A study on seasonal variations in the abundance of Lesser Flamingo (Phoenicopterus minor) in relation to phytoplankton abundance in lake Manyara was conducted for a period of fourteen consecutive months (July 2007 to ...A study on seasonal variations in the abundance of Lesser Flamingo (Phoenicopterus minor) in relation to phytoplankton abundance in lake Manyara was conducted for a period of fourteen consecutive months (July 2007 to August 2008). The aim was to relate the temporal variability in the phytoplankton species abundance and diversity of the lake to the population size of the Lesser Flamingo. Lesser Flamingo population numbers were obtained from monthly ground surveys whereby the lake was subdivided into defined counting vantage points. Water samples for phytoplankton species composition and biomass analyses were taken to the University of Dar es Salaam for laboratory analysis. The flamingo population estimates ranged from 9319 in August 2007 to 640,850 in August 2008. The Lesser Flamingo populations showed that temporal fluctuations were related to the changes in the abundance and diversity of phytoplankton species. The occurrence of Arthrospira associated with the increase in the abundance of Lesser Flamingo. It was observed that changes in the Lesser Flamingo numbers were influenced by the changes in the abundance and availability of their preferred food. The results indicated that microalgae assemblage positively correlated with ammonium and nitrate which were also related to the abundance of lesser flamingo. The phytoplankton community was dominated by cyanobacteria particularly Arthrosipira fusiformis likely due to the high lake salinity and pH that limited the growth of other microalgae. Correlation analysis showed strong correlation between the Lesser Flamingo abundance with the concentration of nitrate and ammonium and between the number of Lesser Flamingo and the cyanobacterium Arthrospira.展开更多
Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.How...Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.展开更多
Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior chara...Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.展开更多
Flower blight on anthurium(Anthurium andraeanum)was observed during August 2018 on an anthurium cultivation farm in the Songkhla Province of southern Thailand.The fungal isolate was identified as Neopestalotiopsis cla...Flower blight on anthurium(Anthurium andraeanum)was observed during August 2018 on an anthurium cultivation farm in the Songkhla Province of southern Thailand.The fungal isolate was identified as Neopestalotiopsis clavispora based on the morphology and DNA sequence of the internal transcribed spacer(ITS),translation elongation factor 1-α(tef1-α),andβ-tubulin(tub)genes.The phylogenetic tree,based on the combined sequences of ITS,tef1-α,and tub,confirmed this pathogen as N.clavispora.Pathogenicity of the species was confirmed according to Koch’s postulate:N.clavispora could infect anthurium.To the best of our knowledge,this is the first report of N.clavispora as a pathogen of anthurium.展开更多
文摘A study on seasonal variations in the abundance of Lesser Flamingo (Phoenicopterus minor) in relation to phytoplankton abundance in lake Manyara was conducted for a period of fourteen consecutive months (July 2007 to August 2008). The aim was to relate the temporal variability in the phytoplankton species abundance and diversity of the lake to the population size of the Lesser Flamingo. Lesser Flamingo population numbers were obtained from monthly ground surveys whereby the lake was subdivided into defined counting vantage points. Water samples for phytoplankton species composition and biomass analyses were taken to the University of Dar es Salaam for laboratory analysis. The flamingo population estimates ranged from 9319 in August 2007 to 640,850 in August 2008. The Lesser Flamingo populations showed that temporal fluctuations were related to the changes in the abundance and diversity of phytoplankton species. The occurrence of Arthrospira associated with the increase in the abundance of Lesser Flamingo. It was observed that changes in the Lesser Flamingo numbers were influenced by the changes in the abundance and availability of their preferred food. The results indicated that microalgae assemblage positively correlated with ammonium and nitrate which were also related to the abundance of lesser flamingo. The phytoplankton community was dominated by cyanobacteria particularly Arthrosipira fusiformis likely due to the high lake salinity and pH that limited the growth of other microalgae. Correlation analysis showed strong correlation between the Lesser Flamingo abundance with the concentration of nitrate and ammonium and between the number of Lesser Flamingo and the cyanobacterium Arthrospira.
基金supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.
文摘Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.
基金supported by Prince of Songkla Universitythe Center of Excellence in Agricultural and Natural Resources Biotechnology(Grant No.CoE-ANRB)phase 3。
文摘Flower blight on anthurium(Anthurium andraeanum)was observed during August 2018 on an anthurium cultivation farm in the Songkhla Province of southern Thailand.The fungal isolate was identified as Neopestalotiopsis clavispora based on the morphology and DNA sequence of the internal transcribed spacer(ITS),translation elongation factor 1-α(tef1-α),andβ-tubulin(tub)genes.The phylogenetic tree,based on the combined sequences of ITS,tef1-α,and tub,confirmed this pathogen as N.clavispora.Pathogenicity of the species was confirmed according to Koch’s postulate:N.clavispora could infect anthurium.To the best of our knowledge,this is the first report of N.clavispora as a pathogen of anthurium.
