Geological materials exhibit spatial variability in their properties as a result of their formation. Many studies have focussed on how to characterise this spatial variation by means of the correlation length θ. Such a characterisation has been applied in the geotechnical design of geostructures at numerous sites where cone penetration tests (CPTs) were available, because θ can be relatively easily estimated from this in-situ information. However, the CPTs available at a given site are often part of the initial site investigation, and hence carried out before the application of any ground improvement technique. This raises the question of how (and by how much) the estimated θ is affected by subsequent stages of the construction project and, more specifically, by the application of ground improvement processes intended to alter the initially poor mechanical condition of the in-situ soil. This paper investigates in-situ data from three trial test sites, where CPT data before and after application of vibro-compaction are available. In addition to the expected overall mechanical improvement of the soil, the application of vibro-compaction has a significant impact on soil heterogeneity, with a substantial reduction in the coefficient of variation and θ of the cone tip resistance and sleeve friction.

Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection cost. For an area with scarce MR samples, it is hard to achieve good accuracy. In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called sf TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy. Specifically, sf TLoc introduces three dedicated components: 1) a new coordinate space to divide an area of interest into smaller domains, 2) a similarity measurement to select best source domains, and 3) an adaptation of an existing transfer learning approach. To the best of our knowledge, sf TLoc is the first framework that demonstrates the efficacy of applying transfer learning in the Telco outdoor position recovery. To exemplify, on the 2G GSM and 4G LTE MR datasets in Shanghai, sf TLoc outperforms a non-transfer approach by 27.58 and 26.12 percent less median errors, and further leads to 47.77 and 49.22 percent less median errors than a recent fingerprinting approach NBL.

While spintronic memories, for example, spin transfer torque magnetic random access memory (STT-MRAM), have shown huge potential for building next-generation memory due to their attractive characteristics, the relatively large write latency and deficient read mechanism preclude their further application for emerging concepts, such as in-memory-processing and neuromorphic computing. A toggle spin torque (TST) MRAM combining STT and spin orbit torque (SOT) has recently been proposed to alleviate the write issue. However, the sensing featuring a good balance between the reliability and speed has not been addressed. In this paper, we propose a self-matching complementary-reference (SMCR) sensing scheme, which provides not only a maximum sensing margin (SM) but also a high-speed read operation. Through applying it in the TST-MRAM, advantageous performance in terms of both write and read processes can be realized. To validate the functionality of our proposal, we design and evaluate an 8Kb TST-MRAM array, in which a read delay of 1 ns and a read bit error rate (BER) of 1.02 × 10-13 are achieved. Moreover, when being operated at 0.8 V supply voltage, it can reduce the read access energy by 7.5% and 20%, compared with conventional voltage sensing and dynamic reference sensing schemes, respectively.

Fossil fuel resources are invaluable to economic growth and social development. Understanding the formation and distribution of fossil fuel resources is critical for the search and exploration of them. Until now, the vertical distribution depth of fossil fuel resources has not been confirmed due to different understandings of their origins and the substantial variation in reservoir depths from basin to basin. Geological and geochemical data of 13 634 source rock samples from 1286 exploration wells in six representative petroliferous basins were examined to identify the maximum burial depth of active source rocks in each basin, which is referred to in this study as the active source rock depth limit (ASDL). Beyond the ASDL, source rocks no longer generate or expel hydrocarbons and become inactive. Therefore, the ASDL also sets the maximum depth for fossil fuel resources. The ASDLs of basins around the world are found to range from 3000 to 16 000 m, while the thermal maturities (Ro) of source rocks at the ASDLs are almost the same, with Ro ≈ 3:5±0:5 %. The Ro of 3.5% can be regarded as a general criterion to identify ASDLs. High heat flow and more oil-prone kerogen are associated with shallow ASDLs. In addition, tectonic uplift of source rocks can significantly affect ASDLs; 21.6 billion tons of reserves in six representative basins in China and 52 926 documented oil and gas reservoirs in 1186 basins around the world are all located above ASDLs, demonstrating the universal presence of ASDLs in petroliferous basins and their control on the vertical distribution of fossil fuel resources. The data used in this study are deposited in the repository of the PANGAEA database at: https://doi.org/10.1594/PANGAEA.900865 (Pang et al., 2019).