing. We also developed an adaptive thresholding method to identify sleep apnea. The error was reduced to 3.98 and 1.43 beats/min (BPM) in heart rate (HR) and respiration estimation, respectively. The apnea was detected with an accuracy of 87%. We optimized the system such that only one edge-measuring sensor can measure the cardiorespiratory parameters. Such a reduction in the complexity and simplification of the instruction of use shows excellent potential for in-home and continuous monitoring