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Prior research has reported differential roles of valence and arousal in guiding memory and attention. However, few studies have systematically examined how these affective dimensions interact across their full spectrum in audiovisual contexts, particularly when multiple images varying in emotional content are simultaneously present. To address this, we used a recognition memory paradigm in which participants viewed arrays of images representing all combinations of positive/negative valence and low/high arousal. Slightly before and during image presentation, music conveying an emotional tone, also varying in valence and arousal, was played. Memory accuracy was assessed in a later recognition phase. We further employed simple eye-tracking measures to explore how visual attention is influenced by image and music valence and arousal. Results revealed that high image arousal increases fixation duration, whilst high music arousal decreases fixation duration. Memorisation likelihood was not influenced by a four-way interaction of image and music valence and arousal, but mainly by an interaction of image valence and arousal, differently depending on music arousal. In high music arousal, all images except low arousal positive images, were memorised regardless of valence. In low arousal music, we observed that memorisation likelihood was mainly driven by high image arousal, but only paired with negative image valence was memorisation significantly higher compared to other image types. Discrimination accuracy was not observed to be influenced by image valence and arousal, but positive high arousal music significantly improved memory discrimination. By systematically manipulating both valence and arousal of images and music, we highlight how the interaction of these unimodal affective qualities can facilitate or hinder memory.
Binaural reproduction of microphone array recordings has become an important technology in the research and consumer sectors. Several commercially available spherical microphone arrays have been introduced over the years along with various methods for binaural rendering of array recordings. Most of these methods have been evaluated individually, typically using only one specific microphone array. However, a comprehensive and systematic perceptual evaluation combining different methods and various microphone arrays is lacking. This study presents the results of a listening experiment comparing the motion-tracked binaural method, various Ambisonic binaural decoders, and the parametric binaural rendering method COMPASS using loudspeaker orchestra recordings with six different microphone arrays from two rooms, the Berliner Philharmonie and a laboratory space resembling a small chamber music venue. The experiment assessed the binaural renderings with respect to overall listening experience and four perceptual attributes from the Spatial Audio Quality Inventory in comparison to a reference recorded with a head and torso simulator. The results provide detailed insights into which rendering method and array combination provides a high overall listening experience while preserving the assessed perceptual attributes externalization, coloration, source position, and presence. Moreover, the results indicate the extent to which the assessed perceptual attributes contribute to overall listening experience.
Naturally ventilated educational buildings frequently struggle to maintain adequate indoor air quality (IAQ) under variable occupancy and user-driven ventilation behavior. This exploratory case study evaluates IAQ using the CO2-concentration as an IAQ-indicator in 14 naturally ventilated rooms of six different educational facilities during the heating season in Germany, examining the effects of room design, occupancy, and ventilation practices. After evaluation against current German design recommendations, all investigated rooms reveal the conditional necessity of a mechanical ventilation system. However, all investigated rooms are exclusively ventilated naturally. Continuous monitoring of occupied rooms revealed mean indoor CO2-concentrations – grouped into categories (I to IV) according to EN 16798–1 – ranging between 233 ppm and 383 ppm (category I), 654 ppm and 691 ppm (category II), 910 ppm and 1097 ppm (category III), and 1409 ppm and 2553 ppm (category IV) above the outdoor CO2-concentration. The corresponding frequency of the prevailing CO2-concentrations during the room occupancy periods in the categories I to IV range between 6% and 63% (category I), 9% and 38% (category II), 10% and 42% (category III), and 0% and 59% (category IV). Occupant-specific ventilation rates – determined based on the CO2-decay method – varied substantially (4–188 m³ h⁻¹ occupant⁻¹). Seven of the 14 investigated rooms failed to meet the German Federal Environment Agency’s minimum ventilation rate of 25–30 m³ h⁻¹ occupant⁻¹ under typical operating conditions. Corresponding area-specific ventilation energy losses during the German heating period were estimated to range between 1 kWh m−2 a−1 to 40 kWh m−2 a−1. To translate exposure patterns into management guidance, a CO₂-based 4 × 4 risk matrix was developed, integrating exceedance severity (based on EN 16798–1 categories) and occurrence frequency. While most rooms showed only moderate CO₂ elevations for limited occupied periods, two of the 14 investigated rooms (14%) were classified as high IAQ risk in the proposed matrix, indicating severe and frequent exceedances of hygienically acceptable conditions. The proposed risk-matrix framework provides a reproducible decision-support tool for prioritizing ventilation improvements in naturally ventilated educational sector buildings. By linking exposure severity, likelihood, and energy implications, the approach supports IAQ management under operational and structural constraints.
The growing burden of mental illness and limited access to evidence-based psychotherapy have increased interest in artificial intelligence (AI)–driven conversational agents as potential supports for mental health care. In this exploratory pilot study, we examined the safety and feasibility of an intelligent virtual agent (IVA) designed to simulate psychotherapeutic interactions, with a focus on high-risk situations involving suicidality and substance use. Two licensed psychotherapists engaged in scripted interactions with the IVA across 12 predefined scenarios addressing suicidality and substance abuse. The IVA was powered by GPT-4omni and embedded in a Unity-based avatar. After each interaction, testers evaluated acceptance, usability, and human–robot interaction. Two independent psychotherapists rated the IVA’s responses using a structured scale assessing guideline adherence, risk recognition, help provision, de-escalation, and empathy. No real patients were involved; all interactions were simulated for safety testing purposes. The IVA showed preliminary indications of good usability and generally empathic responses. However, problematic responses occurred in 29% of conversations, with 12.5% rated as highly critical. Responses rated as “critical” or “highly critical” referred to outputs that failed to provide adequate support, showed insufficient risk recognition, or included ethically problematic suggestions. Key concerns included inadequate recognition of risk, normalization of substance use, and insufficient referral to crisis resources, particularly in scenarios involving underage alcohol access and suicide-related inquiries. In this small, expert-based pilot safety evaluation, the findings suggest that although AI-based agents may improve access to mental health support, rigorous safety evaluation, clinical oversight, and robust safeguards are essential prior to clinical deployment. No clinical conclusions can be drawn from this simulated study.
The COVID-19 pandemic and ensuing lockdowns disrupted social connectivity, prompting individuals to seek alternative sources of socioemotional support. This study investigated whether beat-based music, characterized by the Spotify danceability feature, served as a surrogate for social reward during the first European lockdown (March–May 2020). We integrated large-scale Spotify streaming data with psychological measures of socioemotional support from the COVIDiSTRESS global survey and governmental stringency indices across 11 European countries. Results from a linear-mixed effects model indicate that people listened to music with higher danceability during social distancing after the COVID onset (30 March – 30 May 2020) compared with the same pre-COVID period in the year before. A quasi-Bayesian multilevel mediation analysis further revealed that stricter social distancing policies predicted lower perceived socioemotional support, which in turn was associated with increased listening to more highly danceable music. This effect was specific to certain facets of socioemotional need, namely emotional attachment and reassurance of worth, which delineates the instantaneous rewarding nature of social recognition, often encountered during common activities, such as dinner parties, (band or dance) rehearsals, or (themed) excursions. These findings suggest that individuals may intuitively gravitate toward rhythmically engaging music to compensate for diminished social affirmation and bonding, highlighting beat-based music as a potential non-pharmacological tool for addressing transient socioemotional deficits during social isolation.
This article outlines a pilot study examining the reception of a video clip ("Tous les mêmes" by Stromae) in qualitative and quantitative approaches analysing user comments on social media: What motivates users to watch, interpret, and comment on polarising clips? The overall aim is to carry out an analysis of all (available) comments in order to contribute to the acquisition of knowledge about user motivations and to the methodological debate of comment analysis.
Cross ionization mode chemical similarity prediction between tandem mass spectra in metabolomics
(2026)
Mass spectrometry is a cornerstone of untargeted metabolomics, enabling the characterization of metabolites in both positive and negative ionization modes. However, comparisons across ionization modes have remained a substantial challenge due to the distinct fragmentation patterns produced by each polarity. To overcome this barrier, we present MS2DeepScore 2.0, a machine learning-based model to predict chemical similarity between mass fragmentation spectra, which works both between different and the same ionization modes. We demonstrate the utility of MS2DeepScore 2.0 in three case studies, where MS2DeepScore enabled cross-ionization mode molecular networking, enhancing data exploration and metabolite annotation. To ensure robustness, we have implemented a quality estimation method that flags spectra with low information content or those dissimilar to the training data, thereby minimizing false predictions. Altogether, MS2DeepScore 2.0 extends our current capabilities in organizing, exploring, and annotating untargeted metabolomics profiles.