Reflection silencers are installed in the exhaust system of stationary combustion engines to attenuate low-frequency noise by means of destructive interference. The acoustic properties of mufflers are experimentally determined by the standard two-load method, which only considers measurements without mean flow. In real engine operation, however, exhaust mass flow is always present. Measurements are significantly more complex and expensive if fluid flow is taken into account, which is why the available data is limited. Thus, the impact of mean flow on the attenuation of silencers is not clearly known yet. This work contributes to the state of the art by quantifying the influence of the Mach number on the transmission loss of double-tuned straight-through mufflers based on reproducible, noise corrected measurement results that include uncertainties. A frequency range between 20 Hz and 891 Hz is investigated at eleven different Mach numbers between 0 and 0.1 under ambient conditions. It is found that resonance peaks diminish with increasing Mach number, while other frequencies remain unaffected by mean flow. These findings can be transferred to operating conditions of stationary combustion engines and other exhaust systems. The experimental data will serve as a basis for the validation of analytical and numerical models in subsequent work.

In this work, the Theory of Porous Media (TPM) is employed to model percutaneous vertebroplasty, a medical procedure in which acrylic cement is injected into cancellous vertebral bone. Previously, isothermal macroscale models have been derived to describe this material injection and the arising mechanical interactions. However, the temperature of the injected cement is typically below the human body temperature, necessitating the extension of these existing models to the non-isothermal case. Following the modelling principles of the TPM and considering local thermal non-equilibrium conditions, our model introduces three energy balances as well as constitutive relations for thermal conduction and heat transfer. If restricted to local thermal equilibrium conditions, our model equations are in agreement with other TPM-based models. We observe that our model elicits physically reasonable behaviour in numerical simulations that employ parameter values and initial and boundary conditions relevant for our application. We claim our model to be thermodynamically consistent despite the employment of the Coleman and Noll procedure.

While forests are known to increase turbulence and fatigue loads on wind turbines, the impact of smaller-scale vegetation such as tree rows has received limited attention. This study investigates speed-up effects caused by a tree row and their influence on wind turbine power and blade loads using Detached Eddy Simulation (DES) and OpenFAST. A realistically modeled tree row at the Risø campus is considered, including seasonal variations in leaf area index (LAI). The simulations reveal a speed-up region above the canopy, leading to a relative power increase of approximately 6% for the high-LAI case and about half that value for the low-LAI case. Aeroelastic simulations with the NREL 5 MW wind turbine confirm the power increase and show a rise in flapwise blade root bending loads. Second-order statistics within the rotor plane remain largely unchanged, indicating that the load increase is driven by mean flow acceleration rather than turbulence. These results demonstrate that tree rows can increase power and highlight the influence of local vegetation in wind turbine siting.

Background:
While border screening measures were widely adopted by countries during the COVID-19 pandemic, a lack of consensus on the utility of border screening created a gap in best practice for its implementation. As such, countries adopted a diversity of approaches, providing an opportunity to evaluate the configuration and evolution of border screening systems. The
article addresses three questions: (i) how did countries configure their border screeningsystems for COVID-19? (ii) In what contexts did countries rely on public or private providers of these services? (iii) what do policies and narratives reveal about the perceived role of border screening in global public health? The article contributes to long-standing debates over the
private sector’s role in public health and the perceived value of border screening measures.

Methods:
This article presents results from an international comparative study based on tracking the organisation of border screening in eight countries. Secondary data was collected between  July 2021 – June 2022 from official government websites and policy publications, private sector sources where relevant, and trusted media sources in each study country. The
countries included are Australia, Canada, Germany, Ireland, South Africa, South Korea, Spain, and the United Kingdom.

Results:
All study countries used private provision for pre-departure diagnostic testing for international travellers. In contrast, screening of arriving travellers was more diverse. Countries that opted for private sector post-arrival screening saw governance challenges around accreditation and monitoring of providers, while public service provision saw challenges in capacity and high
resource costs. Travel was often framed as a ‘luxury,’ allowing states to shift responsibility for obtaining tests onto individuals; especially in the context of individuals travelling from low income to high income countries.

Conclusions:
The different approaches countries followed for screening of departing and incoming travellers suggests wealthy countries were more oriented towards defending their populations against disease importation, rather protecting the international community from disease exportation. These findings provide an opportunity to reflect on the purpose and
implementation of border screening. We emphasise a need for further discussion on the efficacy of border screening from both perspectives, given the tendency for countries to rely on these measures

According to the latest reports, selective soldering plays a critical role in the manufacturing of printed circuit boards (PCBs), especially for through-hole technology (THT) components, where molten solder is dispensed from a nozzle to create a continuous 360^° wave around the part. The consistency of this wave is crucial for reliable electrical and mechanical connections. However, repeated exposure to high temperatures (up to 300^°C) and molten solder (e.g., Sn-Pb or SAC alloys) causes nozzle oxidation, leading to impurity buildup and irregular waves. This degradation results in defective solder joints, compromising product quality, increasing rework costs, and potentially causing production downtime. Current monitoring relies on manual visual inspections, which are subjective and timeconsuming, or rule-based image algorithms that are prone to noise and require frequent recalibration. To overcome these limitations, this paper investigates autoencoders-a type of neural network for unsupervised anomaly detection-that reconstruct input images and flag deviations via reconstruction errors. Data was collected from camera-monitored nozzles under controlled conditions. Preprocessing included grayscale conversion, Gaussian blurring, normalization, and resizing. A Flat (fully connected baseline) was evaluated. Models trained on operable images using mean squared error (MSE) loss; performance assessed with MSE distributions and receiver operating characteristic (ROC) curves, using area under the curve (AUC) as the primary metric. Results show the flat autoencoder achieving  ∼ 96 % AUC with a MSE separation (oxidized > 0.02). This is the first application of autoencoders to real-time nozzle oxidation detection in selective soldering, filling a gap in AI for tool-specific degradation. It enables predictive maintenance, reducing defects.

Abstract

German universities of Applied Sciences face declining student numbers and a growing shortage of skilled professionals in technical fields, prompting institutions in Bavaria to increasingly recruit international students, many of them from India. While this strategy stabilizes enrolment and supports national goals for securing a future workforce, it also introduces substantial challenges for teaching staff, administration, and students alike. Many incoming students bring competency profiles shaped by non-European higher-education systems, often marked by a strong emphasis on reproductive learning and limited experience with independent research, critical reflection, and scientific writing. High expectations of German master’s programs, combined with linguistic and cultural barriers, contribute to lower retention rates among international students and create additional burdens on teaching staff.

Drawing on experiences from four international master’s programs at Ansbach University of Applied Sciences, the paper analyses typical difficulties and evaluates measures designed to improve integration, academic success, and study conditions. Programs composed almost exclusively of Indian students tend to reproduce culturally homogeneous learning environments, which limit intercultural exchange, hinder discursive teaching formats, and reinforce established learning habits. In contrast, heterogeneously composed cohorts show better interaction, stronger language development, and improved academic performance. To counteract homogeneity, targeted interventions such as international poster sessions, mixed laboratory groups, and joint courses between German- and English-taught programs were introduced with positive but context-dependent outcomes.

Major challenges arise in the areas of scientific practice, rule compliance, and the unreflected use of AI tools, often rooted in a lack of prior exposure to principles of good scientific practice. The university responded with measures such as training units on academic integrity, adapted assessment formats emphasizing transfer performance, and workshops on literature research and academic writing. Additional structural factors such as particularly limited access to affordable local housing, negatively affect class attendance. Attempts to mitigate this included schedule adaptations to public transport and the introduction of block courses with mandatory practical components.

The paper concludes that successful internationalization requires more than English-language programs. It demands comprehensive support structures, including language training, intercultural competence development for staff, and institutional services tailored to international students’ needs. Given the intensive advising workload, smaller learning groups and recognition of international teaching efforts in workload models are essential. Only through sustained institutional commitment, adequate resources, and openness on all sides can internationalization efforts translate into improved learning outcomes and long-term integration.

Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments. Traditionally this can be achieved by using time-consuming computational fluid dynamics (CFD) simulations on reanalysis data. This study explores the performance of an autoencoder (AE) and a variational autoencoder (VAE) in approximating downscaled wind speed and direction using real-world reanalysis data and reference geo- and vegetation data. The AE model was trained for 2000 epochs and demonstrates the ability to replicate wind patterns with a mean absolute error (MAE) of approximately −0.9. However, the AE model exhibited a consistent underestimation of wind speeds and a directional shift of approximately 10 degrees compared to CFD reference simulations. The VAE model produced visually improved results, capturing complex wind flow structures more accurately than the AE model. It mainly achieves better local accuracy and a reduced variance of the results. The overall result suggests that while autoencoders can approximate wind flow patterns, challenges remain in capturing the full variability of wind speeds and directions with sufficient precision. The study highlights the importance of balancing reconstruction accuracy and latent space regularization in VAE models. Future work should focus on optimizing model architecture and training strategies to enhance accuracy, prediction reliability and generalizability across diverse wind conditions and various locations.

Mit der Einführung des Promotionsrechts an bayerischen Hochschulen für angewandte Wissenschaften widmen sich diese fortan auch der Ausbildung von Promovierenden. Im Gegensatz zu Universitäten ist jedoch eine hochwertige didaktische Qualifikation der Promovierenden bayerischer Hochschulen gegenwärtig noch nicht etabliert. Um diesem Bedarf gerecht zu werden, wurde an der Hochschule Ansbach ein Modul zu digital-analoger Hochschullehre namens »Teaching Skills« für Promovierende entwickelt und im Wintersemester 2023/24 erprobt und evaluiert. In diesem Beitrag wird zunächst der interaktive didaktische Ansatz des Lehrkonzepts erörtert und aufgezeigt, wie die einzelnen Lehreinheiten konzeptioniert wurden. Anschließend folgt ein Einblick in die konkrete Implementierung des Moduls an der Hochschule Ansbach. Abschließend erwachsen aus Begleitevaluation, den Beobachtungen des Projektteams, sowie den Lessons Learned Empfehlungen für die erfolgreiche Implementierung des Konzepts an weiteren Hochschulen.

Printing of polymer parts via digital light processing (DLP) is an established technology to produce functional complex parts within a short period of time. Within recent years this technology has advanced in even higher geometrical accuracy of the parts since printers and materials were constantly improved. Nowadays, it is possible to print parts with features in micro- (µm) or even nanoscale (nm), opening new fields of application and further improvement to already existing parts. Similar to stereolithography (SLA), DLP applies UV light to harden a photopolymer resin in a layer-by-layer process. This enables printing parts with complex geometries such as cavities, implying the resin can later exit the part. However, DLP has an advantage over other 3D-printing technologies when it comes to creating cavities or the printing of overhang structures in general. Since in DLP the entire layer is hardened at once, less support structure is needed to achieve a uniform, stable layer. In consequence, this opens the design option to print micro cavities without any support structures needed. This paper highlights the process of micro DLP printing of polymer parts, focusing on the influence of the most relevant parameters on the successful print of overhang structures. Therefore, the paper presents an experimental analysis, investigating the effects of relevant printing parameters of an overhang on the dimensional accuracy of the resulting parts. The results show a significant dependence of the bridges (overhangs) on gaps size and curing time, and the deformation of the bridges are in the opposite sides of the parts.

• Vor dem Hintergrund sinkender Studierendenzahlen und des Fachkräftemangels in den technischen Berufen in Deutschland, rekrutieren die Hochschulen in Bayern zunehmend Studierende aus dem Ausland. Dabei stoßen die angebotenen internationalen Studiengänge besonders bei Studierenden aus Indien auf großes Interesse. Andersartige Kompetenzprofile aus dem außereuropäischen Bachelorstudium in Kombination mit sehr hohen Erwartungen der Studierenden an ein Masterstudium in Deutschland führen zu enormen Herausforderungen bei allen Beteiligten. Förderlich für den erfolgreichen Studienverlauf ist es, Maßnahmen zur Gruppendurchmischung zwischen ausländischen und deutschen Studierenden wie internationale Postersessions und gemeinsame Studienangebote zu implementieren. Darüber hinaus ist es erforderlich, Regeln der guten wissenschaftlichen Praxis klar zu kommunizieren und deren Befolgung einzufordern. Nicht zuletzt ist die Stärkung der Sprachkompetenz ein Schlüssel für ein erfolgreiches Studium.

In additive manufacturing, the optimisation of process parameters to simultaneously enhance both mechanical and geometrical properties remains a significant challenge. This study presents a comparative analysis between two distinct approaches to multi-response optimisation: a hybrid method combining Artificial Neural Networks with Pattern Search Algorithm, and the traditional Response Surface Methodology. Both methods were applied to optimise the process parameters of Fused Filament Fabrication using Polylactic Acid. The hybrid ANN + PSA model was used to predict process outputs and optimise printing parameters, while RSM employed a statistical approach to identify optimal parameter combinations through designed experiments. Results show that ANN + PSA achieved better outcomes with a maximum tensile strength of 61.88 MPa and dimensional deviations within 3%, compared to RSM's tensile strength of 53.05 MPa and deviations up to 4%. The ANN + PSA model demonstrated higher predictive accuracy with an R² of 94.34% during training and 91.34% during evaluation, versus RSM’s R² of 90.5% for tensile strength prediction. Additionally, ANN + PSA consistently required fewer experimental trials due to its integration with the Pattern Search Algorithm, improving computational efficiency. The findings demonstrate that ANN + PSA is computationally efficient for scenarios with fewer experimental trials, while RSM offers a more detailed understanding of interaction effects. The comparative insights from this study contribute to enhanced multi-response optimisation strategies in additive manufacturing.

Medical products such as mesh implants should demonstrate high material quality and long service life in different
environments over several years. Polypropylene (PP) is a thermoplastic widely used for medical applications due to its
mechanical properties, biocompatibility, and chemical resistance. This study examines the effect of thermal treatment on
the mechanical, thermal, and chemical properties of polypropylene homopolymer (PP-H) and random copolymer (PP-
RC), with and without the addition of palmitic acid (PA) to simulate lipid exposure during implantation. Mechanical and
thermal properties of PA-treated PP-H as analysed by tensile test, differential scanning calorimetry, and thermogravimetric
analysis, showed a higher tensile strength, elastic modulus, and thermal stability than PP-RC. Fourier-transform-infrared
spectroscopy detected no significant chemical changes after thermal treatment, whereas in Raman spectroscopy changes in
the intensity of the peaks and new peaks due to PA were visible. In conclusion, PA incubation accelerated material deg-
radation, with PP-H demonstrating superior stability in the mechanical and thermal properties under the tested conditions.

Selective Laser Sintering of Polymers is a widely used Additive Manufacturing technology that involves a laser to selectively sinter layers of a powder bed, with Polyamide 12 being a common material choice. Despite its favourable processability and component performance, the printing process leaves a significant amount of unsintered powder that undergoes heat treatment due to temperature gradients during printing, leading to material degradation over time. A deep comprehension of the aging behaviour in the powder for rightly planning the successive building process is thus necessary to define the proper recycling methods. This paper presents a comprehensive study of the thermal and structural characteristics of Polyamide 12 after five successive reusing cycles, as well as the dimensional accuracy and the mechanical strength of the corresponding printed parts. The study includes tests on the powder that underwent successive printing, and the parts manufactured using this powder. The results were compared to those obtained from virgin powder. These results were used to justify the differences in mechanical, macro-geometrical, and micro-geometrical performance between virgin and multiple reused powder parts. The results indicate that the powder degradation causes a significant reduction of the mechanical strength, and the texture quality of parts made from reused powder, while the dimensional accuracy remains very high.

Laser technology presents a compelling alternative to conventional methods for removing coatings from plastic and metallic parts, offering advantages over chemical solvents and media blasting for component reuse and recycling. This study investigates laser paint removal from thermoplastic materials, particularly those with complex 3D geometries. Utilizing a 1064 nm pulsed fiber laser, experiments were conducted to analyze paint ablation from plastic substrates and characterize the resulting surfaces. Experimental results on plastic substrates demonstrate the feasibility of complete paint removal while preserving the integrity of the thermoplastic. The laser's energy density, scanning speed, and spot size are identified as key parameters influencing removal efficiency and substrate integrity. The study presents the necessary steps and process conditions and analyzes the resulting quality of the processed surfaces. The study concludes that laser paint removal facilitates the reuse or high-quality recycling of plastic parts and can be an environmentally friendly, flexible, and highly efficient method for paint removal.

The building sector accounts for almost 1/3 of the global final energy consumption, which is mainly made up of heating and cooling, as well as domestic hot water heating, lighting and household appliances. Due to global warming, the energy demand in buildings for heating will decrease, while that for cooling will increase in future (V. Ciancio et al., Sustainable Cities and Society 60, 102213 (2020)). Energy-saving ventilative cooling strategies like e.g. night cooling or hybrid ventilation in mixed-mode buildings therefore will be favourable compared to conventional heating, ventilation and air conditioning (HVAC) systems (L.L. Gomis et al., Energy and Buildings 231, 110597 (2021)).

Our research focuses on evaluating the effectiveness of automated, window-integrated ventilation systems compared to conventional ventilation and air conditioning systems for thermal control during summertime. We use two floors of an office building as a real laboratory for this purpose. Extensive data is collected via KNX sensors installed in the building. The study evaluates scenarios with manual and controlled natural window ventilation, decentralised window-integrated mechanical systems with heat recovery and combinations of all of them. The accuracy of the KNX sensors is checked with the help of laboratory measuring devices.

By connecting to a robust database, we capture detailed environmental metrics including temperature, humidity and CO2 levels, and other parameters such as user presence. Statistical and AI-supported analyses are used to determine the comfort level, user acceptance, energy requirements, CO2 emissions and economic costs for different scenarios. The results are compared with established building performance simulation tools such as EnergyPlus or IDA ICE.

Through detailed analyses and simulations, including user behaviour and the influence of user-specific conditions, our comprehensive approach will provide a better understanding on the optimal dimensioning, configuration and operation of natural, mechanical and hybrid window-integrated ventilation systems, improving operational efficiency in terms of cycles and running times. The study emphasises the need for sensor-based parameter recording to improve the accuracy of demand forecasts and thus support the efficient and sustainable use of energy resources and provides actionable recommendations for optimizing summer thermal control through automated, window-integrated solutions.