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Abstract This article is concerned with a thermoelastic response in a non-local micro-stretch completely covered in the endless non-viscous fluid under temperature dependent, the medium is investigated employing the theory of Green-Naghdi (G-N III) and the model of three-phase-lag (3PHL).Basic equations are derived based on these models. The normal mode technique is employed to achieve an analysis solution to the problem. The study used a magnesium crystal element to compare non-local measurements of thermo-micro-stretch elasticity in water using the (G-N III) theory and the (3PHL) model. The non-local effect has been discovered to have a considerable impact on all physical quantities. Furthermore, comparisons are made between three different frequency values.

This article is concerned with a thermoelastic response in a non-local micro-stretch completely covered in the endless non-viscous fluid under temperature dependent, the medium is investigated employing the theory of Green-Naghdi (G-N III) and the model of three-phase-lag (3PHL).Basic equations are derived based on these models. The normal mode technique is employed to achieve an analysis solution to the problem. The study used a magnesium crystal element to compare non-local measurements of thermo-micro-stretch elasticity in water using the (G-N III) theory and the (3PHL) model. The non-local effect has been discovered to have a considerable impact on all physical quantities. Furthermore, comparisons are made between three different frequency values. [ABSTRACT FROM AUTHOR]

Image itself information is naturally robust to image reconstruction, yet most current super-resolution methods do not fully utilize global feature information. This study proposes a new image super-resolution model mixing multiple and multi-scale attentions, including two new modules: Multi-scale hybrid non-local attention upsampling module and residual dense attention block. Different from previous nonlocal methods, multi-scale hybrid non-local attention upsampling module mixes pixel-based and patch-based nonlocal attention and establishes patch-level upsampling mapping relationships at multiple scales, which enables a wider global search space. The residual dense attention block establishes attention associations in channel and spatial dimensions, which enhances the transfer and fusion of front-to-back attention information through dense connections. In this study, quantitative and qualitative evaluations are conducted on several benchmark datasets, and the experimental results show that the model outperforms similar super-resolution models in terms of performance and reconstruction quality. [ABSTRACT FROM AUTHOR]

This study explores the behavior of laser-exposed photo-excited carriers, investigating the propagation of photoacoustic waves in the thermoelastic domain. It also delves into the theoretical generation of surface acoustic waves in semiconductors through photo-thermoelastic processes. The research considers the interaction between thermomechanical and acoustic waves in a nonlocal medium with temperature-dependent thermal conductivity. Unlike relying on electron–phonon or electron-hole thermalization processes, photoacoustic waves here result from thermoelastic stress induced by the laser-induced temperature increase. The investigation accounts for the optical, mechanical, and thermoelastic properties of nanoscale semiconductor materials. Predictions of photoacoustic signals are derived by solving a combined thermal diffusion issue and a thermoelastic problem, using Laplace and Fourier transforms in the mathematical model. Numerical solutions encompass various physical fields within the time domain using the inversion technique of Laplace and Fourier transforms, such as thermal, acoustic, mechanical, and carrier density diffusion. The study evaluates and compares the influences of thermal memory and thermal conductivity presenting visual representations.

Small-scale pedestrian detection is a challenge. The main issues are as follows: 1) Troubled by their small scale, it is difficult to extract features effectively; 2) During the detection process, it is easily disturbed by background noise such as inter-class occlusion and intra-class occlusion, leading to missed or false detection; 3) The current widely used IoU measurement method is very sensitive to the position deviation of small objects, which seriously reduces the detection performance. To address these problems, we improve YOLOv5 structure by integrating Non-Local and Convolution structures, building a new feature extraction module called ResNet-Conv&NonL, combined with the ResNet structure. This module was then integrated into the backbone of YOLOv5 for better image feature extraction. In addition, we developed a novel model to measure the similarity between bounding boxes, which are embedded in the loss function of the YOLOv5 structure to replace the normal IoU measurement. Experiments on a self-made dataset and a combined dataset from Caltech and CityPersons show the feasibility of the proposed network structure. Results demonstrate the feasibility of the improved network structure is superior to the original method because it increases average precision by 6.0% compared to the original one.

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms. [ABSTRACT FROM AUTHOR]

Although deep learning-based approaches for video processing have been extensively investigated, the lack of generality in network construction makes it challenging for practical applications, particularly in video restoration. As a result, this paper presents a universal video restoration model that can simultaneously tackle video inpainting and super-resolution tasks. The network, called Video-Restoration-Net (VRN), consists of four components: (1) an encoder to extract features from each frame, (2) a non-local network that recombines features from adjacent frames or different locations of a given frame, (3) a decoder to restore the coarse video from the output of a non-local block, and (4) a refinement network to refine the coarse video on the frame level. The framework is trained in a three-step pipeline to improve training stability for both tasks. Specifically, we first suggest an automated technique to generate full video datasets for super-resolution reconstruction and another complete-incomplete video dataset for inpainting, respectively. A VRN is then trained to inpaint the incomplete videos. Meanwhile, the full video datasets are adopted to train another VRN frame-wisely and validate it against authoritative datasets. We show quantitative comparisons with several baseline models, achieving 40.5042 dB/0.99473 on PSNR/SSIM in the inpainting task, while during the SR task we obtained 28.41 dB/0.7953 and 27.25/0.8152 on BSD100 and Urban100, respectively. The qualitative comparisons demonstrate that our proposed model is able to complete masked regions and implement super-resolution reconstruction in videos of high quality. Furthermore, the above results show that our method has greater versatility both in video inpainting and super-resolution tasks compared to recent models. [ABSTRACT FROM AUTHOR]

The present study introduces a theoretical framework according to the Moore-Gibson-Thompson (MGT) Model in the context of generalized thermoelasticity theory. The excited semiconductor material is utilized to formulate the governing equations in one dimensional (1D) with a non-local parameter. According to the photo- thermoelasticity theory, the overlapping between the electronic (optical) deformation and elastic deformation is taken into account. During the photo-excitation processes, the non-local equation of motion and the heat equation is introduced according to the model of Moore-Gibson-Thompson (MGT). The novel model describes the interaction between plasma and thermomechanical waves in a non-dimensional form. Laplace transform for time variable is applied to convert the governing equations into system of ordinary differential equations. The vector-matrix differential equation with the eigenvalues approach method is employed to obtain the solutions of the physical quantities analytically. Laplace transform invers numerically is utilized when some boundary conditions are applied at the semiconductor free surface to obtain the complete general of the main physical quantities. The numerical computations for the input parameters of Silicon as semiconductor material are used to illustrate the obtained results graphically and discussed.

Synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely applied in multiple fields. However, the special imaging mechanism of SAR results in different visual features of the same target at different azimuth angles, so single-aspect SAR target recognition has the limitation of observing the target from a single perspective. Multi-aspect SAR target recognition technology can overcome this limitation by utilizing information from different azimuths and effectively improve target recognition performance. Considering the order dependency and data limitation of existing methods, this paper proposes a multi-aspect SAR recognition method based on Non-Local, which applies a self-attention calculation to feature maps to learn the correlation between multi-aspect SAR images. Meanwhile, in order to improve the generalization ability of the proposed method under limited data, a network based on contrastive learning was designed to pre-train the feature extraction part of the whole network. The experimental results using the MSTAR dataset show that the proposed method has excellent recognition accuracy and good robustness. [ABSTRACT FROM AUTHOR]

Based upon the Green–Lindsay (GL) theory of thermoelasticity and the Eringen non-local elasticity theory, we describe the new constitutive relations and the field equations for generalized non-local thermoelastic model. The propagation of harmonically time-dependent thermoelastic plane waves of assigned frequency is studied in a homogeneous, isotropic, non-local infinite elastic solid by employing these equations. Reflection phenomenon of thermoelastic waves at a stress-free thermally insulated or isothermal flat boundary of a non-local thermoelastic solid half-space is also studied in case of incident coupled longitudinal wave. The amplitude ratios of the reflected waves to that of incident wave are determined analytically and numerically by considering an appropriate material. The numerical results are presented graphically to highlight the effects of various parameters of interest. [ABSTRACT FROM AUTHOR]

We have studied the problem of homogenous, isotropic non-local couple stress micropolar thermoelastic solid in the absence of body forces, couple density and heat resources. The reflection and transmission of waves at the interface of two distinct media have been investigated. It is observed that amplitude ratios of various reflected and transmitted waves are functions of wave number of incident waves and are affected by the non-local parameter of thermoelastic solid.

The images and videos of the high-voltage copper contact are disturbed by various noises in the factory. In this paper, an improved Non-local Self-similarity Recurrent Neural Network(NSRNN) is proposed for image denoising. The sparse representation is used for initializing the images, and then NSRNN is trained and tested based on the image datasets with different noise levels and magnification. Due to the similarity and the time correlation between the sequential images, RNN is used to improve the parameter utilization and model robustness. By measuring the self-similarity of the neighborhood features, NSRNN model outperforms other state-of-the-art methods in term of image denoising performance. [ABSTRACT FROM AUTHOR]

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like, fingerprint or face. Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users' identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session. However, divergent issues remain unaddressed. This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called, Gaussian Weighted Cauchy Krigingbased Continuous Czekanowski's (GWCK-CC). First, a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise present in the raw input face images. Cauchy Kriging Regression function is employed to reduce the dimensionality. Finally, Continuous Czekanowski's Classification is utilized for proficient classification between the genuine user and attacker. By this way, the proposed GWCK-CC method achieves accurate authentication with minimum error rate and time. Experimental assessment of the proposed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset. The results confirm that the proposed GWCK-CC method enhances authentication accuracy, by 9%, reduces the authentication time, and error rate by 44%, and 43% as compared to the existing methods. [ABSTRACT FROM AUTHOR]

The main goal of this research is to provide a novel model that describes an optically heated layer of an excited non-local microelongated semiconductor material. In a rotating field, the model is examined as the photo-excitation processes occur. The model presents the microelongation scalar function, which describes the microelement processes according to the micropolar-thermoelasticity theory. The model analyses the interaction situation between optical-thermomechanical waves under the impact of rotation parameters when the microelongation parameters are taken into consideration according to the photo-thermoelasticity theory. During the electronic and thermoelastic deformation, the fundamental governing equations were obtained in dimensionless form, and they were investigated using the harmonic wave methodology. Two-dimensional general solutions for the fundamental fields of an isotropic, homogeneous, and linear non-local microelongated semiconductor medium are derived (2D). The free surface of the medium is subjected to several conditions to produce complete solutions due to the laser pulse. The physical properties of silicon (Si) material are used to show numerical modeling of the main fields. Some comparisons are made and graphically shown under the impact of various relaxation time and rotational parameters. [ABSTRACT FROM AUTHOR]

The present study introduces a theoretical framework according to the Moore-Gibson-Thompson (MGT) Model in the context of generalized thermoelasticity theory. The excited semiconductor material is utilized to formulate the governing equations in one dimensional (1D) with a non-local parameter. According to the photo- thermoelasticity theory, the overlapping between the electronic (optical) deformation and elastic deformation is taken into account. During the photo-excitation processes, the non-local equation of motion and the heat equation is introduced according to the model of Moore-Gibson-Thompson (MGT). The novel model describes the interaction between plasma and thermomechanical waves in a non-dimensional form. Laplace transform for time variable is applied to convert the governing equations into system of ordinary differential equations. The vector-matrix differential equation with the eigenvalues approach method is employed to obtain the solutions of the physical quantities analytically. Laplace transform invers numerically is utilized when some boundary conditions are applied at the semiconductor free surface to obtain the complete general of the main physical quantities. The numerical computations for the input parameters of Silicon as semiconductor material are used to illustrate the obtained results graphically and discussed. [ABSTRACT FROM AUTHOR]

Although deep learning-based approaches for video processing have been extensively investigated, the lack of generality in network construction makes it challenging for practical applications, particularly in video restoration. As a result, this paper presents a universal video restoration model that can simultaneously tackle video inpainting and super-resolution tasks. The network, called Video-Restoration-Net (VRN), consists of four components: (1) an encoder to extract features from each frame, (2) a non-local network that recombines features from adjacent frames or different locations of a given frame, (3) a decoder to restore the coarse video from the output of a non-local block, and (4) a refinement network to refine the coarse video on the frame level. The framework is trained in a three-step pipeline to improve training stability for both tasks. Specifically, we first suggest an automated technique to generate full video datasets for super-resolution reconstruction and another complete-incomplete video dataset for inpainting, respectively. A VRN is then trained to inpaint the incomplete videos. Meanwhile, the full video datasets are adopted to train another VRN frame-wisely and validate it against authoritative datasets. We show quantitative comparisons with several baseline models, achieving 40.5042 dB/0.99473 on PSNR/SSIM in the inpainting task, while during the SR task we obtained 28.41 dB/0.7953 and 27.25/0.8152 on BSD100 and Urban100, respectively. The qualitative comparisons demonstrate that our proposed model is able to complete masked regions and implement super-resolution reconstruction in videos of high quality. Furthermore, the above results show that our method has greater versatility both in video inpainting and super-resolution tasks compared to recent models.

The key challenge of sequence representation learning is to capture the long-range temporal dependencies. Typical methods for supervised sequence representation learning are built upon recurrent neural networks to capture temporal dependencies. One potential limitation of these methods is that they only model one-order information interactions explicitly between adjacent time steps in a sequence, hence the high-order interactions between nonadjacent time steps are not fully exploited. It greatly limits the capability of modeling the long-range temporal dependencies since the temporal features learned by one-order interactions cannot be maintained for a long term due to temporal information dilution and gradient vanishing. To tackle this limitation, we propose the non-local recurrent neural memory (NRNM) for supervised sequence representation learning, which performs non-local operations by means of self-attention mechanism to learn full-order interactions within a sliding temporal memory block and models global interactions between memory blocks in a gated recurrent manner. Consequently, our model is able to capture long-range dependencies. Besides, the latent high-level features contained in high-order interactions can be distilled by our model. We validate the effectiveness and generalization of our NRNM on three types of sequence applications across different modalities, including sequence classification, step-wise sequential prediction and sequence similarity learning. Our model compares favorably against other state-of-the-art methods specifically designed for each of these sequence applications. [ABSTRACT FROM AUTHOR]

Based on the insights into the phenomenon of bound states in the continuum, a novel approach utilizing the momentum-space polarization morphologies of periodic structures to generate vortex beams (VBs) has been proposed. Such periodic structures modulate beams in a nonlocal way and require no precise alignment. However, the efficiency of such an approach has not been analyzed in detail, and the efficiency in previous realizations is far from optimized. Here, we analyze the factors affecting the efficiency of nonlocal VB generation. We show that the maximal efficiency cannot exceed 25% if the periodic structure carries only singlet resonances. To go beyond this limit, we propose two approaches to improve efficiency. We theoretically analyze the mechanisms and verify the approaches by full-wave simulations. Both of the approaches serve to improve the generation efficiency by several folds.

Multi-source image fusion is very important for improving image representation ability since its essence relies on the complementarity between multi-source information. However, feature-level image fusion methods based on the convolution neural network are impacted by the spatial misalignment between image pairs, which leads to the semantic bias in merging features and destroys the representation ability of the region-of-interests. In this paper, a novel multi-source image fusion transformer (MsIFT) is proposed. Due to the inherent global attention mechanism of the transformer, the MsIFT has non-local fusion receptive fields, and it is more robust to spatial misalignment. Furthermore, multiple classification-based downstream tasks (e.g., pixel-wise classification, image-wise classification and semantic segmentation) are unified in the proposed MsIFT framework, and the fusion module architecture is shared by different tasks. The MsIFT achieved state-of-the-art performances on the image-wise classification dataset VAIS, semantic segmentation dataset SpaceNet 6 and pixel-wise classification dataset GRSS-DFC-2013. The code and trained model are being released upon the publication of the work. [ABSTRACT FROM AUTHOR]

In-loop filters have attracted increasing attention due to the remarkable noise-reduction capability in the hybrid video coding framework. However, the existing in-loop filters in Versatile Video Coding (VVC) mainly take advantage of the image local similarity. Although some non-local based in-loop filters can make up for this shortcoming, the widely-used unsupervised parameter estimation method by non-local filters limits the performance. In view of this, we propose a deformable Wiener Filter (DWF). It combines the local and non-local characteristics and supervisedly trains the filter coefficients based on the Wiener Filter theory. In the filtering process, local adjacent samples and non-local similar samples are first derived for each sample of interest. Then the to-be-filtered samples are classified into specific groups based on the patch-level noise and sample-level characteristics. Samples in each group share the same filter coefficients. After that, the local and non-local reference samples are adaptively fused based on the classification results. Finally, the filtering operation with outlier data constraints is conducted for each to-be-filtered sample. Moreover, the performance of the proposed DWF is analyzed with different reference sample derivation schemes in detail. Simulation results show that the proposed approach achieves 1.16%, 1.92%, and 2.67% bit-rate savings on average compared to the VTM-11.0 for All Intra, Random Access, and Low-Delay B configurations, respectively. [ABSTRACT FROM AUTHOR]

Analysis of non-local, phase-lag, and temperature-dependent properties of modified couple stress thermoelastic diffusive medium is examined in conditions of exciting by thermomechanical sources. The governing equations are framed involving non-local, phase-lag, and temperature-dependent properties. These equations are simplified by using the potential functions and employing the Laplace and Fourier transforms for further study. The problem is solved by deploying suitable thermomechanical loads. A specific type of normal and thermal loading of the ramp-type is considered. The transformed components of the physical field like the displacements, stresses, temperature change, and chemical potential are derived. A numerical analysis is performed for these quantities using the numerical technique. The graphs of the resulting quantities are shown to analyze the impact of non-local, phase-lag, and temperature-dependent properties. The specific cases are also mentioned. [ABSTRACT FROM AUTHOR]

Existing person re-recognition (Re-ID) methods usually suffer from poor generalization capability and over-fitting problems caused by insufficient training samples. We find that high-level attributes, semantic information, and part-based local information alignment are useful for person Re-ID networks. In this study, we propose a person re-recognition network with part-based attribute-enhanced features. The model includes a multi-task learning module, local information alignment module, and global information learning module. The ResNet based on non-local and instance batch normalization (IBN) learns more discriminative feature representations. The multi-task module, local module, and global module are used in parallel for feature extraction. To better prevent over-fitting, the local information alignment module transforms pedestrian attitude alignment into local information alignment to assist in attribute recognition. Extensive experiments are carried out on the Market-1501 and DukeMTMC-reID datasets, whose results demonstrate that the effectiveness of the method is superior to most current algorithms. [ABSTRACT FROM AUTHOR]

We study the existence of sign-changing solutions for a non-local version of the sinh-Poisson equation on a bounded one-dimensional interval I, under Dirichlet conditions in the exterior of I. This model is strictly related to the mathematical description of galvanic corrosion phenomena for simple electrochemical systems. By means of the finite-dimensional Lyapunov–Schmidt reduction method, we construct bubbling families of solutions developing an arbitrarily prescribed number sign-alternating peaks. With a careful analysis of the limit profile of the solutions, we also show that the number of nodal regions coincides with the number of blow-up points.

With the development of advanced manufacturing technologies, the importance of functionally graded materials is growing as they are advantageous over widely used traditional composites. In this paper, we present a novel peridynamic model for higher order functional graded plates for various thicknesses. Moreover, the formulation eliminates the usage of shear correction factors. Euler–Lagrange equations and Taylor's expansion are utilised to derive the governing equations. The capability of the developed peridynamic model is demonstrated by considering several benchmark problems. In these benchmark cases simply supported, clamped and mixed boundary conditions are also tested. The peridynamic results are also verified by their finite element analysis counterparts. According to the comparison, peridynamic and finite element analysis results agree very well with each other. [ABSTRACT FROM AUTHOR]

The main goal of this research is to provide a novel model that describes an optically heated layer of an excited non-local microelongated semiconductor material. In a rotating field, the model is examined as the photo-excitation processes occur. The model presents the microelongation scalar function, which describes the microelement processes according to the micropolar-thermoelasticity theory. The model analyses the interaction situation between optical-thermomechanical waves under the impact of rotation parameters when the microelongation parameters are taken into consideration according to the photo-thermoelasticity theory. During the electronic and thermoelastic deformation, the fundamental governing equations were obtained in dimensionless form, and they were investigated using the harmonic wave methodology. Two-dimensional general solutions for the fundamental fields of an isotropic, homogeneous, and linear non-local microelongated semiconductor medium are derived (2D). The free surface of the medium is subjected to several conditions to produce complete solutions due to the laser pulse. The physical properties of silicon (Si) material are used to show numerical modeling of the main fields. Some comparisons are made and graphically shown under the impact of various relaxation time and rotational parameters.

Tokamak edge (scrape-off layer (SOL)) plasmas can exhibit non-local transport in the direction parallel to the magnetic field due to steep temperature gradients. This effect along with its consequences has been explored at equilibrium for a range of conditions, from sheath-limited to detached, using the 1D kinetic electron code SOL-KiT, where the electrons are treated kinetically and compared to a self-consistent fluid model. Line-averaged suppression of the kinetic heat flux (compared to Spitzer-Härm) of up to 50% is observed, contrasting with up to 98% enhancement of the sheath heat transmission coefficient, γ _e . Simple scaling laws in terms of basic SOL parameters for both effects are presented. By implementing these scalings as corrections to the fluid model, we find good agreement with the kinetic model for target electron temperatures. It is found that the strongest kinetic effects in γ _e are observed at low-intermediate collisionalities, and tend to increase (keeping upstream collisionality fixed) at increasing upstream densities and temperatures. On the other hand, the heat flux suppression is found to increase monotonically as upstream collisionality decreases. The conditions simulated encompass collisionalities relevant to current and future tokamaks.

As the important components of modern facility agriculture, both plastic greenhouses and mulching films have been widely utilized in agriculture production. Due to the similarity of spectral signatures, it remains a challenging task to separate plastic greenhouses and mulching films from each other. Meanwhile, deep learning has achieved great performance in many computer vison tasks, and has become a research hotspot in remote sensing image analysis. However, deep learning has been rarely studied for the accurate mapping of agricultural plastic covers, especially for the long-neglected issue of the separation between plastic greenhouses and mulching films. Therefore, this study aims to propose a deep learning model to detect and separate plastic greenhouses and mulching films from very high resolution (VHR) remotely sensed data, providing the agricultural plastic covered maps for relevant decision-makers. In specific, the proposed model is a dilated and non-local convolutional neural network (DNCNN), which consists of several multi-scale dilated convolution blocks and a non-local feature extraction module. The former contains a series of dilated convolutions with various dilated rates, which is to aggregate multi-level spatial features hence to account for the scale variations of land objects. While the latter utilizes a non-local module to extract the global and contextual features to further enhance the inter-class separability. Experimental results from Shenxian, China and Al-Kharj, Saudi Arabia show that the DNCNN in this study obtains a high accuracy with an overall accuracy of 89.6% and 92.6%, respectively. Compared to standard convolution, the inclusion of dilated convolution could raise the classification accuracy by 2.7%. In addition, ablation analysis shows that the non-local feature extraction module could also improve the classification accuracy by about 2%. This study demonstrates that the proposed DNCNN yields an effective approach for the accurate agricultural plastic cover mapping from VHR remotely sensed imagery.

• A Novel Photoacoustic Model in the context of Photo-thermoelasticity is studied. • The system is heated by laser pulsed when the nonlocal semiconductor medium is used. • The variable thermal conductivity is taken into account. • This photoacoustic model can be used to simulate and optimize the thermal behavior of advanced electronic devices. This study explores the behavior of laser-exposed photo-excited carriers, investigating the propagation of photoacoustic waves in the thermoelastic domain. It also delves into the theoretical generation of surface acoustic waves in semiconductors through photo-thermoelastic processes. The research considers the interaction between thermomechanical and acoustic waves in a nonlocal medium with temperature-dependent thermal conductivity. Unlike relying on electron–phonon or electron-hole thermalization processes, photoacoustic waves here result from thermoelastic stress induced by the laser-induced temperature increase. The investigation accounts for the optical, mechanical, and thermoelastic properties of nanoscale semiconductor materials. Predictions of photoacoustic signals are derived by solving a combined thermal diffusion issue and a thermoelastic problem, using Laplace and Fourier transforms in the mathematical model. Numerical solutions encompass various physical fields within the time domain using the inversion technique of Laplace and Fourier transforms, such as thermal, acoustic, mechanical, and carrier density diffusion. The study evaluates and compares the influences of thermal memory and thermal conductivity presenting visual representations. [ABSTRACT FROM AUTHOR]

In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03.

Peridynamic theory was introduced to overcome the limitations of classical continuum mechanics (CCM) in handling discontinuous material response. However, for certain problems, it is computationally expensive with respect to CCM based approaches. To reduce the computational time, a coarsening method was developed and its capabilities were demonstrated for one-dimensional structures by substituting a detailed model with a surrogate model with fewer degrees of freedom. The objective of this study is to extend the application of coarsening method for linear peridynamics for two-dimensional analysis. Moreover, the existing one-dimensional coarsening method was further explored by considering various different micromodulus functions. The numerical results demonstrated that coarsening approach has a potential to reduce the computational time with high accuracy for both one-dimensional and two-dimensional problems.

• Implementation of a non-local GTN model to avoid spurious mesh dependency. • Applied to model damage evolution and crack propagation until complete fracture. • Validated against corresponding local GTN model. • Over-aggressive regularization is avoided by evaluating on current rather than initial configuration. In this study, we follow the work of Tvergaard and Needleman (1995, 1997) and Needleman and Tvergaard (1998) and present the numerical implementation and initial applications of a non-local Gurson-Tvergaard-Needleman (GTN) model for explicit finite element (FE) analysis. The delocalization relates to the damage mechanism and is incorporated in terms of an integral condition on the rate of change of the porosity. To demonstrate the mesh independence during all stages of ductile damage and fracture, several material test specimens were simulated using different mesh sizes until full fracture occurred. For comparison purposes, the results are also obtained for the corresponding local GTN model in all cases. The effect of the material characteristic length on the ductile damage and fracture behavior and on the mesh sensitivity of the results is discussed. The numerical study shows that simulation results obtained in all stages of the ductile fracture process, including void growth, fracture initiation by coalescence and crack propagation all the way to a fully fractured specimen, are mesh independent for a certain mesh size ratio related to the material characteristic length, provided the non-local integral is evaluated on the current configuration. This ratio is unique for each individually simulated specimen as it depends on the spatial gradients of the porosity and the material parameters adopted for the problem at hand. It is shown that excessive averaging occurs at large deformations if the non-local integral is evaluated on the reference configuration, i.e., without updating the element interaction matrix resulting from the discretization of the non-local integral. [ABSTRACT FROM AUTHOR]

In this article, I seek to investigate how people living in the Šalčininkai district perceive the cultural boundary between locals and newcomers. Based on biographical interviews collected during the ethnographic field research, I argue that historical circumstances and frequent changes in state affiliation have influenced the drawing of the cultural boundary between locals and newcomers. In the article, I present how this division is understood by people of different generations living in the Šalčininkai district. The cultural boundary between locals and newcomers is very important to the oldest generation (born before World War II). People who grew up during Soviet times understand this boundary and its significance, but pay less attention to it. The youngest generation (people born around 1990) perceive this cultural boundary as a useless remnant of the past and want to distance themselves from it.

Functionally graded materials are a potential alternative to traditional fibre-reinforced composite materials as they have continuously varying material properties which do not cause stress concentrations. In this study, a state-based peridynamic model is presented for functionally graded Kirchhoff plates. Equations of motion of the new formulation are obtained using the Euler–Lagrange equation and Taylor's expansion. The formulation is verified by considering several benchmark problems including a clamped plate subjected to transverse loading and a simply supported plate subjected to transverse loading and inclined loading. The material properties are chosen such that Young's modulus is assumed to be varied linearly through the thickness direction and Poisson's ratio is constant. Peridynamic results are compared against finite element analysis results, and a very good agreement is obtained between the two approaches. [ABSTRACT FROM AUTHOR]

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task.

Embryonic exposure to the teratogen nicotine results in brain defects, by disrupting endogenous spatial pre patterns necessary for normal brain size and patterning. Extending prior work in Xenopus laevis that showed that misexpression of ion channels can rescue morphogenesis, we demonstrate and characterize a novel aspect of developmental bioelectricity: channel-dependent repair signals propagate long-range across the embryo. We show that distal HCN2 channel misexpression and distal transplants of HCN2-expressing tissue, non-cell-autonomously reverse profound defects, rescuing brain anatomy, gene expression, and learning. Moreover, such rescue can be induced by small-molecule HCN2 channel activators, even with delayed treatment initiation. We present a simple, versatile computational model of bioelectrical signaling upstream of key patterning genes such as OTX2 and XBF1, which predicts long-range repair induced by ion channel activity, and experimentally validate the predictions of this model. Our results and quantitative model identify a powerful morphogenetic control mechanism that could be targeted by future regenerative medicine exploiting ion channel modulating drugs approved for human use.

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task. [ABSTRACT FROM AUTHOR]

In this paper, we consider the nonlinear-Hilfer impulsive fractional differential equation. Our main objective is to derive the formula for the solution and examine the existence and uniqueness of solutions. The acquired results are extended to the nonlocal-Hilfer impulsive fractional differential equation. We gave an applications to the outcomes we obtained. Further, examples are provided in support of the results we got. [ABSTRACT FROM AUTHOR]

The main aim of the current work is the study of the conditions under which (finite-time) blow-up of a non-local stochastic parabolic problem occurs. We first establish the existence and uniqueness of the local-in-time weak solution for such problem. The first part of the manuscript deals with the investigation of the conditions which guarantee the occurrence of noise-induced blow-up. In the second part we first prove the C 1 -spatial regularity of the solution. Then, based on this regularity result, and using a strong positivity result we derive, for first in the literature of SPDEs, a Hopf's type boundary value point lemma. The preceding results together with Kaplan's eigenfunction method are then employed to provide a (non-local) drift term induced blow-up result. In the last part of the paper, we present a method which provides an upper bound of the probability of (non-local) drift term induced blow-up. [ABSTRACT FROM AUTHOR]

Given a video containing a person, the video-based person re-identification (Re-ID) task aims to identify the same person from videos captured under different cameras. How to embed spatial-temporal information of a video into its feature representation is a crucial challenge. Most existing methods have failed to make full use of the relationship between frames during feature extraction. In this work, we propose a plug-and-play non-local attention module (NLAM) for frame-level feature extraction. NLAM, based on global spatial attention and channel attention, helps the network to determine the location of the person in each frame. Besides, we propose a non-local temporal pooling (NLTP) method used for temporal features' aggregation, which can effectively capture long-range and global dependencies among the frames of the video. Our model obtained impressive results on different datasets compared to the state-of-the-art methods. In particular, it achieved the rank-1 accuracy of 86.3% on the MARS (Motion Analysis and Re-identification Set) dataset without re-ranking, which is 1.4% higher than the state-of-the-art way. On the DukeMTMC-VideoReID (Duke Multi-Target Multi-Camera Video Reidentification) dataset, our method also had an excellent performance of 95% rank-1 accuracy and 94.5% mAP (mean Average Precision). [ABSTRACT FROM AUTHOR]

Existing neural networks proposed for low-level image processing tasks are usually implemented by stacking convolution layers with limited kernel size. Every convolution layer merely involves in context information from a small local neighborhood. More contextual features can be explored as more convolution layers are adopted. However it is difficult and costly to take full advantage of long-range dependencies. We propose a novel non-local module, Pyramid Non-local Block, to build up connection between every pixel and all remain pixels. The proposed module is capable of efficiently exploiting pairwise dependencies between different scales of low-level structures. The target is fulfilled through first learning a query feature map with full resolution and a pyramid of reference feature maps with downscaled resolutions. Then correlations with multi-scale reference features are exploited for enhancing pixel-level feature representation. The calculation procedure is economical considering memory consumption and computational cost. Based on the proposed module, we devise a Pyramid Non-local Enhanced Networks for edge-preserving image smoothing which achieves state-of-the-art performance in imitating three classical image smoothing algorithms. Additionally, the pyramid non-local block can be directly incorporated into convolution neural networks for other image restoration tasks. We integrate it into two existing methods for image denoising and single image super-resolution, achieving consistently improved performance. [ABSTRACT FROM AUTHOR]

In this study, a new peridynamic Mindlin plate formulation is presented which is suitable for the analysis of functionally graded materials. The governing equations of peridynamic formulation are obtained by using Euler-Lagrange equations in conjunction with Taylor’s expansion. To validate the new formulation, three different numerical benchmark problems are considered for a Mindlin plate subjected to simply supported, fully clamped and mixed (clamped-simply supported) boundary conditions. Peridynamic results are compared against results from finite element analysis and a good agreement is observed between the two methods. [ABSTRACT FROM AUTHOR]