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Healthy and good sleep is a prerequisite for a rested mind and body. Both form the basis for physical and mental health. Healthy sleep is hindered by sleep disorders, the medically diagnosed frequency of which increases sharply from the age of 40. This chapter describes the formal specification of an on-course practical implementation for a non-invasive system based on biomedical signal processing to support the diagnosis and treatment of sleep-related diseases. The system aims to continuously monitor vital data during sleep in a patient’s home environment over long periods by using non-invasive technologies. At the center of the development is the MORPHEUS Box (MoBo), which consists of five main conceptualizations: the MoBo core, the MoBo-HW, the MoBo algorithm, the MoBo API, and the MoBo app. These synergistic elements aim to support the diagnosis and treatment of sleep-related diseases. Although there are related developments in individual aspects concerning the system, no comparative approach is known that gives a similar scope of functionality, deployment flexibility, extensibility, or the possibility to use multiple user groups. With the specification provided in this chapter, the MORPHEUS project sets a good platform, data model, and transmission strategies to bring an innovative proposal to measure sleep quality and detect sleep diseases from non-invasive sensors.
This paper introduces the third update/release of the Global Sanctions Data Base (GSDB-R3). The GSDB-R3 extends the period of coverage from 1950–2019 to 1950–2022, which includes two special periods—COVID-19 and the new sanctions against Russia. This update of the GSDB contains a total of 1325 cases. In response to multiple inquiries and requests, the GSDB-R3 has been amended with a new variable that distinguishes between unilateral and multilateral sanctions. As before, the GSDB comes in two versions, case-specific and dyadic, which are freely available upon request at GSDB@drexel.edu. To highlight one of the new features of the GSDB, we estimate the heterogeneous effects of unilateral and multilateral sanctions on trade. We also obtain estimates of the effects on trade of the 2014 sanctions on Russia.
We quantify the effects of GATT/WTO membership on trade and welfare. Using an extensive database covering manufacturing trade for 186 countries over the period 1980–2016, we find that the average partial equilibrium impact of GATT/WTO membership on trade among member countries is large, positive, and significant. We contribute to the literature by estimating country-specific estimates and find them to vary widely across the countries in our sample with poorer members benefitting more. Using these estimates, we simulate the general equilibrium effects of GATT/WTO on welfare, which are sizable and heterogeneous across members. We show that countries not experiencing positive trade effects from joining GATT/WTO can still gain in terms of welfare, due to lower import prices and higher export demand.
Apnea 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 edge-measuring 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.
Die energetische Sanierung von Gebäuden ist von großer Relevanz, um die gesetzlichen Klimaziele zu erreichen. Die Methode des seriellen Sanierens spielt hierbei eine wichtige Rolle. Sie gilt als ganzheitliche Maßnahme zur energetischen Aufwertung von Bestandsgebäuden, durch die nicht nur die Gebäudehülle und die Anlagentechnik, wie etwa das Heizungssystem, effektiv verbessert werden, sondern auch eine Integration von Anlagen zur Strom- und Warmwasseraufbereitung erfolgt. Bei der seriellen Sanierung wird, in Anlehnung an die Industrie und an die modulare Bauweise, eine Vorfertigung der Fassaden-
und Dachelemente durchgeführt. Im Nachgang werden die einzelnen Bauelemente und Anlagen montiert bzw. installiert. Durch die Auslagerung der Produktion und durch die Vorfertigung der Elemente besteht das Potenzial, die Montagezeit und die damit verbundenen Einschränkungen vor Ort für die Bewohner deutlich zu reduzieren.
Multi-faceted stresses of social, environmental, and economic nature are increasingly challenging the existence and sustainability of our societies. Cities in particular are disproportionately threatened by global issues such as climate change, urbanization, population growth, air pollution, etc. In addition, urban space is often too limited to effectively develop sustainable, nature-based solutions while accommodating growing populations. This research aims to provide new methodologies by proposing lightweight green bridges in inner-city areas as an effective land value capture mechanism. Geometry analysis was performed using geospatial and remote sensing data to provide geometrically feasible locations of green bridges. A multi-criteria decision analysis was applied to identify suitable locations for green bridges investigating Central European urban centers with a focus on German cities as representative examples. A cost-benefit analysis was performed to assess the economic feasibility using a case study. The results of the geometry analysis identified 3249 locations that were geometrically feasible to implement a green bridge in German cities. The sample locations from the geometry analysis were proved to be validated for their implementation potential. Multi-criteria decision analysis was used to select 287 sites that fall under the highest suitable class based on several criteria. The cost-benefit analysis of the case study showed that the market value of the property alone can easily outweigh the capital and maintenance costs of a green bridge, while the indirect (monetary) benefits of the green space continue to increase the overall value of the green bridge property including its neighborhood over time. Hence, we strongly recommend light green bridges as financially sustainable and nature-based solutions in cities worldwide.
Insecurity Refactoring is a change to the internal structure of software to inject a vulnerability without changing the observable behavior in a normal use case scenario. An implementation of Insecurity Refactoring is formally explained to inject vulnerabilities in source code projects by using static code analysis. It creates learning examples with source code patterns from known vulnerabilities.
Insecurity Refactoring is achieved by creating an Adversary Controlled Input Dataflow tree based on a Code Property Graph. The tree is used to find possible injection paths. Transformation of the possible injection paths allows to inject vulnerabilities. Insertion of data flow patterns introduces different code patterns from related Common Vulnerabilities and Exposures (CVE) reports. The approach is evaluated on 307 open source projects. Additionally, insecurity-refactored projects are deployed in virtual machines to be used as learning examples. Different static code analysis tools, dynamic tools and manual inspections are used with modified projects to confirm the presence of vulnerabilities.
The results show that in 8.1% of the open source projects it is possible to inject vulnerabilities. Different inspected code patterns from CVE reports can be inserted using corresponding data flow patterns. Furthermore the results reveal that the injected vulnerabilities are useful for a small sample size of attendees (n=16). Insecurity Refactoring is useful to automatically generate learning examples to improve software security training. It uses real projects as base whereas the injected vulnerabilities stem from real CVE reports. This makes the injected vulnerabilities unique and realistic.
Purpose
In order to combat climate change and safeguard a liveable future we need fundamental and rapid social change. Climate communication can play an important role to nurture the public engagement needed for this change, and higher education for sustainability can learn from climate communication.
Approach
The scientific evidence base on climate communication for effective public engagement is summarised into ten key principles, including ‘basing communication on people’s values’, ‘conscious use of framing’, and ‘turning concern into action’. Based on the author’s perspective and experience in the university context, implications are explored for sustainability in higher education.
Findings
The article provides suggestions for teaching (e.g. complement information with consistent behaviour by the lecturer, integrate local stories, and provide students with basic skills to communicate climate effectively), for research (e.g. make teaching for effective engagement the subject of applied research), for universities’ third mission to contribute to sustainable development
in the society (e.g. provide climate communication trainings to empower local stakeholders), andgreening the campus (develop a proper engagement infrastructure, e.g. by a university storytelling exchange on climate action).
Originality
The article provides an up-to-date overview of climate communication research, which is in itself original. This evidence base holds interesting learnings for institutions of higher education, and the link between climate communication and universities has so far not been explored comprehensively.
In this work, a storage study was conducted to find suitable packaging material for tomato powder storage. Experiments were laid out in a single factor completely randomized design (CRD) to study the effect of packaging materials on lycopene, vitamin C moisture content, and water activity of tomato powder; The factor (packaging materials) has three levels (low‐density polyethylene bag, polypropylene bottle, wrapped with aluminum foils, and packed in low‐density polyethylene bag) and is replicated three times. During the study, a twin layer solar tunnel dried tomato slices of var. Galilea was used. The dried tomato slices were then ground and packed (40 g each) in the packaging materials and stored at room temperature. Samples were drawn from the packages at 2‐month interval for quality analysis and SAS (version 9.2) software was used for statistical analysis. From the result, higher retention of lycopene (80.13%) and vitamin C (49.32%) and a nonsignificant increase in moisture content and water activity were observed for tomato powder packed in polypropylene bottles after 6 months of storage. For low‐density polyethylene packed samples and samples wrapped with aluminum foil and packed in a low‐density polyethylene bag, 57.06% and 60.45% lycopene retention and 42.9% and 49.23% Vitamin C retention were observed, respectively, after 6 months of storage. Considering the results found, it can be concluded that lycopene and vitamin C content of twin layer solar tunnel dried tomato powder can be preserved at ambient temperature storage by packing in a polypropylene bottle with a safe range of moisture content and water activity levels for 6 months.
This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical system (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration through error-correcting functions. The parameters of these error-correcting functions are determined during a calibration process. However, due to various sources of noise, these parameters cannot be determined with precision, making it desirable to incorporate uncertainty in the calibration models. Bayesian modeling offers a natural and complete way of reflecting uncertainty by treating the model parameters as variables rather than fixed values. In addition, Bayesian modeling enables the incorporation of prior knowledge, making it an ideal choice for calibration. Nevertheless, it is infrequently used in sensor calibration. This study introduces Bayesian methods for the calibration of MEMS accelerometer data in a straightforward manner using recent advances in probabilistic programming.
Large-scale quantum computers threaten the security of today's public-key cryptography. The McEliece cryptosystem is one of the most promising candidates for post-quantum cryptography. However, the McEliece system has the drawback of large key sizes for the public key. Similar to other public-key cryptosystems, the McEliece system has a comparably high computational complexity. Embedded devices often lack the required computational resources to compute those systems with sufficiently low latency. Hence, those systems require hardware acceleration. Lately, a generalized concatenated code construction was proposed together with a restrictive channel model, which allows for much smaller public keys for comparable security levels. In this work, we propose a hardware decoder suitable for a McEliece system based on these generalized concatenated codes. The results show that those systems are suitable for resource-constrained embedded devices.
This study aims to adapt CEFR in developing an integrative approach-based teaching material model for a pre-basic BISOL class. The method used in this research is the development research design by Borg and Gall. This study was development research. The stages are identification of the problem, formulation of a hypothetical draft model; feasibility testing by experts; product revision; and test product effectiveness. The data were collected through survey techniques, interviews, and documentation. The needs identification results revealed data encompassing 10 themes, 5 tasks per theme, and diverse evaluations comprising theory, in-class practice, and real-world field assignments, both on an individual and group basis. These identified needs require alignment with CEFR A1 for the development of BISOL learning. These findings were subsequently incorporated into the design of the teaching material model, and the results indicated that tailoring CEFR to BISOL as an integrative language teaching material model was feasible for application in the classroom, as assessed by experts. The implications suggest that integrating CEFR into BISOL is highly feasible for the development of teaching materials, and teachers can leverage this instructional model to enhance students' proficiency in the Indonesian language.
Infrastructure-making in interwar India was a dynamic, multilayered process involving roads and vehicles in urban and rural sites. One of their strongest playgrounds was Bombay Presidency and the Central Provinces in central and western India. Focusing on this region in the interwar period, this paper analyzes the varied relationship between peasant households and town-centred modernizing agents in the making of road transport infrastructures. The central argument of this paper is about the persistence of bullock carts over motor cars in the region. This persistence was grounded in the specific regional environment, the effects of the 1930s economic depression, and the priorities of social classes. Pinpointing these connections, the paper highlights that “modernization” of infrastructure was not a simple, linear process of progressivist change, nor did it mean the survival of apparently “old” technologies in the modern era. Instead, the paper pays attention to conflicting social complexities, implications, and meanings of the connection between infrastructure and modernity that modernization assumptions often overlook. Here, the paper shows how technological change occurred as a result of real, material class interests pulling infrastructural technology in different directions. This was where and why arguments of road-motor lobbyists and cart advocates eventually clashed, and Gandhian social workers resisted motor transport in defense of peasant interests.
This paper applies the concept of Soja’s Thirdspace to the phenomenon of Lazgi dance and tourism in Uzbekistan. In doing so it analyses the different levels of perception (including Firstspace and Secondspace) of Lazgi and tourism via an autoethnographic lens. Complemented by expert interviews, the interaction of Lazgi and tourism is examined and characteristics of the Lazgisphere (world of Lazgi) in Uzbekistan are distilled. The results show that Lazgi is often directly or indirectly connected with tourism in Uzbekistan, but even more so serves to reaffirm national identity.
"KI first" braucht Verlierer
(2023)
Aktuell vergeht kaum eine Woche, in der nicht ein Unternehmen den Kampf um die Vorherrschaft im Bereich der Künstlichen Intelligenz (KI) aufnimmt. Tech-Konzerne versprechen sich auch von KI-gesteuerten Bildgeneratoren satte Gewinne. Diese ahmen mit synthetischen Mischbildern stilprägende Künstler/innen nach. Dabei wird auf die Rechtslage verwiesen, die eine zustimmungs- und vergütungsfreie Vervielfältigung ihrer Kunstwerke für Trainingszwecke angeblich zulässt. Doch Widerstand von Künstlern/innen hiergegen ist gesellschaftlich dringend geboten und wäre im Übrigen auch rechtlich gedeckt.
Die Relevanzanalyse
(2023)
Ordnungsgemäße Unternehmensführung ohne adäquates Risiko- und Compliance-Management ist kaum noch vor- und darstellbar. Rechtsprechung, Literatur, Politik und Gesellschaft stellen (mehr oder weniger) klare Anforderungen an ordnungsgemäßes unternehmerisches Verhalten und sanktionieren tatsächliche (und vermeintliche) Regelverstöße. Um die unternehmensspezifischen Risiken zu erfassen ist die Durchführung einer Risikoanalyse (Compliance Risk Assessment – CRA) unumgänglich1. Der eigentlichen Risikoanalyse ist eine Relevanzanalyse voranzustellen, um sich der bei unternehmerischen Aktivitäten naturgemäß nahezu unüberschaubaren potenziellen Risikomenge anzunähern und diese „abarbeitbar“ zu erfassen. Wird diese Relevanzanalyse professionell und strukturiert durchgeführt und dokumentiert, so kann sie einen wertvollen Beitrag zum Schutz und zur Hilfe gegen Compliance-Verstöße und deren Sanktionierung leisten. Der nachfolgende Beitrag stellt die Grundlagen, Ziele, Anforderungen und Ansätze der Relevanzanalyse dar. In einem weiteren Beitrag (erscheint in CB 11/2023) werden sich die Autoren der Durchführung der Relevanzanalyse widmen und Hinweise zu deren Ablauf und Inhalt geben.
Sanktionen gegen Russland
(2023)
Die EU hat aufgrund des völkerrechtswidrigen Angriffskrieges auf die Ukraine umfangreiche Sanktionen gegen Russland erlassen. Die Sanktionspakete umfassen insbesondere Wirtschaftssanktionen in Form von Einfuhr- und Ausfuhrbeschränkungen, die für deutsche Unternehmen mit unmittelbaren oder mittelbaren Geschäftsbeziehungen nach Russland von Bedeutung sind. Im Vordergrund der rechtlichen Thematik steht die Frage, ob und wann deutsche Unternehmen gegen EU-Sanktionen verstoßen. Aber auch deutsche Unternehmen mit Tochtergesellschaften in Drittstaaten stehen vor der großen Herausforderung, den Regelmechanismus der diversen Sanktionspakete zu durchleuchten, um sich nicht der Gefahr des Vorwurfs einer Umgehung der Sanktionen auszusetzen.
Die Relevanzanalyse
(2023)
Um unternehmensspezifische Risiken zu erfassen ist die Risikoanalyse unumgänglich. Ihr ist wiederum eine Relevanzanalyse voranzustellen. Nachdem im Heft 10 des Compliance Berater 2023, S. 400-404 die Grundlagen, Ziele, Anforderungen und Ansätze der Relevanzanalyse dargestellt wurden, widmet sich der nachfolgende Beitrag der Durchführung der Relevanzanalyse und gibt Hinweise zu deren Ablauf und Inhalt.
Compliance meets CSR
(2023)
Was früher Gegenstand freiwilliger Selbstverpflichtung war, wird seit einiger Zeit zunehmend reguliert: die Wahrnehmung der unternehmensspezifischen Verantwortung gegenüber Umwelt und Gesellschaft, neudeutsch Corporate Social Responsibility (CSR). CSR und Compliance rücken damit näher zusammen. Vieles, was früher durch CSR-Abteilungen im besten Fall systematisch gemanagt wurde, ist nun gesetzlich vorgeschrieben und fällt damit in den Aufgabenbereich von Compliance. Liegt es da nicht nahe, die beiden Bereiche miteinander zu verschmelzen respektive CSR dem Bereich unterzuordnen, der seit den spektakulären Korruptions- und Bilanzfälschungsskandalen zu Beginn dieses Jahrtausends über die größere Management-Awareness verfügt?
Der vorliegende Beitrag versucht deutlich zu machen, wie das Verhältnis sachlich-fachlich einzuordnen ist und welche Schlussfolgerungen in der Praxis daraus gezogen werden könnten.
“Crowd contamination”?
(2023)
Misconduct allegations have been found to not only affect the alleged firm but also other, unalleged firms in form of reputational and financial spillover effects. It has remained unexplored, however, how the number of prior allegations against other firms matters for an individual firm currently facing an allegation. Building on behavioral decision theory, we argue that the relationship between allegation prevalence among other firms and investor reaction to a focal allegation is inverted U-shaped. The inverted U-shaped effect is theorized to emerge from the combination of two effects: In the absence of prior allegations against other firms, investors fail to anticipate the focal allegation, and hence react particularly negatively (“anticipation effect”). In the case of many prior allegations against other firms, investors also react particularly negatively because investors perceive the focal allegation as more warranted (“evaluation effect”). The multi-industry, empirical analysis of 8,802 misconduct allegations against US firms between 2007 and 2017 provides support for our predicted, inverted U-shaped effect. Our study complements recent misconduct research on spillover effects by highlighting that not only a current allegation against an individual firm can “contaminate” other, unalleged firms but that also prior allegations against other firms can “contaminate” investor reaction to a focal allegation against an individual firm.
Study design:
Retrospective, mono-centric cohort research study.
Objectives:
The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS).
Methods:
An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; .75 rated as excellent), mean error and additional statistical metrics.
Results:
The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°).
Conclusions:
The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.
A post-growth economy is a comparatively new paradigm in the tourism discourse. The aim of this article is to find out the commonalities between this concept and Māori tourism and in which way the latter can contribute to a post-growth economy. A qualitative mixed method approach, including in-depth-interviews, participant observation, and secondary analysis is applied. The results show that there is a lot of overlap between Māori tourism and a post-growth economy. Differences are visible, as well, regarding the value approach of Māori tourism and the indicator approach of a post-growth economy. Especially the social innovation created in Aotearoa New Zealand at the instigation of Māori groups of granting legal personhood to parts of nature may serve as a driver for a form of tourism that is in line with the idea of a post-growth economy.
Der Wandel des Einzelhandels
(2023)
Die Ursachen der existentiellen Bedrohung vieler Einzelhandelsunternehmen sind nicht nur auf die Nachwirkungen der Coronapandemie und den Ukraine-Krieg mit der daraus resultierenden Inflation und Kaufzurückhaltung zurückzuführen. Auch die Digitalisierung und die wachsende Onlinekonkurrenz sowie ein verändertes Einkaufs- und Konsumverhalten der Kund:innen setzt den Einzelhandel unter Druck. Dabei scheint besonders die junge Generation Z, die mit dem Internet, sozialen Medien und digitalen Anwendungen aufgewachsen ist, nicht mehr den traditionellen Konsummustern zu entsprechen, und erwartet eine Ausrichtung des Einzelhandels an ihre Bedürfnisse. Doch wie ticken junge Konsument:innen und wie unterscheiden sich ihre Erwartungen an den Handel von älteren Generationen? Im Beitrag werden Antworten auf diese Fragen gegeben.
The aim of this paper is to find out in how accommodation providers in the Seychelles perceive climate change and what mitigation and adaptation measures they can provide. In order to answer these questions, a qualitative mixed-method-approach, comprised of twenty semi-structured interviews, an online-survey and participant observation was used. Results show that accommodation providers especially perceive the effects of climate change that directly affect their business and that they have already partly implemented some mitigation and adaptation measures. However, strategies and regulations are needed at the Seychelles’ government level and on a global level to actually achieve CO2 neutral travel.
Nach heutigem Stand der Technik kommen für die Dekontamination von Störstellen wie z.B. Ecken und Innenkanten, weitestgehend Technik aus dem konventionellen Sanierungsbereich zum Einsatz. Maschinen wie Nadelpistolen und Stockgeräte belasten das Arbeitspersonal mit starken Vibrationen und hohen Rückstellkräften. Daher sind entsprechend lange Pausenzeiten erforderlich, wodurch die ohnehin schon geringe Abtragleistung weiter gesenkt wird. Neben dem zusätzlichen Mehraufwand kann die Technik, aufgrund fehlender Absaugungseinrichtungen, unter Umständen zu einer Kontaminationsverschleppung führen. Hierbei werden in bereits dekontaminierten Bereichen kontaminierte Partikel verteilt, wodurch die erzielten Bearbeitungsfortschritte teilweise rückgängig gemacht werden.
Aufgrund der Vielzahl von Nachteilen, die bei den bisher eingesetzten Geräten auftreten, wurde das Forschungsprojekt EKONT-1 zur „Entwicklung eines innovativen, teilautomatisierten Gerätes für eine trocken-mechanische Ecken-, Kanten- und Störstellendekontamination in kerntechnischen Anlagen“ angestoßen und durchgeführt. Im Rahmen dieses Projektes konnten viele neue Erkenntnisse gewonnen und mehrere funktionsfähige Prototypen entwickelt, gebaut und sowohl im Labor als auch im praktischen Einsatz getestet werden. Da im Laufe der Versuche noch einige Verbesserungspotenziale aufgetreten sind, wurde zum 01.07.23 das Folge Projekt EKONT-2 gestartet, was sich mit der Weiterentwicklung der existierenden Prototypen beschäftigt.
Dieser Beitrag untersucht, ob externe Interventionen, in Form von Forschung und/oder Wissenschaftskommunikation, als Mediator für Innovationen in Krisenzeiten in der Tourismusbranche fungieren können. Dabei wird anhand dreier Case Studies diskutiert, inwiefern die Corona-Krise ein Window-
of-opportunity für innovative Geschäftsmodelle im Tourismus darstellen konnte. Die Projektergebnisse geben Hinweise darauf, dass Krisen im Allgemeinen und Wissenschaftskommunikation im Speziellen als Push-Faktoren Innovationen befördern können. Zwar kam es bei den Projektpartnern zu einer Entwicklung von Innovationen im Projektzeitraum, jedoch wurde die Implementierung vermehrt in eine unbestimmte Zukunft verschoben. Durch die damit verbundene Rückkehr zum Status-Quo blieben die angestoßenen Innovationen zu einem Großteil auf einer konzeptionellen Ebene. Dies deutet auf eine Attitude-behavior-gap in Bezug auf die Schaffung und Umsetzung von Innovationen in Krisenzeiten.
Mit Eis erneuerbar Heizen
(2023)
In the last decade, both sustainability and business models for sustainability have increased in importance. Sustainability issues have become the focus of discussion. These issues are interlinked and often negatively impact each other. They are complex and include socio-ecological dilemmas, exist in almost every aspect of our society (economic, environmental, social), and are hard to formulate. They may have multiple, incompatible solutions, competing objectives, and open timeframes. Previous research has not developed satisfactory ways to comprehend and solve problems of this nature. Life Cycle Assessment (LCA) the widely used method to assess sustainable development has reached its limitation to achieve sustainable social goals. System Dynamics (SD) is a valuable methodology that enhances understanding of the structure and internal dynamic behaviours of large, complex, and dynamic systems, leading to improved decision-making. It offers a philosophy and set of tools for modelling, analysing, and simulating dynamic systems. This research applied system dynamics methods in conjunction with simulation software to assess the potential impact of a solution on environmental, social, and economic aspects of a complex system, aims to gain insights into the system's behaviour and identify the potential consequences of interventions or policy changes across multiple dimensions. This paper responds to the urgent need for a new business model by presenting a concept for an adapted dynamic business modelling for sustainability (aDBMfS) using system dynamics. Case studies in the smartphone industry are applied.
Accurate 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.
This study investigates the application of Force Sensing Resistor (FSR) sensors and machine learning algorithms for non-invasive body position monitoring during sleep. Although reliable, traditional methods like Polysomnography (PSG) are invasive and unsuited for extended home-based monitoring. Our approach utilizes FSR sensors placed beneath the mattress to detect body positions effectively. We employed machine learning techniques, specifically Random Forest (RF), K-Nearest Neighbors (KNN), and XGBoost algorithms, to analyze the sensor data. The models were trained and tested using data from a controlled study with 15 subjects assuming various sleep positions. The performance of these models was evaluated based on accuracy and confusion matrices. The results indicate XGBoost as the most effective model for this application, followed by RF and KNN, offering promising avenues for home-based sleep monitoring systems.
This paper compares two popular scripting implementations for hardware prototyping: Python scripts exe- cut from User-Space and C-based Linux-Driver processes executed from Kernel-Space, which can provide information to researchers when considering one or another in their implementations. Conclusions exhibit that deploying software scripts in the kernel space makes it possible to grant a certain quality of sensor information using a Raspberry Pi without the need for advanced real-time operational systems.
The massive use of patient data for the training of artificial intelligence algorithms is common nowadays in medicine. In this scientific work, a statistical analysis of one of the most used datasets for the training of artificial intelligence models for the detection of sleep disorders is performed: sleep health heart study 2. This study focuses on determining whether the gender and age of the patients have a relevant influence to consider working with differentiated datasets based on these variables for the training of artificial intelligence models.
Unintrusive health monitoring systems is important when continuous monitoring of the patient vital signals is required. In this paper, signals obtained from accelerometers placed under a bed are processed with ballistocardiography algorithms and compared with synchronized electrocardiographic signals.
Im Investitionsgüterservice ist Wissen längst zu einem zentralen Erfolgshebel geworden, sowohl zur Steigerung der Prozesseffektivität und -effizienz als auch als Fundament für werthaltige Geschäftsmodelle. Das Management Service-relevanten Wissens ist für kleine und mittelständische Unternehmen der Investitionsgüterindustrie jedoch oftmals eine nicht zu unterschätzende Herausforderung, welche weit über IT-technische Aspekte hinausreicht. In dem vom BMBF sowie vom ESF (ko)finanzierten Projekt „SerWiss“ wurde vor diesem Hintergrund ein umfassender Lösungsansatz entwickelt und bei zwei Projektpartnern aus der Investitionsgüterindustrie prototypisch umgesetzt.
Der Kundenservice von morgen
(2023)
Die digitale Selbstbedienung im Einzelhandel und anderen Dienstleistungsbereichen verändert die Konsumwelt. Self-Services werden zunehmend von Konsumenten aller Altersklassen genutzt. Der Handel muss seine Servicekanäle hinterfragen und vermehrt auf Self-Service als Kundenkontaktpunkt setzen. Andere Branchen haben diesbezüglich bereits Lösungen umgesetzt. Vor diesem Hintergrund analysiert der Beitrag die Nutzung von Self-Service-Lösungen in Abhängigkeit von der Generationen-Zugehörigkeit und gibt Handlungsempfehlungen für KMU aus dem Einzelhandel.
Die durch KMU geprägte Investitionsgüterindustrie steht aufgrund der zunehmenden Internationalisierung im Servicegeschäft, Mitarbeiterengpässen, hohen Prozesskosten sowie fehlendem Wissensmanagment vor großen Herausforderungen. Durch die Digitalisierung entstehen große Nutzenpotenziale im Servicegeschäft. Vor diesem Hintergrund wurde ein auf den Methoden Intelligent Swarming und Knowledge Centered Service basierender, integrierter Ansatz entwickelt, der KMU aus der Investitionsgüterindustrie befähigt, Servicewissen effizient zu generieren, zu strukturieren und international zu vermarkten.
Service in der Investitionsgüterindustrie wird heutzutage in der Regel immer noch manuell und vor Ort beim Kunden ausgeführt. Dazu braucht es qualifizierte Service-Techniker:innen, die über das nötige Produkt- Prozesswissen verfügen. Für kleine und mittelständische Unternehmen (KMU) der Investitionsgüterindustrie stellt insbesondere die Internationalisierung eine Herausforderung dar, da qualifizierte Service-Techniker:innen eine rare Ressource sind. Es gilt sie möglichst effektiv und effizient einzusetzen. Zu diesem Zweck wurde im Rahmen des SerWiss-Projektes eine Lösung entwickelt, die es KMU ermöglicht, service-rele-
vantes Wissen effizient zu generieren, zu strukturieren und am Point-of-Service bereitzustellen sowie im Rahmen geeigneter Geschäftsmodelle zu vermarkten. Im Beitrawird erläutert, wie sich dieses erfasste Wissen als kundenorientiertes Wertangebot einsetzen und erlöswirksam in entsprechenden Geschäftsmodellen umsetzen lässt.
Background
This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).
Methodology
This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers.
Discussion
This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences.
Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking
(2023)
The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer’s, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.
Cardiovascular diseases (CVD) are leading contributors to global mortality, necessitating advanced methods for vital sign monitoring. Heart Rate Variability (HRV) and Respiratory Rate, key indicators of cardiovascular health, are traditionally monitored via Electrocardiogram (ECG). However, ECG's obtrusiveness limits its practicality, prompting the exploration of Ballistocardiography (BCG) as a non-invasive alternative. BCG records the mechanical activity of the body with each heartbeat, offering a contactless method for HRV monitoring. Despite its benefits, BCG signals are susceptible to external interference and present a challenge in accurately detecting J-Peaks. This research uses advanced signal processing and deep learning techniques to overcome these limitations. Our approach integrates accelerometers for long-term BCG data collection during sleep, applying Discrete Wavelet Transforms (DWT) and Ensemble Empirical Mode Decomposition (EEMD) for feature extraction. The Bi-LSTM model, leveraging these features, enhances heartbeat detection, offering improved reliability over traditional methods. The study's findings indicate that the combined use of DWT, EEMD, and Bi-LSTM for J-Peak detection in BCG signals is effective, with potential applications in unobtrusive long-term cardiovascular monitoring. Our results suggest that this methodology could contribute to HRV monitoring, particularly in home settings, enhancing patient comfort and compliance.
Die Kleinwasserkraft stand zuletzt zunehmend in der öffentlichen Kritik wegen des ökologischen Einflusses und der verhältnismäßigen geringen Stromerzeugung. Der vorliegende Beitrag beschreibt die Einschätzung von KWK-Betreibern zum Potenzial einer Effizienzsteigerung ihrer bestehenden Anlagen durch eine intelligente Informationsvernetzung innerhalb des Flusslaufes der Radolfzeller Aach im Süden Baden-Württembergs, um somit die Stromerzeugung der einzelnen Anlagen zu erhöhen.
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
(2023)
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow. In an empirical study with 3639 smart meter customers, our density predictions for 24h-ahead load forecasting compare favorably against Gaussian and Gaussian mixture densities. Furthermore, they outperform a non-parametric approach based on the pinball loss, especially in low-data scenarios.
Das erfolgreiche Gestalten von Organisationen setzt die systematische Analyse ihrer Prozesse voraus. Das gilt auch und insbesondere für kleine und mittelgroße Unternehmen (KMU). Die praktische Durchführung solcher in KMU ist jedoch mit besonderen Herausforderungen verbunden, die in der vorhandenen Literatur bislang kaum reflektiert werden. In diesem Beitrag werden Erfahrungen aus 20 in KMU durchgeführten Prozessanalysen geteilt. Entlang der Prozessphasen werden unterschiedliche Gestaltungsmöglichkeiten vorgestellt und ihre spezifischen Vor- und Nachteile bei der praktischen Anwendung in KMU identifiziert. Der Beitrag unterstreicht die Relevanz von Prozessanalysen in KMU und befähigt zugleich zu ihrer Durchführung.
While managerial mobility is ubiquitously seen as an integral part of the success in firms’ internationalization, discerning its empirical merits has been impaired by the paucity of quasi-experimental evidence, or adequate instrumental variables. To overcome these objective limitations, this paper proposes a novel identification strategy, which uses a control function based on on-the-job search theory to correct estimates for the presence of self-selected mobility flows. Our analysis confirms the finding that managers’ specific market experience matters for firms’ internationalization, especially when it derives from longer tenures at the former jobs.
Regarding the attributes of managerial knowledge, our results reveal that on-the-job earned experience is at least as effective for firms’ internationalization as in born knowledge (i.e. origins) and that managers’ personal network of customers is an important asset in managers’ fund of expertise for the expansion into new markets.
As organizations struggle to cope with digital transformation in
an innovation environment, partnerships between startups and established
companies have become increasingly important. Building upon years of
practical experience and empirical research, we present advantages,
obstacles, and the keys to successful corporate-startup collaboration.
Ignorantia doctorum
(2022)
Since the turn of the millennium, many writing centers have been established at universities in the German-speaking world, in order to support students in academic writing. This essay argues for offering subject-anchored directive guidance and using scientific texts as a basis for model learning. It states that the rhetorical tradition is hardly taken into account in the writing centers. Five arguments for this ignorance are discussed and, if possible, dispelled: the antiquity argument, which considers rhetoric outdated; the orality argument, which understands rhetoric as irrelevant to writing; the moral argument, which condemns rhetoric as a tool for demagogues; the positivist argument, which criticizes rhetoric as unempirical; and the didactic argument, which rejects rhetoric as a rigid doctrine. The discussion shows, however, that the rhetorical tradition, with its normative power and centuries of teaching practice, is a treasure trove of writing didactics that holds many resources, such as well-founded assessment criteria for the quality, appropriateness, and usefulness of texts.
The Black Forest offers renewable energy as a specific tourist destination in the form of bioenergy villages (BEV). Particularly expert tourists tend to visit them. The results of two quantitative surveys on the supply and demand side show that there is, up to now, an untapped potential among experienceoriented
tourists for this type of niche tourism.
Die vorliegende Studie analysiert die Barrierefreiheit der
Stadt Konstanz im Hinblick auf Angebote für und Nachfrage von Touristinnen und Touristen. Die Datenerhebung basierte auf einem Methodenmix aus Interviews und Umfragen von Probanden und Probandinnen mit Behinderungen und zuständigen Akteurinnen und Akteuren in der Stadtplanung sowie Begehungen vor Ort. Als theoretische Grundlage wird das Modell der Unabhängigkeit nach
Nosek and Fuhrer (1992) verwendet. Die Untersuchung zeigt, dass der Bedarf an barrierefreien Angeboten sehr divers ist und die Umsetzung im Sinne eines Universal Design durch die zunehmende Nachfrage zentral. Die Analyse des Tourismusraum Konstanz zeigt Schwachpunkte und Stärken, mit denen sich Implikationen für andere Tourismusregionen ableiten lassen.
In diesem Beitrag wird der finnische Tangotanztourismus
unter Berücksichtigung des Konzeptes des verkörperten Raumes (Low 2003) und des Raumverständnisses von Lefebvre (1991) auf den vielschichtig miteinander verbundenen Ebenen von Körper, Kultur und Raum analysiert. Die finnische „Kultur der Schweigsamkeit“ wird in diesem Zusammenhang im Besonderen
betrachtet. Methodisch werden hierbei sowohl Interviews mit Expertinnen und Experten, teilnehmende Beobachtung als auch die Auswertung von Filmmaterial herangezogen. Im Ergebnis zeigen sich vielfältige Wechselwirkungen von Körper, Kultur und Raum, die zusätzlich Potenziale für den finnischen Tangotanztourismus
aufzeigen.
Welche Kompetenzen brauchen Führungskräfte, damit der Ansatz Compliance und Integrity als Führungsaufgabe in Organisationen verfängt? Und wie lassen sich diese systematisch nutzen und trainieren? Der Beitrag stellt den ersten Baustein eines am Konstanz Institut für Corporate Governance angesiedelten Forschungsprojekts vor, das darauf abzielt, bestehende Compliance-Systeme in Unternehmen praxistauglicher zu machen und die Wirksamkeit der Maßnahmen eines Compliance-Management-Systems (CMS) zu steigern.
Reed-Muller (RM) codes have recently regained some interest in the context of low latency communications and due to their relation to polar codes. RM codes can be constructed based on the Plotkin construction. In this work, we consider concatenated codes based on the Plotkin construction, where extended Bose-Chaudhuri-Hocquenghem (BCH) codes are used as component codes. This leads to improved code parameters compared to RM codes. Moreover, this construction is more flexible concerning the attainable code rates. Additionally, new soft-input decoding algorithms are proposed that exploit the recursive structure of the concatenation and the cyclic structure of the component codes. First, we consider the decoding of the cyclic component codes and propose a low complexity hybrid ordered statistics decoding algorithm. Next, this algorithm is applied to list decoding of the Plotkin construction. The proposed list decoding approach achieves near-maximum-likelihood performance for codes with medium lengths. The performance is comparable to state-of-the-art decoders, whereas the complexity is reduced.
In this letter, we present an approach to building a new generalized multistream spatial modulation system (GMSM), where the information is conveyed by the two active antennas with signal indices and using all possible active antenna combinations. The signal constellations associated with these antennas may have different sizes. In addition, four-dimensional hybrid frequency-phase modulated signals are utilized in GMSM. Examples of GMSM systems are given and computer simulation results are presented for transmission over Rayleigh and deep Nakagami- m flat-fading channels when maximum-likelihood detection is used. The presented results indicate a significant improvement of characteristics compared to the best-known similar systems.
Botenstoffe für Innovationen
(2022)
Uzbekistan is an emerging tourism destination that has experienced a strong increase in tourists since 2017. However, little research on tourism development in Uzbekistan exists to date. This study therefore analyzes possible research topics and proposes a tourism research agenda for Uzbekistan. A mix of methods was used consisting of participant observation, semi-structured qualitative expert interviews and qualitative content anal- ysis. The results revealed a variety of research deficits in different areas, which could be synthesized into a total of ten research fields, which were clustered into three overarching areas, namely market research, management, and culture & environment. The subordi- nate research fields identified are Demand, Statistics, Potentials, Governance, Products, Infrastructure & Development, Marketing, Heritage & Nation-building, Sustainability as well as Peace & Conflict Prevention. A strategic research plan based on this tourism research agenda could help to foster a purposeful scientific debate. Tourism research in these fields has both the potential to investigate and compare theoretical issues in an unique context and to produce applied research results that can make a relevant contri- bution to tourism development in Uzbekistan.
Wie gehen mittelständische Unternehmen mit internationaler Geschäftstätigkeit mit Compliance-Risiken um? Wie gelingt das Risikomanagement spezifischer Herausforderungen der Regelkonformität in Wachstumsländern, die aus Compliance-Gesichtspunkten als Hochrisikoländer eingestuft werden? Und was beschäftigt dabei Compliance-Officer im Mittelstand? Diesen Fragen widmete sich ein anwendungsorientiertes Forschungsprojekt am Konstanz Institut für Corporate Governance.
Die Beständigkeit von hochlegierten korrosions- und säurebeständigen Stählen wird primär durch den Chromgehalt bestimmt. Allerdings gibt es entlang der Wertschöpfungskette von der Stahlerschmelzung bis zum fertigen Produkt eine Vielzahl weiterer Einflussfaktoren. Dem Schleifen kommt hier eine besondere Bedeutung zu, da es je nach Wahl der Prozessparameter sowohl zu einer signifikanten Verschlechterung als auch zu einer Verbesserung der Korrosionsbeständigkeit führen kann. Im vorliegenden Beitrag wird aufgezeigt, dass die erzeugte Rauheit nur eine begrenzte Aussagekraft bietet. Vielmehr erhöhen lokale Mikrodefekte die Anfälligkeit gegen Lochfraß – je nach Ausprägung und Anzahl. Die Automatisierung für die Innenbearbeitung von Behältern im pharmazeutischen Apparatebau kann dabei zu einer signifikanten Verbesserung der Oberfläche und einem homogeneren Erscheinungsbild führen.
Durch eine Aufweitung des Kristallgitters mittels Niedertemperatur-Eindiffusion von Kohlenstoff und/oder Stickstoffatomen können in der Randzone von nichtrostenden Stählen eine hohe Härte und eine hohe Verschleißbeständigkeit erzeugt werden, ohne dass zusätzliche Legierungselemente verwendet werden müssen. Die metallkundlichen Hintergründe für die Härtung, die Wirkung auf Verschleißvorgänge und mögliche Anwendungsbereiche werden geschildert. Anhand von Reibwerten wird gezeigt, in welcher Weise das Reibungsverhalten bei Schraubverbindungen durch die Behandlung verändert wird. Über Migrationsversuche wird nachgewiesen, dass die Ionenabgabe durch die Oberflächenhärtung nicht erhöht, sondern sogar abgesenkt wird. Neben dem besseren Verschleißschutz und einer höheren Dauerfestigkeit sichert diese Oberflächenbehandlung am nichtrostenden Stahl den Schutz gegen die Kontamination von Pharmaprodukten durch Metallabrieb/-ionen. Tests an oberflächengehärteten Edelstahlproben ergaben weiterhin, dass durch die Oberflächenhärtung die Biokompatibilität des nichtrostenden Edelstahls nicht beeinträchtigt wird.
Lignin is a potentially high natural source of biological aromatic substances. However, decomposition of the polymer has proven to be quite challenging, as the complex bonds are fairly difficult to break down chemically. This article is intended to provide an overview of various recent methods for the catalytic chemical depolymerization of the biopolymer lignin into chemical products. For this purpose, nickel-, zeolite- and palladium-supported catalysts were examined in detail. In order to achieve this, various experiments of the last years were collected, and the efficiency of the individual catalysts was examined. This included evaluating the reaction conditions under which the catalysts work most efficiently. The influence of co-catalysts and Lewis acidity was also investigated. The results show that it is possible to control the obtained product selectivity very well by the choice of the respective catalysts combined with the proper reaction conditions.
Reliability Assessment of an Unscented Kalman Filter by Using Ellipsoidal Enclosure Techniques
(2022)
The Unscented Kalman Filter (UKF) is widely used for the state, disturbance, and parameter estimation of nonlinear dynamic systems, for which both process and measurement uncertainties are represented in a probabilistic form. Although the UKF can often be shown to be more reliable for nonlinear processes than the linearization-based Extended Kalman Filter (EKF) due to the enhanced approximation capabilities of its underlying probability distribution, it is not a priori obvious whether its strategy for selecting sigma points is sufficiently accurate to handle nonlinearities in the system dynamics and output equations. Such inaccuracies may arise for sufficiently strong nonlinearities in combination with large state, disturbance, and parameter covariances. Then, computationally more demanding approaches such as particle filters or the representation of (multi-modal) probability densities with the help of (Gaussian) mixture representations are possible ways to resolve this issue. To detect cases in a systematic manner that are not reliably handled by a standard EKF or UKF, this paper proposes the computation of outer bounds for state domains that are compatible with a certain percentage of confidence under the assumption of normally distributed states with the help of a set-based ellipsoidal calculus. The practical applicability of this approach is demonstrated for the estimation of state variables and parameters for the nonlinear dynamics of an unmanned surface vessel (USV).
Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
(2022)
A reliable quantification of the worst-case influence of model uncertainty and external disturbances is crucial for the localization of vessels in marine applications. This is especially true if uncertain GPS-based position measurements are used to update predicted vessel locations that are obtained from the evaluation of a ship’s state equation. To reflect real-life working conditions, these state equations need to account for uncertainty in the system model, such as imperfect actuation and external disturbances due to effects such as wind and currents. As an application scenario, the GPS-based localization of autonomous DDboat robots is considered in this paper. Using experimental data, the efficiency of an ellipsoidal approach, which exploits a bounded-error representation of disturbances and uncertainties, is demonstrated.
Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Virtual Measurement Model
(2022)
Random matrices are widely used to estimate the extent of an elliptically contoured object. Usually, it is assumed that the measurements follow a normal distribution, with its standard deviation being proportional to the object’s extent. However, the random matrix approach can filter the center of gravity and the covariance matrix of measurements independently of the measurement model. This work considers the whole chain from data acquisition to the linear Kalman Filter with extension estimation as a reference plant. The input is the (unknown) ground truth (position and extent). The output is the filtered center of gravity and the filtered covariance matrix of the measurement distribution. A virtual measurement model emulates the behavior of the reference plant. The input of the virtual measurement model is adapted using the proposed algorithm until the output parameters of the virtual measurement model match the result of the reference plant. After the adaptation, the input to the virtual measurement model is considered an estimation for position and extent. The main contribution of this paper is the reference model concept and an adaptation algorithm to optimize the input of the virtual measurement model.
Business Partner Compliance
(2022)
In tomato drying, degradation in final quality may occur based on the drying method used and predrying preparation. Hence, this research was conducted to evaluate the effect of different predrying treatments on physicochemical quality and drying kinetics of twin-layer-solar-tunnel-dried tomato slices. During the experimental work, tomato slices of var. Galilea were used. As predrying treatments, 0.5% calcium chloride (CaCl2), 0.5% ascorbic acid (C6H8O6), 0.5% citric acid (C6H8O7), and 0.5% sodium chloride (NaCl) were used. The tomato samples were sliced to 5 mm thickness, socked in the pretreatments for ten minutes, and dried in a twin layer solar tunnel dryer under the weather conditions of Jimma, Ethiopia. Untreated samples were used as control. The moisture losses from the samples were monitored by weighing samples at 2 h interval from each treatment. SAS statistical software version 9.2 was used for analyzing data on the physicochemical quality of tomato slices in CRD with three replications. From the experimental result, it was observed that dried tomato slices pretreated with 0.5% ascorbic acid gave the best retention of vitamin C and total phenolic content with a high sugar/acid ratio. Better retention of lycopene and fast drying were observed in dried tomato slices pretreated with 0.5% sodium chloride, and pretreating tomatoes with 0.5% citric acid resulted in better color values than the other treatments. Compared to the control, pretreating significantly preserved the overall quality of dried tomato slices and increased the moisture removal rate in the twin layer solar tunnel dryer.
The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
As interest in the investigation of possible sources and environmental sinks of technology-critical elements (TCEs) continues to grow, the demand for reliable background level information of these elements in environmental matrices increases. In this study, a time series of ten years of sediment samples from two different regions of the German North Sea were analyzed for their mass fractions of Ga, Ge, Nb, In, REEs, and Ta (grain size fraction < 20 µm). Possible regional differences were investigated in order to determine preliminary reference values for these regions. Throughout the investigated time period, only minor variations in the mass fractions were observed and both regions did not show significant differences. Calculated local enrichment factors ranging from 0.6 to 2.3 for all TCEs indicate no or little pollution in the investigated areas. Consequently, reference values were calculated using two different approaches (Median + 2 median absolute deviation (M2MAD) and Tukey inner fence (TIF)). Both approaches resulted in consistent threshold values for the respective regions ranging from 158 µg kg−1 for In to 114 mg kg−1 for Ce. As none of the threshold values exceed the observed natural variation of TCEs in marine and freshwater sediments, they may be considered baseline values of the German Bight for future studies.
Die GIGA-Adaptionsmethode
(2022)
Der Aufsatz stellt eine schreibdidaktische Lehrmethode vor, die auf einem Cicero-Zitat über das Aptum, die Angemessenheit des Stils, basiert. Nach einigen psychologischen Vorüberlegungen zur Differenz von Sprech- und Schreibsituation wird das Zitat im Hochschulschreibunterricht in Hinblick auf die darin genannten Stilfaktoren analysiert. Die Ergebnisliste dient als Grundlage einer Methode, mit der sich die stilistische Passgenauigkeit von Texten aller Art stark verbessern lässt. Ziel ist es, eine möglicherweise schreibferne Klientel dazu zu motivieren, zu einem musterhaften, zweckdienlichen, sachgerechten und zielgruppenorientierten Schreiben zu finden.
Technologiebasierte Startups leisten einen wesentlichen Beitrag zur wirtschaftlichen sowie gesellschaftlichen Entwicklung. Im Zuge ihrer Gründung benötigen sie Unterstützung in Form von Risikokapital, das in der Seed- und Early-Stage primär durch Business Angels (BAs) bereitgestellt wird. Die Abläufe und Bewertungskriterien des BA Investmentprozesses sind bisher jedoch unzureichend erforscht. Der vorliegende Beitrag nutzt Experteninterviews im Rahmen einer Fallstudie des baden-württembergischen entrepreneurialen Ökosystems zur Identifikation des Vorgehens von BAs bei der Bewertung und Auswahl technologiebasierter Startups. Zudem werden die Kriterien, nach denen BAs vielversprechende von scheiternden Startups unterscheiden abgeleitet. Somit trägt der Beitrag zur Öffnung der „Black Box” von Investmentaktivitäten in den frühsten Gründungsphasen bei.
Evaluation of tech ventures’ evolving business models: rules for performance-related classification
(2022)
At the early stage of a successful tech venture's life cycle, it is assumed that the business model will evolve to higher quality over time. However, there are few empirical insights into business model evolution patterns for the performance-related classification of early-stage tech ventures. We created relevant variables evaluating the evolution of the venture-centric network and the technological proposition of both digital and non-digital ventures' business models using the text of submissions to the official business plan award in the German State of Baden-Württemberg between 2006 and 2012. Applying a principal component analysis/rough set theory mixed methodology, we explore performance-related business model classification rules in the heterogeneous sample of business plans. We find that ventures need to demonstrate real interactions with their customers' needs to survive. The distinguishing success rules are related to patent applications, risk capital, and scaling of the organisation. The rules help practitioners to classify business models in a way that allows them to prioritise action for performance.
Ziel des Forschungsprojekts "Ekont" ist es, ein handgeführtes Gerät zum Betonabtrag an Innenkanten und Störstellen in Kernkraftwerken (KKW) zu entwickeln. Um die Reaktionskräfte zu reduzieren wird hierbei der neuartige Ansatz eines gegenläufigen Fräsprozesses untersucht. Ergebnis ist eine Getriebelösung, bei der eine mittlere Frässcheibe mit annähernd derselben Umfangsgeschwindigkeit in die entgegengesetzte Richtung von weiteren Frässcheiben rotiert.
The present contribution proposes a novel method for the indirect measurement of the ground reaction forces (GRF) induced by a pedestrian during walking on a vibrating structure. Its main idea is to formulate and solve an inverse problem in the time domain with the aim of finding the optimal time dependent moving point force describing the GRF of a pedestrian (input data), which minimizes the difference between a set of computed and a set of measured structural responses (output data). The solution of the inverse problem is addressed by means of the gradient-based trust region optimization strategy. The moving force identification process uses output data from a set of acceleration and displacement time histories recorded at different locations on the structure. The practicability and the accuracy of the proposed GRF identification method is firstly evaluated using simulated measurements, which revealed a high accuracy, robustness and stability of the results in relation to high noise levels. Subsequently, a comprehensive experimental validation process using real measurement data recorded on the HUMVIB experimental footbridge on the campus of the Technical University of Darmstadt (Germany) was carried out. Besides the conventional sensors for the acquisition of structural responses, an array of biomechanical force plates as well as classical load cells at the supports were used for measurement reference GRFs needed in the experimental validation process. The results show that the proposed method delivers a very accurate estimation of the GRF induced by a subject during walking on the experimental structure.
Outcomes with a natural order commonly occur in prediction problems and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning (DL) models are state-of-the-art for image classification tasks but frequently treat ordinal outcomes as unordered and lack interpretability. In contrast, classical ordinal regression models consider the outcome’s order and yield interpretable predictor effects but are limited to tabular data. We present ordinal neural network transformation models (ontrams), which unite DL with classical ordinal regression approaches. ontrams are a special case of transformation models and trade off flexibility and interpretability by additively decomposing the transformation function into terms for image and tabular data using jointly trained neural networks. The performance of the most flexible ontram is by definition equivalent to a standard multi-class DL model trained with cross-entropy while being faster in training when facing ordinal outcomes. Lastly, we discuss how to interpret model components for both tabular and image data on two publicly available datasets.
The present work proposes the use of modern ICT technologies such as smartphones, NFCs, internet, and web technologies, to help patients in carrying out their therapies. The implemented system provides a calendar with a reminder of the assumptions, ensures the drug identification through NFC, allows remote assistance from healthcare staff and family members to check and manage the therapy in real-time. The system also provides centralized information on the patient's therapeutic situation, helpful in choosing new compatible therapies.
A nonlinear mathematical model for the dynamics of permanent magnet synchronous machines with interior magnets is discussed. The model of the current dynamics captures saturation and dependency on the rotor angle. Based on the model, a flatness-based field-oriented closed-loop controller and a feed-forward compensation of torque ripples are derived. Effectiveness and robustness of the proposed algorithms are demonstrated by simulation results.
Einsatz von Bankettbeton bei schmalen und stark beanspruchten Ortsverbindungs- und Kreisstraßen
(2021)
Introduction. Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed. Objective. This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection. Methods. Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions. Results. The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods. Conclusions. The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.
SyNumSeS is a Python package for numerical simulation of semiconductor devices. It uses the Scharfetter-Gummel discretization for solving the one dimensional Van Roosbroeck system which describes the free electron and hole transport by the drift-diffusion model. As boundary conditions voltages can be applied to Ohmic contacts. It is suited for the simulation of pn-diodes, MOS-diodes, LEDs (hetero junction), solar cells, and (hetero) bipolar transistors.
Background:
One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.
Objective:
This paper aims to review the feasibility of blockchain technology for telemedicine.
Methods:
The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).
Results:
Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).
Conclusions:
These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.
Ferromagnetism is of increasing importance in the growing field of electromobility and data storage. In stable austenitic steels, the occurrence of ferromagnetism is not expected and would also interfere with many applications. However, ferromagnetism in austenitic stainless steels after low-temperature nitriding has already been shown in the past. Herein, the presence of ferromagnetism in austenitic steels is discovered after low-temperature carburization (Kolsterizing), which represents a novel and unique finding. A zone of expanded austenite is established on various austenitic stainless steels by low-temperature carburization and the respective ferromagnetism is investigated in relation to the alloy composition. The ferromagnetism occurring is determined by means of a commercial magnetoinductive sensor (Feritscope). Ferromagnetic domains are visualized by magnetic force microscopy and a ferrofluid. X-ray diffraction measurements indicate a clear difference in the lattice expansion of the different alloys. Furthermore, a different appearance of the magnetizable microstructure regions (magnetic domain structure) is detected depending on the grain orientation determined by electron backscatter diffraction (EBSD). Strongly pronounced magnetic domains show no linear lattice defects, whereas in small magnetizable areas linear lattice defects are detected by electron channeling contrast imaging and EBSD.
Four-Dimensional Hurwitz Signal Constellations, Set Partitioning, Detection, and Multilevel Coding
(2021)
The Hurwitz lattice provides the densest four-dimensional packing. This fact has motivated research on four-dimensional Hurwitz signal constellations for optical and wireless communications. This work presents a new algebraic construction of finite sets of Hurwitz integers that is inherently accompanied by a respective modulo operation. These signal constellations are investigated for transmission over the additive white Gaussian noise (AWGN) channel. It is shown that these signal constellations have a better constellation figure of merit and hence a better asymptotic performance over an AWGN channel when compared with conventional signal constellations with algebraic structure, e.g., two-dimensional Gaussian-integer constellations or four-dimensional Lipschitz-integer constellations. We introduce two concepts for set partitioning of the Hurwitz integers. The first method is useful to reduce the computational complexity of the symbol detection. This suboptimum detection approach achieves near-maximum-likelihood performance. In the second case, the partitioning exploits the algebraic structure of the Hurwitz signal constellations. We partition the Hurwitz integers into additive subgroups in a manner that the minimum Euclidean distance of each subgroup is larger than in the original set. This enables multilevel code constructions for the new signal constellations.
Electricity generation from renewable energies often fluctuates due to weather and other natural effects. The instrument of control energy (balancing energy) can compensate for these fluctuations and thus guarantee the system and supply security of the electricity grid. Luxury hotels on tourist islands could react to fluctuations in electricity generation and provide balancing energy. The purpose of this paper is to investigate the electricity consumption of luxury hotels to assess their potential as a source for providing control energy.