224092 research outputs found
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Towards fibre-like loss for photonic integration from violet to near-infrared
No full textOver the past decades, remarkable progress has been made in reducing the loss of photonic integrated circuits (PICs) within the telecom band, facilitating on-chip applications spanning low-noise optical and microwave synthesis, to lidar and photonic artificial intelligence engines. However, several obstacles arise from the marked increase in material absorption and scattering losses at shorter wavelengths, which prominently elevate power requirements and limit performance in the visible and near-visible spectrum. Here we present an ultralow-loss PIC platform based on germano-silicate—the material underlying the extraordinary performance of optical fibre—but realized by a fully CMOS-foundry-compatible process. These PICs achieve resonator Q factors surpassing 180 million from violet to telecom wavelengths. They also attain a 10-dB higher quality factor without thermal treatment in the telecom band, expanding opportunities for heterogeneous integration with active components. Other features of this platform include readily engineered waveguide dispersion, acoustic mode confinement and large-mode-area-induced thermal stability—each demonstrated by soliton microcomb generation, stimulated Brillouin lasing and low-frequency-noise self-injection locking, respectively. The success of these germano-silicate PICs can ultimately enable fibre-like loss onto a chip, leading to an additional 20-dB improvement in waveguide loss over the current highest performance photonic platforms. Moreover, the performance abilities demonstrated here bridge ultralow-loss PIC technology to optical clocks, precision navigation systems and quantum sensors
Beyond the hospital: what is the role of social networks in acute kidney injury recovery?
No full textSocial networks are vital in providing emotional, practical and informational support that may reduce the risk of readmissions and improve recovery outcomes for patients with acute kidney injuryBackground: acute kidney injury (AKI) is a prevalent and serious condition which can lead to significant short- and long-term health risks—including mortality, readmissions and progression to chronic kidney disease.Aims: this study explored the literature around the role of social networks in self-management support for patients recovering from AKI.Methods: a systematic review was conducted using Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines to examine studies that included social networks and self-management in AKI.Findings: a 3-step search strategy was used in this literature search. An initial limited search of MEDLINE (Ovid), Embase, Psycho info and AMED was undertaken to identify articles on the topic, followed by an analysis of the text words contained in the titles and abstracts of retrieved papers and of the keywords used to describe the articles. After implementing the search strategy no applicable literature was found. Literature on AKI recovery lacks insight into how informal and formal social networks supports patients in the post-AKI period.Conclusions: further research is needed to explore whether social network support can improve AKI recovery
Kirigami metastructures for multidirectional vibration energy harvesting: a mechanics-based investigation
No full textFeeding practices and concerns as mediators between maternal mental health and eating behaviours in early childhood
No full textBACKGROUND: The impact of maternal mental health on child eating beyond infancy is understudied. This study explores whether maternal feeding practices and concerns mediate the association between maternal depression and anxiety symptoms and eating behaviours at age three years.METHODS: Data from 409 mother-child dyads in the Growing Up in Singapore Towards healthy Outcomes cohort were analysed. Maternal mental health was assessed using the Beck Depression Inventory-II and State-Trait Anxiety Inventory, feeding practices and concerns with the Preschooler Feeding Questionnaire, and child eating behaviours with the Children's Eating Behaviour Questionnaire. Structural equation modelling was used to test pathways.RESULTS: Depression symptoms in mothers showed direct and indirect links to child eating behaviours. For example, maternal depression symptoms were directly associated with enjoyment of food (B = 0.011, p = 0.015) and indirectly with food responsiveness (B = 0.004, p = 0.034) via use of food to calm the child. Anxiety symptoms, however, had only indirect associations with child eating behaviours through maternal feeding concerns, not practices. For example, maternal anxiety symptoms were indirectly linked with food responsiveness through perceived difficulty in feeding (B = -0.001, p = 0.011).CONCLUSIONS: Depression and anxiety symptoms influence children's eating behaviours differently. Anxiety symptoms were linked with child eating behaviours only through maternal feeding concerns, whereas depression symptoms were linked with child eating behaviours both directly and indirectly via feeding to calm the child. As maternal anxiety symptoms are linked with more child eating concerns, the validity of mother-reported child eating behaviours requires consideration.</p
tCURLoRA: tensor CUR decomposition based low-rank parameter adaptation and its application in medical image segmentation
No full textTransfer learning, by leveraging knowledge from pre-trained models, has significantly improved the performance of downstream tasks. However, as deep neural networks continue to scale, full fine-tuning poses substantial computational and storage challenges in resource-constrained environments, limiting its practical adoption. To address this, parameter-efficient fine-tuning (PEFT) methods have been proposed to reduce computational complexity and memory requirements by updating only a small subset of parameters. Among them, matrix decomposition-based approaches such as LoRA have shown promise, but often struggle to fully capture the high-dimensional structural characteristics of model weights. In contrast, high-order tensors offer a more natural representation of neural network parameters, enabling richer modeling of multi-dimensional interactions and higher-order features. In this paper, we propose tCURLoRA, a novel fine-tuning method based on tensor CUR decomposition. By stacking pre-trained weight matrices into a third-order tensor and applying tensor CUR decomposition, our method updates only the compressed tensor components during fine-tuning, thereby substantially reducing both computational and storage costs. Experimental results show that tCURLoRA consistently outperforms existing PEFT approaches on medical image segmentation tasks. The source code is publicly available at: https://github.com/WangangCheng/t-CURLora.</p
Who gets in? a conjoint analysis of labour market demand and immigration preferences in England and Japan
No full textWe advance research on attitudes towards immigration using an experimental design that more clearly separates between skill level and labour market demand. In single profile conjoint design experiments fielded in England and Japan, we replicate the well-established finding that high-skill immigrants are generally preferred to low-skill immigrants. However, we also show a more nuanced result in that labour market demand – regardless of skill level – is also important. Indeed, in both England and Japan, the public is willing to accept low-skill workers in high-demand occupations at levels at least as much as for high-skill but low-demand occupations. Labour market demand is an important factor in understanding attitudes towards economic migration
Bioinspired engineering beyond homeostasis
No full textBiological systems operate through precisely coordinated interactions across multiple spatiotemporal scales, from molecules to cells, tissues, and organs. Pathologies often emerge when this homeostatic multiscale organization fails due to elements across different levels pursuing misaligned objectives, creating top-down and bottom-up cascading effects throughout the biological hierarchy. This perspective article explores how understanding these organizational failures provides valuable insights, besides for investigating fundamental processes in pathophysiology and for developing diagnostic and therapeutic strategies targeting biological organization with complex systems approaches, also for designing bioinspired artificial systems across three domains: biomimetic materials, bioinspired devices, and biomorphic computing models. This plethora of paradigms and possibilities is simplified by highlighting selected pathological mechanisms as case studies of multiscale system breakdown, namely, metabolic alterations, cancer, and neurodegenerative conditions, and how these failure modes of biological cooperation, taken in isolation and looked at in a systematic manner, present localized emergent advantages that might offer inspiration for developing adaptive and self-programmable systems, thereby expanding the pool of nature-inspired approaches beyond homeostasis.</p
Advancing Justice in Marine Biodiversity Conservation
No full textDrawing on contemporary political theory, this paper sets out several key normative standards that can be applied to the conservation of marine biodiversity. Such standards ensure that progress in mitigating the biodiversity crisis is achieved fairly and inclusively. The paper suggests that the costs of heading off the marine biodiversity crisis must be allocated in line with contribution to the problem, and ability to pay, and that there can be no justification for leaving the most disadvantaged to bear significant conservation costs. It also clarifies what kinds of activities can count as biodiversity conservation policies, in order to keep the environmental consequences of unsustainable consumption in the global North firmly in view. Finally, it argues that decision-making about marine biodiversity should be opened up much more widely, at all stages of the policy-making process, to ensure that all of those affected by conservation policies have a fair chance to be involved in formulating policies and priorities
An analytical lower bound for a class of minimizing quadratic integer optimization problems
No full textLower bounds for minimization problems are essential for convergence of both branching-based and iterative solution methods for optimization problems. They also serve an important role in evaluating the quality of feasible solutions by providing conservative optimality gaps. We derive a closed-form analytical lower bound for a class of quadratic optimization problems with binary decision variables. Unlike traditional lower bounds obtained by solving relaxed models, our bound is purely analytical and does not require numerically solving any optimization problem. This is particularly valuable for problem instances that are too large to even formulate or load into a solver due to memory limitations. Further, we propose a greedy heuristic for obtaining feasible solutions. Together, the analytical bound and heuristic provide a provable optimality gap without solving any optimization model. Numerical experiments demonstrate that we can solve real-world large-scale instances, that were previously unsolvable due to memory limitations, in under a minute with provable optimality gaps of under 7%. For smaller instances where the optimal solution is computable, our greedy solutions are about 1% away from the optimal. These results highlight the practical value and scalability of our approach when direct solution methods are computationally prohibitive.</p
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