Low Temperature Oxidation Experiments and Kinetics Model of Heavy Oil

Air injection is an effective technique for improved oil recovery. For a typical heavy oil sample, the effects of temperature on the oxidation characteristics were studied by low temperature oxidation (LTO) experiments. Kinetic parameters such as activation energy, frequency factor (pre-exponential factor) and reaction order are determined by using Arrhenius Equation. These parameters provide a theoretical basis for numerical simulation of LTO taking place during air injection in heavy oil reservoirs. The results of LTO experiments show that heavy oil has good low temperature oxidation properties and LTO reaction rate is mainly related to temperature, oxygen partial pressure and properties of crude oil. In the experimental temperature range, the oxidation reaction can effectively consume oxygen and at the same time produce large amount of CO2.Key words: Air injection; Low temperature oxidation; Kinetics model (70-150 oC

High-Resolution Volumetric Reconstruction for Clothed Humans

We present a novel method for reconstructing clothed humans from a sparse set of, e.g., 1 to 6 RGB images. Despite impressive results from recent works employing deep implicit representation, we revisit the volumetric approach and demonstrate that better performance can be achieved with proper system design. The volumetric representation offers significant advantages in leveraging 3D spatial context through 3D convolutions, and the notorious quantization error is largely negligible with a reasonably large yet affordable volume resolution, e.g., 512. To handle memory and computation costs, we propose a sophisticated coarse-to-fine strategy with voxel culling and subspace sparse convolution. Our method starts with a discretized visual hull to compute a coarse shape and then focuses on a narrow band nearby the coarse shape for refinement. Once the shape is reconstructed, we adopt an image-based rendering approach, which computes the colors of surface points by blending input images with learned weights. Extensive experimental results show that our method significantly reduces the mean point-to-surface (P2S) precision of state-of-the-art methods by more than 50% to achieve approximately 2mm accuracy with a 512 volume resolution. Additionally, images rendered from our textured model achieve a higher peak signal-to-noise ratio (PSNR) compared to state-of-the-art methods

What factors determine brand communication? A hybrid brand communication model from utilitarian and hedonic perspectives

IntroductionWith the advancement of new media, brand communication has been taken into consideration by lots of firms. Apparently, customer affection plays a significant role in brand communications, though few studies have determined how the twofold of information function works in this communication mechanism. Based on this research gap and practical background, this paper proposes a hybrid model of communication comprising the utilitarian and hedonic aspects.MethodsFor this study, 575 questionnaires were collected, followed by the structural equation modeling of the derived data to test the research model.ResultsThe results of statistical analysis show that the brand communication can be improved in terms of both utilitarian and hedonic aspects. Moreover, psychological contract and customer engagement play a chain mediation role in this mechanism.DiscussionThese findings contribute to the research of brand communication mechanism in digital era. Likewise, the findings offers several practical implications to the brand management

Zero-Knowledge Proof Vulnerability Analysis and Security Auditing

Zero-Knowledge Proof (ZKP) technology marks a revolutionary advancement in the field of cryptography, enabling the verification of certain information ownership without revealing any specific details. This technology, with its paradoxical yet powerful characteristics, provides a solid foundation for a wide range of applications, especially in enhancing the privacy and security of blockchain technology and other cryptographic systems. As ZKP technology increasingly becomes a part of the blockchain infrastructure, its importance for security and completeness becomes more pronounced. However, the complexity of ZKP implementation and the rapid iteration of the technology introduce various vulnerabilities, challenging the privacy and security it aims to offer. This study focuses on the completeness, soundness, and zero-knowledge properties of ZKP to meticulously classify existing vulnerabilities and deeply explores multiple categories of vulnerabilities, including completeness issues, soundness problems, information leakage, and non-standardized cryptographic implementations. Furthermore, we propose a set of defense strategies that include a rigorous security audit process and a robust distributed network security ecosystem. This audit strategy employs a divide-and-conquer approach, segmenting the project into different levels, from the application layer to the platform-nature infrastructure layer, using threat modelling, line-by-line audit, and internal cross-review, among other means, aimed at comprehensively identifying vulnerabilities in ZKP circuits, revealing design flaws in ZKP applications, and accurately identifying inaccuracies in the integration process of ZKP primitives

Improved Data Transmission Scheme of Network Coding Based on Access Point Optimization in VANET

VANET is a hot spot of intelligent transportation researches. For vehicle users, the file sharing and content distribution through roadside access points (AP) as well as the vehicular ad hoc networks (VANET) have been an important complement to that cellular network. So the AP deployment is one of the key issues to improve the communication performance of VANET. In this paper, an access point optimization method is proposed based on particle swarm optimization algorithm. The transmission performances of the routing protocol with random linear network coding before and after the access point optimization are analyzed. The simulation results show the optimization model greatly affects the VANET transmission performances based on network coding, and it can enhance the delivery rate by 25% and 14% and reduce the average delay of transmission by 38% and 33%

Ultra-Strong Long-Chain Polyamide Elastomers With Programmable Supramolecular Interactions and Oriented Crystalline Microstructures

Polyamides are one of the most important polymers. Long-chain aliphatic polyamides could bridge the gap between traditional polyamides and polyethylenes. Here we report an approach to preparing sustainable ultra-strong elastomers from biomass-derived long-chain polyamides by thiol-ene addition copolymerization with diamide diene monomers. The pendant polar hydroxyl and non-polar butyrate groups between amides allow controlled programming of supramolecular hydrogen bonding and facile tuning of crystallization of polymer chains. The presence of thioether groups on the main chain can further induce metal–ligand coordination (cuprous-thioether). Unidirectional step-cycle tensile deformation has been applied to these polyamides and significantly enhances tensile strength to over 210 MPa while maintaining elasticity. Uniaxial deformation leads to a rearrangement and alignment of crystalline microstructures, which is responsible for the mechanical enhancement. These chromophore-free polyamides are observed with strong luminescence ascribed to the effect of aggregation-induced emission (AIE), originating from the formation of amide clusters with restricted molecular motions