Hierarchical Block Research Articles

Self-Assembly of Hierarchical Silicon-Containing Block Copolymers with Cross-Linkable 3 nm Smectic Motifs for Nanopatterning and Osmotic Energy Conversion Membranes.

Highly-dense small-feature-size nanopatterns and nanoporous membranes are important in advanced microelectronics, nanofiltration, and biomimic device manufacturing. Here, we report the synthesis and self-assembly of a series of high-interaction-parameter (high-χ) silicon-containing hierarchical block copolymers (BCPs) with cross-linkable subordering chalcone motifs, which possess both an intrinsic native etching contrast for nanofabrication and cross-linkability under ultraviolet light for generating free-standing membranes. BCPs with a volume fraction of chalcone block of 55-74% form ordered primary nanostructures with period 15-22 nm including lamellae, double gyroid, hexagonally packed cylinders, and body-centered cubic spheres of the minority Si-containing block. The majority PChMA block self-assembles into a highly ordered 3 nm smectic sublattice, and cross-linking after self-assembly enables the formation of free-standing isoporous membranes. Both silicon oxide nanopatterns and free-standing nanoporous osmotic energy conversion membranes are generated by etching films of these BCPs. This work demonstrates that the combination of hierarchical ordering and cross-linkable motifs in a high-interaction parameter BCP enables applications in both nanofabrication and free-standing functional porous membranes.

Avionics Module Fault Diagnosis Algorithm Based on Hybrid Attention Adaptive Multi-Scale Temporal Convolution Network.

Since the reliability of the avionics module is crucial for aircraft safety, the fault diagnosis and health management of this module are particularly significant. While deep learning-based prognostics and health management (PHM) methods exhibit highly accurate fault diagnosis, they have disadvantages such as inefficient data feature extraction and insufficient generalization capability, as well as a lack of avionics module fault data. Consequently, this study first employs fault injection to simulate various fault types of the avionics module and performs data enhancement to construct the P2020 communications processor fault dataset. Subsequently, a multichannel fault diagnosis method, the Hybrid Attention Adaptive Multi-scale Temporal Convolution Network (HAAMTCN) for the integrated functional circuit module of the avionics module, is proposed, which adaptively constructs the optimal size of the convolutional kernel to efficiently extract features of avionics module fault signals with large information entropy. Further, the combined use of the Interaction Channel Attention (ICA) module and the Hierarchical Block Temporal Attention (HBTA) module results in the HAAMTCN to pay more attention to the critical information in the channel dimension and time step dimension. The experimental results show that the HAAMTCN achieves an accuracy of 99.64% in the avionics module fault classification task which proves our method achieves better performance in comparison with existing methods.

Lifetime stressors relate to invisible symptoms of multiple sclerosis

Aim: Childhood stressors can increase adult stress perception and may accumulate over the lifespan to impact symptoms of multiple sclerosis (MS). Growing evidence links childhood stressors (e.g., abuse, neglect) to fatigue, pain, and psychiatric morbidity in adults with MS; yet literature in this area is lacking a comprehensive lifespan approach. The aim of this cross-sectional study was to examine contributions of childhood and adulthood stressor characteristics (i.e., count, severity), on three individual outcomes: fatigue, pain interference, and psychiatric morbidity in People with MS (PwMS). Methods: An online survey was distributed through the National MS Society. Hierarchical block regression modeling was used to sequentially assess baseline demographics, childhood stressors, and adult stressors per outcome. We hypothesized that child and adult stressors would significantly contribute to fatigue, pain interference, and psychiatric morbidity. Results: Overall, 713 PwMS informed at least one final analytic model. Both childhood and adult stressors significantly contributed to pain interference and psychiatric morbidity. Adult stressor severity independently correlated with psychiatric morbidity (P < 0.0001). Childhood stressors significantly contributed to fatigue (LR test P < 0.0001). Childhood stressor severity independently significantly correlated with both fatigue likelihood (P = 0.03) and magnitude (P < 0.001). Conclusions: This work supports a relationship between stressors across the lifespan and fatigue, pain, and psychiatric morbidity in PwMS. Stressor severity may have an important role which may not be captured in count-based trauma measurement tools. Clinicians and researchers should consider lifetime stress when addressing fatigue, pain, and psychiatric morbidity among PwMS.

Fine-Grained Self-Supervised Learning with Jigsaw puzzles for medical image classification

Classifying fine-grained lesions is challenging due to minor and subtle differences in medical images. This is because learning features of fine-grained lesions with highly minor differences is very difficult in training deep neural networks. Therefore, in this paper, we introduce Fine-Grained Self-Supervised Learning(FG-SSL) method for classifying subtle lesions in medical images. The proposed method progressively learns the model through hierarchical block such that the cross-correlation between the fine-grained Jigsaw puzzle and regularized original images is close to the identity matrix. We also apply hierarchical block for progressive fine-grained learning, which extracts different information in each step, to supervised learning for discovering subtle differences. Our method does not require an asymmetric model, nor does a negative sampling strategy, and is not sensitive to batch size. We evaluate the proposed fine-grained self-supervised learning method on comprehensive experiments using various medical image recognition datasets. In our experiments, the proposed method performs favorably compared to existing state-of-the-art approaches on the widely-used ISIC2018, APTOS2019, and ISIC2017 datasets.

A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations

A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations

Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification.

The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in image segmentation. This innovative model refines image processing without imposing excessive computational demands by honing in on individual pixel intricacies, spatial relationships, and channel-specific attention. Building upon this innovation, our proposed method employs a multi-stage strategy encompassing data pre-processing, feature extraction via a hybrid CNN-SVD model, and classification employing an amalgamation of Improved Support Vector Machine-Radial Basis Function (ISVM-RBF), DT, and KNN techniques. Rigorously tested on the IDRiD dataset classified into five severity tiers, the hybrid model yields remarkable performance, achieving a 99.18% accuracy, 98.15% sensitivity, and 100% specificity in VTDR detection, thus surpassing existing methods. These results underscore a more potent avenue for diagnosing and addressing this crucial ocular condition while underscoring AI's transformative potential in medical care, particularly in ophthalmology.

Computer Model of Thyratron TGI1-270/12

The issues of developing the computer model of a thyratron - an electronic tube triode operating in a switching mode - are considered. A detailed description of the composition of the model is given. The computer model of a single-grid thyratron was developed in the form of a hierarchical block by means of OrCAD. The test circuit for the computer model of a thyratron is suggested. The simulation results of the test circuit operation including a thyratron model are presented (simulated currents and voltages, dynamic characteristics of the thyratron - anode current as a function of the grid voltage). The computer model demonstrated adequacy to the product data sheet.

Parametrization of subsegmental infarcts using high spatial resolution 2DSTE and synthetic ultrasonic data

Abstract Introduction: The purpose of this study was to assess the performance of a set of parameters in characterizing simulated infarcts in left ventricular (LV) models with variations in size and transmural extent. Material and methods: The deformation of the LV models with different infarct sizes was simulated using the Finite Element Method. These simulations provided meshes that were used to generate synthetic ultrasonic data within the FIELD II package. The strain components (longitudinal and circumferential) were then estimated over small subsegments of the of segments 7 and 12 (according to 17-segment left ventricle segmentation standard proposed by the American Heart Association - AHA17), using a hierarchical block matching method. The strain maps obtained were utilized to calculate the Strain Drop Factor (SDF) maps, which represent the percentage ratio of strain observed in the subsegments of the studied model to that observed in the healthy model. Infarct segmentation was performed using these maps, and various parameters were derived, including Infarct Cross-Section Area (ICSA), relative ICSA, Transmurality Ratio (TR), Mean Infarct Transmurality (MIT), strain drop factor in the infarcted region (SDFi), and Strain Contrast (SC). Results: The estimates of ICSA, SC, MIT, and SDFI showed good repeatability and demonstrated the ability to provide a quantitative assessment of the size and transmural extent of the infarcts. Conclusions: The study findings suggest that the evaluated parameters, including ICSA, SC, MIT, and SDFI, can be reliably used to assess the size and transmural extent of infarcts. These parameters offer a quantitative approach for characterizing infarcts based on strain analysis and have the potential to contribute to the diagnosis and evaluation of myocardial infarctions.

Fatigue, pain interference, and psychiatric morbidity in multiple sclerosis: The role of childhood stress.

Multiple sclerosis (MS) is a progressive, autoimmune disease of the central nervous system that affects nearly one million Americans. Despite the existence of immunomodulatory therapies to slow physical and cognitive disability progression, interventions to ameliorate common symptoms of MS, including fatigue and pain, remain limited. Poor understanding of risk factors for these symptoms may contribute to treatment challenges. In recent years, childhood stress has been investigated as a risk factor for chronic autoimmune conditions including MS; yet remarkably few studies have investigated the relationship between childhood stressors and chronic MS symptoms. Our aim was to examine clusters of stressors and three key features of MS: fatigue, pain interference, and psychiatric morbidity. Cross-sectional data were collected from a sample of People with MS (PwMS) via a national web-based survey that assessed the presence and type of childhood stressors and MS clinical features. Hierarchical block regression was used to assess associations among emotional, physical, and environmental childhood stressors and three clinical features commonly experienced by PwMS. N = 719 adults with MS (aged 21-85) completed the survey. Childhood emotional and physical stressors were significantly associated with overall presence of fatigue (p = 0.02; p<0.03) and pain interference (p<0.001; p<0.001) in adulthood, as well as the magnitude of both outcomes. Environmental stressors (p<0.001), in addition to emotional (p<0.001) and physical (p<0.001) stressors were significantly associated with psychiatric morbidity in PwMS. Childhood stress may predict fatigue, psychiatric morbidity, and pain in adults with MS. Further research is needed to show cause and effect; however, if an association exists, strategies to mitigate the impact of childhood stress could offer new pathways to reduce the severity of these symptoms. Broadly, this work adds to the body of evidence supporting upstream preventive measures to help address the stress on children and families.

Coupled clustering strategies for hierarchical matrix preconditioners in saddle point problems

AbstractFluid flow problems can be modeled by the Navier–Stokes or, after linearization, by the Oseen equations. Their discretization results in discrete saddle point problems. These systems of equations are typically very large and need to be solved iteratively. Standard (block‐) preconditioning techniques for saddle point problems rely on an approximation of the Schur complement. Such an approximation can be obtained by a hierarchical (‐) matrix LU‐decomposition, which first approximates the Schur complement explicitly. The computational complexity of this computation depends, among other things, on the hierarchical block structure of the involved matrices. However, widely used techniques do not consider the connection between the discretization grids for the velocity field and the pressure, respectively. Here, we present a hierarchical block structure for the finite element discretization of the gradient operator that is improved by considering the connection between the two involved grids. Numerical results imply that the improved block structure allows for a faster computation of the Schur complement, which is the bottleneck for the set‐up of the ‐matrix LU‐decomposition.

Reversible Data Hiding With Hierarchical Block Variable Length Coding for Cloud Security

Reversible data hiding in encrypted images (RDHEI) can serve as a technical solution to secure data in applications that rely on cloud storage. The key features of an RDHEI scheme are reversibility, security, and data embedding rate. To enlarge the embedding rate, this paper proposes a novel RDHEI scheme based on the median edge detector (MED) and a new proposed hierarchical block variable length coding (HBVLC) technique. In our scheme, the image owner first predicts the pixel values of the carrier image with MED. Then, the prediction error array is sliced into bit-planes and encoded plane by plane. By leveraging the inherent features of the prediction error bit-planes, the image owner adaptively decomposes a bit-plane into blocks of different hierarchical levels based on its local smoothness and encodes the blocks with a variable length coding method. As a result, the carrier image is efficiently compressed to provide spare room for data embedding. The encoded carrier image is then processed with the conventional steps of an RDHEI technique. Experimental results show that the proposed scheme not only can restore the secret data and the carrier image without loss but also outperforms state-of-the-art methods in the embedding rate for images with various features.

GNN-Based Hierarchical Annotation for Analog Circuits

Analog designs consist of multiple hierarchical functional blocks. Each block can be built using one of several design topologies, where the choice of topology is based on circuit performance requirements. A major challenge in automating analog design is in the identification of these functional blocks, which enables the creation of hierarchical netlist representations. This can facilitate a variety of design automation tasks such as circuit layout optimization because the layout is dictated by constraints at each level, such as symmetry requirements, that depend on the topology of the hierarchical block. Traditional graph-based methods find it hard to automatically identify the large number of structural variants of each block. To overcome this limitation, this paper leverages recent advances in graph neural networks (GNNs). A variety of GNN strategies is used to identify netlist elements for circuit functional blocks at higher levels of the design hierarchy, where numerous design variants are possible. At lower levels of hierarchy, where the degrees of freedom in circuit topology are limited, structures are identified using graph-based algorithms. The proposed hierarchical recognition scheme enables the identification of layout constraints such as symmetry and matching, which enable high-quality hierarchical layouts. This method is scalable across a wide range of analog designs. An experimental evaluation shows a high degree of accuracy over a wide range of analog designs, identifying functional blocks such as low-noise amplifiers, operational transconductance amplifiers, mixers, oscillators, and band-pass filters in larger circuits.

Increasing the construction production efficiency based on the use of cyber-physical systems and technologies

Objective: The article proposes a classification of cyber-physical systems and technologies at the stages of the life cycle of a construction project, considers an algorithm for choosing these technologies depending on the effectiveness of the project. Theoretical framework: The study is based on the review of publications in international scientific journals, methodological literature on the topic of the use of digital tools in construction. The experience of different countries in the application of cyber-physical systems and technologies in construction was represented. Method: The study was conducted based on materials published in open sources, including scientific articles, reports, regulatory documents. In addition, the study used the experience of several companies operating in the construction industry. Results and conclusion: The main approaches to planning construction projects at the stage of the life cycle are studied. Approaches to the formation of a graphical hierarchical block diagram of a simulation model, consisting of different levels of nesting, are proposed. The results may be of interest to manufacturing companies that decide to implement digital technologies in construction, as well as individual scientists who study the problem of increasing the efficiency of production organization at the stages of the construction projects life cycles. Implications of the research: The results of the study can be used by construction companies when planning the choice of cyber-physical systems and technologies at the stages of the life cycle of construction objects Originality/value: The authors propose an approach that allows choosing tools for designing and building objects using cyber-physical systems and technologies at the stages of the life cycle. A hierarchical block diagram of the simulation model is proposed, which contains more than a dozen child diagrams that combine several hundred smaller typical modules.

Hierarchical block aggregation network for long-tailed visual recognition

It is usually supposed that training database is manually balanced in traditional visual recognition tasks. However, in nature, data tends to follow long-tailed distributions. In recent years, many plug-and-play methods based on data augmentation or representation learning have been proposed to tackle the long-tailed visual recognition task. Although these methods are effective, we find that when different plug-and-play methods are applied to the same long-tail recognition model, they sometimes fail to promote each other. The reason for this phenomenon may lie in the fact that the overall performance of the model is constrained by the insufficient capability of a traditional feature extractor. Motivated by this fact, we first propose Hierarchical Block Aggregation Network (HBAN), a network structure with stronger feature extraction capability. Then, we design a Quantity-Aware Balanced (QAB) loss and a decoupled training paradigm to optimize HBAN. The effectiveness of HBAN is demonstrated by extensive experiments. In particular, HBAN achieves significant improvements over our baseline on three benchmark datasets, and outperforms the state-of-the-art methods on CIFAR 100-LT.

Associations among stressors across the lifespan, disability, and relapses in adults with multiple sclerosis.

Stress and adversity during childhood, adolescence, and adulthood could impact the present and future health and well-being of people with multiple sclerosis (PwMS); however, a lifespan approach and nuanced stressor data are scarce in this nascent area of research. Our aim was to examine relationships among comprehensively measured lifetime stressors and two self-reported MS outcomes: (1) disability and (2) relapse burden changes since COVID-19 onset. Cross-sectional data were collected from a nationally distributed survey of U.S.-based adults with MS. Hierarchical block regressions were used to sequentially evaluate contributions to both outcomes independently. Likelihood ratio (LR) tests and Akaike information criterion (AIC) were used to evaluate additional predictive variance and model fit. A total of 713 participants informed either outcome. Most respondents (84%) were female, 79% had relapsing remitting multiple sclerosis (MS), and mean (SD) age was 49 (12.7)years. Childhood (R2 =.261, p<.001; AIC=1063, LR p<.05) and adulthood stressors (R2 =.2725, p<.001, AIC=1051, LR p<.001) contributed significantly to disability, above and beyond prior nested models. Only adulthood stressors (R2 =.0534, p<.001; AIC=1572, LR p<.01) significantly contributed above the nested model for relapse burden changes since COVID-19. Stressors across the lifespan are commonly reported in PwMS and could contribute to disease burden. Incorporating this perspective into the "lived experience with MS" could facilitate personalized health care by addressing key stress-related exposures and inform intervention research to improve well-being.

Automatic segmentation of neurovascular bundle on mri using deep learning based topological modulated network.

Radiation damage on neurovascular bundles (NVBs) may be the cause of sexual dysfunction after radiotherapy for prostate cancer. However, it is challenging to delineate NVBs as organ-at-risks from planning CTs during radiotherapy. Recently, the integration of MR into radiotherapy made NVBs contour delineating possible. In this study, we aim to develop an MRI-based deep learning method for automatic NVB segmentation. The proposed method, named topological modulated network, consists of three subnetworks, that is, a focal modulation, a hierarchical block and a topological fully convolutional network (FCN). The focal modulation is used to derive the location and bounds of left and right NVBs', namely the candidate volume-of-interests (VOIs). The hierarchical block aims to highlight the NVB boundaries information on derived feature map. The topological FCN then segments the NVBs inside the VOIs by considering the topological consistency nature of the vascular delineating. Based on the location information of candidate VOIs, the segmentations of NVBs can then be brought back to the input MRI's coordinate system. A five-fold cross-validation study was performed on 60 patient cases to evaluate the performance of the proposed method. The segmented results were compared with manual contours. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95 ) are (left NVB) 0.81±0.10, 1.49±0.88mm, and (right NVB) 0.80±0.15, 1.54±1.22mm, respectively. We proposed a novel deep learning-based segmentation method for NVBs on pelvic MR images. The good segmentation agreement of our method with the manually drawn ground truth contours supports the feasibility of the proposed method, which can be potentially used to spare NVBs during proton and photon radiotherapy and thereby improve the quality of life for prostate cancer patients.

Polymer brushes based on N-methacryloxysuccinimide as platform for versatile post-polymerization modification

In this contribution we present a method for the surface-initiated atom-transfer radical polymerization of active esters based on N-methacryloxysuccinimide and post-polymerization modification in mild conditions. The obtained active ester brushes show quantitative conversion under amidation conditions with various primary amines, including 1-amino-2-propanol, fluorine containing amines and amine-containing fluorophores. Amidation reaction with propargyl amine offers the possibility to introduce a second functionality in the homopolymer brushes. This widens the ability of polymer analogous reactions with functionalized compounds. The effectiveness of the amidation reaction was deeply studied utilizing spectroscopic ellipsometry, infrared reflection-absorption and X-ray photoelectron spectroscopies, contact angle goniometry and surface energy analysis. As validated by functionalized homopolymer brushes, hierarchical block copolymer brushes containing active ester monomer units also undergo quantitative conversion with primary amines. Furthermore, resulting polymer brushes containing fluorophores were studied by fluorescent microscopy to further investigate their film structure and homogeneity.

Bayesian Image Reconstruction Using Weighted Laplace Prior for Lung Respiratory Monitoring With Electrical Impedance Tomography

The poor spatial resolution of electrical impedance tomography (EIT) limits its use in medical devices because of its highly nonlinear and ill-posed nature. A novel hierarchical block sparse Bayesian learning (BSBL) method is designed for lung respiratory monitoring with EIT. It is its excellent modeling capability and noise robustness that allows BSBL to adaptively explore and exploit the internal conductivity distribution, e.g., block sparsity and intra-block correlation. First, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (KNN) strategy is employed to automatically group each block based on the clustering property and incorporated to constrain the intra-block correlation matrix based on the spatial distance. Second, a three-layer hierarchical BSBL model using weighted Laplace (WL) prior is considered to enhance the recovery performance. Finally, an efficient bound optimization (BO) method is performed for Bayesian inference, avoiding the tedious parameter adjustment. This leads to improvements in recovery performance, algorithm robustness, and computational efficiency. Moreover, root mean square error (RMSE), image correlation coefficient (ICC), and relative size coverage ratio (RCR) are applied for quantitative comparisons of image quality. Numerical simulations and in vivo lung respiratory experiments showed that the proposed KNN-BSBL-WL method outperforms existing referenced methods in terms of reconstruction accuracy and computational time. On average, KNN-BSBL-WL obtains the RMSE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 0.07\,\,\pm \,\,0.02$ </tex-math></inline-formula> , ICC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 0.96\,\,\pm \,\,0.01$ </tex-math></inline-formula> , and RCR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 0.98\,\,\pm \,\,0.05$ </tex-math></inline-formula> , which are closest to the true values of the recorded snapshots obtained in the experiments. Meanwhile, the average time of KNN-BSBL-WL is 0.16 s, achieving an acceleration ratio close to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 3$ </tex-math></inline-formula> compared with the referenced KNN-BSBL method. Therefore, the proposed method is an efficient and robust means of imaging reconstruction, which further improves the feasibility of EIT in respiratory clinical applications.

Extending Graph-Based Process Discovery for Hierarchical Block Detection and Incomplete Concurrent Relationship Handling

Extending Graph-Based Process Discovery for Hierarchical Block Detection and Incomplete Concurrent Relationship Handling

Exploration of the health needs of patients with poorly controlled type 2 diabetes using a user-centred co-production approach in the area of mHealth: an exploratory sequential mixed-method protocol

IntroductionResearch on the needs and preferences of patients with poorly controlled type 2 diabetes mellitus (T2DM) with mobile health (mHealth) service is limited. With the principles of co-production, this study...