Ausdünnung beim Apfel: Beim Fadengerät lässt sich noch einiges herausholen!

Die negativen Nebenwirkungen des Fadengerätes lassen sich mit einer Erhöhung der Fahrgeschwindigkeit erheblich verringern ohne dass es dabei zu einer Verminderung der Ausdünnwirkung kommt. Dies erlaubt dem Praktiker zur Erhöhung und Optimierung der Ausdünnwirkung zwei evt. sogar drei Durchgänge mit dem Fadengerät vorzunehmen (gezielt auf gewisse Blütenstadien wie z.B. die Königsblüten oder die späten Blüten am 1-j. Holz). Limitierend ist, dass es nicht so einfach ist, mit derart hohen Geschwindigkeiten durch die Anlage zu preschen. H. Gessler hat in der Zwischenzeit ein doppelspindliges Fadengerät gebaut, dass bei Normalgeschwindigkeit den selben Effekt erzielt wie das schnellere Fahren. Die Erfahrungen mit Vinasse-Produkten sind sehr interessant, bedürfen aber noch einer sehr genauen Überprüfung zumal soeben 2 neue Vinasse Produkte in die BIO-SUISSE Hilfsliste aufgenommen worden sind. Aber auch weil die Südtiroler Kollegen uns von schweren Berostungen und nicht befriedigender Wirkung berichten – auch mit den von uns vermittelten Schweizer Vinassen

Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning

We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests

Towards Enhanced Human Activity Recognition through Natural Language Generation and Pose Estimation

Vision-based human activity recognition (HAR) has made substantial progress in recognizing predefined gestures but lacks adaptability for emerging activities. This paper introduces a paradigm shift by harnessing generative modeling and large language models (LLMs) to enhance vision-based HAR. We propose utilizing LLMs to generate descriptive textual representations of activities using pose keypoints as an intermediate representation. Incorporating pose keypoints adds contextual depth to the recognition process, allowing for sequences of vectors resembling text chunks, compatible with LLMs. This innovative fusion of computer vision and natural language processing holds significant potential for revolutionizing activity recognition. A proof of concept study on a Kinetics700 dataset subset validates the approach's efficacy, highlighting improved accuracy and interpretability. Future implications encompass enhanced accuracy, novel research avenues, model generalization, and ethical considerations for transparency. This framework has real-world applications, including personalized gym workout feedback and nuanced sports training insights. By connecting visual cues to interpretable textual descriptions, the proposed framework advances HAR accuracy and applicability, shaping the landscape of pervasive computing and activity recognition research. As this approach evolves, it promises a more insightful understanding of human activities across diverse contexts, marking a significant step towards a better world.Comment: Presented at the Symposium on Generative AI for Pervasive Computing (GenAI4PC) held at UbiComp/ISWC 202

An interpretable machine learning approach to multimodal stress detection in a simulated office environment

Background and objective: Work-related stress affects a large part of today’s workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providing personalised feedback and allowing for the development of just-in-time adaptive health interventions for stress management. Previous studies on stress detection in work environments have often struggled to adequately reflect real-world conditions in controlled laboratory experiments. To close this gap, in this paper, we present a machine learning methodology for stress detection based on multimodal data collected from unobtrusive sources in an experiment simulating a realistic group office environment (N=90). Methods: We derive mouse, keyboard and heart rate variability features to detect three levels of perceived stress, valence and arousal with support vector machines, random forests and gradient boosting models using 10-fold cross-validation. We interpret the contributions of features to the model predictions with SHapley Additive exPlanations (SHAP) value plots. Results: The gradient boosting models based on mouse and keyboard features obtained the highest average F1 scores of 0.625, 0.631 and 0.775 for the multiclass prediction of perceived stress, arousal and valence, respectively. Our results indicate that the combination of mouse and keyboard features may be better suited to detect stress in office environments than heart rate variability, despite physiological signal-based stress detection being more established in theory and research. The analysis of SHAP value plots shows that specific mouse movement and typing behaviours may characterise different levels of stress. Conclusions: Our study fills different methodological gaps in the research on the automated detection of stress in office environments, such as approximating real-life conditions in a laboratory and combining physiological and behavioural data sources. Implications for field studies on personalised, interpretable ML-based systems for the real-time detection of stress in real office environments are also discussed

The effectiveness and user experience of a biofeedback intervention program for stress management supported by virtual reality and mobile technology: a randomized controlled study

Background: Heart rate variability biofeedback (HRV-BF) can be used for stress management. Recent feasibility studies suggest that delivering HRV-BF in virtual reality (VR) is associated with better user experience (UX) and might yield more beneficial changes in HRV than two-dimensional screens. The effectiveness of a VR-supported HRV-BF intervention program has, however, not been investigated yet. Methods: In this study, 87 healthy women and men were assigned to a VR-supported HRV-BF intervention (INT; $n=44$) or a wait-list control (WLC; $n=43$) group. The INT came to the lab for four weekly HRV-BF sessions in VR using a head-mounted display. Between lab sessions, participants were asked to perform breathing exercises without biofeedback supported by a mobile application. Stress-related psychological and psychophysiological outcomes were assessed pre- and post-intervention and at a follow-up four weeks after the intervention in both groups. A psychosocial stress test was conducted post-intervention to investigate changes in stress reactivity. UX was assessed after each HRV-BF session in the INT. Results: Analysis revealed that LF increased significantly from pre- to post-, whereas pNN50 increased and chronic stress decreased significantly from pre-intervention to follow-up in the INT compared to the WLC. Anxiety and mental fatigue decreased significantly, while mindfulness and health-related quality of life increased significantly from pre- to post- and from pre-intervention to follow-up in the INT compared to the WLC (all small effects). The two groups did not differ in their stress reactivity post-intervention. As for UX in the INT, the degree of feeling autonomous concerning technology adoption significantly decreased over time. Competence, involvement, and immersion, however, increased significantly from the first to the last HRV-BF session, while hedonic motivation significantly peaked in the second session and then gradually returned to first-session levels. Conclusions: This HRV-BF intervention program, supported by VR and mobile technology, was able to significantly improve stress indicators and stress-related symptoms and achieved good to very good UX. Future studies should control for potential placebo effects and emphasize higher degrees of personalization and adaptability to increase autonomy and, thereby, long-term health and well-being. These findings may serve as a first step towards future HRV-BF applications of cutting-edge, increasingly accessible technologies, such as wearables, VR, and smartphones, in the service of mental health and healthcare

JWST UNCOVER: Discovery of z>9z>9 Galaxy Candidates Behind the Lensing Cluster Abell 2744

We present the results of a search for high-redshift ($z>9$) galaxy candidates in the JWST UNCOVER survey, using deep NIRCam and NIRISS imaging in 7 bands over $\sim45$ arcmin$^2$ and ancillary HST observations. The NIRCam observations reach a $5-\sigma$ limiting magnitude of $\sim 29.2$ AB. The identification of high$-z$ candidates relies on a combination of a dropout selection and photometric redshifts. We find 16 candidates at $9<z<12$ and 3 candidates at $12<z<13$, eight candidates are deemed very robust. Their lensing amplification ranges from $\mu=1.2$ to 11.5. Candidates have a wide range of (lensing-corrected) luminosities and young ages, with low stellar masses ($6.8<$ log(M$_{\star}$/M$_{\odot}$) $<9.5$) and low star formation rates (SFR=0.2-7 M$_{\odot}$ yr$^{-1}$), confirming previous findings in early JWST observations of $z>9$. A few galaxies at $z\sim9-10$ appear to show a clear Balmer break between the F356W and F444W/F410M bands, which helps constrain their stellar mass. We estimate blue UV continuum slopes between $\beta=-1.8$ and $-2.3$, typical for early galaxies at $z>9$ but not as extreme as the bluest recently discovered sources. We also find evidence for a rapid redshift-evolution of the mass-luminosity relation and a redshift-evolution of the UV continuum slope for a given range of intrinsic magnitude, in line with theoretical predictions. These findings suggest that deeper JWST observations are needed to reach the fainter galaxy population at those early epochs, and follow-up spectroscopy will help better constrain the physical properties and star formation histories of a larger sample of galaxies.Comment: Submitted to MNRA