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Multifunctional electronic skin with waterproof strain sensing and ultra-stretchable triboelectric energy harvesting
No full textWearable flexible strain sensors and single-electrode triboelectric nanogenerators (TENGs) have emerged as promising building blocks for smart electronic skin applications. However, only a few studies have succeeded in integrating both technologies into a single device while maintaining stable and reliable performance. Here, we present a simple and scalable fabrication approach using spraying, electrostatic spinning, and vacuum filtration to develop a multifunctional system comprising a water-resistant strain sensor and a stretch-insensitive TENG. The strain sensor is constructed from carboxylated carbon nanotubes (CNTs-COOH), fluorinated alkyl silane-modified Ti3C2Tx (FAS-MXene), and a flexible polydimethylsiloxane (PDMS). The TENG consists of a film made of polyvinylpyrrolidone-modified CNTs (PVP-CNTs), Ti3C2Tx (MXene), and electrospun thermoplastic polyurethane nanofibres (TPU) as an electrode. When employed as a strain sensor, the device demonstrates high sensitivity, a wide sensing range (0 % to 100 % strain), excellent water resistance, and outstanding durability (5000 cycles at 50 % strain). These properties are achieved through MXene surface chemical modification and a unique microcrack structure developed under strain. As a highly stretchable TENG, the device exhibits remarkable stability, with minimal changes in relative resistance (0.03 at 20 % strain) even after 5700 cycles, owing to the strong adhesion forces generated by hydrogen bonding interactions between the porous TPU film, PVP-CNTs, and MXene. The integrated device enables simultaneous strain sensing and self-powering capabilities, offering a versatile platform for applications such as health monitoring, encrypted information transmission, and object recognition. The low cost and ease of mass fabrication of this electronic skin mark a significant advancement towards future multifunctional wearable technologies.</p
The Uneven Impact of Big Data in Science:A Literature Review and Reflective Examination of Big Data in Data-Intensive Disciplines
No full textData practices vary widely across scientific disciplines. While Big Data has significantly transformed research activities across various domains and has been described as a revolutionary force in scientific paradigms, its application has not been uniform across all fields. This study examines Big Data research and practices in data-intensive disciplines (DIDs), identifying its distinct features and revealing the uneven adoption and impact of Big Data across scientific domains. Our findings indicate that discussions on the epistemological concepts and definitions of Big Data in DIDs are limited, with little divergence among scholars. Machine learning emerges as a central understanding and technological focus across DIDs, closely integrated with research topics and widely driving scientific advancements. Additionally, this paper highlights the instrumental role of Big Data in scientific inquiry and underscores the disparities in its impact across different disciplines. Through this review, we aim to foster a more comprehensive understanding of Big Data’s evolving role in science, emphasizing the need for continued critical reflection as its influence continues to develop
Reducing cropland fragmentation may not be universally beneficial at increasing land use efficiency:Evidence from multiscale spatial analysis of Huang-Huai-Hai region, China
No full textCropland fragmentation, a global issue affecting agricultural efficiency, poses management challenges while offering opportunities to optimize production and mitigate risks. In this study, we investigated the impact of cropland fragmentation on cropland use intensity in the Huang-Huai-Hai region using data from China's Second National Land Survey (2010). By analysing spatial patterns of fragmentation—focusing on mean plot size, cropland density, and the area-weighted mean shape index—this research applies advanced methods, including empirical orthogonal function (EOF) analysis of the leaf area index (LAI) and sliding window local regression, to capture spatiotemporal variations and localized relationships. The findings reveal substantial spatial variability in fragmentation and cropland use intensity. In plains regions such as the Huang-Huai Plain, the cropland density has reached over 0.7, with the mean plot size exceeding 2.1 ha. In contrast, mountainous areas exhibit lower cropland density (below 0.37) and mean plot sizes often less than 1.3 ha. EOF analysis explained 70.6 % of the spatiotemporal variance in the leaf area index, reflecting clear seasonal patterns of agricultural activity. The relationship between fragmentation and cropland use intensity is complex and context-dependent: while fragmentation may reduce productivity in highly mechanized systems, small-scale farms can adapt fragmented cropland for food production through strategies such as crop diversification. These results suggest that uniform land consolidation policies may be inefficient, and highlight the need for region-specific strategies. For plains regions, consolidation could enhance efficiency, whereas in fragmented mountainous areas, infrastructure improvements and resilient land management practices are more critical. Evidence from the Huang–Huai–Hai region, derived from a multiscale spatial framework, underpins differentiated land governance strategies with empirical and methodological insights.</p
Aligning generalization between humans and machines
No full textRecent advances in artificial intelligence (AI)—including generative approaches—have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals. The responsible use of AI and its participation in human–AI teams increasingly shows the need for AI alignment, that is, to make AI systems act according to our preferences. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalize. In cognitive science, human generalization commonly involves abstraction and concept learning. By contrast, AI generalization encompasses out-of-domain generalization in machine learning, rule-based reasoning in symbolic AI, and abstraction in neurosymbolic AI. Here we combine insights from AI and cognitive science to identify key commonalities and differences across three dimensions: notions of, methods for, and evaluation of generalization. We map the different conceptualizations of generalization in AI and cognitive science along these three dimensions and consider their role for alignment in human–AI teaming. This results in interdisciplinary challenges across AI and cognitive science that must be tackled to support effective and cognitively supported alignment in human–AI teaming scenarios
Dynamic Importance Monte Carlo SPH Vortical Flows with Lagrangian Samples
Get PDFWe present a Lagrangian dynamic importanceMonte Carlo method without non-trivial random walks for solving the Velocity-Vorticity Poisson Equation (VVPE) in SmoothedParticle Hydrodynamics (SPH) for vortical flows. Key to ourapproach is the use of the Kinematic Vorticity Number (KVN)to detect vortex cores and to compute the KVN-based importanceof each particle when solving the VVPE. We use Adaptive KernelDensity Estimation (AKDE) to extract a probability densitydistribution from the KVN for the the Monte Carlo calculations.Even though the distribution of the KVN can be non-trivial,AKDE yields a smooth and normalized result which we dynamically update at each time step. As we sample actual particlesdirectly, the Lagrangian attributes of particle samples ensurethat the continuously evolved KVN-based importance, modeledby the probability density distribution extracted from the KVNby AKDE, can be closely followed. Our approach enables effectivevortical flow simulations with significantly reduced computationaloverhead and comparable quality to the classic Biot-Savart lawthat in contrast requires expensive global particle querying
Cognitive-Developmental Mechanisms in Hallucinations
No full textHallucinations figure prominently in a range of psychiatric disorders but, to date, their developmental origins are not well understood. The aim of the present article is to explore how ideas from mainstream developmental psychology can enhance understanding of how hallucinations develop in different modalities across the lifecourse. Hallucinations vary in their clinical significance depending on at what point they occur in the lifetime of the individual. Key cognitive-developmental processes include engaging with imaginary entities, exposure to adverse events, executive functioning, social cognition, and language development. The presentation of hallucinations in certain developmental conditions suggest that atypical developmental trajectories can also play a key role in shaping hallucination prevalence and phenomenology. In considering prospects for future research at this interface, we propose that two-way benefits may result from further close integration between developmental and psychiatric approaches to hallucinations.</p
Review of Brendan Dooley and Alexander S. Wilkinson (eds.), "Exciting News!: Event, Narration and Impact from Past to Present" (Leiden and Boston: Brill, 2024).
No full textAm I dependent on a lifetime use of antipsychotics? A qualitative analysis of Q&A data about stopping and tapering antipsychotics from the perspective of users and their relatives
No full textBackground: Many antipsychotic users at some point want to stop their antipsychotic. They mention side effects, functioning, experiencing no benefits and health concerns as motivations. The aim was to explore questions antipsychotic users and their relatives have about stopping or tapering antipsychotics. Methods: Data were used from a publicly available anonymous expert Q&A in which experts answered questions about mental health. Questions about stopping or tapering antipsychotics asked by antipsychotics users and their relatives were analysed using an inductive content analysis. Results: A total of 3000 questions were screened, where 426 were about antipsychotics and 194 were about stopping or tapering antipsychotics. The most common question was whether it was sensible to stop. Questions focused on how fast to taper, what their minimum dose should be, where to find support and when withdrawal symptoms or side effects would subside. Motivations were side effects, difficulties in functioning and experiencing no benefits. Barriers were lack of support and return of symptoms. Facilitators were support and experiencing a relief from side effects and/or symptoms. Discussion: Antipsychotic users and their relatives are left with many questions about tapering antipsychotics. These questions reveal attitudes, preferences and concerns that are important to address when discussing antipsychotic treatment.</p