DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

Visually-situated languages such as charts and plots are omnipresent in real-world documents. These graphical depictions are human-readable and are often analyzed in visually-rich documents to address a variety of questions that necessitate complex reasoning and common-sense responses. Despite the growing number of datasets that aim to answer questions over charts, most only address this task in isolation, without considering the broader context of document-level question answering. Moreover, such datasets lack adequate common-sense reasoning information in their questions. In this work, we introduce a novel task named document-level chart question answering (DCQA). The goal of this task is to conduct document-level question answering, extracting charts or plots in the document via document layout analysis (DLA) first and subsequently performing chart question answering (CQA). The newly developed benchmark dataset comprises 50,010 synthetic documents integrating charts in a wide range of styles (6 styles in contrast to 3 for PlotQA and ChartQA) and includes 699,051 questions that demand a high degree of reasoning ability and common-sense understanding. Besides, we present the development of a potent question-answer generation engine that employs table data, a rich color set, and basic question templates to produce a vast array of reasoning question-answer pairs automatically. Based on DCQA, we devise an OCR-free transformer for document-level chart-oriented understanding, capable of DLA and answering complex reasoning and common-sense questions over charts in an OCR-free manner. Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document. We implement and evaluate a set of baselines, and our proposed method achieves comparable results

Systematic Comparison of Power Corridor Classification Methods from ALS Point Clouds

Study examines factors that affect power corridor classification using LiDAR (light detection and ranging) point clouds, including the class distribution, feature selection, classifier type and neighborhood radius for classification feature extraction

Impact of road grid temporal and spatial changes on the ecosystem in the high-altitude plateau area : an empirical study

This empirical research utilized geographic information system (GIS) data and involved kernel density estimation (WKDE), ecological footprint modeling, landscape index analysis, and spatial analysis methods. A plateau landscape ecological risk model is constructed, and the temporal and spatial changes in the road network pattern and the landscape ecological risk in the region in 2012 and 2020 are investigated. The study results identify that the expansion of the road network led to a rapid increase in construction land area and a decrease in cultivated land area. However, there is little impact on other landscape types. The study reveals that road network expansion leads to landscape ecological risk changes, primarily in low-altitude urban centers. The risk levels decrease with increasing ecological risk levels, with the proportion of road level lengths increasing and decreasing. Landscape ecological risk and road level is correlated. This study will interest practitioners engaged in ecosystem management, infrastructure planning, and transportation systems development, as well as researchers in these and related areas.</p

Prevalence of Metabolic Syndrome Among the Adult Population in Western China and the Association With Socioeconomic and Individual Factors: Four Cross-Sectional Studies

Objectives: This study explored the prevalence of and individual influencing factors for metabolic syndrome (MS) as well as associated socioeconomic factors and regional aggregation. Design: Four cross-sectional surveys were analysed for trends in MS and associations with socioeconomic and individual factors through multilevel logistic regression analyses. The risk associated with nutrient intake was also assessed through a dietary survey in 2015. Setting: From 2010 to 2018, 8-15 counties/districts of West China were included. Participants: A total of 28 274 adults were included in the prevalence analysis. A total of 23 708 adults were used to analyse the related factors. Results: The overall prevalence of MS ranged from 21.4% to 27.8% over the 8 years, remaining basically stable within the 95% CI. Our study found that the urbanisation rate and hospital beds per 1000 people were positively associated with MS, and the number of doctors in healthcare institutions per 1000 persons was negatively associated with MS. The ORs for females, people with college education and higher and unmarried or single people were 1.49, 0.67 and 0.51, respectively (p\u3c0.05). The ORs of people who smoked at least 20 cigarettes/day, ate more than 100 g of red meat/day, consumed fruit or vegetable juice and drank carbonated soft drinks less than weekly were 1.10, 1.16, 1.19-1.27 and 0.81-0.84, respectively. The ORs rose with increasing sedentary time and decreased with higher physical activity. Conclusion: The high burden of MS, unreasonable proportions of energy and micronutrient intake and low percentage of high levels of physical activity were the major challenges to public health in western China. Improving the human resources component of medical services, such as the number of doctors, increasing the availability of public sports facilities and E-health tools and improving individual dietary quality and education might help prevent MS

Engineering application and demonstration of intelligent mining control technology for rockburst coal seam

How to achieve intelligent, safe and efficient mining of rockburst coal seams is a major engineering and technical challenge facing deep coal mining. The core connotation of this issue lies in the intelligent, unmanned, and inherently safe mining of coal seams with rockburst. Through accurate perception and intelligent early warning of coal and rock burst risks, as well as self-adaptation and self-optimization of mining engineering, intelligent anti-burst and mining control technologies are formed to achieve a fundamental shift from engineering-induced disasters to engineering-prevented and mitigated disasters. By implementing measures such as mining source design, roof area fracturing, localized targeted management, and strengthening roadway support, a low-stress environment and safe roadway space are created for the safe and efficient mining of coal seams with rock burst. On this basis, a big data analysis software platform that integrates static geological, dynamic monitoring, working conditions, personnel positioning, production organization, historical data and other information, as well as an intelligent mining control system that integrates impact pressure decision-making information, has been developed. The anti-impact information source has been transformed into an intelligent mining control source, forming a new flexible intelligent mining model of "low pressure fast pushing, medium pressure slow mining, and high pressure production stop". Field application shows that in the intelligent anti-impact and control mining area, the degree of mine pressure in working face and roadway is smaller,, and the energy release of coal and rock is more stable, which is conducive to the reasonable release of impact mine production capacity

Unveiling early-life microbial colonization profile through characterizing low-biomass maternal-infant microbiomes by 2bRAD-M

IntroductionThe microbial composition of human breast milk and infant meconium offers critical insights into the early microbial colonization profile, and it greatly contributes to the infant’s immune system and long-term health outcomes. However, analyzing these samples often faces technical challenges and limitations of low-resolution using conventional approaches due to their low microbial biomass.MethodsHere, we employed the type IIB restriction enzymes site-associated DNA sequencing for microbiome (2bRAD-M) as a reduced metagenomics method to address these issues and profile species-level microbial composition. We collected breast milk samples, maternal feces, and infant meconium, comparing the results from 2bRAD-M with those from both commonly used 16S rRNA amplicon sequencing and the gold-standard whole metagenomics sequencing (WMS).ResultsThe accuracy and robustness of 2bRAD-M were demonstrated through its consistently high correlation of microbial individual abundance and low whole-community-level distance with the paired WMS samples. Moreover, 2bRAD-M enabled us to identify clinical variables associated with infant microbiota variations and significant changes in microbial diversity across different lactation stages of breast milk.DiscussionThis study underscores the importance of employing 2bRAD-M in future large-scale and longitudinal studies on maternal and infant microbiomes, thereby enhancing our understanding of microbial colonization in early life stages and demonstrating further translational potential