A bony-crested Jurassic dinosaur with evidence of iridescent plumage highlights complexity in early paravian evolution

The Jurassic Yanliao theropods have offered rare glimpses of the early paravian evolution and particularly of bird origins, but, with the exception of the bizarre scansoriopterygids, they have shown similar skeletal and integumentary morphologies. Here we report a distinctive new Yanliao theropod species bearing prominent lacrimal crests, bony ornaments previously known from more basal theropods. It shows longer arm and leg feathers than Anchiornis and tail feathers with asymmetrical vanes forming a tail surface area even larger than that in Archaeopteryx. Nanostructures, interpreted as melanosomes, are morphologically similar to organized, platelet-shaped organelles that produce bright iridescent colours in extant birds. The new species indicates the presence of bony ornaments, feather colour and flight- related features consistent with proposed rapid character evolution and significant diversity in signalling and locomotor strategies near bird origins

Gender Discrimination towards Borrowers in Online P2PLending

Online peer-to-peer (P2P) lending has developed fast around the world in recent years; however, studies regarding gender discrimination and its rationality for developing countries are limited. Gender discrimination towards borrowers and its rationality in P2P lending in China are studied in this paper. Using data collected from PPdai.com, one of the largest P2P lending platforms in China, we found that, female borrowers are less likely to be funded than male borrowers, but their default rates are lower. Such results suggested that there is significant gender discrimination in P2P lending market in China, but such discrimination is out of prejudice rather than from rational reasoning. Eliminating such gender discrimination is not only beneficial to female borrowers, but also helpful for improving returns of lenders

Profit VS Non-Profit Business Based on P2P Lending: A Cross-Country Multiple Case Study

People-to-People (P2P) lending allows individuals to lend and borrow directly among each other online. Previous research mainly focuses on a single P2P lending marketplace – Prosper.com. This paper uses a multiple-case study approach to explore four P2P lending marketplaces. The study provides in-depth explanations on how different P2P lending marketplaces operate in different business models and in different countries. Our study thus extends current understanding about different operation models of P2P lending marketplaces, and suggests how future research should be done to cover more P2P lending platforms. We also contribute to the literature by indicating the interesting research questions originated from the cross-case analysis

The Roles of Social Capital in Online P2P Lending Markets Under Different Cultures: A Comparison of China and America

Online P2P (People-to-People or Peer-to-Peer) lending has very rapid development since it was appeared in 2005. In order to mitigate asymmetric information between borrowers and lenders, some online P2P market allows members building their social networks (such as Prosper, CommunityLend, PPDai etc). By empirical analyzing the transaction data of Prosper (largest P2P market in US) and PPDai (largest P2P market in China), the paper verifies that the social capital systems have a positive influence on borrower’s loan performance on the markets. However, on both markets, the loan interest rate mainly dependents on borrower’s hard information rather than their social capital. Furthermore, it concludes that borrower’ social network in PPDai is much more useful and effective than in Prosper by comparing the empirical results, which could be helpful for the credit system development of Chinese online P2P lending markets based on the conclusions

Research on Embedded Sensors for Concrete Health Monitoring Based on Ultrasonic Testing

In this article, embedded ultrasonic sensors were prepared using 1–3-type piezoelectric composite and piezoelectric ceramic as the piezoelectric elements, respectively. The frequency bandwidth of the novel embedded ultrasonic sensors was investigated. To obtain the relationship between the receiving ultrasonic velocity and compressive strength, as well as their response signals to crack damage, the sensors were fabricated and embedded into the cement mortar before testing. The results demonstrated that the piezoelectric composite sensor had wider frequency bandwidth than the piezoelectric ceramic sensor. The compressive strength and ultrasonic velocity had a positive linear relationship, with a correlation coefficient of 0.9216. The head wave amplitude of the receiving ultrasonic signal was sensitive to the changing crack damage and gradually decayed with the increasing degree of cement damage. Thus, the novel embedded ultrasonic sensors are suitable for concrete health monitoring via ultrasonic non-destructive testing

Regional Economic Growth Forecast Based on Artificial Intelligence and Computer Vision Model

Introduction: regional economic growth can be predicted to make more effective countermeasures and promote the development of local regions. However, the existing regional economic growth forecasting models have the problems that the forecasting speed is too slow and the forecasting results are inaccurate, which greatly hinders people\u27s understanding of economic growth. Methods: based on artificial intelligence and computer vision model, this paper designed a regional economic growth forecast model and predicted the economic growth of different regions. Through testing different areas, it was found that: The prediction risk index of the economic growth prediction model based on artificial intelligence and computer vision model was lower. Results: Among them, the accuracy rate was increased by 6,9 %, and the prediction speed was improved, as well as the user satisfaction rate was increased by 9,16 %. Conclusion: Therefore, artificial intelligence and computer vision technology could optimize the regional economic growth forecast mode

Design, Fabrication, and Properties of 2-2 Connectivity Cement/Polymer based Piezoelectric Composites with Varied Piezoelectric Phase Distribution

The laminated 2-2 connectivity cement/polymer based piezoelectric composites with variedpiezoelectric phase distribution were fabricated by employing Lead Zirconium Titanate ceramicas active phase, and mixture of cement powder, epoxy resin, and hardener as matrix phase with a mass proportion of 4:4:1. The dielectric, piezoelectric, and electromechanical coupling properties of the composites were studied. The composites with large total volume fraction ofpiezoelectric phase have large piezoelectric strain constant and relative permittivity, and thepiezoelectric and dielectric properties of the composites are independent of the dimensional variations of the piezoelectric ceramic layer. The composites with small total volume fraction of piezoelectric phase have large piezoelectric voltage constant, but also large dielectric loss. The composite with gradually increased dimension of piezoelectric ceramic layer has the smallest dielectric loss, and that with the gradually increased dimension of matrix layer has the largest piezoelectric voltage constant. The novel piezoelectric composites show potential applications in fabricating ultrasonic transducers with varied surface vibration amplitude of thetransducer

CDKN1A as a target of senescence in heart failure: insights from a multiomics study

BackgroundCardiomyocyte senescence plays a crucial role as a pathological mechanism in heart failure (HF). However, the exact triggering factors and underlying causes of HF onset and progression are still not fully understood.ObjectivesBy integrating multi-omics data, this study aimed to determine the genetic associations between cardiomyocyte and HF using cell senescence-related genes (SRGs).MethodsThe study utilized the CellAge database and the SenMayo dataset, combined with high-resolution single-cell RNA sequencing (scRNA-seq) data, to identify SRG and examine differences in cardiac cell expression. To explore the causal relationship with HF using Mendelian Randomization (MR). Genetic variations influencing gene expression, DNA methylation, and protein expression (cis-eQTL, cis-mQTL, and cis-pQTL) were analyzed using the two-sample MR (TSMR) and summary-data-based MR (SMR). Additionally, Bayesian colocalization analysis, germline genetic variation, and bulk RNA data were employed to strengthen the reliability of the results. The application potential of therapeutic targets is ultimately assessed by evaluating their druggability.ResultsThe expression of 39 SRGs in cardiomyocytes was identified. In the discovery set revealed that CDKN1A (OR = 1.09, 95% confidence interval (CI) 1.02–1.15, FDR = 0.048) could be causally related to HF, and the results are also replicated in the validation set (OR = 1.20, 95% confidence interval (CI) 1.10–1.30, FDR <0.0001). Based on the SMR method, CDKN1A was confirmed as a candidate pathogenic gene for HF, and its methylation (cg03714916, cg08179530) was associated with HF risk loci. The result is validated by Bayesian colocalization analysis, genetic variations, and bulk RNA data. The druggability analysis identified two potential therapeutic drugs.ConclusionBased on multi-omics data, this study uncovered the reciprocal regulation of cardiomyocyte senescence through CDKN1A, providing potential targets for HF drug development

Virtual flows and decoupling effects of water-carbon footprints in China’s ICT sector: analysis based on multi-regional input-output and decoupling models

Driven by the digital economy, China’s Information and Communication Technology (ICT) industry has significantly intensified water and carbon pressures. By employing the Environmentally Extended Multi-Regional Input-Output (EE-MRIO) model in conjunction with the Tapio decoupling model and Structural Decomposition Analysis (SDA), this study quantifies interprovincial virtual water-carbon flows and their economic-environmental coupling from 2012 to 2017. The findings reveal a spatial imbalance characterized by “eastern agglomeration–western burden”, as well as a transition from unidirectional outflows to hub-based resource interactions. Decoupling trajectories exhibit regional variations: Jiangsu has achieved strong decoupling, Beijing remains heavily coupled, while Jiangxi and Yunnan demonstrate significant water decoupling. The SDA results indicate that structural expansion continues to be the primary driver: from 2012 to 2017, per capita demand contributed +2.06 and final demand structure +0.54 to the average growth in carbon footprint. Regarding the water footprint, the final demand structure contributed +0.085. Although reductions in intensity have provided localized mitigation, the persistence of structural pressures suggests that path dependence has not been entirely overcome. This study expands the analytical framework for measuring environmental footprints and decoupling within the ICT industry, providing valuable insights for decision-making towards regional green transformation and ecological responsibility reconstruction

Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.Comment: 6 page