Whole Sky Infrared Remote Sensing of Cloud

AbstractClouds are important factors in weather and climate change. Cloud amount, type and height are measured by means of both visual observation on ground and satellites ever before. In recent years, instruments of measuring clouds on ground have been developed. This paper introduces our progress on ground based whole sky infrared remote sensing of cloud. Some results are given. A method for determining clear sky radiance threshold was suggested, and cloud identification combined threshold method with texture method was discussed. An algorithm retrieving cloud base height from downwelling infrared radiance was suggested. Cloud classification of ground based whole sky cloud images was discussed. Structural features are better than texture features in classifying clouds

Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.Comment: TOIS 202

Research on the differential tectonic-thermal evolution of Longmaxi shale in the southern Sichuan Basin

The southern Sichuan Basin in China holds abundant shale gas resources; however, the shale gas bearing property shows great differences due to the multiple stages of tectonic transformation. The key to revealing the shale gas differential enrichment mechanism is to explore the thermal evolution characteristics during tectonic evolution. Therefore, taking the Luzhou and Changning blocks as an example, which have obvious differences in tectonic evolution, the organic geochemical conditions of Longmaxi shale were firstly compared with the test data. Then, the thermal evolution characteristics under the background differential tectonic uplift-erosion were recovered using basin modeling techniques. The results showed that the two blocks contain similar organic geochemical conditions of the Longmaxi shale. Moreover, the hydrocarbon generation condition in Luzhou Block is greater than that in the Changning Block. Influenced by the differential tectonic evolution, the study area experienced a complex burial history and the formation of multiple unconformities. As a result, the present burial depth of Longmaxi Formation in the Luzhou Block is significantly greater than that in the Changning Block. The thermal evolution history of Longmaxi shale in the study area could be divided into three stages, including a low-temperature stage from Caledonian to Hercynian, a middle-temperature stage from Hercynian to Indosinian, and a high-temperature stage from Yanshanian to Himalayan. In addition, it was found that the Himalayan period is the main stage resulting in the differential gas bearing property of Longmaxi shale in the southern Sichuan area. Under the differential structural modification, the peak time of hydrocarbon generation in the Luzhou Block occurred earlier and the conversion rate was slightly higher than that in the Changning Block.Cited as: Zhao, L., Mao, W., Liu, Z., Cheng, S. Research on the differential tectonic-thermal evolution of Longmaxi shale in the southern Sichuan Basin. Advances in Geo-Energy Research, 2023, 7(3): 152-163. https://doi.org/10.46690/ager.2023.03.0

The contributions of key countries, enterprises and refineries to greenhouse gas emissions in global oil refining 2000-2021

The refining industry is the third-largest source of global greenhouse gas (GHG) emissions from stationary sources, so it is at the forefront of the energy transition and net zero pathways. The dynamics of contributors in this sector such as crucial countries, leading enterprises, and key emission processes are vital to identifying key GHG emitters and supporting targeted emission reduction, yet they are still poorly understood. Here, we established a global sub-refinery GHG emission dataset in a long time series based on life cycle method. Globally, cumulative GHG emissions from refineries reached approximately 34.1 gigatons (Gt) in the period 2000–2021 with an average annual increasing rate of 0.7%, dominated by the United States, EU27&UK, and China. In 2021, the top 20 countries with the largest GHG emissions of oil refining accounted for 83.9% of global emissions from refineries, compared with 79.5% in 2000. Moreover, over the past two decades, 53.9–57.0% of total GHG emissions came from the top 20 oil refining enterprises with the largest GHG emissions in 12 of these 20 countries. Retiring or installing mitigation technologies in the top 20% of refineries with the largest GHG emissions and refineries with GHG emissions of more than 0.1 Gt will reduce the level of GHG emissions by 38.0%–100.0% in these enterprises. Specifically, low-carbon technologies installed on furnaces and boilers as well as steam methane reforming will enable substantial GHG mitigation of more than 54.0% at the refining unit level. Therefore, our results suggest that policies targeting a relatively small number of super-emission contributors could significantly reduce GHG emissions from global oil refining

Gust response and body freedom flutter of a flying-wing aircraft with a passive gust alleviation device

The effectiveness of a passive gust alleviation device (PGAD) mounted at the wingtip of aircraft in conventional and flying-wing configurations have been studied in previous research. However the PGAD influence on the aeroelastic stability in particular the body freedom flutter (BFF) of a flying-wing aircraft remains as a concern. This present investigation is focused on evaluating the beneficial effect of PGAD on both gust load alleviation and BFF of a small flying-wing aircraft of high aspect ratio wing made of composite. A small range of (1-cos) type of gust load has been considered to select a representative critical gust load case for the study. A parametric study indicates that there is a narrow band of optimal key parameters for the PGAD design. Subsequently a set of optimal parameters is selected to further the analysis of the PGAD mechanism. The case study results show that the PGAD can make the bending moment at the wing root due to gust reduced by 16%. In addition, the BFF speed of the flying-wing aircraft is increased by 4.2%. The investigation reveals that the PGAD mode and its interaction with the wing bending mode and short period oscillation of the aircraft can have beneficial aeroelastic effect on both gust alleviation and flutter suppression

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that the user is interested in, it may also make the user feel bored and less satisfied. Existing work studies filter bubbles in static recommendation, where the effect of overexposure is hard to capture. In contrast, we believe it is more meaningful to study the issue in interactive recommendation and optimize long-term user satisfaction. Nevertheless, it is unrealistic to train the model online due to the high cost. As such, we have to leverage offline training data and disentangle the causal effect on user satisfaction. To achieve this goal, we propose a counterfactual interactive recommender system (CIRS) that augments offline reinforcement learning (offline RL) with causal inference. The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction. It then uses the learned causal user model to help the planning of the RL policy. To conduct evaluation offline, we innovatively create an authentic RL environment (KuaiEnv) based on a real-world fully observed user rating dataset. The experiments show the effectiveness of CIRS in bursting filter bubbles and achieving long-term success in interactive recommendation. The implementation of CIRS is available via https://github.com/chongminggao/CIRS-codes.Comment: 11 pages, 9 figure