Strong Edge Research Articles

On Conditional Edge-Fault-Tolerant Strong Menger Edge Connectivity Of Folded Hypercubes

Abstract Edge connectivity is an important parameter for the reliability of the inter-connection network. A graph $G$ is strong Menger edge-connected ($SM$-$\lambda $ for short) if there exist min$\{\deg _{G}(u),\deg _{G}(v)\}$ edge-disjoint paths between any pair of vertices $u$ and $v$ of $G$. The conditional edge-fault-tolerance strong Menger edge connectivity of $G$, denoted by $sm_{\lambda }^{r}(G)$, is the maximum integer $m$ such that $G-F$ remains $SM$-$\lambda $ for any edge set $F$ with $|F|\leq m$ and $\delta (G-F)\geq r$, where $\delta (G-F)\geq r$ is the minimum degree of $G-F$. Most of the previous papers discussed $sm_{\lambda }^{r}(G)$ in the case of $r\leq 2$. In this paper, we show that $sm_{\lambda }^{r}(FQ_{n})=2^{r}(n-r+1)-(n+1)$ for $1\leq r\leq n-2$, where $n\geq 4$.

An approach to pseudocoloring of grey scale image using deep learning technique

Image pseudo colorization is the process of adding RGB colours to grayscale images to make them more appealing. Deep learning technology has made progress in the field of automatic colouring. In general, we divide automatic colouring methods into three groups based on where the colour information comes from: colouring based on what you already know and on reference pictures. The colouring method can meet the needs of most users, but there are some drawbacks. For example, users can’t colour different reference graphs for the different things in a picture. In order to solve this problem by recognising several objects and background regions in a picture and combine the final colouring results, the proposed method uses the deep learning approach that regional mixed colours be used more and the method be mastered by using deep learning. Qualitative results (visual perception) validate the effectiveness of pseudocolorisation which split into foreground colour based on a reference picture and background colour based on prior knowledge. Quantitative results such as Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), Image Matching Error and Entropy validates the effectiveness of strong edge strength, visually appealing quality and retention of maximum information without disturbing quality of image.

Accurate edge-preserving stereo matching by enhancing anisotropy

Stereo matching has attracted substantial research attention, as it plays an important role in many visual tasks such as autonomous driving. Despite the remarkable progress has been made by all kinds of stereo matching methods, they still suffer in some challenging scenes like disparity discontinuities. The root cause of blurred depth boundaries lies in information diffusion across edge boundaries. Aiming at this problem, this paper presents a novel edge-preserving stereo matching method by enforcing anisotropy, where information is allowed to propagate throughout the image except through large discontinuities. Specifically, we propose an intra-scale cost aggregation algorithm to smooth out noise in homogeneous regions while well preserving strong edges in the guided image. And weighted averaging scheme, where weights are calculated according to the variances of pixels in different overlapping support windows, is utilized for enhancing anisotropy to improve accuracy at disparity discontinuities. We carry out comprehensive experiments on Middlebury public datasets to demonstrate the accuracy and edge-preserving property of our method. Qualitative and quantitative performance evaluation on Middlebury data sets demonstrate the superior performance of our method for strong edge preservation with 20.77% decreases in wrong matching ratios in discontinuous regions.

Twisted bilayer zigzag-graphene nanoribbon junctions with tunable edge states

Stacking two-dimensional layered materials such as graphene and transitional metal dichalcogenides with nonzero interlayer twist angles has recently become attractive because of the emergence of novel physical properties. Stacking of one-dimensional nanomaterials offers the lateral stacking offset as an additional parameter for modulating the resulting material properties. Here, we report that the edge states of twisted bilayer zigzag graphene nanoribbons (TBZGNRs) can be tuned with both the twist angle and the stacking offset. Strong edge state variations in the stacking region are first revealed by density functional theory (DFT) calculations. We construct and characterize twisted bilayer zigzag graphene nanoribbon (TBZGNR) systems on a Au(111) surface using scanning tunneling microscopy. A detailed analysis of three prototypical orthogonal TBZGNR junctions exhibiting different stacking offsets by means of scanning tunneling spectroscopy reveals emergent near-zero-energy states. From a comparison with DFT calculations, we conclude that the emergent edge states originate from the formation of flat bands whose energy and spin degeneracy are highly tunable with the stacking offset. Our work highlights fundamental differences between 2D and 1D twistronics and spurs further investigation of twisted one-dimensional systems.

Synthesis and Characterization of Sol–Gel Screen Printed Zn:CdO (ZCO) Film for Optoelectronic Technologies

In present report on Zn doped (5%) CdO thick film is synthesized on to the glass substrate (pre-cleaned) via sol–gel screen printing process. The synthesized sintered Zn:CdO film are then characterized via XRD, SEM, EDX, UV–vis spectroscopy, PL spectroscopy, and dark conductivity measurement examinations. The XRD examination of sintered film informs the polycrystalline nature and authenticates that Zn:CdO film holds the cubic crystalline structure. SEM examination dispenses the information on morphology of Zn:CdO film. The EDX examination supports the existence of Cd, O, and Zn in the sintered film. The UV–vis spectroscopy via reflectance mode substantiates the direct transition of band gap with a value of 2.60 eV. PL spectra of the sintered film manifest a strong near band edge emission located around 500 nm. The Arrhenius plot (log σDC vs 1000/T) attests the semiconducting nature for the sintered film.

More than the aggregation of its components: Unveiling the associations between anxiety, depression, and suicidal behavior in adolescents from a network perspective

BackgroundFacing multiple changes, adolescents are prone to have anxiety and depression concurrently, which would accompany a particularly high risk for suicide. However, most previous studies have ignored the heterogeneity of the components and used latent variable methods to explore the associations between these core variables, resulting in a lack of component-level discussions. MethodUsing a large sample of 9300 adolescents (Meanage = 13.51; SDage = 1.33; 49.82 % females), two network pathways of anxiety and depression and the associations between these variables and suicidal behavior were constructed. The central components and the stability of both networks were also identified. ResultsConsidering the network of anxiety and depression, there were two strong bridging symptoms of sleep problems and palpitation or tachycardia. The symptoms of depression showed a more vital centrality than anxiety, and the central symptoms were tachycardia, worthlessness, fatigue, and feeling of choking. For the network of suicidal behavior and symptoms of anxiety and depression, besides sleep problems, the edge linking lifetime suicide ideation and attempt and the frequency of suicide ideation in the past year was also a strong edge. Worthlessness connected symptoms of anxiety and depression with suicidal behavior. The central components were tachycardia, worthlessness, the frequency of suicidal ideation over the past year, and fatigue. Additionally, both networks had higher stability in terms of edge and centrality. ConclusionBased on the identified relevant strong bridging and central components, effective therapies would target these components first, which would lead to the alleviating effects on other components.

The influence of anisotropic electrical resistivity on surface magnetotelluric responses and the design of two new anisotropic inversions

Using the 3-D axial anisotropy, the dipping anisotropy, and the azimuthal anisotropy as case studies, we investigated the influence of each anisotropic resistivity element on the magnetotelluric surface responses. To justify the strong and weak influence and edge effect, we have introduced the influence indices for the impedance components, and the edge effect indices for the tipper components. Interestingly, for decoupled modes, we found that {rho }_{xx} has a strong influence on Zxy, Zyy, and Ty, while {rho }_{yy} strongly affects Zyx, Zxx, and Tx. The three elements {rho }_{zz}, {rho }_{xz}, and {rho }_{yz} have only a very weak influence on all types of responses. For the coupled mode, {rho }_{xx}, {rho }_{yy}, and {rho }_{xy} display a strong influence on all responses. Based on our studies on the influence of the anisotropic resistivity elements, we design and propose two practical processes to replace the conventional axial, dipping, azimuthal, and general anisotropic inversions. First, the axial or dipping inversion can be approximately decoupled into {rho }_{x}-mode and {rho }_{y}-mode inversions. The decoupled mode inversions can be performed either independently and in parallel, or as a joint inversion. Second, since the three resistivity elements always show a weak influence, the general anisotropic inversion can be simplified to just the reduced coupled azimuthal anisotropic inversion with only three resistivity elements as outputs. Both proposed techniques can save a lot of the computational resources. Criteria to choose either the decoupled or coupled modes depend greatly on the magnitudes and distributions of the Zxx and/or Zyy, and Tx and/or Ty responses.Graphical

Influence of Al3+, Ga3+, and In3+ substitution on the structural, microstructural and dielectric characteristics of ZnS nanoparticles

Trivalent metal cations modified polycrystalline ZnS nanoparticles, Zn0.95X0.05S (X = Al3+, Ga3+, & In3+) NPs, were synthesized using co-precipitation technique and their physicochemical investigations were carried out. Structural analysis revealed that all the compositions display cubic crystallinity having Fm3‾m space group as evident from Rietveld refinement technique. The average crystallite size evaluated using Williamson-Hall plot was found to be 4.75 nm, 5.17 nm, and 5.76 nm for Zn0.95Al0.05S NPs, Zn0.95Ga0.05S NPs, and Zn0.95In0.05S NPs respectively. Microstructural studies depicted the nanoscale formation of nanoparticles with uniform grain distribution where the average grain size was observed between 38.2 nm and 45.4 nm. FT-IR spectra analysis confirmed the formation of Zn0.95Al0.05S, Zn0.95Ga0.05S, and Zn0.95In0.05S NPs with high frequency stretching vibrations about 465 cm−1 and 654 cm−1, belonging to the weak and strong edge of elemental regions. The value of the dielectric constant in the lower frequency region was found to be maximum (27.2) for Zn0.95In0.05S NPs and least (17.8) for Zn0.95Al0.05S NPs. It was observed that ac conductivity increases sharply with the increasing frequency of the applied electric field. The measured values of ac conductivity at 1 kHz for Zn0.95Al0.05S NPs, Zn0.95Ga0.05S NPs, and Zn0.95In0.05S NPs were found to be 5.06 × 10−7 Ω−1cm−1, 1.24 × 10−6 Ω−1cm−1, and 1.28 × 10−6 Ω−1cm−1, respectively.

Inner crossover fusion network with pixel-wise sampling for dynamic scene deblurring

Despite dynamic scene deblurring methods based on multi-scale or scale-recursive or hierarchical multi-patch strategy have made great progress, they still have some shortcomings. In the multi-scale or scale-recursive networks, bicubic down-sampling operation may cause some important high frequency information loss, e.g., strong edges. In addition, these networks are limited to using the same receptive field information while neglecting the cross-scale and cross-receptive field relationships inside the subnetwork, which may lead to information drop-out. To alleviate the above problems, we propose an inner crossover fusion network with pixel-wise sampling, called ICFNet, for dynamic scene deblurring. More specifically, we first design a pixel-wise sampling operation to replace bicubic down-sampling in the multi-scale or scale-recursive networks, so as to avoid the loss of the strong edge information. In addition, different from multi-scale and multi-patch architecture, we design a novel inner crossover fusion architecture to make full use of blur information with cross-scale and cross-receptive field in the network. To maximize the advantages of the ICFNet architecture, we design three region-enhanced residual blocks with different receptive fields for learning multiple receptive field features. Finally, extensive experimental results on different datasets have demonstrated that the proposed network can obtain better deblurring effect.

Manganese (Mn2+) doped hexagonal prismatic zinc oxide (ZnO) nanostructures for chemiresistive NO2 sensor

Low-operating temperature gas sensor is of great significance in a range of environmental and health safety. Here, we present the operation and performance of hexagonal prismatic ZnO nanostructures (NSs) for detection of toxic nitrogen dioxide (NO2). Mn nanoparticles (NPs) were incorporated into ZnO at various concentrations, and X-ray diffraction (XRD) patterns delineated a wurtzite hexagonal phase. From Tauc's plot, the band gap (Eg) for the NSs found to be ∼3.15–3.17 eV. The photoluminescence (PL) spectra showed a strong near-band edge emission (NBE) concordant with the calculated Eg. The NO2 response measurements showed a significant perceptual behavior of NO2. The NO2 responses increased with increasing Mn dopant concentrations. The maximum response of 152.3 % and 126.9 % was acquired for 2 % and 4 % Mn-doped ZnO at 75 °C. The response and recovery times for the sensor towards ten ppm NO2 are 107 s and 158 s. Better repeatability was achieved with good stability. These results bring promise to low operating temperature, low cost NO2 sensor based on ZnO, which could possibly used as real time monitoring.

Some Aspects on Fully complete domination in Picture Fuzzy Graphs Based on Strong Edges

Picture fuzzy graph is an efficient mathematical tool for dealing ambiguous real world problems where the fuzzy graph and intuitionistic fuzzy graph would not produce high accuracy. It can be very useful in situations in which there are multiple choices of such type: yes, no, abstain and refusal. The primary aim of this study is to define the fully complete domination in picture fuzzy graph based on strong edges. Due to the importance of the notion of domination and its applications in various situations, The fully complete picture fuzzy dominating set is introduced. In addition, many significant properties related to this parameter are obtained. Further, the relation between the fully complete picture fuzzy domination number and picture fuzzy domination number is discussed. Some theorems are proved with suitable examples. An algorithm is provided to compute the fully complete picture fuzzy dominating set and its domination number and verified through an example.

Adaptive Active Contour Model for Brain Tumor Segmentation

For accurately diagnosing the severity of brain tumors in MRI images, Glioma segmentation is a significant step. The Glioma segmentation is due to noise and weak edges of organs in medical images. The geodesic active contour model (GACM) is a standard method for the segmentation of complex organ structures based on edge maps. The GACM performs poorly due to this noise and weak edges. So, the authors propose a method that uses adaptive kernels instead of a constant kernel for creating strong edge maps for GACM. The kernels used in phase congruency are Log Gabor kernels, which resemble similar anisotropic properties like Gabor kernels. They have replaced these with adaptive kernels. This adaptive kernel-based phase congruency provides a robust edge map to be used in GACM. Experimentation shows that when compared with state-of-the-art edge detection techniques, adaptive kernels enhance the weak as well as strong edges and improve the overall performance. Moreover, the proposed methodology substantially requires fewer parameters compared to existing ACM methods.

Network analysis of anxiety and depression in the functionally impaired elderly.

Evidence from previous studies has confirmed that functionally impaired elderly individuals are susceptible to comorbid anxiety and depression. Network theory holds that the comorbidity emerges from interactions between anxiety and depression symptoms. This study aimed to investigate the fine-grained relationships among anxiety and depression symptoms in the functionally impaired elderly and identify central and bridge symptoms to provide potential targets for intervention of these two comorbid disorders. A total of 325 functionally impaired elderly individuals from five communities in Xi'an, China, were recruited for our investigation. The GAD-7 and PHQ-9 were used to measure anxiety and depression, respectively. SPSS 22.0 software was used for descriptive statistics, and R 4.1.1 software was used for network model construction, expected influence (EI) evaluation and bridge expected influence (BEI) evaluation. In the network, there were 35 edges (indicating partial correlations between symptoms) across the communities of anxiety and depression, among which the strongest edge was A1 "Nervousness or anxiety"-D2 "Depressed or sad mood." A2 "Uncontrollable worry" and D2 "Depressed or sad mood" had the highest EI values in the network, while A6 "Irritable" and D7 "Concentration difficulties" had the highest BEI values of their respective community. In the flow network, the strongest direct edge of D9 "Thoughts of death" was with D6 "Feeling of worthlessness." Complex fine-grained relationships exist between anxiety and depression in functionally impaired elderly individuals. "Uncontrollable worry," "depressed or sad mood," "irritable" and "concentration difficulties" are identified as the potential targets for intervention of anxiety and depression. Our study emphasizes the necessity of suicide prevention for functionally impaired elderly individuals, and the symptom "feeling of worthlessness" can be used as an effective target.

Guided Filtering Combining Neighborhood Variance and Anisotropic Window

Aiming at the problem that guided filtering would cause halo near the edge and it is difficult to identify fine edges, a guided filtering combining neighborhood variance and anisotropic window is proposed. Firstly, the directional selectivity of the anisotropic Gaussian filter is used for the recognition of the fine edge, and the narrow spatial structure of the filter can benefit the information fusion of different pixels inside the local window to suppress the edge blur and halo effect. Secondly, based on the local structure similarity, the neighborhood variance is introduced to optimize the parameters of local linear transformation, so as to achieve the maximum diffusion at non-edge region while preserving strong edges. The experimental results on 102 Category Flower Dataset show that, compared with other edge-preserving filtering methods, the proposed method is superior to other methods in visual effect and quantitative evaluation (PSNR and SSIM), and the distortion of the test image is 46.72%, 48.64% and 29.61% smaller than guided filtering, weighted guided filtering and anisotropic guided filtering respectively. It can effectively suppress the occurrence of artifacts while recognizing precise edges.

An Enhanced Image Patch Tensor Decomposition for Infrared Small Target Detection

Infrared small-target detection is a key technology for the infrared search and track system (IRST), but some problems still exist, such as false detections in complex backgrounds and clutter. To solve these problems, a novel image patch tensor (IPT) model for infrared small-target detection is proposed. First, to better estimate the background component, we utilize the Laplace operator to approximate the background tensor rank. Secondly, we combined local gradient features and highlighted area indicators to model the local targets prior, which can effectively suppress the complex background clutter. The proposed model was solved by the alternating direction method of multipliers (ADMM). The experimental results on various scenes show that our model achieves an excellent performance in suppressing strong edge clutter and estimating small targets.

Defects and oxygen vacancies modified properties of transition metal doped Ce0.95X0.05O2 (X = Fe, Co, Ni) nanoparticles

In this study, pure and transition metal doped (TM = Fe, Co, and Ni), cerium oxide nanoparticles have been synthesized by the co-precipitation techniques. Rietveld refined X-ray diffraction (XRD) patterns affirm the monophase nature with cubic symmetry having an Fm3m space group and crystalline size varies in the range of 9–14 nm calculated by Debye Sherer's formulation. The morphological study has been done by scanning electron microscope (SEM) technique anticipated that agglomerated grains symmetry with the typical grain size distribution varies from 45 to 52 nm. The effect of transition metal doping on bending/stretching vibrational modes has been effectively investigated by FTIR analysis. The strong absorbance edge was seen in the UV region and shifted in the red region with the doping of transition metal ions. The tauc’s plot of UV–vis absorption uncovers the bandgap decreased from 2.87 eV to 2.42 eV on transition metal doping. The photoluminescence spectroscopy (PL) technique was used to investigate the presence of oxygen vacancies and intrinsic defects in the host CeO2 lattice when doped with transition metal ions. These imperfections induce the lattice disorder and shift in the energy bandgap from the UV to the visible region of the spectrum by affecting its electronic transitions. The CIE chromaticity coordinates of pure and transition metal doped compounds observed in the blue region might be applicable in display devices. In this manner, these materials can be fascinating for tuneable electrical and optical devices.

Edge effects constrain endemic but not introduced arthropod species in a pristine forest on Terceira (Azores, Portugal)

Pristine Azorean forests have been deeply fragmented since human colonization. Fragmentation increases the length of edges and it therefore promotes edge habitats. Studying the impact of edge habitat on species assemblages is crucial to highlight the importance of forest connectivity and guide management strategies.This study explores the impact of forest edges on arthropod assemblages, and particularly investigates the differences of arthropod communities between three habitats, along a distance gradient from the forest edge near a pasture matrix to the core forest. We also compare patterns of arthropod communities with different biogeographic status (endemic, native non-endemic, and introduced species), given the island context. We sampled in a pristine forest on Terceira Island bordered by semi-natural pastures, using flight interception traps.Overall, endemic species dominated arthropod abundances whereas species richness and diversity were similar between the three biogeographic categories. We found evidence of a strong edge effect on arthropod assemblages, adjusted both by biogeographic categories and seasonality. Indigenous (endemic and native non-endemic) species abundances were higher in the forest interior than at the edges or intermediate habitats, suggesting that indigenous arthropod assemblages were sensitive to the distance from the edge, a distance extended over 100 m to the core forest, whereas introduced species abundances were not impacted. Species diversity and richness did not differ between the three habitats either, regardless of the biogeographic categories. The composition of arthropods between the three habitats differed significantly when we considered all species or endemic species only, but not with native non-endemic or introduced species. However, the difference got obscured when seasonality was included in the analyses, suggesting that even though edges impact species composition, this impact varies seasonally and endemic species are particularly affected in early summer.Our results indicate that forest edges impact arthropods assemblages but endemic species are more likely to be constrained by the increase of edges than introduced species. Since most of these endemic species are of conservation concern, we urge to avoid forest management strategies that increase fragmentation and call for action to increase the size of protected natural parks.

Addiction Symptom Network of Young Internet Users: Network Analysis.

An increasing number of people are becoming addicted to the internet as a result of overuse. The Internet Addiction Test (IAT) is a popular tool for evaluating internet use behaviors. The interaction between different symptoms and the relationship between IAT and clinical diagnostic criteria are not well understood. This study aimed to explore the core symptoms of internet addiction (IA) and the correlation between different symptoms of the IA symptom network. Network analysis was also conducted to explore the association between the IAT scale and the Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) criteria for IA. We recruited 4480 internet users (aged 14-24 years), and they completed the IAT. The final analysis included 63.50% (2845/4480) of the participants after screening the submitted questionnaires. Participants were classified into IA group and non-IA (NIA) group. By using partial correlation with Lasso regularization networks, we identified the core symptoms of IA in each group and compared the group differences in network properties (strength, closeness, and betweenness). Then, we analyzed the symptom networks of the DSM-5 diagnostic criteria and IAT scale for IA. A total of 12.47% (355/2845) of the patients were in the IA group and 87.52% (2490/2845) of the patients were in the NIA group, and both groups were evaluated for the following nodes: IAT_06 (school work suffers; strength=0.511), IAT_08 (job performance suffers; strength=0.531), IAT_15 (fantasize about being on the web; strength=0.474), IAT_17 (fail to stop being on the web; strength=0.526), and IAT_12 (fear about boredom if offline; strength=0.502). The IA groups had a stronger edge between IAT_09 (defensive or secretive about being on the web) and IAT_18 (hidden web time) than the NIA groups. The items in DSM-5 had a strong association with IAT_12 (weight=-0.066), IAT_15 (weight=-0.081), IAT_17 (weight=-0.106), IAT_09 (weight=-0.198), and IAT_18 (weight=-0.052). The internet use symptom network of the IA group is significantly different from that of the NIA group. Nodes IAT_06 (school work affected) and IAT_08 (work performance affected) are the resulting symptoms affected by other symptoms, whereas nodes IAT_12 (fear about boredom if offline), IAT_17 (inability to stop being on the web), and IAT_15 (fantasize about being on the web) are key symptoms that activate other symptoms of IA and are strongly linked to the inability to control the intention to play games in the DSM-5.

Adaptive Image Steganography Using Fuzzy Enhancement and Grey Wolf Optimizer

Adaptive imagesteganography embeds secret messages into areas of cover images with complex features, including rich edges and complex textures. In this article, an adaptive image steganography technique based on the edge and complex texture areas of images is proposed, by comprehensively considering three rules in the design of image steganography. First, the embedding area is composed of the edge and complex texture areas of images, according to the complexity-first rule. Edge detection is realized by an improved fuzzy enhancement function, optimized by the grey wolf optimizer to detect both the weak and strong edges. Second, the minimum average classification error rate is used to assess the choice of the complex texture areas. Third, under the spreading rule, two different average filters and one KerBohme filter are used to design the cost function in the embedding areas. Finally, confidential information is adaptively embedded through syndrome-trellis codes. Experimental results show that the proposed algorithm outperforms seven classical adaptive image steganography algorithms on two steganalytic feature sets. The performance improvement is particularly significant when the payload is large.

Infrared Maritime Small Target Detection Based on Multidirectional Uniformity and Sparse-Weight Similarity

Infrared maritime target detection is a key technology in the field of maritime search and rescue, which usually requires high detection accuracy. Despite the promising progress of principal component analysis methods, it is still challenging to detect small targets of unknown polarity (bright or dark) with strong edge interference. Using the partial sum of tubal nuclear norm to estimate low-rank background components and weighted l1 norm to estimate sparse components is an effective method for target extraction. In order to suppress the strong edge interference, considering that the uniformity of the target scattering field is significantly higher than that of the background scattering field in the eigenvalue of the structure tensor, a prior weight based on the multidirectional uniformity of structure tensor eigenvalue was proposed and applied to the optimization model. In order to detect targets with unknown polarity, the images with opposite polarity were substituted into the optimization model, respectively, and the sparse-weight similarity is used to judge the polarity of the target. In order to make the method more efficient, the polarity judgment is made in the second iteration, and then, the false iteration will stop. The proposed method is compared with nine advanced baseline methods on 14 datasets and shows significant strong robustness, which is beneficial to engineering applications.