Determination of Compositions and Stability Constants of Holmium and Yttrium Complexes with Tribromoarsenazo by Linear Regression Method

According to the appearing of isosbestic point in the absorption spectra of Ho/Y-Tribromoarsenazo (TBA)systems,the complexation reaction is considered to be M+nL=ML_n.A method has been proposed based on it for calculating the mole fraction of free complexing agent in the solutions from spectral data.and two linear regression formula have been introduced to determine the composition,the molar absorptivity,the conditional stability constant of the complex and the concentration of the complexing agent. This method has been used in Ho-TBA and Y-TBA systems.Ho^(3+)and Y^(3+)react with TBA and form 1: 2 complexes in HCl-NaAc buffer solution at pH 3.80.Their molar absorptivities determined are 1.03×10~8 and 1.10×10~8 cm^2·mol^(-1),and the conditional stability constants(logβ_2)are 11.37 and 11.15 respectively.After considering the pH effect in TBA complexing,their stability constants(log β_2^(ahs))are 43.23 and 43.01. respectively.The new method is adaptable to such systems where the accurate concentration of the complexing agent can not be known conveniently.

Determination of Composition and Stability Constant of Praseodymium(Pr^(3+))Complex with Tribromoarsenazo(TBA)by Dual-Series Linear Regression Method

A new method,dual-series linear regression method,has been used to study the complexation equilibrium of praseodymium(Pr^(3+))with tribromoarsenazo(TBA)without knowing the accurate concentra- tion of the complexing agent TBA.In 1.2 mol/L HCl solution, Pr^(3+)reacts with TBA and forms 1:3 com- plex,the conditional stability constant(lgβ_3)of the complex determined is 15.47,and its molar absorptivity(ε_3^(630))is 1.48×10~5 L·mol^(-1)·cm^(-1).

Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes guides a link failure.This link failure creates more data packet drops that can cause a long time delay.As a result,measuring accurate link failure time is the key factor in the MANET.This paper presents a Fuzzy Linear Regression Method to measure Link Failure(FLRLF)and provide an optimal route in the MANET-Internet of Things(IoT).This work aims to predict link failure and improve routing efficiency in MANET.The Fuzzy Linear Regression Method(FLRM)measures the long lifespan link based on the link failure.The mobile node group is built by the Received Signal Strength(RSS).The Hill Climbing(HC)method selects the Group Leader(GL)based on node mobility,node degree and node energy.Additionally,it uses a Data Gathering node forward the infor-mation from GL to the sink node through multiple GL.The GL is identified by linking lifespan and energy using the Particle Swarm Optimization(PSO)algo-rithm.The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.

Spatio-temporal dynamics of vegetation in Jungar Banner of China during 2000–2017

It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.

Climate change characteristics of Amur River

Unusually severe weather is occurring more frequently due to global climate change. Heat waves, rainstorms, snowstorms, and droughts are becoming increasingly common all over the world, threatening human lives and property. Both temperature and precipitation are representative variables usually used to directly reflect and forecast the influences of climate change. In this study, daily data （from 1953 to 1995） and monthly data （from 1950 to 2010） of temperature and precipitation in five regions of the Amur River were examined. The significance of changes in temperature and precipitation was tested using the Mann-Kendall test method. The amplitudes were computed using the linear least-squares regression model, and the extreme temperature and precipitation were analyzed using hydrological statistical methods. The results show the following： the mean annual temperature increased significantly from 1950 to 2010 in the five regions, mainly due to the warming in spring and winter; the annual precipitation changed significantly from 1950 to 2010 only in the lower mainstream of the Amur River; the frequency of extremely low temperature events decreased from 1953 to 1995 in the mainstream of the Amur River; the frequency of high temperature events increased from 1953 to 1995 in the mainstream of the Amur River; and the frequency of extreme precipitation events did not change significantly from 1953 to 1995 in the mainstream of the Amur River. This study provides a valuable theoretical basis for settling disputes between China and Russia on sustainable development and utilization of water resources of the Amur River.