Role In Many Applications Research Articles

Molecular Interactions and Structural Transformations in Water Complexes with Azoles in Solid Films: IR and DFT Study

AbstractAzoles are widely used as bioactive substances, herbicides, catalysts, corrosion inhibitors, polymeric compositors, organometallic ligands, and optical elements’ components. The interaction with water plays a crucial role in many applications of azoles. The objective of this study is to observe the molecular interaction and structural transformations of 1,2‐ and 1,3‐diazoles’, 1,2,3‐ and 1,2,4‐triazoles’ complexes with water molecules. We have selected the most valuable for application and theoretical interest azoles with different molecular structures, which have allowed us to consider the basic features of their aqueous complexes. We used the IR study method allowing to fix water intermediates in the solid phase. We have also employed the hybrid B3LYP functional DFT method with GD3BJ dispersion correction. At the cluster formation, the hydrogen atom transfer arises, providing a labile spatial arrangement of the water system. The formation of azoles’ complexes with a hydronium ion having relatively low binding energy allows the reshaping of the complexes’ structure at the hydrogen atom cluster‐to‐cluster transition. The complex formation is accompanied by the hydrogen atom transfer with the participation of both water and azole hydrogen atoms. This transfer provides the mobile spatial arrangement of clusters and hydrogen atom transition from donor to acceptor centers.

Near-field enhancement of light by higher-order multipole excitations in a metal nanodisc trimer

Near-field enhancement of light by dipole excitations in plasmonic nanoparticles plays an important role in many applications of optical nanotechnology, including solar cells, plasmonic sensors, and nonlinear optical devices. Recently, we have shown that a seemingly weak octupole resonance in a pair of metal nanospheres can provide a higher near-field enhancement than the dipole resonance. Being motivated by this discovery, we now design a plasmonic nanodisc trimer that supports hybridized higher-order excitations and simultaneously suppresses the dipole excitation. We show that, under these conditions, the near-field enhancement can reach a high level, exceeding the value achievable with a corresponding dimer structure. The interference of the electric currents belonging to different multipole moments is found to play an important role in the enhancement. We believe that arrays of similar metal nanostructures can be designed to enhance optical fields via higher-order resonances for many applications, e.g., in nonlinear optics and optical sensing based on surface-enhanced fluorescence or Raman scattering.

3D-3C measurements of flow reversal in small sessile drops in shear flow

Sessile drops play an important role in nature and the operation of many technical and biological systems as for instance fuel cells, cleaning processes, lab-on-a-chip devices and single-cell analysis. Nonetheless, their dynamic behaviour in a shear flow is still not fully understood (e. g. detachment mechanism). Challenges exist regarding the precise simulation of two-phase flows as well as difficulties in conducting flow measurements with sufficient temporal resolution of more than 1 kHz. In this article, we present the first three-dimensional flow measurements in strongly oscillating drops stemming from a shear flow by using a monocular 3D localization microscope based on a Double-Helix Point Spread Function combined with Particle Tracking Velocimetry. The high temporal resolution and the large measurement volume - in terms of microscopy - make it possible to measure the time- and phase-averaged flow in small drops with Bond numbers (Bo) smaller than 1. Water drops were placed in an air flow channel and measurements were conducted for Reynolds numbers (Red) from about 750 to 1700. Our measurements show that the results of previous investigations for drops with Bo>1 concerning vortex pattern and flow reversal inside the drop apply for smaller drops as well. In addition, we are able to reveal the three-dimensional flow structure and multiple vortex-pattern in time and space. We discover a periodic 3D vortical flow pattern that corresponds to the first and second eigenfrequency of the drop. Moreover, we demonstrate the potential of adaptive optics to correct measurement errors stemming from time-varying light refraction when conducting measurements through the fluctuating drop surface, in particular for opaque substrates. The results may help in understanding the coupling of inner and outer flow for sessile drops in shear flow which allows for an analysis of the onset motion of these drops, i.e. drop removal. Removal of sessile drops plays a crucial role in many applications, which includes among other things the water management of fuel cells where small drops with Bo<1 predominantly occur.

Center-enhanced video captioning model with multimodal semantic alignment

Video captioning aims at automatically generating descriptive sentences based on the given video, establishing an association between the visual contents and textual languages, has attracted great attention and plays a significant role in many practical applications. Previous researches focus more on the aspect of caption generation, ignoring the alignment of multimodal feature and just simply concatenating them. Besides, video feature extraction is usually done in an off-line manner, which leads to the fact that the extracted feature may not adapted to the subsequent caption generation task. To improve the applicability of extracted features for downstream caption generation and to address the issue of multimodal semantic alignment fusion, we propose an end-to-end center-enhanced video captioning model with multimodal semantic alignment, which integrates feature extraction and caption generation task into a unified framework. In order to enhance the completeness of semantic features, we design a center enhancement strategy where the visual–textual deep joint semantic feature can be captured via incremental clustering, then the cluster centers can serve as the guidance for better caption generation. Moreover, we propose to promote the visual–textual multimodal alignment fusion by learning the visual and textual representation in a shared latent semantic space, so as to alleviate the multimodal misalignment problem. Experimental results on two popular datasets MSVD and MSR-VTT demonstrate that the proposed model could outperform the state-of-the-art methods, obtaining higher-quality caption results.

GSK-LocS: Towards a more effective generalisation in population-based neural network training

Despite the effectiveness of deep neural networks, feed-forward neural networks (FFNNs) continue to play a crucial role in many applications, especially when dealing with limited data availability. The primary challenge in FFNNs is determining the optimal weights during the training process, aiming to minimise the disparity between actual and predicted outputs. Although gradient-based techniques like backpropagation (BP) have traditionally been popular for FFNN training, they come with inherent limitations, such as sensitivity to initial weights and susceptibility to getting trapped in local optima. To overcome these challenges, we introduce a novel approach based on the Gaining-Sharing Knowledge-based(GSK) algorithm. To the best of our knowledge, this paper represents the first exploration of GSK for neural network training. After obtaining the appropriate weights for the FFNN by the GSK, the weights and biases are utilised to initialise a Levenberg–Marquardt backpropagation (LMBP) algorithm, serving as a local search component. In other words, our proposed algorithm, GSK-LocS, leverages the global search capabilities of the GSK algorithm and combines them with the local search capabilities of LMBP for neural network training. This integration mitigates sensitivity to initial values and reduces the risk of being trapped in local optima. Experimental results conducted on classification and approximation problems provide compelling evidence that our proposed algorithm is highly competitive compared to other existing methods.

Fluorescence diffuse optical monitoring of bioreactors: a hybrid deep learning and model-based approach for tomography.

Biosynthesis in bioreactors plays a vital role in many applications, but tools for accurate in situ monitoring of the cells are still lacking. By engineering the cells such that their conditions are reported through fluorescence, it is possible to fill in the gap using fluorescence diffuse optical tomography (fDOT). However, the spatial accuracy of the reconstruction can still be limited, due to e.g. undersampling and inaccurate estimation of the optical properties. Utilizing controlled phantom studies, we use a two-step hybrid approach, where a preliminary fDOT result is first obtained using the classic model-based optimization, and then enhanced using a neural network. We show in this paper using both simulated and phantom experiments that the proposed method can lead to a 8-fold improvement (Intersection over Union) of fluorescence inclusion reconstruction in noisy conditions, at the same speed of conventional neural network-based methods. This is an important step towards our ultimate goal of fDOT monitoring of bioreactors.

A cross-fusion of non-local attention network for infrared small target tracking

Infrared small target tracking plays important roles in many applications, such as infrared surveillance and reconnaissance. However, achieving promising performance with existing trackers is difficult due to the following challenges: (1) Infrared small targets lack color and texture information, making it difficult for the network to capture effective features; (2) A slight centroid-shift of the target can lead to tracking failure. To overcome these challenges, this paper proposes the CFNLA network, which consists of two parts: (1) The cross-fusion module is designed to capture the relationship between the target and its surroundings, including a non-local block to enhance the feature information of the target at the current scale and reduce interference such as background; (2) The confidence loss function is proposed to improve tracking stability by forcing the network to concentrate on centroid shift. Furthermore, a self-designed infrared small target tracking dataset, IRSTT, is constructed to evaluate the proposed algorithm. Finally, the experimental results on various open-source datasets and the IRSTT dataset verify the superior performance of the CFNLA network.

Tackling Few-Shot Challenges in Automatic Modulation Recognition: A Multi-Level Comparative Relation Network Combining Class Reconstruction Strategy.

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios. Therefore, this paper endeavored to address the challenge of AMR with limited data and proposed a novel meta-learning method, the Multi-Level Comparison Relation Network with Class Reconstruction (MCRN-CR). Firstly, the method designs a structure of a multi-level comparison relation network, which involves embedding functions to output their feature maps hierarchically, comprehensively calculating the relation scores between query samples and support samples to determine the modulation category. Secondly, the embedding function integrates a reconstruction module, leveraging an autoencoder for support sample reconstruction, wherein the encoder serves dual purposes as the embedding mechanism. The training regimen incorporates a meta-learning paradigm, harmoniously combining classification and reconstruction losses to refine the model's performance. The experimental results on the RadioML2018 dataset show that our designed method can greatly alleviate the small sample problem in AMR and is superior to existing methods.

Accurate learning of graph representation with the consideration of fuzzy overlapping community

Graph classification task plays a crucial role in many practical applications, for which the model is required to be capable of learning an accurate representation with discriminative characteristics, but existing methods typically fail to exploit the most authentic substructures for better representation. Aiming at this limitation, a fuzzy overlapping community guided subgraph neural network is proposed in this paper to make the best use of graph latent topologies for fine-grained representation learning and discriminative feature extraction, namely FS-GRAL. Taking multi-functional elements and their preferences fully into consideration, FS-GRAL allows the nodes to appear in more than one subgraph with different contributions for message passing, so that the model is powerful enough in capturing those natural aggregations inherent in complex networks to achieve accurate representation learning. Meanwhile, on the basis of learned accurate representations, a two-level pooling strategy dominated by intra-subgraph node discarding is presented for feature extraction, which enables graph latent topology can be integrated into extracted discriminative features, and thus the coarsened graph is more representative for graph classification. Extensive experiments on real-world benchmarks demonstrate that our fuzzy overlapping community guided subgraph neural network is highly competitive in learning the accurate graph representation and its intra-subgraph dominated two-level pooling strategy promotes a more promising classification performance.

An efficient and robust varying-parameter projection neural network for sparse signal reconstruction

Sparse signal reconstruction plays an important role in many practical applications, and therefore, many scholars strive to study its various effective (approximate) solution approaches, including some projection neural network models with finite-time or fixed-time convergence due to their parallel nature and convenience for hardware implementation. However, we experimentally find that noise has a significant influence on the convergence performance of these models. To overcome this problem, in this paper, we propose a new efficient varying-parameter projection neural network model (VPPNN) using the sliding mode control technique and a novel time-varying parameter for sparse signal reconstruction. Theoretical analysis shows that this new model not only has shorter fixed convergence time under certain conditions but also has noise-tolerance when compared to the existing projection neural network models. Numerical simulation experiments (via sparse signal/image reconstruction) are performed to illustrate the superiority of the new proposed model over the existing ones in terms of the convergence rate and robustness.

Neural network enabled fringe projection through scattering media

The projection of fringes plays an essential role in many applications, such as fringe projection profilometry and structured illumination microscopy. However, these capabilities are significantly constrained in environments affected by optical scattering. Although recent developments in wavefront shaping have effectively generated high-fidelity focal points and relatively simple structured images amidst scattering, the ability to project fringes that cover half of the projection area has not yet been achieved. To address this limitation, this study presents a fringe projector enabled by a neural network, capable of projecting fringes with variable periodicities and orientation angles through scattering media. We tested this projector on two types of scattering media: ground glass diffusers and multimode fibers. For these scattering media, the average Pearson’s correlation coefficients between the projected fringes and their designed configurations are 86.9% and 79.7%, respectively. These results demonstrate the effectiveness of the proposed neural network enabled fringe projector. This advancement is expected to broaden the scope of fringe-based imaging techniques, making it feasible to employ them in conditions previously hindered by scattering effects.

Enhancing Knowledge Graph Consistency through Open Large Language Models: A Case Study

High-quality knowledge graphs (KGs) play a crucial role in many applications. However, KGs created by automated information extraction systems can suffer from erroneous extractions or be inconsistent with provenance/source text. It is important to identify and correct such problems. In this paper, we study leveraging the emergent reasoning capabilities of large language models (LLMs) to detect inconsistencies between extracted facts and their provenance. With a focus on ``open'' LLMs that can be run and trained locally, we find that few-shot approaches can yield an absolute performance gain of 2.5-3.4% over the state-of-the-art method with only 9% of training data. We examine the LLM architectures' effect and show that Decoder-Only models underperform Encoder-Decoder approaches. We also explore how model size impacts performance and counterintuitively find that larger models do not result in consistent performance gains. Our detailed analyses suggest that while LLMs can improve KG consistency, the different LLM models learn different aspects of KG consistency and are sensitive to the number of entities involved.

Efficient sum-frequency generation of a yellow laser in a thin-film lithium niobate waveguide.

Yellow lasers with high efficiency and tunability play an essential role in many applications. Here, we demonstrate the sum-frequency generation (SFG) of yellow light on a periodically poled thin-film lithium niobate (PP-TFLN) waveguide. Taking advantage of large χ(2) nonlinearity, a high normalized conversion efficiency of 10,097% (W·cm2) is obtained with pump wavelengths of 1317.7 and 1064 nm. An absolute conversion efficiency of 24.17% is recorded with on-chip pump powers of 10.4 dBm (O-band) and 13.5 dBm (1064 nm).

Spatial Invariant Tensor Self-Representation Model for Hyperspectral Anomaly Detection.

With the development of hyperspectral imaging technology, the hyperspectral anomaly has attracted considerable attention due to its significant role in many applications. Hyperspectral images (HSIs) with two spatial dimensions and one spectral dimension are intrinsically three-order tensors. However, most of the existing anomaly detectors were designed after converting the 3-D HSI data into a matrix, which destroys the multidimension structure. To solve this problem, in this article, we propose a spatial invariant tensor self-representation (SITSR) hyperspectral anomaly detection algorithm, which is derived based on the tensor-tensor product (t-product) to preserve the multidimension structure and achieve a comprehensive description of the global correlation of HSIs. Specifically, we exploit the t-product to integrate spectral information and spatial information, and the background image of each band is represented as the sum of the t-product of all bands and their corresponding coefficients. Considering the directionality of the t-product, we utilize two tensor self-representation methods with different spatial modes to obtain a more balanced and informative model. To depict the global correlation of the background, we merge the unfolding matrices of two representative coefficients and constrain them to lie in a low-dimensional subspace. Moreover, the group sparsity of anomaly is characterized by l2.1.1 norm regularization to promote the separation of background and anomaly. Extensive experiments conducted on several real HSI datasets demonstrate the superiority of SITSR compared with state-of-the-art anomaly detectors.

High sensitivity of whitlockite-type phosphor toward optical thermometric

Whitlockite-type structure phosphors have been widely studied due to their adjustable chemical composition and abundant crystal location. Whitlockite-type phosphors can be doped with rare earth elements to achieve the emission of warm white light. White light-emitting diodes (w-LEDs) have become increasingly popular and play a significant role in many applications, such as Biosensing and fluorescent labeling. In this work, The Ca2.6K0.2La0.2(PO4)2(CKLP) whitlockite-type phosphors co-doped with Dy3+ and Sm3+ were entered via a high-temperature solid-phase reaction. Their crystal structure, luminescence characteristics, and temperature sensitivity were explored in detail. Through doping Dy3+ and Sm3+, can obtain white light and warm white light. As the concentration of Sm3+ increased, their lifetime decreased from 0.483 ms to 0.059 ms, confirming interionic energy transfer in the host. The CKLP: 0.06Dy3+, 0.06Sm3+ phosphor prepared by Fluorescence Intensity Ratio (FIR) technology has suitable temperature sensing performance. Its relative sensitivity (Sr) and complete sensitivity (Sa) are high up to 4.48 × 10−3 k−1 and 1.021%K−1 at 298 K. The results show that CKLP: Dy3+ and Sm3+ phosphors used in w-LEDs and display devices will present excellent application potential in new optical thermometry.

A Dual-Way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking

Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia, which plays a key role in many applications. However, existing methods suffer from shortcomings, including modality impurity such as noise in raw image and ambiguous textual entity representation, which puts obstacles to MEL. We formulate multimodal entity linking as a neural text matching problem where each multimodal information (text and image) is treated as a query, and the model learns the mapping from each query to the relevant entity from candidate entities. This paper introduces a dual-way enhanced (DWE) framework for MEL: (1) our model refines queries with multimodal data and addresses semantic gaps using cross-modal enhancers between text and image information. Besides, DWE innovatively leverages fine-grained image attributes, including facial characteristic and scene feature, to enhance and refine visual features. (2)By using Wikipedia descriptions, DWE enriches entity semantics and obtains more comprehensive textual representation, which reduces between textual representation and the entities in KG. Extensive experiments on three public benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance, indicating the superiority of our model. The code is released on https://github.com/season1blue/DWE.

Dual-polarized angle-selective surface based on double-layer frequency selective surface

In this Letter, we propose a dual-polarized angle-selective surface (ASS) based on a double-layer frequency selective surface (FSS) to achieve angular selectivity. By appropriately constructing the structure of the single-layer FSS, bandpass and band-stop are produced by the ASS under normal and oblique incidences, respectively, resulting in exceptional angular selectivity. Simulated results show that the proposed low-pass ASS achieves a bandpass (|S21| &amp;gt; −1 dB) from 0° to 10° and a band-stop (|S21| &amp;lt; −20 dB) from 31° to 85° with a profile thickness of 0.6 λ. A low-pass ASS prototype with an operating frequency of 10 GHz is fabricated to demonstrate the design concept. The measured results of the transmission coefficients are in good agreement with the simulated results. The proposed ASS can play an important role in many applications, as in minimizing the side lobes of the antenna.

Situating the phosphonated calixarene-cytochrome C association by molecular dynamics simulations.

Protein-calixarenes binding plays an increasingly central role in many applications, spanning from molecular recognition to drug delivery strategies and protein inhibition. These ligands obey a specific bio-supramolecular chemistry, which can be revealed by computational approaches, such as molecular dynamics simulations. In this paper, we rely on all-atom, explicit-solvent molecular dynamics simulations to capture the electrostatically driven association of a phosphonated calix-[4]-arene with cytochome-C, which critically relies on surface-exposed paired lysines. Beyond two binding sites identified in direct agreement with the x-ray structure, the association has a larger structural impact on the protein dynamics. Then, our simulations allow a direct comparison to analogous calixarenes, namely, sulfonato, similarly reported as "molecular glue." Our work can contribute to a robust in silico predictive tool to assess binding sites for any given protein of interest for crystallization, with the specificity of a macromolecular cage whose endo/exo orientation plays a role in the binding.

Computer Vision and Image Segmentation

Abstract: Image segmentation is a critical step in image processing, computer vision, and pattern recognition, which involves dividing an image into different regions or segments. Image segmentation plays an essential role in many applications, such as object recognition, medical image analysis, autonomous driving, and robotics. This paper aims to provide an overview of image segmentation techniques, including traditional and deep learning-based approaches. The paper also discusses the challenges associated with image segmentation, such as noise, illumination variations, and occlusions. Finally, the paper provides a brief discussion on the evaluation metrics used to assess the performance of image segmentation algorithms.

A Nonlinear Local Approximation Approach for Catchment Classification.

Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure for catchment classification, through application of a nonlinear local approximation prediction method. The method uses the concept of phase-space reconstruction of a time series to represent the underlying system dynamics and identifies nearest neighbors in the phase space for system evolution and prediction. The prediction accuracy measures, as well as the optimum values of the parameters involved in the method (e.g., phase space or embedding dimension, number of neighbors), are used for classification. For implementation, the method is applied to daily streamflow data from 218 catchments in Australia, and predictions are made for different embedding dimensions and number of neighbors. The prediction results suggest that phase-space reconstruction using streamflow alone can provide good predictions. The results also indicate that better predictions are achieved for lower embedding dimensions and smaller numbers of neighbors, suggesting possible low dimensionality of the streamflow dynamics. The classification results based on prediction accuracy are found to be useful for identification of regions/stations with higher predictability, which has important implications for interpolation or extrapolation of streamflow data.