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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.
In this paper, the mechanical damage behavior is investigated based on the characteristic roughness on the surface and the orientation of superficial structures. The main goal is to explore the surface roughness on mechanically loaded copper conductors as a lifetime indicator. For this purpose, copper conductors are mechanically stressed in accordance with EN 50,396 and then examined metallographically and microscopically. The microstructure examination shows that the roughness is caused by material extrusion and cracks due to work hardening in the surface area. Using confocal microscopy, it is shown for the first time that significant formation of surface roughness takes place over the service life of copper conductors. The roughness increases monotonically, but not linearly with number of cycles, due to internal microstructural processes and can be divided into three sections. First inspections of the conductor surface over lifetime show a correlation between the intensity of structures orientated 45° to the loading direction and the roughness. This phenomenon, already known from microscopic slip lines, is thus also evident in macroscopic roughness formation and is well founded by the research theory on material extrusion along dislocation lines. In summary, a lifetime determination is possible based on its developing roughness which enables the utilization as a sensor element.
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.
Background: Stratified care has the potential to be efficient in addressing the physical and psychosocial components of low back pain (LBP) and optimise treatment outcomes essential in low-income countries. This study aimed to investigate the perceptions of physiotherapists and patients in Nigeria towards stratified care for the treatment of LBP, exploring barriers and enablers to implementation.
Methods: A qualitative design with semistructured individual telephone interviews for physiotherapists and patients with LBP comprising research evidence and information on stratified care was adopted. Preceding the interviews, patients completed the Subgroups for Targeted Treatment tool. The interviews were recorded, transcribed and analysed following grounded theory methodology.
Results: Twelve physiotherapists and 13 patients with LBP participated in the study (11 female, mean age 42.8 (SD 11.47) years). Seven key categories emerged: recognising the need for change, acceptance of innovation, resistance to change, adapting practice, patient’s learning journey, trusting the therapist and needing conviction. Physiotherapists perceived stratified care to be a familiar approach based on their background training. The prevalent treatment tradition and the patient expectations were seen as major barriers to implementation of stratified care by the physiotherapists. Patients see themselves as more informed than therapists realise, yet they need conviction through communication and education to cooperate with their therapist using this approach. Viable facilitators were also identified as patients’ trust in the physiotherapist and adaptations in terms of training and modification of the approach to enhance its use.
Conclusion: Key barriers identified are the patients’ treatment expectations and physiotherapists’ adherence to the tradition of practice. Physiotherapists might facilitate implementation of the stratified care by communication, hierarchical implementation and utilisation of patients’ trust. Possibilities to develop a consensus on key strategies to overcome barriers and on utilisation of facilitators should be tested in future research.
Concerning human and environmental health, safe alternatives to synthetic pesticides are urgently needed. Many of the currently used synthetic pesticides are not authorized for application in organic agriculture. In addition, the developed resistances of various pests against classical pesticides necessitate the urgent demand for efficient and safe products with novel modes of action. Botanical pesticides are assumed to be effective against various crop pests, and they are easily biodegradable and available in high quantities and at a reasonable cost. Many of them may act by diverse yet unexplored mechanisms of action. It is therefore surprising that only few plant species have been developed for commercial usage as biopesticides. This article reviews the status of botanical pesticides, especially in Europe and Mediterranean countries, deepening their active principles and mechanisms of action. Moreover, some constraints and challenges in the development of novel biopesticides are highlighted.