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Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.
The study traces the development of compulsory vaccination in Germany against the background of political discussion and legislative activities, focusing on the area of tension between state health protection and the right to medical self-determination in the context of constitutional balancing. It is based on the assumption that the right to medical self-determination traditionally dominates state decisions in a democratic constitutional state and that the scope for decision-making is constantly being further contoured in the face of current challenges.
This paper analyzes some of the assumptions in which the varied use of technologies to confront the spread of the COVID-19 pandemic and protect people's health has impacted on the fundamental right to the protection of personal data; to do so, it starts from the premise that the use of these technologies cannot mean an affectation to the referred fundamental right, much less an indiscriminate treatment of such data without any minimum control whatsoever.
This paper is structured into two parts, which are closely related: first, the analysis of the parlamentary and governmental measures against the covid-19 pandemic; and second, the future regulatory framework about freedom of movement and other rights in the European area, according to the new European pact on migration and asylum.
Freedom of trade, occupation and profession in times of the Covid-19 pandemic in South Africa
(2022)
This paper evaluates the freedom of trade, occupation, and profession in South Africa from a Covid-19 pandemic context. It does that by focusing on the pertinent provisions and rights contained in the Constitution of the Republic of South Africa, 1996 (the Constitution) and relevant international and regional human rights instruments. It proceeds by discussing the interlinkage between (the freedom of trade, occupation, and profession and other pertinent fundamental) rights, limitation, enforcement, and interpretation of rights. This is followed by some final observations.
Covid-19 outbreak had a huge impact on the economy worldwide as businesses had to close or cease their activities due to the lockdown regulations. The “luckiest” firms were able to operate but under restricted conditions. In order to avoid what certain authors called “bankruptcy epidemic” European countries took economic and fiscal measures to help companies compensate their financial losses. In addition to Government Grants, emergency legislations have been adopted with the aim to adapt insolvency and restructuring procedures to the sanitary situation and specific rules relating to company Law have also been implemented. This paper deals with the measures taken by the state of Luxembourg and gives a brief overview of the legal amendments.
Background: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient’s case.
Objective: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.
Methods: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting–based algorithm and compared their performance on both data sets.
Results: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.
Conclusions: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients’ diagnosis and treatment.
The concept of Circular Economy (CE) is becoming increasingly important in the pursuit of more sustainable societies. CE strategies are being applied in the sustainable management of a plethora of areas, such as energy, water, food and eco-industrial parks. The present paper focuses on the question of how CE principles can support the sustainable management of water in the agricultural sector around the world, considering different legislative environments, water resources management guidelines, environmental stressors, and CE practices. Considering these practices and circumstances, seven countries were compared: Brazil, Germany, Japan, Mexico, Morocco, Portugal, and Taiwan. Together, CE experts in the seven countries developed a set of 44 criteria to assess each of these areas. Broader establishment and respect of water resources legislation was found to be strongly correlated with lower agricultural water use. While the application of CE practices was found to not be correlated with lower consumption, this is still novel in most countries. Based on the studied countries, it can be concluded that a global CE agenda has not been reached for water resources. Further application and variety of practices is required to better represent the impact of CE on a national scale, but local success stories could support the wider application of CE in agriculture. The findings and the framework of the study can be applied to other countries in directing CE strategies for more sustainable water use in agriculture. Increasing CE implementation, motivated by legislation and better management can help ensure water security throughout nations.
The number of additive manufacturing methods and materials is growing rapidly, leaving gaps in the knowledge of specific material properties. A relatively recent addition is the metal-filled filament to be printed similarly to the fused filament fabrication (FFF) technology used for plastic materials, but with additional debinding and sintering steps. While tensile, bending, and shear properties of metals manufactured this way have been studied thoroughly, their fatigue properties remain unexplored. Thus, the paper aims to determine the tensile, fatigue, and impact strengths of Markforged 17-4 PH and BASF Ultrafuse 316L stainless steel to answer whether the metal FFF can be used for structural parts safely with the current state of technology. They are compared to two 316L variants manufactured via selective laser melting (SLM) and literature results. For extrusion-based additive manufacturing methods, a significant decrease in tensile and fatigue strength is observed compared to specimens manufactured via SLM. Defects created during the extrusion and by the pathing scheme, causing a rough surface and internal voids to act as local stress risers, handle the strength decrease. The findings cast doubt on whether the metal FFF technique can be safely used for structural components; therefore, further developments are needed to reduce internal material defects.
Geometrieerzeugung von Evolventenzahntrieben: Profilverschobene schrägverzahnte Stirnzahnräder
(2022)
In dieser Arbeit wird die Zahnradgeometrie von Stirnrädern berechnet und formatiert, um sie in ein CAD-Programm zu übertragen. Dabei werden die Konturen der Evolvente und der Trochoide nach den gleichen Regel wie bei der Herstellung durch Wälzfräsen erzeugt. Der Anwender hat die Möglichkeit die Haupteigenschaften wie Modul, Zahnkopfspiel und Eckenverrundung einzugeben. Zusätzlich können auch schrägverzahnte, profilverschobene Stirnräder mit Hochverzahnung und Kopfkürzung erzeugt werden.
Per Datenausgabe werden die Koordinaten gespeichert und durch ein Makro in das CAD-Programm übertragen. Aus den beiden Konturzügen wird der 3D-Körper durch Austragen entlang der Helix erzeugt.
Zur Weiterverarbeitung wird die Zahnradgeometrie nach manueller Tesselierung in ein universales Dateiformat exportiert.