scholarly journals Retraction Note: The influence of atmospheric pollutant concentration in basin area and the design of architectural space art form

2021 ◽  
Vol 14 (24) ◽  
Author(s):  
Pingyu Wang ◽  
Yun Huang
2021 ◽  
Vol 237 ◽  
pp. 01037
Author(s):  
Haizhen Zhang ◽  
Jiang Wei

During the epidemic period, Urumqi has been sealed off from the city’s management, just as “suspended” state.From an environmental point of view, the reduction of energy consumption during the closure of the city can be considered as an energy control to study the resulting reduction of atmospheric pollutant concentration changes.In this paper, the monitoring data of air pollutant concentration in the same period of city closure and normal years are compared, and the results show that the air pollutant concentration has decreased in different degrees during the period of city closure.The largest decrease was44.66% for NO2, -40.13% for CO, -36.44% for PM2.5, and the smallest was-2.06% for SO2.Multivariate analysis of variance showed that energy control had a significant effect on the concentration of pollutants during the city closure, for example NO2 (F=128.96, Sig.=0.000), PM10 (F=29.58, Sig=0.000), PM2.5 (F=13.98, Sig.=0.000), CO(F=46.34;Sig.=0.000). Through the analysis of the data, it can be concluded that the air quality of Urumqi in winter is poor and the concentration of pollutants is high. The energy control during the closing period played a positive role in pollutant emission reduction and effectively improved the quality of atmospheric environment.


2020 ◽  
Vol 14 (20) ◽  
pp. 4547-4552
Author(s):  
Bin Cao ◽  
Fanghui Yin ◽  
Daiming Yang ◽  
Liming Wang ◽  
Masoud Farzaneh

MRS Bulletin ◽  
1992 ◽  
Vol 17 (1) ◽  
pp. 18-23
Author(s):  
Otto Piene

To avoid misinterpretation, the term “art-and-technology” should be hyphenated because we are looking at an integrated art form which developed, roughly, during the past 70 years (since Naum Gabo's virtual volume, Kinetic Construction, Berlin, 1920). Art-and-technology results from “incorporated” contributions of art, science, and technology or, better, from artists, scientists, and engineers (plus industry, business, government, etc.). Although art-and-technology has frequently been bad-mouthed or even pronouned “dead” by advocates and practitioners of pure art as well as science and technology, it is alive and well and enjoying more vitality, variety, and expansion than ever before. It is currently the only expanding field in the arts; it feeds vitally into technology and industry—most visibly in entertainment but it also provides stimulus beyond fun to areas of science and engineering where “art applications” have abounded since the advent of photography and its vast consequent uses in science.We can claim an eloquent tradition for art-and-technology in ancient historic, cultural manifestations such as the Egyptian pyramids and their “environmental” scale or the Greek theater with its elaborate stage machines. We are aware of elements of that tradition when we observe contemporary art-and-technology such as sky and space art (Figures 1 and 2), computer-generated virtual reality, performance with medical inquiry and medical apparatus, and art concepts inspired by molecular biology (Figure 3). Emphasis of search—whether artistic/expressive, conceptual/philosophical, or inquisitive/scientific—depends on taste and motivation. However, Leonardo is an undisputed idol to both artists and scientists.


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bingchun Liu ◽  
Xiaoling Guo ◽  
Mingzhao Lai ◽  
Qingshan Wang

Air pollutant concentration forecasting is an effective way which protects health of the public by the warning of the harmful air contaminants. In this study, a hybrid prediction model has been established by using information gain, wavelet decomposition transform technique, and LSTM neural network, and applied to the daily concentration prediction of atmospheric pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Beijing. First, the collected raw data are selected by feature selection by information gain, and a set of factors having a strong correlation with the prediction is obtained. Then, the historical time series of the daily air pollutant concentration is decomposed into different frequencies by using a wavelet decomposition transform and recombined into a high-dimensional training data set. Finally, the LSTM prediction model is trained with high-dimensional data sets, and the parameters are adjusted by repeated tests to obtain the optimal prediction model. The data used in this study were derived from six air pollution concentration data in Beijing from 1/1/2014 to 31/12/2016, and the atmospheric pollutant concentration data of Beijing between 1/1/2017 and 31/12/2017 were used to test the predictive ability of the data set test model. The results show that the evaluation index MAPE of the model prediction is 7.45%. Therefore, the hybrid prediction model has a higher value of application for atmospheric pollutant concentration prediction, because this model has higher prediction accuracy and stability for future air pollutant concentration prediction.


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