Search Results (65)

With the advancement of immersive technologies, metaverse museum exhibitions have become an increasingly important medium through which audiences access cultural content and experience artistic works. This study aims to identify the key factors influencing visitors’ processual experiences in metaverse museum exhibitions and to explore how these factors collectively contribute to the formation of satisfaction with the visiting experience. Adopting an interdisciplinary theoretical perspective, the study integrates the Experience Economy theory with the Stimulus–Organism–Response (SOR) model to construct a systematic theoretical framework. This framework reveals how exhibition-related stimuli affect visitors’ behavioral intentions through psychological response pathways. Specifically, perceived educational appeal, interactive entertainment, escapist experience, and perceived visual aesthetics are defined as stimulus variables, while psychological immersion, emotional trigger, and cognitive engagement are introduced as organismic variables to explain their effects on satisfaction with the visiting experience and social sharing intention as response variables. Based on 507 valid responses, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for empirical analysis. The results indicate that interactive entertainment and escapist experience have significant positive effects on psychological responses, serving as key drivers of deep visitor engagement. Emotional Trigger acts as a significant mediator between exhibition stimuli and satisfaction with the visiting experience, which in turn significantly predicts social sharing intention. In contrast, perceived educational appeal and perceived visual aesthetics exhibit weaker impacts at the cognitive and behavioral levels. This study not only identifies these weakened pathways but also proposes optimization strategies grounded in experiential construction and cognitive synergy, offering guidance for enhancing the educational function and deep experiential design of metaverse exhibitions. The findings validate the applicability of the Experience Economy theory and the SOR model in metaverse cultural contexts and deepen our understanding of the psychological mechanisms underlying immersive cultural experiences. This study further provides a pathway for shifting exhibition design from a “content-oriented” to an “experience-driven” approach, offering theoretical and practical insights into enhancing audience engagement and cultural communication effectiveness in metaverse museums. Full article

The conventional-speed Sliding Mode Controller (SMC) for ship PM propulsion motors, which employs exponential reaching laws and linear sliding surface functions, demonstrates susceptibility to oscillatory phenomena. To solve this problem, this paper proposes a saturation adaptive nonlinear integral sliding mode controller (SANI-SMC) which combines a nonlinear integral sliding surface function with an adaptive saturation gain reaching rate. The nonlinear integral sliding surface function improves the system responsiveness, and then enhances the stability and robustness of the system. The adaptive saturation gain reaching rate not only mitigates the chattering effect induced by the sign function in traditional exponential reaching rates, but also weakens the underlying oscillations. This approach effectively solves the overshoot problem inherent in traditional PI controllers, and has better anti-interference ability under sudden load variations. Finally, the proposed controller is experimentally verified based on an electric propulsion semi-physical experimental platform consisting of Rapid Control Prototyping (RCP), and compared with a Proportional–Integral (PI) controller and an SMC. Moreover, the integral absolute error (IAE), integral time-weighted absolute error (ITAE), and integral of the square value (ISV) metrics are calculated for the PI controller, SMC, and SANI-SMC based on experimental data collection. The results demonstrate that the SANI-SMC exhibits superior stability and robustness compared to both the PI controller and SMC. Full article

Glaesserella parasuis (G. parasuis) causes vascular inflammation in piglets, resulting in vascular damage. However, the mechanism causing vascular inflammation remains unclear. Baicalin possesses an anti-inflammatory function. Tumor necrosis factor-like weak inducer of apoptosis (TWEAK) has been implicated in immunosuppression. CD163, a scavenger receptor expressed on macrophages that acts as a decoy receptor for TWEAK, plays a crucial role in the regulation of autophagy and inflammation. This research investigated the efficacy of baicalin in reducing immunosuppression elicited by G. parasuis through the regulation of CD163/TWEAK-mediated autophagy. The data demonstrated that G. parasuis altered routine blood indicators and biochemical parameters, increased cytokine production, and induced blood vessel tissue damage. G. parasuis reduced the CD3+ T cell proportion, CD3+CD4+ T cell proportion, and CD3+CD8+ T cell proportion in piglet blood. The proteomic analysis revealed that CD163 was differentially expressed in the blood vessels of challenged piglets. Baicalin was found to regulate CD163/TWEAK axis expression, inhibit Notch/Wnt signaling pathway activation, promote autophagy, and reduce NLRP3/Caspase 1 signaling pathway activation. Baicalin also decreased cytokine production and alleviated pathological tissue damage in the blood vessels of G. parasuis-challenged piglets. Taken together, this study indicates that baicalin alleviates G. parasuis-induced immunosuppression and might promote CD163/TWEAK-mediated autophagy. This finding suggests that baicalin could serve as a potential therapeutic agent to control G. parasuis infection and related vascular inflammation. Full article

Glaesserella parasuis (G. parasuis) infection is responsible for Glässer’s disease in pigs. G. parasuis could trigger piglet immunosuppression, but the mechanism of inducing immunosuppression by G. parasuis remains unknown. Macrophage migration inhibitory factor (MIF)/CD74 axis has been shown to participate in inflammation response and immunosuppression, but the function of MIF/CD74 during immunosuppression elicited by G. parasuis has not been fully explored. This experiment explored the efficacy of baicalin on immunosuppression elicited by G. parasuis alleviation through regulating polarization via the MIF/CD74 signaling pathway. Our data indicated that baicalin reduced IL-1β, IL-6, IL-8, IL-18, TNF-α, and COX-2 expression, and regulated MIF/CD74 axis expression in the spleen. Immunohistochemistry analysis showed that baicalin enhanced CD74 protein levels in the spleen of piglets induced by G. parasuis. Baicalin regulated the PI3K/Akt/mTOR signaling pathway and RAF/MEK/ERK signaling activation, modified the expression of the autophagy-related proteins Beclin-1, P62, and LC3B, promoted M2 polarization to M1 polarization, and enhanced CD3, CD4, CD8, and TIM3 levels in the spleen of piglets elicited by G. parasuis. Our study reveals the important functions of the MIF/CD74 axis in G. parasuis-induced immunosuppression and may offer a new therapeutic method to control G. parasuis infection. Full article

A method for joint Direction of Arrival (DOA) and frequency estimation based on the Direct Data Domain (DDD-JDFE) is proposed. This algorithm, designed to work within an array antenna reception system, utilizes space-time adaptive processing techniques to acquire short-term samples through both spatial and temporal smoothing. It employs a broadband array direction-finding system to simultaneously determine the frequency and arrival direction of targets. Compared to traditional spatial-domain array signal processing methods, this approach provides improved direction-finding performance, particularly at low signal-to-noise ratios. Meanwhile, it shows great potential for long-distance signal detection, and simulation results have verified the effectiveness of the method. Full article

Glaesserella parasuis (G. parasuis) causes Glässer’s disease and systemic inflammatory responses in the host. The currently available therapies have limited efficacy and fail to achieve a balance between anti-inflammatory and antibacterial effects. In this study, we investigated the effects of baicalin, amoxicillin, and probenecid on blood biochemical parameters, routine blood indicators, survival rate, bacterial burden, and pathological tissue damage in G. parasuis-challenged mice. Treatment with baicalin, amoxicillin, and probenecid significantly modified the blood biochemical parameters and routine blood test indicators, increased the survival rate, attenuated the bacterial burden, and alleviated pathological tissue damage in G. parasuis-challenged mice. Treatment with baicalin, amoxicillin, and probenecid also increased the number of CD3⁺, CD3⁺CD4⁺, and CD3⁺CD8⁺ T cells as measured by flow cytometry, and restored the intensity of the CD3, CD4, and CD8 protein expression in the blood vessels of G. parasuis-challenged mice by immunohistochemistry. These compounds reduced interleukin 1β (IL-1β), IL-18, tumor necrosis factor alpha (TNF-α), and high mobility group box 1 protein (HMGB1) expression in the spleen of G. parasuis-challenged mice. Furthermore, baicalin, amoxicillin, and probenecid inhibited activation of the family pyrin domain containing 3 (NLRP3) inflammasome and apoptosis in the spleen of G. parasuis-challenged mice. This study showed the important roles of baicalin, amoxicillin, and probenecid in the modulation of the inflammatory response of Glässer’s disease. The findings might provide new strategies for combination therapy using antibiotics and anti-inflammatory drugs to control G. parasuis infection. Full article

In order to explore the effects of planting two economic crops in Moso plantations on the composition of soil phosphorus-functional microbial community, this study collected soil samples of Persimmon and Tea-oil plantations cultivated on the original bamboo soil for 3 years for comparison. Soil physical and chemical measurements and metagenomic sequencing were used to evaluate the effects of crop cultivation on the diversity of soil phosphorus-functional microorganisms. Results show that (1) Moso forests are converted to different crops after the soil pH values decline, and other physical and chemical properties of soil and microbial biomass phosphorus (MBP) content rise. (2) Soil microbial community structure changed with crop planting. The number of phosphorus-functional bacteria in Persimmon soil was higher than Tea-oil and Moso soils, with the total number of phosphorus-functional bacteria and unique phosphorus-functional bacteria in Persimmon soil being the highest. (3) The relative abundance of phoU, phoR, ugpA, ugpB, gcd and ppaC genes was significantly increased, while the abundance of pstA, pstB and pstC genes was decreased by crop replanting. (4) The dominant phosphorus-functional microorganisms under different crop cultivation were closely related to basic soil properties. Bradyrhizobium and Camellia abundances were significantly positively correlated with soil total phosphorus (TP), while Sphingomonas was significantly negatively correlated with soil TP. Soil electrical conductivity (EC), soil total nitrogen (TN) and soil MBP were positively correlated with the ppx–gppA gene. AP, EC and TN were positively correlated with the phoB gene, while TN and MBP were negatively correlated with the phoP gene. These results suggested that land use patterns could directly change soil environmental conditions, thereby affecting phosphorus-functional microbial communities. In conclusion, the conversion of Moso plantations to commercial crops is beneficial for the optimization of the soil system, promoting the activation and release of soil phosphorus to maintain the dynamic balance of soil microbial community. Full article

Fresh sweet and waxy maize (Zea mays) are valuable specialty crops in southern China. Hyperspectral remote sensing offers a powerful tool for detecting maize foliar nutrients non-destructively. This study aims to investigate the capability of leaf spectroscopy (SVC HR-1024i spectrometer, wavelength range: 400–2500 nm) to retrieve maize foliar nutrients. Specifically, we (1) explored the effects of nitrogen application rates (0, 150, 225, 300, and 450 kg·N·ha⁻¹), maize cultivars (GLT-27 and TGN-932), and growth stages (third leaf (vegetation V3), stem elongation stage (vegetation V6), silking stage (reproductive R2), and milk stage (reproductive R3)) on foliar nutrients (nitrogen, phosphorus, and carbon) and leaf spectra; (2) evaluated the transferability of the regression and physical models in retrieving foliar nutrients across maize cultivars. We found that the PLSR (partial least squares regression), SVR (support vector machine regression), and RFR (random forest regression) regression model accuracies were fair within a specific cultivar, with the highest R² of 0.60 and the lowest NRMSE (normalized RMSE = RMSE/(Max − Min)) of 17% for nitrogen, R² of 0.19 and NRMSE of 21% for phosphorous, and R² of 0.45 and NRMSE of 19% for carbon. However, when these cultivar-specific models were used to predict foliar nitrogen across cultivars, lower R² and higher NRMSE values were observed. For the physical model, which does not rely on the dataset, the R² and NRMSE for foliar chlorophyll-a and -b (C_ab), carotenoid (C_xc), and equivalent water thickness (EWT) were 0.76 and 15%, 0.67 and 34%, and 0.47 and 21%, respectively. However, the prediction accuracy for foliar nitrogen, expressed as foliar protein in PROSPECT-PRO, was lower, with an R² of 0.22 and NRMSE of 27%, which was comparable to that of the regression models. The primary reasons for this limited transferability were attributed to (1) the insufficient number of samples and (2) the lack of strong absorption features for foliar nutrients within the 400–2500 nm wavelength range and the confounding effects of other foliar biochemicals with strong absorption features. Future efforts are needed to investigate the physical mechanisms underlying hyperspectral remote sensing of foliar nutrients and incorporate transfer learning techniques into foliar nutrient models. Full article

Estuarine constructed wetlands (ECWs) play a role as ecological barriers in the control of external pollution in lakes. Usually, ECWs show reduced water treatment efficiency after many years of operation compared to their initial performance. However, it is unclear how the water purification efficiency of an ECW changes over time. After over a decade of tracking analysis on an ECW, this study found that it indeed played a significant role in achieving water quality improvement effects. The average removal rates for total nitrogen (TN) and total phosphorus (TP) and the permanganate index (COD_Mn) were 36.2%, 26.7%, and 30.7%, respectively, with annual reductions of 1.6 t/a, 20.8 t/a, and 44.6 t/a. The surface hydraulic load is a critical indicator for the design and operational management of ECWs. The reduction loads of TP, TN, and COD_Mn increased with the rise in surface hydraulic load, indicating that this ECW project had certain advantages in treating large-volume water bodies. However, when strict COD_Mn treatment is needed, the surface hydraulic load should be reduced. During the high-efficiency period (2010–2015), the treatment effects on TN and TP were more than twice those during the degradation period (2016–2021), and the effect on COD_Mn was about 1.5 times greater. With increased operation years, the TN removal rate declined most rapidly due to pollutant accumulation and sediment release. Full article

The film superposition prediction model is a crucial tool in the preliminary design of the turbine outer ring, enabling the rapid estimation of adiabatic wall temperatures and significantly reducing computational costs. This study established the relationship between the mainstream temperature correction coefficient and the air bleed ratio based on energy conservation principles in the boundary layer during film injection. A superposition model grounded in mainstream temperature corrections was developed. The proposed model utilizes cooling efficiency characteristics based on the equivalent blowing ratio to accurately predict the cooling efficiency of multi-row hole layouts with varying hole spacings. Experiments on the adiabatic film cooling efficiency were conducted with four different hole configurations at various blowing ratios. The limitations of the traditional Sellers superposition method in predicting cooling efficiency distributions are discussed by comparing them with experimental data. The comparison reveals that the Sellers method accumulates prediction errors as the number of hole rows increases, leading to an overestimation of the cooling efficiency. Introducing a mainstream temperature correction factor effectively addressed this issue. The prediction accuracy of the improved model was higher at $M=0.3$, with relative deviations remaining within 10% across different test plates. As the blowing ratio increased, the prediction deviation gradually increased when the number of hole rows was less than 10. At a blowing ratio of 1.0, the deviation exceeded 20%. However, as the number of hole rows increased, the deviation remained within 10% under various blowing ratios. Compared to existing advanced models in the literature, the improved model demonstrated higher prediction accuracy under most conditions. For Case 1 in this study, the model predicted an average surface cooling efficiency deviation of 3.4% at a blowing ratio of 0.5. Similarly, for Case 14 in the literature, the deviation was 3.4% at a blowing ratio of 0.6. In contrast, the prediction deviations in the literature models were 16.1% and 8%, respectively. Furthermore, the introduction of the equivalent blowing ratio reduced the data requirements for calculating the single-hole-row cooling efficiency when performing cooling efficiency superposition predictions. Full article

The relationship between circulating metabolites and sarcopenia-related phenotypes remains unclear. We explored the causality between circulating metabolites and sarcopenia-related phenotypes. Instrumental variables for the human metabolome were derived from the recently published GWAS, which included 690 plasma metabolites. Summary statistics for four sarcopenia phenotypes (whole-body lean mass (WBLM), usual walking pace, appendicular lean mass (ALM), and handgrip strength (HGS)) (both sexes, males and females) were obtained from relevant GWASs. We used MR to evaluate the association between circulating metabolites and sarcopenia-related phenotypes. Colocalization analysis was utilized to determine whether two associated signals were consistent with a shared causal variant rather than the confounding effect of linkage disequilibrium. Subsequently, we explored associations between modifiable risk factors and sarcopenia-related metabolites to explore which metabolites may serve as potential intervention targets through lifestyle modification. Genetically predicted plasma levels of 95 known metabolites were associated with sarcopenia-related phenotypes, and 27 metabolites were supported by robust evidence of colocalization, among which 13 metabolites had a cross-sarcopenia effect. These metabolites primarily included acyl carnitines, amino acids and their derivatives, and phospholipids. Specifically, our analyses supported causal relationships between 23, 6, and 15 metabolites and ALM, HGS, and WBLM, respectively. Seven relevant metabolites might be associated with six modifiable factors. We identified 27 metabolite biomarkers with robust causal evidence for sarcopenia-related phenotypes, highlighting 13 metabolites with a cross-sarcopenia effect, and prioritized several metabolites as the potential interventional targets of lifestyle changes. Our study provided new insight into the etiology and prevention of sarcopenia. Full article

This study investigates the key factors which influence design learners’ behavioral intention to collaborate with AI in the educational metaverse (EMH-AIc). Engaging design learners in EMH-AIc enhances learning efficiency, personalizes learning experiences, and supports equitable and sustainable design education. However, limited research has focused on these influencing factors, leading to a lack of theoretical grounding for user behavior in this context. Drawing on social cognitive theory (SCT), this study constructs a three-dimensional theoretical model comprising the external environment, individual cognition, and behavior, validated within an EMH-AIc setting. By using Spatial.io’s Apache Art Studio as the experimental platform and analyzing data from 533 design learners with SPSS 27.0, SmartPLS 4.0, and partial least squares structural equation modeling (PLS-SEM), this study identifies those rewards, teacher support, and facilitating conditions in the external environment, with self-efficacy, outcome expectation, and trust in cognition also significantly influencing behavioral intention. Additionally, individual cognition mediates the relationship between the external environment and behavioral intention. This study not only extends SCT application within the educational metaverse but also provides actionable insights for optimizing design learning experiences, contributing to the sustainable development of design education. Full article

To understand the role of paddy soils in the global carbon cycle, it is necessary to analyze the characteristics of the organic carbon pool at different soil depths. It was hypothesized that soil organic carbon fractions including labile organic carbon fraction I (LOCF-I), labile organic carbon fraction II (LOCF-II), and recalcitrant organic carbon (ROC) distributed differently within the soil profile. In this study, soil was collected from 27 typical rice fields in Guangdong Province, south China. The carbon fractions of the paddy field soils were analyzed and compared over a 0–60 cm depth profile. The relationship between carbon content and the physical and chemical properties of the soils was further analyzed using correlation analysis and structural equation modeling. The results showed that soil total organic carbon concentration in paddy fields was increased by 22.1% during the last four decades. In the soil organic carbon pool of 0–60 cm profile, the proportion of 67.31 to 70.31% in ROC, 21.75 to 22.06% in LOCF-I, and 7.7 to 10.63% was recorded, respectively, indicating that ROC was the dominating fraction. Storage of soil total organic carbon and fractions all decreased with the increase in soil depth. Correlation and path analysis showed that total nitrogen was the main driving factor affecting the soil carbon fractions, whereas pH and soil bulk density indirectly affected the content of carbon fractions by influencing total nitrogen. The results imply the importance of soil total nitrogen in paddy carbon management of rice cultivation. Full article

The metal-free porphyrins protonation has gained interest over five decades because its structure modification and hardly monoacid intermediate isolation. Here, upon the hydrogen atom abstraction processes, one step diproptonated H₃STTP(BF₄)₂ (STTP = 5,10,15,20-tetraphenyl-21-thiaporphyrin) (3) and stepwise protonated HS₂TTPSbCl₆ (5) and diprotonated H₂S₂TTP(BF₄)₂ (6) (S₂TTP = 5,10,15,20-tetraphenyl-21,23-thiaporphyrin) compounds were obtained using HSTTP and S₂TTP with oxidants. The closed-shell protonated compounds were fully characterized using XRD, UV-vis, IR and NMR spectra. In addition, the reduced 19π compounds [K(2,2,2)]HSTTP (2) and [K(2,2,2)]S₂TTP (7) were synthesized by the ligands with reductant KC₈ in THF solution. These two open-shell compounds were characterized with UV-vis, IR and EPR spectroscopies. The semiempirical ZINDO/S method was employed to analyze the HOMO/LUMO gap lever and identify the electronic transitions of the UV-vis spectra of the closed- and open-shell porphyrin compounds. Full article

In this study, changes in abiotic environmental factors were analyzed based on measured data from Dawangtan Reservoir in Nanning City during 2021–2022. The Carlson Integrated Trophic State Index method was applied to evaluate water quality and eutrophication in the reservoir, considering both exogenous inputs and internal water quality conditions. Additionally, by investigating water quality and biological factors (zooplankton and fish) in the reservoir, this study identified the main drivers influencing phytoplankton outbreaks in Dawangtan Reservoir through redundancy analysis (RDA) and Pearson correlation analysis. The results showed that the combined trophic state index of the reservoir varied between 29.2 and 56.5 throughout the year, with each water quality indicator performing worse during the summer months when temperatures were higher. There was a positive correlation between zooplankton biomass and phytoplankton biomass, and the increase in the proportion of phytophagous fish contributed to the reduction in phytoplankton. The redundancy analysis revealed that the distribution of phytoplankton species was significantly correlated with total phosphorus (TP), pH, dissolved oxygen (DO), and chlorophyll a (Chl-a), with different phytoplankton species responding variably to these environmental factors. Finally, a multiple stepwise regression analysis was used to construct the optimal response equation between Chl-a concentration and environmental factors. The results indicated that pH, Secchi disk (SD), DO, and biological oxygen demand (BOD) were the main factors affecting Chl-a concentration. Therefore, the outcomes of abiotic environmental factors and lake biological resources should be considered in the restoration of eutrophic lakes to achieve the desired restoration effect. Full article