Search

With the development of communication technology, the birth of 5G technology has epoch-making significance for data transmission and storage. Because of its very large network bandwidth and small network delay, it is very reliable in data transmission. 5G and other engineering, such as artificial intelligence and other technologies are often linked together, from the technical means and data transmission efficiency and other aspects of the technical level, promote and make artificial intelligence technology can be further expanded and extended. Based on this, the application and development of 5G technology and artificial intelligence under the background of new era information technology are expanded and studied in this paper. In addition, based on the prediction process of deep neural network construction, this paper studies its operability on FPGA platform from this process, and carries out a series of related studies on the implementation means of neural network on FPGA platform. In this paper, an accelerated system is designed, and the model can be established based on the key factors extracted from the accelerated system. Applying models set under different acceleration systems in different scenarios can be suitable for different network architectures. In the final research phase, we analyzed the materials and performed experimental verification. In the context of the market economy, we elaborated on the responsibility of the government in a certain region to maintain public security. For a region, it needs to be based on local actual conditions, specific analysis of specific issues, learning from the experience of some areas where public health incidents are handled well, and establishing a system with local characteristics. From the process of elaboration, we found that public health safety is not only closely related to each of our lives, but also further related to social stability and security.

The influencing factors of community risk are complex. For the low accuracy of traditional prediction model, a multichannel convolutional neural network community risk prediction model is proposed by improving convolutional neural network of deep learning. First of all, in the community risk prediction model, the structure of multichannel input convolutional neural network is selected. Then, add it into the full connection layer. Subsequently, the DenseNet layer is added to establish connections between different network layers. Finally, the receptive field is improved, and the gradient disappearance is solved. Thus, the prediction accuracy of model is improved. Compared with the traditional model, the proposed multichannel convolutional neural network model has better prediction accuracy. In addition, it performs better on the three indicators, namely, correlation coefficient R , coefficient of determination R 2 , and mean square root error RMSE. Compared with the commonly used LSTM model and logic regression model, the proposed model also has certain advantages, which is more suitable for community risk prediction.

With the rapid development of the Internet in our country, the information on the Internet has spread rapidly like an explosion. The convenience of the Internet shortens the time it takes for people to exchange information, which is likely to cause the outbreak of hot-spot incidents of online public opinion. The public opinion orientation on the Internet will directly affect the real thoughts and behaviors of offline people. In order to avoid the occurrence of major influential public opinion events, timely grasping the hot topics discussed by people, and paying attention and positive guidance are important means of maintaining social harmony and stability. Markov logic network is a first-order logic knowledge base with additional weights for formulas. From the perspective of probability theory, Markov logic network provides a concise description language for Markov network, and can flexibly introduce a large amount of domain knowledge into Markov network. Therefore, this article studies the prediction model of public policy public opinion based on Markov model.

Expanding the application range of flame-retardant polymer biocomposites remains a huge challenge for a sustainable society. Despite largely enhanced flame retardancy, until now the resultant poly(lactic acid) (PLA) composites still suffer reduced tensile strength and impact toughness due to improper material design strategies. We, herein, demonstrate the design of a green flame retardant additive (ammonium polyphosphate (APP)@cellulose nanofiber (CNF)) via using the cellulose nanofibers (CNFs) as the green multifunctional additives hybridized with ammonium polyphosphate (APP). The results show that PLA composite with 5 wt % loading of APP@CNF can pass the UL-94 V-0 rating, besides a high limited oxygen index of 27.5%, indicative of a significantly enhanced flame retardancy. Moreover, the 5 wt % of APP@CNF enables the impact strength (σi) of the PVA matrix to significantly improve from 7.63 to 11.8 kJ/m2 (increase by 54%), in addition to a high tensile strength of 50.3 MPa for the resultant flame-retardant PLA composite. The enhanced flame retardancy and mechanical strength performances are attributed to the improved dispersion of APP@CNF and its smaller phase size within the PLA matrix along with their synergistic effect between APP and CNF. This work opens up a facile innovative methodology for the design of high-performance ecofriendly flame retardants and their advanced polymeric composites.

Greenspace in urban areas is closely related to urban ecosystems, economy, culture, and society. Recently, rapid urban development and expansion are always dominated by a series of human–environmen...

Background: Heat stroke (HS) is a physically dysfunctional illness caused by hyperthermia. Lung, as the important place for gas-exchange and heat-dissipation organ, is often first to be injured. Lung injury caused by HS impairs the ventilation function of lung, which will subsequently cause damage to other tissues and organs. Nevertheless, the specific mechanism of lung injury in heat stroke is still unknown. Methods: Rat lung tissues from controls or HS models were harvested. The gene expression profile was identified by high-throughput sequencing. DEGs were calculated using R and validated by qRT-PCR. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and cell-enrichment were performed using differential expression genes (DEGs). Finally, lung histopathology was accessed by H&E staining. Results: About 471 genes were identified to be DEGs, of which 257 genes were up-regulated, and 214 genes were down-regulated. The most up-regulated and down-regulated DEGs were validated by qRT-PCR, which confirmed the tendency of expression. GO, KEGG, and protein–protein interaction (PPI)-network analyses disclosed DEGs were significantly enriched in leukocyte migration, response to lipopolysaccharide, NIK/NF-kappaB signaling, response to reactive oxygen species, response to heat, and the hub genes were Tnf, Il1b, Cxcl2, Ccl2, Mmp9, Timp1, Hmox1, Serpine1, Mmp8 and Csf1, most of which were closely related to inflammagenesis and oxidative stress. Finally, cell-enrichment analysis and histopathologic analysis showed Monocytes, Megakaryotyes, and Macrophages were enriched in response to heat stress. Conclusions: The present study identified key genes, signal pathways and infiltrated-cell types in lung after heat stress, which will deepen our understanding of transcriptional response to heat stress, and might provide new ideas for the treatment of HS.

The demand for urban construction has placed growing pressure on biodiversity conservation. In China, landscape fragmentation caused by rapid urbanization has resulted in an immense amount of damage to the ecological system. Taking the Haidian District of Beijing as the study area, this study distinguishes the vital patches and corridors for landscape connectivity maintenance through morphological spatial pattern analysis (MSPA), the probability of connectivity (PC), and the least-cost path analysis. Although we adopted and combined these methods from the existing research about landscape modeling, we went deeper into two parameters: the resistance value and the distance threshold. In order to get a species-specific result, we selected two focal species: the ring-necked pheasant (Phasianus colchius) and the raccoon dog (Nyctereutes procyonoides), whose biological characteristics and habitat types are assumed to represent most of the habitats in Beijing. The result shows the different habitats and corridors for two species. Then, the results of two simulated scenarios are overlaid to obtain the final landscape pattern. Based on this case study, we propose a paradigm of ecological network identification of multiple species, which may contribute to landscape modeling and green space planning.

Olgan larch is a traditional construction material used for the renovation of historical timber-frame buildings in China. However, acquiring the necessary large-sized larch trees from old-growth forests has become a challenge in China because of the rare and inaccessible distribution of these trees. In recent years, remote sensing imagery has provided a more effective alternative for delineating tree crowns automatically with high accuracy. In this study, an object-based method for delineating old-growth larch tree crowns using Geoeye-1 imagery is developed. Tree crown delineation results are tested and evaluated by field data. In addition, the correlation between delineated tree-crown and basal areas are quantitatively validated to ensure that the developed method can be applied for estimating the distribution of old-growth larch trees. Results demonstrate that the developed object-based larch tree-crown delineation method is reliable, thus providing a new technique for detecting old-growth larch tree re...

Historic wooden buildings are a symbol of China’s “culture of wood” and require extraction of forest resources for their renovation. In the 21st century, natural resources are limited globally, and sustainable solutions are needed. In this study, we established a new method to connect building and forest sites for efficient utilization of limited forest resources for the renovation of historic buildings. We obtained measurements of large wooden components from Shenyang Imperial Palace. We also performed morphometric analyses on 47 thinned, old-growth larch trees to determine the relative taper curve, and selected 108 standing trees for simulation of the tree-height curve in the Mt. Changbai area, Jinlin Province, Northeast China. On the basis of forest metrology, we established an upper tree prediction method. By measuring the diameter at breast height (DBH) alone, we could compare size information (e.g., diameter, length) for standing trees and wooden building components. This method was then applied to estimate the required DBH class of standing trees for the renovation of Shenyang Imperial Palace.

Timber-frame buildings are an important architectural heritage of China, and they play a pivotal role in Chinese architectural history. However the restoration of wood components in ancient buildin...

Estimation of forest carbon storage can be of great significance to the research on the productivity of terrestrial ecosystem, carbon cycle, and global warming. China has more than 54 million hm2barren hills and waste land suitable for forestation, which provides a great potential for developing carbon sink forestry by means of forestation. This research analyzed the volume increments, volume densities, and carbon contents of 15 analytical samples of five main plantation tree species in North China, includingPinus tabulaeformis(A),Robinia pseudoacacia(B),Populus euramericana(C),Larix olgenisis(D), andLarix kaempferi(E). Results showed that carbon storage dynamic process can be expressed as follows: the ages of quantitative maturity of each tree species are 67a, 40a, 30a, 48a, 49a, respectively; the average wood densities of each tree species at different age classes are 550.93 kg/m3, 629.25 kg/m3, 404.56 kg/m3, 592.33 kg/m3, and 544.11 kg/m3,t. The average carbon contents of each tree species at different age classes are 51.48%, 46.88%, 47.81%, 46.76%, and 47.24%. It showed a significant difference between the above tree species through variance test. The maximum values of average carbon storage are 70a, 40a, 30a, 48.7a, and 49.2a, respectively. The corresponding average carbon storages are A 2.527 kg, B 3,794 kg, C 2.781 kg, D 2.996 kg, and E 3,322 kg, in a descending order ofC>E>D>B>A. This research, through experiment on four tree species with clear growth rings and one tree species with unclear growth rings, verified the scientific character and the scope of application of the carbon storage dynamic analysis method, providing a new method for the measurement and analysis of forest carbon storage.