Since COVID-19, clean indoor air has become more of a focus due to airborne viruses. Conventional filters often fail to capture ultrafine particles. This work investigates how standing ultrasonic fields manipulate aerosols for more efficient cleaning. Gor’kov theory and FEM simulations used to evaluate the acoustic forces on particles. Experiments by light refractive vibrometry and high-speed camera observations confirm the model quality.

Media bias is an enduring feature of news dissemination, reflecting the subjective perspectives of its creators across history. From archaic records like "The Victory Stele of Naram-Sin" to contemporary news channels, bias permeates media, influencing political, social, and public health narratives. This research aims to investigate the persistent phenomenon of media bias and the potential of large language models (LLMs)(Kojima et al., 2022) in its detection and classification, in order to deploy publicly available software tools aiming to enhance media literacy among news consumers.

Traditionally, media bias served the interests of ruling powers; even with the rise of modern journalism, objectivity is often compromised by commercial pressures and inherent human biases. (Rodrigo-Ginés et al., 2024). As media landscapes evolve, bias continues to shape public opinion, impacting democratic processes and public health perceptions—evident during the COVID-19 pandemic, where polarized media narratives swayed public health decisions and fueled misinformation. (Recio-Román et al., 2023)

Current research on the effects of labeling media bias or propaganda, whether automatically or with human involvement, highlights the complexity of the issue. Depending on different circumstances, labeling can lead to negative outcomes (such as reinforcing filter bubbles by providing means to avoid news with a different perspective), no change in news consumption behavior at all, or, in some cases, an actual improvement in media literacy as intended (Zavolokina et al., 2024).

This research aims to develop a technical solution for the automatic labeling of biased media content, emphasizing several proposals that we hope will lead to a positive effect on media literacy among those presented with the system’s assessments.

These proposals include using a fine-grained taxonomy of bias types rather than a simple binary left/right labeling, focusing on detailed explanations for each model decision in natural language, marking bias at the sentence level rather than at the article or publication level to provide more insights, fine-tuning autoregressive models like GPT-3.5 or Mistral with high-quality examples instead of using “simple” bidirectional models like BERT(Brown et al., 2020) or non-finetuned models, and focusing on the German language, which has not yet been properly explored for such systems.

Understanding readers' perceptions when exposed to bias-labeled content is another facet of this research. It will explore how bias labeling influences readers' views on credibility and neutrality and whether real-time bias indicators affect news consumption behaviors. As mentioned, practical applications serve as a cornerstone of this research. One aim is to implement bias detection systems in real-world settings, such as search engines and news aggregators, to promote balanced information consumption. The development of user tools, like browser extensions highlighting media bias, intends to address public need for transparent information evaluation.

In essence, this research contributes to media literacy enhancement by demystifying media bias through advanced computational methods. By refining detection mechanisms, classifying bias more effectively, and implementing practical tools, it aims to fortify democratic discourse and public understanding, thereby addressing the pervasive influence of media bias in today’s interconnected world.

References

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 1877–1901). Curran.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 35, 22199–22213.

Recio-Román, A., Recio-Menéndez, M., & Román-González, M. V. (2023). Influence of Media Information Sources on Vaccine Uptake: The Full and Inconsistent Mediating Role of Vaccine Hesitancy. Computation (Basel). https://doi.org/10.3390/computation11100208

Rodrigo-Ginés, F.-J., Carrillo-de-Albornoz, J., & Plaza, L. (2024). A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it. Expert Systems with Applications, 237, 121641.

Keywords: Media Bias, Large Language Models, Bias Detection, Natural Language Processing, Journalism, Public Opinion, Taxonomy

This paper explores an alternative approach to ultrasonic flow measurement using guided acoustic waves in cylindrical modes. Unlike conventional methods with diagonal sound propagation, the entire pipe including the fluid is excited to vibrate, reducing path-dependent correction factors. A ring-shaped sensor was developed for a DN15 steel pipe. Results show a signal time shift 2.5 times greater than with Lamb wave-based sensors, adjustable over distance. This approach enables precise, non-invasive flow measurement across various pipe diameters.

Background, Motivation and Objective

Hip joint prostheses (HJP) are increasingly common with an aging population. The most frequent complication is aseptic loosening, linked to bone resorption and a growing soft tissue gap between bone and implant. However, integration monitoring and loosening diagnosis still rely on expensive, static X-ray imaging. Ultrasound, despite cheaper, dynamic, and radiation-free, is not yet viable due to its limits in resolving tissue beyond the bone.

This work presents how a novel quantitative ultrasound (QUS) data processing approach could improve HJP monitoring by quantitatively assessing osteointegration. While the basic concept was already tested on artificial models, we now show first clinical results for ultrasonic thickness measurements of the bone-implant gap at hip implant patients compared to X-ray imaging.

Statement of Contribution/Methods

Our approach is based on an analysis of raw (RF) beamformed ultrasonic data. A scan line perpendicular to the bone surface is extracted and a certain signal range following the dominant bone reflection is transformed to the frequency domain using a Fast Fourier Transform (FFT) (a). The gap thickness, indicating local osteointegration quality and potential loosening signs, is then determined by evaluating the frequency spacing of minima in the amplitude spectrum.

To demonstrate the potential of our QUS method, we analyzed ultrasonic scans from six HJP patients at one fixed position each (sagittal and transversal) using a handheld scanner (C3 HD3, Clarius, Canada) and compared the measured gap thicknesses with x-ray images.

Results/Discussion

(b) shows the gap thicknesses determined using our QUS method in comparison with the visual assessment of corresponding X-ray images. Despite the small sample size and some simplifying assumptions used for this first feasibility test, the clear trend highlights the approach’s potential for assessing local implant integration. The cases where no gap could be seen in the x-ray image illustrate its potential for gap detection beyond X-ray resolution limits.

Besides gap thickness, our QUS approach could also reveal elasticity changes in the soft-tissue gap, potentially indicating critical biofilm formation. Further steps also include extending our method to an automated thickness detection during dynamic scanning and integrating results into B-mode images, e. g., using color coding.

Chronic kidney disease (CKD) is characterized by persistent inflammation and tubulointerstitial fibrosis leading to end-stage renal disease. Transient receptor potential canonical 6 (TRPC6) channel inhibition mitigates tubular injury and renal fibrosis in murine models of unilateral ureteral obstruction (UUO) and 2-month chronic post-ischemia-reperfusion injury (2m post-I/R). Through integrated analysis of single-cell-RNA-sequencing (scRNA-Seq) data from UUO mice treated with the selective TRPC6 inhibitor SH045, here the renoprotective cell composition and cell type-specific transcriptional programs are defined. We explored translational aspects by conducting an in-depth scRNA-Seq analysis of kidney samples from patients with CKD. These results reveal global transcriptional shifts with a dramatic diversification of inflammatory cells, endothelial cells and fibroblasts. Notably, a distinct subpopulation of novel endothelial cells is delineated, which is termed ECRIN, that regulate inflammatory networks implicating VEGF and GAS signaling pathways. The data also indicates that inhibition of TRPC6 channels triggers a Prnp transcription factor regulatory network, which contributes to the alleviation of renal fibrosis. The key findings are supported at the protein level by immunofluorescence and western blot analysis. We observed similar patterns in the chronic 2m postI/R injury model. These findings provide novel insights into the potential therapeutic benefits of TRPC6 inhibition in CKD.

Wärmepumpen (WP) stellen eine Schlüsseltechnologie für die Energiewende im Gebäudesektor dar. Die Erkenntnis, dass die Effizienz von WP vom Temperaturhub zwischen Wärmequelle und Wärmesenke und damit von der benötigten Vorlauftemperatur abhängt, ist mittlerweile weit verbreitet. Vor diesem Hintergrund werden Raumheizeinrichtungen auch so ausgewählt und ausgelegt, dass sie mit einem möglichst niedrigen Temperaturniveau auskommen. Wenig Aufmerksamkeit wird bisweilen aber der Tatsache geschenkt, dass in den hydraulischen Systemen ein Abfall der Vorlauf- bzw. Versorgungstemperatur (Drop of Supply Temperature (DoST)) von der WP bis zum Verbraucher auftritt, der im weiten Bereich zwischen 0 und 20 K variiert. Dies führt mitunter zu einer Effizienzverschlechterung des WP-Betriebs von bis zu 50 %. Der vorliegende Beitrag beschäftigt sich mit den oft vermeidbaren Ursachen und gibt Größenordnungen für den DoST an. Hieraus kann die Effizienzverschlechterung für das Wärmepumpen-System (WPS) nach „CARNOT“ abgeschätzt werden.

Der vorliegende Beitrag befasst sich mit neueren Erkenntnissen der Mobilitätsforschung zur Erklärung von Verkehrshandeln. Aus der Perspektive verschiedener Disziplinen wird die Wechselwirkung zwischen Mobilität und sozialen Strukturen analysiert und hinsichtlich ihrer Bedeutung für eine sozial-ökologische Transformation des Mobilitätssystems bewertet. Zunächst wird das Verhältnis von Raumstrukturen und Mobilität geklärt, bevor grundlegende Theorien der Umweltpsychologie vorgestellt werden. Anschließend wird die Bedeutung von Routinen sowie von Umbruchsituationen im Lebenslauf für die Veränderung des Verkehrshandelns diskutiert und das darauf basierende Konzept der Mobilitätsbiografien erläutert. Folgerungen für eine sozial-ökologische Transformation des Mobilitätssystems und die dafür notwendige Mobilitätspolitik schließen den Beitrag ab.