Reconstructing 3D models from a single image remains challenges in computer graphics and vision, especially when dealing with free-hand sketches. Fragmented strokes and distorted lines often introduce ambiguities, leading to 3D models that deviate from the intended shape. Moreover, variations in sketching styles frequently result in incomplete object representations. To address the above challenges, we present the sketch-orientated Autoencoder SkFC-AE for high-quality 3D voxelized model reconstruction. Our approach features a Dual-space Feature Encoding mechanism, which extracts sketch semantics from both image space and geometric space using three encoders. To explore the details and common appearance of sketches, this study specifically proposes two additional modules , Feature Fusion Module (FFM) and Feature Complement Module (FCM), to fuse sketch features to create detailed embeddings and complement them with common embeddings derived from the sketch prior. Extensive experiments on two public sketch-to-voxel benchmarks, i.e., Sketch-Voxel ShapeNet and ModelNet-Canny, demonstrate that our SkFC-AE model significantly outperforms state-of-the-art models in terms of 3D reconstruction quality and detail preservation. Our code can be found at this link.

Enhancing carbon productivity is fundamental to achieving carbon neutrality while sustaining economic growth. Utilizing a comprehensive dataset of Chinese cities from 2010, 2015, and 2020, this study investigates the spatiotemporal patterns and underlying drivers of urban carbon productivity (UCP). Methods including kernel density estimation, spatial autocorrelation analysis, and the spatial Durbin model (SDM) are employed. The results reveal that China's UCP has improved significantly overall, yet with increasing internal disparities among cities. The SDM decomposition indicates a fundamental shift in driving mechanisms. Green technological innovation has supplanted generalized R&D expenditure as the most dependable core driving force for improving local carbon productivity. Moreover, the economic development level also exerts positive spatial spillover effects in the later stage, which jointly contribute to the formation of a multi-centered pattern. Urban form metrics exert dual influences: urban compactness (ENN_MN) shows a stable positive local effect, whereas urban fragmentation (PD) and urban sprawl (CONTAG) exhibit a paradoxical "local inhibition-neighborhood promotion" effect, highlighting intricate inter-city spatial interactions. The findings underscore the necessity for differentiated local practices, namely, policy must target differentiated city roles and manage spatial spillovers for synergistic regional green and sustainable transition.

Urban agglomerations increasingly confront disaster risks that overwhelm individual cities' reserve capacities. Collaborative emergency reserves offer a potential remedy but also introduce coordination burdens and distributional concerns. To address this, this study formulates a multi-objective optimization problem that integrates economic utility with perceived fairness. The model explicitly accounts for residents' shortage and delay experiences alongside managers' perceptions of coordination costs, reserve burdens, and cross-city disparities. It is solved using an enhanced multi-objective particle swarm optimization algorithm coupled with the ε-constraint method and driven by Monte Carlo disaster scenario generation. A numerical illustration based on 21 mainland prefecture-level cities in Guangdong Province evaluates the framework under heterogeneous risk, budget, and coordination conditions. Sobol's global sensitivity analysis identifies regional budget, inter-city collaboration experience, disaster intensity, and nonlinear perceived-loss parameters as dominant drivers of collaborative advantage, whereas transportation costs and standalone city resilience exert weaker effects. The results demonstrate that collaboration generally outperforms independent reserves, especially under severe disasters and fiscal constraints. This advantage diminishes when disasters are widespread or when transaction and coordination costs are prohibitive. Distributional stress tests further reveal that resource-abundant hub cities may perceive lower fairness when acting primarily as providers yet stand to gain substantially when severe shocks occur locally. This research provides a decision-making framework for jointly evaluating efficiency and perceived fairness. It highlights mutual-aid agreements, fiscal compensation, contribution-credit mechanisms, and joint exercises as institutional complements to optimized reserve strategies.

Industry market risk, a critical systemic risk component, requires precise measurement and timely warnings. This study analyzes risk spillovers among ten Chinese industries post-2008, integrating historical patterns and future risk trends. Using elastic net combined with generalized variance decomposition, we quantify static and dynamic risk spillover networks and transmission under exogenous shocks. Advancing existing methods, we develop a CNN-based model that outperforms alternatives in accuracy and stability for early warnings. Key findings: 1) Consumer Discretionary, IT, and Raw Materials are primary risk sources, while Financial and Real Estate absorb most risks; 2) Domestic events amplify cross-industry spillovers; 3) The CNN system effectively predicts aggregate and sector-specific risks. This framework equips regulators to prioritize prevention and enables investors to adjust portfolios dynamically. Our work bridges systemic risk measurement and forecasting, offering actionable strategies for managing interconnected market risks.

Urban digital development plays a pivotal role in achieving carbon mitigation and advancing environmental sustainability. While existing studies have begun to examine the relationship between digitalization and carbon dioxide emissions, discussions regarding how urban digitalization influences carbon dioxide emissions under multi-dimensional conditions remain limited. In particular, there is a need for further exploration of specific dimensions such as digital governance, digital innovation, digital industrialization, digital infrastructure, and digital finance. Therefore, this study employs the Spatial Durbin Model (SDM) and Geographically Weighted Regression (GWR) to investigate the spatial effects of urban digitalization on carbon dioxide emissions. The analysis utilizes cross-sectional data from 245 prefecture-level cities in China for the years 2015 and 2020. The results indicate the following: (1) In comparison to 2015, the influence of urban digitalization on carbon dioxide emission reduction weakened by 2020 and exhibited a diffusion effect toward surrounding regions, demonstrating a spatial distribution trend that decreases from north to south; (2) The internal dimensions of urban digitalization, such as digital governance, innovation, infrastructure, industrialization, and finance, exhibit geographical spatial heterogeneity in their impact on carbon dioxide emissions. (3) Digital governance has exhibited a transformative impact on carbon dioxide emissions. In 2015, digital governance was associated with an increase in local carbon dioxide emissions, yet its spatial spillover effects were limited. By contrast, in 2020, while its influence on local emissions became statistically insignificant, it demonstrated a substantial carbon reduction effect in neighboring regions. (4) Digital innovation has consistently played a critical role in reducing carbon dioxide emissions. However, compared to 2015, the overall effectiveness of digital innovation in mitigating carbon dioxide emissions has shown signs of weakening, both locally and in adjacent areas. The results emphasize the pivotal role of digital transformation in mitigating carbon dioxide emissions, thereby providing a robust empirical foundation for the development of low-carbon cities.

Cities serve as the primary arenas for achieving the strategic objectives of “carbon peak and carbon neutrality”. This study employed the LMDI method to systematically analyze the evolution trend of energy-related carbon emissions in Hong Kong and their influencing factors from 1980 to 2023. The main findings are as follows: (1) Hong Kong’s energy consumption structure remains dominated by coal and oil. Influenced by energy prices, significant shifts in this structure occurred across different periods. Imported electricity from mainland China, in particular, has exerted a promoting effect on the optimization of its energy consumption mix. (2) Economic output and population concentration are the primary drivers of increased carbon emissions. However, the contribution of economic growth to carbon emissions has gradually weakened in recent years due to a lack of new growth drivers. (3) Energy consumption intensity, energy consumption structure, and carbon intensity are the primary influencing factors in curbing carbon emissions. Among these, the carbon reduction impact of energy consumption intensity is the most significant. Hong Kong should continue to adopt a robust strategy for controlling total energy consumption to effectively mitigate carbon emissions. Additionally, it should remain vigilant regarding the potential implications of future energy price fluctuations. It is also essential to sustain cross-border energy cooperation, primarily based on electricity imports from the Pearl River Delta, while simultaneously expanding international and domestic supply channels for natural gas.

Oxidative stress that is induced by excessive reactive oxygen species (ROS) is considered to be a key pathophysiological mechanism of inflammatory bowel disease (IBD), and restoring redox homeostasis in the inflammatory region by eliminating ROS is an effective way to treat IBD. Herein, ultrasmall Au25 nanoclusters (Au25 NCs) were synthesized using a simple improved protocol, which has good physiological stability and biosafety and can be noninvasively monitored by clinical computed tomography (CT) after oral administration. Au25 NCs can eliminate ROS such as ABTS radicals, superoxide free radicals (•O2−), and hydroxyl free radicals (•OH), upregulate the expression level of antioxidant enzymes, inhibit the expression of proinflammatory cytokines, and finally interrupt the inflammatory circuit of IBD to achieve the effective prevention and delayed treatment of IBD. This work will demonstrate the protective effect of Au25 NCs on IBD in living animals, which suggests a new nanomedicine strategy for IBD treatment.

Liver ischemia-reperfusion injury (LIRI) is an inevitable detrimental event after liver transplantation. The MAS receptor plays a protective role in various diseases. However, the specific roles of MAS in myeloid cell innate immunity and the maintenance of hepatic tissue homeostasis remain unclear. Here, we showed that mice with systemic, Kupffer cell-specific, or myeloid cell-specific Mas1 deficiency were vulnerable to LIRI. Single-cell RNA sequencing, spatial transcriptomics, and intravital imaging revealed that myeloid deficiency of Mas1 resulted in impaired macrophage efferocytosis by down-regulating MER tyrosine kinase (MERTK), leading to the accumulation of aged neutrophils and exacerbation of inflammation and pathology. Mechanistic studies indicated that the MAS receptor regulated the Krüppel-like factor 4 (KLF4)/MERTK axis in macrophages via the protein kinase A (PKA)/cAMP response element-binding protein (CREB) signaling pathway. KLF4 directly bound to the promoter region of MERTK and transcriptionally promoted its expression in macrophages, leading to attenuation of the liver inflammatory response. Macrophage-specific knockout of KLF4 and MERTK in the mice also resulted in impaired macrophage efferocytosis with the accumulation of aged neutrophils. Macrophage-specific overexpression of KLF4 in vivo effectively reversed the phenotype exacerbated by myeloid Mas1 deficiency. In addition, we demonstrated that MAS+MERTK+ macrophages actively migrated toward aged neutrophils in ischemia-stressed human livers, thereby promptly clearing aged neutrophils. In summary, this study documented the regulatory function of the MAS/KLF4/MERTK axis in macrophage efferocytosis via PKA/CREB signaling. This axis may thus serve as a therapeutic target and checkpoint regulator of homeostasis in response to LIRI.

When industries face systemic shocks, enterprise risks extend beyond the effects of isolated incidents and propagate through intricate competitive networks. Existing research predominantly addresses risk spillovers initiated by isolated events, such as financial scandals and product recalls, while neglecting the risk spillover effects associated with firms embedded in multi-market competitive networks. This study undertakes an empirical analysis within the context of China's electronic information manufacturing industry. The findings reveal that the broader a firm's product market competition boundary, the more pronounced the peer risk spillover effects it encounters. Furthermore, the degree of product diversification and analyst coverage positively moderate the relationship between competition boundaries and risk spillovers. Firms positioned downstream in the supply chain, those with lower corporate social responsibility performance, and non-state-owned enterprises experience more significant impacts of competition boundary breadth on risk spillovers. This study conceptualizes the boundaries of product market competition as a crucial lens for comprehending industry risk spillovers, thereby advancing risk spillover research from static event analysis to dynamic network analysis. The findings indicate that, in the context of systemic shocks, expansive competition boundaries exacerbate risk contagion through two primary channels: direct business linkages and indirect cognitive biases. This study offers a novel decision-making framework for firms to navigate the balance between market opportunities and risk exposure within intricate competitive environments.

Macrophage-mediated inflammation has been implicated in the pathogenesis of metabolic dysfunction-associated steatohepatitis (MASH); however, the immunometabolic program underlying the regulation of macrophage activation remains unclear. Beta-arrestin 2, a multifunctional adaptor protein, is highly expressed in bone marrow tissues and macrophages and is involved in metabolism disorders. Here, we observed that β-arrestin 2 expression was significantly increased in the liver macrophages and circulating monocytes of patients with MASH compared with healthy controls and positively correlated with the severity of metabolic dysfunction-associated steatotic liver disease (MASLD). Global or myeloid Arrb2 deficiency prevented the development of MASH in mice. Further study showed that β-arrestin 2 acted as an adaptor protein and promoted ubiquitination of immune responsive gene 1 (IRG1) to prevent increased itaconate production in macrophages, which resulted in enhanced succinate dehydrogenase activity, thereby promoting the release of mitochondrial reactive oxygen species and M1 polarization. Myeloid β-arrestin 2 depletion may be a potential approach for MASH.

Sketch-guided point cloud reconstruction aims to provide an efficient and flexible pathway to generate plausible 3D shapes automatically from a free-hand sketch shaping the modeling intentions of end users. However, such a task is still in its infancy due to the complex, challenging, and highly variable patterns of sketches by nature. In this paper, we present a novel sketch-guided framework based on free-form deformation (FFD) for 3D point cloud generation. To capture sufficient meaningful features from a sketch, a dual-branch encoding architecture is devised to extract complementary semantic and geometric clues by formulating the input as a binary image and a 2D point cloud, respectively. The proposed encoder also learns useful features to guide content generation from a template point cloud before decoding the resultant global features into a set of control points for the use of FFD. We have also developed a large and diverse manually collected dataset, Sketch-3DPC, in which there are a total of 13,754 sketch and 3D point cloud pairs categorized into 11 classes. Both qualitative and quantitative experiment results demonstrate the superiority of the proposed methodology and dataset.

Nowadays, accurate and efficient short-term traffic flow forecasting plays a critical role in intelligent transportation systems (ITS). However, due to the fact that traffic flow is susceptible to factors such as weather and road conditions, traffic flow data tend to exhibit dynamic uncertainty and nonlinearity, making the construction of a robust and reliable forecasting model still a challenging task. Aiming at this nonlinear and complex traffic flow forecasting problem, this paper constructs a short-term traffic flow forecasting hybrid optimization model, SSA-ELM, based on extreme learning machine by embedding the sparrow search algorithm in order to solve the above problem. Extreme learning machine has been widely used in short-term traffic flow forecasting due to its characteristics such as low computational complexity and fast learning speed. By using the sparrow search algorithm to optimize the input weight values and hidden layer deviations in the extreme learning machine, the sparrow search algorithm is utilized to search for the global optimal solution while taking into account the original characteristics of the extreme learning machine, so that the model improves stability while increasing prediction accuracy. Experimental results on the Amsterdam A10 road traffic flow dataset show that the traffic flow forecasting model proposed in this paper has higher forecasting accuracy and stability, revealing the potential of hybrid optimization models in the field of short-term traffic flow forecasting.

Background: Hepatic inflammatory and fibrotic lesions promote the development of cirrhosis and hepatocellular carcinoma in patients with chronic Hepatitis B (CHB). Early recognition of hepatic histopathological changes and timely initiation of antiviral therapy can delay or even reverse disease progression. Objectives: This study aimed to analyze non-invasive diagnostic indicators associated with different inflammation grades, fibrosis stages, and Hepatitis degrees in CHB patients, and their outcomes after antiviral therapy. Methods: A total of 91 CHB patients treated at the Third People's Hospital of Shenzhen from January 2016 to December 2019 were selected for inflammation grading (G) and fibrosis staging (S) based on liver puncture examination results. The patients were further divided into mild, moderate, and severe chronic Hepatitis groups. Correlation analysis was conducted via Spearman. The diagnostic performance of the relevant indexes for inflammation grading, fibrosis staging, and Hepatitis degree grading was evaluated using receiver operator characteristics (ROC). The performance of different ROC curves was further compared using the DeLong test. The effects of antiviral drugs on patients with different liver histopathological degrees were comparatively analyzed after 24, 48, 72, and 96 weeks. Results: Data analysis at baseline showed that 86.81% (79 of 91) of all patients were male. Additionally, about 61.54% (16 of 26), 30.77% (8 of 26), 85.19% (23 of 27), and 62.96% (17 of 27) of patients with normal ALT had G ≥ 2, G ≥ 3, S ≥ 2, and S ≥ 3, respectively. Inflammation grade, fibrosis stage, and Hepatitis degree were positively correlated with portal vein internal diameter, spleen thickness, LN, GPR, FIB-4, and S-index, and negatively correlated with PLT (P < 0.05). The area under the curve (AUC) of the ROC-assessed multifactorial combinations PSBPTL, PSWPAHPCL, and PSWPHCL for predicting the risk of developing G ≥ 3 inflammation, S ≥ 3 fibrosis, and moderate-to-severe Hepatitis in patients with CHB were 0.806, 0.843, and 0.778, respectively. The diagnostic accuracies were higher than some of these factors applied individually and some commonly used serological markers. HBV DNA levels were significantly lower in different Hepatitis groups after 24 weeks of antiviral therapy than before treatment (P < 0.05). Furthermore, the ALT normalization rate and HBV DNA clearance rate were slightly higher in the moderate and severe groups than in the mild group after 48 weeks of treatment (P > 0.05). The serum Hepatitis B envelope antigen (HBeAg) level was significantly lower in the severe group than in the mild group after 72 weeks of antiviral therapy (H = 7.043, P = 0.030). Although only the HBeAg serologic conversion rate was significantly different at 96 weeks among the three groups (χ2 = 12.389, P = 0.002), HBeAg-negative and the serologic conversion rates were higher in the severe group at each time point than in the mild and moderate groups. Conclusions: Multiple non-invasive indicators are strongly associated with different degrees of liver histopathology in patients with chronic HBV infection. Therefore, PSBPTL, PSWPAHPCL, and PSWPHCL can be used to predict the risk of developing G ≥ 3 inflammation, S ≥ 3 fibrosis, and moderate-to-severe Hepatitis in these patients, respectively. Moreover, short-term antiviral therapy has a more pronounced effect on patients with severe Hepatitis by improving hepatic inflammation and inhibiting viral replication.

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods.

In the realm of addressing large-scale emergencies, interorganizational collaboration emerges as a crucial strategy for effectively mitigating crises. However, the existence of institutional and procedural disparities poses significant challenges in establishing collaborative networks among diverse organizations. Moreover, the determinants influencing cooperative formations exhibit heterogeneity across distinct network structures. This research employs proximity theory and the Exponential Random Graph Model (ERGM) to comprehensively examine the dynamic evolution of interorganizational collaboration networks. Our primary objective is to investigate the effects of institutional proximity, organizational proximity, geographical proximity and social proximity on the formation of interorganizational collaboration networks. Textual data was meticulously collected from the Wisers database and official websites. The findings underscore the pivotal roles of institutional and organizational proximity in fostering interorganizational collaboration networks, while the impact of geographical proximity is relatively insignificant. Additionally, we delve into the driving factors that influence the formation of diverse structural networks. Social proximity emerges as a significant catalyst for horizontal collaborative networks but impedes the establishment of vertical collaborative relationships. Furthermore, we partition the entire emergency response process into six stages to explore the dynamic evolution characteristics of these networks. The results reveal an overall decentralized structure with a tendency toward clustering in the collaborative network. These findings hold substantial significance as they elucidate the influential factors driving the formation of diverse collaborative networks in a dynamic context. Therefore, this research bears implications for policymakers and practitioners aiming to enhance their emergency response capabilities with optimal efficiency.

As one of the leading cases of acute liver failure triggered by excessive Acetaminophen (APAP), breakdown of the antioxidant system, inflammatory response, and inescapable apoptosis following overaccumulation of reactive oxygen species (ROS) play crucial roles in the mechanisms of APAP-induced liver injury (AILI). Therefore, cutting off ROS overproduction at the source is considered promising. Here, manganese Prussian blue nanozymes (MPBZs) with superior antioxidant enzyme-like activity are prepared as an effective strategy for hepatocyte protection, in which MPBZs accumulated in the liver show anti-oxidation properties by scavenging superfluous ROS. Importantly, in addition to alleviating oxidative stress, bioactive MPBZs with abundant variable valence states as a natural antioxidant enzymes mediated the responses of multi-biological signaling pathways in vitro and in vivo, including Nrf2-Keap1, NF-κB, and mitochondrial-induced apoptosis signaling pathways, enhancing tolerance for imminent AILI. Taking nanomedicine, hepatology, and catalytic chemistry into consideration, the revealed superior performance of AILI prevention suggests that MPBZ-based nano-detoxification therapy may offer an effective alternative against AILI.

In recent years, governments have successively introduced a variety of policies to promote the development of the autonomous vehicle (AV) industry and technology. Existing research sheds light on individuals' decision process regarding their willingness to purchase AVs or legislation and policy design from the perspective of policymakers, regulators and experts. Little research has been conducted to explore what incentive policies consumers demand and how the policies affect their purchase intentions. In this study, a decision model of AV purchase intentions is proposed through a combination of the planned risk information seeking model (PRISM) and innovation diffusion theory (IDT), and the decision model is utilized to explore the mechanism of the influence of incentive policies on purchase intentions. The early stage of AV commercialization is the research background, and potential AV consumers are the research object. A national survey was conducted in China, and the empirical results indicate that consumers show higher risk perceptions of AVs and that they are most concerned about the unclear risk involved after the occurrence of traffic accidents by unattended cars. The perceived attractiveness of policies such as tax subsidy policies, insurance policies, and commute policies are all significantly and positively correlated with purchase intentions toward AVs. Rational industrial policies have significant effects on mitigating consumer risk perceptions and stimulating purchasing intentions.

Background:Since boosting stem cell resilience in stressful environments is critical for the therapeutic efficacy of stem cell-based transplantations in liver disease, this study aimed to establish the efficacy of a transient plasmid-based preconditioning strategy for boosting the capability of mesenchymal stromal cells (MSCs) for anti-inflammation/antioxidant defenses and paracrine actions in recipient hepatocytes.Methods:Human adipose mesenchymal stem cells (hADMSCs) were subjected to transfer, either with or without the nuclear factor erythroid 2-related factor 2 (Nrf2)/Dickkopf1 (DKK1) genes, followed by exposure to TNF-α/H2O2. Mouse models were subjected to acute chronic liver failure (ACLF) and subsequently injected with either transfected or untransfected MSCs. These hADMSCs and ACLF mouse models were used to investigate the interaction between Nrf2/DKK1 and the hepatocyte receptor cytoskeleton-associated protein 4 (CKAP4).Results:Activation of Nrf2 and DKK1 enhanced the anti-stress capacity of MSCs in vitro. In a murine model of ACLF, transient co-overexpression of Nrf2 and DKK1 via plasmid transfection improved MSC resilience against inflammatory and oxidative assaults, boosted MSC transplantation efficacy, and promoted recipient liver regeneration due to a shift from the activation of the anti-regenerative IFN-γ/STAT1 pathway to the pro-regenerative IL-6/STAT3 pathway in the liver. Importantly, the therapeutic benefits of MSC transplantation were nullified when the receptor CKAP4, which interacts with DKK1, was specifically removed from recipient hepatocytes. However, the removal of the another receptor low-density lipoprotein receptor-related protein 6 (LRP6) had no impact on the effectiveness of MSC transplantation. Moreover, in long-term observations, no tumorigenicity was detected in mice following transplantation of transiently preconditioned MSCs.Conclusions:Co-stimulation with Nrf2/DKK1 safely improved the efficacy of human MSC-based therapies in murine models of ACLF through CKAP4-dependent paracrine mechanisms.

Background and aim: Both chronic hepatitis B (CHB) and non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH) are the most common liver diseases over the world, but their underlying pathological mechanisms and interrelations are poorly understood. Methods: Histological analysis and NLRP3 protein expression were performed on 130 CHB patients with liver-biopsied. Wild-type or NLRP3 knockdown hepatitis B virus (HBV) -transgenic mice were fed with high-fat diet to induce steatosis, with or without co-administration with a novel NLRP3 inhibitor MCC950. Results: Hepatic NLRP3 inflammasome is markedly up-regulated in the CHB, NASH, superimposed NASH with CHB patients and their corresponding model mice. Hepatic knock-down of NLRP3 significantly inhibits HBV replication and surface antigen expression, as well as ameliorates NASH typical symptoms of HBV transgenic mice with or without high-fat diet consumption. In addition, administration of MCC950 successfully inhibits pathological features of both CHB and steatosis-induced liver damage without detectable adverse effects. Conclusions: NLRP3 inflammasome is activated during the progression of both CHB and NASH and may play a critical role in their pathogenesis by regulating hepatic inflammation. Targeting this protein platform may represent an effective and novel strategy for the treatment of CHB, NASH and the superimposed patients. (c) 2022 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Shear layers separating from opposite sides of a bluff body are inherently connected with near wake flow structures; interfering one of the shear layers may lead to dramatic changes in the near wake flow and fluid forces on the bluff body. Being motivated by this, we investigate the square cylinder flow disturbed by a synthetic jet at one leading edge of the cylinder. Large-eddy simulations are conducted at a Reynolds number Re = 5.0 × 103. The synthetic jet is driven by a sine function with frequency fj = 0–1.65 fo and magnitude Vj,o = 0–1.0 Uo (corresponding to momentum coefficient Cμ = 0–1.01%), where Uo is the free stream velocity, and fo is the dominant vortex-shedding frequency of the uncontrolled flow. The results indicate a strong dependence of fluid forces and flow structures on Cμ and fj. Time-mean drag (C¯d) and fluctuating lift (Cl,rms) are significantly reduced at high fj (>1.21 fo) and Cμ (>0.25%), compared to those of the uncontrolled flow; the maximum reductions in C¯d and Cl,rms are up to 39% and 33%, respectively, at the highest fj = 1.65 fo and Cμ = 1.01% considered presently. Modifications of the near wake flows by the synthetic jet perturbations of different frequencies are discussed based on instantaneous, time-mean, and phased-averaged results. A high efficiency is attained by the present control strategy.