Search Results (21,685)

(1) Background: Air rudders are used to control the flight attitude of aircraft, and their surface quality directly affects flight accuracy and safety. (2) Method: Traditional positioning methods can only obtain defect location information at the image level but cannot determine the defect’s physical surface position on the air rudder, which lacks guidance for subsequent defect repair. We propose a defect physical surface positioning method based on a camera mapping model. (3) Results: Repeated positioning experiments were conducted on three typical surface defects of the air rudder, with a maximum absolute error of 0.53 mm and a maximum uncertainty of 0.26 mm. Through hardware systems and software development, the real-time positioning function for surface defects on the air rudder was realized, with the maximum axial positioning error for real-time defect positioning being 0.38 mm. (4) Conclusion: The proposed defect positioning method meets the required accuracy, providing a basis for surface defect repair in the air rudder manufacturing process. It also offers a new approach for surface defect positioning in similar products, with engineering application value. Full article

This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the G-DN formula deals with measured data, it introduces a degree of uncertainty and fuzziness that traditional probability theory struggles to address. Possibility theory, an extension of fuzzy set theory, offers a suitable framework for managing this uncertainty and fuzziness. In this study, the G-DN formula is adapted to incorporate fuzzy logic, and the possibilistic nature of reservoir capacity is translated into a probabilistic framework using α-cuts from the possibility theory. These α-cuts approximate probability confidence intervals with high confidence. Applying the proposed methodology, in the present crisp case with the storage capacity D = 0.75, the value of the capacity C was found to be $1271\times {10}^6 {\mathrm{m}}^3$, and that for D = 0.5 was $634.5\times {10}^6 {\mathrm{m}}^3$. On the other hand, in the fuzzy case using the possibility theory, the value of the capacity for D = 0.75 is the internal $[315,5679]\times {10}^6 {\mathrm{m}}^3$ and for D = 0.5 the value is interval $[158,2839]\times {10}^6 {\mathrm{m}}^3$, with a probability of ≥95% and a risk level of α = 5% for both cases. The proposed approach could be used as a robust tool in the toolkit of engineers working on irrigation, drainage, and water resource projects, supporting informed and effective engineering decisions. Full article

In the digital context, smart homestays have developed rapidly in the post-epidemic era and have become a new form of accommodation. Homestays are favored by many young people in China, such as those belonging to Generation Z. According to data concerning China’s national tourism and related reports, the demand for homestays has increased dramatically in recent years. Thus, we need to consider how to improve the smart homestay user experience. Based on the American Customer Satisfaction Index (ACSI) model, this study explores the factors that affect the user experience of smart homestays. An online survey of 370 respondents of Generation Z in China was conducted, followed by descriptive statistical analysis and hypothesis model validation using SPSS 26.0. The data show that among the five service variables (reservation, check-in, living, check-out, and information sharing), perceived value has a positive and positive impact on service variables in all aspects. Finally, machine learning is used for emotion text analysis, and the results show that users are biased towards smart homestays in the sentiment analysis of the comments. Although smart homestays have a certain amount of attention, there is still a lot of room for progress in technology and services. The purpose of this study is to improve and perfect the rules for making smart homestay service standards based on understanding the satisfaction of Generation Z when using smart homestays while also providing a theoretical basis and practical manuals for the industry to promote the development of the industry and improve user experience. Based on the research results of the above literature, it is imperative to carry out research on Generation Z, the main force of future consumption, especially in the field of artificial intelligence. Full article

As a major coal-producing area, the Shanxi section of the Yellow River Basin has been significantly affected by coal mining activities in the local ecological environment. Therefore, an in-depth study of the ecological evolution in this region holds great scientific significance and practical value. In this study, the Shanxi section of the Yellow River Basin, including its planned coal mining area, was selected as the research subject. An improved remotely sensed ecological index model (NRSEI) integrating the remotely sensed ecological index (RSEI) and net primary productivity (NPP) of vegetation was constructed utilizing the Google Earth Engine platform. The NRSEI time series data from 2003 to 2022 were calculated, and the Sen + Mann–Kendall analysis method was employed to comprehensively assess the ecological environment quality and its evolutionary trends in the study area. The findings in this paper indicate the following data: (1) The contribution of the first principal component of the NRSEI model is more than 70%, and the average correlation coefficient is higher than 0.79. The model effectively integrates the information of multiple ecological indicators and enhances the applicability of regional ecological environment evaluation. (2) Between 2003 and 2022, the ecological environment quality in the Shanxi section of the Yellow River Basin showed an overall upward trend, with the average NRSEI value experiencing phases of fluctuation, increase, decline, and stabilization. The NRSEI values in non-coal mining areas consistently remained higher than those in coal mining areas. (3) Over 60% of the areas have improved ecological conditions, especially in coal mining areas. (4) The impact of coal mining on the ecological environment is significant within a 6 km radius, while the effects gradually diminish in the 6 to 10 km range. This study not only offers a reliable methodology for evaluating ecological environment quality on a large scale and over a long time series but also holds significant guiding value for the ecological restoration and sustainable development of the Shanxi section of the Yellow River Basin and its coal mining area. Full article

Silene viscidula Franch is mainly produced in southwest China. The region has a vast area and rich climate, which has an impact on the quality of the plants due to the differences in distribution between the origins. There is a lack of systematic research on its chemical compounds in the existing literature, and fewer studies have been reported for the active compounds of this plant. Therefore, high-resolution liquid mass spectrometry was used in this study. Sixty batches of Silene viscidula Franch samples from twenty origins in three provinces were analyzed for compounds. A database of chemical compounds of Silene viscidula Franch was established through node-to-node information in the GNPS molecular network, as well as literature records. The ion fragmentation information obtained was compared with the literature data and analyzed and identified by importing the mass spectrometry software PeakView 1.2. Then, the MarkerView t-test was applied to analyze and identify the compounds of Silene viscidula Franch from different origins. Afterwards, the antioxidant activity of Silene viscidula Franch from different origins was preliminarily evaluated using DPPH and ABTS free radical scavenging assays. The results showed a total of 78 compounds, including 34 steroids, 14 triterpenoid saponins, 30 flavonoid glycosides, and other classes of compounds, such as alkaloids. The cleavage patterns of steroids, triterpenoid saponins, and flavonoids in positive-ion mode were also summarized. Based on the p-value of the t-test (p < 0.05), 29 differential compounds were screened out. The relative contents of saponins and steroidal compounds in these samples were found to be associated with antioxidant activity. This study provided a preliminary reference for the establishment of a comprehensive evaluation system for the quality of Silene viscidula Franch. Full article

Background: The neutrophil-to-lymphocyte ratio (NLR) is an independent predictor of the severity of coronary heart disease and COVID-19. This study aims to assess the predictive ability of the NLR in patients with in-stent restenosis after COVID-19. Materials and Methods: a cross-sectional study included 931 patients who underwent repeated myocardial revascularization between May 2020 and May 2023. The 420 patients of the main group had in-stent restenosis, of which 162 patients had COVID-19 previously. The control group included 511 patients without stent restenosis (107 patients had COVID-19 previously). All reported events were verified by hospital electronic records from the Complex Medical Information System. Results: The mean values of the NLR were 2.51 and 2.68 in the study groups, respectively. A statistically significant positive relationship in both groups was found between the NLR and troponin, D-dimer, C-reactive protein, creatinine, ALT, and AST. A statistically significant positive relationship was found between NLR and myocardial infarction (MI) in patients of both groups (p = 0.004; p < 0.001, respectively) and a negative relationship with the ejection fraction (p = 0.001; p < 0.036, respectively). An evaluation of the predictive ability of the clinical and laboratory predictors of recurrent myocardial infarction shows a high degree of utility of this model. The area under the ROC curve for AUC for NLR was 0.664 with 95% CI from 0.627 to 0.700 (p < 0.001). Conclusions: NLR is one of the significant factors for predicting the development of adverse outcomes in patients with revascularized myocardium after COVID-19. Full article

Tropical trees that remain evergreen and exhibit leaf litterfall that is gradual over time coexist with trees that are seasonally deciduous and exhibit pulsed litterfall. The manner in which these trees acquire, store, and contribute nutrients to the biogeochemical cycle may differ. Green and senesced leaves from deciduous Ficus prolixa trees were compared with those from Ficus tinctoria on the island of Guam. The results enabled stoichiometry and resorption calculations. F. prolixa’s young green leaf nitrogen (N) and potassium (K) concentrations were double, and the phosphorus (P) concentration was triple, those of F. tinctoria. Concentrations converged as the leaves aged such that no differences in concentration occurred for senesced leaves, indicating that nutrient resorption proficiency did not differ between the two species. In contrast, the resorption efficiency was greater for F. prolixa than F. tinctoria for all three nutrients. The N:P values of 6–11 and K:P values of 5–7 were greater for young F. tinctoria leaves than young F. prolixa leaves. The N:K values were 1.1–1.6 and did not differ between the two species. No differences in pairwise stoichiometry occurred for senesced leaves for any of the nutrients. These Guam results conformed to global trends indicating that seasonally deciduous plants are more acquisitive and exhibit greater nutrient resorption efficiency. The differences in how these two native trees influence the community food web and nutrient cycling lies mostly in the volume and synchronicity of pulsed F. prolixa litter inputs, and not in differences in litter quality. These novel findings inform strategic foresight about sustaining ecosystem health in Guam’s heavily threatened forests. Full article

The accurate prediction of the state of health (SOH) for lithium-ion batteries is a key factor for improving the performance of battery management systems (BMS). However, traditional point prediction methods are difficult to effectively eliminate errors due to the uncertainty of variables and application environments. This paper presents a model for predicting the interval of lithium-ion batteries based on health indicators (HIs) during charging, which addresses the limitations of current point prediction in practical applications. First, twelve HIs are extracted from the current and voltage variables of the charging process. Secondly, feature selection is performed by random forest (RF) training, and the selected HIs are dimensioned using partial least squares (PLS). Finally, a long short-term memory network (LSTM) combined with quantile regression (QR) is used to derive the quantile values of the prediction points and each quantile is employed as input information for Gaussian kernel density estimation (KDE) to obtain the SOH probability density distribution. The experimental results based on the NASA PCOE Li-ion battery dataset and CALCE Li-ion battery dataset show that the SOH interval coverage is more than 90% and the average width of the interval is less than 0.294. Full article

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions. Full article

Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study proposes a hybrid model based on 3D convolutional neural networks (CNNs) and transformers for predicting the N-staging and survival rates of NSCLC patients within the NSCLC radiogenomics and Nsclc-radiomics datasets. The model achieved accuracies of 0.805, 0.828, and 0.819 for the training, validation, and testing sets, respectively. By leveraging the strengths of CNNs in local feature extraction and the superior performance of transformers in global information modeling, the model significantly enhances predictive accuracy and efficacy. A comparative analysis with traditional CNN and transformer architectures demonstrates that the CNN-transformer hybrid model outperforms N-staging predictions. Furthermore, this study extracts the one-year survival rate as a feature and employs the Lasso–Cox model for survival predictions at various time intervals (1, 3, 5, and 7 years), with all survival prediction p-values being less than 0.05, illustrating the time-dependent nature of survival analysis. The application of time-dependent ROC curves further validates the model’s accuracy and reliability for survival predictions. Overall, this research provides innovative methodologies and new insights for the early diagnosis and prognostic evaluation of NSCLC. Full article

Online reviews that consist of texts and images are an essential source of information for alleviating data sparsity in recommender system studies. Although texts and images provide different types of information, they can provide complementary or substitutive advantages. However, most studies are limited in introducing the complementary effect between texts and images in the recommender systems. Specifically, they have overlooked the informational value of images and proposed recommender systems solely based on textual representations. To address this research gap, this study proposes a novel recommender model that captures the dependence between texts and images. This study uses the RoBERTa and VGG-16 models to extract textual and visual information from online reviews and applies a co-attention mechanism to capture the complementarity between the two modalities. Extensive experiments were conducted using Amazon datasets, confirming the superiority of the proposed model. Our findings suggest that the complementarity of texts and images is crucial for enhancing recommendation accuracy and performance. Full article

Achieving precise and swift monitoring of aquaculture ponds in coastal regions is essential for the scientific planning of spatial layouts in aquaculture zones and the advancement of ecological sustainability in coastal areas. However, because the distribution of many land types in coastal areas and the complex spectral features of remote sensing images are prone to the phenomenon of ‘same spectrum heterogeneous objects’, the current deep learning model is susceptible to background noise interference in the face of complex backgrounds, resulting in poor model generalization ability. Moreover, with the image features of aquaculture ponds of different scales, the model has limited multi-scale feature extraction ability, making it difficult to extract effective edge features. To address these issues, this work suggests a novel semantic segmentation model for aquaculture ponds called MPG-Net, which is based on an enhanced version of the U-Net model and primarily comprises two structures: MS and PGC. The MS structure integrates the Inception module and the Dilated residual module in order to enhance the model’s ability to extract the features of aquaculture ponds and effectively solve the problem of gradient disappearance in the training of the model; the PGC structure integrates the Global Context module and the Polarized Self-Attention in order to enhance the model’s ability to understand the contextual semantic information and reduce the interference of redundant information. Using Sentinel-2 and Planet images as data sources, the effectiveness of the refined method is confirmed through ablation experiments conducted on the two structures. The experimental comparison using the FCN8S, SegNet, U-Net, and DeepLabV3 classical semantic segmentation models shows that the MPG-Net model outperforms the other four models in all four precision evaluation indicators; the average values of precision, recall, IoU, and F1-Score of the two image datasets with different resolutions are 94.95%, 92.95%, 88.57%, and 93.94%, respectively. These values prove that the MPG-Net model has better robustness and generalization ability, which can reduce the interference of irrelevant information, effectively improve the extraction precision of individual aquaculture ponds, and significantly reduce the edge adhesion of aquaculture ponds in the extraction results, thereby offering new technical support for the automatic extraction of aquaculture ponds in coastal areas. Full article

Some US-listed country exchange-traded funds (ETFs) suffer from chronic and meaningful mispricing in the form of premiums or discounts relative to their fundamental value despite the presence of the creation/redemption mechanism. This mispricing is mainly attributed to the staggered information flow due to nonoverlapping time zones between the market where the ETF is listed and its underlying home market. This study provides out-of-sample evidence on the price formation of Gulf Cooperation Council (GCC) country ETFs and gauges the impact of mispricing on their underlying home markets. The GCC context is particularly insightful because these markets have nonoverlapping time zones with the US and follow distinct trading schedules. Our sample comprises daily data from three countries’ iShares that exclusively track the Qatari, Saudi, and Emirati stock markets from 17 September 2015 to 14 March 2023. The results show that GCC ETFs are driven mainly by their net asset values (NAVs), albeit imperfectly, while the S&P500 exerts a relatively mild influence on these ETFs compared to other country ETFs, as reported by prior studies. Moreover, we find that crude oil prices positively and significantly impact GCC ETFs’ pricing. When we control for unsynchronized trading days between the US and the GCC home markets, we find a structural difference between overlapping and nonoverlapping trading days. This structural difference manifests in a sluggish adjustment to correct mispricing in the ETF market on the day the home market is closed; however, other variables, including the S&P500, show no discernible difference, which refutes the overreaction explanation. This recurrent pattern is reflected in a clear day-of-the-week pattern in the price discovery these ETFs offer to their underlying home markets. Full article

The remediation and exploitation of sandy saline soils, an underutilized resource, can be enhanced by a greater comprehension of the impact of plants and microorganisms on nutrient cycling. However, there is scant research information on the capacity of different trees and shrubs to improve carbon and nitrogen cycling in saline soils at different depth layers. This study investigated the effect of the trees Zelkova serrata (ZS) and Ligustrum lucidum (LL) and shrub Hibiscus syriacus (HS) on the carbon and nitrogen fractions, soil enzyme activities and microbial communities in sandy saline soils. Planting ZS, LL or HS improved soil quality, increased soil carbon and nitrogen content, changed rhizosphere soil metabolites and enhanced soil enzyme activities and microbial abundance and diversity. Compared to values in the bare soil, the highest reduction in soil salinity was noticed under Zelkova serrata (49%) followed by Ligustrum lucidum (48%). The highest increase in total soil organic carbon (SOC) was noted under Ligustrum lucidum and Hibiscus syriacus (62% each), followed by Zelkova serrata (43%), as compared to levels in the bare soil. In the 0–10 cm soil layer, the total N in bare soil was 298 ± 1.48 mg/kg, but after planting LL, ZS or HS, the soil total N increased by 101%, 56% and 40%, respectively. Compared with that of the bare soil, cbbL sequencing showed that the relative abundance of Bradyrhizobium increased and that of Bacillus decreased due to planting. Similarly, the nifH sequencing results indicated that the relative abundance of Bradyrhizobium and Motiliproteu increased and that of Desulfuromonas and Geoalkalibacter decreased. These findings suggested that soil microorganisms could play a pivotal role in the carbon and nitrogen cycle of saline soils by influencing the content of soil carbon and nitrogen. Full article

Polar lows can pose serious threats to maritime operations and coastal communities in polar regions, especially due to their extreme wind speeds. The accurate and reliable representation of their wind field thus plays a crucial role in forecasting and mitigating the risks associated with this phenomenon. This study aims to evaluate the value of the SAR-based Sentinel-1 Ocean Wind Field product compared to two reanalysis products—regional CARRA and global ERA5—in studying the spatial wind speed distribution of polar lows. A visual comparison of the wind direction and wind speed fields was performed, as well as a brief quantitative analysis of wind speeds. Despite notable differences in spatial resolution, all of the data sources are able to identify the polar lows. However, the SAR-based product remains unmatched in capturing the intricate structure of the wind field. Although CARRA resolves more details than ERA5, it still deviates from the SAR image to a degree that suggests that the difference in spatial resolution is not the only source of disparity between the sources. Both CARRA and ERA5 underestimate the maximum wind speed as compared to the SAR data. Only the SAR data seems capable of providing the information necessary to study the details of the wind field of polar lows. Full article