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Renal fibrosis is a major pathological event in the development of diabetic nephropathy (DN). Baicalin is a flavonoid glycoside that possesses multiple pharmacological properties including anti-fibrotic activity. In the present study, the effects of baicalin on renal fibrosis along with related molecular basis were investigated in streptozotocin (STZ)-induced DN mouse model and high glucose (HG)-treated HK-2 human proximal tubule epithelial cell model. Renal injury was evaluated through blood urea nitrogen (BUN) and serum creatinine (Scr) levels and urine albumin creatine ratio (ACR). Renal fibrosis was assessed by type IV collagen (COLIV) and fibronectin (FN) protein expression and histopathologic analysis via Masson trichrome staining. Protein levels of COLIV, FN, NF-κB inhibitor alpha (IκBα), phosphorylated IκBα (p-IκBα), p65, phosphorylated p65 (p-p65), and toll-like receptor 4 (TLR4) were measured by western blot assay. MicroRNA-124 (miR-124) and TLR4 mRNA levels were detected by RT-qPCR assay. The interaction of miR-124 and TLR4 was examined by bioinformatics analysis, luciferase reporter assay, and RIP assay. Baicalin or miR-124 attenuated renal injury and fibrosis in STZ-induced DN mice. Baicalin inhibited the increase of COLIV and FN expression induced by HG through upregulating miR-124 in HK-2 cells. TLR4 was a target of miR-124. MiR-124 inhibited TLR4/NF-κB pathway activation and the inactivation of the NF-κB pathway hindered COLIV and FN expression in HG-stimulated HK-2 cells. Baicalin prevented renal fibrosis by increasing miR-124 and inactivating downstream TLR4/NF-κB pathway in DN, hinting the pivotal values of baicalin and miR-124 in the management of DN and renal fibrosis.

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research.

Nowadays, additive manufacturing (AM) is advanced to deliver high-value end-use products rather than individual components. This evolution necessitates integrating multiple manufacturing processes to implement multi-material processing, much more complex structures, and the realization of end-user functionality. One significant product category that benefits from such advanced AM technologies is 3D microelectronics. However, the complexity of the entire manufacturing procedure and the various microstructures of 3D microelectronic products significantly intensified the risk of product failure due to fabrication defects. To respond to this challenge, this work presents a defect detection technology based on deep learning and machine vision for real-time monitoring of the AM fabrication process. We have proposed an enhanced YOLOv8 algorithm to train a defect detection model capable of identifying and evaluating defect images. To assess the feasibility of our approach, we took the extrusion 3D printing process as an application object and tailored a dataset comprising a total of 3550 images across four typical defect categories. Test results demonstrated that the improved YOLOv8 model achieved an impressive mean average precision (mAP50) of 91.7% at a frame rate of 71.9 frames per second.

With increasing interest in the rapid development of lattice structures, hybrid additive manufacturing (HAM) technology has become a competent alternative to traditional solutions such as water jet cutting and investment casting. Herein, a HAM technology that combines vat photopolymerization (VPP) and electroless/electroplating processes is developed for the fabrication of multifunctional polymer-metal lattice composites. A VPP 3D printing process is used to deliver complex lattice frameworks, and afterward, electroless plating is employed to deposit a thin layer of nickel-phosphorus (Ni-P) conductive seed layer. With the subsequent electroplating process, the thickness of the copper layer can reach 40 μm within 1 h and the resistivity is around 1.9×10−8 Ω⋅m, which is quite close to pure copper (1.7 ×10−8 Ω⋅m). The thick metal shell can largely enhance the mechanical performance of lattice structures, including structural strength, ductility, and stiffness, and meanwhile provide current supply capability for electrical applications. With this technology, the frame arms of unmanned aerial vehicles (UAV) are developed to demonstrate the application potential of this HAM technology for fabricating multifunctional polymer-metal lattice composites.

Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin system construction methodology tailored for the long-term monitoring of port terminals. This study elaborates on the organization and processing of foundational geospatial data, sensor monitoring information, and oceanic hydrometeorological data essential for constructing a digital twin of the terminal. By mapping relationships between physical and virtual spaces, we developed comprehensive dynamic and static models of terminal facilities. Employing a “particle model” approach, we visually represented oceanic and meteorological elements. Additionally, we developed a multi-source heterogeneous data fusion model to facilitate the rapid creation of data indexes for harbor elements under high concurrency conditions, effectively addressing performance issues related to scene-rendering visualization and real-time sensor data storage efficiency. Experimental validation demonstrates that this method enables the rapid construction of digital twin systems for port terminals and supports practical application in business scenarios. Data analysis and comparison confirm the feasibility of the proposed method, providing an effective approach for the long-term monitoring of port terminal operations.

Lattice structures are a group of cellular materials composed of regular repeating unit cells. Due to their extraordinary mechanical properties, such as specific mechanical strength, ultra-low density, negative Poisson’s ratio, etc., lattice structures have been widely applied in the fields of aviation and aerospace, medical devices, architecture, and automobiles. Hybrid additive manufacturing (HAM), an integrated manufacturing technology of 3D printing processes and other complementary processes, is becoming a competent candidate for conveniently delivering lattice structures with multifunctionalities, not just mechanical aspects. This work proposes a HAM technology that combines vat photopolymerization (VPP) and electroless plating process to fabricate smart metal-coated lattice structures. VPP 3D printing process is applied to create a highly precise polymer lattice structure, and thereafter electroless plating is conducted to deposit a thin layer of metal, which could be used as a resistive sensor for monitoring the mechanical loading on the structure. Ni-P layer and copper layer were successfully obtained with the resistivity of 8.2×10−7Ω⋅m and 2.0 ×10−8 Ω⋅m, respectively. Smart lattice structures with force-loading self-sensing functionality are fabricated to prove the feasibility of this HAM technology for fabricating multifunctional polymer-metal lattice composites.

Since the effect of the facet braiding phenomenon, the display quality of reconstructed image degrades with increasing depth of field in tabletop three-dimensional light-field display. Here, to analysis the facet braiding, the imaging process of the tabletop 360-degree three-dimensional light-field display based on conical lens array is mathematically modeled. A viewpoint-fitting encoding algorithm is proposed to reduce the effect of the facet-braiding phenomenon and improve the range of depth of field, which is optimized to form the best synthetic encoded image by fitting the reconstructed image seen by the simulated human eye to the parallax image captured at the corresponding location. The effectiveness of the proposed optimization algorithm is verified by simulation analysis and optical experiments, respectively. In the experiment, the clear depth of field range of the display system is increased from 13 cm to 15 cm, and the visualization effect of the reconstructed three-dimensional image is enhanced.

Skin converts multisensory stimuli into bioelectrical signals through cutaneous receptors and then transmits them to the central nervous system (CNS), implementing an analog-digital response to perceive the environment. However, target engagement components that access multisensory stimuli face significant challenges in multimodal interaction, especially the intrinsic decoupling in stretchable heterogeneous integrating systems and the dimensional broadening in traditional human five sensations. In this work, we propose a passive wireless multimodal self-decoupling methodology paradigm to optimize the signal scheduling of systems and broaden the cognitive dimensions of humans, which engages the strategic configuration of symmetrical inductor-capacitor (LC) resonant circuit combined with LC tank to unlock the single-port output self-decoupling sensing, thereby decoding five sensible stimuli to augment situational awareness of human. Systematic theoretical model is established to verify the self-decoupling methodology and the multimodal sensing scheme based on RLC-modulated mechanism. Multiple prototypes of single-port liquid metal (LM)-based wireless multimodal electronic skin implement targeted responses of skin-like receptors. That incorporating pressure (0 kPa~40 kPa), temperature (25 °C ~ 45 °C), humidity (5%RH ~ 90%RH), ultraviolet (0 lm~20 lm) and inclination (30°, 45°, 60°, 90°) through accessing corresponding sensing components. This technique proposal is designed to render a self-decoupling methodology for stretchable wireless multimodal unperturbed platforms and bridge the spatial sensory dimensions in traditional multisensory mechanisms for human-machine interaction.

Tabletop integral imaging display with a more realistic and immersive experience has always been a hot spot in three-dimensional imaging technology, widely used in biomedical imaging and visualization to enhance medical diagnosis. However, the traditional structural characteristics of integral imaging display inevitably introduce the flipping effect outside the effective viewing angle. Here, a full-parallax tabletop integral imaging display without the flipping effect based on space-multiplexed voxel screen and compound lens array is demonstrated, and two holographic functional screens with different parameters are optically designed and fabricated. To eliminate the flipping effect in the reconstruction process, the space-multiplexed voxel screen consisting of a projector array and the holographic functional screen is presented to constrain light beams passing through the corresponding lens. To greatly promote imaging quality within the viewing area, the aspherical structure of the compound lens is optimized to balance the aberrations. It cooperates with the holographic functional screen to modulate the light field spatial distribution. Compared with the simulation results, the distortion rate of the imaging display is reduced to less than 9% from more than 30%. In the experiment, the floating high-quality reconstructed three-dimensional image without the flipping effect can be observed with the correct 3D perception at 96° × 96° viewing angle, where 44,100 viewpoints are employed.

Trehalose is known for its effect of improving the stability of freeze-dried foods. In this work, vacuum freeze-drying (VFD) technology was employed to prepare northeast black bee royal jelly into lyophilized powder and a novel method mixing trehalose into royal jelly is successfully developed to enhance the free radical scavenging ability and the nutrition stability of royal jelly lyophilized powder. The effects of different trehalose content (0, 0.1, 0.3, 0.5, 0.7 and 0.9 wt.%) on the physicochemical properties of lyophilized royal jelly powder were studied. With systematic analysis, it was found that the incorporation of suitable trehalose content in lyophilized royal jelly powder can reduce the loss of the protein, total sugar, total flavone content during the VFD process and enhance the total phenolic antioxidant capacity, solubility, angle of repose, and bulk density of the royal jelly powder. Finally, lyophilized royal jelly with 0.5 wt.% trehalose is selected as the suitable addition content which exhibits the best radical scavenging ability as well as the lowest hygroscopicity. From the perspective of sensory evaluation, all royal jelly lyophilized powders with trehalose are acceptable.