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A new wall function method for hypersonic laminar boundary layers (HLBLs) is proposed to reduce the near-wall grid dependence of skin friction $c_f$ and wall heat flux $q_w$ in numerical simulations, aiming for fast and accurate predictions. First, an analytic laminar velocity law of the wall is derived, which achieves a universal scaling of the near-wall velocity of HLBLs. Then an accurate temperature–velocity relation is deduced by introducing the general recovery factor to address the invalidation of the Walz relation under the cold wall effect. Based on the laminar laws of the wall, a new wall function method for HLBLs is proposed. To avoid introducing the boundary layer edge quantities, the laminar laws of the wall are reformed by modifying the outer boundary conditions of the differential equation in deriving the temperature–velocity relation. Unlike the wall function method in turbulence, the new wall function obtains directly the accurate $c_f$ and $q_w$ by post-processing without being involved in the simulation iteration. The numerical experiments of a Mach 8 HLBL over the flat plate show that effectively, the new wall function can enlarge the distance of the first grid point off the wall $\\Delta y_1$ from $10^{-6}$ m to $10^{-3}$ m, which brings a 50 times enhancement of the simulation efficiency. Meanwhile, the simulation errors of $c_f$ and $q_w$ of the mesh with $\\Delta y_1=10^{-3}$ m are reduced significantly from 24.2 % and 18.5 % to 0.5 % and 0.1 %, respectively. Due to the new wall function removing the boundary layer edge quantities, success is also achieved under the curved walls.

Abstract Background and Hypothesis Neuroimaging studies investigating the neural substrates of auditory verbal hallucinations (AVH) in schizophrenia have yielded mixed results, which may be reconciled by network localization. We sought to examine whether AVH-state and AVH-trait brain alterations in schizophrenia localize to common or distinct networks. Study Design We initially identified AVH-state and AVH-trait brain alterations in schizophrenia reported in 48 previous studies. By integrating these affected brain locations with large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we then leveraged novel functional connectivity network mapping to construct AVH-state and AVH-trait dysfunctional networks. Study Results The neuroanatomically heterogeneous AVH-state and AVH-trait brain alterations in schizophrenia localized to distinct and specific networks. The AVH-state dysfunctional network comprised a broadly distributed set of brain regions mainly involving the auditory, salience, basal ganglia, language, and sensorimotor networks. Contrastingly, the AVH-trait dysfunctional network manifested as a pattern of circumscribed brain regions principally implicating the caudate and inferior frontal gyrus. Additionally, the AVH-state dysfunctional network aligned with the neuromodulation targets for effective treatment of AVH, indicating possible clinical relevance. Conclusions Apart from unifying the seemingly irreproducible neuroimaging results across prior AVH studies, our findings suggest different neural mechanisms underlying AVH state and trait in schizophrenia from a network perspective and more broadly may inform future neuromodulation treatment for AVH.

On admission, his white blood cell count was 6·62 × 109 per L, lymphocyte count was 2·02 × 109 per L, C-reactive protein level was 26·6 mg/L (normal range 0–8·0 mg/L), and fasting blood glucose level was 12·5 mmol/L. On admission, her white blood cell count was 4·01 × 109/L, lymphocyte count was 0·71 × 109/L, and C-reactive protein level was 2·58 mg/L. Sputum induction is a safe and simple non-invasive method for detecting various lung diseases.3 Moreover, the risk of medical staff exposure to COVID-19 is lower with sputum induction than with nasal or throat swabs and bronchoalveolar lavage methods.

The diverse functional roles of the insula may emerge from its heavy connectivity to an extensive network of cortical and subcortical areas. Despite several previous attempts to investigate the hierarchical organization of the insula by applying the recently developed gradient approach to insula‐to‐whole brain connectivity data, little is known about whether and how there is variability across connectivity gradients of the insula to different cerebral systems. Resting‐state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel‐wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems. The connectivity gradients to the higher‐order transmodal associative systems, including the prefrontal, posterior parietal, temporal cortices, and limbic lobule, showed a ventroanterior‐dorsal axis across the insula; those to the lower‐order unimodal primary systems, including the motor, somatosensory, and occipital cortices, displayed radiating transitions from dorsoanterior toward both ventroanterior and dorsoposterior parts of the insula; the connectivity gradient to the subcortical nuclei exhibited an organization along the anterior–posterior axis of the insula. Apart from complementing and extending previous literature on the heterogeneous connectivity patterns of insula subregions, the presented framework may offer ample opportunities to refine our understanding of the role of the insula in many brain disorders. Resting‐state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel‐wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems.

In modern power systems, distributed generation (DG) clusters such as wind and solar resources are increasingly being integrated into active distribution networks through DG cluster control, which enhances the economic efficiency and adaptability of the DGs. However, cyber attacks on cyber–physical systems (CPS) may disable control links within the DG cluster, leading to the loss of control over slave DGs and resulting in power deficits, thereby threatening system stability. Existing CPS security assessment methods have limited capacity to capture cross-domain propagation effects caused by cyber attacks and lack a comprehensive evaluation framework from the attacker’s perspective. This paper establishes a CPS system model and control–communication framework and then analyzes the cyber–physical interaction characteristics under DG cluster control. A logical model of cyber attack strategies targeting DG cluster inverters is proposed. Based on the control topology and master–slave logic, a probabilistic failure model for DG cluster control is developed. By considering power deficits at cluster point of common coupling (PCC) and results in internal network of the DG cluster, a physical consequence quantification method is introduced. Finally, a cyber risk assessment method is proposed for DG cluster control under cyber attacks. Simulation results validate the effectiveness of the proposed method.

Neuroimaging studies have documented brain structural changes in schizophrenia at different stages of the illness, including clinical high-risk (cHR), genetic high-risk (gHR), first-episode schizophrenia (FES), and chronic schizophrenia (ChS). There is growing awareness that neuropathological processes associated with a disease fail to map to a specific brain region but do map to a specific brain network. We sought to investigate brain structural damage networks across different stages of schizophrenia. We initially identified gray matter alterations in 523 cHR, 855 gHR, 2162 FES, and 2640 ChS individuals relative to 6963 healthy controls. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to four specific networks. Brain structural damage networks of cHR and gHR had limited and non-overlapping spatial distributions, with the former mainly involving the frontoparietal network and the latter principally implicating the subcortical network, indicative of distinct neuropathological mechanisms underlying cHR and gHR. By contrast, brain structural damage networks of FES and ChS manifested as similar patterns of widespread brain areas predominantly involving the somatomotor, ventral attention, and subcortical networks, suggesting an emergence of more prominent brain structural abnormalities with illness onset that have trait-like stability over time. Our findings may not only provide a refined picture of schizophrenia neuropathology from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for individuals at different schizophrenia stages.

Feature matching is pivotal when using multi-view stereo (MVS) to reconstruct dense 3D models from calibrated images. This paper proposes PAC-MVSNet, which integrates perspective-aware convolution (PAC) and metadata-enhanced cost volumes to address the challenges in reflective and texture-less regions. PAC dynamically aligns convolutional kernels with scene perspective lines, while the use of metadata (e.g., camera pose distance) enables geometric reasoning during cost aggregation. In PAC-MVSNet, we introduce feature matching with long-range tracking that utilizes both internal and external focuses to integrate extensive contextual data within individual images as well as across multiple images. To enhance the performance of the feature matching with long-range tracking, we also propose a perspective-aware convolution module that directs the convolutional kernel to capture features along the perspective lines. This enables the module to extract perspective-aware features from images, improving the feature matching. Finally, we crafted a specific 2D CNN that fuses image priors, thereby integrating keyframes and geometric metadata within the cost volume to evaluate depth planes. Our method represents the first attempt to embed the existing physical model knowledge into a network for completing MVS tasks, which achieved optimal performance using multiple benchmark datasets.

Background Excessive microglial activation is implicated in the pathogenesis of various age-related neurodegenerative diseases. In addition to neurons, brain-derived neurotrophic factor (BDNF) and its receptor TrkB are also expressed in microglia. However, the direct effect of BDNF on age-related microglial activation has rarely been investigated. Methods We began to address this question by examining the effect of age on microglial activation and the BDNF-TrkB pathway in mice. By using pharmacological and genetic approaches, the roles of BDNF and downstream signaling pathways in microglial activation and related neurotoxicity were examined in microglial cell line and primary microglial cells. Results We showed that microglial activation was evident in the brains of aged mice. The levels of BDNF and TrkB in microglia decreased with age and negatively correlated with their activation statuses in mice during aging. Interestingly, aging-related microglial activation could be reversed by chronic, subcutaneous perfusion of BDNF. Peripheral lipopolysaccharide (LPS) injection-induced microglial activation could be reduced by local supplement of BDNF, while shTrkB induced local microglial activation in naïve mice. In cultured microglial cell line and primary microglial cells, BDNF inhibited LPS-induced microglial activation, including morphological changes, activations of p38, JNK, and NF-кB, and productions of proinflammatory cytokines. These effects were blocked by shTrkB. BDNF induced activations of ErK and CREB which then competed with LPS-induced activation of NF-кB for binding to a common coactivator, CREB-binding protein. Conclusions Decreasing BDNF-TrkB signaling during aging favors microglial activation, while upregulation BDNF signaling inhibits microglial activation via the TrkB-Erk-CREB pathway.

Hydrogels have drawn considerable attention in the field of flexible chemical sensors due to their unique 3D structure, high permeability, ion‐conductivity, and tissue‐like mechanical properties. These structures and properties allow them to be functionalized into diverse sensing components and respond to chemical signals in complex environments. Herein, an overview of functional hydrogel‐based flexible chemical sensors is provided. First, the representative hydrogel materials are introduced and the operating principles for flexible chemical sensors are discussed. Then, state‐of‐the‐art functional hydrogel‐based flexible chemical sensing applications are highlighted including gas sensors, humidity sensors, pH sensors, glucose sensors, wound monitoring, and others. Finally, major challenges and opportunities in this field are provided.

Porous noble metal nanomaterials have attracted extensive attention due to their high specific surface area and surface plasmon resonance effect. However, it is difficult to form porous structures due to the high mobility and low reduction potential of noble metal precursors. In this article, we developed a facile method for preparing porous Ag with a controllable structure at room temperature. Two kinds of Ag crystals with different porous structures were successfully prepared by using AgCl cubes as sacrificial templates. Through the galvanic replacement reaction of Zn and AgCl, Ag crystals with a sponge-like porous structure were successfully prepared. Additionally, using NaBH4 as the reducing agent, we prepared granular porous Ag cubes by optimizing the amount of reducing agent. Both the sponge-like and granular porous Ag cubes have clean and accessible surfaces. In addition, we used the prepared two porous Ag cubes as substrate materials for SERS detection of five kinds of methamphetamine analogs. The experimental results show that the enhancement effect of granular porous Ag is better than that of sponge-like porous Ag. Furthermore, we probed the hot spot distribution of granular porous Ag by Raman mapping. By using granular porous Ag as the substrate material, we have achieved trace detection of 5 kinds of methamphetamine analogs including Ephedrine, Amphetamine, N-Methyl-1-(benzofuran-5-yl)propan-2-amine (5-MAPB), N-Methyl-1-(4-methoxyphenyl)propan-2-amine (PMMA) and N-Methyl-1-(4-fluorophenyl)propan-2-amine (4-FMA). Furthermore, to achieve qualitative differentiation of analogs with similar structures we performed density functional theoretical (DFT) calculations on the Raman spectra of the above analogs. The DFT calculations provided the vibrational frequencies, Raman activities, and normal mode assignment for each analog, enabling the qualitative differentiation of the above analogs.