Asphalt pavement often experiences structural failure due to repeated vehicle loading. The discrete element method (DEM) model was established based on the semicircle bending test (SCB) to investigate the fracture damage mechanism of emulsified asphalt cold recycled mixture (CRME) under loading. The micro-mechanical parameters of CRME were determined through a reliable validation process using the uniaxial compression static creep test. The microscopic fracture characteristics of CRME were investigated through the load-displacement curve, stress distribution, and force chain distribution. The fracture energy was used as the evaluation index to analyze the influence of prefabricated notch length and aggregate gradation on the fracture performance of CRME. The results indicate that the emulsified asphalt mortar-aggregate interface was the critical weak position of the mixture fracture; the failure of the tension chain was the main destructional form of the SCB test. The development of cracks affected the stress concentration phenomenon and stress concentration level of the mixture. Fine-grained mixture exhibited crack resistance. The number and length of cracks were affected by gradation. As the prefabricated notch length increased, the influence gradually diminished. The research results could provide theoretical and data support for the design of CRME.

The Mesozoic volcanic rocks are widely developed in the Bohai Bay basin. The D oilfield, located in the southeast of the Bohai Bay Basin, is a Cenozoic depression developed on the base of the Mesozoic. The types of the volcanic rocks are complex and the reservoir space is diverse. According to the characteristics of the volcanic reservoir, such as vertical multi-stage and strong heterogeneity, and based on the analysis of the volcanic core observation, thin section identification, logging data and seismic data, we analyzed the reservoir space type, physical property characteristics and reservoir physical property control factors of volcanic reservoir in the study area. The results show that the volcanic rocks in the study area are mainly volcanic breccia, andesite and tuff; the lithofacies types mainly include volcanic eruption facies, effusion facies and volcanic sedimentary facies, and the volcanic eruption facies is the most developed. Four types of volcanic reservoirs and 14 effective storage space types have been identified from the macroscopic and microscopic multi-scale, mainly intergranular pores, intergranular dissolution pores, intracrystalline pores, structural fractures and weathering dissolution fractures. Reservoir performance is mainly affected by lithology, lithofacies, tectonic activity and diagenesis. The primary pores in the upper part of exhalative and explosive facies are the most developed. Early cement filling is beneficial to the preservation of primary intergranular pore space and is an important prerequisite for the formation of secondary dissolution pores. Under the action of multi-stage tectonic movement and weathering leaching, the reservoir performance of volcanic rocks has been greatly improved, and the volcanic rocks with superimposed fractures and porosities are effective volcanic reservoirs.

Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models. These components have drastically different characteristics, are required to work in collaboration, and interface with each other as microservices. This leads to increasingly high complexity in developing, optimizing, configuring, and deploying scale-out applications, raising the barrier to entry for most individuals and small teams. We developed a novel co-designed runtime system, Jaseci , and programming language, Jac , which aims to reduce this complexity. The key design principle throughout Jaseci's design is to raise the level of abstraction by moving as much of the scale-out data management, microservice componentization, and live update complexity into the runtime stack to be automated and optimized automatically. We use real-world AI applications to demonstrate Jaseci's benefit for application performance and developer productivity.