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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.
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.
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.
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.
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.
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.
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.
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.
Die digitale Transformation von Geschäftsprozessen und die stärkere Integration von IT-Systemen führen zu Chancen und Risiken für kleine und mittlere Unternehmen (KMU). Risiken, die zu fehlender IT-Governance, Risk und Compliance (GRC) führen können. Ziel dieses Beitrags ist es, die Design- und Evaluierungsphase der Erstellung eines Artefakts darzustellen. Dabei wird der Design Science Research Ansatz nach Hevner verwendet. Das Artefakt wird für die Auswahl von Standards entwickelt, indem KMU-relevante Ausprägungen und bestehende Rahmenwerke auf die definierten Kriterien angepasst werden.
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.
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.
"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.
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.
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.
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.
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.
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?