Metal-organic framework-derived Ni 2 P/nitrogen-doped carbon porous spheres for enhanced lithium storage

Transition metal phosphides (TMPs)/carbonaceous matrices have gradually attracted attention in the field of energy storage. In this study, we presented nickel phosphide (Ni2P) nanoparticles anchored to nitrogen-doped carbon porous spheres (Ni2P/NC) by using metal-organic framework-Ni as the template. The comprehensive encapsulation architecture provides closer contact among the Ni2P nanoparticles and greatly improves the structural integrity as well as the electronic conductivity, resulting in excellent lithium storage performance. The reversible specific capacity of 286.4 mA h g−1 has been obtained even at a high current density of 3.0 A g−1 and 450.4 mA h g−1 is obtained after 800 cycles at 0.5 A g−1. Furthermore, full batteries based on LiNi1/3Co1/3Mn1/3O2||Ni2P/NC exhibit both good rate capability and cycling life. This study provides a powerful and in-depth insight on new advanced electrodes in high-performance energy storage devices

Stress-oriented structural optimization for frame structures

To fabricate a virtual shape into the real world, the physical strength of the shape is an important consideration. We introduce a framework to consider both the strength and complexity of 3D frame structures. The key to the framework is a stress-oriented analysis and a semi-continuous condition in the shape representation that can both strengthen and simplify a structure at the same time. We formulate a novel semi-continuous optimization and present an elegant method to solve this optimization. We also extend our framework to general solid shapes by considering them as skeletal structures with non-uniform beams. We demonstrate our approach with applications such as topology simplification and structural strengthening

Effects of novel coronavirus Omicron variant infection on pregnancy outcomes: a retrospective cohort study from Guangzhou

ObjectiveSince 2022, Omicron has been circulating in China as a major variant of the novel coronavirus, but the effects of infection with Omicron variants on pregnant women and newborns are unknown. The purpose of this study was to determine the clinical characteristics of Omicron infection during pregnancy and its effect on pregnancy outcomes.MethodsThis study retrospectively analyzed the data of 93 confirmed cases of novel coronavirus infection and 109 non-infected patients admitted to the isolation ward of Guangdong Maternal and Child Health Hospital from December 1, 2022 to January 31, 2023, and statistically analyzed the clinical features of Omicron variant infection during pregnancy and its impact on pregnancy outcomes. Further effects of underlying diseases on Omicron infection in pregnant women were analyzed.ResultsThe incubation period of COVID-19 infection was 0.99±0.86 days, 94.38% of patients had fever or other respiratory symptoms, the lymphocyte count in the infected group was lower than that in the uninfected group, and the lymphocyte count was further reduced in the patients with pregnancy complications or complications. Compared with the uninfected group, APTT and PT were prolonged, platelet count and fibrinogen were decreased in the infected group, all of which had statistical significance. COVID-19 infection during pregnancy increased the rate of cesarean section compared to uninfected pregnant patients, and COVID-19 infection in gestational diabetes resulted in a 4.19-fold increase in cesarean section rate. There was no statistically significant difference in gestational age between the two groups. The incidence of intrauterine distress, turbidity of amniotic fluid and neonatal respiratory distress were higher in the infection group. No positive cases of neonatal COVID-19 infection have been found.ConclusionThe patients infected with omicron during pregnancy often have febrile respiratory symptoms with lymphocyopenia, but the incidence of severe disease is low. Both Omicron infection and gestational diabetes further increase the incidence of cesarean section, and this study found no evidence of vertical transmission of Omicron

A4. En tekst om å ville â og ikke ville være vanlig

People living outside conventional families have to grapple with the concept of ordinariness. If their lives are not seen as ordinary intimate lives, what life choices and narrative choices do they have in claiming and responding to this extraordinariness? The article explores ordinariness as a theoretical and cultural concept, and shows how both theoretical approaches and self-narratives can have very different as well as ambivalent attitudes towards ordinariness

Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion

The spiking neural network (SNN) is regarded as a promising candidate to deal with the great challenges presented by current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a great gap between SNNs and the online meta-learning performance of artificial neural networks. Importantly, existing spike-based online meta-learning models do not target the robust learning based on spatio-temporal dynamics and superior machine learning theory. In this invited article, we propose a novel spike-based framework with minimum error entropy, called MeMEE, using the entropy theory to establish the gradient-based online meta-learning scheme in a recurrent SNN architecture. We examine the performance based on various types of tasks, including autonomous navigation and the working memory test. The experimental results show that the proposed MeMEE model can effectively improve the accuracy and the robustness of the spike-based meta-learning performance. More importantly, the proposed MeMEE model emphasizes the application of the modern information theoretic learning approach on the state-of-the-art spike-based learning algorithms. Therefore, in this invited paper, we provide new perspectives for further integration of advanced information theory in machine learning to improve the learning performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic systems

Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion

The spiking neural network (SNN) is regarded as a promising candidate to deal with the great challenges presented by current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a great gap between SNNs and the online meta-learning performance of artificial neural networks. Importantly, existing spike-based online meta-learning models do not target the robust learning based on spatio-temporal dynamics and superior machine learning theory. In this invited article, we propose a novel spike-based framework with minimum error entropy, called MeMEE, using the entropy theory to establish the gradient-based online meta-learning scheme in a recurrent SNN architecture. We examine the performance based on various types of tasks, including autonomous navigation and the working memory test. The experimental results show that the proposed MeMEE model can effectively improve the accuracy and the robustness of the spike-based meta-learning performance. More importantly, the proposed MeMEE model emphasizes the application of the modern information theoretic learning approach on the state-of-the-art spike-based learning algorithms. Therefore, in this invited paper, we provide new perspectives for further integration of advanced information theory in machine learning to improve the learning performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic systems

Heterogeneous ensemble-based spike-driven few-shot online learning

Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the major challenges of current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a great gap between SNNs and the few-shot learning performance of artificial neural networks. Importantly, existing spike-based few-shot learning models do not target robust learning based on spatiotemporal dynamics and superior machine learning theory. In this paper, we propose a novel spike-based framework with the entropy theory, namely, heterogeneous ensemble-based spike-driven few-shot online learning (HESFOL). The proposed HESFOL model uses the entropy theory to establish the gradient-based few-shot learning scheme in a recurrent SNN architecture. We examine the performance of the HESFOL model based on the few-shot classification tasks using spiking patterns and the Omniglot data set, as well as the few-shot motor control task using an end-effector. Experimental results show that the proposed HESFOL scheme can effectively improve the accuracy and robustness of spike-driven few-shot learning performance. More importantly, the proposed HESFOL model emphasizes the application of modern entropy-based machine learning methods in state-of-the-art spike-driven learning algorithms. Therefore, our study provides new perspectives for further integration of advanced entropy theory in machine learning to improve the learning performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic systems.This study was funded partly by the National Natural Science Foundation of China (Grant Nos. 62006170, 62088102, and U21A20485) and partly by China Postdoctoral Science Foundation (Grant Nos. 2020M680885 and 2021T140510).Peer reviewe

Atomic-level molybdenum oxide nanorings with full-spectrum absorption and photoresponsive properties.

Superthin nanostructures, particularly with atomic-level thicknesses, typically display unique optical properties because of their exceptional light-matter interactions. Here, we report a facile strategy for the synthesis of sulfur-doped molybdenum oxide nanorings with an atomic-level size (thickness of 0.5 nm) and a tunable ring-in-ring architecture. These atomic-level nanorings displayed strong photo-absorption in both the visible and infrared-light ranges and acted as a photothermal agent. Under irradiation with an 808 nm laser with an intensity of 1 W/cm2, a composite of the nanorings embedded in polydimethylsiloxane showed an ultrafast photothermal effect, delivering a local temperature of up to 400 °C within 20 s, which to the best of our knowledge is the highest temperature by light irradiation reported to date. Meanwhile, the resulting nanorings were also employed as a photoinitiator to remotely induce a visible-light shape memory response, self-healing, reshaping performance and reversible actuation of dynamic three-dimensional structures. This study demonstrates an advancement towards controlling atomic-level-sized nanostructures and achieving greatly enhanced optical performances for optoelectronics