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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.
AbstractSanctions encompass a wide set of policy instruments restricting cross‐border economic activities. In this paper, we study how different types of sanctions affect the export behavior of firms to the targeted countries. We combine Danish register data, including information on firm‐destination‐specific exports, with information on sanctions imposed by Denmark from the Global Sanctions Database. Our data allow us to study firms' export behavior in 62 sanctioned countries, amounting to a total of 453 country‐years with sanctions over the period 2000–2015. Methodologically, we apply a two‐stage estimation strategy to properly account for multilateral resistance terms. We find that, on average, sanctions lead to a significant reduction in firms' destination‐specific exports and a significant increase in firms' probability to exit the destination. Next, we study heterogeneity in the effects of sanctions across (i) sanction types and sanction packages, (ii) the objectives of sanctions, and (iii) countries subject to sanctions. Results confirm that the effects of sanctions on firms' export behavior vary considerably across these three dimensions.
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.
IT-Kosten machen heute einen immer größeren Anteil an den Gesamtkosten von Unternehmen aus. Die Verantwortlichen sind aufgefordert die IT-Kosten zu senken oder zumindest ein effizientes Management sicherzustellen. Oftmals fehlt es dafür an Transparenz und Verständnis für diese Ausgaben. Die Analyse der IT-Kostentreiber ermöglicht ein tieferes Verständnis der Ursachen und Auswirkungen strategischer Entscheidungen. Dieser Beitrag zielt darauf ab, die strategischen IT-Kostentreiber bezüglich des Wirkungshorizonts und des Entscheidungsortes zu analysieren. Die durchgeführte Delphi-Studie zeigt, dass Entscheidungen über diese Kostentreiber größtenteils mittel- bis langfristige Auswirkungen haben. Zudem wird deutlich, dass die IT-Abteilung zwar in den Entscheidungsprozess eingebunden ist, während die finalen Entscheidungen häufig stärker im Fachbereich liegen. Zusammenarbeit und effektive Kommunikation sind deshalb entscheidend und die Verantwortung für IT-Kosten sollte von allen EntscheidungsträgerInnen getragen werden. Dieser Beitrag erweitert die Forschung im IT-Kostenmanagement und sensibilisiert PraktikerInnen für Kostenbeeinflussungshebel und die strategische Diskussion über IT-Kosten und das Wertversprechen der IT.
Black-box variational inference (BBVI) is a technique to approximate the posterior of Bayesian models by optimization. Similar to MCMC, the user only needs to specify the model; then, the inference procedure is done automatically. In contrast to MCMC, BBVI scales to many observations, is faster for some applications, and can take advantage of highly optimized deep learning frameworks since it can be formulated as a minimization task. In the case of complex posteriors, however, other state-of-the-art BBVI approaches often yield unsatisfactory posterior approximations. This paper presents Bernstein flow variational inference (BF-VI), a robust and easy-to-use method flexible enough to approximate complex multivariate posteriors. BF-VI combines ideas from normalizing flows and Bernstein polynomial-based transformation models. In benchmark experiments, we compare BF-VI solutions with exact posteriors, MCMC solutions, and state-of-the-art BBVI methods, including normalizing flow-based BBVI. We show for low-dimensional models that BF-VI accurately approximates the true posterior; in higher-dimensional models, BF-VI compares favorably against other BBVI methods. Further, using BF-VI, we develop a Bayesian model for the semi-structured melanoma challenge data, combining a CNN model part for image data with an interpretable model part for tabular data, and demonstrate, for the first time, the use of BBVI in semi-structured models.
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.
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis—polysomnography (PSG)—is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
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.
Sanktionen stellen Zwangsmaßnahmen dar, die bei der Bewältigung politischer Spannungen zwischen Nationen eine lange und wiederkehrende Stellung einnehmen. Sie werden sowohl einseitig als auch in Staatenbündnissen verhängt und besonders nach dem 2. Weltkrieg mit zunehmender Häufigkeit eingesetzt. Während im letzten Jahrhundert, insbesondere vor dem 2. Weltkrieg, Handelsbeschränkungen und umfassende Wirtschaftsblockaden die vorherrschenden Sanktionsinstrumente darstellten, werden heute in einer stärker integrierten und globalisierten Welt Sanktionen in verschiedenen weiteren Formen verhängt, einschließlich internationaler Finanzbeschränkungen, Reiseverbote, Handelseinschränkungen für bestimmte Gütergruppen, Aufhebung militärischer Hilfen und spezifische Einschränkungen, wie beispielsweise Flugverbote und Hafensperrungen.
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.
Increasing demand for sustainable, resilient, and low-carbon construction materials has highlighted the potential of Compacted Mineral Mixtures (CMMs), which are formulated from various soil types (sand, silt, clay) and recycled mineral waste. This paper presents a comprehensive inter- and transdisciplinary research concept that aims to industrialise and scale up the adoption of CMM-based construction materials and methods, thereby accelerating the construction industry’s systemic transition towards carbon neutrality. By drawing upon the latest advances in soil mechanics, rheology, and automation, we propose the development of a robust material properties database to inform the design and application of CMM-based materials, taking into account their complex, time-dependent behaviour. Advanced soil mechanical tests would be utilised to ensure optimal performance under various loading and ageing conditions. This research has also recognised the importance of context-specific strategies for CMM adoption. We have explored the implications and limitations of implementing the proposed framework in developing countries, particularly where resources may be constrained. We aim to shed light on socio-economic and regulatory aspects that could influence the adoption of these sustainable construction methods. The proposed concept explores how the automated production of CMM-based wall elements can become a fast, competitive, emission-free, and recyclable alternative to traditional masonry and concrete construction techniques. We advocate for the integration of open-source digital platform technologies to enhance data accessibility, processing, and knowledge acquisition; to boost confidence in CMM-based technologies; and to catalyse their widespread adoption. We believe that the transformative potential of this research necessitates a blend of basic and applied investigation using a comprehensive, holistic, and transfer-oriented methodology. Thus, this paper serves to highlight the viability and multiple benefits of CMMs in construction, emphasising their pivotal role in advancing sustainable development and resilience in the built environment.
Digital federated platforms and data cooperatives for secure, trusted and sovereign data exchange will play a central role in the construction industry of the future. With the help of platforms, cooperatives and their novel value creation, the digital transformation and the degree of organization of the construction value chain can be taken to a new level of collaboration. The goal of this research project was to develop an experimental prototype for a federated innovation data platform along with a suitable exemplary use case. The prototype is to serve the construction industry as a demonstrator for further developments and form the basis for an innovation platform. It exemplifies how an overall concept is concretely implemented along one or more use cases that address high-priority industry pain points. This concept will create a blueprint and a framework for further developments, which will then be further established in the market. The research project illuminates the perspective of various governance innovations to increase industry collaboration, productivity and capital project performance and transparency as well as the overall potential of possible platform business models. However, a comprehensive expert survey revealed that there are considerable obstacles to trust-based data exchange between the key stakeholders in the industry value network. The obstacles to cooperation are predominantly not of a technical nature but rather of a competitive, predominantly trust-related nature. To overcome these obstacles and create a pre-competitive space of trust, the authors therefore propose the governance structure of a data cooperative model, which is discussed in detail in this paper.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
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.
Driver assistance systems are increasingly becoming part of the standard equipment of vehicles and thus contribute to road safety. However, as they become more widespread, the requirements for cost efficiency are also increasing, and so few and inexpensive sensors are used in these systems. Especially in challenging situations, this leads to the fact that target discrimination cannot be ensured which may lead to false reactions of the driver assistance system. In this paper, the Boids flocking algorithm is used to generate semantic neighborhood information between tracked objects which in turn can significantly improve the overall performance. Two different variants were developed: First, a free-moving flock whereby a separate flock is generated per tracked object and second, a formation-controlled flock where boids of a single flock move along the future road course in a pre-defined formation. In the first approach, the interaction between the flocks as well as the interaction between the boids within a flock is used to generate additional information, which in turn can be used to improve, for example, lane change detection. For the latter approach, new behavioral rules have been developed, so that the boids can reliably identify control-relevant objects to a driver assistance system. Finally, the performance of the presented methods is verified through extensive simulations.
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.
In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a “similarity level” parameter increases the adaptability and reusability of study findings in individual practical cases.
"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.
Non-volatile NAND flash memories store information as an electrical charge. Different read reference voltages are applied to read the data. However, the threshold voltage distributions vary due to aging effects like program erase cycling and data retention time. It is necessary to adapt the read reference voltages for different life-cycle conditions to minimize the error probability during readout. In the past, methods based on pilot data or high-resolution threshold voltage histograms were proposed to estimate the changes in voltage distributions. In this work, we propose a machine learning approach with neural networks to estimate the read reference voltages. The proposed method utilizes sparse histogram data for the threshold voltage distributions. For reading the information from triple-level cell (TLC) memories, several read reference voltages are applied in sequence. We consider two histogram resolutions. The simplest histogram consists of the zero-and-one ratios for the hard decision read operation, whereas a higher resolution is obtained by considering the quantization levels for soft-input decoding. This approach does not require pilot data for the voltage adaptation. Furthermore, only a few measurements of extreme points of the threshold voltage distributions are required as training data. Measurements with different conditions verify the proposed approach. The resulting neural networks perform well under other life-cycle conditions.
Background: Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL).
Materials and methods: We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM).
Results: The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy.
Conclusion: The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI.
Die jüngeren Entscheidungen des EuGH sowie des BAG zur Arbeitszeiterfassung haben trotz ihrer eigentlich klaren Aussagen in der betrieblichen Praxis zu teils erheblichen Verunsicherungen geführt: Müssen nun wirklich die Arbeitszeiten der Beschäftigten erfasst werden und wie wirkt sich das auf die in vielen Unternehmen gelebte „Vertrauensarbeitszeit“ aus?
Urlaub, Urlaub und kein Ende – Die aktuelle Rechtslage auf Basis der Rechtsprechung von EuGH und BAG
(2023)
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.
Low-Code Development Plattformen (LCDPs) fördern die digitale Transformation von Organisationen, indem sie die Applikationsentwicklung durch FachbereichsmitarbeiterInnen ohne tiefgreifende Programmierkenntnisse – sogenannte Citizen Developer – ermöglichen. Marktforschungsinstitute prognostizieren, dass in den nächsten Jahren mehr als die Hälfte aller Applikationen mit LCDPs entwickelt werden. Nichtsdestotrotz stehen Organisationen vor der Herausforderung, sich für die richtigen Implementierungs- und Anwendungsansätze von LCDPs zu entscheiden. Dieser Artikel liefert daher ein umfassendes Bild über das praktische Verständnis und aktuelle Ansätze in verschiedenen Organisationen und leitet daraus Handlungsempfehlungen ab. Dafür wurden 16 Experteninterviews durchgeführt und wissenschaftlich analysiert. Die Ergebnisse zeigen, dass die Praxis grundsätzlich ein ähnliches Verständnis des Begriffs LCDP hat. Die Initiative für die Einführung kommt meist aus den Fachbereichen, die Entscheidung für oder gegen die LCDP-Implementierung wird jedoch meist von der Geschäftsführung in Kooperation mit der IT-Abteilung getroffen. Dabei unterscheiden sich die aktuellen Anwendungsansätze: Unternehmen nutzen entweder einen Self-Service-Ansatz durch die Fachbereiche oder integrieren die Entscheidung über eine potenzielle LCDP-Entwicklung durch die Citizen Developer in das bestehende Demand-Management der IT-Abteilung. Eine etablierte und adaptive Governance ist für beide Ansätze eine wichtige Voraussetzung. Die Erkenntnisse des Beitrags tragen zur wissenschaftlichen Diskussion bei, da dieser Artikel eine der ersten umfassenden und wissenschaftlich fundierten qualitativen Analysen über aktuelle praktische Adoptionsansätze der Praxis liefert. PraktikerInnen erfahren zudem, wie andere Unternehmen mit aktuellen Herausforderungen umgehen und welche Ansätze erfolgversprechend sind.
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.
While driving, stress is caused by situations in which the driver estimates their ability to manage the driving demands as insufficient or loses the capability to handle the situation. This leads to increased numbers of driver mistakes and traffic violations. Additional stressing factors are time pressure, road conditions, or dislike for driving. Therefore, stress affects driver and road safety. Stress is classified into two categories depending on its duration and the effects on the body and psyche: short-term eustress and constantly present distress, which causes degenerative effects. In this work, we focus on distress. Wearable sensors are handy tools for collecting biosignals like heart rate, activity, etc. Easy installation and non-intrusive nature make them convenient for calculating stress. This study focuses on the investigation of stress and its implications. Specifically, the research conducts an analysis of stress within a select group of individuals from both Spain and Germany. The primary objective is to examine the influence of recognized psychological factors, including personality traits such as neuroticism, extroversion, psychoticism, stress and road safety. The estimation of stress levels was accomplished through the collection of physiological parameters (R-R intervals) using a Polar H10 chest strap. We observed that personality traits, such as extroversion, exhibited similar trends during relaxation, with an average heart rate 6% higher in Spain and 3% higher in Germany. However, while driving, introverts, on average, experienced more stress, with rates 4% and 1% lower than extroverts in Spain and Germany, respectively.
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.
Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship, new skills, and jobs, especially in small communities and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being, and protect digital rights, we propose data cooperatives as a vehicle for secure, trusted, and sovereign data exchange. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted application programming interfaces (“APIs”) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This review article analyses an array of transformative use cases that underline the potential of cooperative data governance. These case studies exemplify how data and platform cooperatives, through their innovative value creation mechanisms, can elevate digital commons and value chains to a new dimension of collaboration, thereby addressing pressing societal issues. Guided by our research aim, we propose a policy framework that supports the practical implementation of digital federation platforms and data cooperatives. This policy blueprint intends to facilitate sustainable development in both the Global South and North, fostering equitable and inclusive data governance strategies.
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.