Monitoring of heavy metals contamination of agricultural products and their transfer and bioaccumulation in crops like rice has become a hot topic worldwide over the last two decades. The present study was carried out...Monitoring of heavy metals contamination of agricultural products and their transfer and bioaccumulation in crops like rice has become a hot topic worldwide over the last two decades. The present study was carried out to determine the accumulation of heavy metals in rice fields and their transfer to rice grains. Soil, irrigation water and rice grains samples were gathered in Maga-Pouss, Far-North, Cameroon. Concentrations of six heavy metals (lead, cadmium, zinc, copper, iron and mercury) were evaluated by Atomic Absorption Spectrophotometer (AAS). Mercury was not detected in this study. Average concentrations of metals were in this order (in mg/kg): Fe (188.60 ± 97.06) > Pb (63.63 ± 7.11) > Cd (2.59 ± 0.29) > Zn (1.10 ± 1.05) > Cu (0.80 ± 0.73) in water and Pb (105.50 ± 31.11) > Fe (105.50 ± 31.11) > Cu (45.93 ± 14.39) > Zn (22.52 ± 6.40) > Cd (3.15 ± 0.49) in soil. Water in Maga-Pouss rice fields appears to be more harmful than the soil, notably for lead, cadmium and copper. In rice grains, heavy metals were found in this order (mg/kg): Fe (188.01 ± 82.62) > Cu (27.20 ± 0.00) > Zn (23.61 ± 12.42) > Pb (19.50 ± 19.91) > Cd (2.02 ± 1.05). The mean bioconcentration factor (BCF) of metals from soil to rice grains was in the following order: Fe (2.60) > Zn (1.05) > Cd (0.64) > Cu (0.59) > Pb (0.18). From water to rice grains, the order is: Cu (37.26) > Zn (22.49) > Cd (6.97) > Pb (2.74) > Fe (1.94). Rice field pH and electrical conductivity favored the uptake of lead, copper and cadmium by rice grains. The findings of this study will be good documentation for risk assessment, and decision-making by environmental managers in this region.展开更多
For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machin...For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.展开更多
文摘Monitoring of heavy metals contamination of agricultural products and their transfer and bioaccumulation in crops like rice has become a hot topic worldwide over the last two decades. The present study was carried out to determine the accumulation of heavy metals in rice fields and their transfer to rice grains. Soil, irrigation water and rice grains samples were gathered in Maga-Pouss, Far-North, Cameroon. Concentrations of six heavy metals (lead, cadmium, zinc, copper, iron and mercury) were evaluated by Atomic Absorption Spectrophotometer (AAS). Mercury was not detected in this study. Average concentrations of metals were in this order (in mg/kg): Fe (188.60 ± 97.06) > Pb (63.63 ± 7.11) > Cd (2.59 ± 0.29) > Zn (1.10 ± 1.05) > Cu (0.80 ± 0.73) in water and Pb (105.50 ± 31.11) > Fe (105.50 ± 31.11) > Cu (45.93 ± 14.39) > Zn (22.52 ± 6.40) > Cd (3.15 ± 0.49) in soil. Water in Maga-Pouss rice fields appears to be more harmful than the soil, notably for lead, cadmium and copper. In rice grains, heavy metals were found in this order (mg/kg): Fe (188.01 ± 82.62) > Cu (27.20 ± 0.00) > Zn (23.61 ± 12.42) > Pb (19.50 ± 19.91) > Cd (2.02 ± 1.05). The mean bioconcentration factor (BCF) of metals from soil to rice grains was in the following order: Fe (2.60) > Zn (1.05) > Cd (0.64) > Cu (0.59) > Pb (0.18). From water to rice grains, the order is: Cu (37.26) > Zn (22.49) > Cd (6.97) > Pb (2.74) > Fe (1.94). Rice field pH and electrical conductivity favored the uptake of lead, copper and cadmium by rice grains. The findings of this study will be good documentation for risk assessment, and decision-making by environmental managers in this region.
文摘For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.
