Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of t...Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.展开更多
Faster and predictable osseointegration is crucial for the success of dental implants, especially in patients with compromised local or systemic conditions. Despite various surface modifications on the commercially av...Faster and predictable osseointegration is crucial for the success of dental implants, especially in patients with compromised local or systemic conditions. Despite various surface modifications on the commercially available Titanium (Ti) dental implants, the bioactivity of Ti is still low. Thus, to achieve both biological and therapeutic activity on titanium surfaces, surface modification techniques such as titanium nanotubes have been studied as nanotube surfaces can hold therapeutic drugs and molecules. The main aim of the present research work is to study the early osseointegration around the novel Simvastatin drug eluting nanotubular dental implant. In the present research, the titanium nanotubes were fabricated on the screw-shaped dental implant surface and the Simvastatin drug was loaded into the nanotubes using the ultrasonication dip method. In vitro and In vivo studies were carried out on the modified dental implants. In vitro cell culture study reported enhanced osteogenic activity on the drug-loaded nanotube surface implants. The in vivo animal studies were evaluated by micro-CT, histopathology, and reverse torque removal analysis methods. The test results showed faster osseointegration with the strong interface on the Simvastatin drug-loaded implant surface at 4 weeks of healing as compared to the control implants.展开更多
文摘Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers.Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution,and an inefficient task scheduling algorithm results in under-or over-utilization of the resources,which in turn leads to degradation of the services.Therefore,in the proposed work,load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.In this paper,we propose an adaptive genetic algorithm-based load balancing(GALB)-aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan,performance improvement ratio,and degree of imbalance.The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination.CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.
文摘Faster and predictable osseointegration is crucial for the success of dental implants, especially in patients with compromised local or systemic conditions. Despite various surface modifications on the commercially available Titanium (Ti) dental implants, the bioactivity of Ti is still low. Thus, to achieve both biological and therapeutic activity on titanium surfaces, surface modification techniques such as titanium nanotubes have been studied as nanotube surfaces can hold therapeutic drugs and molecules. The main aim of the present research work is to study the early osseointegration around the novel Simvastatin drug eluting nanotubular dental implant. In the present research, the titanium nanotubes were fabricated on the screw-shaped dental implant surface and the Simvastatin drug was loaded into the nanotubes using the ultrasonication dip method. In vitro and In vivo studies were carried out on the modified dental implants. In vitro cell culture study reported enhanced osteogenic activity on the drug-loaded nanotube surface implants. The in vivo animal studies were evaluated by micro-CT, histopathology, and reverse torque removal analysis methods. The test results showed faster osseointegration with the strong interface on the Simvastatin drug-loaded implant surface at 4 weeks of healing as compared to the control implants.
