Granitic gneiss (orthogneiss) and Himalayan leucogranite are widely distributed in the Himalayan orogen, but whether or not the granitic gneiss made a contribution to the Himalayan leucogranite remains unclear. In this study, we present the petrological, geochronological and geochemical results for orthogneisses and leucogranites from the Zhada area, Western Himalayas. Zhada orthogneiss is composed mainly of quartz, plagioclase, K‐feldspar, biotite and muscovite, with accessory zircon and apatite. Orthogneiss zircon cathodoluminescence (CL) images show that most grains contain a core with oscillatory zoning, which indicates an igneous origin. Sensitive high‐resolution ion microprobe (SHRIMP) U‐Pb dating of the zircon cores in the orthogneiss shows a weighted 206Pb/238U age of 515 ± 4 Ma (early Paleozoic), with spongelike zircon rims of 17.9 ± 0.5 Ma (Miocene). Zhada leucogranite shows 206Pb/238U ages ranging from 19.0 ± 0.4 Ma to 12.4 ± 0.2 Ma, the weighted average age being 16.2 ± 0.4 Ma. The leucogranites have a low Ca content (<1 wt%), FeOt content (<1 wt%), Rb content (67.0–402 ppm), Sr content (<56.6 ppm), Ba content (3.35–238 ppm) and Rb/Sr ratio (0.5–14.7), which are similar to the geochemical characteristics of the Himalayan leucogranite derived from muscovite dehydration partial melting of metasediments and representative of most Himalayan leucogranites. The highly variable Na2O + K2O (4.33 wt%–9.13 wt%), Al2O3 (8.44 wt%–13.51 wt%), ∑REE (40.2–191.0 ppm), Rb (67.0–402 ppm) and Nb (8.23–26.4 ppm) contents, 87Sr/86Sr(t) ratios (0.7445–0.8605) and εNd(t) values (–3.6 to –8.2) indicate that the leucogranite is derived from a heterogenetic source. The nonradiogenic Nd isotope values of the studied Zhada leucogranite and orthogneiss range from –8.2 to –3.6 and from –8.7 to –4.1, respectively. Therefore, the general mixing equation was used to perform the Sr and Nd isotope mixing calculations. The results indicate that the heterogenetic source was the Tethyan Himalayan Sequence (THS)/Higher Himalayan Crystalline (HHC) metasediments and Zhada orthogneiss. The Zhada area experienced crustal anatexis during the Miocene and the heterogenetic source of the orthogneiss and metasediment may have experienced crustal anatexis controlled by muscovite dehydration. The Zhada leucogranite inherited not only the geochemical characteristics of the Himalayan metasediment (muscovite dehydration melting), but also the trace elements and Sr‐Nd isotopic characteristics of the Zhada orthogneiss. These results indicate that the Paleozoic Zhada orthogneiss was involved in crustal anatexis at 17.9 ± 0.5 Ma (Miocene) and that the muscovite dehydration of the metasediments in the heterogenetic source produced fluid, which may have caused the orthogneiss solidus lines to decline, triggering a partial melting of the Zhada orthogneiss. It is therefore proposed that Himalayan leucogranite is a crust‐derived granite rather than a S‐type granite, as previously hypothesized.
Rewards are critical hyperparameters in reinforcement learning (RL), since in most cases different reward values will lead to greatly different performance. Due to their commercial value, RL rewards become the target of reverse engineering by the inverse reinforcement learning (IRL) algorithm family. Existing efforts typically utilize two metrics to measure the IRL performance: the expected value difference and the mean reward loss, which we call them EVD and MRL respectively. Unfortunately, in some cases, EVD and MRL can give completely opposite results, due to MRL focusing on whole state-space rewards while EVD only considering partly sampled rewards. Such situation naturally rises to one fundamental question: whether current metrics and assessment are sufficient and accurate for more general use. Thus, in this paper, based on the metric called normalized mutual information of reward clusters (C-NMI) we propose a novel IRL assessment; we aim to fill this research gap by considering a middle-granularity state space between the entire state space and the specific sampling space. We utilize the agglomerative nesting algorithm (AGNES) to control dynamical C-NMI computing via a 4-order tensor model with injected manipulated trajectories. With such a model, we can uniformly capture different-dimension values of MRL, EVD, and C-NMI, and perform more comprehensive and accurate assessment and analyses. Extensive experiments on several mainstream IRLs are experimented in Object World, hence revealing that the assessing accuracy of our method increases 110.13% and 116.59% respectively when compared with the EVD and MRL. Meanwhile, C-NMI is more robust than EVD and MRL under different demonstrations.Impact Statement-In this work, we pay attention to the inconformity problem of MRL-and EVD-based IRL assessment. There are two main challenges for us to address: (1) how to design a novel metric by combining the advantages of both MRL and EVD, and (2) how to construct a comprehensive assessment method for accurate comparison and analysis. To address such challenges, we craft a novel assessment of IRL based on the metric called normalized mutual information of reward clusters (C-NMI). Hence we attempt to fill the existing research gap by considering a middle-granularity state space between the entire state space and the certain sampled space. We list all of the notation and parameters used in the rest of this paper in Table I.
Inefficient signal control will not only exaggerate traffic congestion, but also increase the fuel consumption and exhaust emissions. Thus, signal planning is highly important in green transportation. As the Connected vehicle (CV) technology has transformed today's transportation systems by connecting vehicles and the transportation infrastructure through wireless communication, the CV-based signal control system has seen significant studies recently. Unfortunately, existing signal planning algorithms in use are developed for the signal-intersection, showing low traffic efficiency in the multi-intersection collaborative planning due to ignoring the traffic correlation among the neighboring intersections. In this work, we target the USDOT (U.S. Department of Transportation) sponsored CVbased traffic control system, and implement a multi-intersection traffic network. We model the multi-intersection collaborative signal planning problem as a multi-agent reinforcement learning problem, and present an actor-attention-critic algorithm to improve transportation efficiency and energy efficiency in green transportation, as well as resist congestion attack. Experiment results on the multi-intersection traffic network indicates that 1) compared to the baseline, our approach reduces the total delay by as high as 44.24%; 2) our method transports more vehicles passing the intersections meanwhile reduces the total CO 2 emissions by 2.40%; 3) under the congestion attack, our approach shows robustness and reduces the total delay by as high as 64.33%.
The Bayan Obo rare earth element (REE) deposit in Inner Mongolia, northern China, is the largest REE deposit in the world, whose mineralization process remains controversial. There are dozens of carbonatite dykes that are tightly related to the deposit. Here we report the petrological and mineralogical characteristics of a typical dolomite carbonatite dyke near the deposit. The dolomite within the dyke experienced intense post-emplacement fluids metasomatism as evidenced by the widespread hydrothermal REE-bearing minerals occurring along the carbonate mineral grains. REE contents of bulk rocks and constituent dolomite minerals (>90 vol.%) are 1407–4184 ppm and 63–152 ppm, respectively, indicating that dolomite is not the dominant mineral controlling the REE budgets of the dyke. There are three types of apatite in the dyke: Type 1 apatite is the primary apatite and contains REE2O3 at 2.35–4.20 wt.% and SrO at 1.75–2.19 wt.%; Type 2 and Type 3 apatites are the products of replacement of primary apatite. The REE2O3 (6.10–8.21 wt.%) and SrO (2.83–3.63 wt.%) contents of Type 2 apatite are significantly elevated for overprinting of REE and Sr-rich fluids derived from the carbonatite. Conversely, Type 3 apatite has decreased REE2O3 (1.17–2.35 wt.%) and SrO (1.51–1.99 wt.%) contents, resulting from infiltration of fluids with low REE and Na concentrations. Our results on the dyke suggest that post-magmatic fluids expelled from the carbonatitic melts dominated the REE mineralization of the Bayan Obo deposit, and a significant fluid disturbance occurred but probably provided no extra REEs to the deposit.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.