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Additives and polymer composition influence the interaction of microplastics with xenobiotics
(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.
Biosynthesis of iron oxide magnetic nanoparticles using clinically isolated Pseudomonas aeruginosa
(2021)
Comparison of a histology based multi layer artery model to its simplified axisymmetric model
(2021)
Comparison of Geometrical Lung Models to Calculate Tidal Volumes during Spontaneous Breathing
(2021)
Cross-cultural adaptation and validation of the German Central Sensitization Inventory (CSI-GE)
(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.
Demethylating therapy increases anti-CD123 CAR T cell cytotoxicity against acute myeloid leukemia
(2021)
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.
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.