This study assessed the effect of cyanogenic potential (CNP) in leaf tissue on grasshopper incidence and severity of damage in cassava for the identification of parents with desired complementary traits for crossing. T...This study assessed the effect of cyanogenic potential (CNP) in leaf tissue on grasshopper incidence and severity of damage in cassava for the identification of parents with desired complementary traits for crossing. The experiment was conducted at the Foya Wulleh, Njala experimental site in Sierra Leone during 2020 and 2021 cropping seasons in a randomized complete block design with three replications. A total of 30 genotypes comprising 26 breeding lines, two improved and two local genotypes were assessed. Results showed a significant (p < 0.05) linear relationship between leaf CNP and grasshopper infestation (incidence and severity of damage) among cassava genotypes. Findings showed that the higher leaf CNP, the lower the grasshopper infestation in cassava genotypes. About two genotypes (Cooksoon and Cocoa) had low leaf CNP;three genotypes (TR0020, TR0037 and TR0013) CNP had moderately low leaf CNP;eight genotypes (SLICASS 6, TR0029, TR0032, TR0011, TR0012, TR0016-1/17, TR0002 and TR0010) had intermediate leaf CNP;seven (TR0009, TR0015-1/17, TR0036, TR0022-1/17, SLICASS 4, TR0007 and TR0026-1/17) had moderately high leaf CNP;eight (TR0008, TR0019-1/17, TR0006, TR0005, TR0021, TR0021-1/17, TR0022 and TR0024-1/17) had high leaf CNP;and two genotypes (TR0001 and TR0018-1/17) had very high leaf CNP. This suggests the indirect dependence of leaf cyanogenic potential on grasshopper infestation (incidence and severity of damage) in cassava that could be exploited for the genetic improvement of cassava for improved resistance to grasshopper infestation, nutrition and utilization of the crop.展开更多
In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending netw...In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.展开更多
文摘This study assessed the effect of cyanogenic potential (CNP) in leaf tissue on grasshopper incidence and severity of damage in cassava for the identification of parents with desired complementary traits for crossing. The experiment was conducted at the Foya Wulleh, Njala experimental site in Sierra Leone during 2020 and 2021 cropping seasons in a randomized complete block design with three replications. A total of 30 genotypes comprising 26 breeding lines, two improved and two local genotypes were assessed. Results showed a significant (p < 0.05) linear relationship between leaf CNP and grasshopper infestation (incidence and severity of damage) among cassava genotypes. Findings showed that the higher leaf CNP, the lower the grasshopper infestation in cassava genotypes. About two genotypes (Cooksoon and Cocoa) had low leaf CNP;three genotypes (TR0020, TR0037 and TR0013) CNP had moderately low leaf CNP;eight genotypes (SLICASS 6, TR0029, TR0032, TR0011, TR0012, TR0016-1/17, TR0002 and TR0010) had intermediate leaf CNP;seven (TR0009, TR0015-1/17, TR0036, TR0022-1/17, SLICASS 4, TR0007 and TR0026-1/17) had moderately high leaf CNP;eight (TR0008, TR0019-1/17, TR0006, TR0005, TR0021, TR0021-1/17, TR0022 and TR0024-1/17) had high leaf CNP;and two genotypes (TR0001 and TR0018-1/17) had very high leaf CNP. This suggests the indirect dependence of leaf cyanogenic potential on grasshopper infestation (incidence and severity of damage) in cassava that could be exploited for the genetic improvement of cassava for improved resistance to grasshopper infestation, nutrition and utilization of the crop.
文摘In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.
