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This work describes a novel approach for the synthesis of (−)-epigallocatechin gallate (EGCG) palmitate by a chemical-synthesis method, where the elevated stability of the EGCG derivative is achieved. Various parameters affecting the acylation process, such as the base, solvent, as well as the molar ratio of palmitoyl chloride, have been studied to optimize the acylation procedure. The optimized reaction condition was set as follows: EGCG/palmitoyl chloride/sodium acetate was under a molar ratio of 1:2:2, with acetone as the solvent, and the reaction temperature was 40 °C. Under the optimized condition, the yield reached 90.6%. The EGCG palmitate (PEGCG) was isolated and identified as 4′-O-palmitoyl EGCG. Moreover, the stability of PEGCG under different conditions was proved significantly superior to EGCG. Finally, PEGCG showed better inhibition towards α-amylase and α-glucosidase, which was 4.5 and 52 times of EGCG, respectively. Molecular docking simulations confirmed the in vitro assay results. This study set a novel and practical synthetic approach for the derivatization of EGCG, and suggest that PEGCG may act as an antidiabetic agent.

A simple and fast high-performance liquid chromatography (HPLC) fingerprint method combining reference standard extract for the identification of bilberry extract was developed and validated. Six batches of bilberry extract collected from different manufactures were used to establish the HPLC fingerprint. Other berry extracts—such as blueberry extracts, mulberry extracts, cranberry extracts, and black rice extracts—were also analyzed for their HPLC chromatograms. The fingerprints of five batches of bilberry extract showed high similarities, while one batch was distinguished from others. Additionally, the content of anthocyanin Cyanidin-3-O-glucoside (Cy-3-glc) in each berry extract was analyzed and compared. The results indicate that this HPLC fingerprint method, combining reference standard extracts, could be used for the authentication and quality control of bilberry extracts.

Conventional air-carried evaporating separation (ACES) technology, to achieve complete separation and recovery of water and salt in brine, tends to necessitate heating air above a critical temperature (typically>90 °C). In this paper, a novel concept of process-heat-supplied and an ACES cycle with this technique is proposed. A comprehensive thermodynamic analytical investigation is conducted. The results indicate that at heat source supply temperature T supply of only 45.17 °C, this novel unit is capable of achieving complete separation of water and salt from 5 wt% concentration brine. Meanwhile, thermodynamic mechanism analysis reveals that sufficient process-heat-supplied affords the fluid self-adaptive regulation on the driving potential of heat and mass transfer, thus circumventing traditional heat and mass transfer limitation. Additionally, a solar ACES system with process-heat-supplied incorporating heat pump is further proposed. For this system, theoretical evaporation rate for unit area of solar irradiation m e-solar  = 2.23 kg/(m 2 ·h), integrated solar utilization efficiency η i  = 188%; while considering overall losses m e-solar  = 1.41 kg/(m 2 ·h), η i  = 95.2%.

The moiré superlattice, arising from the interface of mismatched single crystals, intricately regulates the physical and mechanical properties of materials, giving rise to phenomena such as superconductivity and superlubricity. This study delves into the profound impact of moiré superlattices on the interfacial mechanical behavior of van der Waals (vdW) layered materials, with a particular focus on tribological properties. A comprehensive review of continuum modeling approaches for vdW layered materials is presented, accentuating the incorporation of moiré superlattice effects in theoretical models to unravel their distinctive interfacial frictional behavior and thermodynamic properties. The exploration of moiré superlattices has significantly advanced our fundamental understanding of interface phenomena in vdW layered materials. This progress provides crucial theoretical insights that can inform the design of multifunctional devices based on the unique properties of twisted layered materials. Interlayer twist will form moiré superlattices at the interface of two contacting crystal planes, which can induce in‐plane atomic reconstruction and out‐of‐plane deformation field, playing a key role in determining the physical and mechanical properties of van der Waals layered materials.

Incentive policies for prefabricated buildings (PBIP) can effectively promote the development of prefabricated buildings (PB) and improve the sustainability of the construction industry, attracting increasing attention from academia and industry. The government has issued many PBIPs (including land policy, plot ratio reward policy, fund policy, financial support policy, preferential tax policy, research and development support policy, and construction process management policy) but the implementation effect of PBIP remains to be clarified, especially regarding the research gap from a dynamic perspective. This study proposes an analytical framework of policy implementation effect based on the chain of “policy text content–policy impact path–policy implementation effect”, aiming to analyze the implementation stage and effect of PBIPs using the system theory analysis method. Combining the main factors affecting the PBIP impact system, a quantitative model containing 50 variables is established based on the system dynamics (SD) model. Finally, using Shenyang, one of China’s first PB pilot cities, as an example, the system simulation and sensitivity analysis of main parameters are carried out in Vensim software. The research results indicate that PBIP in Shenyang has not been fully utilized and targeted improvements and strengthened implementation of PBIP are needed. In the simulation of individual policies, the implementation effect of fund policy is the best, and the impact of research and development support policy on carbon reduction is the most significant. The promotional effect of the policy combination on PB development is more prominent. Using the policy combination reasonably is necessary to leverage the incentive effect fully. Simulation and sensitivity analysis results provide valuable insights for government departments to enhance the implementation effectiveness of the existing PBIP. This study responds to the global trend of promoting sustainable building development. It proposes a new framework for systematically analyzing the implementation effects of PBIPs, filling the research gap in policy evaluation from a dynamic perspective. Its methods and findings are not only applicable to the Chinese context but also provide valuable experience for other countries to develop and optimize PBIPs.

Building production increases energy demand, which raises carbon dioxide emissions and leads to environmental degradation. The use of prefabricated buildings has the potential to play an important role in promoting sustainable development in the construction industry, and prefabricated building incentive policies (PBIP) are an effective means of improving the development level of prefabricated buildings (PBDL). This study investigated the significance of PBIP using a structural equation model (SEM) analysis of the results of 519 questionnaire surveys obtained in 10 prefabricated building demonstration cities in China. The results indicate that policy satisfaction has the most substantial impact on PBDL, with direct funding subsidy policy being the most influential factor. Policy application enthusiasm ranks second according to influence on PBDL, while policy awareness has the most negligible impact on PBDL among the examined factors. This research provides a reference for the government to formulate reasonable and effective prefabricated building incentive policies, and it may be useful for promoting the development of prefabricated buildings.

The application of deep learning in crack detection has become a research hotspot in Structural Health Monitoring (SHM). However, the potential of detection models is often limited due to the lack of large-scale training data, and this issue is particularly prominent in the crack detection of ancient wooden buildings in China. To address this challenge, “CrackModel”, an innovative dataset construction model, is proposed in this paper. This model is capable of extracting and storing crack information from hundreds of images of wooden structures with cracks and synthesizing the data with images of intact structures to generate high-fidelity data for training detection algorithms. To evaluate the effectiveness of synthetic data, systematic experiments were conducted using YOLO-based detection models on both synthetic images and real data. The results demonstrate that synthetic images can effectively simulate real data, providing potential data support for subsequent crack detection tasks. Additionally, these findings validate the efficacy of CrackModel in generating synthetic data. CrackModel, supported by limited baseline data, is capable of constructing crack datasets across various scenarios and simulating future damage, showcasing its broad application potential in the field of structural engineering.

Deep hypothermic circulatory arrest (DHCA) can induce systemic inflammatory response syndrome, including neuroinflammation. Finding suitable compounds is necessary for attenuating neuroinflammation and avoiding cerebral complications following DHCA. In the present study, we established DHCA rat models and monitored the vital signs during the surgical process. After surgery, we found significantly increased proinflammatory cytokines (IL-6, IL-1β, and TNF-α) in DHCA rats. Quantitative proteomics analysis was performed for exploring the differentially expressed proteins in hippocampus of DHCA rats and the data showed the adiponectin receptor 1 protein was upregulated. More importantly, administration of AdipoRon, a small-molecule adiponectin receptor agonist, could improve the basic vital signs and attenuate the increased IL-6, IL-1β, and TNF-α in DHCA rats. Furthermore, AdipoRon inhibits the activation of microglia (M1 state) and promotes their transition to an anti-inflammatory state, via promoting the phosphorylation of adenosine monophosphate-activated protein kinase (AMPK), and downregulating nuclear factor kappa B (NF-κB) in DHCA rats. Consistently, we used LPS-treated BV2 cells to mimic the neuroinflammatory condition and found that AdipoRon dose-dependently decreased cytokines, along with increased phosphorylation of AMPK and downregulated NF-κB. In conclusion, our present data supported that AdipoRon inhibited DHCA-induced neuroinflammation via activating the hippocampal AMPK/NF-κB pathway.

Deep learning has achieved good results in the crack detection of roads and bridges. However, the timber structures of ancient architecture have strong orthotropic anisotropy and complex microscopic structures, and the law of cracks development is extremely complex. The image data has a large proportion of pixels, which is obviously different from the background gray value, and there is timber grain noise, thus the existing methods cannot accurately extract the complex texture contour feature of cracks. In previous studies, we have verified that YOLO v5s is effective in crack detection in timber structures of ancient architecture. However, there are many different versions of YOLO series models. In order to find a better algorithm, this paper mainly adopts three models including YOLO v3, YOLO v4s-mish, and YOLO v5s to detect cracks in the timber structures of ancient architecture, and compares and analyzes the advantages and disadvantages of the three models. In the comparing process, we mainly have discussed the index performance of the three models in terms of training time, loss function, recall rate, and mAP value. We have summarized and analyzed the advantages and disadvantages of the three models in cracks detection of the timber structures of ancient architecture, and concluded the comparing results of the three models in cracks detection based on experiments. We published the first picture data set of cracks in timber structures of ancient architecture, and applied YOLO model in the intelligent identification field of cracks in timber structures of ancient architecture for the first time, which opened up a new idea for the intelligent operation and maintenance of the timber structures of ancient architecture.

A total of 8 mechanically ventilated residential buildings and 8 naturally ventilated residential buildings were selected to analyze the pollution characteristics of indoor VOCs under different ventilation modes in the severe cold area of northeast China. On typical meteorological days in each season, VOCs were detected on site, and ventilation modes were investigated by long-term online monitoring. The test results showed that the TVOC (total volatile organic compounds) concentrations varied greatly in different seasons or different functional rooms, and the TVOC concentration was the highest in winter, with a value of 0.994 mg/m3. The kitchen was the place with the most serious VOC pollution, and the TVOC concentration could reach 1.403 mg/m3. Benzene series and methylsiloxane had the highest detection rates, but the detected concentrations were low, and the average concentrations were 0.025 mg/m3 and 0.013 mg/m3 respectively. Among the VOC types with a detection rate greater than 50%, the average proportions of aldehydes, alkanes, and benzene series were 18.7%, 15.39%, and 14.38%, respectively. And their mass ratios were also high, which were 14.90%, 30.85%, and 15.70%, respectively. The annual daily average ventilation duration of mechanically ventilated residential buildings was 7.84 h longer than that of naturally ventilated residential buildings. The median TVOC concentrations of mechanically ventilated residential buildings and naturally ventilated residential buildings were 0.621 mg/m3 and 0.707 mg/m3, respectively. The fresh air system was applicable in the severe cold area of northeast China.