scholarly journals Dynamics of epicotyl emergence of Quercus robur from different climatic regions is strongly driven by post-germination temperature and humidity conditions

Dendrobiology ◽  
2019 ◽  
Vol 81 ◽  
pp. 73-85 ◽  
Author(s):  
Szymon Jastrzębowski ◽  
Joanna Ukalska
Keyword(s):  
Quercus Robur ◽  
Climatic Regions ◽  
Germination Temperature ◽  
Temperature And Humidity

Effects of Temperature and Humidity on the Germination of Two Pioneer Species in Ecological Restoration

2012 ◽  
Vol 209-211 ◽  
pp. 1265-1268 ◽  
Author(s):  
Di Zhang ◽  
Xi Liu ◽  
Fang Qing Chen
Keyword(s):  
Seed Germination ◽  
Ecological Restoration ◽  
Ecological Engineering ◽  
Treatment Level ◽  
Pioneer Species ◽  
Important Species ◽  
Germination Temperature ◽  
Temperature And Humidity ◽  
Effects Of Temperature ◽  
Increasing Temperature

Seed germination and seedling establishment is a critical stage in the life cycle. Cynodon dactylon and Medicago sativa are two important species using in ecological restoration. Control experiments were employed to test the effects of temperature and humidity on the germination of the two pioneer species. The experiment included three temperature treatment level (15, 20, 25 °C) and four humidity treatment (5, 10, 15, 20 %) with three repeats. Results showed that temperature and humidity had significant effects on the germination of both species seeds. Seed germination of C. dactylon fluctuated with the increasing temperature, but increased with the increasing humidity. The optimal germination temperature and humidity for C. dactylon seeds was 20 °C and 20 % respectively. Seed germination of M. sativa increased with the increasing temperature meanwhile fluctuated with the increasing humidity. The optimal germination temperature and humidity for M. sativa seeds was 25 °C and 10 % respectively. It is critical to provide suitable soil humidity for seed germination in the ecological engineering.

scholarly journals Temperature and Humidity Control for the Next Generation Greenhouses: Overview of Desiccant and Evaporative Cooling Systems

2021 ◽  
Author(s):  
Muhammad Sultan ◽  
Hadeed Ashraf ◽  
Takahiko Miyazaki ◽  
Redmond R. Shamshiri ◽  
Ibrahim A. Hameed
Keyword(s):  
Molecular Sieves ◽  
Air Conditioning ◽  
Activated Carbons ◽  
Evaporative Cooling ◽  
Next Generation ◽  
Cooling Systems ◽  
Humidity Control ◽  
Climatic Regions ◽  
Multi Stage ◽  
Temperature And Humidity

Temperature and humidity control are crucial in next generation greenhouses. Plants require optimum temperature/humidity and vapor pressure deficit conditions inside the greenhouse for optimum yield. In this regard, an air-conditioning system could provide the required conditions in harsh climatic regions. In this study, the authors have summarized their published work on different desiccant and evaporative cooling options for greenhouse air-conditioning. The direct, indirect, and Maisotsenko cycle evaporative cooling systems, and multi-stage evaporative cooling systems have been summarized in this study. Different desiccant materials i.e., silica-gels, activated carbons (powder and fiber), polymer sorbents, and metal organic frameworks have also been summarized in this study along with different desiccant air-conditioning options. However, different high-performance zeolites and molecular sieves are extensively studied in literature. The authors conclude that solar operated desiccant based evaporative cooling systems could be an alternate option for next generation greenhouse air-conditioning.

Exogenous phytoghormones effect on the growth of winter wheat, depending on the seeds germination temperature and humidity

2020 ◽  
Vol 1 (83) ◽  
pp. 130-135
Author(s):  
Yuriy Podushin ◽  
◽  
Yuriy Fedulov ◽  
Eugeniy Degtyarev ◽  
Alexander Donskoy ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Germination Temperature ◽  
Temperature And Humidity ◽  
Seeds Germination

scholarly journals Classification of pavement climatic regions through unsupervised and supervised machine learnings

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Qiao Dong ◽  
Xueqin Chen ◽  
Shi Dong ◽  
Jun Zhang
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Common Factors ◽  
Climatic Variables ◽  
Supervised Machine Learning ◽  
Misclassification Rate ◽  
Climatic Data ◽  
Climatic Regions ◽  
Temperature And Humidity

AbstractThis study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snowfall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Around one third of the 800 weather stations record variation of freeze and precipitation classifications and a few of them show significant change of classifications over time based on the results of logistic regression analyses. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.

scholarly journals Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

2021 ◽  
Author(s):  
Qiao Dong ◽  
Xueqin Chen ◽  
Shi Dong ◽  
Jun Zhang
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Common Factors ◽  
Climatic Variables ◽  
Supervised Machine Learning ◽  
Misclassification Rate ◽  
Climatic Data ◽  
Climatic Regions ◽  
Temperature And Humidity

Abstract This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snow fall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.

Cryo-TEM of amphiphilic polymer and amphiphile/polymer solutions

1993 ◽  
Vol 51 ◽  
pp. 876-877
Author(s):  
Yeshayahu Talmon
Keyword(s):  
Microstructural Characterization ◽  
Building Blocks ◽  
Quantitative Information ◽  
Physical Models ◽  
Specimen Preparation ◽  
Time Resolved ◽  
Temperature Changes ◽  
Temperature And Humidity ◽  
Microstructured Fluids ◽  
Air Streams

To achieve complete microstructural characterization of self-aggregating systems, one needs direct images in addition to quantitative information from non-imaging, e.g., scattering or Theological measurements, techniques. Cryo-TEM enables us to image fluid microstructures at better than one nanometer resolution, with minimal specimen preparation artifacts. Direct images are used to determine the “building blocks” of the fluid microstructure; these are used to build reliable physical models with which quantitative information from techniques such as small-angle x-ray or neutron scattering can be analyzed.To prepare vitrified specimens of microstructured fluids, we have developed the Controlled Environment Vitrification System (CEVS), that enables us to prepare samples under controlled temperature and humidity conditions, thus minimizing microstructural rearrangement due to volatile evaporation or temperature changes. The CEVS may be used to trigger on-the-grid processes to induce formation of new phases, or to study intermediate, transient structures during change of phase (“time-resolved cryo-TEM”). Recently we have developed a new CEVS, where temperature and humidity are controlled by continuous flow of a mixture of humidified and dry air streams.

Sign in / Sign up

Export Citation Format

Share Document