In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with l...In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.展开更多
Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies,uneven Security Edge Protection Proxy(SEPP)deployment,and the dynamic nature o...Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies,uneven Security Edge Protection Proxy(SEPP)deployment,and the dynamic nature of inter-Public Land Mobile Network(inter-PLMN)signaling.Traditional rule-based defenses are inadequate for protecting cloud-native 5G core networks,particularly as roaming expands into enterprise and Internet of Things(IoT)domains.This work addresses these challenges by designing a scalable 5G Standalone testbed,generating the first intrusion detection dataset specifically tailored to roaming threats,and proposing a deep learning based intrusion detection framework for cloud-native environments.Six deep learning models including Multilayer Perceptron(MLP),one-dimensional Convolutional Neural Network(1D CNN),Autoencoder(AE),Recurrent Neural Network(RNN),Gated Recurrent Unit(GRU),and Long Short-Term Memory(LSTM)were evaluated on the dataset using both weighted and balanced metrics to account for strong class imbalance.While all models achieved over 99%accuracy,recurrent architectures such as GRU and LSTM outperformed others in balanced accuracy and macro-level evaluation,demonstrating superior effectiveness in detecting rare but high-impact attacks.These results confirm the importance of sequence-aware Artificial Intelligence(AI)models for securing roaming scenarios,where transient and contextdependent threats are common.The proposed framework provides a foundation for intelligent,adaptive intrusion detection in 5G and offers a path toward resilient security in Beyond 5G and 6G networks.展开更多
生物氧化是生物化学代谢部分的重要内容,因其复杂性和抽象性不易被学生理解。采用非变性聚丙烯酰胺凝胶电泳(Native polyacrylamide gel electrophoresis, Native-PAGE)构建了适用于本科实验教学的“线粒体复合体酶I活性方法”。从锥虫...生物氧化是生物化学代谢部分的重要内容,因其复杂性和抽象性不易被学生理解。采用非变性聚丙烯酰胺凝胶电泳(Native polyacrylamide gel electrophoresis, Native-PAGE)构建了适用于本科实验教学的“线粒体复合体酶I活性方法”。从锥虫中提取线粒体粗体物,以10μg/孔样品蛋白上样量,进行非变性聚丙烯酰胺凝胶电泳, NADH脱氢酶酶促反应时间为30分钟,可得到分辨率高、条带清晰的特异性酶活性条带。将Native-PAGE方法应用到本科生化实验中,可有效拓展教学内容,有助于教学紧密联系科研,对本科生化实验教学的开展有借鉴意义。展开更多
Tree endophytic fungi play an important role in reducing insect herbivory,either by repelling them or kill-ing them directly.Identifying which fungi show such activ-ity could lead to new environmentally friendly pesti...Tree endophytic fungi play an important role in reducing insect herbivory,either by repelling them or kill-ing them directly.Identifying which fungi show such activ-ity could lead to new environmentally friendly pesticides.In this study,the Mediterranean basin climate conditions are projected to harshen in the next decades,will increase vulnerability of tree species to pest invasions.Endophytic fungi were isolated from wood and leaves of Quercus pyr-enaica,Q.ilex and Q.suber and tested for virulence against adults of the mealworm beetle,Tenebrio molitor L.using a direct contact method.Only 3 of 111 sporulating isolates had entomopathogenic activity,all identified as Lecanicillium lecanii.The pathogenicity of L.lecanii on T.molitor resulted in a median lethal time(TL50)of 14-16 d.Compared with commercial products,L.lecanii caused faster insect death than the nematode Steinernema carpocapsae and nuclear polyhedrosis virus(no effect on T.molitor survival),and slower than Beauveria bassiana(TL50=5),Beauveria pseu-dobassiana(TL50=8d)and Bacillus thuriengensis(80%mortality first day after inoculation).Mortality was also accelerated under water stress,reducing TL50 by an addi-tional 33%.Remarkably,water stress alone had a comparable effect on mortality to that of L.lecanii isolates.This study confirms T.molitor as a good model insect for pathogenicity testing and agrees with management policies proposed in the EU Green Deal.展开更多
Exotic tree species,though widely used in forestry and restoration projects,pose great threats to local ecosystems.They need to be replaced with native species from natural forests.We hypothesized that natural forests...Exotic tree species,though widely used in forestry and restoration projects,pose great threats to local ecosystems.They need to be replaced with native species from natural forests.We hypothesized that natural forests contain large,fast-growing,dominant native tree species that are suitable for specific topographic conditions in forestry.We tested this hypothesis using data from a 50-ha forest dynamics plot in subtropical China.We classified the plot into the ridge,slope,and valley habitats and found that 34/87 species had significant associations with at least one topographic habitat.There were 90 tree species with a maximum diameter≥30 cm,and their abundances varied widely in all habitat types.In all habitat types,for most species,rate of biomass gain due to recruitment was<1%of its original biomass,and rate of biomass gain due to tree growth was between 1 and 5%of its original biomass.For most species,biomass loss due to tree mortality was not significantly different than biomass gain due to recruitment,but the resulting net biomass increment rates did not significantly differ from zero.The time required to reach a diameter of 30 cm from 1 cm diameter for Altingia chinensis in the slope habitat,for Quercus chungii and Morella rubra in the ridge habitat and for Castanopsis carlesii in all habitats could be as short as 30 years in our simulations based on actual distributions of tree growth observed in the forest.Principal component analyses of maximum diameter,abundance and net biomass increment rates suggested several species were worthy of further tests for use in forestry.Our study provides an example for screening native tree species from natural forests for forestry.Because native tree species are better for local ecosystems,our study will also contribute to biodiversity conservation in plantations.展开更多
N-methyl-D-aspartate receptors(NMDARs)play crucial roles in neuronal plasticity and brain function by sensing key neurotransmitters,such as glutamate and glycine.In a tour-de-force,Zhang et al.[1]provide the first ima...N-methyl-D-aspartate receptors(NMDARs)play crucial roles in neuronal plasticity and brain function by sensing key neurotransmitters,such as glutamate and glycine.In a tour-de-force,Zhang et al.[1]provide the first images of native NMDARs directly extracted from rat brains,revealing key aspects of NMDAR assembly and diversity.展开更多
Although mollusks represent Earth’s second most diverse invertebrate group,their natural history and ecology are still scarcely known.The compilation of non-traditional data,such as those from citizen science,represe...Although mollusks represent Earth’s second most diverse invertebrate group,their natural history and ecology are still scarcely known.The compilation of non-traditional data,such as those from citizen science,represents an alternative to fill these gaps,particularly on striking land snail species such as Macrocyclis peruvianus.Based on long-term citizen science,we aimed to update and describe some basic ecological aspects,such as the distribution and protected area types used by M.peruvianus.We performed pairwise comparisons to test potential changes in occurrence and occupancy among administrative regions,forest types,and protected area types using chi-squared tests.The citizen scientists were also asked to provide the number of M.peruvianus individuals observed and the tree species that dominated their habitat.Thus,we tested if the number of land snails found by citizen scientists could be related to forest and protected area types using a generalized linear mixed model.We expanded the northern distributional limit,with Nothofagus,evergreen,and mixed forests far the most frequented by M.peruvianus.Parallelly,the occurrence of M.peruvianus in official protected areas(65.73%)was significantly higher than in privately owned areas.Moreover,we did not find associations between forest and protected area types with the number of M.peruvianus recorded.Although citizen science is a helpful method for obtaining novel information regarding the ecology of neglected species such as M.peruvianus,it also introduces spatial and occurrence biases explained by the access and attractiveness of the officially protected areas compared to privately owned patches of native forest.展开更多
Non-typhoid Salmonella is a common foodborne infection.[1]In the setting of immunosuppression,the classical symptom of diarrhea,that is an immune defense mechanism,may be absent,[2,3]allowing the bacteria to hematogen...Non-typhoid Salmonella is a common foodborne infection.[1]In the setting of immunosuppression,the classical symptom of diarrhea,that is an immune defense mechanism,may be absent,[2,3]allowing the bacteria to hematogenous spread and settle in other organs.[4,5]As a result,in the setting of acute pericarditis in immunosuppressed patients,a bacterial etiology must always be considered,which requires pericardiocentesis to complete drainage and pathogen identification.展开更多
Group B Streptococcus(GBS;Streptococcus agalactiae)is a gram-positive coccus that colonizes the gastrointestinal and genital tracts in adults,as well as the upper respiratory tract in infants.While it has been thought...Group B Streptococcus(GBS;Streptococcus agalactiae)is a gram-positive coccus that colonizes the gastrointestinal and genital tracts in adults,as well as the upper respiratory tract in infants.While it has been thought that GBS only results in invasive disease in pregnant females and neonates,recent literature has suggested an increasing incidence of invasive GBS among non-pregnant individuals within the United States.展开更多
The goal of this project is to use the Semantic Web Technologies and Data Mining for disease diagnosis to assist health care professionals regarding the possible medication and drug to prescribe (Drug recommendation) ...The goal of this project is to use the Semantic Web Technologies and Data Mining for disease diagnosis to assist health care professionals regarding the possible medication and drug to prescribe (Drug recommendation) according to the features of the patient. Numerous Decision Support Systems (DSS) and Expert Systems allow medical collaboration, like in the differential diagnosis specific or general. But, a medical recommendation system using both Semantic Web technologies and Data mining has not yet been developed which initiated this work. However, it should be mentioned that there are several system references about medicine or active ingredient interactions, but their final goal is not the Drug recommendation which uses above technologies. With this project we try to provide an assistant to the doctor for better recommendations. The patient will also able to use this system for explanation of drugs, food interaction and side effects of corresponding drugs.展开更多
Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphys...Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
文摘In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00441484,Development of Open Roaming Technology for Private 5G Network)。
文摘Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies,uneven Security Edge Protection Proxy(SEPP)deployment,and the dynamic nature of inter-Public Land Mobile Network(inter-PLMN)signaling.Traditional rule-based defenses are inadequate for protecting cloud-native 5G core networks,particularly as roaming expands into enterprise and Internet of Things(IoT)domains.This work addresses these challenges by designing a scalable 5G Standalone testbed,generating the first intrusion detection dataset specifically tailored to roaming threats,and proposing a deep learning based intrusion detection framework for cloud-native environments.Six deep learning models including Multilayer Perceptron(MLP),one-dimensional Convolutional Neural Network(1D CNN),Autoencoder(AE),Recurrent Neural Network(RNN),Gated Recurrent Unit(GRU),and Long Short-Term Memory(LSTM)were evaluated on the dataset using both weighted and balanced metrics to account for strong class imbalance.While all models achieved over 99%accuracy,recurrent architectures such as GRU and LSTM outperformed others in balanced accuracy and macro-level evaluation,demonstrating superior effectiveness in detecting rare but high-impact attacks.These results confirm the importance of sequence-aware Artificial Intelligence(AI)models for securing roaming scenarios,where transient and contextdependent threats are common.The proposed framework provides a foundation for intelligent,adaptive intrusion detection in 5G and offers a path toward resilient security in Beyond 5G and 6G networks.
基金supported by LIFE project MYCORESTORE“Innovative use of mycological resources for resilient and productive Mediterranean forests threatened by climate change,LIFE18 CCA/ES/001110”projects VA178P23 and VA208P20 funded by JCYL(Spain),both co-financed by FEDER(UE)budget.
文摘Tree endophytic fungi play an important role in reducing insect herbivory,either by repelling them or kill-ing them directly.Identifying which fungi show such activ-ity could lead to new environmentally friendly pesticides.In this study,the Mediterranean basin climate conditions are projected to harshen in the next decades,will increase vulnerability of tree species to pest invasions.Endophytic fungi were isolated from wood and leaves of Quercus pyr-enaica,Q.ilex and Q.suber and tested for virulence against adults of the mealworm beetle,Tenebrio molitor L.using a direct contact method.Only 3 of 111 sporulating isolates had entomopathogenic activity,all identified as Lecanicillium lecanii.The pathogenicity of L.lecanii on T.molitor resulted in a median lethal time(TL50)of 14-16 d.Compared with commercial products,L.lecanii caused faster insect death than the nematode Steinernema carpocapsae and nuclear polyhedrosis virus(no effect on T.molitor survival),and slower than Beauveria bassiana(TL50=5),Beauveria pseu-dobassiana(TL50=8d)and Bacillus thuriengensis(80%mortality first day after inoculation).Mortality was also accelerated under water stress,reducing TL50 by an addi-tional 33%.Remarkably,water stress alone had a comparable effect on mortality to that of L.lecanii isolates.This study confirms T.molitor as a good model insect for pathogenicity testing and agrees with management policies proposed in the EU Green Deal.
基金supported by the National Natural Science Foundation of China(31925027,31300455).
文摘Exotic tree species,though widely used in forestry and restoration projects,pose great threats to local ecosystems.They need to be replaced with native species from natural forests.We hypothesized that natural forests contain large,fast-growing,dominant native tree species that are suitable for specific topographic conditions in forestry.We tested this hypothesis using data from a 50-ha forest dynamics plot in subtropical China.We classified the plot into the ridge,slope,and valley habitats and found that 34/87 species had significant associations with at least one topographic habitat.There were 90 tree species with a maximum diameter≥30 cm,and their abundances varied widely in all habitat types.In all habitat types,for most species,rate of biomass gain due to recruitment was<1%of its original biomass,and rate of biomass gain due to tree growth was between 1 and 5%of its original biomass.For most species,biomass loss due to tree mortality was not significantly different than biomass gain due to recruitment,but the resulting net biomass increment rates did not significantly differ from zero.The time required to reach a diameter of 30 cm from 1 cm diameter for Altingia chinensis in the slope habitat,for Quercus chungii and Morella rubra in the ridge habitat and for Castanopsis carlesii in all habitats could be as short as 30 years in our simulations based on actual distributions of tree growth observed in the forest.Principal component analyses of maximum diameter,abundance and net biomass increment rates suggested several species were worthy of further tests for use in forestry.Our study provides an example for screening native tree species from natural forests for forestry.Because native tree species are better for local ecosystems,our study will also contribute to biodiversity conservation in plantations.
文摘N-methyl-D-aspartate receptors(NMDARs)play crucial roles in neuronal plasticity and brain function by sensing key neurotransmitters,such as glutamate and glycine.In a tour-de-force,Zhang et al.[1]provide the first images of native NMDARs directly extracted from rat brains,revealing key aspects of NMDAR assembly and diversity.
基金supported by the Agencia Nacional de Investigacion y Desarrollo(ANID)with the ANID grant[SIA 85220045].
文摘Although mollusks represent Earth’s second most diverse invertebrate group,their natural history and ecology are still scarcely known.The compilation of non-traditional data,such as those from citizen science,represents an alternative to fill these gaps,particularly on striking land snail species such as Macrocyclis peruvianus.Based on long-term citizen science,we aimed to update and describe some basic ecological aspects,such as the distribution and protected area types used by M.peruvianus.We performed pairwise comparisons to test potential changes in occurrence and occupancy among administrative regions,forest types,and protected area types using chi-squared tests.The citizen scientists were also asked to provide the number of M.peruvianus individuals observed and the tree species that dominated their habitat.Thus,we tested if the number of land snails found by citizen scientists could be related to forest and protected area types using a generalized linear mixed model.We expanded the northern distributional limit,with Nothofagus,evergreen,and mixed forests far the most frequented by M.peruvianus.Parallelly,the occurrence of M.peruvianus in official protected areas(65.73%)was significantly higher than in privately owned areas.Moreover,we did not find associations between forest and protected area types with the number of M.peruvianus recorded.Although citizen science is a helpful method for obtaining novel information regarding the ecology of neglected species such as M.peruvianus,it also introduces spatial and occurrence biases explained by the access and attractiveness of the officially protected areas compared to privately owned patches of native forest.
文摘Non-typhoid Salmonella is a common foodborne infection.[1]In the setting of immunosuppression,the classical symptom of diarrhea,that is an immune defense mechanism,may be absent,[2,3]allowing the bacteria to hematogenous spread and settle in other organs.[4,5]As a result,in the setting of acute pericarditis in immunosuppressed patients,a bacterial etiology must always be considered,which requires pericardiocentesis to complete drainage and pathogen identification.
文摘Group B Streptococcus(GBS;Streptococcus agalactiae)is a gram-positive coccus that colonizes the gastrointestinal and genital tracts in adults,as well as the upper respiratory tract in infants.While it has been thought that GBS only results in invasive disease in pregnant females and neonates,recent literature has suggested an increasing incidence of invasive GBS among non-pregnant individuals within the United States.
文摘The goal of this project is to use the Semantic Web Technologies and Data Mining for disease diagnosis to assist health care professionals regarding the possible medication and drug to prescribe (Drug recommendation) according to the features of the patient. Numerous Decision Support Systems (DSS) and Expert Systems allow medical collaboration, like in the differential diagnosis specific or general. But, a medical recommendation system using both Semantic Web technologies and Data mining has not yet been developed which initiated this work. However, it should be mentioned that there are several system references about medicine or active ingredient interactions, but their final goal is not the Drug recommendation which uses above technologies. With this project we try to provide an assistant to the doctor for better recommendations. The patient will also able to use this system for explanation of drugs, food interaction and side effects of corresponding drugs.
基金National Natural Science Foundation of China(42375153,42105153,42205157)Development of Science and Technology at Chinese Academy of Meteorological Sciences(2023KJ038)。
文摘Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
