Identifying the microbial population diversity in the gut of cashew stem girdler,Analeptes trifasciataFabricius (Coleoptera: Cerambycidae)

2016 ◽  
Author(s):  
Victor Adegoke Oyedokun
2021 ◽  
Author(s):  
Kehinde Oluwadamilare Sowunmi

Abstract A study investigated impact of cement dust pollution from Ewekoro cement industry on soil microbes. pH of the soil ranged from 6.27±0.03- 6.47 and soil moisture content ranged from 15.78±2.52- 9.65±1.16. The levels of heavy metals except Mg, Zn and Na were higher within the factory than in the control. Microbial population diversity increased steadily away from the factory and this variation could be attributed to the impact of pH and heavy metals on microbial population. The lower counts of bacteria compared to fungi may be as a result of the nutrient status of the soil and the bacteria counts in polluted soil were lower than the fungal counts in control soil. The bacteria and fungi was influenced by the cement dust deposition. The study was published in the journal ‘Phenomenon: Microbes and the Cement Industry’.


2003 ◽  
Vol 41 (5) ◽  
pp. 1977-1986 ◽  
Author(s):  
W. A. Riemersma ◽  
C. J. C. van der Schee ◽  
W. I. van der Meijden ◽  
H. A. Verbrugh ◽  
A. van Belkum

Author(s):  
Mebom Princess Chibuike ◽  
N. David Ogbonna ◽  
Williams Janet Olufunmilayo

Wetland soils constitute vast, under-exploited and sometimes undiscovered ecologies in many countries of the World, including Nigeria. A total of 54 wetland soil samples including surface and subsurface soil at depths of 0-15 cm and 15-30 cm were collected using a sterile hand auger for a period of three months between August and October and subjected to standard and analytical microbiological procedures. The wetland soil samples were further subjected to atomic absorption spectroscopy (AAS) to check for presence and concentration of heavy metals. Results obtained showed that apart from heterotrophic bacterial and fungal counts, hydrocarbon utilizing bacteria (HUB) counts were higher in the surface soil ranging from 12.06±3.43bX107 cfu/g at Iwofe to 6.19±2.67aX107 cfu/g at Chokocho while subsurface soil had HUB ranging from 8.91±6.67aX103 cfu/g at Eagle Island to 4.93±3.95aX103cfu/g at Chokocho. Heavy metals such as Fe, Pb, Cd and Ni were recorded in concentrations above FEPA permissible limit in the surface and subsurface soil across the three wetlands. The heavy metal concentration in each wetland however, decreased with an increase in soil depth. According to literatures, elevated levels of heavy metals in soils decrease microbial population, diversity and activities. However, the microbial population in this study increased with increasing heavy metal concentration which indicates that the microbes can tolerate or utilize heavy metals in their systems; as such can be used for bioremediation of heavy metal polluted soils. 


2017 ◽  
Vol 17 (10) ◽  
pp. 2490-2499 ◽  
Author(s):  
Yumei Jiang ◽  
Di Lin ◽  
Xianjiao Guan ◽  
Jinfeng Wang ◽  
Guangpan Cao ◽  
...  

2018 ◽  
Vol 111 (1) ◽  
pp. 31-37 ◽  
Author(s):  
S DOOSTI ◽  
MR YAGHOOBI-ERSHADI ◽  
MM SEDAGHAT ◽  
SH MOOSA-KAZEMI ◽  
K AKBARZADEH ◽  
...  

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>


Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.


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