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Generative AI in music (GAIM) technologies are rapidly transforming music production, yet little is known about how working musicians perceive and respond to these changes. This study presents findings from in-depth interviews with 43 musicians, spanning diverse genres, professional roles and experience with music technology. Our analysis, informed by a reflexive thematic analysis approach, suggests complex tensions between perceived benefits and risks of GAIM adoption. Key themes were generated around tensions between (i) fear of reduced job opportunities for professional musicians and appreciation of the potential of AI to make individual musicians more independent and productive; (ii) fear about the exploitation of artists’ work and benefits of open music exchanges; (iii) fear that AI will exacerbate inequities and recognition of AI’s potential to increase access to music production. Our findings highlight the need for careful consideration of justice and fairness in GAIM development and deployment, suggesting that different types of GAIM use (from assistant to replacement) carry distinct ethical implications. This work provides a foundation for understanding how GAIMs can be integrated into music production while respecting artists’ rights and creative agency. 

Abstract Extreme weather events, such as hurricanes with intense rainfall and storm surges, are posing increasing challenges to local communities worldwide. These hazards not only result in substantial property damage but also lead to significant population displacement. Federal disaster assistance programs are crucial for providing financial support for disaster response and recovery, but the allocation of these resources often unequal due to the complex interplay of environmental, social, and institutional factors. Relying on datasets collected from diverse sources, this study employs a structural equation model to explore the complex relationships between disaster damage (DD), social vulnerability (SV), public disaster assistance (PDA), the national flood insurance (NFI), and population migration (PM) across counties in the contiguous US. Our findings reveal that communities with lower SV tend to experience higher levels of DD across US counties. SV is negatively associated with PM, PDA, and NFI, both directly and indirectly. Furthermore, PDA is positively linked to PM, whereas DD has a direct negative effect on PM but an indirect positive effect through PDA. 

Radiative cooling textiles hold promise for achieving personal thermal comfort under increasing global temperature. However, urban areas have heat island effects that largely diminish the effectiveness of cooling textiles as wearable fabrics because they absorb emitted radiation from the ground and nearby buildings. We developed a mid-infrared spectrally selective hierarchical fabric (SSHF) with emissivity greatly dominant in the atmospheric transmission window through molecular design, minimizing the net heat gain from the surroundings. The SSHF features a high solar spectrum reflectivity of 0.97 owing to strong Mie scattering from the nano-micro hybrid fibrous structure. The SSHF is 2.3°C cooler than a solar-reflecting broadband emitter when placed vertically in simulated outdoor urban scenarios during the day and also has excellent wearable properties. 

Abstract The geographic center of El Niño has shifted from the tropical eastern Pacific (EP) in the 20th century to the tropical central Pacific (CP) in the 21st century. Analyzing data spanning 1948–2018, this study uncovers notable alterations in the impact of the changing El Niño patterns on California market squid (Doryteuthis opalescens) landings. While the traditional EP El Niño in the 20th century significantly reduces squid landings, this impact diminishes with the ascent of the CP type of El Niño in the 21st century. Remarkably, the CP‐I type of El Niño, a specific variant where warming occurs predominantly in the central Pacific and is often less intense but more frequent than traditional El Niño events, can even amplify squid landings. These transformations stem from variations in sea surface temperature, trade winds, and Sverdrup transport associated with different El Niño types. These findings suggest that the fishery community should consider developing adaptive approaches to address the evolving impacts of El Niño. 

Abstract One-dimensional (1D) olivine iron phosphate (FePO₄) is widely proposed for electrochemical lithium (Li) extraction from dilute water sources, however, significant variations in Li selectivity were observed for particles with different physical attributes. Understanding how particle features influence Li and sodium (Na) co-intercalation is crucial for system design and enhancing Li selectivity. Here, we investigate a series of FePO₄particles with various features and revealed the importance of harnessing kinetic and chemo-mechanical barrier difference between lithiation and sodiation to promote selectivity. The thermodynamic preference of FePO₄provides baseline of selectivity while the particle features are critical to induce different kinetic pathways and barriers, resulting in different Li to Na selectivity from 6.2 × 10²to 2.3 × 10⁴. Importantly, we categorize the FePO₄particles into two groups based on their distinctly paired phase evolutions upon lithiation and sodiation, and generate quantitative correlation maps among Li preference, morphological features, and electrochemical properties. By selecting FePO₄particles with specific features, we demonstrate fast (636 mA/g) Li extraction from a high Li source (1: 100 Li to Na) with (96.6 ± 0.2)% purity, and high selectivity (2.3 × 10⁴) from a low Li source (1: 1000 Li to Na) with (95.8 ± 0.3)% purity in a single step. 

Abstract Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient.