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Microwave deicing is an efficient and environment-friendly technology for asphalt pavement. Previous studies have shown that silicon carbide can improve the deicing efficiency of pavement, but the influence of drastic temperature change during microwave deicing on the temperature stress and temperature fatigue life is undefined. In this study, the temperature stress during heating and cooling were analysed by numerical simulation, and the mechanism of stress change was discussed. Finally, the temperature fatigue performance of pavement was evaluated. The simulation results showed that an increase in the temperature rising rate did not necessarily increase the temperature stress, and there was no stress accumulation after microwave deicing. The temperature stress was affected by both the temperature change rate and the heat transfer performance, and good heat transfer performance of asphalt mixture could reduce the temperature stress. Besides, the drastic temperature change did not necessarily reduce the temperature fatigue life of pavement.

ABSTRACTMyasthenia gravis is a major disease in the context of an ageing society, and the discovery of effective herbal compound and herbal active ingredients is a highly promising direction for the treatment of myasthenia gravis. In this study, we selected shujiao, dried ginger and ginseng from the compound ingredients through a network pathology approach. The three ingredients were used to obtain drug targets in Traditional Chinese Medicine Systems Pharmacology (TCMSP), HERB and BATMAN-TCM data and intersected with the disease targets of myasthenia gravis. The resulting regulatory network maps were then used to identify core genes through the String database, and finally the core genes were molecularly aligned with the corresponding active ingredients using Autodock vina software. The ‘herbal-component-target’ regulatory network of the Chinese herbal formulae was constructed, which is important for finding the potential molecular mechanism for the treatment of myasthenia gravis. It will provide a theoretical basis for the therapeutic and clinical research of myasthenia gravis.

Introduction:Acute kidney injury (AKI) is a common clinical disease, especially in the intensive care unit. Identification of reliable biomarker is of great clinical significance and benefit the therapy and prevention of AKI. The clinical significance and function of miR-874-3p in AKI development were evaluated in this study aiming to explore a novel biomarker for AKI. Methods:There were 83 AKI patients and 56 healthy individuals included, and the serum samples were collected. The AKI animal models were established via ischemic/reperfusion (I/R) and LPS on C57BL/6 mice. The expression of miR-874-3p was evaluated using PCR, while the potential downstream targets were also validated in AKI mice. The regulatory mechanism of miR-874-3p was investigated in AKI cell model established with HK-2 cell by I/R. Results:miR-874-3p was downregulated in both AKI patients and established AKI mice models. The downregulation of miR-874-3p could discriminate against AKI patients and predict poor prognosis of patients. miR-874-3p was negatively correlated with the levels of serum creatine, blood urea nitrogen, CRP, NEU, and PCT and positively correlated with the eGFR of AKI patients. In I/R- and LPS-induced AKI mice, overexpressing miR-874-3p could alleviate renal dysfunction, oxidative stress, and inflammation induced by AKI. Additionally, miR-874-3p could negatively regulate the expression of MSRB3, which was speculated as the potential mechanism underlying the function of miR-874-3p in AKI. Overexpression of miR-874-3p could alleviate the I/R-induced HK-2 cell apoptosis and decreased proliferation, which was reversed by the upregulation of MSRB3. Conclusion:miR-874-3p served as a diagnostic and prognostic biomarker of AKI and mediate the severity and development of AKI via targeting MSRB3.

The aluminum alloy components on automobile often experience high strain rate deformation during accidents. The dynamic deformation behavior and constitutive model are crucial for analysis of vehicle body collisions and failures. In this work, the 7A75 aluminum alloy plate was heat treated according to standard body manufacturing procedures. Quasi-static and dynamic tensile experiments were conducted on the aged 7A75 alloy in a wide strain rate range from 0.001 s−1to 500 s−1. The dynamic deformation behavior of the alloy was revealed, and the constitutive models were established. It was found that the 7A75 alloy has strong strain rate sensitivity, and its mechanical properties can be significantly improved with the increase of strain rate. As the strain rate increases, the necking instability strain shifts towards higher values, which leads to a larger uniform deformation ability and fracture elongation. This behavior results in a better energy absorption capacity at high strain rates, thus providing a good collision protection property. The constitutive models, including Johnson-Cook (J-C) and Zerilli-Armstrong (Z-A) types, were established and compared. Note that the conventional J-C and Z-A models exhibit severe prediction distortion. Therefore, a modified J-C model incorporating strain rate compensation was introduced, which decrease the errors by 149% and achieves accurate predictions of both dynamic and static deformation behaviors over a wide range of strain rates.

A dual-band circular polarized antenna with broad bandwidth is proposed. Two circular grooves with different radii are carved on the ground to generate two frequency bands. Two rectangular slots are engraved on the outside of the circular groove. By adjusting the length of the two slots, this antenna can radiate right-handed circularly polarized (RHCP) waves at two frequency bands, which can cover GPS L5 (1.176 GHz), L2 (1.227 GHz), L1 (1.575 GHz), BDS, GALILEO, and GLONASS bands. The measured −10 dB bandwidth is 46.3 % (1.13–1.81 GHz) and 3 dB axial ratio bandwidths are 15.6 % (1.11–1.30 GHz) and 17.8 % (1.38–1.65 GHz). The measured gains are 2.51 dBi and 2.67 dBi at 1.2 GHz and 1.56 GHz, respectively.

• The addition of Fe-based amorphous alloys alters element distribution and solidification crystallization process. • The transformation of grain morphology plays a crucial role in crack healing. • Element precipitation has a positive effect on crack healing and strength improvement. • The strength and plasticity of high-entropy alloy are improved simultaneously after adding Fe-based amorphous alloy. Solidification cracking issues during additive manufacturing (AM) severely prevent the rapid development and broad application of this method. In this work, a representative Co 34 Cr 32 Ni 27 Al 4 Ti 3 high-entropy alloy (HEA) susceptible to crack formation was fabricated by selective laser melting (SLM). As expected, many macroscopic cracks appeared. The crack issues were successfully solved after introducing a certain amount of Fe-based metallic glass (MG) powder as a glue during SLM. The effect of MG addition on the formation and distribution of defects in the SLM-processed HEA was quantitatively investigated. With an increasing mass fraction of the MG, the dominant defects transformed from cracks to lack of fusion (LOF) defects and finally disappeared. Intriguingly, the MG preferred to be segregated to the boundaries of the molten pool. Moreover, the coarse columnar crystals gradually transformed into equiaxed crystals in the molten pool and fine-equiaxed crystals at the edge of the molten pool, inhibiting the initiation of cracks and providing extra grain boundary strengthening. Furthermore, multiple precipitates are formed at the boundaries of cellular structures, which contribute significantly to strengthening. Compared to the brittle SLM-processed Co 34 Cr 32 Ni 27 Al 4 Ti 3 HEA, the SLM-processed HEA composite exhibited a high ultimate tensile strength greater than 1.4 Ga and enhanced elongation. This work demonstrates that adding Fe-based MG powders as glues into SLM-processed HEAs may be an attractive method to heal cracks and simultaneously enhance the mechanical properties of additively manufactured products. [ABSTRACT FROM AUTHOR]

Objectives: The process of measuring morphological parameters of the cervical spine through computed tomography (CT) is repetitive and time-consuming. Deep learning (DL) improves efficiency and consistency. In this study, we built a series of DL-based segmentation algorithms to automatically measure five key parameters for the evaluation of cervical spondylotic myelopathy. Subsequently, we compared the performance of our algorithm with that of physicians to assess its accuracy and clinical application value. Methods: Cervical spine CT images of 685 patients were divided into a training (n= 548) and a test set (n= 137). The training set was used to develop a VB-Net DL model, including a 3D segmentation model of multiple cervical spine subregions and a key point location model on the midsagittal slice of the cervical spine CT. The parameters measured included sagittal vertebral canal diameter (SCD), sagittal vertebral body diameter (VBD), Pavlov’s ratio, transverse vertebral canal diameter (TCD), and osseous spinal canal area (OSCA). Manual measurements were performed by a radiologist and a spinal surgeon. The model’s performance was evaluated using the Mann–Whitney Utest, Pearson correlation, mean absolute error, and Bland–Altman plots. Results: DL and manual measurements of the Pavlov’s ratio, SCD, VBD, TCD, and OSCA in the test set showed similar accuracy and consistency. The VB-Net model’s Pearson correlation coefficient exceeded 0.8 for most parameters. Conclusions: Our VB-Net-based DL approach can effectively approximate the manual measurements of cervical CT morphological parameters in humans, offing physicians an accurate and efficient auxiliary diagnostic tool.

Magnetic resonance angiography (MRA) contrast agents are extensively utilized in clinical practice due to their capability of improving the image resolution and sensitivity. However, the clinically approved MRA contrast agents have the disadvantages of a limited acquisition time window and high dose administration for effective imaging. Herein, albumin-coated gadolinium-based nanoparticles (BSA-Gd) were meticulously developed for in vivo ultrahigh-resolution MRA. Compared to Gd-DTPA, BSA-Gd exhibits a significantly higher longitudinal relaxivity (r1= 76.7 mM–1s–1), nearly 16-fold greater than that of Gd-DTPA, and an extended blood circulation time (t1/2= 40 min), enabling a dramatically enhanced high-resolution imaging of microvessels (sub-200 μm) and low dose imaging (about 1/16 that of Gd-DTPA). Furthermore, the clinically significant fine vessels were successfully mapped in large mammals, including a circle of Willis, kidney and liver vascular branches, tumor vessels, and differentiated arteries from veins using dynamic contrast-enhanced MRA BSA-Gd, and have superior imaging capability and biocompatibility, and their clinical applications hold substantial promise.

ABSTRACTThe VPS37Agene encodes a subunit of the endosomal sorting complex required for transport (ESCRT)-I complex that is frequently lost in a wide variety of human solid cancers. We have previously demonstrated the role of VPS37A in directing the ESCRT membrane scission machinery to seal the phagophore for autophagosome completion. Here, we report that VPS37A-deficient cells exhibit an accumulation of the apoptotic initiator CASP8 (caspase 8) on the phagophore and are primed to undergo rapid apoptosis through the intracellular death-inducing signaling complex (iDISC)-mediated CASP8 activation upon exposure to endoplasmic reticulum (ER) stress. Using CRISPR-Cas9 gene editing and comparative transcriptome analysis, we identified the ATF4-mediated stress response pathway as a crucial mediator to elicit iDISC-mediated apoptosis following the inhibition of autophagosome closure. Notably, ATF4-mediated iDISC activation occurred independently of the death receptor TNFRSF10B/DR5 upregulation but required the pro-apoptotic transcriptional factor DDIT3/CHOP to enhance the mitochondrial amplification pathway for full-activation of CASP8 in VPS37A-deficient cells stimulated with ER stress inducers. Our analysis also revealed the upregulation of NFKB/NF-kB signaling as a potential mechanism responsible for restraining iDISC activation and promoting cell survival upon VPS37Adepletion. These findings have important implications for the future development of new strategies to treat human cancers, especially those with VPS37Aloss.Abbreviations:ATG: autophagy related; BMS: BMS-345541; CASP: caspase; CHMP: charged multivesicular body protein; DKO: double knockout; Dox: doxycycline; ER: endoplasmic reticulum; ESCRT: endosomal sorting complex required for transport; gRNA: guide RNA; GSEA: gene set enrichment analysis; GSK157: GSK2656157; iDISC: intracellular death-inducing signaling complex; IKK: inhibitor of NFKB kinase; IPA: ingenuity pathway analysis; KO: knockout; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NFKB/NF-kB: nuclear factor kappa B; OZ: 5Z-7-oxozeaenol; RNA-seq: RNA sequencing; UPR: unfolded protein response; TFT: transcription factor target; THG: thapsigargin; TUN: tunicamycin; VPS: vacuolar protein sorting.

This article presents an energy-efficient single-ended transceiver featuring the proposed AC/DC-coupled pre-emphasis feed-forward equalizer (PE-FFE) and edge-dependent phase-modulation decision feedback equalizer (PM-DFE) for low-power memory controllers. Specifically: 1) on the transmitter (Tx) side, an AC/DC-coupled PE-FFE is implemented in a ground-terminated Tx to minimize the equalization (EQ) power and maximize the output swing; 2) on the receiver (Rx) side, an edge-dependent PM-DFE operating on the full-swing signal edges is proposed for time-domain EQ, which improves sampling margin and reduces the linear EQ requirement as well as the power consumption. The design, fabricated in 22-nm CMOS, achieves a data rate of 20 Gb/s/pin with a 41 mV increase in the Tx output eye height and a 0.12 UI increase in the Rx sampling margin over a channel with a 10.3 dB loss. Measurement results reveal an energy efficiency of 0.45 pJ/b and 0.38 pJ/b for the Tx and the Rx, respectively, and a figure-of-merit of 0.081 pJ/b/dB (Tx+Rx).

The ongoing process of decarbonizing the power system and the frequent occurrence of extreme weather have led to a significant increase in operating uncertainty and greatly reduced the system controllability. An effective risk assessment and early-warning tool will significantly assist system operators in monitoring and controlling power systems. However, existing methods mainly rely on simplified analytical conditional probability models to describe the component failure probability under different operating conditions and are not suitable for low-probability risk assessment. Inspired by the idea of Bayesian statistics, a Bayesian deep learning-based probabilistic risk assessment and early-warning model considering meteorological conditions is proposed in this article. A new Bayesian neural network (BNN) is proposed which efficiently utilizes expert experience and domain knowledge as prior to model the contingency probability. And a hybrid neural network is developed to rationally utilize the multisource heterogeneous data and comprehensively analyze the historical and forecast information. Finally, a novel risk assessment and early-warning model for high-impact, low-probability extreme events is proposed, which can predict the risk for the next n time-steps. The proposed method is tested on a modified IEEE 118-bus system and a real-world provincial grid, and the results verify its effectiveness.

Purpose This study aimed to explore sex differences in the association between emotional support and self-rated health among the elderly.Design This was a cross-sectional survey based on the sub-project of China’s National Basic Public Health Service Project—Health Management Services for the Elderly.Setting Participants were recruited from ten rural townships in Jingyuan County, Gansu Province, Northwestern China.Subjects 1405 subjects aged 60 or above.Methods Emotional support (consisting of 5 items) and self-rated health (evaluated by EQ-VAS) were investigated in this study. Multiple linear regression was conducted to consider the potential relationship.Results The frequency of children visit and the number of providers of emotional support were positively associated with self-rated health among older women (β = 1.13, 95%CI = 0.25-2.02; β = 1.80, 95%CI = 1.01-2.58), whereas the number of close friends had a positive association with self-rated health among older men (β = 1.11, 95%CI = 0.20-2.01). The number of close relatives and the frequency of seeking emotional support were not found to be associated with self-rated health among both older men and older women.Conclusion The study has found that the relationship between emotional support and self-rated health was differed by sex, calling attention to the need for sex-specific interventions.

ABSTRACT In view of the fact that the internal pore structure of electrolytic manganese residue based permeable brick (EMRB) is as difficult to observe as 'black box', which is not [...]

•Two leukemic evolutionary patterns, “clonal shift” and “clonal drift” are unveiled in relapsed T-ALL via single-cell multiomics profiling.•High RNA-binding protein MSI2 level accounts for persistent clones at relapse through the posttranscriptional regulation of MYC in T-ALL.

Buttermilk, a potential material used to produce milk fat globule membrane (MFGM), is obtained as a byproduct of butter making from milk whole cream and cheese whey cream. This study investigated the effects of rennet and acid coagulation on the protein profiles of buttermilk rennet-coagulated whey (BRW) and buttermilk acid-coagulated whey (BAW). They were compared to those of whey cream buttermilk (WCB). Rennet coagulation was more efficient in removing casein, while retaining more IgG and lactoferrin than acid coagulation. BRW had more MFGM than BAW. Butyrophilin, xanthine dehydrogenase, and mucin1 were significantly higher (P< 0.05) in BRW, while fatty acid-binding protein 3 was enriched in BAW. KEGG analysis showed that complement and coagulation cascades had the greatest differences, and the abundance of proteins involved in this signaling pathway in BRW and BAW was higher, suggesting their potential anticoagulant and anti-inflammatory activity. BAW had higher apolipoprotein A4 and transcobalamin 2, which are essential carriers for transporting long-chain fatty acids and vitamin B12 from the intestine to the blood. Therefore, BAW intake might improve lipids and vitamin B12 absorption. This study can help deepen the understanding of protein composition of MFGM-enriched whey and facilitate the production of MFGM proteins for infants and old-aged populations.

With the growing intelligence of power grids, the application of data-driven AI technologies has been widely studied in distribution network (DN) control and operation. However, most existing studies can only address a specific task. The recent surge of powerful, versatile AI models has inspired us to explore whether the grid controller can also evolve toward greater intelligence, enabling it to perform multiple DN operation tasks. To this end, this letter proposes a novel generic data-driven Large Distribution network operation Model (LDM) based on multitask reinforcement learning (MTRL). It can concurrently learn multiple DN operation skills and perform distinct tasks separately. Specifically, to effectively handle the unaligned heterogeneous action spaces across different tasks, action-masking is incorporated. Case studies on a modified 33-bus system prove the generalization capabilities of LDM.

Since wind resources increase with altitude, many wind turbines are installed in high-altitude areas, where blade icing may occur frequently in cold weather. Ice accretion on wind turbines can lead to severe aerodynamic performance degradation or even shutdown. Furthermore, considering the spatiotemporal uncertainty of wind resources, wind power prediction (WPP) in cold weather will be extremely complex. However, existing methods mostly focus on icing-related shutdown detection of wind turbines and pay little attention to the associated WPP during cold weather. To address this problem, a novel Bayesian deep learning-based WPP (BDL-WPP) model is proposed. First, hybrid features related to WPP are extracted based on the actual operational characteristics of wind turbines, and the whole process of blade icing and de-icing is considered for the first time. Then, a BDL-WPP model is proposed based on the extracted features. In order to process the time series information within the BDL framework, a variational Bayesian gated recurrent unit is developed to implement the proposed BDL-WPP model. Finally, a posterior inference algorithm is derived for the BDL-WPP model based on stochastic variational inference. The proposed method is tested on a real-world provincial grid, and the results show that its mean absolute error is consistently below 0.025 under both normal and icing conditions, verifying its effectiveness.

The volatility from the high penetration of distributed energy resources (DERs) makes distribution networks more susceptible to voltage violations. Besides, with the increasing coupling of cyber and physical sides in modern power systems, the risk of potential cyber contingencies (CCs) is rising, which can weaken existing voltage regulation methods. Addressing these issues, this paper proposes a novel graph-based deep reinforcement learning (DRL) framework for enhancing the tolerance of voltage regulation to CCs. Firstly, typical CCs including data missing, data noise, and time delay are modeled in a unified manner, based on the cyber-physical architecture of distribution network. The voltage regulation problem is formulated into a Markov decision process (MDP) with a pertinently designed reward function, while the partial observability exhibited in scenarios involving CCs is also described. In the proposed framework, a novel graph feature representation (GFR) algorithm aiming to mitigate the impact of CCs, which fully utilizes the graph information in the cyber-physical distribution network, is developed in detail and embedded into the proximal policy optimization (PPO) algorithm, whose implementation is specified to ensure its feasibility. Case studies on the 33-bus and 141-bus networks prove the effectiveness and tolerance of the proposed method to CCs of different severities.

The objective of this paper is to learn dense 3D shape correspondence for topology-varying generic objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a 3D point given a shape latent code. Instead, our novel implicit function produces a probabilistic embedding to represent each 3D point in a part embedding space. Assuming the corresponding points are similar in the embedding space, we implement dense correspondence through an inverse function mapping from the part embedding vector to a corresponded 3D point. Both functions are jointly learned with several effective and uncertainty-aware loss functions to realize our assumption, together with the encoder generating the shape latent code. During inference, if a user selects an arbitrary point on the source shape, our algorithm can automatically generate a confidence score indicating whether there is a correspondence on the target shape, as well as the corresponding semantic point if there is one. Such a mechanism inherently benefits man-made objects with different part constitutions. The effectiveness of our approach is demonstrated through unsupervised 3D semantic correspondence and shape segmentation.

The low voltage direct current (LVdc) system effectively integrates renewable energy sources and diverse dc loads. It eliminates unnecessary energy conversion steps between dc distribution units and ac grids, thereby enhancing energy efficiency. In LVdc systems, voltage source converters (VSCs) serve as vital interfaces for converting energy between ac and dc systems, however, their capability on dc fault ride-through is usually lacked. Furthermore, the existing dc circuit breakers struggle to reliably isolate faults before VSCs blocked, thereby compromising VSC safety. To address these issues, this article introduces a novel topology self-adjusted fault current limiter (NSAFCL). In normal operating mode, the impedance of NSAFCL is controlled in a parallel state, and a bias power with adaptable output is designed to bypass NSAFCL, minimizing its influence during normal operation. In fault mode, the impedance of NSAFCL is controlled in a series state, and a current limiting resistor is introduced, shaving the fault current and maintaining the fault voltage. Finally, the simulation and experiment are conducted to verify the feasibility of NSAFCL, and results demonstrate that compared to traditional schemes, the proposed NSAFCL offers extended current limitations, prevents VSC blocking, and reduces the peak fault current by 70%.

Bone defects are often difficult to treat due to their complexity and specificity, and therefore pose a serious threat to human life and health. Currently, the clinical treatment of bone defects is mainly surgical. However, this treatment is often more harmful to patients and there is a potential risk of rejection and infection. Hydrogels have a unique three-dimensional structure that can accommodate a variety of materials, including particles, polymers and small molecules, making them ideal for treating bone defects. Therefore, emerging composite hydrogels are considered one of the most promising candidates for the treatment of bone defects. This review describes the use of different types of composite hydrogel in the treatment of bone defects. We present the basic concepts of hydrogels, different preparation techniques (including chemical and physical crosslinking), and the clinical requirements for hydrogels used to treat bone defects. In addition, a review of numerous promising designs of different types of hydrogel doped with different materials (e.g., nanoparticles, polymers, carbon materials, drugs, and active factors) is also highlighted. Finally, the current challenges and prospects of composite hydrogels for the treatment of bone defects are presented. This review will stimulate research efforts in this field and promote the application of new methods and innovative ideas in the clinical field of composite hydrogels.

Quantitative analysis of biomarkers in Optical Coherence Tomography (OCT) images plays an import role in the diagnosis and treatment of retinal diseases. However, biomarker segmentation in retinal OCT images is very hard due to the large variations in size and shape of retinal biomarkers, blurred boundaries, low contrast, and speckle interference. We proposed a novel Multi-scale Local-Global Transformer network (MsLGT-Net) for biomarker segmentation in retinal OCT images. The network combines the proposed Multi-scale Fusion Attention (MFA) module, Local-Global Transformer (LGT) module, and Contrastive Learning Enhancement (CLE) module to tackle the challenges of biomarker segmentation. Specifically, the proposed MFA module aims to enhance the network’s ability to learn multi-scale features of retinal biomarkers by effectively combining the local detail information and contextual semantic information of biomarkers at different scales, and improve the representation ability for different classes of biomarkers. The LGT module is designed to learn local and global information adaptively from multi-scale fused features to address the challenge of small biomarker segmentation. In addition, to distinguish features between different types of retinal biomarkers, we propose the CLE module to enhance the feature representation of different biomarkers. Our proposed method is validated on one public dataset and one local dataset. The experimental results show that the proposed method is more effective than other state-of-the-art methods.

State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-realistic images that are hard for humans to distinguish from genuine photos. Identifying and understanding manipulated media are crucial to mitigate the social concerns on the potential misuse of GMs. We propose to perform reverse engineering of GMs to infer model hyperparameters from the images generated by these models. We define a novel problem, “model parsing”, as estimating GM network architectures and training loss functions by examining their generated images – a task seemingly impossible for human beings. To tackle this problem, we propose a framework with two components: a Fingerprint Estimation Network (FEN), which estimates a GM fingerprint from a generated image by training with four constraints to encourage the fingerprint to have desired properties, and a Parsing Network (PN), which predicts network architecture and loss functions from the estimated fingerprints. To evaluate our approach, we collect a fake image dataset with 100 K images generated by 116 different GMs. Extensive experiments show encouraging results in parsing the hyperparameters of the unseen models. Finally, our fingerprint estimation can be leveraged for deepfake detection and image attribution, as we show by reporting SOTA results on both the deepfake detection (Celeb-DF) and image attribution benchmarks.

Background:: Cartilage, bone, and teeth, as the three primary hard tissues in the human body, have a significant application value in maintaining physical and mental health. Since the development of bacterial cellulose-based composite materials with excellent biomechanical strength and good biocompatibility, bacterial cellulose-based composites have been widely studied in hard tissue regenerative medicine. This paper provides an overview of the advantages of bacterial cellulose-based for hard tissue regeneration and reviews the recent progress in the preparation and research of bacterial cellulose-based composites in maxillofacial cartilage, dentistry, and bone. Method:: A systematic review was performed by searching the PubMed and Web of Science databases using selected keywords and Medical Subject Headings search terms. Results:: Ideal hard tissue regenerative medicine materials should be biocompatible, biodegradable, non-toxic, easy to use, and not burdensome to the human body; In addition, they should have good plasticity and processability and can be prepared into materials of different shapes; In addition, it should have good biological activity, promoting cell proliferation and regeneration. Bacterial cellulose materials have corresponding advantages and disadvantages due to their inherent properties. However, after being combined with other materials (natural/ synthetic materials) to form composite materials, they basically meet the requirements of hard tissue regenerative medicine materials. We believe that it is worth being widely promoted in clinical applications in the future. Conclusion:: Bacterial cellulose-based composites hold great promise for clinical applications in hard tissue engineering. However, there are still several challenges that need to be addressed. Further research is needed to incorporate multiple disciplines and advance biological tissue engineering techniques. By enhancing the adhesion of materials to osteoblasts, providing cell stress stimulation through materials, and introducing controlled release systems into matrix materials, the practical application of bacterial cellulose-based composites in clinical settings will become more feasible in the near future. Graphical abstract:

The second near-infrared (NIR-II) window laser-activated agents have attracted broad interest in an orthotopic cancer theranostic. However, developing NIR-II photothermal agents (PTAs) with advanced photothermal conversion efficiency (PTCE) and tumor-specific response elevation remains a crucial challenge. Herein, a hollow gold nanorod (AuHNR) with a strong localized surface plasmon resonance (LSPR) peak in the NIR-II window was coated with MnO2and chitosan to obtain AuHNR@MnO2@CS (termed AuMC) by a one-step method. Upon exposure to the tumor microenvironment (TME), the overexpressed GSH triggered degradation of the MnO2layer to release Mn2+and resulted in the PTCE elevation owing to exposure of the AuHNR. Consequently, photoacoustic and magnetic resonance imaging for accurate diagnosis, Mn2+-mediated chemodynamic therapy, and AuHNR elevating PT therapy for precise treatment could be achieved. Both in vitroand in vivoexperiments confirmed the good performance of the AuMC on an orthotopic bladder cancer precise theranostic. This study provided NIR-II activated, TME-response PT conversion efficiency enhanced PTAs and offered a tumor-selective theranostic agent for orthotopic bladder cancer in clinical application.

In 2014, IEEE Intelligent Transportation Systems Society established a Technical Committee on Transportation 5.0 with the mission of promoting and transforming the deployment of advanced and innovative technologies, especially Artificial Intelligence in transportation. This paper briefly summarizes our main research and findings over the last decade. Transportation Foundation Models, Transportation Scenarios Engineering, and Transportation Operating Systems have been identified as the main directions for the research and development of next-generation intelligent transportation systems.

Speciation leads to adaptive changes in organ cellular physiology and creates challenges for studying rare cell-type functions that diverge between humans and mice. Rare cystic fibrosis transmembrane conductance regulator (CFTR)-rich pulmonary ionocytes exist throughout the cartilaginous airways of humans1,2, but limited presence and divergent biology in the proximal trachea of mice has prevented the use of traditional transgenic models to elucidate ionocyte functions in the airway. Here we describe the creation and use of conditional genetic ferret models to dissect pulmonary ionocyte biology and function by enabling ionocyte lineage tracing (FOXI1-CreERT2::ROSA-TG), ionocyte ablation (FOXI1-KO) and ionocyte-specific deletion of CFTR(FOXI1-CreERT2::CFTRL/L). By comparing these models with cystic fibrosis ferrets3,4, we demonstrate that ionocytes control airway surface liquid absorption, secretion, pH and mucus viscosity—leading to reduced airway surface liquid volume and impaired mucociliary clearance in cystic fibrosis, FOXI1-KO and FOXI1-CreERT2::CFTRL/Lferrets. These processes are regulated by CFTR-dependent ionocyte transport of Cl−and HCO3−. Single-cell transcriptomics and in vivo lineage tracing revealed three subtypes of pulmonary ionocytes and a FOXI1-lineage common rare cell progenitor for ionocytes, tuft cells and neuroendocrine cells during airway development. Thus, rare pulmonary ionocytes perform critical CFTR-dependent functions in the proximal airway that are hallmark features of cystic fibrosis airway disease. These studies provide a road map for using conditional genetics in the first non-rodent mammal to address gene function, cell biology and disease processes that have greater evolutionary conservation between humans and ferrets.

Modular multilevel converter (MMC) is a novel circuit that has appeared in the last few decades, and has been extensively applied to medium/high voltage scenarios, especially in motor drive owing to its scalability and easy to supply a higher number of output voltage levels. As theoretical analysis shows, the sub-module (SM) capacitor voltage magnitude is positively related to the converter output current amplitude and conversely connected to the fundamental frequency. Therefore, the prime challenge of applying MMC to variable speed drives is that there is a huge voltage oscillation in SM capacitor voltage during low-speed working. An innovative high-frequency voltage injection modality to diminish the fluctuation of the capacitor voltage in variable speed drives is proposed in this paper, in which the frequency of the injected component can be changed with the operating frequency of the converter and the low frequency component of power can be converted into high frequency component. The discussed measure not only significantly reduces the voltage float in capacitor, but also avoids the overmodulation of arm voltage modulation waveform caused by high frequency of injection component when converter works in low frequency. With the improved method, the performance of motor at low frequency is enhanced. Finally, the feasibility and effectiveness of the proposed improved high-frequency voltage injection method are verified by simulation and experiment.

Multi-terminal flexible high-voltage-direct-current (HVDC) transmission and DC grid technology are developing rapidly, and the hybrid DC circuit breaker (DCCB) is usually used to protect the DC system, which can quickly remove the fault current and make sure the safe and stable work of system. However, the existing hybrid DCCBs have restrictions regarding one or more of the following characteristics: economy, fast adaptive reclosing, low on-state losses, and no additional pre-charge power source. This paper presents an economical hybrid DCCB with pre-current-limiting capability (EP-HCB), in which the main branch only is made up of mechanical switch (MS) and the fault handling branch consists of fault current transfer circuit, current limiting circuit and breaking circuit. And the proposed EP-HCB itself has inherent pre-charging and adaptive reclosing capabilities and low on-state losses, which efficiently limit the magnitude and rise rate of fault current and thus weaken the opening requirements on the DCCB. Detailed topology structure of the EP-HCB is presented, and the work principle of the fault current commutation and limiting are analyzed. Then, a design method for parameters of the proposed EP-HCB is provided. Finally, based on the 4-terminal simulation system in PSCAD/EMTDC, the feasibility and economy of the EP-HCB are proved by comparison with ABB hybrid DCCB.

This article presents a nested delta-sigma modulator (DSM) structure, where an inner analog DSM is embedded in an outer analog-digital-hybrid DSM. The outer hybrid DSM is composed of the inner analog DSM, a digital filter, and a hysteresis-comparison MSB-pass quantizer. The internal signal swing of the inner analog DSM can be significantly suppressed by the outer DSM loop, thus achieving good power efficiency. Compared to conventional system-level signal swing optimization techniques, the proposed nested DSM does not suffer from residual signal error issues and distortion leakage and is more digital-friendly with great flexibility. Fabricated in a 180 nm CMOS, the prototype analog-to-digital converter (ADC), using the nested DSM structure with hysteresis-comparison MSB-pass quantization, achieves signal-to-noise ratio (SNR)/signal-to-noise-and-distortion ratio (SNDR)/spurious-free dynamic range (SFDR)/dynamic range (DR) of 110 dB/109.2 dB/119.6 dBc/110.3 dB, respectively. It achieves a measured Schreier figure of merit (FoM $_{\mathrm {S}}$ ) of 183.5 dB, which is among the best FoMS of the state-of-the-arts.

ABSTRACT Fiber-reinforced polymer (FRP)-concrete-steel double-skin tubular column (DSTC) is a new type of composite columns which consist of an outer FRP tube and an inner steel tube, with concrete filled [...]

Graphite carbon nitride (g-C3N4) has become an emerging metal-free photocatalyst and attracted the attention of researchers. However, pure g-C3N4(PCN) prepared by traditional thermal condensation polymerization has a low polymerization of heptazine ring chains and a large number of charge recombination centers, which hinder in-plane charge transport, leading to undesirable photocatalytic performance. Herein, PCN fabrication by thermal treatment with ammonium oxalate monohydrate was performed to produce highly dispersed porous g-C3N4nanosheets, and the integrity of their in-plane heptazine ring chains was successfully improved without introducing heteroatoms. Electron paramagnetic resonance and 13C nuclear magnetic resonance analyses revealed significantly increased electron density and delocalization of the π-conjugated structure. The obtained samples exhibited acceptable stability and photocatalytic activity, and the hydrogen reduction rates without and with the Pt co-catalyst were 11.2 and 5.3 times that of PCN.

Inflammatory bowel diseases (IBDs) are chronic disorders of the gastrointestinal tract with the following two subtypes: Crohn’s disease (CD) and ulcerative colitis (UC). To date, most IBD genetic associations were derived from individuals of European (EUR) ancestries. Here we report the largest IBD study of individuals of East Asian (EAS) ancestries, including 14,393 cases and 15,456 controls. We found 80 IBD loci in EAS alone and 320 when meta-analyzed with ~370,000 EUR individuals (~30,000 cases), among which 81 are new. EAS-enriched coding variants implicate many new IBD genes, including ADAP1and GIT2. Although IBD genetic effects are generally consistent across ancestries, genetics underlying CD appears more ancestry dependent than UC, driven by allele frequency (NOD2) and effect (TNFSF15). We extended the IBD polygenic risk score (PRS) by incorporating both ancestries, greatly improving its accuracy and highlighting the importance of diversity for the equitable deployment of PRS.

ABSTRACT Due to its low strength and high brittleness, lightweight aggregate concrete (LWAC) can mainly be used to fabricate non-load bearing structures. Wrapping LWAC with fiber-reinforced polymer (FRP) can effectively [...]

ABSTRACT Distribution of the conductive paths in the conductive asphalt concrete seems as rough, disorder and random, but it has self-similar fractal characteristics in statistics. It use the fractal theory [...]

ABSTRACT An eco-concrete prepared by silica-alumina based cementitious material was developed and defined as silica-alumina based concrete. Systematic properties investigation showed that the silica-alumina based concrete exhibited good mechanical properties, [...]

Two-dimensional covalent–organic frameworks (2D COFs) have recently emerged as great prospects for their applications as new photocatalytic platforms in solar-to-hydrogen conversion; nevertheless, their inefficient solar energy capture and fast charge recombination hinder the improvement of photocatalytic hydrogen production performance. Herein, two photoactive three-component donor−π–acceptor (TCDA) materials were constructed using a multicomponent synthesis strategy by introducing electron-deficient triazine and electron-rich benzotrithiophene moieties into frameworks through sp2carbon and imine linkages, respectively. Compared with two-component COFs, the novel TCDA-COFs are more convenient in regulating the inherent photophysical properties, thereby realizing outstanding photocatalytic activity for hydrogen evolution from water. Remarkably, the first sp2carbon-linked TCDA-COF displays an impressive hydrogen evolution rate of 70.8 ± 1.9 mmol g–1h–1with excellent reusability in the presence of 1 wt % Pt under visible-light illumination (420–780 nm). Utilizing the combination of diversified spectroscopy and theoretical prediction, we show that the full π-conjugated linkage not only effectively broadens the visible-light harvesting of COFs but also enhances charge transfer and separation efficiency.

• Hot extrusion reduces grain sizes and simultaneously accelerates precipitation. • Pre-existing dislocation slips and hierarchical precipitates cause high strength. • Interaction among dislocations, stacking faults, and twins ensures acceptable ductility. Annealing-regulated precipitation strengthening combined with cold-working is one of the most efficient strategies for resolving the conflict between strength and ductility in metals and alloys. However, precipitation control and grain refinement are mutually contradictory due to the excellent phase stability of multicomponent alloys. This work utilizes the high-temperature extrusion and annealing to optimize the microstructures and mechanical properties of the Co 34 Cr 32 Ni 27 Al 3.5 Ti 3.5 multicomponent alloy. Hot extrusion effectively reduces grain sizes and simultaneously accelerates the precipitation of coherent L1 2 nanoparticles inside the face-centered cubic (FCC) matrix and grain boundary precipitations (i.e., submicron Cr-rich particles and L1 2 -Ni 3 (Ti, Al) precipitates), resulting in strongly reciprocal interaction between dislocation slip and hierarchical-scale precipitates. Subsequent annealing regulates grain sizes, dislocations, twins, and precipitates, further allowing to tailor mechanical properties. The high yield strength is attributed to the coupled precipitation strengthening effects from nanoscale coherent L1 2 particles inside grains and submicron grain boundary precipitates under the support of pre-existing dislocations. The excellent ductility results from the synergistic activation of dislocations, stacking faults, and twins during plastic deformation. The present study provides a promising approach for regulating microstructures, especially defects, and enhancing the mechanical properties of multicomponent alloys. [ABSTRACT FROM AUTHOR]

In order to improve the flexibility and reduce the energy consumption of cooperative guidance laws considering the impact angle constraint, this paper proposes a three-dimensional event-triggered fixed-time cooperative guidance law with the constraint of relative impact angles. First, for the purpose of avoiding the precision degradation due to the estimation error of time-to-go especially facing a maneuvering target, the range-to-go and velocity along the line-of-sight (LOS) are taken as the coordination variables for achieving time-cooperative guidance. Secondly, instead of assigning specific desired impact angles for each missile, only the consensus errors of relative impact angles are utilized as the coordination variables for achieving space-cooperative guidance, which can avoid continually maneuvering for maintaining the constant desired impact angles, thus reducing the fuel consumption. Next, the guidance laws along the LOS and perpendicular to the LOS are developed, and the event-triggering mechanisms are designed to reduce the update frequency of cooperative guidance commands, thus further reducing the energy consumption. To guarantee the convergence rate, the fixed-time control theory is adopted and the stability of proposed event-triggered cooperative guidance laws are rigorously proved. In addition, it is also proved that there is no Zeno behavior when implementing the proposed event-triggered cooperative guidance laws. Finally, numerical simulations indicate that the strictly simultaneous attack is achieved and the constraint of relative impact angles is satisfied. Comparative studies demonstrate that the computation burden of cooperative guidance commands is relaxed and the fuel consumption is reduced by the proposed event-triggered cooperative guidance laws with the constraint of relative impact angles. [ABSTRACT FROM AUTHOR]

Accurate diagnosis and timely therapeutic intervention of inflammatory bowel disease (IBD) is essential in preventing the progression of the disease, although it still represents an insurmountable challenge. Here we report the design of bacterial-flagella-inspired polydiiododiacetylene (PIDA) nanofibers and its performance in targeted computed tomography (CT) imaging and on-demand therapeutic intervention of IBD. With a morphology mimicking bacterial flagella, PIDA nanofibers attach on the mucus layer of the gastrointestinal (GI) tract after oral administration, evenly distributing on the GI surface to portray the GI lining under CT scan within 2 h. PIDA can retain for a longer time in the damaged mucosa at the inflamed lesions than in normal GI tissues to enable the targeted CT visualization of IBD. PIDA also scavenges reactive oxygen species and ameliorates gut dysbiosis attributed to its iodine-substituted polydiacetylene structure, so that the enriched PIDA nanofibers at the targeted IBD lesions can alleviate the inflammation while maintaining the gut microbiota homeostasis, thus promoting the rebalance of GI microenvironment and the mucosal healing.

An area-efficient 28-GHz four-element phased-array transceiver front-end (TRx FE) is proposed in this article. Transmitter/receiver (T/R) switch, phase shifter (PS), and power combining/splitting network all use customized coupler-based structures to replace area-consuming quarter-wavelength ( $\lambda $ /4) transmission line (T-line) structures. In Tx mode, a three-stage power amplifier (PA) is designed by cascading a class-AB predriver amplifier, a deep class-AB driver amplifier, and a class-A output amplifier for high output power. In Rx mode, a two-stage low noise amplifier (LNA) is designed with a 3-bit resistive adjusting load for high dynamic range of input power. A coupler is adopted in the variable gain amplifier (VGA) to achieve constant phase during gain adjustment. The proposed TRx FE is fabricated in a 40-nm CMOS process. Measurement results show that output 1-dB compression point (OP1dB) of 14.99 dBm and saturated output power (OPsat) of 15.68 dBm with 25.2% power efficiency are achieved in each Tx element. In each Rx element, noise figure (NF) of 4.54 dB, input 1-dB compression point (IP1dB) of −10.15 dBm, and maximum gain of 26.3 dB with 56.1-mW power consumption are realized. The gain range of Rx/Tx mode is 9.9–26.3 dB/24.2–44.6 dB with a resolution of 0.3 dB and phase variation less than 2° at 28 GHz. Thanks to the area-efficient coupler-based structures, the phased-array TRx FE occupies an area of 0.42 mm2 for each element and 0.084 mm2 for total area of combiner/splitter, achieving high area efficiency for Ka-band phased-array TRx FEs.

This article presents a 60-MS/s 5-MHz BW noise-shaping (NS) successive-approximation-register (SAR) analog-to-digital converter (ADC) with an integrated highly linear input buffer in a 40-nm CMOS process. A dynamic level-shifting (DLS) technique is proposed to adaptively adjust the output common-mode voltage of the integrated input buffer for different operation phases, achieving the optimal linearity while alleviating the voltage breakdown problem at the capacitive digital-to-analog converter (CDAC) top plate. The mismatch-error-shaping (MES) technique is utilized to minimize the CDAC mismatch, which also generates undesired inter-symbol-interference (ISI) errors as a side effect. An ISI-error correction (IEC) technique is proposed to mitigate this problem and further improve the linearity. Both foreground and background calibrations are adopted in the subranging ADC and the NS filter to improve the gain accuracy. This SAR ADC prototype, with an integrated input buffer driving a 4.4-pF CDAC within 2.7 ns, achieves signal-to-noise ratio (SNR)/signal-to-noise-and-distortion ratio (SNDR)/spurious-free dynamic range (SFDR)/dynamic range (DR)/total harmonic distortion (THD) of 85.4 dB/84.2 dB/97.3 dBc/86.4 dB/−92.9 dBc while consuming 8.06 mW from 2.5- and 1.1-V dual supply voltages. The Walden FoM (FoM $_{\mathrm {W}}$ ) and figure-of-merit (FoM $_{\mathrm {S}}$ ) are 60.6 fJ/conv.-step and 172.1 dB, respectively.

In the reflector-based compact antenna test range (CATR), the aperture usage is one of our most concerned problems. Higher aperture usage means that in a CATR with the same aperture reflector, a larger quiet zone (QZ) can be generated to measure a larger aperture antenna. In this work, we theoretically derive a method to design a single-reflector CATR with high aperture usage. Specifically, the radiation function of the ideal feed for the offset-fed CATR is deduced. An offset-fed parabolic reflector CATR with this ideal feed can generate a QZ with 100% aperture usage analyzed by geometrical optics (GO). An offset-fed CATR with a 3 m side-length rectangular aperture parabolic reflector is designed and simulated by physical optics (PO) and physical theory of diffraction (PTD) in GRASP10. With the proposed feed, the aperture efficiency can reach about 74% at 100 GHz and over 86% from 200 to 500 GHz. The simulation results of the QZ field distribution in the system with an ideal feed and a manufactured coaxial cavity horn feed are presented and analyzed. Besides, we also summarize amplitude ripple, phase ripple, and cross-polarization isolation in the QZ to illustrate the performance of the single-reflector CATR.

It has been gradually recognized that mobile phones can be used as a practical and promising way to identify individual travel trajectories. Researchers have developed various approaches to detecting human mobility and trip characteristics including trip origin–destination, travel modes, trip purposes based on mobile phone data. Among these researches, trip purpose detection has drawn less attention from researchers. This paper presents our work to investigate a set of machine learning approaches to identifying the trip purposes for tourists based on mobile signaling data combined with sampling surveys and point of interest (POI) data. Five machine learning algorithms, including support vector machine, decision tree, random forest, artificial neural network, and deep stacked auto-encoded (DSAE), have been employed to infer trip purposes under multiple scenarios. Four scenarios have been designed by considering the POI information around trip end [a 500 m buffer or Thiessen polygon (the coverage of the base station theoretically)] and training dataset selection (equal probabilities selection or equal proportion selection). The accuracy of trip purpose classification with machine learning algorithms has compared under different scenarios. The highest accuracy of 93.47% for the test dataset is achieved based on DSAE model under the scenario of a trip end 500 m buffer and equal probabilities selection. The experimental results indicate that the methodology developed with machine learning algorithms based on mobile signaling data combined with sample travel survey is expected as an alternative way to traditional travel surveys for trip purposes.

ABSTRACT The electrical and road properties of asphalt materials were modified using different conductive materials, such as graphite and carbon fiber. The material property changes were studied using DMA (Dynamic [...]

Accurate traffic anomaly prediction offers an opportunity to save the wounded at the right location in time. However, the complex process of traffic anomaly is affected by both various static factors and dynamic interactions. The recent evolving representation learning provides a new possibility to understand this complicated process, but with challenges of imbalanced data distribution and heterogeneity of features. To tackle these problems, this paper proposes a spatio-temporal evolution model named SNIPERfor learning intricate feature interactions to predict traffic anomalies. Specifically, we design spatio-temporal encoders to transform spatio-temporal information into vector space indicating their natural relationship. Then, we propose a temporally dynamical evolving embedding method to pay more attention to rare traffic anomalies and develop an effective attention-based multiple graph convolutional network to formulate the spatially mutual influence from three different perspectives. The FC-LSTM is adopted to aggregate the heterogeneous features considering the spatio-temporal influences. Finally, a loss function is designed to overcome the ’over-smoothing’ and solve the imbalanced data problem. Extensive experiments show that SNIPER averagely outperforms state-of-the-arts by 3.9%, 0.9%, 1.9% and 1.6% on Chicago datasets, and 2.4%, 0.6%, 2.6% and 1.3% on New York City datasets in metrics of AUC-PR, AUC-ROC, F1 score, and accuracy, respectively.

The L12-strengthened Co34Cr32Ni27Al4Ti3medium-entropy alloy (MEA) with precipitations of grain boundaries has been developed through selective laser melting (SLM) followed by cold rolling and annealing, exhibiting excellent strength-ductility synergy. The as-printed alloy exhibits low yield strength (YS) of ~ 384 MPa, ultimate tensile strength (UTS) of ~ 453 MPa, and uniform elongation (UE) of 1.5% due to the existence of the SLM-induced defects. After cold rolling and annealing, the YS, UTS, and UE are significantly increased to ~ 739 MPa, ~ 1230 MPa, and ~ 47%, respectively. This enhancement primarily originates from the refined grain structure induced by cold rolling and annealing. The presence of coherent spherical γ' precipitates (L12phases) and Al/Ti-rich precipitates at the grain boundaries, coupled with increased lattice defects such as dislocations, stacking faults, and ultrafine deformation twins, further contribute to the property’s improvement. Our study highlights the potential of SLM in producing high-strength and ductile MEA with coherent L12nanoprecipitates, which can be further optimized through subsequent rolling and annealing processes. These findings offer valuable insights for the development of high-performance alloys for future engineering applications.

The multistage economic dispatch solution is of paramount importance for achieving the optimal unit commitment and the optimal decentralized decision-making in smart grid operations. Through the approximations of the optimal expected cost-to-go functions, the stochastic dual dynamic programming (SDDP) has been shown effective to obtain the optimum for the multistage stochastic programming (SP) problem, while without finite termination guarantee. Thus, we propose a fast stochastic dual dynamic programming (FSDDP) method to accelerate the convergence rate of the SDDP, including updating the candidate points based on the case with the maximum difference between the upper and lower bounds of the optimal value functions, adding a backward-forward-backward inner scheme in the backward pass, and initializing the bounds with finite constant values. We can verify that the FSDDP method enjoys a nice property with a finite convergence guarantee for the multistage SP problems. Numerical tests on 4-, 33-, 34-, 69-, 118- and 123-bus distribution systems have demonstrated that FSDDP can approach the optimal economic performance at significantly improved computational efficiency.

Satellite ocean color products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration (NOAA)-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A (S3A) and Sentinel-3B (S3B) have been widely used for surveillance of the ocean environment and research on ocean physical, biological, biogeochemical, and ecological processes. However, either VIIRS or OLCI daily ocean color images are often incomplete in spatial coverage due to cloud cover, contamination of high sun glint, narrow swath width, high sensor-zenith angle, high solar-zenith angle, and/or other unfavorable retrieval conditions. Although merging daily ocean color images from multiple satellite sensors can help reduce the number of invalid pixels, gap-filling methods, such as the data interpolating empirical orthogonal function (DINEOF), are often used to reconstruct invalid pixels and generate gap-free images. The 9-km spatial resolution global gap-free ocean color data have been routinely produced by the NOAA Ocean Color Science Team and distributed through NOAA CoastWatch (https://coastwatch.noaa.gov/cw/index.html). In this study, we aim to develop and produce improved spatial resolution gap-free products, including chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient at the wavelength of 490 nm [ $K_{d}$ (490)], and suspended particulate matter (SPM) concentration for spatial resolutions of 0.5-, 1-, and 2-km. Two-sensor (VIIRS-SNPP and VIIRS-NOAA-20), three-sensor (two-sensor + OLCI-S3A), and four-sensor (three-sensor + OLCI-S3B) daily merged global Level-3 ocean color data are created and compared. It is found that, by merging data from the two VIIRS sensors, ~38% more valid ocean product data are retrieved compared with a single sensor from either SNPP or NOAA-20. Adding OLCI-S3A to the two-sensor merged data can increase the number of valid pixels by ~12%, and adding OLCI-S3B to the three-sensor merged data can further increase the number of valid pixels by ~8%. The DINEOF method is applied to daily two-, three-, and four-sensor merged data to generate global 2-km resolution gap-free images. Results show that 2-km resolution gap-free images are able to resolve fine ocean features, such as coastal eddies and filaments, which are not available in the 9-km resolution images. While adding OLCI-S3A data significantly improves the three-sensor-derived gap-free images over the two-sensor images, no significant enhancement is found in the four-sensor-derived gap-free images by adding OLCI-S3B data. The DINEOF method is also applied to 1- and 0.5-km resolution four-sensor merged Chl-a, $K_{d}$ (490), and SPM images in the Gulf of Mexico and U.S. West Coast region. It is found that both 0.5- and 1-km resolution images show more detailed ocean structures and features in coastal regions.