Prediction model for the number of crucian carp hypoxia based on the fusion of fish behavior and water environment factors

2021 ◽  
Vol 189 ◽  
pp. 106386
Author(s):  
Longqing Sun ◽  
Yuhan Wu ◽  
Daoliang Li ◽  
Boning Wang ◽  
Xibei Sun ◽  
...  
Keyword(s):  
Prediction Model ◽  
Crucian Carp ◽  
Water Environment ◽  
Fish Behavior ◽  
Environment Factors

CORRELATION OF MICROCYSTINS AND WATER ENVIRONMENT FACTORS IN MIYUN RESERVOIR OF BEIJING, CHINA

2011 ◽  
Vol 10 (6) ◽  
pp. 849-853 ◽  
Author(s):  
Guisen Du ◽  
Huimin Li ◽  
Yumei Wu ◽  
Dianwei Wu ◽  
Jingshi Wang
Keyword(s):  
Water Environment ◽  
Miyun Reservoir ◽  
Environment Factors ◽  
Beijing China

scholarly journals STUDI EKOLOGI SUMBERDAYA TERIPANG DI NEGERI PORTO PULAU SAPARUA MALUKU TENGAH

2014 ◽  
Vol 1 (1) ◽  
pp. 32-42
Author(s):  
Yona Aksa Lewerissa
Keyword(s):  
Sea Cucumber ◽  
Water Environment ◽  
Turbidity Current ◽  
Line Transect ◽  
Community Structures ◽  
Sea Cucumbers ◽  
Environment Factors ◽  
Line Transect Method ◽  
Environment Measurement

Background: Decreasing of sea cucumber resources on numbers and size of fishing capture in Negeri Porto waters influenced its community structures. Further, water environment steadiness will questionable. For this reason, study was done to analyzed community structures of sea cucumbers include of numbers species found, density, potency, species presence frequency, distribute pattern, ecology index and environment measurement. Method: Research done on February to May, 2008 at Negeri Porto used Line Transect Method. Result: showed there were eight species of sea cucumbers found (potency as much as 12.286 individuals). Bohadschia marmorata got highest in numbers species found, density, potency and species presence frequency. There were three distribute pattern of sea cucumbers found wich are uniform, random and group. Conclusion: Sea cucumbers diversity was in lower, species are closed to compatible. B. marmorata and Holothuria edulis are tends to dominate this water. Environment factors such temperature, pH, salinity, DO, turbidity, current and sediment distribute showed that Porto waters was suitable to sea cucumbers growth.

An improved atmospheric corrosion prediction model considering various environmental factors

CORROSION ◽  
10.5006/3879 ◽  
2021 ◽  
Author(s):  
Ziguang Ji ◽  
Xiaobing Ma ◽  
Kun Zhou ◽  
Yikun Cai
Keyword(s):  
Environmental Factors ◽  
Prediction Model ◽  
Process Model ◽  
Atmospheric Corrosion ◽  
Corrosion Process ◽  
Atmospheric Environment ◽  
Suitable Model ◽  
Environment Factors ◽  
Climatic Regions ◽  
Fitting In

There are obvious differences in the corrosion process of materials in different climatic regions, and it is of great significance to establish the corrosion process model considering the effects of environmental factors to improve environmental adaptability. However, many existing models do not take comprehensive environmental factors into account, and the selection of environmental factors in these corrosion models is not sufficiently investigated. Meanwhile, the comparison of models is also based on goodness-of-fitting. In order to improve the atmospheric corrosion prediction model and use an improved method to judge the suitable model instead of traditional goodness-of-fitting, this paper firstly studies the effects of various atmospheric environment factors on corrosion loss. Then a corrosion prediction model considering the influence of various atmospheric environment factors is proposed, and the fitting effect is compared with the previous model with experimental data. Considering that the best fitting can no guarantee the best prediction, a new criterion of model selection is also proposed for corrosion model confirmation. Corrosion prediction results reveal that the proposed model is more effective than the existing models. Finally, the influence of the environmental factors on the model output is discussed by sensitivity analysis.

Verapamil – L type voltage gated calcium channel inhibitor diminishes aggressive behavior in male Siamese fighting fish

2015 ◽  
Vol 18 (2) ◽  
pp. 401-406 ◽  
Author(s):  
B.F. Kania ◽  
B. Dębski ◽  
D. Wrońska ◽  
E. Zawadzka
Keyword(s):  
Aggressive Behavior ◽  
Water Environment ◽  
Total Duration ◽  
Siamese Fighting Fish ◽  
Fish Behavior ◽  
Control Group ◽  
Hormonal Changes ◽  
Voltage Gated ◽  
Living Organisms ◽  
Cluster Headaches

Abstract Verapamil is a L-type voltage gated calcium channels inhibitor (VGCCI), which is a highly prescribed drug used in the treatment of hypertension, angina pectoris, cardiac arrhythmia and cluster headaches. Its common use caused its appearance in water environment. VGCC inhibit epinephrine release and cause many neuro-hormonal changes influencing also fish behavior. Siamese fighting fish was chosen to study the influence of verapamil given to the water on the beginning of experiment in 3 different concentrations of 0 (control), 8 and 160 μg · L−1, on aggressive behavior in these fish. The experimental fish were placed in individual glass containers for 3 weeks and the mirror test was used. The highest concentration led to a significant modulation of fish behavior after 1 week and the lower dose caused statistically significant behavioral changes after 2 weeks of verapamil treatment. Siamese fighting fish males exposed to verapamil had longer latencies to the first chase – 12.6 s (8 μg · L−1 of verapamil) and 18.8 s (160 μg · L−1 of verapamil) compared to 5.6 s in the control group, decreased attack frequency and shorter duration of these attacks. The number of attacks within 10 min was decreased from 38.3 in the control group to 27.1 and 16.1, respectively. Also the total duration of these attacks decreased from 354.8 (control) to 326.4 (decrease statistically insignificant) and to 194.8 s in verapamil treated groups. It was shown, that even relatively low concentrations of verapamil in water may have adverse effects on fish and probably other living organisms.

scholarly journals Impact of water environment factors on eutrophication status of Lake Ulansuhai based on monitoring data in 2013-2018

2020 ◽  
Vol 32 (6) ◽  
pp. 1610-1619
Author(s):  
QUAN Dong ◽  
◽  
ZHANG Sheng ◽  
SHI Xiaohong ◽  
SUN Biao ◽  
...  
Keyword(s):  
Water Environment ◽  
Monitoring Data ◽  
Environment Factors
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