Shuttle-like supramolecular nanostructures formed by self-assembly of a porphyrin via an oil/water system

In this paper, in terms of the concentration of an aqueous solution of a surfactant, we investigate the self-assembly behavior of a porphyrin, 5, 10, 15, 20-tetra(4-pyridyl)-21H, 23H-porphine [H2TPyP], by using an oil/water system as the medium. We find that when a chloroform solution of H2TPyP is dropwise added into an aqueous solution of cetyltrimethylammonium bromide [CTAB] with a lower concentration, a large amount of irregular nanoarchitectures, together with a small amount of well-defined shuttle-like nanostructures, hollow nanospheres, and nanotubes, could be produced. While a moderate amount of shuttle-like nanostructures accompanied by a few irregular nanoarchitectures, solid nanospheres, and nanorods are produced when a CTAB aqueous solution in moderate concentration is employed, in contrast, a great quantity of shuttle-like nanostructures together with a negligible amount of solid nanospheres, nanofibers, and irregular nanostructures are manufactured when a high-concentration CTAB aqueous solution is involved. An explanation on the basis of the molecular geometry of H2TPyP and in terms of the intermolecular π-π interactions between H2TPyP units, and hydrophobic interactions between CTAB and H2TPyP has been proposed. The investigation gives deep insights into the self-assembly behavior of porphyrins in an oil/water system and provides important clues concerning the design of appropriate porphyrins when related subjects are addressed. Our investigation suggests that an oil/aqueous system might be an efficient medium for producing unique organic-based nanostructures

Finite size effects on hinge states in three-dimensional second-order topological insulators

We investigate the finite size effects of a three-dimensional second-order topological insulator with fourfold rotational symmetry and time-reversal symmetry. Starting from the effective Hamiltonian of the three-dimensional second-order topological insulator, we derive the effective Hamiltonian of four two-dimensional surface states with gaps derived by perturbative methods. Then, the sign alternation of the mass term of the effective Hamiltonian on the adjacent surface leads to the hinge state. In addition, we obtain the effective Hamiltonian and its wave function of one-dimensional gapless hinge states with semi-infinite boundary conditions based on the effective Hamiltonian of two-dimensional surface states. In particular, we find that the hinge states on the two sides of the same surface can couple to produce a finite energy gap

Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction

We propose a continuous neural network architecture, termed Explainable Tensorized Neural Ordinary Differential Equations (ETN-ODE), for multi-step time series prediction at arbitrary time points. Unlike the existing approaches, which mainly handle univariate time series for multi-step prediction or multivariate time series for single-step prediction, ETN-ODE could model multivariate time series for arbitrary-step prediction. In addition, it enjoys a tandem attention, w.r.t. temporal attention and variable attention, being able to provide explainable insights into the data. Specifically, ETN-ODE combines an explainable Tensorized Gated Recurrent Unit (Tensorized GRU or TGRU) with Ordinary Differential Equations (ODE). The derivative of the latent states is parameterized with a neural network. This continuous-time ODE network enables a multi-step prediction at arbitrary time points. We quantitatively and qualitatively demonstrate the effectiveness and the interpretability of ETN-ODE on five different multi-step prediction tasks and one arbitrary-step prediction task. Extensive experiments show that ETN-ODE can lead to accurate predictions at arbitrary time points while attaining best performance against the baseline methods in standard multi-step time series prediction

Effect of Height Difference on The Performance of Two-phase Thermosyphon Loop Used in Air-conditioning System

Two-phase thermosyphon loops (TPTLs) are highly effective devices for spontaneously transferring heat through a relatively long distance. Therefore, TPTLs are extensively used in various fields, such as cooling of electronic components, light water reactors, etc. Recently, the TPTL has also been found to be an effective way to recover or transfer heat in air-conditioning systems for energy saving. A typical TPTL consists of an evaporator, a riser (gas tube), a condenser, and a downcomer (liquid tube), and the condenser is higher than the evaporator by a certain vertical distance. The TPTL is powered by the natural force --- gravity, which means the pressure drop in the cycle always equals to the liquid head caused by the density difference of the liquid in the downcomer and the vapor or vapor/liquid mixture in the riser. Therefore, the liquid head is a key factor affecting the circulation flow rate and energy performance of TPTLs. In the traditional applications, the liquid head is considered to be proportional to the height difference between the condenser and the evaporator based on the underlying assumption: the downcomer is fully liquid filled. According to that, the TPTL will perform better with a larger height difference. The conclusion may be correct in the cases with large temperature difference and heat flux, such as in the field of cooling of electronic and light water reactors. However, when the TPTL is used in air-conditioning system, which has quite small temperature difference and small heat flux, some special phenomena were observed: the liquid heat is lower than the height difference and the downcomer is partially liquid filled. That is largely different from the thermosyphons in traditional applications. What’s the thermodynamic mechanism of partially liquid filled in the downcomer? How to determine the liquid head and the height difference? These are the fundamental questions that required answers before using two-phase thermosyphon in air conditioner field. In this study, the thermodynamic mechanism of partially liquid filled in the downcomer is researched and the effect of height difference on the performance of TPTL is investigated theoretically and experimentally. Firstly, a visual experimental setup is established, and the performance of a water-water TPTL is measured when the height difference ranges from 0 m to 2.4 m. Based on it, the basic phenomena are observed and the thermodynamic mechanism is investigated. Secondly, a generalized distributed-parameter model is developed based on the conservations of momentum, energy, and mass, which can determine liquid head and overall performance simultaneously according to external conditions. The model is verified by experiments. Then the model is used to analyze the variation of liquid head, circulation flow rate, heat transfer rate, system pressure under different height differences. The results show that with the increase of the height difference, the liquid head rises continuously until remain stable. Therefore, the liquid head is less than the height difference in some cases. Consequently, with the increase of height difference, the circulation flow rate and thermal performance firstly increases then remains constant

Outpainting by Queries

Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision. However, CNNs rely on inherent inductive biases to achieve effective sample learning, which may degrade the performance ceiling. In this paper, motivated by the flexible self-attention mechanism with minimal inductive biases in transformer architecture, we reframe the generalised image outpainting problem as a patch-wise sequence-to-sequence autoregression problem, enabling query-based image outpainting. Specifically, we propose a novel hybrid vision-transformer-based encoder-decoder framework, named \textbf{Query} \textbf{O}utpainting \textbf{TR}ansformer (\textbf{QueryOTR}), for extrapolating visual context all-side around a given image. Patch-wise mode's global modeling capacity allows us to extrapolate images from the attention mechanism's query standpoint. A novel Query Expansion Module (QEM) is designed to integrate information from the predicted queries based on the encoder's output, hence accelerating the convergence of the pure transformer even with a relatively small dataset. To further enhance connectivity between each patch, the proposed Patch Smoothing Module (PSM) re-allocates and averages the overlapped regions, thus providing seamless predicted images. We experimentally show that QueryOTR could generate visually appealing results smoothly and realistically against the state-of-the-art image outpainting approaches

EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables

While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods. The dynamical systems of multivariate time series could be modelled with complex unknown partial differential equations (PDEs) which play a prominent role in many disciplines of science and engineering. In this paper, we propose a continuous-time model for arbitrary-step prediction to learn an unknown PDE system in multivariate time series whose governing equations are parameterised by self-attention and gated recurrent neural networks. The proposed model, \underline{E}xogenous-\underline{g}uided \underline{P}artial \underline{D}ifferential \underline{E}quation Network (EgPDE-Net), takes account of the relationships among the exogenous variables and their effects on the target series. Importantly, the model can be reduced into a regularised ordinary differential equation (ODE) problem with special designed regularisation guidance, which makes the PDE problem tractable to obtain numerical solutions and feasible to predict multiple future values of the target series at arbitrary time points. Extensive experiments demonstrate that our proposed model could achieve competitive accuracy over strong baselines: on average, it outperforms the best baseline by reducing $9.85\%$ on RMSE and $13.98\%$ on MAE for arbitrary-step prediction

AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design

Structure-based drug design (SBDD), which aims to generate molecules that can bind tightly to the target protein, is an essential problem in drug discovery, and previous approaches have achieved initial success. However, most existing methods still suffer from invalid local structure or unrealistic conformation issues, which are mainly due to the poor leaning of bond angles or torsional angles. To alleviate these problems, we propose AUTODIFF, a diffusion-based fragment-wise autoregressive generation model. Specifically, we design a novel molecule assembly strategy named conformal motif that preserves the conformation of local structures of molecules first, then we encode the interaction of the protein-ligand complex with an SE(3)-equivariant convolutional network and generate molecules motif-by-motif with diffusion modeling. In addition, we also improve the evaluation framework of SBDD by constraining the molecular weights of the generated molecules in the same range, together with some new metrics, which make the evaluation more fair and practical. Extensive experiments on CrossDocked2020 demonstrate that our approach outperforms the existing models in generating realistic molecules with valid structures and conformations while maintaining high binding affinity

High resolution magnetic microscopy based on semi-encapsulated graphene Hall sensors

The realization of quantitative, noninvasive sensors for ambient magnetic imaging with high spatial and magnetic field resolution remains a major challenge. To address this, we have developed a relatively simple process to fabricate semi-encapsulated graphene/hBN Hall sensors assembled by dry transfer onto pre-patterned gold contacts. 1 lm-sized Hall cross sensors at a drive current of 0.5 lA exhibit excellent room temperature sensitivity, SI 700 V/AT, and good minimum detectable fields, Bmin ¼ 0.54 G/Hz0.5 at a measurement frequency of 1 kHz, with considerable scope for further optimization of these parameters. We illustrate their application in an imaging study of labyrinth magnetic domains in a ferrimagnetic yttrium iron garnet film

Albumin Binding Function: The Potential Earliest Indicator for Liver Function Damage

Background. Currently there is no indicator that can evaluate actual liver lesion for early stages of viral hepatitis, nonalcoholic fatty liver disease (NAFLD), and cirrhosis. Aim of this study was to investigate if albumin binding function could better reflect liver function in these liver diseases. Methods. An observational study was performed on 193 patients with early NAFLD, viral hepatitis, and cirrhosis. Cirrhosis patients were separated according to Child-Pugh score into A, B, and C subgroup. Albumin metal ion binding capacity (Ischemia-modified albumin transformed, IMAT) and fatty acid binding capacity (total binding sites, TBS) were detected. Results. Both IMAT and TBS were significantly decreased in patients with NAFLD and early hepatitis. In hepatitis group, they declined prior to changes of liver enzymes. IMAT was significantly higher in cirrhosis Child-Pugh class A group than hepatitis patients and decreased in Child-Pugh class B and class C patients. Both IMAT/albumin and TBS/albumin decreased significantly in hepatitis and NAFLD group patients. Conclusions. This is the first study to discover changes of albumin metal ion and fatty acid binding capacities prior to conventional biomarkers for liver damage in early stage of liver diseases. They may become potential earliest sensitive indicators for liver function evaluation