Mobility of Li+, Na+, and Cs+ cations in Nafion membrane,as studied by NMR techniques

The local mobility and diffusion of Li⁺, Na⁺, and Cs⁺ cations in Nafion 117 membrane were explored by ⁷Li, ²³Na, and ¹³³Cs spin relaxation and pulsed field gradient NMR techniques. It was shown that the macroscopic mass transfer of cations is controlled by ion motion near sulfonate groups. Lithium and sodium cations, whose hydrated energy is higher than the water hydrogen bond energy, are moving together with water molecules, but cesium cations possessing a low hydrated energy are jumping directly between the neighboring sulfonate groups.     

Light-Based 3D Printing of Gelatin-Based Biomaterial Inks to Create a Physiologically Relevant In Vitro Fish Intestinal Model

To provide prominent accessibility of fishmeal to the European population, the currently available, time- and cost-extensive feeding trials, which evaluate fish feed, should be replaced. The current paper reports on the development of a novel 3D culture platform, mimicking the microenvironment of the intestinal mucosa in vitro. The key requirements of the model include sufficient permeability for nutrients and medium-size marker molecules (equilibrium within 24 h), suitable mechanical properties (G' < 10 kPa), and close morphological similarity to the intestinal architecture. To enable processability with light-based 3D printing, a gelatin-methacryloyl-aminoethyl-methacrylate-based biomaterial ink is developed and combined with Tween 20 as porogen to ensure sufficient permeability. To assess the permeability properties of the hydrogels, a static diffusion setup is utilized, indicating that the hydrogel constructs are permeable for a medium size marker molecule (FITC-dextran 4 kg mol⁻¹). Moreover, the mechanical evaluation through rheology evidence a physiologically relevant scaffold stiffness (G' = 4.83 ± 0.78 kPa). Digital light processing-based 3D printing of porogen-containing hydrogels results in the creation of constructs exhibiting a physiologically relevant microarchitecture as evidenced through cryo-scanning electron microscopy. Finally, the combination of the scaffolds with a novel rainbow trout (Oncorhynchus mykiss) intestinal epithelial cell line (RTdi-MI) evidence scaffold biocompatibility.     

Hydrochemistry of an ancient traditional irrigation reservoir in Padaviya,Sri Lanka

Padaviya Wewa Reservoir constructed in 2nd century BC is an important traditional reservoir in Sri Lanka. The construction and the use of these traditional Sri Lankan reservoirs are different from modern large-scale hydroelectric and multi-purpose reservoirs in Sri Lanka and the rest of the world. The hydrochemistry of these unique water bodies has been rarely studied and understood. In this study, depth-wise distribution of Fe, Mn, Zn, Cd, and As and their correlation with pH, dissolved oxygen, conductivity, temperature, BOD, COD, and total phosphate were investigated to identify the mechanisms that drive the water quality in Padaviya Wewa Reservoir. There is a well distinguishable chemical gradient from the surface to the bottom of the water column suggesting that Padaviya is not a well-mixed water body even though the maximum water depth during the study period was 4.75 m. There was a strong negative correlation between Fe, Mn, Zn, and Cd and DO in both surface and bottom waters. In addition, a strong positive correlation between Fe, Mn, Zn, Cd, and As, and total phosphate was observed in bottom waters. The suggested driving force behind the depth-wise distribution of chemical species is intense biological activities in the reservoir. The weak thermal stratification is strengthened by photosynthesis at the surface waters and organic matter degradation at bottom of the reservoir. The end-result of these biological activities are oxygen rich surface waters with low concentrations of nutrients and metal ions and oxygen depleted bottom waters with higher concentrations of nutrients and metal ions.     

Strain analysis for moiré interferometry using the two-dimensional continuous wavelet transform

In this paper, we introduce the two-dimensional continuous wavelet transform for the automated strain analysis of the moiré interference fringe pattern. The Fourier transform method has been widely used for automated analysis of an optical interference fringe pattern. However, this method is hardly applicable to the analysis of the fringe pattern, which includes large displacement range or discontinuities. We show the advantages of the wavelet transform method by applying it to experimental results on composite laminates.     

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

Shadow is one of the fundamental indicators of remote sensing image which could cause loss or interference of the target data. As a result, the detection and removal of shadow has already been the hotspot of current study because of the complicated background information. In the following passage, a model combining the Atmospheric Transport Model (hereinafter abbreviated as ATM) with the Poisson Equation, AP ShadowNet, is proposed for the shadow detection and removal of remote sensing images by unsupervised learning. This network based on a preprocessing network based on ATM, A Net, and a network based on the Poisson Equation, P Net. Firstly, corresponding mapping between shadow and unshaded area is generated by the ATM. The brightened image will then enter the Confrontation identification in the P Net. Lastly, the reconstructed image is optimized on color consistency and edge transition by Poisson Equation. At present, most shadow removal models based on neural networks are significantly data-driven. Fortunately, by the model in this passage, the unsupervised shadow detection and removal could be released from the data source restrictions from the remote sensing images themselves. By verifying the shadow removal on our model, the result shows a satisfying effect from a both qualitative and quantitative angle. From a qualitative point of view, our results have a prominent effect on tone consistency and removal of detailed shadows. From the quantitative point of view, we adopt the non-reference evaluation indicators: gradient structure similarity (NRSS) and Natural Image Quality Evaluator (NIQE). Combining various evaluation factors such as reasoning speed and memory occupation, it shows that it is outstanding among other current algorithms.     

Quantum Correlation Swapping between Two Werner States Undergoing Local and Nonlocal Unitary Operations

In this paper, quantum correlation (QC) swapping between two Werner-like states, which are transformed from Werner states undergoing local and nonlocal unitary operations, are studied. Bell states measures are performed in the middle node to realize the QC swapping and correspondingly final correlated sates are obtained. Two different QC quantifiers, i.e., measurement-induced disturbance (MID) and ameliorated MID, are employed to characterize and quantify all the concerned QCs in the swapping process. All QCs in the concerned states are evaluated analytically and numerically. Correspondingly, their characteristics and properties are exposed in detail. It is exposed that, through the QC swapping process, one can obtain the long-distance QC indeed. Moreover, the similarities of monotony features of MID and AMID between the initial states and final states are exposed and analyzed.     

A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.     

An Efficient Supervised Deep Hashing Method for Image Retrieval

In recent years, searching and retrieving relevant images from large databases has become an emerging challenge for the researcher. Hashing methods that mapped raw data into a short binary code have attracted increasing attention from the researcher. Most existing hashing approaches map samples to a binary vector via a single linear projection, which restricts the flexibility of those methods and leads to optimization problems. We introduce a CNN-based hashing method that uses multiple nonlinear projections to produce additional short-bit binary code to tackle this issue. Further, an end-to-end hashing system is accomplished using a convolutional neural network. Also, we design a loss function that aims to maintain the similarity between images and minimize the quantization error by providing a uniform distribution of the hash bits to illustrate the proposed technique’s effectiveness and significance. Extensive experiments conducted on various datasets demonstrate the superiority of the proposed method in comparison with state-of-the-art deep hashing methods.     

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms

Despite the increasing applications, demands, and capabilities of drones, in practice they have only limited autonomy for accomplishing complex missions, resulting in slow and vulnerable operations and difficulty adapting to dynamic environments. To lessen these weaknesses, we present a computational framework for deducing the original intent of drone swarms by monitoring their movements. We focus on interference, a phenomenon that is not initially anticipated by drones but results in complicated operations due to its significant impact on performance and its challenging nature. We infer interference from predictability by first applying various machine learning methods, including deep learning, and then computing entropy to compare against interference. Our computational framework begins by building a set of computational models called double transition models from the drone movements and revealing reward distributions using inverse reinforcement learning. These reward distributions are then used to compute the entropy and interference across a variety of drone scenarios specified by combining multiple combat strategies and command styles. Our analysis confirmed that drone scenarios experienced more interference, higher performance, and higher entropy as they became more heterogeneous. However, the direction of interference (positive vs. negative) was more dependent on combinations of combat strategies and command styles than homogeneity.