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Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data
(2021)
In dem vorliegenden Beitrag wird der aktuelle Trend, Payas-you-live-Systeme (PAYL) in Verbindung mit Krankenversicherungen anzubieten, analysiert. PAYL-Systeme bedeutet konkret die kontinuierliche Erfassung von Gesundheitsdaten mithilfe technischer Geräte und Übermittlung dieser Daten an Versicherungen sowie die Auszahlung von Boni für erreichte Fitnessziele. Zunächst wird eine Definition von PAYL-Systemen dargelegt, dann werden die technischen Rahmenbedingungen erläutert, schließlich werden die Auswirkungen dieser soziotechnischen Systeme näher beleuchtet. Dies geschieht auf Grundlage unserer empirischen Untersuchung. Die drei identifizierten Hauptkonflikte betreffen die Genese von PAYL, seine Auswirkungen auf den Wert der Datensouveränität besonders für Versicherte und die Schwierigkeit, für den Anspruch auf Prävention und Kostenersparnis durch PAYL Evidenz zu erzeugen. Diese Konflikte werden diskutiert, um sowohl die direkten wie auch indirekten Auswirkungen der Digitalisierung und der Ökonomisierung des Sozialen durch PAYL zu beleuchten.
Nonlinearity of Magnetostrictive Torque Sensor under Varying External Magnetic Field Strength
(2021)
Auch während Pandemien müssen die Blutspendedienste kontinuierlich BlutspenderInnen rekrutieren, um die Versorgung mit Blutprodukten zu gewährleisten. Während im Anschluss an andere Katastrophen wie Erdbeben oder Terroranschlägen die Spendebereitschaft meist sprunghaft ansteigt, zeigte sich in der Vergangenheit bei beginnenden Pandemien zunächst ein Rückgang des Spendeaufkommens. Viele SpenderInnen fürchten eine Infektion oder eine Schwächung ihres Immunsystems und bleiben zu Hause. Auch fällt es den Blutspendediensten zunächst schwer, die gewohnte Anzahl an mobilen Spendeterminen zu organisieren, wodurch das Spendenaufkommen zurückgeht. In der aktuellen SARS-CoV2-Pandemie betrug dieser Rückgang in vielen Ländern mehr als 10%.
SpenderInnen, die auch während einer Pandemie spenden, sind in der Regel erfahrener und besitzen bereits eine ausgebildete Spenderidentiät. Viele dieser SpenderInnen berichten, dass sie gezielt einen Beitrag zur Überwindung der Krise leisten möchten. Auch während einer Pandemie ist demnach eine hohe Solidarität unter Blutspendern zu finden. Potentielle ErstpenderInnen lassen sich durch die unsicheren Rahmenbedingungen dagegen von einer Spende eher abhalten und es bedarf gezielter Rekrutierungsstrategien unter Einsatz von Social-Media-Kanälen, um neue SpenderInnen zu gewinnen. Erste Befunde unter deutschen Blutspendern lassen hierbei eine hohe Rückkehrintention der ErstspenderInnen erwarten.
Um die Blutversorgung auch während einer Pandemie aufrechterhalten zu können, sollten die Blutspendedienste neben der Rekrutierung von ErstspenderInnen versuchen, schnell die Anzahl ihrer Spendetermine zu erhöhen. Die Ansprache bestehender SpenderInnen sollte vor allem die Verunsicherung reduzieren und das Vertrauen in die Blutspendedienste stärken. Je größer das Vertrauen in die Spendeeinrichtung ausfällt, desto geringer ist die Risikowahrnehmung der SpenderInnen. Auch sollten alternative Kontaktwege etwa über Messenger-Dienste getestet werden, da diese eine schnelle Ansprache erlauben.
In Industry 4.0 machine learning approaches are a state-of-the art for predictive maintenance, machine condition monitoring, and others. Distributed decision trees are one of the learning algorithms for such applications. A new approach of node based parallelization for the construction is presented and allows to classify data through a network of nodes. Attacks on the nodes are discussed based on different attack scenarios and attack classifications are presented. A thorough analysis of protection measurements is given, such that classification is not maliciously modified by an attacker. Different countermeasures are proposed and analyzed. A quorum-based system allows for a good balance between computational overhead and robustness of the algorithm.
Chest Compresses with Ginger or Mustard Affect Warmth Regulation in Healthy Adults - A Randomized Co
(2021)
In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case.
The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.
Nowadays, machine learning projects have become more and more relevant to various real-world use cases. The success of complex Neural Network models depends upon many factors, as the requirement for structured and machine learning-centric project development management arises. Due to the multitude of tools available for different operational phases, responsibilities and requirements become more and more unclear. In this work, Machine Learning Operations (MLOps) technologies and tools for every part of the overall project pipeline, as well as involved roles, are examined and clearly defined. With the focus on the inter-connectivity of specific tools and comparison by well-selected requirements of MLOps, model performance, input data, and system quality metrics are briefly discussed. By identifying aspects of machine learning, which can be reused from project to project, open-source tools which help in specific parts of the pipeline, and possible combinations, an overview of support in MLOps is given. Deep learning has revolutionized the field of Image processing, and building an automated machine learning workflow for object detection is of great interest for many organizations. For this, a simple MLOps workflow for object detection with images is portrayed.
Cameras play a prominent role in the context of 3D data, as they can be designed to be very cheap and small and can therefore be used in many 3D reconstruction systems. Typical cameras capture video at 20 to 60 frames per second, resulting in a high number of frames to select from for 3D reconstruction. Many frames are unsuited for reconstruction as they suffer from motion blur or show too little variation compared to other frames. The camera used within this work has built-in inertial sensors. What if one could use the built-in inertial sensors to select a set of key frames well-suited for 3D reconstruction, free from motion blur and redundancy, in real time? A random forest classifier (RF) is trained by inertial data to determine frames without motion blur and to reduce redundancy. Frames are analyzed by the fast Fourier transformation and Lucas–Kanade method to detect motion blur and moving features in frames to label those correctly to train the RF. We achieve a classifier that omits successfully redundant frames and preserves frames with the required quality but exhibits an unsatisfied performance with respect to ideal frames. A 3D reconstruction by Meshroom shows a better result with selected key frames by the classifier. By extracting frames from video, one can comfortably scan objects and scenes without taking single pictures. Our proposed method automatically extracts the best frames in real time without using complex image-processing algorithms.
Cross-cultural adaptation and validation of the German Central Sensitization Inventory (CSI-GE)
(2021)
Impact of restricted contact between grandparents and grandchildren during the COVID-19 pandemic
(2021)
Experimental study of single grit scratch test on carbon fiber-reinforced polyether ether ketone
(2021)
The influence of bath hydrodynamics on the resultant micromechanical properties of electrodeposited nickel-cobalt alloy system is investigated. The bath hydrodynamics realized by magnetic stirring is simulated using COMSOL Multiphysics and a region of minimum variation in velocity within the electrolytic cell is determined and validated experimentally. Nickel-cobalt alloy and nickel coating samples are deposited galvanostatically (50 mA/cm2) with varying bath velocity (0 to 42 cm/s). The surface morphology of samples gradually changed from granular (fractal dimension 2.97) to more planar (fractal dimension 2.15) growth type, and the according average roughness decreased from 207.5 nm to 11 nm on increasing the electrolyte velocity from 0 to 42 cm/s for nickel-cobalt alloys; a similar trend was also found in the case of nickel coatings. The calculated grain size from the X-ray diffractograms decreased from 31 nm to 12 nm and from 69 nm to 26 nm as function of increasing velocity (up to 42 cm/s) for nickel-cobalt and nickel coatings, respectively. Consecutively, the measured Vickers microhardness values increased by 43% (i.e., from 393 HV0.01 to 692 HV0.01) and by 33% (i.e., from 255 HV0.01 to 381 HV0.01) for nickel-cobalt and nickel coatings, respectively, which fits well with the Hall–Petch relation.
A sensor fusion concept integrating the optical and microfabricated eddy-current sensor for the non-destructive testing of the grinding burn is reported. For evaluation, reference grinding burn with varying degrees are fabricated on 42CrMo4 tool steel cylinder. The complementary sensing nature of the proposed sensors for the non-destructive testing of the grinding burn is successfully achieved, wherein both the superficial and an in-depth quantitative profile information of the grinding zone is recorded. The electrical output (voltage) of the optical sensor, which is sensitive to the optical surface quality, dropped only by 20 % for moderate degree of grinding burn and by ca. 50 % for stronger degree of grinding burn (i.e. by exclusively considering the superficial surface morphology of the grinding burn). Moreover, a direct correlation among the average surface roughness of the grinding burn, the degree of grinding burn and the optical sensor’s output voltage was observed. The superficial and in-depth information of the grinding burn was recorded using a microfabricated eddy-current sensor (planar microcoil with circular spiral geometry with 20 turns) by measuring the impedance change as function of the driving frequency. The depth of penetration of induced eddy-current in the used 42CrMo4 workpiece (with a sensor to workpiece distance of 700 µm) varied from 223 µm to 7 µm on increasing the frequency of the driving current from 1kHz to 10 MHz, respectively. A very interesting nature of the grinding burn was observed with two distinct zones within the grinding zone, namely, the superficial zone (starting from the workpiece surface to 15 µm in grinding zone) and a submerged zone (>15 µm within the grinding zone). The impedance of the microcoils changed by ca. 8 % and 4 % for the superficial and submerged zone for regions with stronger degree of grinding burn at a frequency of 10 MHz and 2.5MHz, respectively. Furthermore, a correlation between the microhardness of the grinding burn and the impedance change is also observed.
Demethylating therapy increases anti-CD123 CAR T cell cytotoxicity against acute myeloid leukemia
(2021)
Biosynthesis of iron oxide magnetic nanoparticles using clinically isolated Pseudomonas aeruginosa
(2021)
While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.