Study on gas emission prediction of steeply inclined coal seam applied horizontal slice mining

Calculation of emission quantities from different gas emission sources is important to predict gas emission quantity on the horizontally layered working face of a steep seam. However, existing gas emission prediction model has many problems, such as complicated calculation links, poor accuracy and non-applicability to the steep seam. In this study, a model applicable to predict gas emission from horizontal mining layer of steep seam was constructed based on the different-source prediction method. This model was applied to the west wing working face in the 45# seam of WuDong Mine in Xinjiang Autonomous Region in China. Prediction results of this model were compared with existing standard data of the different-source prediction method. According to analysis results, existing different-source prediction model is optimized specifically and the proposed model adds predictions of pressure relief gas emission in coal mass below the mining seam and gas emission in top goaf. The calculated gas emission in the working face presents an error of +8.63% with actual gas emission. This error conforms to the practical situation of horizontal layered mining of steep seam and verified the reliability of the proposed model. Conclusions obtained in this study serve as theoretical references to gas emission prediction in horizontal mining layer of steep seam

Open-ended Commonsense Reasoning with Unrestricted Answer Scope

Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional ways of formulating the commonsense question into a question-answering form or utilizing external knowledge to learn retrieval-based methods are less applicable in the open-ended setting due to an inherent challenge. Without pre-defining an answer scope or a few candidates, open-ended commonsense reasoning entails predicting answers by searching over an extremely large searching space. Moreover, most questions require implicit multi-hop reasoning, which presents even more challenges to our problem. In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. The reasoning paths can help to identify the most precise answer to the commonsense question. We conduct experiments on two commonsense benchmark datasets. Compared to other approaches, our proposed method achieves better performance both quantitatively and qualitatively.Comment: Findings of EMNLP 202

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS's individually, and do not leverage the dynamic distributions underlying the MTS's, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting timeseries. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.Comment: This paper is accepted by AAAI 202

Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to make large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to better summarize and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China

With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China’s LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China’s LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation

A novel iterative detection method based on a lattice reduction-aided algorithm for MIMO OFDM systems

Abstract The lattice reduction-aided algorithm has received broad attention from researchers since it operates as a maximum likelihood receiver with better system performance for multiple-input multiple-output orthogonal frequency division multiplexing systems and contains a full diversity. A novel iterative detection algorithm canceling parallel iterations that employ the lattice reduction-aided approach is proposed. Soft information is exchanged through the detector itself. Its iteration occurs inside the detector, which reduces much of the exchange cost between the multiple-input multiple-output orthogonal frequency division multiplexing detector and the turbo decoder. Since the parallel interference cancellation algorithm is constrained by the accuracy of the initial value of the detection, it is easy to form error propagation after several iterations. Due to the lattice reduction-aided algorithm, its performance is approximated with the maximum likelihood algorithm. Therefore, the lattice reduction-aided algorithm is introduced into the parallel interference cancellation algorithm to make its detection algorithm more accurate and overcome the effect of error propagation in the manuscript. Simulation results indicate that the proposed algorithm leads to an improvement of 0.8–2 dB when the bit error rate is set to 10–4 when compared to other algorithms

Adoption of image surface parameters under moving edge computing in the construction of mountain fire warning method.

In order to cope with the problems of high frequency and multiple causes of mountain fires, it is very important to adopt appropriate technologies to monitor and warn mountain fires through a few surface parameters. At the same time, the existing mobile terminal equipment is insufficient in image processing and storage capacity, and the energy consumption is high in the data transmission process, which requires calculation unloading. For this circumstance, first, a hierarchical discriminant analysis algorithm based on image feature extraction is introduced, and the image acquisition software in the mobile edge computing environment in the android system is designed and installed. Based on the remote sensing data, the land surface parameters of mountain fire are obtained, and the application of image recognition optimization algorithm in the mobile edge computing (MEC) environment is realized to solve the problem of transmission delay caused by traditional mobile cloud computing (MCC). Then, according to the forest fire sensitivity index, a forest fire early warning model based on MEC is designed. Finally, the image recognition response time and bandwidth consumption of the algorithm are studied, and the occurrence probability of mountain fire in Muli county, Liangshan prefecture, Sichuan is predicted. The results show that, compared with the MCC architecture, the algorithm presented in this study has shorter recognition and response time to different images in WiFi network environment; compared with MCC, MEC architecture can identify close users and transmit less data, which can effectively reduce the bandwidth pressure of the network. In most areas of Muli county, Liangshan prefecture, the probability of mountain fire is relatively low, the probability of mountain fire caused by non-surface environment is about 8 times that of the surface environment, and the influence of non-surface environment in the period of high incidence of mountain fire is lower than that in the period of low incidence. In conclusion, the surface parameters of MEC can be used to effectively predict the mountain fire and provide preventive measures in time

A novel approach to treating nickel-containing electroplating sludge by solidification with basic metallurgical solid waste

Nickel-containing electroplating sludge is a solid waste generated during the treatment of electroplating wastewater, often managed through solidification with materials such as cement and lime. This paper presents a novel approach for solidifying nickel-containing electroplating sludge using basic metallurgical solid waste. A mathematical model was employed to determine the kinetics of Ni2+ leaching, yielding the kinetic equation α(t) = 0.97∗(1-exp (-0.19∗t)). Rapid leaching occurred in the initial 10 min, reaching a concentration of 86.78 mg/L, followed by a slower release, peaking at 104.54 mg/L within 40 min. Through the solidification of electroplating sludge with basic waste materials, it was found that the inclusion of 2.0 % lime, 12.2 % sintering dust, and 2.5 % steel slag resulted in Ni2+ leaching concentrations of 0.28, 0.41, and 0.61 mg/L, respectively, all meeting the discharge standard of <1.00 mg/L. Phase analysis indicated that the main constituents of the solidified products were C–S–H and CaSO4, with no detectable change in the crystallinity of Ni2+ before and after solidification. Thermodynamic calculations further demonstrated that under basic solidification conditions, Ni2+ transforms from NiSO4 to Ni(OH)2. Microscopic morphology analysis revealed that the encapsulation of Ni2+ by C–S–H and CaSO4 led to the densification of the porous microstructure of the electroplating sludge, achieving effective solidification

Corrosion mechanism and microstructure evolution of yttrium-doped marine steel

The effects of Yttrium dosage and heat treatment process on the microstructure, inclusions, and corrosion resistance of marine steel, as well as the electrochemical parameters on corrosion behavior were studied in this work. The results show that Yttrium mainly exists in Yttrium oxide and Yttrium sulfide. In the process of the optimal heat treatment temperature (800°C-5h), the movement of grain boundaries is hindered by the nailing action of Y2O3, which makes the grain size refined. Yttrium can refine the corrosion products and improve the adhesion ability between the corrosion layer and the substrate, which can effectively improve the corrosion resistance of marine steel. The electrochemical parameters mainly affect the proportion and structure of α-FeOOH and α-Fe2O3 in the corrosion layer by affecting the ion diffusion rate or oxygen content, thus affecting the corrosion resistance property. With the increase of temperature, the diffusion coefficient of oxygen in solution increases, and the limiting current density also increases, which is not conducive to the formation of dense corrosion layer and will lead to faster corrosion speed

Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph

We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence. While previous work has demonstrated effective syntax-guided MRC models, we propose to adopt the inter-sentence syntactic relations, in addition to the rudimentary intra-sentence relations, to further utilize the syntactic dependencies in the multi-sentence input of the MRC task. In our approach, we build the Inter-Sentence Dependency Graph (ISDG) connecting dependency trees to form global syntactic relations across sentences. We then propose the ISDG encoder that encodes the global dependency graph, addressing the inter-sentence relations via both one-hop and multi-hop dependency paths explicitly. Experiments on three multilingual MRC datasets (XQuAD, MLQA, TyDiQA-GoldP) show that our encoder that is only trained on English is able to improve the zero-shot performance on all 14 test sets covering 8 languages, with up to 3.8 F1 / 5.2 EM improvement on-average, and 5.2 F1 / 11.2 EM on certain languages. Further analysis shows the improvement can be attributed to the attention on the cross-linguistically consistent syntactic path. Our code is available at https://github.com/lxucs/multilingual-mrc-isdg