Inspired by the developments in photonic metamaterials, the concept of thermal metamaterials has promised new avenues for manipulating the flow of heat. In photonics, the existence of natural ...materials with both positive and negative permittivities has enabled the creation of metamaterials with a very wide range of effective parameters. In contrast, in conductive heat transfer, the available range of thermal conductivities in natural materials is far narrower, strongly restricting the effective parameters of thermal metamaterials and limiting possible applications in extreme environments. Here, we identify a rigorous correspondence between zero index in Maxwell's equations and infinite thermal conductivity in Fourier's law. We also propose a conductive system with an integrated convective element that creates an extreme effective thermal conductivity, and hence by correspondence a thermal analogue of photonic near-zero-index metamaterials, a class of metamaterials with crucial importance in controlling light. Synergizing the general properties of zero-index metamaterials and the specific diffusive nature of thermal conduction, we theoretically and experimentally demonstrate a thermal zero-index cloak. In contrast with conventional thermal cloaks, this meta-device can operate in a highly conductive background and the cloaked object preserves great sensitivity to external temperature changes. Our work demonstrates a thermal metamaterial which greatly enhances the capability for molding the flow of heat.
Particle trapping and binding in optical potential wells provide a versatile platform for various biomedical applications. However, implementation systems to study multi-particle contact interactions ...in an optical lattice remain rare. By configuring an optofluidic lattice, we demonstrate the precise control of particle interactions and functions such as controlling aggregation and multi-hopping. The mean residence time of a single particle is found considerably reduced from 7 s, as predicted by Kramer's theory, to 0.6 s, owing to the mechanical interactions among aggregated particles. The optofluidic lattice also enables single-bacteria-level screening of biological binding agents such as antibodies through particle-enabled bacteria hopping. The binding efficiency of antibodies could be determined directly, selectively, quantitatively and efficiently. This work enriches the fundamental mechanisms of particle kinetics and offers new possibilities for probing and utilising unprecedented biomolecule interactions at single-bacteria level.
Recently, intensive efforts have been poured into the real-time voltage stability assessment (VSA) by machine learning methods using measurement data. However, one serious and open problem of ...learning methods for VSA is that they heavily suffer from sudden topology changes and significant variation of system parameters. If a sudden topology change occurs, traditional learning methods usually need a retraining process that consumes excessive time and large amount of pre-labelled post-change data. To address this problem, this paper proposes an online adaptive VSA method based on data domain adaptation, which can rapidly adapt to the new topology after the change by limited amount of unlabelled post-change data. Besides, to improve the VSA accuracy, this method presents an enhanced temporal convolution network (ETCN), which can both extract the long-term time series characteristics of VSA and the key information at current time, as the primary neural network framework. The proposed method performs high accuracy and efficiency in both stages before and after topology changes, as well as the weak requirement for the amount of post-change data and high robustness for measurement noise. Case studies in different testing systems and the comparison with other learning algorithms demonstrate the effectiveness and advantages of the proposed approach.
Aims/hypothesis Diabetes has been related to Alzheimer's disease with inconsistent findings. We aimed to clarify the association of diabetes with different dementing disorders taking into account ...glycaemic control, and to explore the link between glucose dysregulation and neurodegeneration. Methods A dementia-free cohort (n = 1,248) aged >=75 years was longitudinally examined to detect dementia, Alzheimer's disease and vascular dementia (VaD) cases (Diagnostic and Statistical Manual of Mental Disorders, revised third edition DSM-III-R criteria). The Alzheimer's disease diagnoses were subdivided into Alzheimer's disease with stroke and Alzheimer's disease without hypertension, heart disease and stroke. Diabetes was ascertained based on medical history, or hypoglycaemic medication use, or a random blood glucose level >=11.0 mmol/l, which included undiagnosed diabetes when neither a history of diabetes nor hypoglycaemic drugs use was present. Uncontrolled diabetes was classified as a random blood glucose level >=11.0 mmol/l in diabetic patients. Borderline diabetes was defined as a random blood glucose level of 7.8-11.0 mmol/l in diabetes-free individuals. Cox models were used to estimate HRs. Results During the 9 year follow-up, 420 individuals developed dementia, including 47 with VaD and 320 with Alzheimer's disease (of the 320 Alzheimer's disease cases, 78 had previous, temporally unrelated stroke, and 137 had no major vascular comorbidities). Overall diabetes was only related to VaD (HR 3.21, 95% CI 1.20-8.63). Undiagnosed diabetes led to an HR of 3.29 (95% CI 1.20-9.01) for Alzheimer's disease. Diabetic patients with random blood glucose levels <7.8 mmol/l showed no increased dementia risk. Uncontrolled and borderline diabetes were further associated with Alzheimer's disease without vascular comorbidities. Conclusions/interpretation Uncontrolled diabetes increases the risk of Alzheimer's disease and VaD. Our findings suggest a direct link between glucose dysregulation and neurodegeneration.
Infections are considered important environmental triggers of autoimmunity and can contribute to autoimmune disease onset and severity. Nucleic acids and the complexes that they form with ...proteins-including chromatin and ribonucleoproteins-are the main autoantigens in the autoimmune disease systemic lupus erythematosus (SLE). How these nuclear molecules become available to the immune system for recognition, presentation, and targeting is an area of research where complexities remain to be disentangled. In this review, we discuss how bacterial infections participate in the exposure of nuclear autoantigens to the immune system in SLE. Infections can instigate pro-inflammatory cell death programs including pyroptosis and NETosis, induce extracellular release of host nuclear autoantigens, and promote their recognition in an immunogenic context by activating the innate and adaptive immune systems. Moreover, bacterial infections can release bacterial DNA associated with other bacterial molecules, complexes that can elicit autoimmunity by acting as innate stimuli of pattern recognition receptors and activating autoreactive B cells through molecular mimicry. Recent studies have highlighted SLE disease activity-associated alterations of the gut commensals and the expansion of pathobionts that can contribute to chronic exposure to extracellular nuclear autoantigens. A novel field in the study of autoimmunity is the contribution of bacterial biofilms to the pathogenesis of autoimmunity. Biofilms are multicellular communities of bacteria that promote colonization during chronic infections. We review the very recent literature highlighting a role for bacterial biofilms, and their major components, amyloid/DNA complexes, in the generation of anti-nuclear autoantibodies and their ability to stimulate the autoreactive immune response. The best studied bacterial amyloid is curli, produced by enteric bacteria that commonly cause infections in SLE patients, including
and
. Evidence suggests that curli/DNA complexes can trigger autoimmunity by acting as danger signals, molecular mimickers, and microbial chaperones of nucleic acids.
In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning. To achieve sparsity and/or ...low-rankness inducing, the <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula> norm and nuclear norm are of the most popular regularization penalties due to their convexity. While the <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula> and nuclear norm are convenient as the related convex optimization problems are usually tractable, it has been shown in many applications that a nonconvex penalty can yield significantly better performance. In recent, nonconvex regularization-based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization. This paper gives an overview of this topic in various fields in signal processing, statistics, and machine learning, including compressive sensing, sparse regression and variable selection, sparse signals separation, sparse principal component analysis (PCA), large covariance and inverse covariance matrices estimation, matrix completion, and robust PCA. We present recent developments of nonconvex regularization based sparse and low-rank recovery in these fields, addressing the issues of penalty selection, applications and the convergence of nonconvex algorithms. Code is available at https://github.com/FWen/ncreg.git .
A low-alloyed Mg-1.2Zn-0.1Ca (wt.%) alloy was extruded at low temperatures. The alloy extruded at 150 °C exhibits a yield strength (YS) of 374 MPa and an elongation of 14.2%. The YS is superior to ...most Mg–Zn–Ca alloys, and can compete with high-alloyed AZ91 and Mg-RE alloys.
Due to increasing complexity, uncertainty and data dimensions in power systems, conventional methods often meet bottlenecks when attempting to solve decision and control problems. Therefore, ...data-driven methods toward solving such problems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems, including those in power systems. This paper firstly reviews the basic ideas, models, algorithms and techniques of DRL. Applications in power systems such as energy management, demand response, electricity market, operational control, and others are then considered. In addition, recent advances in DRL including the combination of RL with other classical methods, and the prospect and challenges of applications in power systems are also discussed.
Mesoscale eddies help regulate ocean energy cascades. Eddies deformation influences barotropic instability, which represents kinetic energy transfer between scales; however, the barotropic ...instability structure has not been well studied. We investigated an intra‐thermocline eddy (ITE) and developed a novel anisotropic method to examine the horizontal barotropic instability. The development of the ITE was monitored using a state‐of‐the‐art autonomous underwater vehicle from May to July. The ITE became trapped in June and moved eastward in July. Based on anisotropic theory, the barotropic instability was separated into isotropic and anisotropic productions. The anisotropy contained information regarding shape and mean flow feedback of the eddy. Barotropic instability was the main source for ITE eastward propagation and was dominated by anisotropic production. Following a shape and anisotropy change, the ITE gained the mean‐flow kinetic energy by the anisotropy shear production in June and by the anisotropy stretch production when moving eastward in July.
Plain Language Summary
Mesoscale eddies play vital roles in ocean circulation and are important in energy cascades between large‐scale ocean circulation and dissipate scales. The barotropic instability could induce kinetic energy transition between scales, however, the underlying mechanism has not been well studied. We developed a novel method to decompose the horizontal barotropic instability into isotropic production and anisotropic production. The development of a mesoscale eddy was monitored continuously using a state‐of‐the‐art autonomous underwater vehicle. This mesoscale eddy became stationary in June and began to move eastward in July. In the observation case, the barotropic instability mainly controlled the eddy kinetic energy budget and is dominated by anisotropic production, indicating the contribution of the mean‐flow strain. We found that, the mesoscale eddy gained the mean‐flow kinetic energy via the anisotropy shear production when it was stationary, while via anisotropy stretch production when it moved eastward.
Key Points
For 3 months, a state‐of‐the‐art AUV monitored the development of an intra‐thermocline eddy (ITE) in terms of vertical transects
Novel anisotropy analysis was developed and revealed interactions between eddy anisotropy and mean‐flow strain in barotropic instability
The anisotropic partitioning of the ITE at different stages helped gain kinetic energy from the mean flow
Broadband quantum memories hold great promise as multiplexing elements in future photonic quantum information protocols. Alkali-vapor Raman memories combine high-bandwidth storage, on-demand readout, ...and operation at room temperature without collisional fluorescence noise. However, previous implementations have required large control pulse energies and have suffered from four-wave-mixing noise. Here, we present a Raman memory where the storage interaction is enhanced by a low-finesse birefringent cavity tuned into simultaneous resonance with the signal and control fields, dramatically reducing the energy required to drive the memory. By engineering antiresonance for the anti-Stokes field, we also suppress the four-wave-mixing noise and report the lowest unconditional noise floor yet achieved in a Raman-type warm vapor memory, (15±2)×10^{-3} photons per pulse, with a total efficiency of (9.5±0.5)%.
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