Ride-sharing has received great attention recently and is considered to be a sustainable transportation mode. Understanding the determinants of the consumers’ intention to use ride-sharing services ...is critical to promote such services. In this research, an extended technology acceptance model is used as a theoretical research framework. This extension was implemented by incorporating three new constructs: personal innovativeness, environmental awareness, and perceived risk. The model was empirically tested using questionnaire survey data collected from 426 participants. The results indicate that personal innovativeness, environmental awareness, and perceived usefulness are positively associated with consumers’ intention to use ride-sharing services, while perceived risk is negatively associated with the intention and perceived usefulness. The analysis shows that, contrary to our expectations, the perceived ease of use has no significant effect on intention to use ride-sharing services. In addition, personal innovativeness is positively related to perceived usefulness and perceived ease of use but negatively related to perceived risk. Based on these results, implications for practice and suggestions for further research are discussed.
The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural ...environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.
•A mark-free vision-based dynamic real-time measurement technique is proposed for full field strain and 3D deformation detection.•Point cloud combination and 3D reconstruction are applied.•The ...non-contact method results truly reflect the deformation process and full-field strain values.•The standard deviation mean value measured by the proposed method relative to the true value is 1.23%.•The proposed method can be used as a reasonable alternative to traditional contact methods.
This study presents a dynamic real-time detection method for surface deformation and full field strain in recycled aggregate concrete-filled steel tubular columns (RACSTCs). Automatic calibration and dynamic surface tracking measurement are utilized and mathematical models combining the four-ocular visual coordinates and point cloud matching are proposed. The 3D deformation of the RACSTCs under low cyclic loading are gathered every 60 s as sample images. The four-ocular vision system constitutes two groups of binocular stereoscopic vision systems. The 3D deformation surface is reconstructed by multi-ocular vision coordinate association, image preprocessing, and point cloud registration. The measurement results are validated by comparison against laser range finder data. The standard deviation mean value of 10 specimens measured by the proposed method relative to the true value is 1.23%; the mean error of the maximum absolute value is 2.82%.
Urban last-mile express delivery in China encounters several challenges. This paper presents the establishment of a sharing logistics center aimed at enhancing the overall efficiency of urban ...last-mile express delivery while optimizing the utilization of essential resources. The successful implementation of shared delivery within sharing logistics center necessitates the creation of a robust collaborative mechanism. Recognizing that cooperative benefit allocation is dynamically influenced by factors such as resource input, operational efficiency, risk management, and other cost-related considerations, this study introduces a multi-weight H-Shapley value method for benefit allocation. By conducting empirical analyses of urban last-mile express delivery in Beijing within a sharing logistics service framework, our findings reveal that the revised benefit allocation model better aligns with the interests of participating entities and positively correlates with their contributions. Analyzing the impact of delivery volume and express operational costs changes, it is found that when the delivery volume and express operational costs of the sharing logistics center change, the benefits of participating enterprises move in the same direction. The benefit allocation model established in this study enriches the existing body of research in the field of shared delivery and offers valuable insights for benefit allocation issues that necessitate consideration of the dynamic effects of multiple parameter variations.
The Transformer-based Siamese networks have excelled in the field of object tracking. Nevertheless, a notable limitation persists in their reliance on ResNet as backbone, which lacks the capacity to ...effectively capture global information and exhibits constraints in feature representation. Furthermore, these trackers struggle to effectively attend to target-relevant information within the search region using multi-head self-attention (MSA). Additionally, they are prone to robustness challenges during online tracking and tend to exhibit significant model complexity. To address these limitations, We propose a novel tracker named ASACTT, which includes a backbone network, feature fusion network and prediction head. First, we improve the Swin-Transformer-Tiny to enhance its global information extraction capabilities. Second, we propose an adaptive sparse attention (ASA) to focus on target-specific details within the search region. Third, we leverage position encoding and historical candidate data to develop a dynamic template updater (DTU), which ensures the preservation of the initial frame's integrity while gracefully adapting to variations in the target's appearance. Finally, we optimize the network model to maintain accuracy while minimizing complexity. To verify the effectiveness of our proposed tracker, ASACTT, experiments on five benchmark datasets demonstrated that the proposed tracker was highly comparable to other state-of-the-art methods. Notably, in the GOT-10K
evaluation, our tracker achieved an outstanding success score of 75.3% at 36 FPS, significantly surpassing other trackers with comparable model parameters.
The purpose of our study was to explore potential characteristic biomarkers in patients with sepsis. Peripheral blood specimens from sepsis patients and normal human volunteers were processed by ...liquid chromatography-mass spectrometry-based analysis. Outlier data were excluded by principal component analysis and orthogonal partial least squares-discriminant analysis using the metabolomics R software package metaX and MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/home.xhtml ) online analysis software, and differential metabolite counts were identified by using volcano and heatmaps. The obtained differential metabolites were combined with KEGG (Kyoto Gene and Kyoto Encyclopedia) analysis to screen out potential core differential metabolites, and ROC curves were drawn to analyze the changes in serum metabolites in sepsis patients and to explore the potential value of the metabolites in the diagnosis of sepsis patients. By metabolomic analysis, nine differential metabolites were screened for their significance in guiding the diagnosis and differential diagnosis of sepsis namely: 3-phenyl lactic acid, N-phenylacetylglutamine, phenylethylamine, traumatin, xanthine, methyl jasmonate, indole, l-tryptophan and 1107116. In this study, nine metabolites were finally screened based on metabolomic analysis and used as potential characteristic biomarkers for the diagnosis of sepsis.
Separating sensitive characteristic signals from original vibration data is an important challenge for rolling bearing fault diagnosis. Because it is difficult to obtain large number of damaged ...bearings, Rolling bearing fault datasets are often small sample datasets. For the classification of small sample rolling bearing fault datasets, we propose a coupling vibration data classification method based on triplet embedding. The method is divided into two steps: feature extraction and fault identification. First, build a triple embedding based on the CNN model to reduce the original vibration signal, and then train the SVM model for classification. Compared with traditional features and autoencoder, triplet network can learn the differences between samples. Make classification training easier and more accurate. We have evaluated the performance of this method through two bearing experiment examples. The experimental results show that this method is superior to stacked autoencoder, stacked denoising autoencoder and CNN.
Wnt/β-catenin signaling has been broadly implicated in human cancers and experimental cancer models of animals. Aberrant activation of Wnt/β-catenin signaling is tightly linked with the increment of ...prevalence, advancement of malignant progression, development of poor prognostics, and even ascendence of the cancer-associated mortality. Early experimental investigations have proposed the theoretical potential that efficient repression of this signaling might provide promising therapeutic choices in managing various types of cancers. Up to date, many therapies targeting Wnt/β-catenin signaling in cancers have been developed, which is assumed to endow clinicians with new opportunities of developing more satisfactory and precise remedies for cancer patients with aberrant Wnt/β-catenin signaling. However, current facts indicate that the clinical translations of Wnt/β-catenin signaling-dependent targeted therapies have faced un-neglectable crises and challenges. Therefore, in this study, we systematically reviewed the most updated knowledge of Wnt/β-catenin signaling in cancers and relatively targeted therapies to generate a clearer and more accurate awareness of both the developmental stage and underlying limitations of Wnt/β-catenin-targeted therapies in cancers. Insights of this study will help readers better understand the roles of Wnt/β-catenin signaling in cancers and provide insights to acknowledge the current opportunities and challenges of targeting this signaling in cancers.
•The possible mechanism of acquired resistance to Smo inhibitors was reviewed.•Development of second-generation inhibitors is critical to combat Smo resistance.•Gli inhibition could benefit to ...patients with Smo-inhibitor resistant tumors.•The Hh-associated oncogenic signaling pathways represent potential therapeutic targets.
Smoothened (Smo), the main transducer of the Hedgehog (Hh) signaling pathway, is a promising target for anticancer therapy. Although vismodegib and sonidegib have demonstrated effectiveness for the treatment of basal cell carcinoma (BCC), their clinical use has been associated with mutation-related drug resistance. In this review, we outline the resistance mechanisms of Smo inhibitors and point the way for future endeavors. We focus in particular on the development of second-generation Smo inhibitors based on co-crystal structures, inhibition of downstream components, and the regulation of other interacting pathways or mediators that could compensate for the inhibitory activity of upstream inhibitors.
During tooth development, dental papilla cells differentiate into odontoblasts with polarized morphology and cell function. Our previous study indicated that the C-Jun N-terminal kinase (JNK) pathway ...regulates human dental papilla cell adhesion, migration, and formation of focal adhesion complexes. The aim of this study was to further examine the role of the JNK pathway in dental papilla cell polarity formation. Histological staining, qPCR, and Western Blot suggested the activation of JNK signaling in polarized mouse dental papilla tissue. After performing an in vitro tooth germ organ culture and cell culture, we found that JNK inhibitor SP600125 postponed tooth germ development and reduced the polarization, migration and differentiation of mouse dental papilla cells (mDPCs). Next, we screened up-regulated polarity-related genes during dental papilla development and mDPCs or A11 differentiation. We found that Prickle3, Golga2, Golga5, and RhoA were all up-regulated, which is consistent with JNK signaling activation. Further, constitutively active RhoA mutant (RhoA Q63L) partly rescued the inhibition of SP600125 on cell differentiation and polarity formation of mDPCs. To sum up, this study suggests that JNK signaling has a positive role in the formation of dental papilla cell polarization.
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