scholarly journals Study and Application on Big Data Information Fusion System Based on IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lina Zheng
Keyword(s):  
Big Data ◽  
Relative Humidity ◽  
Light Intensity ◽  
Prediction Model ◽  
Information Fusion ◽  
Co2 Concentration ◽  
Training Data ◽  
Fusion Technology ◽  
Climate Data ◽  
Zigbee Technology

In our study, we first illustrated information fusion technology and Internet of Things (IoT), and then we built farmland IoT information collection platform on the basis of ZigBee technology and agricultural sensors to collect climate data including air pressure, temperature, soil water content, light intensity, and relative humidity. Finally, prediction model was used to evaluate crop growth condition. Results show that temperature increases with time and reaches the maximum at 13:00 PM. But relative humidity decreases with time and reaches the maximum at 3:30 AM. Light intensity presents a straight trend with time and reaches the maximum at 13:30 PM. CO2 concentration presents a fluctuation trend with time and reaches high point at 7:00 AM. Prediction model presented a high accuracy outcome with 99% accuracy in training data and 100% in testing set. Therefore, we can conclude that big data fusion technology on the basis of IoT has a good future in many fields excepting agriculture crop, which is also an irreversible trend.

Photosynthesis of Cotton Leaves Under a Range of Environmental Conditions in Relation to Internal and External Diffusive Resistances

1964 ◽  
Vol 17 (2) ◽  
pp. 348 ◽  
Author(s):  
PA Parsons
Keyword(s):  
Water Stress ◽  
Relative Humidity ◽  
Light Intensity ◽  
Environmental Conditions ◽  
Co2 Concentration ◽  
Leaf Chamber

Experiments have been described in which photosynthesis of cotton leaves enclosed in a leaf chamber was measured under various conditions of light intensity (1000-6000f.o., corresponding to 3�8x 104-22�5x 104 erg cm-2 sec-I), CO2 concentration (200-2000 p.p.ro.}, temperature (30-40�C), relative humidity (40-80%), and windBpeed (0�6-3�1 em sec-I). The plants were well watered in order to minimize water stress.

scholarly journals Enterprise Financial Risk Management Using Information Fusion Technology and Big Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Huabo Yue ◽  
Haojie Liao ◽  
Dong Li ◽  
Ling Chen
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Information Fusion ◽  
Classification Accuracy ◽  
Financial Risk ◽  
Financial Risks ◽  
Fusion Technology ◽  
Financial Risk Management ◽  
Big Data Mining ◽  
Big Data Technology

This paper aims to study enterprise Financial Risk Management (FRM) through Big Data Mining (BDM) and explore effective FRM solutions by introducing information fusion technology. Specifically, big data technology, Support Vector Machine (SVM), Logistic regression, and information fusion approaches are employedto study the enterprise financial risks in-depth.Among them, the selection offinancial risk indexes has a great impact on the monitoring results of the SVM-based FRM model; the Logistic regression-based FRM model can efficientlyclassify financial risks; theinformation fusion-based FRM model uses a fusion algorithm to fuse different information sources. The results show that the SVM-based and Logistic regression-based FRM models can manage and classify enterprise financial risks effectively in practice, with a classification accuracy of 90.22% and 90.88%, respectively; by comparison, the information fusion-based FRM modelbeats SVM-based and Logistic regression-based FRM models by presenting a classification accuracy as high as 95.18%. Therefore, it is concluded that the information fusion-based FRM is better than the SVM-based and Logistic regression-based models; it can integrate and calculate multiple enterprise financial risk data from different sources and obtain higher accuracy; besides, big data technology can provide important research methods for enterprise financial risk problems; SVM-based FRM model and Logistic regression-based FRM model can well classify enterprise financial risks, with relatively high accuracy.

EL NINO RELATED CLIMATE AND DENGUE FEVER CASES IN JEDDAH, SAUDI ARABIA

2019 ◽  
Vol 25 (1) ◽  
Author(s):  
MASROOR ALI KHAN ◽  
KHALID AL GHAMDI ◽  
JAZEM A. MEHYOUB ◽  
RAKHSHAN KHAN
Keyword(s):  
Relative Humidity ◽  
Dengue Fever ◽  
El Niño ◽  
Significant Relationship ◽  
El Nino ◽  
Climate Data ◽  
Light Traps ◽  
Mosquito Abundance ◽  
Dengue Transmission ◽  
The Relationship

The focus of this study is to find the relationship between El Nino and dengue fever cases in the study area.Mosquito density was recorded with the help of light traps and through aspirators collection. Climate data were obtained from National Meteorology and Environment centre. (Year wise El Nino and La Nina data are according to NOAA & Golden Gate Weather Services). Statistical methods were used to establish the correlation coefficient between different factors. A high significant relationship was observed between Relative Humidity and Dengue fever cases, but Aedes abundance had no significant relationship with either Relative humidity and Temperature. Our conclusion is that the El Nino does not affect the dengue transmission and Aedes mosquito abundance in this region, which is supported by earlier works.

scholarly journals Optimizing Planting Density and Impact of Panicle Types on Grain Yield and Microclimatic Response Index of Hybrid Rice (Oryza sativa L.)

2021 ◽  
Author(s):  
Guotao Yang ◽  
Xuechun Wang ◽  
Farhan Nabi ◽  
Hongni Wang ◽  
Changkun Zhao ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Light Intensity ◽  
Grain Yield ◽  
Hybrid Rice ◽  
Plant Density ◽  
Oryza Sativa L ◽  
Planting Density ◽  
Response Index ◽  
Lower Position ◽  
High Plant Density

AbstractThe architecture of rice plant represents important and complex agronomic traits, such as panicles morphology, which directly influence the microclimate of rice population and consequently grain yield. To enhance yield, modification of plant architecture to create new hybrid cultivars is considered a sustainable approach. The current study includes an investigation of yield and microclimate response index under low to high plant density of two indica hybrid rice R498 (curved panicles) and R499 (erect panicles), from 2017 to 2018. The split-plot design included planting densities of 11.9–36.2 plant/m2. The results showed that compared with R498, R499 produced a higher grain yield of 8.02–8.83 t/ha at a higher planting density of 26.5–36.2 plant/m2. The response index of light intensity and relative humidity to the planting density of R499 was higher than that of R498 at the lower position of the rice population. However, the response index of temperature to the planting density of R499 was higher at the upper position (0.2–1.4%) than at the lower position. Compared with R498, R499 at a high planting density developed lower relative humidity (78–88%) and higher light intensity (9900–15,916 lx) at the lower position of the rice population. Our finding suggests that erect panicles are highly related to grain yield microclimatic contributors under a highly dense rice population, such as light intensity utilization, humidity, and temperature. The application of erect panicle rice type provides a potential strategy for yield improvement by increasing microclimatic conditions in rice.

scholarly journals Effect Factors of Benzene Adsorption and Degradation by Nano-TiO2Immobilized on Diatomite

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Lijun Cheng ◽  
Yong Kang ◽  
Guishui Li
Keyword(s):  
Relative Humidity ◽  
Light Intensity ◽  
Experimental Results ◽  
Benzene Adsorption ◽  
Effect Factors ◽  
Benzene Concentration ◽  
Effects Of Temperature ◽  
Safe Concentration

Difference between adsorption of benzene by diatomite and nano-TiO2immobilized on diatomite was investigated. And effects of temperature, light intensity, relative humidity, and initial benzene concentration on adsorption and degradation of benzene by nano-TiO2immobilized on diatomite were also studied. The experimental results showed that when initial benzene concentration was2.2×10−3 mg L−1, it could be degraded to below safe concentration (1.1×10−4 mg L−1) after 50 h when temperature was 20°C, but it just needed 30 h at 35°C. When light intensity was 6750 Lx, it needed 30 h for benzene to be degraded to below safe concentration, but benzene could barely be degraded without light. When relative humidity was 50%, benzene could be degraded to1.0×10−4 mg L−1after 30 h, while its concentration could be reduced to7.0×10−5 mg L−1at the relative humidity of 80%.

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