Inherent Structure Research Articles

Smooth Tensor Qatar Riyal Decomposition for Dynamic MRI Reconstruction.

Dynamic magnetic resonance imaging (dMRI) speed and imaging quality have always been a crucial issue in medical imaging research. Most existing methods characterize the tensor rank-based minimization to reconstruct dMRI from sampling k- t space data. However, (1) these approaches that unfold the tensor along each dimension destroy the inherent structure of dMR images. (2) they focus on preserving global information only, while ignoring the local details reconstruction such as the spatial piece-wise smoothness and sharp boundaries. To overcome these obstacles, we suggest a novel low-rank tensor decomposition approach by integrating tensor Qatar Riyal (QR) decomposition, low-rank tensor nuclear norm, and asymmetric total variation to reconstruct dMRI, named TQRTV. Specifically, while preserving the tensor inherent structure by utilizing tensor nuclear norm minimization to approximate tensor rank, QR decomposition reduces the dimensions in the low-rank constraint term, thereby improving the reconstruction performance. TQRTV further exploits the asymmetric total variation regularizer to capture local details. Numerical experiments demonstrate that the proposed reconstruction approach is superior to the existing ones.

Unleash the Power of State Space Model for Whole Slide Image with Local Aware Scanning and Importance Resampling.

Whole slide image (WSI) analysis is gaining prominence within the medical imaging field. However, previous methods often fall short of efficiently processing entire WSIs due to their gigapixel size. Inspired by recent developments in state space models, this paper introduces a new Pathology Mamba (PAM) for more accurate and robust WSI analysis. PAM includes three carefully designed components to tackle the challenges of enormous image size, the utilization of local and hierarchical information, and the mismatch between the feature distributions of training and testing during WSI analysis. Specifically, we design a Bi-directional Mamba Encoder to process the extensive patches present in WSIs effectively and efficiently, which can handle large-scale pathological images while achieving high performance and accuracy. To further harness the local information and inherent hierarchical structure of WSI, we introduce a novel Local-aware Scanning module, which employs a local-aware mechanism alongside hierarchical scanning to adeptly capture both the local information and the overarching structure within WSIs. Moreover, to alleviate the patch feature distribution misalignment between training and testing, we propose a Test-time Importance Resampling module to conduct testing patch resampling to ensure consistency of feature distribution between the training and testing phases, and thus enhance model prediction. Extensive evaluation on nine WSI datasets with cancer subtyping and survival prediction tasks demonstrates that PAM outperforms current state-of-the-art methods and also its enhanced capability in modeling discriminative areas within WSIs. The source code is available at https://github.com/HKU-MedAI/PAM.

Scalable Data-based Diagnostic Concept -Introduction and application in automotive field

This paper introduces and applies the Scalable Data-based Diagnostic Concept. At its core, the concept consists of (Kernel) Principal Component Analysis (PCA) and Autoencoder (AE), which are used to perform accurate fault diagnosis in technical systems, e.g. in automotive or railroad sectors, including various sub-methods for fault detection, identification and isolation. The analysis of real automotive fault cases is done, where a new smoothed comparative detection chart is presented. The findings prove the necessity of choosing the right method, regarding efficiency and the inherent data structure, which is one of the main objectives of the comprehensive scalable diagnostic concept.

Hole transport layer engineering in high performance quasi-2D perovskite blue light emitting diodes.

Quasi-2D perovskites have emerged as highly promising materials for application in perovskite light-emitting diodes (PeLEDs), garnering significant attention due to their outstanding semiconductor properties. These materials boast an inherent multi-quantum well structure that imparts a robust confinement effect, particularly advantageous for blue emission. However, the development of blue emitters utilizing quasi-2D perovskites encounters challenges, notably colour instability, multipeak emission, and suboptimal fluorescence yield. The hole transfer layer (HTL) on which the perovskite layer is deposited in PeLEDs further affects the performance and efficiency. In this review, we delve into the evolution of blue PeLEDs and elucidate the optical properties of quasi-2D perovskites with the primary focus on HTL materials. We explore different HTL materials like PEDOT:PSS, metal oxides, and conjugated polyelectrolytes as well as ionic liquids, and their role in enhancing the colour stability, minimizing interfacial defects and increasing the fluorescence yield. This review endeavours to provide a holistic perspective of the different HTLs and serve as a valuable reference for researchers navigating the realm of HTL engineering towards the realization of high-performance blue quasi-2D PeLEDs.

Unlocking OER catalytic potential and chiral Fe3O4 film as a game-changer for electrochemical water oxidation pathway and by-product control

This work report a Fe3O4 film with inherent chiral structure that effectively promoted OER, owing to its CISS effect on spin aligning intertwined with the capability of half-metallic materials to facilitate the transfer of spin-aligned electrons.

IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training.

In the field of medical Vision-Language Pretraining (VLP), significant efforts have been devoted to deriving text and image features from both clinical reports and associated medical images. However, most existing methods may have overlooked the opportunity in leveraging the inherent hierarchical structure of clinical reports, which are generally split into 'findings' for descriptive content and 'impressions' for conclusive observation. Instead of utilizing this rich, structured format, current medical VLP approaches often simplify the report into either a unified entity or fragmented tokens. In this work, we propose a novel clinical prior guided VLP framework named IMITATE to learn the structure information from medical reports with hierarchical vision-language alignment. The framework derives multi-level visual features from the chest X-ray (CXR) images and separately aligns these features with the descriptive and the conclusive text encoded in the hierarchical medical report. Furthermore, a new clinical-informed contrastive loss is introduced for cross-modal learning, which accounts for clinical prior knowledge in formulating sample correlations in contrastive learning. The proposed model, IMITATE, outperforms baseline VLP methods across six different datasets, spanning five medical imaging downstream tasks. Comprehensive experimental results highlight the advantages of integrating the hierarchical structure of medical reports for vision-language alignment.

Ultrafast and accurate prediction of polycrystalline hafnium oxide phase-field ferroelectric hysteresis using graph neural networks.

Polycrystalline hafnium oxide emerges as a promising material for the future of nanoelectronic devices. While phase-field modeling stands as a primary choice tool for forecasting domain structure evolution and electromechanical properties of ferroelectric materials, it suffers from a high computational cost, which impedes its applicability to real-size systems. Here, we propose a Graph Neural Network (GNN) machine-learning framework to predict the ferroelectric hysteresis of polycrystalline hafnium oxide, with the goal of significantly accelerating computations in contrast to high-fidelity phase-field methods. By leveraging the inherent graph structure of the polycrystalline system and incorporating edge-level feature properties through graph attentional layers, our approach accurately predicts hysteresis behaviors across a broad range of polycrystalline structures, grain numbers, and Landau coefficients. The GNN framework exhibits high accuracy, with an average relative error of ∼4%, and demonstrates remarkable computational efficiency with respect to ground truth phase-field simulations, offering speed-ups exceeding a million-fold. Furthermore, we showcase the transferability of our model to efficiently scale predictions in polycrystals comprising up to a thousand grains, paving the way for effective simulations of real-sized systems. Our approach, by overcoming computational limitations in polycrystalline hafnium oxide, opens doors for accelerating discovery and design in ferroelectric materials.

Molecular dynamics simulation of effect of non-condensable gases on heat transfer of water molecule flow in nanochannels

With the development of higher performance and miniaturization of electronic components, the flow heat transfer of working fluids in nanochannels has received more attention. To elucidate this phenomenon, molecular dynamics simulations are used to simulate the behaviors of fluids within nanochannels at temperatures of 300 K, 325 K, and 350 K. Water serves as a flow medium, with argon substituted for any non-condensable gases. In the flow process, argon atoms aggregate into clusters that are characterized by high potential energy. As the temperature rises, the concomitant increases in the fluid’s potential energy, which leads to the gradual diminution or complete dissipation of these clusters. A minor presence of gas atoms can facilitate fluid movement; however, an excess of argon promotes the formation of larger gaseous clusters in the central region of the channel, thereby impeding fluid flow. Concurrently, the application of heat to the fluid appreciably diminishes the coefficient of flow resistance. The temperature of the fluid in the near-wall region exceeds that of the central area. In the clusters, the atoms exhibit heightened activity, leading to an increase in the average molecular kinetic energy and a concomitant rise in temperature. The inherent hydrogen-bonding structure of water enhances heat transfer within the nanochannels. Argon atoms exert an influence on the number of hydrogen bonds, and rising temperatures disrupts the hydrogen-bond network established by water molecules, ultimately leading to a decrease of the Nusselt number. This investigation offers insights into the heat transfer dynamics of water molecular flow within microchannels under the perturbation of non-condensable gases, thereby furnishing theoretical guidance for enhancing heat transfer within electronic devices.

Topologically indistinguishable relations and separation axioms

<abstract><p>This study focuses on defining separation axioms for sets without an inherent topological structure. By utilizing a mapping to relate such sets to a topological space, we first define a distinguishable relation over the universal set with respect to the neighborhood systems inspired by a topology of the co-domain set and elucidate its basic properties. To facilitate the way of discovering this distinguishable relation, we initiate a color technique for the equivalence classes inspired by a given topology. Also, we provide an algorithm to determine distinguishable members (or objects) under study. Then, we establish a framework for introducing separation properties within these structureless sets and examine their master characterizations. To better understand the obtained results and relationships, we display some illustrative instances.</p></abstract>

Multi-gate Mixture of Multi-view Graph Contrastive Learning on Electronic Health Record.

Electronic Health Record (EHR) is the digital form of patient visits containing various medical data, including diagnosis, treatment, and lab events. Representation learning of EHR with deep learning methods has been beneficial for patient-related prediction tasks. Recently, studies have focused on revealing the inherent graph structure between medical events in EHR. Graph neural network (GNN) methods are prevalent and perform well in various prediction tasks. However, the inherent relationships between various medical events must be marked, which is complicated and time-consuming. Most research works adopt the straightforward structure of GNN models on a single prediction task which could not fully exploit the potential of EHR representations. Compared with previous work, the multi-task prediction could utilize the latent information of concealed correlations between different prediction tasks. In addition, self-contrastive learning on graphs could improve the representation learned by GNN. We propose a multi-gate mixture of multi-view graph contrastive learning (MMMGCL) method, aiming to get a more reasonable EHR representation and improve the performances of downstream tasks. First, each patient visit is represented as a graph with a well-designed hierarchically fully-connected pattern. Second, node features in the manually constructed graph are pre-trained via the Glove method with hierarchical ontology knowledge. Finally, MMMGCL processes the pre-trained graph and adopts a joint learning strategy to simultaneously optimize task and contrastive losses. We verify our method on two large open-source medical datasets, Medical Information Mart for Intensive Care (MIMIC-III) and the eICU Collaborative Research Database (eICU). Experiment results show that our method could improve performance compared to straightforward graph-based methods on prediction tasks of patient readmission, mortality, and length of stay.

What are the minimal folding seeds in proteins? Experimental and theoretical assessment of secondary structure propensities of small peptide fragments.

Certain peptide sequences, some of them as short as amino acid triplets, are significantly overpopulated in specific secondary structure motifs in folded protein structures. For example, 74% of the EAM triplet is found in α-helices, and only 3% occurs in the extended parts of proteins (typically β-sheets). In contrast, other triplets (such as VIV and IYI) appear almost exclusively in extended parts (79% and 69%, respectively). In order to determine whether such preferences are structurally encoded in a particular peptide fragment or appear only at the level of a complex protein structure, NMR, VCD, and ECD experiments were carried out on selected tripeptides: EAM (denoted as pro-'α-helical' in proteins), KAM(α), ALA(α), DIC(α), EKF(α), IYI(pro-β-sheet or more generally, pro-extended), and VIV(β), and the reference α-helical CATWEAMEKCK undecapeptide. The experimental data were in very good agreement with extensive quantum mechanical conformational sampling. Altogether, we clearly showed that the pro-helical vs. pro-extended propensities start to emerge already at the level of tripeptides and can be fully developed at longer sequences. We postulate that certain short peptide sequences can be considered minimal "folding seeds". Admittedly, the inherent secondary structure propensity can be overruled by the large intramolecular interaction energies within the folded and compact protein structures. Still, the correlation of experimental and computational data presented herein suggests that the secondary structure propensity should be considered as one of the key factors that may lead to understanding the underlying physico-chemical principles of protein structure and folding from the first principles.

A Novel Unsupervised Feature Selection for High-Dimensional Data Based on FCM and k -Nearest Neighbor Rough Sets.

Large amounts of high-dimensional unlabeled data typically contain only a small portion of truly effective information. Consequently, the issue of unsupervised feature selection methods has gained significant attention in research. However, current unsupervised feature selection approaches face limitations when dealing with datasets that exhibit uneven density, and they also require substantial computational time. To address this problem, this research article proposes a feature extraction technique that combines the Fuzzy C-Means (FCM) and k -nearest neighbor rough sets. FCM is a clustering algorithm grounded in fuzzy theory, which takes into account the inherent data structure and the correlations between different features. Consequently, FCM is particularly well-suited for datasets with uneven density. Our proposed method consists of three steps. First, the FCM algorithm is used to cluster the unlabeled data. Second, a measure that evaluates the importance of features is defined and sorted based on the clustering results. Finally, redundant features are filtered using k -nearest neighbor rough sets while retaining important features, significantly reducing the running time. In addition, we designed the feature selection algorithm (KND-UFS) and conducted experiments on 12 public datasets. We compared KND-UFS with eight existing algorithms in terms of running time, classification accuracy, and the number of selected features. The experimental results provided strong evidence supporting the superior performance of the KND-UFS algorithm.

In situ energy absorption and negative Poisson’s ratio behaviors of 3D weft knitted auxetic fabrics under variant measurements

Speculative negative Poisson’s ratio (NPR) materials exhibit an auxetic response to strain in a predetermined direction due to their unusual morphologies, making them highly suitable for various specialized applications. For the rationale, these materials have immense potential in various specialized contexts. In weft-knitted auxetic fabrics, uneven tension in the purl loops leads to the distinctive curling architecture. Due to their inherent curly structure and adaptability, weft-knitted fabrics are an excellent choice for emulating NPR materials. In this study, 3D weft-knitted auxetic fabrics were created using flatbed purl knitting technology. Two distinctive patterns, comprising knit and tuck stitches, were employed. The number of stitches per repeat (SPR) was varied to assess the impact of grid size on NPR behavior. Additionally, the variation in measurement points within standard-sized characterization specimens was analyzed, revealing slight differences in auxeticity. Increasing the SPR size initially enhanced NPR behavior, up to a certain limit, beyond which the trend declined. A significant shift in NPR was observed due to material variation and inter-yarn friction, with a 27% and 28% shift of Poisson’s ratio was experienced above and below the mean, respectively. Poisson’s ratio was also positively correlated with energy absorption, suggesting that the designed materials could be effective for protective and energy-absorbing applications.

Ultrafast, in situ transformation of a protective layer on lithium-rich manganese-based layered oxides for high-performance Li-ion batteries

We successfully used ultrafast high-temperature heating to create a homogeneous surface layer with a mixed-phase oxygen-vacancy composition on the cathode material, preserving the inherent layered structure within the cathode particles.

Multiview Deep Subspace Clustering Networks.

Multiview subspace clustering aims to discover the inherent structure of data by fusing multiple views of complementary information. Most existing methods first extract multiple types of handcrafted features and then learn a joint affinity matrix for clustering. The disadvantage of this approach lies in two aspects: 1) multiview relations are not embedded into feature learning and 2) the end-to-end learning manner of deep learning is not suitable for multiview clustering. Even when deep features have been extracted, it is a nontrivial problem to choose a proper backbone for clustering on different datasets. To address these issues, we propose the multiview deep subspace clustering networks (MvDSCNs), which learns a multiview self-representation matrix in an end-to-end manner. The MvDSCN consists of two subnetworks, i.e., a diversity network (Dnet) and a universality network (Unet). A latent space is built using deep convolutional autoencoders, and a self-representation matrix is learned in the latent space using a fully connected layer. Dnet learns view-specific self-representation matrices, whereas Unet learns a common self-representation matrix for all views. To exploit the complementarity of multiview representations, the Hilbert-Schmidt independence criterion (HSIC) is introduced as a diversity regularizer that captures the nonlinear, high-order interview relations. Because different views share the same label space, the self-representation matrices of each view are aligned to the common one by universality regularization. The MvDSCN also unifies multiple backbones to boost clustering performance and avoid the need for model selection. Experiments demonstrate the superiority of the MvDSCN.

On Exploring Multiplicity of Primitives and Attributes for Texture Recognition in the Wild.

Texture recognition is a challenging visual task since its multiple primitives or attributes can be perceived from the texture image under different spatial contexts. Existing approaches predominantly built upon CNN incorporate rich local descriptors with orderless aggregation to capture invariance to the spatial layout. However, these methods ignore the inherent structure relation organized by primitives and the semantic concept described by attributes, which are critical cues for texture representation. In this paper, we propose a novel Multiple Primitives and Attributes Perception network (MPAP) that extracts features by modeling the relation of bottom-up structure and top-down attribute in a multi-branch unified framework. A bottom-up process is first proposed to capture the inherent relation of various primitive structures by leveraging structure dependency and spatial order information. Then, a top-down process is introduced to model the latent relation of multiple attributes by transferring attribute-related features between adjacent branches. Moreover, an augmentation module is devised to bridge the gap between high-level attributes and low-level structure features. MPAP can learn representation through jointing bottom-up and top-down processes in a mutually reinforced manner. Experimental results on six challenging texture datasets demonstrate the superiority of MPAP over state-of-the-art methods in terms of accuracy, robustness, and efficiency.

...Or We Will Do Without the Theatre. Challenging the Urban Space, Drafting a New City Map Through Performances.

The article discusses the evolution of urban mapping and innovative urban design. It explores the transition from functional urban planning to more creative approaches inspired by artists such as Guy Debord, Yoko Ono and others. It suggests that the city is a stage for events, similar to Antonin Artaud’s vision. The central question is how the city can be transformed into a theatre through practical design rules. The paper presents two experiments. The first involves random map rearrangement, encouraging new exploration of familiar neighbourhoods. The second experiment introduces guidance and unpredictability, reflecting the uncertainties of architectural projects. These experiments aim to apply theatrical concepts to urban design. The article seeks to develop a manual for dramatic urban navigation, highlighting the inherent dramatic structure of the city and promoting innovative design regulations.

Construction of 2D/3D g-C3N4/BiOI Photocatalysts with p-n Heterojunction and Their Performance in Photocatalytic Degradation of Amaranth Dye.

The g-C3N4 (graphitic carbon nitride)/BiOI (bismuth oxyiodide) photocatalysts, boasting a unique nanomicrosphere architecture, were synthesized through a tripartite process involving heat polycondensation, hydrothermal treatment, and hybrid methods, using melamine, bismuth nitrate, and potassium iodide as starting materials. The photocatalyst was comprehensively characterized and analyzed while its efficacy in photocatalytic degradation of amaranth (AR) under various lighting conditions was investigated, and the catalytic mechanism was determined by kinetic analysis and free radical scavenging experiments. The results showed that g-C3N4 formed a strong bond with BiOI. The resulting composite catalyst retains the inherent 2D lamellar structure of g-C3N4, as well as the 3D microsphere structure of BiOI, thereby creating heterojunctions via p-n interactions. Under visible light exposure, g-C3N4/BiOI-15% demonstrated optimal catalytic performance, achieving a degradation rate of 74.64% for AR and exhibiting the highest rate constant. Radical tests confirmed that -O2- (superoxide anion) plays a crucial role as active species in the photocatalytic reaction.

Dynamic modelling and parametric analysis of a bolted joint rotor-bearing system considering stick-slip behavior at mating interface

Bolted joint structure is widely utilized in the aeroengine compress rotor systems with convenience in assembly and maintenance. Due to the inherent structure discontinuity and stick-slip behavior at the mating interface, stiffness softening and frictional energy dissipation would be generated at the mating interface, resulting in complex vibration features of the rotor system. In this work, to describe the stick-slip behavior at the mating interface of the rotor system, an Iwan-based model is introduced to describe the contact state of asperities at the multi-scale level. The corresponding model identification method is proposed based on the partial hysteresis curve of the bolted joint. After that, a dynamic model of the bolted joint rotor system is established, and its dynamic response is obtained using the Runge–Kutta method. The effect of preload and friction coefficient on the nonlinear vibration of the bolted joint rotor is evaluated in detail based on the dynamic model. The result indicates that the stiffness softening would lead to the critical speed decreasing and aggravating the vibration response of the rotor system, while the frictional energy dissipation would restrain the response amplitude and induce the self-excited vibration within the super-critical speed range. The higher preload or friction coefficient has little effect on the motion state of the rotor system within the sub-critical speed range and makes system vibration stable between the 1st- and 2nd-order critical speed while unstable at the super-critical speed range. This research helps understand the vibration features of bolted joint rotor systems under the condition of stick-slip behaviors at the mating interface.

Construction of core‐shell heterostructured MnO@Co/NC for enhancing the oxygen electrocatalytic performance

AbstractThe development of non‐precious metal electrocatalysts for efficient oxygen reduction reaction (ORR) is a significant research field vital for the progress of zinc−air battery technology. Here, we purposely constructed a core‐shell heterostructured electrocatalyst consisting of MnO nanotubes covered by Co nanoparticles anchored on N‐doped carbon substrate. MnO@Co/NC displayed superior ORR capability with a half‐wave potential of 0.85 V and almost no potential decay after 10,000 cycles CV test, outform that of commercial Pt/C catalysts. Employing MnO@Co/NC as the air electrode catalyst in primary Zn−air batteries leads to a top power density of 145 mW cm−2 and an enhanced specific capacity of 869 mAh g−1 as compared to commercial noble metal catalysts. Such superb ORR performance outcomes primarily stem from the synergistic effect between Co/NC shell and MnO core since the coupling structure not only regulates the inherent electronic structure but also accelerates electron transfer speed and boosts the active sites. Besides, the Co/NC outer layer can also help to improve the corrosion resistance of MnO@Co/NC in the electrolyte. This work demonstrated that the core‐shell heterostructured non‐precious catalysts open up a new avenue for obtaining sufficiently effective ORR catalysts in Zn−air batteries and other related applications.