The field of environmental sciences is abundant with various interfaces and is the right place for the application of new fundamental approaches leading towards a better understanding of environmental phenomena. Follo...The field of environmental sciences is abundant with various interfaces and is the right place for the application of new fundamental approaches leading towards a better understanding of environmental phenomena. Following the definition of environmental interface by Mihailovic and Bala? [1], such interface can be, for example, placed between: human or animal bodies and surrounding air, aquatic species and water and air around them, and natural or artificially built surfaces (vegetation, ice, snow, barren soil, water, urban communities) and the atmosphere, cells and surrounding environment, etc. Complex environmental interface systems are (i) open and hierarchically organised (ii) interactions between their constituent parts are nonlinear, and (iii) their interaction with the surrounding environment is noisy. These systems are therefore very sensitive to initial conditions, deterministic external perturbations and random fluctuations always present in nature. The study of noisy non-equilibrium processes is fundamental for modelling the dynamics of environmental interface regarded as biophysical complex system and for understanding the mechanisms of spatio-temporal pattern formation in contemporary environmental sciences. In this paper we will investigate an aspect of dynamics of energy flow based on the energy balance equation. The energy exchange between interacting environmen- tal interfaces regarded as biophysical complex systems can be represented by coupled maps. Therefore, we will numerically investigate coupled maps representing that exchange. In ana- lysis of behaviour of these maps we applied Lyapunov exponent and cross sample entropy.展开更多
We have defined the environmental interface through the exchange processes between media forming this interface. Considering the environmental interface as a complex system we elaborated the advanced mathematical tool...We have defined the environmental interface through the exchange processes between media forming this interface. Considering the environmental interface as a complex system we elaborated the advanced mathematical tools for its modelling. We have suggested two coupled maps serving the exchange processes on the environmental interfaces spatially ranged from cellular to planetary level, i.e. 1) the map with diffusive coupling for energy exchange simulation and 2) the map with affinity, which is suitable for matter exchange processes at the cellular level. We have performed the dynamical analysis of the coupled maps using the Lyapunov exponent, cross sample as well as the permutation entropy in dependence on different map parameters. Finally, we discussed the map with affinity, which shows some features making it a promising toll in simulation of exchange processes on the environmental interface at the cellular level.展开更多
Soft robots can exhibit better performance in specific tasks compared to conventional robots,particularly in healthcare related tasks.However,the field of soft robotics is still young,and designing them often involves...Soft robots can exhibit better performance in specific tasks compared to conventional robots,particularly in healthcare related tasks.However,the field of soft robotics is still young,and designing them often involves mimicking natural organisms or relying heavily on human experts’creativity.A formal automated design process is required.The use of neuroevolution-based algorithms to automatically design initial sketches of soft actuators that can enable the movement of future medical devices,such as drug-delivering catheters,is proposed.The actuator morphologies discovered by algorithms like Age-Fitness Pareto Optimisation,NeuroEvolution of Augmenting Topologies(NEAT),and Hypercube-based NEAT(HyperNEAT)were compared based on the maximum displacement reached and their robustness against various control methods.Analysing the results granted the insight that neuroevolution-based algorithms produce better-performing and more robust actuators under diverse control methods.Specifically,the best-performing morphologies were discovered by the NEAT algorithm.展开更多
Nanoparticles promise to improve the treatment of cancer through their increasingly sophisticated functionalisations and ability to accumulate in certain tumours.Yet recent work has shown that many nanomedicines fail ...Nanoparticles promise to improve the treatment of cancer through their increasingly sophisticated functionalisations and ability to accumulate in certain tumours.Yet recent work has shown that many nanomedicines fail during clinical trial.One issue is the lack of understanding of how nanoparticle designs impact their ability to overcome transport barriers in the body,including their circulation in the blood stream,extravasation into tumours,transport through tumour tissue,internalisation in the targeted cells,and release of their active cargo.Increased computational power,as well as improved multi-scale simulations of tumours,nanoparticles,and the biological transport barriers that affect them,now allow us to investigate the influence of a range of designs in biologically relevant scenarios.展开更多
We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments.Our work aims to decrease both the time and cost required to develop nanoparticle designs.EVONANO includes ...We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments.Our work aims to decrease both the time and cost required to develop nanoparticle designs.EVONANO includes a simulator to grow tumours,extract representative scenarios,and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution.The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments.We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments.Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.展开更多
基金funded by the Serbian Ministry of Science and Technology under the project No.III 43007“Research of climate changes and their impact on environment.Monitoring of the impact,adaptation and moderation”for 2011-2014.
文摘The field of environmental sciences is abundant with various interfaces and is the right place for the application of new fundamental approaches leading towards a better understanding of environmental phenomena. Following the definition of environmental interface by Mihailovic and Bala? [1], such interface can be, for example, placed between: human or animal bodies and surrounding air, aquatic species and water and air around them, and natural or artificially built surfaces (vegetation, ice, snow, barren soil, water, urban communities) and the atmosphere, cells and surrounding environment, etc. Complex environmental interface systems are (i) open and hierarchically organised (ii) interactions between their constituent parts are nonlinear, and (iii) their interaction with the surrounding environment is noisy. These systems are therefore very sensitive to initial conditions, deterministic external perturbations and random fluctuations always present in nature. The study of noisy non-equilibrium processes is fundamental for modelling the dynamics of environmental interface regarded as biophysical complex system and for understanding the mechanisms of spatio-temporal pattern formation in contemporary environmental sciences. In this paper we will investigate an aspect of dynamics of energy flow based on the energy balance equation. The energy exchange between interacting environmen- tal interfaces regarded as biophysical complex systems can be represented by coupled maps. Therefore, we will numerically investigate coupled maps representing that exchange. In ana- lysis of behaviour of these maps we applied Lyapunov exponent and cross sample entropy.
文摘We have defined the environmental interface through the exchange processes between media forming this interface. Considering the environmental interface as a complex system we elaborated the advanced mathematical tools for its modelling. We have suggested two coupled maps serving the exchange processes on the environmental interfaces spatially ranged from cellular to planetary level, i.e. 1) the map with diffusive coupling for energy exchange simulation and 2) the map with affinity, which is suitable for matter exchange processes at the cellular level. We have performed the dynamical analysis of the coupled maps using the Lyapunov exponent, cross sample as well as the permutation entropy in dependence on different map parameters. Finally, we discussed the map with affinity, which shows some features making it a promising toll in simulation of exchange processes on the environmental interface at the cellular level.
基金funding from the European Union’s Horizon Europe research and innovation programme(101070328)UWE researchers were funded by the UK Research and Innovation(10044516).
文摘Soft robots can exhibit better performance in specific tasks compared to conventional robots,particularly in healthcare related tasks.However,the field of soft robotics is still young,and designing them often involves mimicking natural organisms or relying heavily on human experts’creativity.A formal automated design process is required.The use of neuroevolution-based algorithms to automatically design initial sketches of soft actuators that can enable the movement of future medical devices,such as drug-delivering catheters,is proposed.The actuator morphologies discovered by algorithms like Age-Fitness Pareto Optimisation,NeuroEvolution of Augmenting Topologies(NEAT),and Hypercube-based NEAT(HyperNEAT)were compared based on the maximum displacement reached and their robustness against various control methods.Analysing the results granted the insight that neuroevolution-based algorithms produce better-performing and more robust actuators under diverse control methods.Specifically,the best-performing morphologies were discovered by the NEAT algorithm.
基金This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 800983.
文摘Nanoparticles promise to improve the treatment of cancer through their increasingly sophisticated functionalisations and ability to accumulate in certain tumours.Yet recent work has shown that many nanomedicines fail during clinical trial.One issue is the lack of understanding of how nanoparticle designs impact their ability to overcome transport barriers in the body,including their circulation in the blood stream,extravasation into tumours,transport through tumour tissue,internalisation in the targeted cells,and release of their active cargo.Increased computational power,as well as improved multi-scale simulations of tumours,nanoparticles,and the biological transport barriers that affect them,now allow us to investigate the influence of a range of designs in biologically relevant scenarios.
基金This project hs mcelved funding from the European Unlon's Horfzon 2020 meseardh and Innovation progamme under gant agremmnt No.80098.
文摘We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments.Our work aims to decrease both the time and cost required to develop nanoparticle designs.EVONANO includes a simulator to grow tumours,extract representative scenarios,and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution.The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments.We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments.Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.
