Abstract
Alzheimer's disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.

Abstract
This work presents a new dump load controller for active power management of hydraulic generator unit using the power system parameters at the generator's terminals. An existing system with an analog controller is evaluated and a new digital open source HPP (Hydraulic power plant) power controller with extended functionality is proposed, designed and realized. A simulation of the voltage measurement sensor in LTspice is performed in order to determine the appropriate hardware parameters. Comparison between the features of the existing analog controller and the new digital controller is performed. Based on the results achieved, valuable conclusions are made. The new solution proposed offers simplified hardware, high reliability, easiness and flexibility of controller settings.

Abstract
The process of developing Dockerfiles is perceived by many developers as slow and based on trial-and-error, and it is hardly immediate to see the result of a change introduced into a Dockerfile. In this work we propose a plugin for Visual Studio Code, which we name Dockerlive, and that has the purpose of shortening the length of feedback loops. Namely, the plugin is capable of providing information to developers on a number of Dockerfile elements, as the developer is writing the Dockerfile. We achieve this through dynamic analysis of the resulting container, which the plugin builds and runs in the background.

Abstract
<p><br>The automatic classification of peaks in gamma radiation time series is relevant for both scientific and practical applications. From the practical perspective, the classification of  peaks is fundamental for  early-warning systems for radiation protection and detection of radioactive material. From the scientific point of view, peaks in gamma radiation are often driven by precipitation  and consequent  scavenging of airborne radon progeny radionuclides to the ground (mainly Pb-214 and Bi-214). Thus measurements of gamma radiation at the earth's surface have the potential to provide information on micro-physical processes occurring high above in the clouds, as the dominant source of radon progeny is thought to be associated with in-cloud processes – nucleation scavenging and interstitial aerosol collection by cloud or rain droplets. </p><p>The present study addresses the classification of peaks in high-resolution (1-minute) gamma radiation time series from the GRM (Gamma Radiation Monitoring) campaign, which is being carried out since 2015 at the Eastern North Atlantic (ENA) station of the ARM (Atmospheric Radiation Measurements) programme. In addition to the gamma time series, precipitation information from laser disdrometer measurements is considered, including rain rate, liquid water content, median drop diameter and droplet concentration. Diverse machine learning algorithms are examined with the goal to identify and classify gamma peaks driven by precipitation events, and further examine the association between precipitation characteristics and the resulting gamma radiation peak on the ground.</p><p> </p>

Abstract

Abstract
There is an increasing demand for stateful edge computing for both complex Virtual Network Functions (VNFs) and application services in emerging 5G networks. Managing a mutable persistent state in the edge does however bring new architectural, performance, and dependability challenges. Not only it has to be integrated with existing cloud-based systems, but also cope with both operational and analytical workloads and be compatible with a variety of SQL and NoSQL database management systems. We address these challenges with AIDA-DB, a polyglot data management architecture for the edge and cloud continuum. It leverages recent development in distributed transaction processing for a reliable mutable state in operational workloads, with a flexible synchronization mechanism for efficient data collection in cloud-based analytical workloads.