Effect of secondary oxidation of pre-oxidized coal on early warning value for spontaneous combustion of coal

The indicative ability of a gas indicator for the spontaneous combustion of coal is affected by the secondary oxidation of oxidized coal, from old goafs, entering a new goaf through air leakages. This phenomenon can affect the accuracy of early warning systems regarding the spontaneous combustion of coal in a goaf. In this research, three kinds of coal were selected to carry out a spontaneous combustion simulation experiment in which a temperature-programmed experimental device was used to analyze the behavior of the index gas towards raw coal and oxidized coal, for which the latter was oxidized at 70 ¿C, 90 ¿C, 130 ¿C, and 150 ¿C. The results show that the chain alkane ratio in the secondary oxidation process and the trends of oxygen, CO, and C2H4 concentrations are the same as those in the primary oxidation process. On the other hand, the temperature at which C2H4 initially appears, during secondary oxidation, is lower than in primary oxidation. The CO produced in the early stage of secondary oxidation is greater than the CO produced, at the same temperature, in primary oxidation. In this regard, the usage of C2H4 concentration as an indicator with which to judge the occurrence of the spontaneous combustion of coal would allow for an earlier response. In the secondary oxidation process, the temperature of the extreme value of the alkene ratio appears higher than in primary oxidation. The presence of a higher pre-oxidation temperature and a higher proportion of secondary oxidation gas will affect an indicator’s judgement when the primary oxidation enters the severe oxidation stage. The gas produced by secondary oxidation will affect the early warning of the spontaneous combustion of coal in the coal mine goaf, which should be considered in the establishment of an early warning system.This work was supported by the National Natural Science Foundation of China [52074285].Peer ReviewedObjectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

Analysis of video quality induced spatio-temporal saliency shifts

Human viewers’ eye movements reflect their perceptual responses to visual signals. Previous research has shown that distortions in videos cause spatio-temporal gaze shifts, which means gaze behaviour is related to video quality perception. It would be highly beneficial to understand gaze behaviour of viewing videos of varying perceived quality. However, little is known about the interactions between gaze, video content and distortions. In this paper, based on our eye-tracking database for video quality (SVQ160), we perform systematic analyses to reveal the impact of video content (VC) and time order (TO) on gaze shifts. Findings and quantitative methods for gaze behaviour can be used to develop advanced video quality metrics and video processing algorithms

Identifying pitfalls in the evaluation of saliency models for videos

Saliency prediction has been extensively studied for natural images. In the area of video coding and video quality assessment, researchers attempt to integrate a saliency model to individual frames of a video sequence. In selecting best-performing saliency models for these applications, the evaluation only considers the average model performance over all frames of a video. This may mask the defects of a saliency model and consequently hinder further improvement of the model. In this paper, we present the identification of pitfalls in the evaluation of saliency models for videos. We demonstrate the importance of considering the video content classification and temporal effect. Building on the findings, we make recommendations for saliency model evaluation and selection for videos

The driving factors of spatial differences on the whole life cycle carbon emissions of the construction industry: from the analysis perspective of total factor productivity

The energy saving and emissions reduction of the construction industry are crucial for China to achieve the “carbon peaking and carbon neutrality” goals. In order to promote the green development of the life cycle of the construction industry and improve the efficiency of emissions reduction. This paper examines the spatial-temporal distribution of life cycle carbon emissions in China’s construction industry (LCCECI) from 2004 to 2018. It uses the SBM-Malmquist total factor productivity (TFP) index to measure technological progress and establishes the spatial econometric model based on the STIRPAT model. The study investigates the driving factors of the LCCECI at the provincial and regional levels, aiming to provide suggestions for low-carbon development in the construction industry. The research results are as follows. ① The growth in the SBM-Malmquist TFP index of the construction industry distinctly curbs the LCCECI. ② Total population and urbanization level are not the primary driving factors for the LCCECI. The growth of per capita GDP significantly induces the LCCECI, while concurrently exhibiting a notable inhibitory effect on the LCCECI of neighboring regions. ③ The improvement of the SBM-Malmquist TFP index is conducive to the reduction of the LCCECI in the three major regions. The per capita GDP has the largest positive driving effect of the LCCECI in the eastern region, and the urbanization level the urbanization rate only significantly inhibits the growth of the LCCECI in the central region

An Intronic Signal for Alternative Splicing in the Human Genome

An important level at which the expression of programmed cell death (PCD) genes is regulated is alternative splicing. Our previous work identified an intronic splicing regulatory element in caspase-2 (casp-2) gene. This 100-nucleotide intronic element, In100, consists of an upstream region containing a decoy 3′ splice site and a downstream region containing binding sites for splicing repressor PTB. Based on the signal of In100 element in casp-2, we have detected the In100-like sequences as a family of sequence elements associated with alternative splicing in the human genome by using computational and experimental approaches. A survey of human genome reveals the presence of more than four thousand In100-like elements in 2757 genes. These In100-like elements tend to locate more frequent in intronic regions than exonic regions. EST analyses indicate that the presence of In100-like elements correlates with the skipping of their immediate upstream exons, with 526 genes showing exon skipping in such a manner. In addition, In100-like elements are found in several human caspase genes near exons encoding the caspase active domain. RT-PCR experiments show that these caspase genes indeed undergo alternative splicing in a pattern predicted to affect their functional activity. Together, these results suggest that the In100-like elements represent a family of intronic signals for alternative splicing in the human genome

An Intronic Signal for Alternative Splicing in the Human Genome

An important level at which the expression of programmed cell death (PCD) genes is regulated is alternative splicing. Our previous work identified an intronic splicing regulatory element in caspase-2 (casp-2) gene. This 100-nucleotide intronic element, In100, consists of an upstream region containing a decoy 3′ splice site and a downstream region containing binding sites for splicing repressor PTB. Based on the signal of In100 element in casp-2, we have detected the In100-like sequences as a family of sequence elements associated with alternative splicing in the human genome by using computational and experimental approaches. A survey of human genome reveals the presence of more than four thousand In100-like elements in 2757 genes. These In100-like elements tend to locate more frequent in intronic regions than exonic regions. EST analyses indicate that the presence of In100-like elements correlates with the skipping of their immediate upstream exons, with 526 genes showing exon skipping in such a manner. In addition, In100-like elements are found in several human caspase genes near exons encoding the caspase active domain. RT-PCR experiments show that these caspase genes indeed undergo alternative splicing in a pattern predicted to affect their functional activity. Together, these results suggest that the In100-like elements represent a family of intronic signals for alternative splicing in the human genome.</p

Multiplexed Monitoring of Neurochemicals via Electrografting- Enabled Site-Selective Functionalization of Aptamers on Field-Effect Transistors

Neurochemical corelease has received much attention in understanding brain activity and cognition. Despite many attempts, the multiplexed monitoring of coreleased neurochemicals with spatiotemporal precision and minimal crosstalk using existing methods remains challenging. Here, we report a soft neural probe for multiplexed neurochemical monitoring via the electrografting-assisted site-selective functionalization of aptamers on graphene field-effect transistors (G-FETs). The neural probes possess excellent flexibility, ultralight mass (28 mg), and a nearly cellular-scale dimension of 50 μm × 50 μm for each G-FET. As a demonstration, we show that G-FETs with electrochemically grafted molecular linkers (−COOH or −NH2) and specific aptamers can be used to monitor serotonin and dopamine with high sensitivity (limit of detection: 10 pM) and selectivity (dopamine sensor \u3e22-fold over norepinephrine; serotonin sensor \u3e17-fold over dopamine). In addition, we demonstrate the feasibility of the simultaneous monitoring of dopamine and serotonin in a single neural probe with minimal crosstalk and interferences in phosphate-buffered saline, artificial cerebrospinal fluid, and harvested mouse brain tissues. The stability studies show that multiplexed neural probes maintain the capability for simultaneously monitoring dopamine and serotonin with minimal crosstalk after incubating in rat cerebrospinal fluid for 96 h, although a reduced sensor response at high concentrations is observed. Ex vivo studies in harvested mice brains suggest potential applications in monitoring the evoked release of dopamine and serotonin. The developed multiplexed detection methodology can also be adapted for monitoring other neurochemicals, such as metabolites and neuropeptides, by simply replacing the aptamers functionalized on the G-FETs

Co-seismic signatures in magnetometer, geophone, and infrasound data during the Meinong Earthquake

This paper utilizes 10 stations of co-located seismometer, QuakeFinder/infrasound to observe co-seismic signatures triggered by the 6 February 2016 M 6.6 Meinong Earthquake. Each QuakeFinder system consists of a 3-axes induction magnetometer, an air conductivity sensor, a geophone, and temperature/relative humidity sensors. There are no obvious charges in the positive/negative ions, the temperature, and the humidity, while the magnetometer, the geophone, and infrasound data detect clear co-seismic signatures, similar to seismic waves recorded by seismometers. The magnetometers register high-frequency pulsations, like seismic waves, and superimpose with low-frequency variations, which could be caused by the magnetometer shaking/tilting and/or the underground water level change, respectively, upon the arrival of seismic waves. The spectrum centering around 2.0 Hz of the co-seismic geophone fluctuations is similar to that of the seismic waves. However, the energy of co-seismic geophone fluctuations (also magnetometer pulsations) yields an exponential decay to the distance of a station to the epicenter, while the energy of the seismic waves is inversely proportional to the square of the distance. This suggests that the mechanisms for detecting seismic waves of the QuakeFinder system and seismometers are different. In general, the geophone and magnetometer/infrasound system are useful to record high- and low-frequency seismic waves, respectively

Gadolinium‐Doped Iron Oxide Nanoprobe as Multifunctional Bioimaging Agent and Drug Delivery System

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116012/1/adfm201502868.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116012/2/adfm201502868-sup-0001-S1.pd

A unified understanding of deep NLP models for text classification

The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually. Existing methods cannot meet the need for understanding different models in one framework due to the lack of a unified measure for explaining both low-level (e.g., words) and high-level (e.g., phrases) features. We have developed a visual analysis tool, DeepNLPVis, to enable a unified understanding of NLP models for text classification. The key idea is a mutual information-based measure, which provides quantitative explanations on how each layer of a model maintains the information of input words in a sample. We model the intra- and inter-word information at each layer measuring the importance of a word to the final prediction as well as the relationships between words, such as the formation of phrases. A multi-level visualization, which consists of a corpus-level, a sample-level, and a word-level visualization, supports the analysis from the overall training set to individual samples. Two case studies on classification tasks and comparison between models demonstrate that DeepNLPVis can help users effectively identify potential problems caused by samples and model architectures and then make informed improvements