Squaramide and bis-urea supramolecular gels

In the field of supramolecular chemistry, supramolecular low molecular weight gelators (LMWG) have attracted the public attention. Supramolecular gel is a very attractive soft materials formed by non-covalent interactions. The frameworks based on non-covalent interactions give the supramolecular gels the most important properties in dynamics and reversibility. Dynamic characters provide a variety of characterisation methods and reversibility enables them to heal structures in respond of external stimuli such as light, heat and so on. This project can be divided into two main parts. At first, gel screening and characterization have done for squaramides 2.1-2.8 which came from the Dr Rob Elmes cooperative laboratory in Maynooth University. Half of the squaramaides were gelators, and one of them was metallogelator which gelled selected solvents together with copper chlorides and nitrates. In order to characterize the physical properties of gels, rheology has been carried out. Secondly, analogues 2.9-2.12 have been synthesised. After that, gel screening and characterisation proceeded for isoniazid and nicotinic hydrazide terminated gelators with both meta-disbstituted aryl linker and tetraethyl diphenylmethane linker 2.9- 2.12. All compands have been analysed by Nuclear Magnetic Resonance (NMR), Mass spectroscopy (MS) and elemental analysis. Analogues 2.9 and 2.11 were found to be good metallogelators in the presence of copper and cadmium chlorides. Gelator 2.10 was able to give a list of partial gels while 2.12 was found to be a non-gelator. The crystals obtained from the gel screen process were send for the single crystal diffraction in order to find out the structure. What is more, typical gels formed from each gelator were charicterised by rheology

Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation

Medical image data are often limited due to the expensive acquisition and annotation process. Hence, training a deep-learning model with only raw data can easily lead to overfitting. One solution to this problem is to augment the raw data with various transformations, improving the model's ability to generalize to new data. However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions. Therefore, automatic data augmentation is proposed to learn favorable augmentation strategies for different datasets while incurring large GPU overhead. To this end, we present a novel method, called Dynamic Data Augmentation (DDAug), which is efficient and has negligible computation cost. Our DDAug develops a hierarchical tree structure to represent various augmentations and utilizes an efficient Monte-Carlo tree searching algorithm to update, prune, and sample the tree. As a result, the augmentation pipeline can be optimized for each dataset automatically. Experiments on multiple Prostate MRI datasets show that our method outperforms the current state-of-the-art data augmentation strategies

Accelerating Globally Optimal Consensus Maximization in Geometric Vision

Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially as a function of the dimensionality of the problem at hand. In this work, we convey a novel, general technique that allows us to branch over an $n-1$ dimensional space for an n-dimensional problem. The remaining degree of freedom can be solved globally optimally within each bound calculation by applying the efficient interval stabbing technique. While each individual bound derivation is harder to compute owing to the additional need for solving a sorting problem, the reduced number of intervals and tighter bounds in practice lead to a significant reduction in the overall number of required iterations. Besides an abstract introduction of the approach, we present applications to three fundamental geometric computer vision problems: camera resectioning, relative camera pose estimation, and point set registration. Through our exhaustive tests, we demonstrate significant speed-up factors at times exceeding two orders of magnitude, thereby increasing the viability of globally optimal consensus maximizers in online application scenarios

Recognizing Dew as an Indicator and an Improver of Near-Surface Air Quality

The relationship between dew and airborne particles is important in urban ecosystems, but the capability of dew to remove airborne particles remains unclear. During 2015 in Changchun, China, 74 dew and particle samples were collected simultaneously to investigate their chemical characteristics under normal, fog, and haze conditions. Analyses included measuring total dissolved solids, total suspended particulates, PM2.5 and PM10 concentrations, major cations (NH4+, Na+, K+, Ca2+, and Mg2+), major anions (F−, Cl−, SO42-, and NO3-), and organic and elemental carbon. Results showed that air quality deteriorated during haze but improved in fog. The particle size distributions, major cations, and carbonaceous species documented in the dew and airborne particles demonstrated consistent synchronous patterns with values descending in the order haze > normal > fog conditions. We found that dew is a good indicator of near-surface air quality. Specifically, its water-soluble ions and carbonaceous species could be used to distinguish emission sources and to identify the presence of secondary organic carbon. Dew is more effective at removing airborne particles in normal weather than in haze or fog conditions; PM2.5 removal rates were 21.5%, 15.2%, and 13.7% on normal, foggy, and hazy days, respectively. Dew condensation processes reduce concentrations of gaseous and particulate pollutants in the near-surface layer

How FinTech affects total factor energy efficiency? Evidence from Chinese cities

The advancement of Financial Technology (FinTech) is crucial for government entities, the National Grid, and various energy corporations to facilitate the transition towards sustainable and green production methods. This study investigates the relationship between FinTech and Total Factor Energy Efficiency (TFEE) using data from a selected sample of 254 city groups in China. We examine how the development of FinTech impacts TFEE from both non-spatial and spatial perspectives. The results from the non-spatial panel model indicate that FinTech development has a significant positive impact on TFEE. Comparative studies were conducted using fixed effects (FE), feasible generalized least squares (FGLS) models, and system generalized method of moments (GMM) models, and the main findings remained consistent, confirming the robustness of our conclusions. Spatial autocorrelation results reveal a significant positive spatial spillover effect on TFEE. Both the spatial Durbin model and the dynamic spatial Durbin model demonstrate that FinTech also has a significant positive impact on TFEE, and this effect increases over time. These conclusions remain robust even after considering various spatial weight matrices and alternative methods for calculating TFEE. Additionally, we discovered that the digital economy plays a vital role in strengthening the relationship between FinTech and TFEE. Heterogeneity analysis indicates that, compared to cities without resource-based economies, FinTech development in growing resource-based cities has a more substantial impact on TFEE