Publikationsserver der Ostbayerischen Technischen Hochschule Regensburg
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Miniaturized Pirani vacuum sensor with active heat-loss compensation
No full textPirani sensors measure the thermal conductivity of the residual gas in a vacuum by creating a thermal gradient between a heated sensor element and a heat sink. The heat flux from the sensor element to the heat sink over the residual gas is a measure of the vacuum and can be determined by the electrical power applied. In addition to the heat flux over the gas, there are further energy losses from the heating structure due to radiation and parasitic heat fluxes via the suspensions of the sensor element. These losses reduce the sensitivity of the sensor. For this reason, a Micro-Pirani sensor in the shape of a microhotplate was developed that actively compensates the heat flux via the suspensions. This was achieved by placing additional heating structures on the suspensions, which interrupt the heat flow from the sensor element via the suspensions during operation. This active compensation improves the sensitivity at low pressures, enabling vacuum measurements from atmospheric pressure down to 10e-6 mbar
Fast simulation of hemodynamics in intracranial aneurysms for clinical use
No full textBACKGROUND: A widely accepted tool to assess hemodynamics, one of the most important factors in aneurysm pathophysiology, is Computational Fluid Dynamics (CFD). As current workflows are still time consuming and difficult to operate, CFD is not yet a standard tool in the clinical setting. There it could provide valuable information on aneurysm treatment, especially regarding local risks of rupture, which might help to optimize the individualized strategy of neurosurgical dissection during microsurgical aneurysm clipping.
METHOD: We established and validated a semi-automated workflow using 3D rotational angiographies of 24 intracranial aneurysms from patients having received aneurysm treatment at our centre. Reconstruction of vessel geometry and generation of volume meshes was performed using AMIRA 6.2.0 and ICEM 17.1. For solving ANSYS CFX was used. For validational checks, tests regarding the volumetric impact of smoothing operations, the impact of mesh sizes on the results (grid convergence), geometric mesh quality and time tests for the time needed to perform the workflow were conducted in subgroups.
RESULTS: Most of the steps of the workflow were performed directly on the 3D images requiring no programming experience. The workflow led to final CFD results in a mean time of 22 min 51.4 s (95%-CI 20 min 51.562 s-24 min 51.238 s, n = 5). Volume of the geometries after pre-processing was in mean 4.46% higher than before in the analysed subgroup (95%-CI 3.43-5.50%). Regarding mesh sizes, mean relative aberrations of 2.30% (95%-CI 1.51-3.09%) were found for surface meshes and between 1.40% (95%-CI 1.07-1.72%) and 2.61% (95%-CI 1.93-3.29%) for volume meshes. Acceptable geometric mesh quality of volume meshes was found.
CONCLUSIONS: We developed a semi-automated workflow for aneurysm CFD to benefit from hemodynamic data in the clinical setting. The ease of handling opens the workflow to clinicians untrained in programming. As previous studies have found that the distribution of hemodynamic parameters correlates with thin-walled aneurysm areas susceptible to rupture, these data might be beneficial for the operating neurosurgeon during aneurysm surgery, even in acute cases
Pain Neuroscience Education (PNE) - Anwendungsbereitschaft deutscher Physiotherapeut*innen
No full textDeutsche Physiotherapeut*innen scheinen in ihrem Arbeitsalltag von einer Implementierung des PNE- Konzepts tendenziell Abstand zu nehmen. Parallel zu dieser Vermutung offenbarte sich eine mehrheitliche Unsicherheit bei der Behandlung von chronischen Schmerzpatient*innen. Vor allem Therapeut*innen, welche sich weniger unsicher im Umgang mit chronischen Schmerzpatient*innen fühlen, neigen zur Verwendung der „Why-you-Hurt?“ – Karten. In diesem Falle wäre zu diskutieren, ob der Grund hierfür in der Tatsache liegt, dass diese Therapeut*innen eine geeignete und gut ergänzende Methode in dem Edukations-Tool sehen oder, ob ein sicherer Umgang mit chronischen Patient*innen die Voraussetzung für eine erfolgreiche Implementierung ist. Umfangreichere Erhebungen müssen durchgeführt werden, um diese Erkenntnisse zu festigen
Sharing health data for research purposes: results of a population survey in Germany
No full textBACKGROUND: Increased use of health data has the potential to improve both health care and health policies. Several recent policy initiatives at the European and German legislative levels aim to increase the primary and secondary use of health data. However, little is known about general population views on health data access for research. Most studies are based on subsets defined by specific illnesses.
METHODS: We commissioned a national computer-assisted dual-frame telephone survey (landline and mobile). Logit estimation models were used to identify predictors of willingness to provide access to health data to different organizations (universities in Germany, universities worldwide, German government organizations, pharmaceutical companies).
RESULTS: A high willingness to share health data for research purposes is observed, depending on the specific data recipient. The willingness is highest for research at universities in Germany and German governmental organizations, and lowest regarding research by pharmaceutical companies. The main drivers for sharing health data are the level of trust in public institutions, the respondents' assessment of the seriousness and likelihood of data misuse, and the level of digital literacy. Age, gender, and level of education have small effects and do not determine the willingness to share health data for all organizations.
CONCLUSION: We present evidence from a random sample of the German population. The results indicate widespread support among the population for providing access to health data for research purposes. Similar to findings in other countries, the willingness depends strongly on the recipient of the data. This paper evaluates the impact of various determinants - identified in previous qualitative and quantitative research - on the willingness of the German population to share health data. While previous studies have found that patients are generally more willing to share health data, we found that the presence of a medical precondition does not translate into respondents' unequivocal support for health data sharing. We identify privacy concerns, general trust, and digital literacy as key factors influencing the willingness to share health data. Therefore, policymakers and stakeholders need to ensure and communicate the necessary privacy protection measures to increase the willingness of the German population to share health data
Herausforderungen für Wissenschaft und Forschung im Kontext von nachhaltiger Entwicklung. Ein Essay mit gesundheitswissenschaftlichem Fokus
No full textIn der Agenda 2030 wird dem Zusammenspiel von Technologie, Innovation und Wissenschaft eine zentrale Bedeutung zur nachhaltigen Entwicklung beigemessen. Wissenschaft und Forschung werden neben Politik, Gesellschaft und Wirtschaft als Co-Akteure zur Lösung komplexer gesellschaftlicher und globaler Herausforderungen betrachtet. Ausgehend von den Entwicklungslinien der UN-Debatte und den darin zutage tretenden Erwartungen an Wissenschaft und Forschung setzt sich der Artikel kritisch mit dem Innovationsverständnis und den Anforderungen an Forschungs- und Entwicklungszusammenhänge unter besonderer Berücksichtigung von Transdisziplinarität und Gesundheitsforschung auseinander
Artificial Intelligence and anamnesis: Results of a population survey
No full textDigital procedures are increasingly implemented to enhance efficiency in healthcare, with Artificial Intelligence (AI) — particularly chatbot s— showing significant potential for future applications. However, little is known about patients’ acceptance of such technologies. The study “AI and Anamnesis” addresses this gap by investigating the German population’s acceptance of and willingness to use AI-driven technologies for digital anamnesis. This poster presents results from the first wave of the survey, offering initial insights into public attitudes and potential barriers to adoption
Multi-Body Simulation of a Dynamic Hand Orthosis based on a Prestressed Compliant Structure Incorporating the Human Hand
No full textMany dynamic hand orthoses use one degree of freedom joints, such as hinge joints. Therefore, these orthoses can only partially replicate the complex, multi-axis movement of the hand. A possible solution for this is the use of prestressed compliant structures as the basis for orthoses. Determining the joint forces in the wrist and optimizing the dynamic orthosis to influence these forces as well as acting muscle forces are important steps in the development of these orthoses. For this reason, in this work multi-body simulation models of an orthosis with human hand models are presented. Based on these theoretical investigations, more detailed orthosis models as well as initial prototypes of prestressed compliant dynamic hand orthoses can be developed
Numerische Untersuchungen zum Last-Verformungsverhalten von Untergrundverbesserungen organischer Böden mit nichtummantelten Sandsäulen
No full textWeiche organische Böden sind als Baugrund problematisch, dennoch lässt es sich immer häufiger nicht vermeiden, diese mit Infrastrukturprojekten zu queren. Hierfür sind Bodenverbesserungsmaßnahmen erforderlich. Der Einsatz von granularen Säulen kann den Untergrund verbessern und die Konsolidation beschleunigen. Jedoch weisen die organischen Böden eine geringe undrainierte Scherfestigkeit auf, so dass das in Deutschland weit verbreitete Verfahren der Rüttelstopfsäulen nicht zum Einsatz kommen kann. Es konnte in vorausgehenden Arbeiten bereits gezeigt werden, dass das aus Japan stammende Verfahren der Sandverdichtungspfähle auch in Mudden und Torfen angewandt werden kann, ohne dass die Säulen auf Grund
fehlender radialer Stützung versagen. In der vorliegenden Arbeit wurden verschiedene Laborversuche, u.a. Oedometer- und Triaxialversuche, nachgerechnet um die Eignung der Stoffmodelle Soft Soil Creep und Visco-Hypoplastizität für die untersuchten Böden mit unterschiedlichen organischen Anteilen zu verifizieren.
Die Modellierung des Säule-Boden-Verbundsystems mit dem FE-Programm Plaxis soll einen Beitrag zum Verständnis des Setzungsverhaltens liefern und
Kenntnisse zu den relevanten Einflussfaktoren. Die Ergebnisse der Variation des Flächenverhältnisses von Säule zu Boden wurde mit analytischen Bemessungsverfahren und Modellversuchen aus der Literatur verglichen. Außerdem wurde eine Sensitivitätsanalyse bezüglich verschiedener Bodenparameter und Modellannahmen durchgeführt. Des Weiteren wurde die Baugrundverbesserung von Böden mit unterschiedlichen organischen Anteilen untersucht. Neben der Modellierung des Bodenersatzverfahrens als ”wished in place“, wurde das Bodenverdrängungsverfahren mit Hilfe einer Hohlraumaufweitung simuliert.
Am Beispiel eines dokumentierten Bauvorhabens wurde aufgezeigt, dass es mit den gewonnenen Erkenntnissen möglich ist, durch eine FE-Simulation eine gute Prognose der Messergebnisse aufzustellen. Im Zuge der Weiterentwicklung des numerischen Versuchsstands und der Ableitung
eines Bemessungsansatzes für das Bodenersatzverfahren wären ergänzende Untersuchungen zur Anisotropie des Materials und eine zusätzliche Verifizierungen in situ wünschenswert. Um den Versuchs- und Simulationsaufwand möglichst gering zu halten, wird eine statistische Versuchsplanung (DOE) empfohlen
Influence of the Process-Related Surface Structure of L-PBF Manufactured Components on Residual Stress Measurement Using the Incremental Hole Drilling Method
No full textLaser Powder Bed Fusion (L-PBF) parts combine geometric freedom with process-induced rough surfaces that challenge residual-stress metrology. We evaluated the accuracy of the incremental hole-drilling (IHD) method with electronic speckle pattern interferometry (ESPI) by applying defined stresses via four-point bending to stress-relieved AlSi10Mg coupons, rather than measuring unknown process stresses. Flat specimens (2 mm, thin per ASTM E837) were analyzed on up-skin, side-skin, and CNC-milled surfaces; thin-specimen calibration coefficients were used. After a preliminary inter-specimen check (three specimens per surface; spread < 8 MPa), one representative specimen per surface was tested with three drill sites to assess intra-specimen uniformity. Measured IHD–ESPI stresses agreed best at 70 MPa: deviations were ~4.1% (up-skin), 6.0% (side-skin), and 6.24% (CNC-milled). At 10 MPa the relative errors increased (23.6%, 18.4%, and 1.40%), consistent with reduced ESPI signal-to-noise and fixture compliance in the low-stress regime. At 140 MPa, deviations rose again (21.1%, 14.3%, and 13.1%), reflecting operation near the ~60% Rp0.2 elastic limit of hole-drilling and potential local plasticity. Surface-dependent artifacts also mattered as follows: the side-skin required no coating and performed comparably to CNC-milled, whereas the up-skin’s roughness plus matting spray introduced fringe distortions and chip/coating debris near the hole. This controlled study indicates that IHD–ESPI can provide reliable results on L-PBF AlSi10Mg in the mid-stress range when surface preparation, coating, and rig compliance are carefully managed. Limitations include excluding down-skin surfaces and testing only one specimen per condition; thus, results should be generalized cautiously
A heuristic packet processing model for improved encrypted network analysis
No full textModern networked systems, such as those in the automotive sector, face increasing complexity and growing attack surfaces due to the rise of interconnected and data-driven technologies. Detecting malicious behavior in these environments requires efficient and scalable methods that can operate reliably despite limited resources and high communication volumes. This paper proposes a heuristic packet processing model designed to support intrusion detection based on structural and temporal characteristics of encrypted network traffic. The model follows a modular architecture consisting of four key phases: recording, sorting, prioritizing, and analyzing. At the core of the approach is the Polymetric Queueing Topology Space, a feature space that combines statistical and time series attributes derived from model structure and flow data. These features serve as input for machine learning models, which can effectively distinguish between benign and intrusion traffic patterns without relying on packet data beyond the transport layer. The approach was evaluated using the publicly available ToN_IoT dataset and demonstrated that reliable classification is achievable using a subset of the developed feature space that contains model-derived traffic features. We used Random Forest for supervised binary and multi-class classification achieving high accuracy scores of 99% for binary and 98% for multi-class classification. Additionally, for unsupervised anomaly detection, we created an Isolation Forest model accomplishing F1-scores of 0.92 for the benign and 0.96 for intrusion class. The architecture is designed to enable dynamic traffic prioritization and to offer a flexible foundation that can observe diverse network domains while maintaining efficient performance under constrained computational conditions