The Genetic Mechanism of Inertinite in the Middle Jurassic Inertinite-Rich Coal Seams of the Southern Ordos Basin

Inertinite is an important type of organic maceral in coal deposits, andalso an important geological information carrier of coal forming environments. In the southern section of the Ordos Basin, the No. 4 inertinite-richcoal seam of the Middle Jurassic Yan’an Formation in the Binchang Coalfield was selected as an example to study the genetic mechanism of theinertinite. In this study, the results obtained from experimental tests ofcoal rock, including principal and trace elements, stable carbon isotopes,scanning electron microscopy, inertinite reflectance, sporopollen andfree radical retorting methods, were analyzed. Then, the findings werecombined with the previous understanding of the oxygen content in theatmosphere and ground fire characteristics, in order to discuss the genesismechanism of inertinite in the No. 4 coal seam. The obtained researchresults were as follows: (1) During the coal forming period of the No. 4coal seam, the overall climate had been relatively dry. There were fourrelatively dry-wet climate cycles in the No.4 coal seam, which werecontrolled by the eccentricity astronomical period. The inertinite contentwere relatively high during the dry periods; (2) The temperature rangesuitable for microorganism activities during the oxidation processes wasbetween 0 and 80℃ . The simulation results of the free radical concentrations showed that the maximum temperature of fusain in the No. 4 coalseam during the process of coalification had not exceeded 300℃ , whichwas significantly higher than the temperature range of microorganismactivities. Therefore, these were not conducive to the activities of microorganism and formation of inertinite during the coal-forming period;(3) The genesis temperature of the inertinite in the No. 4 coal seam wascalculated according to the reflectance of the inertinite, which was lowerthan 400 ℃ . This result supported the cause of wildfire of the inertiniteand reflected that the type of wildfire was mainly ground fire, along withpartially surface fire. Moreover, the paleogeographic location, climaticconditions, atmospheric oxygen concentration, etc. of the study areashowed that the conditions for wildfire events were in fact available; (4)There were dense and scattered fusinite observed in the No. 4 coal seam,and the thickness of cell walls were found to differ. It was speculated thatthis was related to the type of wildfire, combustion temperatures, combustion timeframes, and different initial conditions of the burned objectsduring the coal forming periods

HLT-MT: High-resource Language-specific Training for Multilingual Neural Machine Translation

Multilingual neural machine translation (MNMT) trained in multiple language pairs has attracted considerable attention due to fewer model parameters and lower training costs by sharing knowledge among multiple languages. Nonetheless, multilingual training is plagued by language interference degeneration in shared parameters because of the negative interference among different translation directions, especially on high-resource languages. In this paper, we propose the multilingual translation model with the high-resource language-specific training (HLT-MT) to alleviate the negative interference, which adopts the two-stage training with the language-specific selection mechanism. Specifically, we first train the multilingual model only with the high-resource pairs and select the language-specific modules at the top of the decoder to enhance the translation quality of high-resource directions. Next, the model is further trained on all available corpora to transfer knowledge from high-resource languages (HRLs) to low-resource languages (LRLs). Experimental results show that HLT-MT outperforms various strong baselines on WMT-10 and OPUS-100 benchmarks. Furthermore, the analytic experiments validate the effectiveness of our method in mitigating the negative interference in multilingual training.Comment: 7 pages, 7 figures, IJCAI-ECAI 202

A case study of the comparison between rubberized and polymer modified asphalt on heavy traffic pavement in wet and freeze environment

Ground tire rubber (GTR) usage in asphalt pavement with the dry process has gained more prominence in recent times. The objective of this work is to investigate the pavement performance of GTR-modified asphalt pavement and polymer-modified asphalt pavement on heavy volume of traffic conditions in Michigan\u27s wet and freeze environment. A suite of laboratory tests was done to evaluate the pavement performance of GTR-modified and polymer-modified asphalt mixtures. To reveal the strain and stress relationship under different frequencies and temperatures, the dynamic modulus test was applied. The Hamburg wheel tracking device (HWTD) was used to assess the high-temperature deformation resistance. The disc-shaped compact tension (DCT) test was used to evaluate the low-temperature cracking characteristics. The characteristics of the asphalt binder were assessed by the dynamic shear rheometer (DSR) for high-temperature properties and the asphalt binder cracking device (ABCD) for low-temperature properties. After the construction, a field noise test was conducted. The experimental results stated that the polymer-modified asphalt mixture and GTR-modified asphalt mixture showed higher dynamic modulus and better ability to prevent cracking than the conventional asphalt mixture at low temperatures, as well as better permanent deformation and stripping resistance than the conventional asphalt mixture. The fracture energy of the GTR-modified hot mix asphalt (HMA) is 13–16 % larger than the polymer-modified HMA. The number of passes to the stripping point of GTR-modified was 510–518 % higher than the conventional HMA. When compared to the field core, the lab-compacted HMA offers superior pavement performance. The extracted asphalt binder test results show the GTR-modified asphalt has better rutting resistance and cracking resistance than polymer-modified asphalt, and the results in the noise test demonstrated that the rubber-modified asphalt pavement mitigated the noise level by 2–3 dB on the road at different vehicle speeds. Moreover, the pavement condition was noticeably enhanced after the reconstruction of the surface course. The total number of passenger tires to be used in this project is about 2270. To summarize, better rutting and cracking properties in asphalt pavement are shown by the project\u27s utilization of rubber technology. And the GTR-modified HMA is comparable to polymer-modified HMA. Therefore, it may be appropriate to utilize rubber technology on high-traffic volume asphalt pavement in Michigan\u27s wet and freeze climate

ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry

Increase in ocean-onto-land droughts and their drivers under anthropogenic climate change

Ocean-onto-land droughts (OTLDs)—i.e., droughts originating over the oceans and migrating onto land—are a recently identified phenomenon with severe natural and human impacts. However, the influence of anthropogenic emissions on past and future changes in OTLDs and their underlying mechanisms remain unclear. Here, using precipitation-minus-evaporation deficits to identify global OTLDs, we find OTLDs have intensified due to anthropogenic climate change during the past 60 years. Under a future high-emissions scenario, the OTLDs would become more frequent (+39.68%), persistent (+54.25%), widespread (+448.92%), and severe (+612.78%) globally. Intensified OTLDs are associated with reduced moisture transport driven by subtropical anticyclones in the northern hemisphere and complex circulation patterns in the southern hemisphere. The reduction in moisture transport during OTLDs is mainly caused by the atmospheric thermodynamic responses to human-induced global warming. Our results underscore the importance of improving understanding of this type of drought and adopting climate mitigation measures

Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction

Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success in web search. However, many state-of-the-art text ranking approaches only focus on core relevance while ignoring other dimensions that contribute to user satisfaction, e.g., document quality, recency, authority, etc. In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner. In particular, we leverage the capacities of PLMs on both textual and numerical inputs, and apply a multi-field input that modularizes each dimension of user satisfaction as an input field. Overall, SAT-Ranker is an effective, extensible, and data-centric framework that has huge potential for industrial applications. On rigorous offline and online experiments, SAT-Ranker obtains remarkable gains on various evaluation sets targeting different dimensions of user satisfaction. It is now fully deployed online to improve the usability of our search engine

Chronic Glucocorticoid Exposure Induces Depression-Like Phenotype in Rhesus Macaque (Macaca Mulatta)

It has long been observed in humans that the occurrence of depressive symptoms is often accompanied by the dysfunction of hypothalamic-pituitary-adrenal (HPA) axis. The rodent experiments also showed that chronic corticosterone exposure could induce depression-like phenotype. However, rodents are phylogenetically distant from humans. In contrast, non-human primates bear stronger similarities with humans, suggesting research on primates would provide an important complement. For the first time, we investigated the effects of chronic glucocorticoid exposure on rhesus macaques. Seven male macaques were selected and randomized to glucocorticoid or vehicle groups, which were subjected to either prednisolone acetate or saline injections, respectively. The depression-like behaviors were assessed weekly, and the body weights, HPA axis reactivity, sucrose solution consumption and monoaminergic neurotransmitters were further compared between these two groups. The glucocorticoid group was not found to display more depression-like behaviors than the vehicle group until 7 weeks after treatment. Chronic glucocorticoid exposure significantly decreased the levels of cortisol determined from blood (a biomarker for acute HPA axis reactivity) but increased the hair cortisol concentrations (a reliable indicator of chronic HPA axis reactivity) compared with controls. The glucocorticoid group was also found to consume less sucrose solution than controls, a good manifestation of anhedonia. This could be possibly explained by lower dopamine (DA) levels in cerebrospinal fluid induced by chronic glucocorticoid treatment. The results presented here indicate that chronic glucocorticoid exposure could disturb both the acute and chronic HPA axis reactivity, which eventually disturbed the neurotransmitter system and led monkeys to display depression-like phenotype

Deep Reflection Prior

Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass. The problem of removing reflection artifacts is important but challenging due to its ill-posed nature. Recent learning-based approaches have demonstrated a significant improvement in removing reflections. However, these methods are limited as they require a large number of synthetic reflection/clean image pairs for supervision, at the risk of overfitting in the synthetic image domain. In this paper, we propose a learning-based approach that captures the reflection statistical prior for single image reflection removal. Our algorithm is driven by optimizing the target with joint constraints enhanced between multiple input images during the training stage, but is able to eliminate reflections only from a single input for evaluation. Our framework allows to predict both background and reflection via a one-branch deep neural network, which is implemented by the controllable latent code that indicates either the background or reflection output. We demonstrate superior performance over the state-of-the-art methods on a large range of real-world images. We further provide insightful analysis behind the learned latent code, which may inspire more future work