Fachverband Gebäude-Klima (Germany)

Driver monitoring systems are increasingly relying on physiological signals to assess cognitive and emotional states for improved safety and user experience. Electrodermal activity (EDA) is a particularly informative biomarker of arousal but is conventionally measured with skin-contact electrodes, limiting its applicability in vehicles. This work explores the feasibility of non-contact EDA estimation using Light Detection and Ranging (LiDAR) as a novel sensing modality. In a controlled laboratory setup, LiDAR reflection intensity from the forehead was recorded simultaneously with conventional finger-based EDA. Both classification and regression tasks were performed as follows: feature-based machine learning models (e.g., Random Forest and Extra Trees) and sequence-based deep learning models (e.g., CNN, LSTM, and TCN) were evaluated. Results demonstrate that LiDAR signals capture arousal-related changes, with the best regression model (Temporal Convolutional Network) achieving a mean absolute error of 14.6 on the normalized arousal factor scale (–50 to +50) and a correlation of r = 0.85 with ground-truth EDA. While random split validations yielded high accuracy, performance under leave-one-subject-out evaluation highlighted challenges in cross-subject generalization. The algorithms themselves were not the primary research focus but served to establish feasibility of the approach. These findings provide the first proof-of-concept that LiDAR can remotely estimate EDA-based arousal without direct skin contact, addressing a central limitation of current driver monitoring systems. Future research should focus on larger datasets, multimodal integration, and real-world driving validation to advance LiDAR towards practical in-vehicle deployment.

Spiking Neural Networks (SNN) is a machine learning model inspired by the spiking nature of biological human brain neurons. These neural models lead to the creation of neuromorphic computing chips, which can execute at a very low power profile, less than 1 Watt. Such a low profile can be very useful in developing low-power sensors. In this paper, we investigate the feasibility of implementing our iToF LiDAR depth correction model for static scenes using SNN, leveraging the advantages of neuromorphic chips to develop low-power sensors suitable for electric vehicles and battery-powered autonomous mobile robots. We present our results, findings, and recommendations to implement such a system. We also discuss the challenges we have faced and the possible solutions to overcome them. The paper has benchmarked the new SNN-based model versus the original ANN model in terms of accuracy, and it has been found that SNN-based models can provide comparable accuracy under certain conditions.

<bold>Background:</bold> There is a need for more effective treatments for idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis (PPF). Nerandomilast (BI 1015550), an oral preferential inhibitor of PDE4B, is being evaluated in two randomised Phase III trials: FIBRONEER-IPF (<ext-link>NCT05321069</ext-link>) and FIBRONEER-ILD (<ext-link>NCT05321082</ext-link>). FIBRONEER-ON is an open-label extension (OLE) of these studies that will evaluate the long-term safety and efficacy of nerandomilast. <bold>Aims and objectives:</bold> To describe the design of the OLE. <bold>Methods:</bold> This prospective 2-year OLE will follow the Phase III parent trials. Approximately 1,700 patients from 44 countries are expected to complete the parent trials and will be eligible for continuing into the OLE. Whether previously on active treatment or placebo, all patients in the OLE will be treated with nerandomilast at either 9 mg or 18 mg twice daily, depending on which dose demonstrates the most favourable benefit–risk profile in the parent trial. <bold>Results:</bold> The primary endpoint will be the occurrence of any adverse event over the course of the OLE. This trial will also monitor long-term efficacy outcomes, including forced vital capacity change, and time to first exacerbation, disease progression, hospitalisation and death. <bold>Conclusions:</bold> This trial will provide information on the long-term safety, tolerability and efficacy of nerandomilast in patients with IPF and PPF. <fig><object-id>erj;64/suppl_68/PA672/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>

BackgroundAs the climate and environmental crises unfold, eco-anxiety, defined as anxiety about the crises’ devastating consequences for life on earth, affects mental health worldwide. Despite its importance, research on eco-anxiety is currently limited by a lack of validated assessment instruments available in different languages. Recently, Hogg and colleagues proposed a multidimensional approach to assess eco-anxiety. Here, we aim to translate the original English Hogg Eco-Anxiety Scale (HEAS) into German and to assess its reliability and validity in a German sample.MethodsFollowing the TRAPD (translation, review, adjudication, pre-test, documentation) approach, we translated the original English scale into German. In total, 486 participants completed the German HEAS. We used Bayesian confirmatory factor analysis (CFA) to assess whether the four-factorial model of the original English version could be replicated in the German sample. Furthermore, associations with a variety of emotional reactions towards the climate crisis, general depression, anxiety, and stress were investigated.ResultsThe German HEAS was internally consistent (Cronbach’s alphas 0.71–0.86) and the Bayesian CFA showed that model fit was best for the four-factorial model, comparable to the factorial structure of the original English scale (affective symptoms, rumination, behavioral symptoms, anxiety about personal impact). Weak to moderate associations were found with negative emotional reactions towards the climate crisis and with general depression, anxiety, and stress.DiscussionOur results support the original four-factorial model of the scale and indicate that the German HEAS is a reliable and valid scale to assess eco-anxiety in German speaking populations.

It is important from a techno-economic perspective to understand the challenges associated with developing scalable processes to extract functional proteins from rapeseed cake for applications as functional food ingredients. One of these challenges is to develop a better understanding of the link between upstream mechanical processing of the rapeseed cake and the yield of extracted protein. An initial trial with cold pressed rapeseed cake (CPR) indicated that wet milling had no impact on protein yield. Measurement of protein yields with the Bradford assay indicated that 14% would be recovered. The total protein recovered (5.4% by Kjeldahl analysis) contained a glucosinolate and phytate content that was similar to the rapeseed cake at the start of the process. The fate of the protein extraction was followed during a pilot scale trial with hot-pressed rapeseed cake (HPR), using only stirring followed by sieving, revealing an increase in protein yields, as determined by the Bradford assay, from 8.0% at the start to 22.6% at the end of processing. In addition, only 16% of HPR was solubilized. A similar pilot scale trial with HPR using wet milling and stirring followed by decanting also showed an increase in protein concentration from 15.3% at the start to 23.7% at the end of processing. In addition, 38% of HPR was solubilized. This study provides an insight into what the effect of upstream mechanical processing (wet milling) has in releasing protein from rapeseed cake in order to improve process efficiency. This could have important consequences for upscaling and commercial extraction of rapeseed cake protein.

Modular cascaded circuits offer attractive qualities in reconfigurable battery applications, including improved fault tolerance and flexibility. In contrast to conventional hard-wired dc battery packs, however, cascaded topologies, such as modular multilevel circuits (MMC) with serial or serial and parallel connectivity, load modules with substantial low-frequency current ripple, which generates additional loss and can accelerate battery aging. Recent studies reveal that low-frequency ripple can cause noticeable battery aging, whereas high frequencies are insignificant, presumably mainly as they can be absorbed by the dielectric electrode capacitance, and reduce heating associated with lower high-frequency battery impedance. Previous MMC–battery control methods solely focus on state-of-charge and thermal balancing of individual modules, while the few existing methods for suppressing ripple tend to form low-frequency patterns in the modules’ load, which increase battery cycling as well as loss. This paper presents a ripple-oriented high-bandwidth control technique that minimizes low-frequency components in the module load spectrum and improves battery treatment, while maintaining the average switching frequency. The control method takes limitations related to module data acquisition into account and enhances the feedback bandwidth using observers. It works with a wide range of topologies including modules with series connectivity only as well as series/parallel. The measurements in the laboratory verify the shift of the module load from the 10 – 100 Hz range to ~5 kHz and a reduction of battery losses by up to 20 %.

Models of vulnerability and resilience within environmental history enrich contemporary debates (see Ruckert 2019; Ruckert (in print)). But it is mainly modern history and social geography that conduct research on historical climatic processes and catastrophes, in terms of what they reveal about vulnerable and resilient societies in the face of crisis. Only very gradually, some voices within medieval studies are beginning to engage with the concept of resilience.