Non-Abelian Chiral Spin Liquid on the Kagome Lattice

We study $S=1$ spin liquid states on the kagome lattice constructed by Gutzwiller-projected $p_x+ip_y$ superconductors. We show that the obtained spin liquids are either non-Abelian or Abelian topological phases, depending on the topology of the fermionic mean-field state. By calculating the modular matrices $S$ and $T$, we confirm that projected topological superconductors are non-Abelian chiral spin liquid (NACSL). The chiral central charge and the spin Hall conductance we obtained agree very well with the $SO(3)_1$ (or, equivalently, $SU(2)_2$) field theory predictions. We propose a local Hamiltonian which may stabilize the NACSL. From a variational study we observe a topological phase transition from the NACSL to the $Z_2$ Abelian spin liquid.Comment: 12 pages, 7 figures, 1 tabl

A Deep Learning Framework for Hydrogen-fueled Turbulent Combustion Simulation

The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related design, research and optimization. In this study, we propose a new framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired from a U-Net architecture and inception module is designed for constructing the framework of the deep learning solver, named CFDNN. CFDNN is then trained on the simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from CFDNN solver show excellent consistency with the conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration is achieved by using CFDNN solver compared to the conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets

Scientometric trends and knowledge maps of global health systems research

Background: In the last few decades, health systems research (HSR) has garnered much attention with a rapid increase in the related literature. This study aims to review and evaluate the global progress in HSR and assess the current quantitative trends. Methods: Based on data from the Web of Science database, scientometric methods and knowledge visualization techniques were applied to evaluate global scientific production and develop trends of HSR from 1900 to 2012. Results: HSR has increased rapidly over the past 20 years. Currently, there are 28,787 research articles published in 3,674 journals that are listed in 140 Web of Science subject categories. The research in this field has mainly focused on public, environmental and occupational health (6,178, 21.46%), health care sciences and services (5,840, 20.29%), and general and internal medicine (3,783, 13.14%). The top 10 journals had published 2,969 (10.31%) articles and received 5,229 local citations and 40,271 global citations. The top 20 authors together contributed 628 papers, which accounted for a 2.18% share in the cumulative worldwide publications. The most productive author was McKee, from the London School of Hygiene \& Tropical Medicine, with 48 articles. In addition, USA and American institutions ranked the first in health system research productivity, with high citation times, followed by the UK and Canada. Conclusions: HSR is an interdisciplinary area. Organization for Economic Co-operation and Development countries showed they are the leading nations in HSR. Meanwhile, American and Canadian institutions and the World Health Organization play a dominant role in the production, collaboration, and citation of high quality articles. Moreover, health policy and analysis research, health systems and sub-systems research, healthcare and services research, health, epidemiology and economics of communicable and non-communicable diseases, primary care research, health economics and health costs, and pharmacy of hospital have been identified as the mainstream topics in HSR fields. These findings will provide evidence of the current status and trends in HSR all over the world, as well as clues to the impact of this popular topic; thus, helping scientific researchers and policy makers understand the panorama of HSR and predict the dynamic directions of research

Artificial intelligence techniques for ground fault line selection in power systems: State-of-the-art and research challenges

In modern power systems, efficient ground fault line selection is crucial for maintaining stability and reliability within distribution networks, especially given the increasing demand for energy and integration of renewable energy sources. This systematic review aims to examine various artificial intelligence (AI) techniques employed in ground fault line selection, encompassing artificial neural networks, support vector machines, decision trees, fuzzy logic, genetic algorithms, and other emerging methods. This review separately discusses the application, strengths, limitations, and successful case studies of each technique, providing valuable insights for researchers and professionals in the field. Furthermore, this review investigates challenges faced by current AI approaches, such as data collection, algorithm performance, and real-time requirements. Lastly, the review highlights future trends and potential avenues for further research in the field, focusing on the promising potential of deep learning, big data analytics, and edge computing to further improve ground fault line selection in distribution networks, ultimately enhancing their overall efficiency, resilience, and adaptability to evolving demands

Plasmoid ejection and secondary current sheet generation from magnetic reconnection in laser-plasma interaction

Reconnection of the self-generated magnetic fields in laser-plasma interaction was first investigated experimentally by Nilson {\it et al.} [Phys. Rev. Lett. 97, 255001 (2006)] by shining two laser pulses a distance apart on a solid target layer. An elongated current sheet (CS) was observed in the plasma between the two laser spots. In order to more closely model magnetotail reconnection, here two side-by-side thin target layers, instead of a single one, are used. It is found that at one end of the elongated CS a fan-like electron outflow region including three well-collimated electron jets appears. The ($>1$ MeV) tail of the jet energy distribution exhibits a power-law scaling. The enhanced electron acceleration is attributed to the intense inductive electric field in the narrow electron dominated reconnection region, as well as additional acceleration as they are trapped inside the rapidly moving plasmoid formed in and ejected from the CS. The ejection also induces a secondary CS

Production of Transgenic Pigs with an Introduced Missense Mutation of the Bone Morphogenetic Protein Receptor Type IB Gene Related to Prolificacy

In the last few decades, transgenic animal technology has witnessed an increasingly wide application in animal breeding. Reproductive traits are economically important to the pig industry. It has been shown that the bone morphogenetic protein receptor type IB (BMPR1B) A746G polymorphism is responsible for the fertility in sheep. However, this causal mutation exits exclusively in sheep and goat. In this study, we attempted to create transgenic pigs by introducing this mutation with the aim to improve reproductive traits in pigs. We successfully constructed a vector containing porcine BMPR1B coding sequence (CDS) with the mutant G allele of A746G mutation. In total, we obtained 24 cloned male piglets using handmade cloning (HMC) technique, and 12 individuals survived till maturation. A set of polymerase chain reactions indicated that 11 of 12 matured boars were transgene-positive individuals, and that the transgenic vector was most likely disrupted during cloning. Of 11 positive pigs, one (No. 11) lost a part of the terminator region but had the intact promoter and the CDS regions. cDNA sequencing showed that the introduced allele (746G) was expressed in multiple tissues of transgene-positive offspring of No.11. Western blot analysis revealed that BMPR1B protein expression in multiple tissues of transgene-positive F1 piglets was 0.5 to 2-fold higher than that in the transgene-negative siblings. The No. 11 boar showed normal litter size performance as normal pigs from the same breed. Transgene-positive F1 boars produced by No. 11 had higher semen volume, sperm concentration and total sperm per ejaculate than the negative siblings, although the differences did not reached statistical significance. Transgene-positive F1 sows had similar litter size performance to the negative siblings, and more data are needed to adequately assess the litter size performance. In conclusion, we obtained 24 cloned transgenic pigs with the modified porcine BMPR1B CDS using HMC. cDNA sequencing and western blot indicated that the exogenous BMPR1B CDS was successfully expressed in host pigs. The transgenic pigs showed normal litter size performance. However, no significant differences in litter size were found between transgene-positive and negative sows. Our study provides new insight into producing cloned transgenic livestock related to reproductive traits