Extra Connections Research Articles

TRANSFORMER BASED ATTENTION GUIDED NETWORK FOR SEGMENTATION AND HYBRID NETWORK FOR CLASSIFICATION OF LIVER TUMOR FROM CT SCAN IMAGES

When a liver disease causes changes in the image's pixel quality, an ultrasonic filter can identify these changes as potential indicators of cancer. An ultrasonic filter may detect changes in the quality of an image's pixels based on the state of the liver, which are indicators of the closeness of malignant development. It is possible that alcohol, rather than liver disease, is the cause of cirrhosis because such alterations are more prevalent in alcoholic liver diseases. Current 2D ultrasound data sets have an accuracy degree of 85.9%, whereas a 2D CT data set has an accuracy rating of 91.02%. This work presents TAGN, a new Transformer-based Attention Guided Network that aims to improve the semantical segmentation architecture's performance through a combination of multi-level assembly. In order to efficiently learn the non-local interactions among encoder characteristics, TAGN incorporates the self-aware attention (SAA) element with Transformer Self Attention (TSA) besides Global Spatial Attention (GSA), which are inspired by Transformer. In addition, the work aggregates the upsampled features with distinct semantic scales by using extra multi-scale skip connections across decoder blocks. By doing so, the capacity to produce discriminative features from multi-scale context information is enhanced. For the purpose of reliable and accurate liver tumor classification using segmented pictures, this study suggests a system that integrates a Vision with a Gated Recurrent Unit (GRU). By analyzing the input image, the ViT finds important characteristics, and the GRU finds obvious relationships between them. Іn the experimental analysis of the projected ViT-GRU model achieved a recall rate of 95.21, accuracy as a 97.57, precision of 95.62, specificity of 98.33, and an f-score of 95.88. Based on segmentation and classification analyses performed on publically accessible datasets, the suggested classifier achieved a total accuracy of 98.79% in the experiments. When used optimally, the suggested strategy improves the accuracy of liver tumor diagnoses by medical professionals.

Dynamics and isolation performance of a vibration isolator with a yoke-type nonlinear inerter

Previous studies have shown that nonlinear inerters can achieve superior vibration suppression compared to linear inerters, such as broadening the effective frequency band of the vibration isolator. However, nonlinear inerters are typically implemented by incorporating linear inerters into nonlinear configurations, resulting in indirect configurations and extra connections. This study introduces a direct and simple way to realize a yoke-type nonlinear inerter by employing the Scotch yoke mechanism, which is named the Scotch yoke inerter (SYI). The primary contributions of this paper are introducing a concept about the physical realization of a nonlinear inerter capable of providing both apparent mass and nonlinear inertial effects and investigating the dynamic responses and isolation performances of a vibration isolator enhanced by the proposed nonlinear inerter. The working mechanism for how the proposed SYI generates the nonlinear inertia is introduced and a simple configuration that facilities the application of this nonlinear inerter is proposed. The mechanical model of the SYI is built to describe its inertial behavior using the Euler-Lagrange method. Then, the SYI is incorporated into a vibration isolator to improve its performances. The averaging method is adopted to obtain the analytical responses and dynamic characteristics of the isolator with SYI subjected to force excitation. The stability of this nonlinear isolator is analyzed, and numerical integration using the fourth-order Runge-Kutta method is employed to verify the analytical results. The isolation performance evaluation of the isolator with SYI is conducted through three indices including the peak dynamic displacement, peak force transmissibility and effective isolation frequency band. The mechanical behavior indicates that the SYI can successfully generate the apparent mass effect of an inerter and nonlinear inertial behavior. The analytical responses agree well with the numerical results for different parameters of the isolator with SYI, which verifies the analytical solutions. The use of SYI in the vibration isolator can bend the backbone curve to the low frequencies, which indicates the softening characteristic of the frequency response. The isolator with SYI demonstrates beneficial performances in terms of force transmissibility and effective isolation frequency band. It is concluded that the proposed SYI can be seen as an alternative nonlinear inerter and is beneficial for improving the performance of the vibration isolator.

Investigation of zinc epoxy coatings modified with pyrolyzed and gasified biochar nanoparticles for corrosion protection

Pyrolyzed and gasified biochar nanoparticles (BCN) were manufactured and incorporated into a series of zinc epoxy coatings to investigate how the anticorrosive performance is affected. Electrochemical measurements were conducted to test the coating alongside the exposure. Overall, BCN-modified formulations exhibited enhanced barrier protection, especially on formulations with lower zinc content (10 vol% and 22 vol%). More than 600 h of efficient galvanic protection was found on the gasified BCN-modified formulation with 30 vol% zinc. BCN improved the zinc utilization rate as well, where the unreacted zinc was reduced from ca. 50 wt% to ca. 20 wt% after 930 h of immersion. Scanning electron microscopy and X-ray diffraction results also verified the effects. The enhancement was attributed to the filling of micropores and extra electrical connections provided by BCN. This study imparts a sustainable solution for enhancing the anticorrosive performance of zinc epoxy coatings.

Project Spotlight: High-altitude visitor center weathers wind, soil loads with extra connections

Project Spotlight: High-altitude visitor center weathers wind, soil loads with extra connections

Multi-Dimensional Graph Interactional Network for Progressive Point Cloud Completion

Point cloud completion refers to inferring the complete and visually plausible shape from a partial input. Existing point cloud completion methods focus on recovering the global integrity of partial point clouds but lack local structural details. Furthermore, they seldom consider the shape faithfulness of completed results, that some completed points fail to fall into the ground-truth position faithfully. To meet the above challenges, we present a multidimensional graph interactional network for progressive point cloud completion. Specifically, we propose a multiresolution multidimensional graph encoder (MRMD-GE) to capture the information from both within-dimension and cross-dimension interactions for the purpose of enhancing the perception of local geometry. Inspired by the FPN, we develop a recursive point cloud pyramid decoder (RPPD) for generating multistage completed point clouds progressively, which incorporates extra feedback connections into the bottom-up backbone layers. In addition, we design a depth map discriminator combined with differentiable rendering to match the distribution of generated and real point clouds, making the completed point clouds more faithful to the ground truth. Quantitative and qualitative experiments on Completion3D, Shapenet-Part, and KITTI datasets demonstrate that our proposed method has compelling advantages over the state-of-the-art methods.

Training deep networks with only synthetic data: Deep-learning-based near-offset reconstruction for (closed-loop) surface-related multiple estimation on shallow-water field data

Accurate removal of surface-related multiples remains a challenge in shallow-water cases. One reason is that the success of surface-related multiple estimation (SRME)-related algorithms is sensitive to the quality of the near-offset reconstruction. When it comes to a larger missing gap and a shallower water bottom, the state-of-the-art near-offset gap construction method — the parabolic Radon transform — fails to provide reliable recovery of the shallow reflections due to the limited information from the data and highly curved events at near offsets with strong lateral amplitude variations. Therefore, we have developed a novel workflow that first deploys a deep-learning-based reconstruction of the shallow reflections and then uses the reconstructed data as the input for the subsequent surface multiple removal. In particular, we use a convolutional neural network architecture — U-net that was developed from convolutional autoencoders with extra direct skip connections between different levels of encoders and the corresponding decoders. Instead of using field data directly in network training, the training set is carefully synthesized based on the prior water-layer information of the field data; thus, a fully sampled field data set, which is difficult to obtain, is not needed for training in our workflow. An inversion-based approach — closed-loop SRME — is used for the surface multiple removal, in which the primaries are directly estimated via full-waveform inversion in a data-driven manner. Finally, the effectiveness of our workflow is determined based on 2D North Sea field data in a shallow-water scenario (92.5 m water depth) with a relatively large minimum offset (150 m).

GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment

Glaucoma is a chronic eye condition causing irreversible vision damage and presently stands as the second leading cause of blindness worldwide. Damaged optic disc and optic cup assessment in color fundus image has been shown to be a promising method for glaucoma screening. In this paper, we propose a new accurate and efficient optic disc and cup segmentation methodology using a residual deep learning approach. The proposed patch-based deep network (GlaucoNet) is trained with a large number of preprocessed input image patches, big enough to include the important discriminatory information around each pixel. The proposed architecture gives better segmentation results as extra skip connections are introduced in the framework which explicitly reformulate the layers such that the learning function is dependent on a residual function of the input layer. It addresses the degradation problem of the deep network by improving the information flow. The convex hull transformation is applied on the initial results to obtain the final segmentation output. The proposed method is tested on publicly available DRISHTI-GS, RIM-ONE and ORIGA-light datasets. The experimental results for optic disc segmentation show an overlapping score (OS) of 0.9106, 0.8972 and 0.8835 and the optic cup segmentation achieves an OS of 0.8229, 0.7401 and 0.8106 in DRISHTI-GS, RIM-ONE and ORIGA-light, respectively. Finally, the glaucoma risk index is obtained by computing the cup-to-disc height ratio obtained from the segmented regions. Experimental results showing better or comparable segmentation performance and decrease in cup-to-disc height ratio estimation errors demonstrate the efficacy of the proposed GlaucoNet-based segmentation methodology.

Sustainability through reuse: a reconfigurable structural system for residential and office buildings

Current load-bearing systems for buildings rarely have a beneficial end of life. Modular design is a proven solution for revalorizing obsolete structures, but it hardly competes with conventional solutions: the range of future spatial configurations that the modules will accommodate is usually too limited to balance additional upfront costs due to necessary oversized elements and extra connections. Through a review of building demolition cases, this paper first presents motives, challenges and requirements for overcoming adverse end-of-life environmental impacts of building structures. Then a new structural system addressing the specified design constraints is introduced. The system is a highly versatile kit of slab and column elements. Contrary to existing modular solutions, its element dimensions do neither constrain the positioning of columns nor the shape of floor plans. Slab elements are stacked vertically to tune bending and shear stiffnesses locally and ensure serviceability requirements for a wide range of column and load cases layouts. All connections between elements are reversible and ready for reuse. Accordingly, the proposed structural system is well-suited for multiple service cycles and architectural needs, mitigating the detrimental effects that buildings have on the environment.

Increased functional connectivity supports language performance in healthy aging despite gray matter loss

Although language is quite preserved from aging, it remains unclear whether age-related differences lead to a deterioration or reorganization in language functional networks, or to different dynamics with other domains (e.g., the multiple-demand system). The present study is aimed at examining language networks, using resting-state functional magnetic resonance imaging in typical aging in relation to language performance. Twenty-three (23) younger adults and 24 healthy older adults were recruited. Volumetric gray matter differences between the 2 groups were assessed using voxel-based morphometry. Then, seed-based analyses, integrated local correlations in core regions of the language network, and within- and between-network connectivity were performed. We expected less extended connectivity maps, local coherence diminution, and higher connectivity with the multiple-demand system in older adults. On the contrary, analyses showed language network differences in healthy aging (i.e., increased connectivity with areas inside and outside language network), but no deterioration, despite widespread atrophy in older adults. Integrated local correlation revealed alterations that were unnoticeable with other analyses. Although gray matter loss was not correlated with language performance, connectivity differences were positively correlated with fluency performance in the older group. These results differ from the literature concerning other cognitive networks in aging in that they show extra internetwork connections without a decrease in intranetwork language connections. This reorganization could explain older adults' good language performance and could be interpreted in accordance with the scaffolding theory of aging and cognition.

An End-to-End Novel Forecasting Model for Crime Prediction Based on Big Data

Representation strategies are a decent extra investigation device for investigating high dimensional showcasing information by advertising specialists. Other at that point frequently utilized procedures they give a superior understanding into the first information. By utilizing the picture design acknowledgment conceivable outcomes of the human minds, perception gives a ground-breaking extra investigating apparatus for information mining and unique model making in showcasing research. Utilizing the relationship scene the showcasing specialists could recognize significant connection designs in their information. By choosing factors with fascinating connections with regards to an extra second representation obscure connections between groups of factors are found. This brought about new models to foresee client conduct.

Synthesis and Characterization of Free-Stand Graphene/Silver Nanowire/Graphene Nano Composite as Transparent Conductive Film with Enhanced Stiffness

As-grown graphene via chemical vapor deposition (CVD) has potential defects, cracks, and disordered grain boundaries induced by the synthesis and transfer process. Graphene/silver nanowire/graphene (Gr/AgNW/Gr) sandwich composite has been proposed to overcome these drawbacks significantly as the AgNW network can provide extra connections on graphene layers to enhance the stiffness and electrical conductivity. However, the existing substrate (polyethylene terephthalate (PET), glass, silicon, and so on) for composite production limits its application and mechanics behavior study. In this work, a vacuum annealing method is proposed and validated to synthesize the free-stand Gr/AgNW/Gr nanocomposite film on transmission electron microscopy (TEM) grids. AgNW average spacing, optical transmittance, and electrical conductivity are characterized and correlated with different AgNW concentrations. Atomic force microscope (AFM) indentation on the free-stand composite indicates that the AgNW network can increase the composite film stiffness by approximately 460% with the AgNW concentration higher than 0.6 mg/mL. Raman spectroscopy shows the existence of a graphene layer and the disturbance of the AgNW network. The proposed method provides a robust way to synthesize free-stand Gr/AgNW/Gr nanocomposite and the characterization results can be utilized to optimize the nanocomposite design for future applications.

Multiscale Resilience in Water Distribution and Drainage Systems

Multiscale resilience, i.e., coordinating different scales within a system to jointly cope and mitigate risks on any single scale, is identified as the feature of a complex resilient system. However, in water distribution systems (WDSs) and urban drainage systems (UDSs), the inherent resilience is usually not multiscale resilience. By referring to the larger scale to larger pipes serving both local users and some other users at smaller scales, it can be found that smaller scales are not responsible for providing resilience to cope with failures in larger scales. These are because the main function of traditional water systems is to deliver water from upstream to downstream. This study demonstrates that improving multiscale resilience in WDSs and UDSs needs to allow water to travel reversely in the system via providing extra capacities and/or connections at smaller scales. This hypothesis is verified via case studies on a real world WDS and UDS.

Almost Complete and Equable Heteroclinic Networks

Heteroclinic connections are trajectories that link invariant sets for an autonomous dynamical flow: these connections can robustly form networks between equilibria, for systems with flow-invariant spaces. In this paper we examine the relation between the heteroclinic network as a flow-invariant set and directed graphs of possible connections between nodes. We consider realizations of a large class of transitive digraphs as robust heteroclinic networks and show that although robust realizations are typically not complete (i.e. not all unstable manifolds of nodes are part of the network), they can be almost complete (i.e. complete up to a set of zero measure within the unstable manifold) and equable (i.e. all sets of connections from a node have the same dimension). We show there are almost complete and equable realizations that can be closed by adding a number of extra nodes and connections. We discuss some examples and describe a sense in which an equable almost complete network embedding is an optimal description of stochastically perturbed motion on the network.

An Indirect Approach to Control an Active Capacitor

We propose an indirect approach to control an active capacitor, realized using a bidirectional converter, meant for dc voltage filtering (ripple elimination). This active capacitor can replace large, expensive, and unreliable electrolytic capacitors connected on a dc bus for power filtering. It can deal with a wide frequency band of harmonics so that it does not need a priori knowledge of the ripple frequencies. Thanks to the simple decentralized controller proposed in this paper, our active capacitor can be connected directly to the dc bus like a passive filtering capacitor, and no extra connections are needed. Compared with the various direct approaches, a unique feature of the proposed indirect approach is that the buffering capacitor energy $V_{S}^{2}$ is directly regulated and much better dynamics of $V_{S}^{2}$ under sudden load variations are obtained. The control algorithm has been verified by both simulation and experiments, with our circuit being connected to a 350-W commercial power factor compensator.

Plug-and-Play Control of the Virtual Infinite Capacitor

The virtual infinite capacitor (VIC) is a nonlinear active capacitor for dc voltage filtering (ripple elimination). It can replace large and unreliable electrolytic capacitors, and it is intended for dc systems where random fluctuations of the dc bus voltage may occur. We propose a plug-and-play (PnP) realization of the VIC, which can be connected directly to the dc bus like a passive capacitor without any extra connections. The proposed control algorithm automatically adjusts the reference voltage of the VIC so that it will be equal to the equilibrium voltage of the dc bus. For the stability of complex interconnected systems, it is recommended that the impedance of the components should be strictly positive real. By studying the output impedance of PnP VIC, we discuss how to achieve the strictly positive-real property of the overall dc bus by a careful choice of controllers and filters. The results were tested by simulations and experiments on a circuit comprising a commercial power factor compensator with an average of 390 V output and a variable resistive load (up to 345 W).

A D-Band Rectangular Waveguide-to-Coplanar Waveguide Transition Using Wire Bonding Probe

This paper presents a rectangular waveguide-to-coplanar waveguide (CPW) transition at D-band (110–170 GHz) using wire bonding probe. A conventional CPW is designed and fabricated based on both quartz and glass substrates for estimating the losses at D-band and testing the fabrication processes. Two transition prototypes at D-band using E-plane probe and wire bonding probe are designed, fabricated, and measured in a back-to-back configuration. The system packaging approaches and scattering parameters of the transition prototypes are compared. For both on-chip and carrier substrate approaches, chips can be tested individually before packaged into a system and extra connections are not required. The fabricated rectangular waveguide-to-CPW transition at D-band using wire bonding probe in a back-to-back configuration exhibits a bandwidth of 56.3 GHz ranging from 110 to 166.3 GHz in which the return loss is better than 10 dB with an associated insertion loss of 2 dB. Compared with the fabricated rectangular waveguide-to-CPW transition using E-plane probe, an equivalent bandwidth is achieved at D-band while the system packaging approaches are more versatile and compact for the proposed transition using wire bonding probe.

Profiling the airway in the macaque model of tuberculosis reveals variable microbial dysbiosis and alteration of community structure

BackgroundThe specific interactions of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), and the lung microbiota in infection are entirely unexplored. Studies in cancer and other infectious diseases suggest that there are important exchanges occurring between host and microbiota that influence the immunological landscape. This can result in alterations in immune regulation and inflammation both locally and systemically. To assess whether Mtb infection modifies the lung microbiome, and identify changes in microbial abundance and diversity as a function of pulmonary inflammation, we compared infected and uninfected lung lobe washes collected serially from 26 macaques by bronchoalveolar lavage over the course of infection.ResultsWe found that Mtb induced an initial increase in lung microbial diversity at 1 month post infection that normalized by 5 months of infection across all macaques. Several core genera showed global shifts from baseline and throughout infection. Moreover, we identified several specific taxa normally associated with the oral microbiome that increased in relative abundance in the lung following Mtb infection, including SR1, Aggregatibacter, Leptotrichia, Prevotella, and Campylobacter. On an individual macaque level, we found significant heterogeneity in both the magnitude and duration of change within the lung microbial community that was unrelated to lung inflammation and lobe involvement as seen by positron emission tomography/computed tomography (PET/CT) imaging. By comparing microbial interaction networks pre- and post-infection using the predictive algorithm SPIEC-EASI, we observe that extra connections are gained by Actinomycetales, the order containing Mtb, in spite of an overall reduction in the number of interactions of the whole community post-infection, implicating Mtb-driven ecological reorganization within the lung.ConclusionsThis study is the first to probe the dynamic interplay between Mtb and host microbiota longitudinally and in the macaque lung. Our findings suggest that Mtb can alter the microbial landscape of infected lung lobes and that these interactions induce dysbiosis that can disrupt oral-airway boundaries, shift overall lung diversity, and modulate specific microbial relationships. We also provide evidence that this effect is heterogeneous across different macaques. Overall, however, the changes to the airway microbiota after Mtb infection were surprisingly modest, despite a range of Mtb-induced pulmonary inflammation in this cohort of macaques.

Background-Driven Salient Object Detection

The background information is a significant prior for salient object detection, especially when images contain cluttered background and diverse object parts. In this paper, we propose a background-driven salient object detection (BD-SOD) method to more comprehensively exploit the background prior, aiming at generating more accurate and robust salient maps. To be specific, we first exploit the background prior to conduct the saliency estimation, i.e., computing the regional saliency values. In this stage, the background prior is utilized in threefold: restricting the reference regions to only the background regions, weighting the contribution of reference regions, and leveraging the importance of different features. Benefiting from such an explicit utilization, the proposed model can greatly mitigate the negative interference of the cluttered background and diverse object parts. We then embed the background prior into the optimization graph for saliency refinement. Specifically, two virtual supernodes (representing the background and foreground, respectively) are introduced with extra connections, and the nonlocal feature connections between similar regions are also set up. These connections enhance the power of optimization graph to alleviate the perturbations from diverse parts, and thus help to achieve the uniformity of saliency values. Finally, we provide systematical studies to investigate the effectiveness of the proposed BD-SOD in exploiting the valuable background prior. Experimental results on multiple public benchmark datasets, including MSRA-1000, THUS-10000, PASCAL-S, and ECSSD, clearly show that BD-SOD consistently outperforms the well-established baselines and achieves state-of-the-art performance.

A novel double pipe pulsating heat pipe design to tackle inverted heat source arrangement

Traditional pulsating heat pipe with fewer turns is functional only when the heat source is placed at the bottom while the heat sink is on top. This study proposes a novel design having alternative double-pipe tube diameter with some extra open connections of these double-pipes. By introducing extra un-balanced pressure force, capillary force and gravity force, the PHP can be easily starting up even for an opposite heat source/sink arrangement while the conventional single tube design fails to start up with this opposite arrangement. The thermal resistance of double pipe PHP can be lowered to 0.0729W/K with an effective thermal conductivity of 12,603W/m·K when the heat source is placed on top. Yet the thermal resistance for the proposed double pipe design is only half of the single tube design.

Abnormalities of the executive control network in multiple sclerosis phenotypes: An fMRI effective connectivity study.

The Stroop interference task is a cognitively demanding task of executive control, a cognitive ability that is often impaired in patients with multiple sclerosis (MS). The aim of this study was to compare effective connectivity patterns within a network of brain regions involved in the Stroop task performance between MS patients with three disease clinical phenotypes [relapsing-remitting (RRMS), benign (BMS), and secondary progressive (SPMS)] and healthy subjects. Effective connectivity analysis was performed on Stroop task data using a novel method based on causal Bayes networks. Compared with controls, MS phenotypes were slower at performing the task and had reduced performance accuracy during incongruent trials that required increased cognitive control. MS phenotypes also exhibited connectivity abnormalities reflected as weaker shared connections, presence of extra connections (i.e., connections absent in the HC connectivity pattern), connection reversal, and loss. In SPMS and the BMS groups but not in the RRMS group, extra connections were associated with deficits in the Stroop task performance. In the BMS group, the response time associated with correct responses during the congruent condition showed a positive correlation with the left posterior parietal → dorsal anterior cingulate connection. In the SPMS group, performance accuracy during the congruent condition showed a negative correlation with the right insula → left insula connection. No associations between extra connections and behavioral performance measures were observed in the RRMS group. These results suggest that, depending on the phenotype, patients with MS use different strategies when cognitive control demands are high and rely on different network connections. Hum Brain Mapp, 37:2293-2304, 2016. © 2016 Wiley Periodicals, Inc.