Numerical Simulation on Instability Mechanism of Section Coal Pillar in Shallow Buried Coal Seams

There is abundant coal resource of Jurassic period in Yulin City, and the problem of shallow buried coal seams mining will occur in every mine area. FLAC3D is used to analyze the plastic failure, deformation characteristic, and vertical stress on shallow buried coal seams mining, based on north No. 2 engineering geological conditions of Hongliulin Mining Corporation. In this paper, the results are shown as follows. The plastic failure fields of section coal pillar forming are smaller; the coal seam hosting is shallower. The elastic region of section coal pillar has a relatively large proportion, which is the coal seam first mined. The value of Z-Displacement for the first coal seam is relatively small. However, for the lower coal seam it is relatively big. The value of X-Displacement for each coal seam is relatively small. The coal wall of section coal pillar is in a steady state. The coal seam hosting is deeper; the more distant is from coal wall to peak point abutment pressure. The stress concentration factor for the first coal seam is relatively big. The research conclusion reveals instability mechanism of section coal pillar, while coal seams mining, which provides a theoretical basis for designing width vale and optimizing supporting scheme of section coal pillar, has engineering experience application value to other coalmines in Yulin City

Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms

By integrating the self-attention capability and the biological properties of Spiking Neural Networks (SNNs), Spikformer applies the flourishing Transformer architecture to SNNs design. It introduces a Spiking Self-Attention (SSA) module to mix sparse visual features using spike-form Query, Key, and Value, resulting in the State-Of-The-Art (SOTA) performance on numerous datasets compared to previous SNN-like frameworks. In this paper, we demonstrate that the Spikformer architecture can be accelerated by replacing the SSA with an unparameterized Linear Transform (LT) such as Fourier and Wavelet transforms. These transforms are utilized to mix spike sequences, reducing the quadratic time complexity to log-linear time complexity. They alternate between the frequency and time domains to extract sparse visual features, showcasing powerful performance and efficiency. We conduct extensive experiments on image classification using both neuromorphic and static datasets. The results indicate that compared to the SOTA Spikformer with SSA, Spikformer with LT achieves higher Top-1 accuracy on neuromorphic datasets (i.e., CIFAR10-DVS and DVS128 Gesture) and comparable Top-1 accuracy on static datasets (i.e., CIFAR-10 and CIFAR-100). Furthermore, Spikformer with LT achieves approximately 29-51% improvement in training speed, 61-70% improvement in inference speed, and reduces memory usage by 4-26% due to not requiring learnable parameters.Comment: Under Revie

Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network

Learning from the interaction is the primary way biological agents know about the environment and themselves. Modern deep reinforcement learning (DRL) explores a computational approach to learning from interaction and has significantly progressed in solving various tasks. However, the powerful DRL is still far from biological agents in energy efficiency. Although the underlying mechanisms are not fully understood, we believe that the integration of spiking communication between neurons and biologically-plausible synaptic plasticity plays a prominent role. Following this biological intuition, we optimize a spiking policy network (SPN) by a genetic algorithm as an energy-efficient alternative to DRL. Our SPN mimics the sensorimotor neuron pathway of insects and communicates through event-based spikes. Inspired by biological research that the brain forms memories by forming new synaptic connections and rewires these connections based on new experiences, we tune the synaptic connections instead of weights in SPN to solve given tasks. Experimental results on several robotic control tasks show that our method can achieve the performance level of mainstream DRL methods and exhibit significantly higher energy efficiency

Recent Advances and New Frontiers in Spiking Neural Networks

In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware. With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot. This paper reviews recent advances and discusses new frontiers in SNNs from five major research topics, including essential elements (i.e., spiking neuron models, encoding methods, and topology structures), neuromorphic datasets, optimization algorithms, software, and hardware frameworks. We hope our survey can help researchers understand SNNs better and inspire new works to advance this field.Comment: Accepted at IJCAI202

Comparative anatomical and transcriptomic analyses of the color variation of leaves in Aquilaria sinensis

Color variation in plant tissues is a common phenomenon accompanied with a series of biological changes. In this study, a special-phenotype Aquilaria sinensis (GS) with color variation of leaf was firstly reported, and DNA barcode sequences showed GS samples could not be discriminated clearly with the normal A. sinensis sample (NS), which suggested that the variety was not the cause of the GS formation. To reveal the characteristics of GS compared to NS, the anatomical and transcriptome sequencing studies were carried out. In microscopic observation, the leaves of golden-vein-leaf sample (LGS) and normal-vein-leaf sample (LNS) showed significant differences including the area of the included phloem in midrib and the thickness parameters of palisade and spongy tissues; the stems of golden-vein-leaf sample (SGS) and normal-vein-leaf sample (SNS) were also different in many aspects such as the area of vessels and included phloem. In addition, the structure of chloroplast was more complete in the midrib of LNS than that of LGS, and some particles suspected as virus were found through transmission electron microscope as well. Genes upregulated in LGS in contrast with LNS were mainly enriched in photosynthesis. As for stems, most of the genes upregulated in SGS compared to SNS were involved in translation and metabolism processes. The pathways about photosynthesis and chlorophyll metabolism as well as some important transcription factors may explain the molecular mechanism of the unique phenotypes of leaves and the genes related to suberin biosynthesis may result in the difference of stems. In addition, the genes about defense response especially biotic stress associated with numerous pathogenesis-related (PR) genes upregulated in LGS compared to LNS indicated that the pathogen may be the internal factor. Taken together, our results reveal the macro- and micro-phenotype variations as well as gene expression profiles between GS and NS, which could provide valuable clues for elucidating the mechanism of the color variation of Aquilaria

Whole Genome Distribution and Ethnic Differentiation of Copy Number Variation in Caucasian and Asian Populations

Although copy number variation (CNV) has recently received much attention as a form of structure variation within the human genome, knowledge is still inadequate on fundamental CNV characteristics such as occurrence rate, genomic distribution and ethnic differentiation. In the present study, we used the Affymetrix GeneChip® Mapping 500K Array to discover and characterize CNVs in the human genome and to study ethnic differences of CNVs between Caucasians and Asians. Three thousand and nineteen CNVs, including 2381 CNVs in autosomes and 638 CNVs in X chromosome, from 985 Caucasian and 692 Asian individuals were identified, with a mean length of 296 kb. Among these CNVs, 190 had frequencies greater than 1% in at least one ethnic group, and 109 showed significant ethnic differences in frequencies (p<0.01). After merging overlapping CNVs, 1135 copy number variation regions (CNVRs), covering approximately 439 Mb (14.3%) of the human genome, were obtained. Our findings of ethnic differentiation of CNVs, along with the newly constructed CNV genomic map, extend our knowledge on the structural variation in the human genome and may furnish a basis for understanding the genomic differentiation of complex traits across ethnic groups

An analysis of farmers' perception of the new cooperative medical system in Liaoning Province, China

<p>Abstract</p> <p>Background</p> <p>Since 2003, the number of pilot areas of the New Rural Cooperative Medical System (NRCMS) has increased in rural China. And the major efforts have been concentrated on the enrollment of prospective members. In this study, we examined the satisfaction of the rural residents with the NRCMS as well as factors affecting their attitudes towards the NRCMS.</p> <p>Methods</p> <p>The data for this study were collected from a survey involving twenty counties in Liaoning Province. Interviews and focus groups were conducted between 10^thJanuary and 20^thAugust 2008. A total of 2,780 people aged 18-72 were randomly selected and interviewed. Data were evaluated by nonparametric tests and ordinal regression models.</p> <p>Results</p> <p>71.6% of the study subjects were satisfied with the NRCMS. Single factor analysis showed that attitudes towards the NRCMS were influenced by gender, age, marital status, and self-rated health status. In the ordinal regression analysis, gender, age, and self-rated health status affect satisfaction (P < 0.05).</p> <p>Conclusions</p> <p>We found that a considerable proportion of farmers were satisfied with the NRCMS. Gender, age, and self-rated health status had significant effects on farmers' attitudes towards the NRCMS. The Chinese Central Government attempted to adopt active measures in the future to continuously improve the NRCMS, including initiating educational programs, building new medical facilities and increasing financial investment.</p

On the Design and Implementation of a Geometric-Object-Oriented Language

International audienceThis paper presents the design and implementation of a geometric-object-oriented language Gool for constructing, representing, manipulating, and visualizing symbolic geometric objects and relations and performing symbolic geometric computation and formal reasoning. The language uses case distinction to formalize symbolic geometric objects and relations, reducing the problem of dealing with uncertainty and degeneracy to that of handling geometric constraints. We describe the capabilities, features, and main components of Gool, propose several techniques for geometric constraint handling, and discuss some of the implementation issues

Oxidative Degradation of AMP/MEA Blends for Post-combustion CO2 Capture

AbstractThe oxidative degradation of aqueous, concentrated 2-amino-2-methyl-1-propanol (AMP)/monoethanolamine (MEA) mixture has been investigated in batch reactors at temperatures in the range of 100-140°C, and oxygen pressures in the range of 250-350kPa. The oxidation pathway of individual AMP was proposed before attempting to investigate the oxidation of AMP/MEA blend. As compared with degradation of single MEA and AMP, no cross product was found in the degraded AMP/MEA blend under the experimental conditions. This result showed that AMP and MEA could be oxidized in parallel in the mixture. Both the overall degradation rates of MEA and AMP increased with raising temperature and oxygen partial pressure. MEA degraded faster than AMP in the blend under all the experimental conditions. The degradation rate of AMP decreased indicating MEA protects AMP from oxidation in the mixture when initial MEA concentration was increased