In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance...In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance delivery.Task execution failure becomes common in the CC environment.Therefore,fault-tolerant scheduling techniques in CC environment are essential for handling performance differences,resourcefluxes,and failures.Recently,several intelli-gent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics.With this motivation,this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme(GTO-FTASS)in CC environment.The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account.The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops.Besides,the GTO-FTASS model derives afitness function involving two parameters such as expected time of completion(ETC)and failure probability of executing a task.In addition,the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model.The experimental vali-dation of the GTO-FTASS model has been performed and the results are inspected under several aspects.Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.展开更多
The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent ...The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices.Anomaly detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual activities.Machine learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device behaviour.In addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to botnets.Various ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT systems.To overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)technique.The GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT environment.For data preprocessing,the min-max data normalization approach is primarily used.The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets.Moreover,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet detection.Finally,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM method.The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset.The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.展开更多
Climate change represents an unprecedented challenge for the conservation and management of endangered species and habitats. Effective climate smart conservation will require robust predictions of vulnerability and fu...Climate change represents an unprecedented challenge for the conservation and management of endangered species and habitats. Effective climate smart conservation will require robust predictions of vulnerability and future changes, along with the design and prioritisation of effective adaptation planning and management responses that are clearly linked to projected climate impacts. To achieve this goal, conservation managers urgently need practical tools and approaches for vulnerability assessment and adaptation planning. This article explores lessons emerging from a recent vulnerability assessment and adaptation planning exercise conducted on the impact of climate change for mountain gorilla (Gorilla beringei beringei). We describe the main findings emerging from this initiative and explore key lessons for climate change vulnerability assessment and adaptation planning for conservation management. Data limitations were a key factor determining the utility of model outputs and we stress the importance of stakeholder engagement and collaboration throughout the vulnerability assessment and adaptation planning cycle. These findings are of relevance to conservation practitioners seeking to incurporate climate change considerations into ongoing management planning for endangered species conservation.展开更多
The discovery of Paranthropus deyiremeda in 3.3 - 3.5 million-year-old fossil sites in Afar, together with 30% of the gorilla genome showing lineage sorting between humans and chimpanzees, and a NUMT (“nuclear mitoch...The discovery of Paranthropus deyiremeda in 3.3 - 3.5 million-year-old fossil sites in Afar, together with 30% of the gorilla genome showing lineage sorting between humans and chimpanzees, and a NUMT (“nuclear mitochondrial DNA segment”) on chromosome 5 that is shared by both gorillas, humans and chimpanzees, and shown to have diverged at the time of the Pan-Homo split rather than the Gorilla/Pan-Homo split, provides conclusive evidence that introgression from the gorilla lineage caused the Pan-Homo split, and the speciation of both the Australopithecus lineage and the Paranthropus lineage.展开更多
Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and ...Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.展开更多
为了有效地验证类人猿型机器人"GOROBOT"的双足动步行能力,通过合理地将可变ZMP(Zero Moment Point)的变化规律定义为余弦曲线,并基于三维倒立摆的动力学原理,推导出了单脚支撑期内机器人质心轨迹方程。在此基础上,采用样条...为了有效地验证类人猿型机器人"GOROBOT"的双足动步行能力,通过合理地将可变ZMP(Zero Moment Point)的变化规律定义为余弦曲线,并基于三维倒立摆的动力学原理,推导出了单脚支撑期内机器人质心轨迹方程。在此基础上,采用样条插值函数来保证机器人质心加速度的连续性,从而提出了基于这种可变ZMP的双足动步行关节轨迹生成方法。最后,在虚拟物理环境下利用仿真软件实现了虚拟的3-D类人猿机器人"GOROBOT"双足动步行,验证了方法的正确性和实际类人猿机器人"GOROBOT"的双足动步行能力。展开更多
Ecosystems in Mediterranean climate regions are projected to undergo considerable changes as a result of shifting climate, including from extreme drought and heat events. A severe and sudden dieback event, occurring i...Ecosystems in Mediterranean climate regions are projected to undergo considerable changes as a result of shifting climate, including from extreme drought and heat events. A severe and sudden dieback event, occurring in regionally significant Eucalyptus gomphocephala woodland in Western Australia, coincided with extreme drought and heat conditions in early 2011. Using a combination of remote sensing and field- based approaches, we characterized the extent and severity of canopy dieback following the event, as well as highlighted potential predisposing site factors. An estimated 500 ha of woodland was severely affected between February and March 2011. Tree foliage rapidly discolored and died over this period. In the af-fected portion of the woodland, approximately 90% of trees greater than 20 cm DBH were impacted, while in the adjacent unaffected woodland 6% showed signs of damage. Tree density in the unaffected area had approximately 4.5 times more trees than the affected woodland. Precipitation drainage patterns are thought to explain the difference between affected and unaffected woodland. Dropping groundwater levels, a relatively shallow soil profile, and extreme drought and heat in 2010-2011 are thought to predispose water-shedding sites to drought-triggered canopy dieback during extended periods of dryness. Tracking forest health changes in response to severe disturbance is an important key to deciphering past and future vegetation change.展开更多
文摘In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance delivery.Task execution failure becomes common in the CC environment.Therefore,fault-tolerant scheduling techniques in CC environment are essential for handling performance differences,resourcefluxes,and failures.Recently,several intelli-gent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics.With this motivation,this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme(GTO-FTASS)in CC environment.The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account.The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops.Besides,the GTO-FTASS model derives afitness function involving two parameters such as expected time of completion(ETC)and failure probability of executing a task.In addition,the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model.The experimental vali-dation of the GTO-FTASS model has been performed and the results are inspected under several aspects.Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.
文摘The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices.Anomaly detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual activities.Machine learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device behaviour.In addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to botnets.Various ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT systems.To overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)technique.The GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT environment.For data preprocessing,the min-max data normalization approach is primarily used.The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets.Moreover,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet detection.Finally,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM method.The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset.The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.
文摘Climate change represents an unprecedented challenge for the conservation and management of endangered species and habitats. Effective climate smart conservation will require robust predictions of vulnerability and future changes, along with the design and prioritisation of effective adaptation planning and management responses that are clearly linked to projected climate impacts. To achieve this goal, conservation managers urgently need practical tools and approaches for vulnerability assessment and adaptation planning. This article explores lessons emerging from a recent vulnerability assessment and adaptation planning exercise conducted on the impact of climate change for mountain gorilla (Gorilla beringei beringei). We describe the main findings emerging from this initiative and explore key lessons for climate change vulnerability assessment and adaptation planning for conservation management. Data limitations were a key factor determining the utility of model outputs and we stress the importance of stakeholder engagement and collaboration throughout the vulnerability assessment and adaptation planning cycle. These findings are of relevance to conservation practitioners seeking to incurporate climate change considerations into ongoing management planning for endangered species conservation.
文摘The discovery of Paranthropus deyiremeda in 3.3 - 3.5 million-year-old fossil sites in Afar, together with 30% of the gorilla genome showing lineage sorting between humans and chimpanzees, and a NUMT (“nuclear mitochondrial DNA segment”) on chromosome 5 that is shared by both gorillas, humans and chimpanzees, and shown to have diverged at the time of the Pan-Homo split rather than the Gorilla/Pan-Homo split, provides conclusive evidence that introgression from the gorilla lineage caused the Pan-Homo split, and the speciation of both the Australopithecus lineage and the Paranthropus lineage.
基金This work is financially supported by the Fundamental Research Funds for the Central Universities under Grant 2572014BB06.
文摘Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.
文摘为了有效地验证类人猿型机器人"GOROBOT"的双足动步行能力,通过合理地将可变ZMP(Zero Moment Point)的变化规律定义为余弦曲线,并基于三维倒立摆的动力学原理,推导出了单脚支撑期内机器人质心轨迹方程。在此基础上,采用样条插值函数来保证机器人质心加速度的连续性,从而提出了基于这种可变ZMP的双足动步行关节轨迹生成方法。最后,在虚拟物理环境下利用仿真软件实现了虚拟的3-D类人猿机器人"GOROBOT"双足动步行,验证了方法的正确性和实际类人猿机器人"GOROBOT"的双足动步行能力。
文摘Ecosystems in Mediterranean climate regions are projected to undergo considerable changes as a result of shifting climate, including from extreme drought and heat events. A severe and sudden dieback event, occurring in regionally significant Eucalyptus gomphocephala woodland in Western Australia, coincided with extreme drought and heat conditions in early 2011. Using a combination of remote sensing and field- based approaches, we characterized the extent and severity of canopy dieback following the event, as well as highlighted potential predisposing site factors. An estimated 500 ha of woodland was severely affected between February and March 2011. Tree foliage rapidly discolored and died over this period. In the af-fected portion of the woodland, approximately 90% of trees greater than 20 cm DBH were impacted, while in the adjacent unaffected woodland 6% showed signs of damage. Tree density in the unaffected area had approximately 4.5 times more trees than the affected woodland. Precipitation drainage patterns are thought to explain the difference between affected and unaffected woodland. Dropping groundwater levels, a relatively shallow soil profile, and extreme drought and heat in 2010-2011 are thought to predispose water-shedding sites to drought-triggered canopy dieback during extended periods of dryness. Tracking forest health changes in response to severe disturbance is an important key to deciphering past and future vegetation change.
