561975 research outputs found
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What’s in a name? Fair labelling, legal goods, and the criminalisation of image-based sexual abuse
No full textHierarchical Manufacturing of Anisotropic and High-Efficiency Electromagnetic Interference Shielding Modules for Smart Electronics
No full textTo shield electronics from complicated electromagnetic environments caused by wireless electromagnetic waves, achieving elaborately structural manufacturing while not sacrificing electromagnetic interference shielding performances remains crucial challenges. Herein, we propose a hierarchical manufacturing method that combines the use of 3D printing shear flow field and layer-by-layer assembly for fabricating the structurally customizable and multifunctional polylactic acid@graphene nanoparticle (PLA@GNs) materials. The dynamic behavior of polymer fluids is firstly explored via computational fluid dynamic simulation, and a Weissenberg number is employed to quantitatively analyze the disordered-to-ordered structural evolution of molecular chains and nanoparticles, allowing to tailor the micro-scale ordered structures. Subsequently, the macro-scale 3D architectures of PLA@GNs modules are fabricated by layer-by-layer assembly. Owing to the aligned GNs, the shielding performance reaches 41.2 dB, simultaneously accompanied by a directional thermal conductivity of 3.2 W m−1 K−1. Moreover, the potential application of 3D-printed shielding modules in specific civilian frequency bands such as 4G (1800–2100 MHz), Bluetooth (2402–2480 MHz), and 5G (3300–3800 MHz) is fully demonstrated. Overall, this work not only establishes a universal methodology about 3D printing shear flow field-driven orientation of two-dimensional nanoparticles within polymer fluids, but also gives a scientific method for advanced manufacturing of the next-generation electromagnetic functional modules for smart electronics
Triphasic synthesis of MXenes with uniform and controlled halogen terminations
No full textSurface terminations critically govern the properties of two-dimensional transition metal carbides and/or nitrides (MXenes), yet a universal strategy to obtain MXenes with uniform and controllable terminations remains elusive. Here we introduce a ‘gas–liquid–solid’ triphasic etching strategy that employs iodine vapour, halide molten salts and MAX phases to produce MXenes with pure and precisely tunable halogen terminations (Cl, Br, I or their combinations). In this process, halide molten salts dissolve iodine via interhalogen anion formation while efficiently transporting etching by-products. The resulting MXenes retain excellent structural integrity, yielding uniformly ordered surfaces. As a representative example, Ti3C2Cl2 shows a 160-fold enhancement in macroscopic conductivity and a 13-fold enhancement in terahertz conductivity relative to conventional Cl/O-terminated Ti3C2, attributed to minimized electron trapping and scattering. Beyond single-halogen terminations, the gas–liquid–solid approach enables dual- and triple-halogen termination control, providing a general platform for tailoring MXene surface chemistry towards advanced (opto)electronic applications
Human-induced temperature rise is driving Africa towards drought-prone climatic conditions
No full textThis study focuses on the role of human activities in shaping climate forcings and their impact on surface air temperature (SAT) and drought intensification over Africa, emphasizing the human contributions to these phenomena. Through the analysis of observations, various model experiments, and Regularized Optimal Fingerprinting detection technique, our findings indicate that human-induced factors have contributed to an increase in surface air temperatures ranging from 0.8 to C above pre-industrial benchmarks. Greenhouse gases (GHGs) emerge as the primary driver of this rise (0.47 to C), followed by land use (LU) changes (0.47 to C). In contrast, anthropogenic aerosols (Aaer) exert a cooling effect (-1.82 to C) on SAT. The analysis reveals that SAT anomalies, particularly during the industrial period, have significantly contributed to the intensification of drought-prone climatic conditions. During the pre-industrial period, the absence of anthropogenic warming kept SAT stable, resulting in mildly wet conditions (Standardized Precipitation Evapotranspiration Index (SPEI)=0.54). However, in the industrial period, the sharp rise in SAT due to GHG and LU forcings led towards significantly drought-prone climatic conditions (SPEI=-0.73), while the cooling effect of Aaer was insufficient to offset the warming trend. Estimates based on Representative Concentration Pathways (RCP) 4.5 and 8.5 suggest that the SAT over Africa could rise by around C and C, respectively, by the end of the century, highlighting the significant influence of human-driven factors in driving temperature rise. Strategic oversight of GHG emissions, LU changes, and aerosol concentrations in Africa offers the possibility potential to mitigate further warming and consequent drought intensification in this region
[Book Review] Blackout by Blueprint: Rilinger, Georg: Failure by Design: The California Energy Crisis and the Limits of Market Planning (Chicago: University of Chicago Press, 2024)
No full textEffect of temperature on wood modification with citric acid
No full textCitric acid (CA) has emerged as a promising biosourced reagent for wood modification, improving the wood’s dimensional stability and durability. However, CA treatments are often associated with reduced mechanical strength, and the specific role of curing temperature in this balance remains insufficiently explored. This study investigates the effect of curing temperature on the chemical, physical, and mechanical behavior of CA-modified wood. Scots pine ( Pinus sylvestris L.) sapwood samples were impregnated with 30 % CA solution and cured at 100, 120, 140, and 160 °C for 24 h. Chemical changes were analyzed by FTIR and solid-state 13C NMR, while physical and mechanical performance was assessed through weight gain, cell wall bulking, anti-swelling efficiency, moisture exclusion efficiency, Brinell hardness, and three-point bending strength. Results demonstrated that esterification between CA and wood hydroxy groups increased with temperature, enhancing dimensional stability, especially at 140–160 °C. However, curing at 160 °C also promoted acid-catalyzed hydrolysis of cell wall components, leading to significant reductions in bending strength. The best compromise between stability and strength was achieved at 140 °C. These findings highlight curing temperature as a critical factor for optimizing CA treatments, providing a pathway for industrially viable wood modification strategies that balance performance with sustainability
Manufacturing and high heat flux testing of advanced target mock-ups for the EU-DEMO divertor target
No full textAsynchronous federated learning: a scalable approach for decentralized machine learning
No full textFederated learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data. However, traditional FL approaches often face limitations in scalability and efficiency due to their reliance on synchronous client updates, which can result in significant delays and increased communication overhead, particularly in heterogeneous and dynamic environments. To address these challenges in this article, we propose an asynchronous FL (AFL) algorithm, which allows clients to update the global model independently and asynchronously. Our key contributions include a comprehensive convergence analysis of AFL in the presence of client delays and model staleness. By leveraging martingale difference sequence theory and variance bounds, we ensure robust convergence despite asynchronous updates. Assuming strongly convex local objective functions, we establish bounds on gradient variance under random client sampling and derive a recursion formula quantifying the impact of client delays on convergence. Furthermore, we demonstrate the practical applicability of the AFL algorithm by training decentralized linear regression and support vector machine (SVM)-based classifiers and compare its results with synchronous FL algorithm to effectively handle non-IID data distributed among clients. The proposed AFL algorithm addresses key limitations of traditional FL methods, such as inefficiency due to global synchronization and susceptibility to client drift. It enhances scalability, robustness, and efficiency in real-world settings with heterogeneous client populations and dynamic network conditions. Our results underscore the potential of AFL to drive advancements in distributed learning systems, particularly for large-scale, privacy-preserving applications in resource-constrained environments.I. Introduction A. Motivation B. Contributions II. Preliminaries and main assumptions A. Assumptions III. Problem formulation IV. Theoretical requisite for convergence analysis V. Convergence analysis of AFL A. Finding the Recursion in AFL VI. Simulations A. Implementation Details B. Code and Hardware VII. Conclusio
Causally informed, multifactorial pathways linking cognition and personality to adolescent mental health
No full textAdolescence is a sensitive period for the emergence of psychopathology. During this time, physiological changes and environmental exposures jointly shape brain development and influence cognitive and personality maturation, collectively heightening vulnerability to mental disorders. However, the complexity of interactions between these factors has hindered a systems-level understanding of mental health and the causal roles of cognition and personality in psychopathology. In this study, we proposed a multifactorial causal framework integrating brain, pubertal, environmental, and behavioral factors to characterize heterogeneity in adolescent mental health trajectories at the individual level. We then investigated latent causal pathways linking cognition and personality to mental health outcomes and identified potential personalized intervention targets. Leveraging the Adolescent Brain Cognitive Development (ABCD) dataset (N = 4,501), we analyzed 165 behavioral pairs connecting cognition and personality traits to mental health symptoms. Using cross-sectional multivariate mediation and longitudinal interaction-inclusive analyses, we identified 68 behavioral pairs showing significant causal relationships, with brain and environmental exposures contributing to most pathways, while pubertal factors exhibited limited involvement. Individualized interpretive analyses further revealed 23 pairs suggesting potential interventions with response rates exceeding 50%. Among these, behavioral inhibition, negative urgency, and processing speed emerged as the most common intervention targets, whereas psychosis symptoms and attention problems were the most likely issues to improve. Overall, our study advances a comprehensive framework capturing the multifactorial and heterogeneous nature of adolescent mental health, delineates specific causal pathways from cognitive and personality traits to psychopathology, and provides a principled basis for potential individualized intervention strategies
