The human serotonin transporter (hSERT) terminates neurotransmission by removing serotonin from the synaptic cleft, which is an essential process plays an important role in depression. In addition to substrate serotonin, hSERT is also the target of drugs of abuse like cocaine and clinically used antidepressants such as escitalopram and paroxetine. To date, few studies attempt to investigate the unbinding mechanism underlying the orthosteric and allosteric modulation of hSERT. The high-resolution X-ray structure of hSERT resolved recently enables us to theoretically study the unbinding of the above four ligands against the S1 or S2 site of hSERT, by means of molecular docking, molecular dynamics (MD) and potential of mean force (PMF) simulations. We proved that for either the S1 or S2 site, the other three ligands (cocaine, escitalopram and paroxetine) are much more favorable than the original substrate serotonin, whether in kinetics along the unbinding pathways or in thermodynamics at the equilibrium states. Furthermore, the S1 site is much more favorable than the S2 site, for each ligand. Interestingly, inspection revealed that there are ~ 3Å lengths between the allosteric site of serotonin and cocaine, and an unseen un-binding pathway for escitalopram at the S1 site except for verification of the broadest trail.

Silicon carbide single crystal (SiC) has been widely used in the field of power devices, while it is difficult to fabricate ultra-flat surface by traditional manufacturing technologies due to its difficult-to-machine property. This paper investigates the interaction process during femtosecond laser ablation of crystalline silicon carbide based on the two-temperature model simulation and experimental study. The findings reveal that parameters such as laser pulse energy, scanning interval, and defocus amount exert significant influence on ablation depth, roughness, and surface morphology. The femtosecond laser proves effective in removing surface defects from cut SiC substrates, albeit resulting in increased surface roughness. However, subsequent polishing treatment of the laser-ablated silicon carbide surface can mitigate this roughness, underscoring the feasibility of laser surface ablation for silicon carbide single crystals.

The development of mobile internet technology has enabled companies to use social media for E-commerce. Some companies use personified images and languages to communicate with consumers in this context. How does personified communication affect consumer behavior in social commerce? Are consumers willing to accept this new form of communication under social commerce? To answer these questions, this paper explores consumers' willingness to take per-sonified communication in the context of social commerce. It investigates the role of cognitive needs in regulating the internal mechanism and proposes some suggestions for enterprises to improve social media communication. Specifically, this paper presents an improved model based on the TAM model. In our model, perceived interaction is introduced as a new independent variable, and cognitive need is added as a regulatory variable, which is more suitable for social commerce. We conduct a questionnaire survey on the Internet and analyze data using AMOS and SPSS. The results show that perceived usefulness and perceived interaction positively im-pact attitude, which influences consumers' willingness to purchase. Furthermore, the cognitive need as the regulatory variable significantly affects the influencing path from perceived use-fulness to attitude and purchase intention.

Fractures of the talar neck and body are associated with spine fractures and scoliosis deformity, which affect cosmetic appearance and cause difficulty in ambulation. The implant design for talus surgery is thriving as a functional alternative in case of severe talar destruction, focusing on segmentation and reconstruction of the talus’s shape. However, manual segmentation of the talus is time-consuming and subjective. In this study we exploited the automatic segmentation framework to efficiently train a deep learning-based model to accurately segment the talus based on computed tomography imaging. We developed three model configurations with nnU-Net and investigated their Dice similarity coefficients (DSC) and 95% Hausdorff distances (HD95) for talus segmentation on a CT image dataset. The three configurations performed well (DSC > 0.95, HD95 < 0.6). When tested on the same samples, one of the configurations was more efficient while ensuring higher accuracy. We propose to focus on talus anatomic variations with increasing age based on this framework and apply it to clinical trials at the next stage.

Though the energy density of lithium-ion batteries continues to increase, safety issues related with the internal short-circuit and the resulting combustion of highly flammable electrolyte impede the further development of lithium-ion batteries. It has been well-accepted that a thermal stable separator is important to postpone the entire battery short-circuit and thermal-runaway. Traditional methods to improve the thermal stability of separators includes surface modification and/or developing alternate material systems for separators which may always affect the battery performance negatively. Herein, a thermostable and shrink-free separator with little compromise in battery performance is prepared by coaxial electrospinning and tested. The separator consists of core-shell fiber networks where poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) layer serves as shell and polyacrylonitrile (PAN) as the core. This core-shell fiber network exhibits little or even no shrinking/melting at elevated temperature over 250 °C. Meanwhile, it shows excellent electrolyte wettability and can take large amount of liquid electrolyte three times more than that of conventional Celgard 2400 separator. In addition, the half-cell using LiNi1/3Co1/3Mn1/3O2 as cathode and the aforementioned electrospun core-shell fiber network as separator demonstrates superior electrochemical behavior, stably cycling for 200 cycles at 1 C with a reversible capacity of 130 mAh g-1 and little capacity decay.

Tourism activities generally have a ∩-type lock on the level of tourism ecological security in an area, but when applied to the border areas of China, there are certain specificities in the spatial evolution of tourism ecological security (TES) compared to traditional findings. This paper measures tourism ecological security in China’s border areas from 2009 to 2020 by using the DPSIR model with the superefficient SBM-DEA and analyzes the spatial differences, evolutionary characteristics, and driving factors of tourism ecological security in border areas by using Pearson’s correlation coefficients, center of gravity models, and geographic probes: (1) The overall tourism ecological security index of China’s border provinces is relatively good. Tourism activities do not completely affect the traditional “∩ lock” of the border provinces. The tourism ecological security level of the border provinces presents three spatial-temporal changes (“∩” type, “U” type, “\” type) and four evolution trends (“high–high–high”, “middle–middle–medium”, “medium–low–low”, and “low–low–low”). (2) The overall tourism ecological security level in border areas is polarized between high and low levels, and the ecological security efficiency of the three large areas is spatially characterized as “Southwest Area &gt; Northeast Area &gt; Northwest Area”, and the center of gravity of ecological security is mostly concentrated in Xinjiang, Tibet, and Neimenggu, where the ecological security level is higher. (3) Social and environmental factors are the main factors that influence tourism ecological security in border areas, while economic factors account for a smaller proportion. Accordingly, this thesis also proposes the driving mechanism of the ecological security of tourism sites in border areas in China with a view to providing theoretical support for policy formulation.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

Lithium-ion batteries have attracted enormous interests recently as promising power sources. However, the safety issue associated with the employment of highly flammable liquid electrolyte impedes the further development of next-generation lithium-ion batteries. Recently, researchers reported the use of electrospun core-shell fiber as the battery separator consisting of polymer layer as protective shell and flame retardants loaded inside as core. In case of a typical battery shorting, the protective polymer shell melts during thermal-runaway and the flame retardants inside would be released to suppress the combustion of the electrolyte. Due to the use of a single precursor solution for electrospinning containing both polymer and flame retardants, the weight ratio of flame retardants is limited and dependent. Herein, we developed a dual-nozzle, coaxial electrospinning approach to fabricate the core-shell nanofiber with a greatly enhanced flame retardants weight percentage in the final fibers. The weight ratio of flame retardants of triphenyl phosphate in the final composite reaches over 60 wt.%. The LiFePO4-based cell using this composite nanofiber as battery separator exhibits excellent flame-retardant property without compromising the cycling stability or rate performances. In addition, this functional nanofiber can also be coated onto commercial separators instead of being used directly as separators.

Aromatic azo compounds have -N=N- double bonds as well as larger π electron conjugation system, which endows aromatic azo compounds with wide applications in the fields of functional materials. The properties of aromatic azo compounds are closely related to the substituents on their aromatic rings. However, traditional synthesis methods, such as the coupling of diazo salts, have a great limitation on the structural design of aromatic azo compounds. Therefore, many scientists have devoted their efforts to developing new synthetic methods. Moreover, recent advances in the synthesis of aromatic azo compounds led to the improvement of design and preparation of light-response materials at molecular level. This review summarizes the important synthetic progress of aromatic azo compounds in recent years, with an emphasis on the pioneering contribution of functional nanomaterials in the field.

The response of boreal vegetation to global warming has shown a weakening trend over the last three decades. However, in previous studies, models of vegetation activity responses to temperature change have often only considered changes in mean daily temperature (Tmean), with the diurnal temperature range (DTR) being neglected. The goal of this study is to evaluate the temporal trends of the relationships between two temperature factors (Tmean and DTR) and vegetation activity across the boreal regions on both annual and seasonal timescales, by simultaneously employing satellite and climate datasets. We found that the interannual partial correlation between growing season (GS) NDVI and Tmean (RNDVI−Tmean) showed a significant decreasing trend over the last 34 years. At the seasonal scale, RNDVI−Tmean shows a significant upward trend in the spring, while in the summer and autumn, RNDVI−Tmean exhibits a significant downward trend. The temporal trend characteristics of the partial correlation between NDVI and DTR (RNDVI−DTR), at both the GS and seasonal scales, are fully consistent with RNDVI−Tmean. The area with a significant decrease in GS RNDVI−Tmean and RNDVI−DTR accounted for approximately 44.40% and 41.21% of the boreal region with the 17-year moving window, respectively. In stark contrast, the area exhibiting a significant increasing trend in GS RNDVI−Tmean and RNDVI−DTR accounted for only approximately 14.65% and 13.59% of the boreal region with the 17-year moving window, respectively. With respect to the seasonal patterns of RNDVI−Tmean and RNDVI−DTR, the area with a significant upward trend in spring was greater than that with a significant downward trend. Nevertheless, more areas had a significant downward trend of RNDVI−Tmean and RNDVI−DTR in summer and autumn than had a significant upward trend. Overall, our research reveals a weakening trend in the impact of temperature on vegetation activity in the boreal regions, and contributes to a deeper understanding of the vegetation response to global warming.

Pool boiling heat transfer is recognized as one of the most effective heat transfer methods and has extensive applications in industry. The application of non-azeotropic binary mixtures can not only meet the environmental requirements of modern industrial development, but also improve the coefficient of performance (COP) of many systems. Therefore, studying the boiling heat transfer characteristics of non-azeotropic mixtures is important on improving their potential industrial application. This article selected ethylene glycol/deionized water (EG/DW) at varies concentration as the research working fluids, and conducted experimental research on the pool boiling performance. At the same time, the thermophysical parameters of the mixtures were studied and analyzed. The results showed that the concentration of EG in the mixture had a significant impact on the thermal properties and temperature glide of the mixture, where both affected the heat transfer deterioration. Based on experimental data, six widely used heat transfer coefficient prediction correlations were used for calculation, and two of them were modified. The deviation between the modified correlations and the experimental results remained within 20%.

The point cloud is a form of three-dimensional data that comprises various detailed features at multiple scales. Due to this characteristic and its irregularity, point cloud analysis based on deep learning is challenging. While previous works utilize the sampling-grouping operation of PointNet++ for feature description and then explore geometry by means of sophisticated feature extractors or deep networks, such operations fail to describe multi-scale features effectively. Additionally, these techniques have led to performance saturation. And it is difficult for standard MLPs to directly "mine" point cloud. To address above problems, we propose the Detail Activation (DA) module, which encodes data based on Fourier transform after sampling and grouping. We activate the channels at different frequency levels from low to high in the DA module to gradually recover finer point cloud details. As training progresses, the proposed Point-MDA can uncover local and global geometries of point cloud progressively. Our experiments show that Point-MDA achieves superior classification accuracy, outperforming PointNet++ by 3.3% and 7.9% in terms of overall accuracy on the ModelNet40 and ScanObjectNN dataset, respectively. Furthermore, it accomplishes this without employing complicated operations, while exploring the full potential of PointNet++.

Flash flood is one of the most significant natural disasters in China, particularly in mountainous area, causing heavy economic damage and casualties of life. Accurate risk assessment is critical to an efficient flash flood management. There are more than 530,000 small watersheds in 2058 counties in China where flash flood should be prevented. In practice, with limited fund and different risk levels, the priorities of each small watershed for flash flood prevention and control are also needed for an efficient flash flood management. This paper, take Licheng county in China as an example, aims to give out these priorities for management. First, sensitive indexes are identified among index system, which includes 9 indexes based on underlying surface characteristics of small watershed in hilly region. Second, the range of each index and the rank division of each index for evaluation are determined. Based on the rank divisions for evaluation, the flash flood risk grade eigenvalue (H) is calculated by Variable Fuzzy Method (VFM ) using 1000 samplings generated by Latin hypercube sampling method. Third, the key sensitivity factors that affect flash flood risk grade eigenvalue (H) are assessed by two different global sensitivity analysis methods -- stepwise regression analysis and mutual entropy. Both results indicate that watershed slope (S) is the most sensitive factor; the second is antecedent precipitation index (CN); while other factors are slightly different sensitive in sequence. This study shows that stepwise regression analysis and mutual information analysis are appropriate for the sensitivity analysis of mountain flash flood risk. Finally, based on watershed slope (S), the priorities of flash flood prevention and control of 119 small watersheds in Licheng county are given out.

Early and accurate mapping of winter canola planting areas provides critical support for sustainable cropland management. Although some methods have been proposed to map the winter canola at the flowering or later stage, mapping winter canola planting areas at the early stage is still challenging, due to the insufficient understanding of the multi-source remote sensing features sensitive for winter canola mapping. This study proposed an early-stage winter canola area mapping method by using the combination of optical and synthetic aperture radar (SAR) data. We analyzed the spectral features, backscatter coefficients, and textural features of winter canola based on Sentinel-2 and Sentinel-1 images. Random forest (RF) and Support Vector Machine (SVM) classification models were built to map winter canola based on early-stage images and field samples in 2017 and then apply the best model to corresponding satellite data in 2018-2022. Results showed that: (1) The red edge and near-infrared-related spectral features were most important for the mapping of early-stage winter canola, followed by VV, DVI and GOSAVI, which also showed greater differences than the other features; (2) Based on Sentinel-1 and Sentinel-2 data, winter canola could be earliest mapped around 130 days prior to ripening (i.e., early overwinter stage) with the F-score over 0.85 and the OA over 81%; (3) Adding Sentinel-1 could improve the OA by about 2%-4% and the F-score by about 1% -2% in winter canola mapping. (4) The F-score of winter canola mapping based on the classifier transfer approach in 2018-2022 varied between 0.75 and 0.97, and the OA ranged from 79% to 86%. This study demonstrates the potential of early-stage winter canola mapping using the combination of Sentinel-2 and Sentinel-1 images, which could provide valuable and timely information for stakeholders and decision makers.

In GNSS/IMU integrated navigation systems, factors such as satellite occlusion and non-line-of-sight conditions can lead to degradation of satellite positioning results, Subsequently affecting the overall accuracy of the integrated navigation system. To address this issue and ef-fectively utilize historical pseudorange information from satellites, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints are con-structed using satellite ‘s position from previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint can serve as an alternative solution for individual satellites in case of signal anomalies, ensuring the integrity and continuity of the graph optimi-zation model. Additionally, this paper conducts an analysis of the graph optimization model constructed using these virtual constraints, comparing it with traditional integrated navigation graph optimization model, and analyzes and reconstructs the marginalization process based on these virtual constraints. The experimental results, compared with tightly coupled Kalman fil-tering and the original graph optimization method on a set of real-world data, demonstrate that the introduction of virtual pseudoranges maintains high positioning accuracy. The RMSE error between it and the original graph optimization remains within 5%, affirming the high feasibility of this approach.

With the rapid development of Internet of Things (IoT)-based near-earth remote sensing technology, the problem of network intrusion for near-earth remote sensing systems has become more complex and large-scale. Therefore, it is essential to seek an intelligent, automated, and robust network intrusion detection method. In recent years, network intrusion detection methods based on graph neural networks (GNNs) have been proposed. However, there are still some practical issues with these methods. For example, they have not taken into consideration the characteristics of near-earth remote sensing systems, the state of the nodes, and the temporal features. Therefore, this article analyzes the characteristics of existing near-earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes. The proposed network applies spatiotemporal graph neural networks to the network intrusion detection of near-earth remote sensing systems and validates the effectiveness of the proposed method on the latest flow-based dataset.

This article focuses on obtaining the analytical solutions for parabolic Volterra integro- differential equations in d-dimensional with different types frictional memory kernel. Based on theories of Laplace transform, Fourier transform, the properties of Fox-H function and convolution theorem, analytical solutions of the equations in the infinite domain are derived under three frictional memory kernel functions respectively. The analytical solutions are expressed by infinite series, the generalized multi-parameter Mittag-Leffler function, Fox-H function and convolution form of Fourier transform. In addition, the graphical representations of the analytical solution under different parameters are given for one-dimensional parabolic Volterra integro-differential equation with power-law memory kernel. It can be seen that the solution curves subject to Gaussian decay at any given moment.

This study focuses on the nuanced phenomenon of gendered parenting by exploring “how” and “when” maternal son preference associates with depressive symptoms among Chinese early adolescents. Focusing on 1,093 junior high school students from a relatively affluent city in western China, the research examined the mediating roles of mother-child attachment and father-child attachment, as well as the moderating role of school connectedness in the relationship between maternal son preference and depressive symptoms. The results revealed a noteworthy positive correlation between maternal son preference and depressive symptoms among female adolescents, with no significant association observed in males. In addition, mother-child attachment and father-child attachment mediated the relationship between maternal son preference and depressive symptoms, which supported the “spillover effect” and “crossover effect”. Moreover, moderation effect analysis indicated that higher level of school connectedness can buffer the effect of maternal son preference on girls’ depressive symptoms, while lower level of school connectedness can enhance the effect of maternal son preference on girls’ depressive symptoms. These insights help to enhance people's understanding of gendered parenting, emphasizing the enduring necessity of addressing son preference within the broader context of promoting gender equality.

L-Theanine, a unique non-protein amino acid, is an important bioactive component of green tea. Previous studies have shown that L-theanine has many potent health benefits, such as anti-anxiety effect, regulation of immune response, relaxing neural tension, reducing oxidative damage. However, little is known concerning whether L-theanine can improve the clearance of mitochondrial DNA (mtDNA) damage in organisms. Here, we reported that L-theanine treatment increased ATP production and improved mitochondrial morphology to extend the lifespan of UVC-exposed nematodes. Mechanistic investigations showed that L-theanine treatment enhanced the removal of mtDNA damage and extended lifespan by activating autophagy, mitophagy, mitochondrial dynamics, and mitochondrial unfolded protein response (UPRmt) in UVC-exposed nematodes. In addition, L-theanine treatment also up-regulated the expression of genes related to mitochondrial energy metabolism in UVC-exposed nematodes. Our study provided a theoretical basis for the possibility that tea drinking may prevent mitochondrial related diseases.

Senescence-accelerated mouse prone 8 (SAMP8) mice exhibit cognitive defects and neuron loss with aging, and are used to study anti-aging effects of Dendrobium nobile alkaloids (DNLA). SAMP8 mice were orally given DNLA from ages 6 to 10 months. At 10 months of age, behavioral tests and neuron damage were evaluated. Protein was extracted and subjected to phosphorylated proteomic analysis. The cognitive deficits and neuron loss in hippocampus and cortex of aged SAMP8 mice were improved by DNLA. Hippocampal proteomic analysis showed differentially expressed protein/genes in SAMP8 compared to age-matched senescence-accelerated resistant mice, including altered tubulin binding, microtubule binding, etc. via Gene Oncology. KEGG revealed endocytosis, mRNA surveillance, tight junction, protein processing in endoplasmic reticulum, aldosterone synthesis and secretion, and glucagon signaling pathway changes. Upregulated protein/genes in the hippocampus of SAMP8 mice, such as Lmtk3, Usp10, Dzip1, Csnk2b, and Rtn1, were attenuated by DNLA; whereas downregulated protein/genes, such as Kctd16, Psd3, Bsn, Atxn2l, and Kif1a, were rescued by DNLA. The aberrant protein/gene expressions of SAMP8 mice were correlated with transcriptome changes of Alzheimer’s disease in the GEO database, and were attenuated by DNLA. Thus, DNLA improved cognitive dysfunction and ameliorated neuronal injury in aged SAMP8 mice, and attenuated aberrant protein/gene expressions.

Aflatoxin biosynthesis is correlated with oxidative stress and is proposed to function as a secondary defense mechanism to redundant intracellular reactive oxygen species (ROS). We find that the antioxidant gallic acid inhibits aflatoxin formation and growth in A. flavus in a dose-dependent manner. Global expression analysis (RNA-Seq) of gallic acid treated A. flavus showed that 0.8% (w/v) gallic acid revealed two possible routes of aflatoxin inhibition. Gallic acid significantly inhibited the expression of farB, encoding a transcription factor that participates in peroxisomal fatty acid β oxidation, a fundamental contributor to aflatoxin production. Secondly, the carbon repression regulator encoding gene creA was significantly down regulated by gallic acid treatment. CreA is necessary for aflatoxin synthesis and aflatoxin biosynthesis genes were significantly downregulated in DcreA mutants. In addition, the results of antioxidant enzyme activities and the lipid oxidation levels coupled with RNA-Seq data of antioxidant genes indicated that gallic acid may reduce oxidative stress through the glutathione- and thioredoxin-dependent systems in A. flavus.

The eco-efficiency of cultivated land use (ECLU) is an important indicator for the construction of ecological civilization in China. Exploring the spatiotemporal dynamic evolution of the ECLU is helpful for sustainable use of arable land, ensuring food security and ecological security. However, previous studies have mostly focused on the use of a slacks-based measure (SBM) model for ECLU measurement, ignoring the more accurate epsilon-based measure (EBM) model. Therefore, in this study, firstly, we explored the conceptual framework of ECLU, and then, based on the panel data of the counties in the region around Beijing and Tianjin from 2005 to 2020, we investigated the spatial and temporal evolution of ECLU by using the Super-EBM model, kernel density estimation method, and spatial Markov chain model. Results displayed: (1) From 2005 to 2020, the ECLU in the region around Beijing and Tianjin displayed an increasing state, but the average value was only 0.55. (2) The time evolution of the ECLU has gradually polarized, the internal gap has widened, but it tends to stabilize. (3) The ECLU in the region around Beijing-Tianjin was more inclined to keep it the same and there was a "club convergence" phenomenon, which was meaningfully affected by the background of neighboring areas. In the light of local conditions, the government should reasonably formulate the path to optimize the ECLU, strengthen the linkage with the surrounding cities, and bring into play the positive spillover effect.

The guided ultrasonic wave (GUW) is extensively employed in non-destructive testing (NDT) for the purpose of detecting defects in aerospace vehicles, oil pipelines and mechanical equipment. The filtration of GUW signals, which often contain substantial environmental noise, is a crucial procedure in signal processing. This paper presents a novel denoising approach that combines Variational Mode Decomposition (VMD) with an enhanced Singular Value Decomposition (SVD). The VMD method is employed to preprocess the initial signal, thereby segregating the signal component from the noise component. Subsequently, the SampEn-SVD (Sample Entropy-SVD) method is utilized to extract the effective component from the VMD-preprocessed noise component. Finally, the VMD-preprocessed signal component and the SampEn-SVD-processed effective component are combined to yield the resultant signal, which effectively filters out the noise. The efficacy of this integrated denoising approach is substantiated through the examination of experimental signals. Furthermore, a comparative analysis is conducted to evaluate the efficacy of this method in relation to other denoising techniques. The results indicate that the SampEn-SVD method yields a superior signal-to-noise ratio (SNR) when processing the GUW signal transmitted through the steel strand. Moreover, the denoising procedure significantly reduces the discretization of characteristic parameters in the signal waveform, thereby addressing the issue of inadequate reproducibility in testing outcomes. Consequently, the denoised signal exhibits high fidelity and demonstrates a strong correlation between its combined eigenvector and strand stress.

In this paper, the catalytic combustion of DMDS (dimethyl disulfide, CH₃SSCH₃) over bimetallic supported catalysts were investigated. It was confirmed that Cu/γ-Al₂O₃-CeO₂ showed best catalytic performance among the five single-metal catalysts. Furthermore, six different metals were separately added into Cu/γ-Al₂O₃-CeO₂ to investigate the promoting effect. The experiments revealed Pt as the most effective promoter and the the best catalytic performance was achieved as the adding amount of 0.3 wt%. The characterization results indicated that high activity and resistance to sulfur poisoning of Cu-Pt/γ-Al₂O₃-CeO₂ could be attributed to the synergistic effect between Cu and Pt.

Abstract: The EMU(electric multiple units) traction motors are powered by converters. The PWM(pulse width modulation) voltage increases the voltage stress borne by the motor insulation system, making the ITSC(inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power was proposed in the article to evaluate the non-metallic ITSC faults degree. The apFFT(all phase FFT) time-shift phase difference correction with double Hanning-windows is used to calculate the fundamental frequency of the traction motor's ZSVC（zero se-quence voltage component）, the fundamental amplitudes of ZSVC and three-phase current. The five parameters are used as fault features to train the SVM (support vector machine)fault diagnosis model. The SVM hyper-parameters C and g are optimized by K-CV (K fold cross-validation) and grid search methods. The experimental verification was carried out by the EMU electric traction simulation experimental platform. According to the non-metallic degree index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training data set and 93.33 % on the test data set. There was no misclassification between normal and serious ITSC fault samples.

Reducing the hardness of hard water is of great concern nowadays due to some adverse effects on water pipes, boilers and soap consumption. Using the method of biomineralization to precipitate calcium and magnesium ions to become carbonate minerals was one of the most important innovations for reducing the hardness of hard water. The present study sought to explore the physical and chemical conditions of carbonates bio-precipitation and the potential use of Bacillus licheniformis SRB2 strain (GenBank: KM884945.1) isolated from sludge sample of Moshui River (Shandong University of Science and Technology, Qingdao, China) in reducing the hardness of hard waters by the induction of carbonate minerals. In this study, B. licheniformis SRB2 strain was identified based on the morphological, biochemical and 16S rDNA gene sequence homology analysis. The carbonate minerals induced by B. licheniformis bacteria in the liquid culture medium with 3% NaCl and Mg/Ca molar ratio of 0, 6, 8, 10 and 12 were investigated. The culture medium was inoculated with the bacterial liquid seed was set as the experimental group and the other culture medium was inoculated with the same volume of distilled water was set as the control group. The mineral phases, micromorphologies, and crystal structures were analyzed using X-ray powder diffraction, scanning electron microscope, energy dispersive X-ray detector, high resolution transmission electron microscopy and selected area electron diffraction. The bacterial concentrations and pH values of the solution were measured by a spectrophotometer and a pH meter, respectively. The urease secreted by B. licheniformis SRB2 was found to greatly increase the pH values of the liquid medium, which favored the formation of calcium carbonate.As a result, Mg²⁺ and Ca²⁺ ion concentrations decreased greatly due to the biomineralization of calcium carbonate and nesquehonite minerals in the presence of B. licheniformis SRB2 bacterium. There were only few calcium carbonates and no nesquehonite minerals in the control groups. It was also found that the minerals of nesquehonite induced by B. licheniformis SRB2 had a phenomenon of preferred orientation. What was more, even though Mg²⁺ ions inhibited the precipitation of Ca²⁺ ions, but under the action of B. licheniformis SRB2 bacteria, the inhibition effect was significantly declined. The bio-precipitation of calcium carbonate and nesquehonite minerals may represent a new method of pretreatment for the hardness reduction of hard water. The accomplished study is of certain interest for interpretation of the carbonates biomineralization in natural environment, and maybe also has a certain application value in the former processing of hard water

Broad bean paste (BBP) is a traditional fermented soy food, and its high salt content not only prolongs the fermentation time but also threatens human health. In this study, three BBP-meju with different salt concentrations were prepared, and the effects of varying salinity on the fermentation process were comprehensively compared. The results showed that salt-reduced fermentation contributed to the accumulation of amino acid nitrogen, reducing sugars, free amino acids, and organic acids. Alcohols, esters, aldehydes, and acids were the main volatile flavor compounds in BBP-meju, and the highest total volatile flavor compounds were found in medium-salt meju. Bacillus, Staphylococcus, Aspergillus, and Mortierella were the dominant microbial communities during fermentation, and there were also three opportunistic pathogens, Enterobacter, Pantoea, and Brevundimonas, respectively. According to Spearman correlation analysis, Wickerhamomyces, Bacillus, Staphylococcus, and Mortierella all showed highly significant positive correlations with ≥3 key flavor compounds, which may be the core functional flora. Furthermore, the dominant microbial genera worked synergistically to promote the formation of high-quality flavor compounds and inhibit the production of off-flavors during salt-reduced fermentation. This study provides a theoretical reference for the quality and safety control of low-salt fermented soy foods.

Preventing the rebound of black and odorous water bodies is critical for improving the ecological environment of water bodies. This study investigated the effect and mechanism of in-situ improvement of sediment microenvironment by nitrate in tailwater of wastewater treatment plants combined with aerobic denitrifying bacteria under low-DO regulation (TailN+CFM+LDO). On the 60th day of remediation, the dissolved oxygen and oxidation-reduction potential in the overlying water increased to 5.6 mg/L and 300 mV, respectively, the acid volatile sulfide in the sediment decreased by 70.4%, and the organic matter in the sediment was reduced by 62.7%, in which the HFOM was degraded from 105 g/kg to 56 g/kg, and the potential risk of water reverting to black and odorous conditions significantly decreased. Amplicon sequencing analysis revealed that the relative abundance of electroactive bacteria Thiobacillus and Pseudomonas with denitrification capacity was found to be significantly higher in the TailN+CFM+LDO group than in the other remediation groups. Functional prediction of 16S sequencing results indicated that both the quantity and activity of critical microbial enzymes involved in nitrification and denitrification processes could enhance in the TailN+CFM+LDO group. These results improved our understanding of the improvement of the sediment microenvironment and could thus facilitate its application.

In this work, nylon 6/ P(N-phenylmaleimide-alt-styrene) blends were prepared by melt blending, and the mechanical, heat-resistant, crystallographic and dynamical mechanical properties of nylon 6/ P(N-phenylmaleimide-alt-styrene) blends with different contents were investigated and analyzed. The results showed that the mechanical properties decreased with increasing PNS, while the heat deflection temperature (HDT), relative crystallinity (Xn), and storage modulus (G’) increased with increasing PNS. The results of differential Scanning Calorimetry (DSC) proved the PNS played the positive role of nucleating PA6. And the results of dynamic mechanical analysis (DMA) proved the PNS could improve the rigidity of PA6/PNS blends. From the SEM, these PNS domains were between 0.2 and 4 μm in diameter. The experimental results indicated that the addition of PNS improved the rigidity of PA6/PNS blends, and then improved the heat-resistant property.

Outliers seriously affect the accuracy in geometric model fitting. Previous works in coping with outliers involve threshold selection and scale estimation. However most scale estimators suppose the inlier distribution as a Gaussian model, which usually poorly meets the cases in geometric model fitting. Outliers, considered as the points with big residuals to all the true models, share common items with big values in quantized residual preferences, thus making the outliers gather away from the inliers in quantized residual preference space. In this paper we make use of this outlier consensus in quantized residual preference space by extending the usage of energy minimization to combine the model error and the spatial smoothness in quantized residual preference space for outlier detection. The energy minimization based outlier detection process follows an alternate sampling and labeling framework. After the outlier detection, an ordinary energy minimization method is employed to optimize the inlier labels, which also follows the framework of alternate sampling and labeling. The experimental results in this study show that the energy minimization based outlier detection method can detect most of the outliers in the data. Furthermore, the proposed energy minimization based inlier segmentation process segments the inliers into different models quite accurately. Overall, the performance of the proposed method is better than most of the state-of-the-art methods.

Structure/material requires simultaneous consideration of both its design and manufacturing processes to dramatically enhance its manufacturability, assembly and maintainability. In this work, we present a novel design framework for structure/material with requested mechanical performances in virtue of the compelling properties of topological design and origami techniques. The framework comprises four procedures, including topological design, unfold, reduction manufacturing, and fold. Topological design method, i.e. Solid Isotropic Material Penalization (SIMP) method, serves to optimize the structure to achieve preferred mechanical characteristics and origami technique is exploited to make the structure rapidly and easily fabricated. Topological design and unfold procedures can be conveniently completed in a computer; then, reduction manufacturing, i.e. cutting, is performed to remove materials from the unfolded flat plate; the final structure is finally obtained by folding the plate of the previous procedure. A series of cantilevers, consisting of origami with parallel creases and Miura-ori (usually regarded as a metamaterial), made of paperboard are designed with least weight and required stiffness by using the proposed framework. The findings here furnish an alternative design framework for engineering structures which could be better than 3D printing technique, especially for large structures made of thin metal materials.

The thesis proposes a argument that a mass point moving in a horizontal circle on the Earth's surface can generate a radial centrifugal force of the Earth's center of mass, thus realizing an unconventional anti-gravity path. And through the theoretical analysis of the difference in the distribution of gravitational acceleration at different latitudes caused by the rotation of the earth, and the centrifugal force of a mass point moving in a horizontal circle on the earth's surface is analyzed, the validity of the argument is demonstrated. At the same time, the paper puts forward two possible basic principles, and makes theoretical analysis and demonstration. Conceive of the concrete experimental verification of principle two is proposed. Four technical schemes for antigravity ascent are proposed and basic analysis is made. On the premise of not fully considering the practical technical ability, it is theoretically proved that three of them have realistic operability and development potential. In theory, this paper opens up a different way of anti-gravity for human beings, which will bring new ideas and prospects for human beings to get rid of the bondage of gravity.

In this paper, the Hafele‐Keating experiment and the geostationary orbit satellite of related physics problems of are analyzed and discussed in detail. If researchers read the paper and think about it, It will be found that the Hafele‐Keating experiment and the geostationary satellite problem prove that some of the core ideas and theories of relativity are seriously wrong. Isnʹt that a very serious problem in physics? Canʹt this cause the academic circles to pay great attention to it? Isnʹt this worthy of in‐depth analysis and research? The core of the paper is to point out that there are problems with the space concept and the reference system theory of classical physics and relativity, And that would bring up a whole host of physics problems. It takes a concerted effort from the academic community, Do furtherin‐depth analysis and research. Strive forthe further discovery and perfection of physics theory.

The molecular clusters ((HO)3Si-O-Si(OH)3, and (HO)3Al-O-Si(OH)3 ) representative of aluminosilicate mineral surface were employed to study the dissolution of aluminosilicate in acidic condition via density functional theory with the M06-2X+G(d,p) methodology. The surface termination sites (Si and Al) were both tetra-coordinated and the terminal oxygen was protonated in acidic condition. In the dissolution reaction, the calculated barrier height of the six-membered ring transition state complex containing two water molecules was predicted to be lower than that of four-membered ring transition state complex containing one water molecule. In addition, the calculated barrier heights for Al-terminated sites were predicted to be lower than those for the Si-terminated sites, suggesting that breaking the Al-O bond is easier than Si-O bond in the aluminosilicate mineral surface; the barrier heights of the surface termination sites with protonated terminal oxygen were lower than those without protonated terminal oxygen. With the fracture of Si-O and Al-O bonds, the Si and Al release from the aluminosilicate. These results confirm the experimental conclusions that the acidic condition facilitates the release of Si and Al from the aluminosilicate, and the concentration of Al leaching from aluminosilicate is higher than Si.

The heterogeneous structure implies that a few nodes may be crucial in maintaining network structural and functional properties. Identifying these crucial nodes correctly and quickly is a primary issue as contemporarily may face the mushrooming of large-scale datasets. Besides, the ‘weight issue’ is always ignored in this field which edge weight may play a positive/negative role in contributing to the node importance in different weighted networks. This paper provides a novel algorithm, Weighted Expectation Algorithm (WEA), which aims to ensure accuracy and speed of computation by taking advantage of dynamic programming to better handle the task of large-scale networks. Additionally, the weight issue that edge weights may contribute differently is addressed by a simply quantitative definition. Two standard experiments show WEA can maintain the network structure in connectivity well (by the lowest average robustness 0.192) and identify the node importance better in the spreading function test of spreading dynamics (by the highest average Kendall’s tau-b 0.678). In addition, the time complexities of different algo-rithms are evaluated, and their time-consuming are tested, proving that WEA consumes a rela-tively short time.

The degree of ore rock fragmentation of mine blasting is an important index for evaluating the effect of engineering blasting. In order to study the effect of millisecond delayed blasting on the internal fragmentation of the ore rock, LS-DYNA numerical simulation software was used to obtain the damage characteristics of the ore rock in the blasting area using the RHT model. The Mohr-Coulomb strength damage criterion was applied to analyze the internal stress state of the ore rock, and the effects of 25ms, 50ms and 75ms delay time conditions on ore rock fragmentation were studied. The results show that, within a certain range of delay time, the degree of rock fragmentation increases with the increase of delay time. The stress state in the ore rock due to blasting is strengthened with the increase of delay time in the direction of the blast hole, which shows that the stress concentration is strengthened from the excavation face to the interior of the ore rock. The damage and the degree of fragmentation show a characteristic relationship of 25ms＜50ms＜75ms, which is verified by carrying out blasting experiments on site.

The performance of recycled aggregate concrete deteriorates due to the defects of adhesive mortar, pores and cracks on the surface of recycled aggregate. It is of great engineering significance to modify the reclaimed aggregate to improve its performance and adaptability. In this paper, the physical and chemical properties of waste concrete recycled aggregate are reviewed. Then, the existing modification methods and technologies of recycled aggregate, including physical, chemical and biological methods, are reviewed. Finally, the existing problems of the current modification technology are summarized, and the future research direction is proposed. It is expected to provide a new idea and method for the modification of waste concrete recycled aggregate.

In (Semigroup Forum 77: 325-338, 2008) Mahmoudi M. and Renshaw J. solved a study that covers of cyclic $S$-acts over monoids. This article is an attempt to initiate the covers of finitely generated $S$-acts. We give a necessary and sufficient condition for a monoid to have the properties that $n$-generated $S$-acts have strongly flat covers, Condition $(P)$ covers and projective covers. The main conclusions extend some known results. We show also that Condition $(P)$ covers of finitely generated $S$-acts are not unique, unlike the situation for strongly flat covers. Additionally, we demonstrate that the property of Enochs' $\mathcal{X}$-precover of $S$-act $A$, where $\mathcal{X}$ denotes a class of $S$-acts that are closed under isomorphisms.

China Xihu Longjing tea famous for its good flavor and quality. However, the related metabolites information except flavonoids is largely deficient. Different processing methods of China Xihu Longjing tea fixing both by machine at first and second step(A1), first step by machine and second step by hand(A2), first step by hand and second step by machine(A3), both by hand at first and second step (A4) were compared using a UHPLC-QE-MS-based metabolomics approach. Liquid chromatography-mass spectrometry was used to analyze the metabolic profiles of the processed samples. A total of 490 metabolites (3 alkaloids, 3 anthracenes, 15 benzene and substituted derivatives, 2 benzopyrans, 13 coumarins and derivatives, 128 flavonoids, 4 furanoid lignans, 16 glycosides and derivatives, 5 indoles and derivatives, 18 isocoumarins and derivatives, 4 chalcones and dihydrochalcones, 4 naphthopyrans, 3 nucleosides, 78 organic acids and derivatives, 55 organooxygen compounds, 5 phenols, 109 prenol lipids, 3 saccharolipids, 3 steroids and steroid derivatives, 17 tannins) were identified. The different metabolism profiles were distinguished using PCA and OPLS-DA. There were differences in the types and contents of metabolites, especially flavonoids, furanoid lignans, glycosides and derivatives, organic acids and derivatives and organooxygen compounds. There was a positive correlation between flavonoid metabolism and amino acid metabolism. However, there was a negative correlation between flavonoid metabolism and amino acid metabolism, which had the same trend as prenol lipids metabolism, tannin.This study provides new valuable information for regarding differences in the metabolite profile of China Xihu Longjing tea processed based on machine fixing and manual fixing methods.

In this paper, we study the well-posedness and asymptotics of a one-dimensional thermoelastic laminated beam system either with or without structural damping, where the heat conduction is given by Fourier's law effective in the rotation angle displacements. We show that the system is well-posed by using Lumer-Philips theorem, and prove that the system is exponentially stable if and only if the wave speeds are equal, by using the perturbed energy method and Gearhart-Herbst-Prüss-Huang theorem. Furthermore, we show that the system with structural damping is polynomially stable provided that the wave speeds are not equal, by using the second-order energy method.

The stator winding insulation system is the most critical and weak part of the EMU's (electric multiple unit) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent the fault from expanding into phase-to-phase or ground short-circuits. The TCU(traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is stationary. Three ITSC fault diagnostic conditions are based on different IGBTs control logic. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on random forest. The proposed method was validated using a simulation experimental platform for ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with the increase of ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location both in train and test samples are 100%.

Controlling the collective behavior of micro/nanomotors with ultrasound may enable new functionality in robotic, medicine and other engineering disciplines. Currently, various collective behaviors of nanomotors, such as assembly, reconfiguration and disassembly, have been explored by using acoustic fields with a fixed frequency, while regulating their collective behaviors by varying the ultrasound frequency still remains challenging. In this work, we design an ultrasound manipulation methodology allowing nanomotors to exhibit different collective behaviors by regulating the applied ultrasound frequency. This work has important implications for design of artificial actuated nanomotors and optimize their performances.

This paper outlines our effort on optimizing Pulsed Magneto-Oscillation (PMO) design in order to improve the efficiency of ingot manufacturing under PMO. Our calculation and experiment provided optimized PMO coil design and special configuration. Our optimized PMO design offered us a device that enabled the operator to examine and operate the melt without losing the efficiency. In one of the designs, the equipment even refined the grain sizes. Our work also showed an optimized distance range between the coil and the melt surface that maximized the refinement effects.

We generalize the idea of quantum clock time to quantum spacetime reference frame via physical realization of a reference system by quantum rulers and clocks. Omitting the internal degrees of freedom (such as spins) of the physical rulers and clocks, only considering their metric properties, the spacetime reference frame is described by a bosonic non-linear sigma model (NLSM). We study the quantum behavior of the system under approximations, and obtain (1) a cosmological constant valued (2/π)ρ_c0 (ρ_c0 the critical density at near current epoch) which is very close to the observations; (2) an effective Einstein-Hilbert term; (3) the ratio of variance to mean-squared of spacetime interval tends to a universal constant 2/π in the infrared region. This effect is testable by observing a linear dependence between the inherent quantum variance and mean-squared of the redshifts from cosmic distant spectral lines. The proportionality is expected to be the observed percentage of the dark energy. The equivalence principle is also generalized to the quantum level.

Objective: To investigate the effects and mechanisms of different concentrations of CCCP on mitophagy in human peripheral blood regulatory T cells. Methods: Tregs were isolated, identified and then grouped, treating with CCCP at a concentration of 2.5 μM, 5 μM, 10 μM, 20 μM and 40 μM for 24h in an incubator. Flow cytometry detected the reactive oxygen species (ROS), mitochondrial membrane potential (MMP), mitochondrial quality, and fluorescence microscopy observed the co-localization of mitochondria and lysosomes in each group. Results: The purity of CD4⁺CD25⁺Tregs was (93.36 ± 1.87) %. With the increase of CCCP concentration, the level of ROS gradually increased, while the MMP decreased gradually. About the mitochondria and lysosome fusion, the fluorescence intensity of orange (yellow) was the highest when the concentration of CCCP was in the range of 5-10 μM while decreased with the CCCP concentration continually increasing. The mitochondrial quality decreased with the increase of CCCP concentration. However, there was no significant difference between groups C, D and E. The mitochondrial quality of groups F and G were significantly lower than that of group E. Conclusions: With the concentration of CCCP gradually increased, the level of ROS in Treg cells increased, and MMP decreased, which promoted the mitophagy, mitochondrial quality maintains homeostasis; When ROS accumulated, and MMP decreased significantly, the mitophagy was inhibited, and the mitochondrial quality was significantly decreased.

Using the method of the beta function, Sun has recently evaluated some series of the type $\sum_{k=0}^{\infty}(ak+b)x^k/\binom{mk}{nk}$. By factorization of the polynomial $t^{m-n}(1-t)^n-x$, we will give a general method to find new $\pi$-series.

Long-haul COVID-19 illness first gained widespread recognition among social support groups and later in scientific and medical communities. This illness is mysterious as it affects COVID-19 survivors at all levels of disease severity, even younger adults and children. While the precise definition may be lacking, the defining symptoms are fatigue, dyspnea, and headache that last for months after hospital discharge. The less typical symptoms may include cognitive impairments, chest and joint pains, myalgia, smell and taste dysfunctions, cough, mood changes, and gastrointestinal and cardiac issues. Presently, there is limited literature discussing the possible pathophysiology, risk factors, and treatments in long-haul COVID-19, which the current review aims to address. In brief, long-haul COVID-19 may be driven by long-term lung and brain damage and unresolved inflammation from multiple sources. The associated risk factors may include female sex, more than five early symptoms, early dyspnea, and specific biomarkers like D-dimer. While only rehabilitation training has been useful for long-haul COVID-19, therapeutics repurposed from mast cell activation syndrome, myalgic encephalomyelitis/chronic fatigue syndrome, and pulmonary fibrosis also hold potential. In sum, this review hopes to provide the current understanding of what is known about long-haul COVID-19.

Probiotics have emerged as a prominent alternative to antibiotics in antimicrobial-free broiler farming. To assess the effect of the probiotics Sphingomonas sp. Z392 isolated and identified by this this laboratory on broiler growth, we conducted a study to examine the changes in broiler body weight, EPI, intestinal histological structure and gut microbiota. The results showed that supplementation of broiler drinking water with 4 × 105 CFU/mL of Sphingomonas resulted in an increase in the relative abundance of Lactobacillus, Bacteroides, Lachnospiraceae, Aminobacterium, Oribacterium, Christensenellaceae, Faecalibacterium, Barnesiella, Ruminococcus, Parabacteroides, Phascolarctobacterium, Butyricicoccaceae, and Caproiciproducens, which were reported to be positively correlated with the digestion and absorption of broiler chickens. The relative abundance was increased of Odoribacter, Alistipes, Parabacteroides, and Rikenellaceae, which were reported to be negatively correlated with the occurrence of intestinal diseases. The relative abundance was decreased of Campylobacter, Shigella Castellani, Bilophila, Campylobacter, Clostridia, and Anaerotruncus, which were reported positively correlated with the occurrence of intestinal diseases. At the same time, it was increased in the integrity of small intestinal villus morphology, the number of goblet cells in small intestinal epithelial cells, the health of mitochondria in the cytoplasm of jejunal villous epithelial cells, and the number of lysosomes in the cytoplasm of goblet cells in small intestinal epithelium, ileal villous epithelial cells, mitochondria in the cytoplasm of large intestinal villous epithelial cells, the VH/CD of the ileum, digestive, absorption, and defense capabilities. Especially the final weight was increased by 4.33% and the EPI increased by 10.10%. Therefore, the supplementation of broiler drinking water with Sphingomonas generated better economic benefits for the broiler chickens.

Microwave radiation diagnosis technology can detect thermal changes in skin tissue earlier than anatomical changes, but its detection accuracy is limited by non-target radiation interference in the measurement environment, additional measurement errors of system units, and energy scattering and transmission between skin tissues. This paper aims to address the scientific challenges of analyzing the forward and inversion modelling detection mechanism of layered accurate temperature measurement of human skin tissue based on multi-band. The study focuses on the construction of a pencil beam antenna optimization system, the optimization strategy of high-sensitivity correlated radiometer architecture, and the high-precision multi-band forward and inversion modelling detection algorithm. The key technologies include: (1) A new method of integrated modeling and multi-index optimization of antenna with high directivity and small aperture is proposed, and a priori knowledge-guided neural network of pencil beam distribution is constructed to realize the inversion model of antenna structural parameters; (2) The influence mechanism of high sensitivity correlated radiometer architecture error is analyzed, and a periodic phase shift error correction algorithm based on uniform polar circle is designed; (3) Combining deep learning theory and hyperparameter optimization framework, an iterative model is established, and the objective function modified by the penalty factor is defined to realize a new detection method combining forward and inversion. This paper presents a theoretical foundation for the industrialization of microwave radiation diagnostic technology.

This study aimed to investigate how transplanting date affects the agronomic and grain quality traits of two early maturing rice varieties. The experiment was conducted in the rice research field of Chungnam Agricultural Research and Extension Services, South Korea and rice materials were transplanted at intervals of approximately 15 days from April 16 to July 16 in 2019 and 2020. Results showed that agronomic and grain quality traits varied according to the transplanting date, and earlier transplanting resulted in the longer period of days from transplanting to heading (DTH). The spikelet number m-2 was highly correlated with the milled rice yield (r = 0.963** for Jinbuol, r = 0.909** for Yeoreumi) and it significantly decreased as the transplanting date was delayed, which was leading to lower yield. Environmental factors, the mean temperature during the grain filling stage, had a negative correlation with head rice rate (r2 = 0.825** for Jinbuol, r2 = 0.803** for Yeoreumi), and the number of days from transplanting to heading showed negative correlation with protein content (r2 = 0.777** for Jinbuol, r2 = 0.833** for Yeoreumi). Therefore, increasing the number of days from transplanting to heading date can lead to higher milled rice yield and lower protein content, and avoiding heading dates on July 17 can improve the appearance traits. As a results, it is suggested that early transplanting is advantageous to increase the milled rice yield and grain quality of early maturing rice.

A PLS-based quality prediction method is proposed for batch processes using two-dimensional extended windows. To realize the adoption of information in the direction of sampling time and batch, a new definition region of support (ROS), k-i-back-extended region of support (KIBROS), is proposed to establish the extended window, which including the data of the previous sampling times in the same batch, the data of the same sampling time in the historical batches, the data of the previous sampling times in the historical batches and the data of the later sampling times in the historical batches. According to these regions of support, extended windows are established, and different models are proposed based on the extended windows for batch processes quality prediction. Further, using a typical batch process as an example, the injection molding process, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and the prediction stability is also improved.

The intense motion of a ship can greatly impacts the comfort of crew members and the safety of the vessel. Therefore, accurately estimating and predicting ship attitudes has become an important issue. This paper introduces the latest development in functional deep learning model called DeepOnet. It takes wave height as input and ship motion as output, using a cause-to-result prediction approach. The modeling data used in this study is sourced from publicly available experimental data from the Iowa Institute of Hydraulic Research. Firstly, parameters system tuning was conducted for the neural network's hyperparameters to determine the appropriate combination of parameters. Secondly, the DeepOnet model for wave height and multi-degree-of-freedom motion was established, and the influence of increasing time steps on prediction accuracy was examined. Finally, a comparison was made between the DeepOnet model and the classical time series model LSTM. It was found that the DeepOnet model had a 10-fold improvement in accuracy for roll and heave attitudes. Moreover, as the forecast duration increased, the advantage of DeepOnet showed a trend of strengthening. As a functional prediction model, DeepOnet provides a new promising tool for very short-term prediction of ship motion.

The transforming growth factor-β (TGF-β), in which overexpression have been associated with various diseases, has become an attractive molecular target for the treatment of cancers. Three series of 3-substituted-4-(quinoxalin-6-yl) pyrazoles 14a–h, 15a–h, 16a–h, 22a, 22b, 22d, 23a, 23b, 23d, 24b, and 24d were synthesized and evaluated for their activin receptor-like kinase 5 (ALK5) and p38α mitogen activated protein (MAP) kinase inhibitory activity in an enzymatic assays. Among these compounds, the most active compound 16f inhibited ALK5 phosphorylation with an IC₅₀ value of 0.28 µM, with 98% inhibition at 10 µM. Compound 16f also had good selectivity index of >35 against p38α MAP kinase, with 9.0-fold more selective than clinical candidate, compound 3 (LY-2157299). Molecular docking study was performed to identify the mechanism of action of the synthesized compounds and their good binding interactions were observed. ADMET prediction of good active compounds showed that these ones possess good pharmacokinetics and drug-likeness behavior.

We constrain two dynamical dark energy models that are parametrized by the logarithm form of $w(z)=w_{0}+w_{1}\left(\frac{\ln (2+z)}{1+z}-\ln 2\right)$ and the oscillating form of $w(z)=w_{0}+w_{1}\left(\frac{\sin(1+z)}{1+z}-\sin(1)\right)$. Comparing with the Chevallier-Polarski-Linder (CPL) model, the two parametrizations for dark energy can explore the whole evolution history of the universe properly. Using the current mainstream observational data including the cosmic microwave background data and the baryon acoustic oscillation data as well as the type Ia supernovae data, we perform the $\chi^2$ statistic analysis to global fit these models, finding that the logarithm parametrization and the oscillating parametrization are almost as well as the CPL scenario in fitting these data. We make a comparison for the impacts of the dynamical dark energy on the cosmological constraints on the total mass of active neutrinos. We find that the dark energy properties could significantly change the fitting results of neutrino mass. Looser constraints on $\sum m_{\nu}$ are obtained in the logarithm and oscillating models than those derived in the CPL model. Consideration of the possible mass ordering of neutrinos reveals that the most stringent constraint on $\sum m_{\nu}$ appears in the degenerate hierarchy case.

Macrobrachium rosenbergii is a valuable freshwater prawn in Asian aquaculture. In recent years, a new symptom that was generally called as ‘white head’ caused high mortality in M. rosenbergii farms in China. Samples of M. rosenbergii, M. nipponense, Procambarus clarkii, M. superbum, Penaeus vannamei, and Cladocera from a farm suffering from ‘white head’ in Jiangsu Province were collected and analyzed in this study. Pathogen detection showed that all samples were positive for Shrimp hemocyte iridescent virus (SHIV). Histopathological examination revealed dark eosinophilic inclusions and pyknosis in hematopoietic tissue, hepatopancreas and gills of M. rosenbergii and M. nipponense. Blue signals of in situ DIG-labeled LAMP (ISDL) appeared in hematopoietic tissue, hemocytes, hepatopancreatic sinus, and antennal gland. TEM of ultrathin sections showed a large number of SHIV particles with a mean diameter about 157.9 nm. The virogenic stromata and budding virions were observed in hematopoietic cells. Quantitative detection by TaqMan probe based real-time PCR of different tissues in natural infected M. rosenbergii showed that hematopoietic tissue contained the highest SHIV load with a relative abundance of (25.4±16.9)%. Hepatopancreas and muscle contained the lowest SHIV load with a relative abundance at (2.44±1.24)% and (2.44±2.16)%, respectively. Above results verified that SHIV is the pathogen causing ‘white head’ in M. rosenbergii, and M. nipponense and Pr. clarkii are also the susceptible species of SHIV.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Transcutaneous injection laryngoplasty is a well-known procedure for treating paralyzed vocal fold by injecting augmentation material into it. Hence, vocal fold localization plays a vital role in the preoperative planning as the fold location is required to determine the optimal injection route. In this communication, we propose a mirror environment based reinforcement learning (RL) algorithm for localizing the right and left vocal folds in preoperative neck CT. RL-based methods commonly showed noteworthy outcome in general anatomic landmark localization problem in the recent years. However, such methods suggest training individual agent for localizing each fold, though the right and left vocal folds are located in close proximity and have high feature-similarity. Utilizing the lateral symmetry between the right and left vocal folds, the proposed mirror environment allows for a single agent for localizing both the folds by treating the left fold as a flipped version of the right fold. Thus, localization of both folds can be trained using a single training session which utilizes the inter-fold correlation and avoids redundant feature learning. Experiment with 120 CT volumes showed improved localization performance and training efficiency of the proposed method compared with the standard RL method.

In this paper, the sol−gel method was used to synthesize powders of LaMnO3 (LMO), La0.85Ca0.15MnO3 (LCM), and La0.85Sr0.15MnO3 (LSM). We investigated the impact of substituting Ca and Sr at the A−site on the perovskite crystal structure and electrochemical capabilities of LMO. LCM retained its orthogonal structure in comparison to the parent LMO components, whereas LSM transitioned to a rhombic structure. At 0.5 A/g, the specific capacitance of LCM and LSM electrodes is 185.5 F/g and 248 F/g, respectively.The specific capacitance of LCM was more three times than that of the LMO electrode. Among the three samples (LMO, 22.25 m2 g−1; LSM, 31.56 m2 g−1), the LCM sample exhibited the highest specific surface area of 38.79 m2 g−1. The charge transfer resistances of the LMO, LCM, and LSM are 0.48 Ω, 0.36 Ω, and 0.38 Ω, correspondingly. The LCM electrode exhibits the greatest capacitance performance due to its more refined morphology, increased concentration of oxygen vacancy, and more complete utilization of the perovskite bulk structure. The above results demonstrate that Ca or Sr substitution of A−site compounds has great potential for supercapacitor applications.

A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or system. Recent advancements have extended the concept of digital twins to humans, incorporating complex biological data such as DNA (Predictiv Care, Inc.) and immune system profiles. These sophisticated models go beyond mere pictorial representations, offering a more holistic digital reflection. However, a significant gap remains. The current human digital twin models are not capable of learning one’s preferences. In this pilot study, we introduce Diginality, a chatbot powered by HyperCLOVA X, a Large Language Model (LLM) developed by NAVER, Inc. Diginality, learns one’s preferences with custom training from data collected by interview-style questions on the user’s topic of interest. Our findings demonstrate that Diginality successfully answers one’s preferences, thereby adding a new dimension to the concept of human digital twins. This work represents a pioneering step towards creating a more comprehensive and psychologically nuanced human digital twin.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

The human microbiome is an extremely complex ecosystem considering the amount of bacterial species, their interactions, and its variability over time. Here we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder in human populations. Based on a novel information theoretic network inference model we detect species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interactions and scale-free topology versus random unhealthy networks. We detect an inverse scaling relationship between species total outgoing information flow, meaningful of node interactivity, and relative species abundance (RSA). The top ten interacting species are also the least relatively abundant for the healthy microbiome and the most detrimental. These findings support the idea about the diminishing role of network hubs and hubs should be defined considering the total outgoing information flow rather than the node degree. Macroecologically, the healthy microbiome is characterized by the highest total species diversity growth rate, the lowest species turnover, and the smallest variability of RSA for all species. This result challenges current views that posit a universal association between healthy states and the highest absolute species diversity in ecosystems. Additionally, we show how the transitory microbiome is unstable and microbiome criticality is not at the phase transition between healthy and unhealthy states. We stress out the importance of considering interacting pairs versus single node dynamics when characterizing the microbiome and of ranking these pairs in terms of their dynamics. Interactions (i.e. species collective behavior) shape transition from healthy to unhealthy states.\\ The macroecological characterization of the microbiome is useful for diagnostic purposes and disease etiognosis, while species-specific analyses can detect species that are more beneficial leading to personalized design of pre- and pro-biotic treatments and microbiome engineering.

This study investigates the impact of specimen size on the mechanical properties of wood, specifically focusing on compression strength, elastic modulus, and Poisson's ratio. Compression tests were conducted using three different specimen sizes (20mm×20mm×30mm, 40mm×40mm×60mm, 60mm×90mm×90mm) in the longitudinal, radial, and tangential directions. Mechanical parameters, failure mechanisms, load-displacement curves, and stress-strain relationships were systematically analyzed for each size. The study also evaluated the influence of specimen size on the accuracy of finite element numerical simulations by utilizing the obtained mechanical parameters. The results reveal a significant correlation between compressive strength and specimen size, indicating a decrease in compressive strength with increasing specimen size. Conversely, elastic modulus and Poisson's ratio exhibit less sensitivity to variations in specimen size. Notably, parameters derived from small-sized specimens (20mm×20mm×30mm) exhibited substantial errors, while those obtained from medium-sized (40mm×40mm×60mm) and large-sized specimens (60mm×90mm×90mm) demonstrated greater reliability, providing precise results in finite element numerical simulations.

With the daily increasing demands for higher-quality services, mobile Internet of Things (MIoT) are facing various challenges, such as communication security, availability, scalability, etc. Its changing topology features bring a lot of trouble when solving the above problems. Though the evolved cellular network is expected to bring promising solutions, some inherent problems in traditional MIoTs may keep hindering its development. Thus, to overcome above problems, we propose a software-defined MIoTs-based model providing communication security and privacy protection under emergencies. In our scheme, the control plane is responsible to compute routes for online learning devices (OLDs), and forward entries for switches. Taking use of information that OLDs and facilities collect, controller is able to coordinate the overall situation. To ensure the authenticity and reliability of messages sent by OLDs, signature and authentication should be provided. We also introduce an emergency-dealing system. It transplants the multicast technology into software-defined MIoTs to generate a Steiner Tree among impacted nodes, so that OLDs will be informed as soon as emergency happening. The security analysis proves that our scheme is able to ensure the communication security in software-defined MIoTs. The evaluation of performance indicates that our scheme outperforms other existing schemes.

This study presented a novel and sustainable method for producing chitin nanocrystals (PA-ChNCs) using phosphoric acid hydrolysis. The produced PA-ChNCs were used as stabilizers for Pickering emulsions and their physicochemical properties as well as stabilization mechanisms were systematically investigated. The PA-ChNCs exhibited small particle sizes and high surface areas, enabling an effective adsorption at the oil/water interface, which prevented coalescence in Pickering emulsions. The particle size and surface charge PA-ChNCs were strongly pH-dependent, which were critical factors for stabilizing Pickering emulsions. The study showed that emulsions with higher O:W ratios (50%) exhibited larger droplets but greater viscosity, forming a stable gel-like matrix that was more stable than emulsions with other O:W ratios after storage for 30 days. Emulsions with higher PA-ChNCs concentrations (2.0 wt%) and higher pH values (pH 10) showed small droplets and high viscosity, leading to stable emulsions. This research contributes to the development of sustainable and effective emulsifiers for use in the food industry, making utilization of seafood waste in an eco-friendly manner possible. Overall, this study highlights the potential of using newly developed PA-ChNCs as stabilizers for Pickering emulsions in food products.

This study evaluated the effects of high-temperature and high-pressure treatment on the antioxidant and anti-inflammatory activities of carrot extracts. The total polyphenol and flavonoid content of heated carrot extract increased as the heating temperature increased, with 403.93 mg GAE/100 g and 53.14 mg CE/100 g at 150°C. DPPH and ABTs radical scavenging activity (IC50) of heated carrot extract also increased as the heating temperature increased, with 4.65 mg/mL and 7.31 mg/mL, respectively, at 150°C. The reducing power increased with the heating temperature. Heated carrot extract did not exhibit any temperature- or concentration-dependent cytotoxicity. Nitric oxide (NO) generation was suppressed as the heating temperature and NO production increased. The heated carrot extract showed a decrease in the production of dose-dependent TNF-α, IL-1β, and IL-6 compared to the control group. The above results show that heated carrot extract had effective antioxidant and anti-inflammatory activities.

A portable Fourier Transform Spectrometer (B3M-IR) is built and used to measure atmospheric trace gases in the city of Beijing during Olympic Games in 2008. A short description of the instrument is first provided in this paper. A detailed spectral analysis is then presented. The total columns of ozone (O3), carbon monoxide (CO), methane (CH4) and nitrous oxide (N2O) are retrieved from the ground-based solar absorption spectra recorded by the B3M-IR during the Olympic Games. Lacking validation data, only the retrieved total column of O3 is compared with that retrieved by MAX-DOAS, which is deployed at the same station. The mean difference between the two methods of measurement is 6.5%, demonstrating the performance and reliability of B3M-IR.

Considering the aging society and budget constraints, a strategy for reforming public transportation welfare policies is essential for sustainability. Among the various transportation welfare policies, fare discount policies are provided to specific age groups, and most countries offer benefits to several public transportation. South Korea provides a fare-free policy limited to subways, and the need for policy modification is emerging due to sustainability problems caused by an aging society. Hence, the scope of the policy should be expanded to all travel modes, but the discount rate should be reduced, or the target group should be reduced in consideration of the budget. This study proposes a travel mode to focus on by city through the preference of the elderly. The latent class model is employed to classfy stated preference data collected from the survey. The estimation results show a significant preference heterogeneity depending on the level of subway supply by region, and a policy focused on subways would be more reasonable in cities with sufficient subway infrastructure. Hence, regarding sustainability, it may be more appropriate to focus on specific means than to provide the same discount policy for all public transport.

The advent of deep learning has completely reshaped our world. Now, our daily life is fulfilled with many well-known applications that adopt deep learning techniques, such as self-driving cars and face recognition. Furthermore, robotics developed more forms of technology which share the same principle with face recognition, such as hand pose recognition and fingerprint recognition. Image recognition technology requires a huge database and various learning algorithms, such as convolutional neural network and recurrent neural network, that requires lots of computational power, such as CPUs and GPUs. Thus, clients could not be satisfied with the computational resource of the local machine. The cloud resource platform emerged at a historic moment. Docker containers play a significant role of microservices-based applications in the next generation. However, it could not guarantee the quality of service. From clients’ perspective, they have to balance the budget and quality of experiences (e.g. response time). The budget leans on individual business owners and the required Quality of Experience (QoE) depends on usage scenarios of different applications, for instance, an autonomous vehicle requires real-time response, but, unlocking your smartphone can tolerate delays. Plenty of on-going projects developed user-oriented optimization resource allocation to improve the quality of the service. Considering the users’ specifications, including accelerating the training process and specifying the quality of experience, this thesis proposes two differentiate containers scheduling for deep learning applications: TRADL and DQoES .

Buckling defect will appear on the outer surface of the deformed ring during constrained ring rolling (CRR) of aluminum alloy thin wall conical ring with inner high ribs (AATWCRIHR), if the geometrical dimension of the ribs doesn't match the wall thickness. To avoid the buckling defect, a quantitative method for characterizing the degree of the buckling defect is proposed using the area of buckling profile. Then an orthogonal experimental scheme was designed taking the width of the middle rib, thickness of wall, and height of the middle rib as design variables and defining the area of buckling profile as optimization objective. Subsequently, a quadratic polynomial response surface model was established by combining the optimization algorithm with the finite element method (FEM), and the geometrical dimension of the middle ribs of the deformed AATWCRIHR is optimized. Also the optimal parameter combination to minimize the area of buckling profile is obtained and verified using FE simulation. Results show that the AATWCRIHR after optimization doesn’t generate the buckling defect during constrained ring rolling, and it is proved that the quantitative buckling defect representation method and the optimization design method based on the response surface model and the finite element simulation results are feasible for the constrained ring rolling process of the AATWCRIHR.

To estimate the progressive collapse resistance capacity of a multi-column frame tube structure with the assembled truss beam composite floor (ATBCF), the pushdown analysis, and the nonlinear dynamic analysis as well, are conducted for such a structure by using the alternate load path (ALP) method. The bearing capacities of the remaining structures at three different work conditions, which are the side middle column failure, the edge middle column failure, and the corner column failure, are individually studied, and the collapse mechanism for the remaining structures is analyzed from the aspects of the internal force redistribution and the failure mode of the second defense line. Simultaneously, the influence of the column failure time on the dynamic response of the remaining structure and the dynamic amplification coefficient are discussed. The results show that the residual bearing capacity of the remaining structure with the bottom corner column failure is higher than that of the one with the side or edge middle column failure, while the latter has a stronger plastic deformation capacity. When the ALP method is adopted to operate the progressive collapse analysis, it is reasonable to take the column failure time as 0.1 times of the first-order vertical vibration period of the remaining structure, and it is suitable to set the dynamic amplification coefficient as of 2.0, which is the ratio of the maximum dynamic displacement to the static displacement of the remaining structure under the transient loading condition.

Intercropping of maize (Zea mays L.) and peanuts (Arachis hypogaea L.) (M||P) significantly enhances crop yield. In a long-term M||P field experiment with two P fertilizer levels, we examined how long-term M||P affects topsoil aggregate fractions and stability, organic carbon (SOC), available phosphorus (AP), and total phosphorus (TP) in each aggregate fraction, along with crop yields. Compared to their respective monocultures, long-term M||P substantially increased the proportion of topsoil mechanical macroaggregates (7.6–16.3%) and water-stable macroaggregates (>1 mm) (13.8–36.1%), while reducing the unstable aggregate index (ELT) and the percentage of aggregation destruction (PAD). M||P significantly boosted the concentration (12.9–39.9%) and contribution rate (4.1–47.9%) of SOC in macroaggregates compared to single crops. Moreover, the concentration of TP in macroaggregates (>1 mm) and AP in each aggregate fraction of M||P exceeded that of the respective single crops (P<0.05). Furthermore, M||P significantly increased the Ca2-P, Ca8-P, Al-P, and Fe-P concentrations of intercropped maize (IM) and the Ca8-P, O-P, and Ca10-P concentrations of intercropped peanuts (IP). The land equivalent ratio (LER) of M||P was higher than one, and M||P stubble improved the yield of subsequent winter wheat (Triticum aestivum L.) compared with sole-crop maize stubble. P application augmented the concentration of SOC, TP, and AP in macroaggregates, resulting in improved crop yields. In conclusion, our findings suggest that long-term M||P combined with P application, sustains farmland productivity in the North China Plain by increasing SOC and macroaggregate fractions, improving aggregate stability, and enhancing soil P availability.

This study analyzes the relationship between economic policy uncertainty (hereinafter EPU) and corporate value, and verifies the differential effects of EPU on corporate value according to corporate social responsibility (hereinafter CSR) activities. The study period was from 2010 to 2020, and the EPU indexes measured by Baker et al. (2016)'s method and Hexun's CSR scores for companies listed on the Chinese stock market were used. Hexun is one of the three major evaluation agencies in China, and the CSR scores evaluated by this agency have been widely used in previous studies as a representative indicator of Chinese companies' CSR performance. The results of this study are as follows. First, EPU and corporate value were found to have a negative (-) relationship. Second, active CSR activities relieved the negative (-) relationship between EPU and corporate value. Finally, companies were divided into groups according to CSR activity scores to analyze the relationship and according to the result, the negative (-) relationship between EPU and corporate value was greater in the groups with low CSR activity scores than in the groups with high CSR activity scores. A lag model was applied to control endogeneity, and the results were consistent with the main results above. Previous studies have mainly focused on microscopic factors to investigate factors affecting corporate value. This study focused on macroeconomic policies and paid attention to the effects of changes in the macroenvironment caused by economic policy uncertainty on corporate value. In addition, this study is meaningful in that it expanded previous studies related to CSR activities by confirming that active CSR activities can be an advantageous tool for overcoming EPU.

Leucyl-aminopeptidase (LAP), an important metallopeptidase, hydrolyses amino acid residues from the N-terminal of polypeptides and proteins, acting preferentially on the peptide bond formed by N- terminus leucine. A new leucyl-aminopeptidase was found in Bacillus cereus CZ. Its gene (bclap) contained a 1485 bp ORF encoding 494 amino acids with a molecular weight of 54 kDa. The bcLAP protein was successfully expressed in E. coli BL21(DE3). Optimal activity is obtained at pH 9.0 and 58°C. The bcLAP displays a moderate thermostability and an alkaline pH adaptation range. Enzymatic activity is dramatically enhanced by Ni2+. EDTA significantly inhibits the enzymatic activity, and bestatin and SDS also show strong inhibition. The three-dimensional model of bcLAP monomer and homohexamer is simulated by Phyre2 server and SWISS-MODEL server. The docking of bestatin, Leu-Trp, Asp-Trp and Ala-Ala-Gly to bcLAP is performed using AutoDock4.2.5 , respectively. Molecular docking results show that the residues Lys260, Asp265, Lys272, Asp283, Asp342, Glu344, Arg346, Gly372, His437 are involved in the hydrogen bonding with the ligands and zinc ions. There may be two nucleophilic catalytic mechanisms in bcLAP, one involving His 437 or Arg346 and the other involving His437 and Arg346. The bcLAP can hydrolyse the peptide bonds in Leu-Trp, Leu-Trp and Ala-Ala-Gly.

Polypyrrole (PPy)-capped caped silver nanowire (Ag NW) nanomaterials (core–shell rod-shaped Ag NW@PPy) were synthesized suing a one–port suspension polymerization technique. The thickness of PPy layer on the 50-nm-thickness/15-μm-length Ag NW was effectively controlled to 10, 40, 50, and 60 nm. Thin films cast from one-dimensional conductive Ag NW@PPy formed a three-dimensional (3-D) conductive porous network structure and provided excellent electrochemical performance. The 3-D Ag NW@PPy network can significantly reduce the internal resistance of the electrode and maintain structural stability. As a result, a high specific capacitance of 625 F/g at a scan rate of 1 mV/s was obtained from the 3-D porous Ag NW@PPy composite film. The cycling performance over a long period exceeding 10,000 cycles was also evaluated. We expect that our core–shell-structured Ag NW@PPy composites and their 3-D porous structure network films can be applied as electrochemical materials for the design and manufacture of supercapacitors and other energy storage devices.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

Information encryption and anti-counterfeiting play an important role in many aspects of daily life, such as in minimizing economic losses, protecting secure communication and public security, and so on. Owing to the high information capacity and ease of operation, luminescent materials for advanced information encryption and anti-counterfeiting are essential to meet the increasing demand on encryption security. Herein, we summarize two emerging luminescent materials for information encryption and anti-counterfeiting, the AIE materials and room-temperature phosphorescent materials. At last, we discuss the opportunities and anticipations of these two information encryption and anti-counterfeiting materials.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

The research examines the relationship between self-rated health situation and personal percep-tion of heat waves, and how social linkage of communities would be a moderator variable in residents’ perception of heat waves in Taiwan. This study uses the questionnaire conducted by Sinica “Responsive Capacity under Heat Wave: The Perspectives of the Locals”(2019), using OLS method for estimating the unknown parameters in multiple regression model. The author finds that the correlation of self-rated health situation and perception toward heat is significantly posi-tive. Also, social linkage in communities affects strongly as a moderator variable: While the sat-isfaction to with their community could reduce negative reaction to heat, contacts with neighbors could increase possibility people feel uncomfortable in high-temperature situation. This study ex-hibits the effects of social environment on community, and expects further related researches or practices to strengthen capability to resist heat wavesƒ for Taiwanese residents.

Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A number of deep learning approaches have been developed in recent years to improve the accuracy of such affinity prediction. While the predicting power of these systems have advanced to some degrees depending on the dataset used for model training and testing, the effects of the quality and quantity of the underlying data have not been thoroughly examined. In this study, we employed erroneous datasets and data subsets of different sizes, created from one of the largest databases of experimental binding affinities, to train and evaluate a deep learning system based on convolutional neural networks. Our results show that data quality and quantity do have significant impacts on the prediction performance of trained models. Depending on the variations in data quality and quantity, the performance discrepancies could be comparable to or even larger than those observed among different deep learning approaches. In particular, the presence of proteins during model training leads to a dramatic increase in prediction accuracy. This implies that continued accumulation of high-quality affinity data, especially for new protein targets, is indispensable for improving deep learning models to better predict protein-ligand binding affinities.

Plant height is significantly correlated with grain traits, which is a component of wheat yield. The purpose of this study is to investigate the main QTLs that control plant height and grain-related traits in multiple environments. In this study, we constructed a high-density genetic linkage map using the Wheat50K SNP Array to map quantitative trait loci (QTLs) for these traits in 198 recombinant inbred lines (RILs). The two ends of the chromosome were identified as re-combination-rich areas in all chromosomes except chromosome 1B. The middle area of the chro-mosomes was identified as the recombination-barren area. Both the genetic map and the physical map showed a significant correlation when p=0.001, with a correlation coefficient between 0.63 and 0.99. However, there was almost no recombination between 1RS and 1BS. In terms of plant height, 1RS contributed to the reduction of plant height by 3.43cm. In terms of grain length, 1RS contributed to the elongation of grain by 0.11mm. A total of 43 QTLs were identified, including 8 QTLs for Plant height(PH), 11 QTLs thousand grain weight(TGW), 15 QTLs for grain length(GL),and 9 QTLs for grain width(GW), which explained 1.36%–33.08% of the phenotypic variation. Seven were environment-stable QTLs, including two loci Qph.nwafu-4B and Qph.nwafu-4D that determined plant height. The explanation rates of phenotypic variation were 7.39%-12.26% and 20.11%-27.08%, respectively. One QTL, Qtgw.nwafu-4B, which influenced TGW, showed an explanation rate of 3.43%-6.85% for phenotypic variation, two co-segregating KASP markers were developed, the physical locations corresponding to KASP_AX-109316968 and KASP_AX-109519968 were 25.888344 MB and 25.847691 MB. Another QTL, Qgw.nwafu-4D, which determined grain width, had an explanation rate of 3.43%-6.85%. Three loci that affected the grain length were Qgl.nwafu-5A, Qgl.nwafu-5D.2 and Qgl.nwafu-6B, illustrating the explana-tion rates of phenotypic variation as 6.72%-9.59%, 5.62%-7.75%, and 6.68%-10.73%, respectively. Two QTL clusters were identified on chromosomes 4B and 4D.

To clarify whether cryptochrome contributes to stem elongation and flowering promoted by blue lights associated with low phytochrome activity, wild-type Arabidopsis was compared with its cryptochrome-deficient mutants and cryptochrome-overexpressing transgenic plants. Results indicated that the promotion effects were mainly related to low CRY1 activity, despite partial involvement of high-activity CRY2.

In this paper, we investigate the new degenerate Daehee polynomials and numbers which are called the degenerate Daehee polynomials of the second kind, and derive some new and interesting identities and properties of those polynomials.

Hypoglossal nerve stimulator (HGNS) is a minimally invasive device used for treating obstructive sleep apnea (OSA). The conventional implantable HGNS is a device that consists of a stimuli generator, a breathing sensor, and electrodes connected to the hypoglossal nerve via leads. However, this implant is bulky and causes significant trauma. In this paper, we propose a minimally invasive HGNS based on an electrocardiogram (ECG) sensor and a wireless power transfer (WPT), consisting of a wearable breathing monitor and an implantable stimulator. The breathing external monitor utilizes an ECG sensor to identify abnormal breathing patterns associated with OSA with 88.68$\%$ accuracy, achieved through the utilization of a Convolutional Neural Network (CNN) algorithm. With a skin thickness of 5mm and a receiving coil diameter of 9mm, the power conversion efficiency was measured at 31.8$\%$. The implantable device, on the other hand, is composed of a front-end CMOS Power Management Module (PMM), a Binary Phase Shift Keying (BPSK)-based data demodulator, and a bipolar biphasic current stimuli generator. The PMM, with a silicon area of 0.06 $mm^2$ (excluding pads), demonstrates a power conversion efficiency of 77.5$\%$ when operating at a receiving frequency of 2 MHz. Furthermore, it offers three-voltage options (1.2V, 1.8V, and 3.1V). Within the data receiver component, a low-power BPSK demodulator has been ingeniously incorporated, consuming only 42 $\mu$W when supplied with a voltage of 0.7V. The performance is achieved through the implementation of the self-biased phase-locked loop (PLL) technique. The stimuli generator delivers biphasic constant currents, providing a 5-bit programmable range spanning from 0 to 2.4 mA. The functionality of proposed ECG and WPT-based HGNS was validated, representing a highly promising solution for the effective management of OSA, all while minimizing trauma and space requirements.

Mountain ecosystems are crucial for global biodiversity conservation. However, their landscape features are constantly changing owing to urban expansion. Understanding the relationships between biotic communities and landscape features is essential for biodiversity conservation. This study aimed to examine the effect of land-cover type on avian communities in Lishui, a mountainous urban area in eastern China. Avian surveys were conducted using a total of 168 line transects across different land-cover types once per season from December 2019 to January 2021. We assessed the diversity of bird communities by calculating various metrics at both taxonomic and functional levels. Among the land-cover types measured, woodland, built-up land, cultivated land, and water bodies significantly influenced bird community diversity and composition. Species richness, species abundance, and functional richness were negatively correlated with the proportion of woodland but positively correlated with the proportion of non-natural land cover, such as built-up and cultivated land. In contrast, functional evenness was positively correlated with the proportion of woodland and grassland but negatively correlated with the proportion of non-natural land cover. Land-cover type also exhibited significant correlations with avian functional characteristics such as diet, foraging strata, and body mass, thereby influencing the overall community structure. Our results indicated that mountainous landscape patterns substantially affect avian communities. Different land-cover types possess varying resource endowments that affect the distribution of avian species. Therefore, urban landscape planning in mountainous areas should carefully consider the various functions provided to organisms by different types of land cover to promote biodiversity.

The early elongation events in transcription or translational control in the N-terminal of genes are assuming critical roles in their heterologous expression. The secretory expression of the alkaline pectin lyase APL in B. subtilis were studied to explore the effects of the N-terminal 5-7 amino acids sequences of different signal peptides on gene transcription and protein secretion in this study. The first 5 amino acid sequences of the N-terminal of the signal peptide LipA were identified to play an important role in promoting the expression of APL for the first time. Meanwhile, it also revealed that the signal peptide LipA could lead to higher secretory expression than other signal peptides not because of its sequence structure associated with secretion is stronger but because the N-terminal sequences showed greater advantages in gene transcription. At this basis, the recombinant strain structed in this work showed a new record for the highest extracellular yields (12 295 U/ml) of APL in B. subtilis which was 1.9 times higher than that expressed in the recombinant Escherichia coli strain we had reported before. The novel theories revealed in this study would assuming significance roles for improving expression of foreign proteins in or out of the cells.

Digital Elevation Models (DEMs) are widely used in geographic and environmental studies. In the current work, the fusion of multi-source DEMs is investigated to improve the overall accuracy of public domain DEMs. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods – the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD) and nonlinear adaptive multi-scale decomposition (N-AMD) - are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D – 30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the ice cloud and land elevation satellite (ICESat) global land surface altimetry data as a reference. The vertical accuracy of the fused DEM shows significant improvements of 29.6% and 19.3% in a mountainous region and 27.4% and 15.5% in a low-relief region, compared to the SRTM-1 and AW3D30 respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.

Gut microbiota dysfunction is a key factor affecting chronic kidney disease (CKD) susceptibility. Puerariae Lobatae Radix (PLR), a traditional Chinese medicine and food homologous herb, is known to promote gut microbiota homeostasis, however, its role in renoprotection remains unknown. This study aimed to investigate the efficacy and potential mechanism of PLR to alleviate CKD. An 8-week 2% NaCl-feeding murine model was applied to induce CKD and evaluate the therapeutic effect of PLR supplementary. After gavage for 8 weeks, PLR significantly alleviated CKD-associated creatinine, urine protein increasement and nephritic histopathological injury. Moreover, PLR protected kidney from fibrosis by reducing inflammatory response and down-regulating the canonical Wnt / β-catenin pathway. Further, PLR rescued gut microbiota dysbiosis and protected high salt-induced gut barrier dysfunction. We found enrichment of Akkermansia and Bifidobacterium after PLR intervention, the relative abundance of which were in positive correlation with normal maintenance of renal histology and function. Next, fecal microbiota transplantation experiment verified that the positive effect of PLR on CKD was at least partially exerted through gut microbiota reestablishment and down-regulation of the Wnt / β-catenin pathway. Our study provides evidence for new function of PLR on kidney protection and put forward a potential therapeutic strategy target for CKD.

The monitoring of lake waterbody area in the Qinghai Tibet Plateau (QTP) is of great significance to deal with global climate change. As the latest generation of Landsat series satellites, Landsat-9 data not only have higher radiometric resolution, but also cooperate with other Landsat satellites to greatly improve the temporal resolution. It has great application potential in lake waterbody area monitoring. In order to explore the performance of different algorithms for extracting waterbody and lake waterbody area in Landsat-9 data under large-scale QTP regions, this study relies on Google Earth Engine (GEE) platform and selects 10 waterbody extraction algorithms as the basis to realize the quantitative evaluation of QTP lake waterbody area extraction results. The results show that the Random Forest (RF) algorithm performs best in all models. The overall accuracy of waterbody extraction is 95.84%, and the average error of lake waterbody area extraction is 1.505%. Among the traditional threshold segmentation waterbody extraction algorithms, the overall accuracy of the NDWI waterbody extraction method is 89.89%, and the average error of lake waterbody area extraction is 3.501%, which is the highest performance model in this kind of algorithms. This study proves that Landsat-9 data can effectively classify QTP waterbodies. With the development of cloud computing technologies such as Gee, more complex models such as RF can be selected to improve the extraction accuracy of water body and Lake area in large-scale research.

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

Aging is associated with muscle atrophy and fatty infiltration of skeletal muscle. The multifidus muscle stabilizes the lumbar spine and undergoes adipose accumulation with age, leading to functional decline in the elderly. Therefore, quantitative assessment of the multifidus muscle can be beneficial for the elderly when formulating treatment strategies and reducing future complications. Fifty-seven patients (mean age, 73.89 ± 6.09; 23 male patients) who underwent lumbar Magnetic resonance imaging (MRI) were prospectively recruited. The cross-sectional area (CSA) of the multifidus from the L2-S1 level and the CSA of the L4-5 level psoas muscle were measured. The functional CSA (fCSA) of the multifidus muscle was measured by excluding the fat infiltration area from the multifidus CSA. The CSA to fCSA ratio was obtained by multiplying 100 by the value obtained by dividing CSA by the fCSA. Pfrrmann classification was used to evaluate the degree of disc degeneration. The functional disability measurements were the Short Physical Performance Battery (SPPB), Berg Balance Scale (BBS), grip strength, and functional reach test (FRT). Pearson's correlation analysis was used to examine the relationship between the functional disability measurements and the multifidus muscle. The CSA to fCSA ratio value was relatively constant at each spine level and showed a significant correlation with the SPPB, grip strength, FRT, and psoas index (p&lt;0.05). However, degree of disc and multifidus muscle degeneration were not statistically significant. So, age-related changes play a significant role in developing back muscle fatty infiltration than disc dgeneration. Moreover, Grip strength showed a stronger relationship with the quality of the multifidus muscle than other functional disability measurements.

The betel nut chewing habit of taxi drivers has shown that can promote companionship, relaxation, and emotional stability at work. 19.3% of betel nut behaviors are instigated by an invitation among drivers to engage in the habit. The objective of this study was to explore the impact of social learning, subjective norm, and self-efficacy on betel nut chewing in taxi drivers in Taiwan. We conducted a cross-sectional study on taxis’ driver of betel nut behaviour and measured their evaluations of its consequences using a self-report questionnaire. Other variables include socio-demographic characteristics and chewing-related experience. The overall variance is 44.5% in socio-demographic variable, social learning, self-efficacy, and subjective norm. Results show that the variables of affected betel nut chewing behavior, driving years has the strongest influence, followed by learning about betel nut from the media, relatives, and friends, and education level, which positively affect betel nut chewing behavior. Findings indicated that education related to quitting betel nut chewing, significant others, and rules of the taxi fleet are adopted to design a plan for quitting betel chewing to reduce the positive expectations of taxi drivers for betel nut chewing and decrease their betel nut chewing behavior.

Thyroid nodules (TNs) are common thyroid lesions in older population. Few studies focused on the prevalence of TNs and its relationship to lifestyle characteristics and dietary habits in centenarians. The current study aimed to determine the prevalence of TNs in Chinese centenarians using high-resolution ultrasound equipment and investigate its relationship to lifestyles and dietary habits. The current study was part of China Hainan Centenarian Cohort Study (CHCCS) which conducted in Hainan, an iodine sufficient region in China. A total of 874 permanent residents aged 100 years or older (mean age, 102.8 ± 2.8 years) without any missing data were included in the analysis. Among the participants, 649 of them were detected at least one thyroid nodule under the ultrasound examinations. The overall prevalence rate of TNs was 74.3%. The prevalence of TNs was higher in participants who were females, hypertension, diabetes, and underweight than their counterparts. Multivariate logistic regression analyses showed that being female, hypertension, diabetes, betel quid consumption, red meat consumption were independent risk factors, while being underweight, and nut consumption were independent protective factors for TNs. Our findings indicate that the presence of thyroid nodules was highly prevalent in Chinese centenarians, particularly in females. In addition to gender, hypertension, diabetes, and underweight, the presence of TNs was independently associated with betel quid, red meat, and nut consumptions. Further prospective studies are warranted to verify these associations in population from different age strata, races, cultures, and iodine backgrounds.

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.

The recyclable paper based on photochromic materials not only reduces the pollution in the paper manufacture process, but also reduces the pollution caused by the use of ink, which receives wide attention. In this paper, a serial of PMoA-PWA/ZnO/PVP hybrid films which had different ratio of PMoA/PWA was prepared by the ultrasonic composite method. The results indicated that the hybrid film prepared when the ratio of PMoA to PWA was 3 had the best photochromic performance. In this system, ZnO was the photosensitizer while PMoA/PWA was the chromophore. The photochromic mechanism of PMoA-PWA/ZnO/PVP hybrid film was based on the photogenerated electron transfer mechanism. ZnO generated photoelectron under the excitation of visible light, then PMoA and PWA obtained the photoelectron and produced photoreduction reaction to generate heteropolyblue. The visible light photochromic paper was prepared by loaded PMoA-PWA/ZnO/PVP hybrid film (A3) on A4 paper. Application test showed that the prepared paper had extremely stable, excellent and reversible visible light photochromic properties, whether it was printing patterns or words and could replace the ordinary paper to realize the reuse of paper.

Embracing innovative digital technologies within educational systems is essential for overcoming challenges in modern learning environments, especially in the post-COVID-19 era. Teaching sustainability management in higher education institutions with innovative digital tools plays a fundamental role in the transition toward sustainable societies. Suitable game design elements play a significant role in facilitating sustainable learning. This study explored the effectiveness of incorporating business simulation games with project-based learning (PBL) in a flipped classroom setting. This approach was adopted within the context of a university cross-border e-commerce course to prepare students for acquiring 21st -century skills as higher-order thinking skills in a rapidly changing educational landscape. A quasi-experimental method was employed, involving a total of 60 university students from China's Zhejiang Province. Participants completed an online questionnaire designed to assess their learning engagement across three dimensions (cognitive, emotional, and behavioral) as well as their higher-order thinking skills (problem-solving, critical thinking, and creativity). The results show that the business simulation games combined with flipped classroom learning had a significantly positive impact on students’ learning outcomes, enhancing their problem-solving, critical thinking, and creative capabilities. Importantly, this approach also improved student engagement and promoted sustainable practices by applying real-life scenarios in an interactive environment. We conclude that business simulation games integrated with project-based learning (PBL) in flipped classroom settings represent a valuable educational approach. This approach not only enhances learning engagement but also fosters the development of higher-order thinking skills, encouraging students to adopt sustainable learning practices.

By combing continuum topology optimization (TO) method and lattice structure technique, a sandwich aircraft spoiler with a high stiffness-to-weight is designed. TO method is served to produce the shell of the aircraft spoiler and the lattice structure, used as cores, is employed to support the shell. TO problem is established as maximizing the stiffness of the structure with limited material volume. Density-based method is utilized to achieve a 0/1 solution. We then empirically replace the core of the aircraft spoiler by using 3D kagome lattice structure. Two different materials, i.e., aluminum alloy and titanium alloy, are synthetically applied to further reduce the weight and simultaneously improve the strength of the aircraft spoiler. Numerical simulations are conducted to show that the designed aircraft spoiler can meet the service environment with a reduction of its weight by approximately 80% when compared with that of the initial design model. Finally, we have fabricated the designed model with photosensitive resin by using 3D printing technique.

The performance of the absolute atom gravimeters used on moving platforms, such as vehicles, ships and aircrafts, is strongly affected by the vibration noise. To suppress its influence, we summarize a vibration compensation method utilizing data measured by a classical accelerometer. The measurements with the accelerometer show that the vibration noise in the vehicle can be 2 order of magnitude greater than that in the lab during daytime, and can induce an interferometric phase fluctuation with a standard deviation of 16.70π. With the compensation method, our vehicle-mounted atom gravimeter can work normally in these harsh conditions. Comparing the Allan standard deviations before and after the vibration noise correction, we find a suppression factor of 22.74 can be achieved in static condition with an interrogation time of T = 20 ms, resulting a sensitivity of 1.35 mGal/Hz1/2, and a standard deviation of 0.5 mGal with an average time of 10 s. We also demonstrate the first test of an atom gravimeter in a moving vehicle, in which a suppression factor of 50.85 and a sensitivity of 60.88 mGal/Hz1/2 were realized with T = 5 ms.

This paper is based on the concept of absolute airspace and its local absolute stationary reference system proposed by the author, on the premise of abandoning space concept and the relativity principle of relativity theory, It is proposed that general relativity and special relativity can be unified into Simple and practical a space-time transformation theory which fully includes other basic physical principles of relativity. And the unified and simple expression of the time transformation effect is proved and deduced. By comparing with the theoretical derivation and checking calculation of Hafele-Keating experiment in an ideal state, It is proved that the expression of time transformation effect proposed in this paper is completely correct. Compared with the theory of relativity, the calculation is simpler and more accurate. The paper also puts forward two checking prediction of physical cases about the time transformation effect, which are left for the capable competent unit or individual to verify in practice.

With the aging of China's population, the problem of pension security has become more and more prominent, and whether delayed retirement can effectively alleviate the pension fund gap and ensure the sustainability of the pension fund has become the focus of social concern. This study predicts the income and expenditure of urban workers' basic pension insurance fund from 2021 to 2050 by constructing an actuarial model of pension insurance fund income and expenditure, and simulates the effect of delayed retirement policy. The prediction results show that under the existing system, the basic pension insurance fund for urban workers will have a shortfall for the first time in 2027, and the shortfall will expand year by year. Compared with the non-implementation of delayed retirement policy, the simulation of the implementation of delayed retirementt program delayed the emergence of the fund gap until 2029, and the forecast period of the pension fund gap significantly narrowed, indicating that delayed retirement policy has a certain positive impact on alleviating the pressure of pension payments, but delayed retirementt can not completely eliminate the pension fund gap. In view of this, this paper suggests that a progressive and flexible delayed retirement policy should be introduced as soon as possible to better adapt to the needs of different groups. At the same time, differentiated policies should be formulated for different groups of people and a pension incentive mechanism for delayed retirement should be set up to improve public acceptance of delayed retirement policy. In addition, delayed retirement policy should be combined with other measures, such as lowering the corporate contribution rate and enhancing the value-added capacity of the pension fund, so as to ensure the sustainability of the pension fund.