In this paper, an empirical model based on self-evolving neural network is proposed for predicting the flexural behavior of ferrocement elements. The model is meant to serve as a simple but reliable tool for estimatin...In this paper, an empirical model based on self-evolving neural network is proposed for predicting the flexural behavior of ferrocement elements. The model is meant to serve as a simple but reliable tool for estimating the moment capacity of ferrocement members. The proposed model is trained and validated using experimental data obtained from the literature. The data consists of information regarding flexural tests on ferrocement specimens which include moment capacity and cross-sectional dimensions of specimens, concrete cube compressive strength, tensile strength and volume fraction of wire mesh. Comparisons of predictions of the proposed models with experimental data indicated that the models are capable of accurately estimating the moment capacity of ferrocement members. The proposed models also make better predictions compared to methods such as the plastic analysis method and the mechanism approach. Further comparisons with other data mining techniques including the back-propagation network, the adaptive spline, and the Kriging regression models indicated that the proposed models are superior in terms prediction accuracy despite being much simpler models. The performance of the proposed models was also found to be comparable to the GEP-based surrogate model.展开更多
The paper investigates applicability of the developed high-level model and technology for solution of diverse problems in large distributed dynamic systems which can provide sufficient awareness of their structures,or...The paper investigates applicability of the developed high-level model and technology for solution of diverse problems in large distributed dynamic systems which can provide sufficient awareness of their structures,organization,and functionalities.After the review of meanings of awareness and existing approaches for its expression and support,the paper shows application of the Spatial Grasp Model and Technology(SGT)and its basic Spatial Grasp Language(SGL)for very practical awareness solutions in large distributed dynamic systems,with obtaining any knowledge from any point inside or outside the system.The self-evolving,self-replicating,and self-recovering scenario code in SGL can effectively supervise distributed systems under any circumstances including rapidly changing number of their elements.Examples are provided in SGL for distributed networked systems showing how in any node any information about other nodes and links,including the whole system,can be obtained by using network requesting patterns based on recursive scenarios combining forward and backward network matching and coverage.The returned results may be automatically organized in networked patterns too.The presented exemplary solutions are parallel and fully distributed,without the need of using vulnerable centralized resources,also very compact.This can be explained by fundamentally different philosophy and ideology of SGT which is not based on traditional partitioned systems representation and multiple agent communications.On the contrary,SGT and its basic language supervise and control distributed systems by holistic self-spreading recursive code in wavelike,virus-like,and even“soul-like”mode.展开更多
Electrical stimulation has recently received attention as noninvasive treatment in skin wound healing with its outstanding biological property for clinical setting.However,the complexity of equipment for applying appr...Electrical stimulation has recently received attention as noninvasive treatment in skin wound healing with its outstanding biological property for clinical setting.However,the complexity of equipment for applying appropriate electrical stimulation remains an ongoing challenge.Here,we proposed a strategy for skin scar inhibition by providing electrical stimulation via a multilayer stacked electret(MS-electret),which can generate direct current(DC)electric field(EF)without any power supply equipment.In addition,the MS-electret can easily control the intensity of EFs by simply stacking electret layers and maintain stable EF with the surface potential of 3400 V over 5 days owing to the injected charges on the electret surface.We confirmed inhibition of type 1 collagen andα-SMA expression of human dermal fibroblasts(hDFs)by 90%and 44%in vitro,indicating that the transition of hDFs to myofibroblasts was restricted by applying stable electrical stimulation.We further revealed a 20%significant decrease in the ratio of myofibroblasts caused by the MS-electret in vivo.These findings present that the MS-electret is an outstanding candidate for effective skin scar inhibition with a battery-free,physiological electrical microenvironment,and noninvasive treatment that allows it to prevent external infection.展开更多
文摘In this paper, an empirical model based on self-evolving neural network is proposed for predicting the flexural behavior of ferrocement elements. The model is meant to serve as a simple but reliable tool for estimating the moment capacity of ferrocement members. The proposed model is trained and validated using experimental data obtained from the literature. The data consists of information regarding flexural tests on ferrocement specimens which include moment capacity and cross-sectional dimensions of specimens, concrete cube compressive strength, tensile strength and volume fraction of wire mesh. Comparisons of predictions of the proposed models with experimental data indicated that the models are capable of accurately estimating the moment capacity of ferrocement members. The proposed models also make better predictions compared to methods such as the plastic analysis method and the mechanism approach. Further comparisons with other data mining techniques including the back-propagation network, the adaptive spline, and the Kriging regression models indicated that the proposed models are superior in terms prediction accuracy despite being much simpler models. The performance of the proposed models was also found to be comparable to the GEP-based surrogate model.
文摘The paper investigates applicability of the developed high-level model and technology for solution of diverse problems in large distributed dynamic systems which can provide sufficient awareness of their structures,organization,and functionalities.After the review of meanings of awareness and existing approaches for its expression and support,the paper shows application of the Spatial Grasp Model and Technology(SGT)and its basic Spatial Grasp Language(SGL)for very practical awareness solutions in large distributed dynamic systems,with obtaining any knowledge from any point inside or outside the system.The self-evolving,self-replicating,and self-recovering scenario code in SGL can effectively supervise distributed systems under any circumstances including rapidly changing number of their elements.Examples are provided in SGL for distributed networked systems showing how in any node any information about other nodes and links,including the whole system,can be obtained by using network requesting patterns based on recursive scenarios combining forward and backward network matching and coverage.The returned results may be automatically organized in networked patterns too.The presented exemplary solutions are parallel and fully distributed,without the need of using vulnerable centralized resources,also very compact.This can be explained by fundamentally different philosophy and ideology of SGT which is not based on traditional partitioned systems representation and multiple agent communications.On the contrary,SGT and its basic language supervise and control distributed systems by holistic self-spreading recursive code in wavelike,virus-like,and even“soul-like”mode.
基金National Research Foundation of Korea(NRF),Grant/Award Numbers:2021R1A4A1032782,2022R1C1C1008831Korean Fund for Regenerative Medicine(KFRM),Grant/Award Number:21A0102L1-12Postdoctoral Research Program of Sungkyunkwan University。
文摘Electrical stimulation has recently received attention as noninvasive treatment in skin wound healing with its outstanding biological property for clinical setting.However,the complexity of equipment for applying appropriate electrical stimulation remains an ongoing challenge.Here,we proposed a strategy for skin scar inhibition by providing electrical stimulation via a multilayer stacked electret(MS-electret),which can generate direct current(DC)electric field(EF)without any power supply equipment.In addition,the MS-electret can easily control the intensity of EFs by simply stacking electret layers and maintain stable EF with the surface potential of 3400 V over 5 days owing to the injected charges on the electret surface.We confirmed inhibition of type 1 collagen andα-SMA expression of human dermal fibroblasts(hDFs)by 90%and 44%in vitro,indicating that the transition of hDFs to myofibroblasts was restricted by applying stable electrical stimulation.We further revealed a 20%significant decrease in the ratio of myofibroblasts caused by the MS-electret in vivo.These findings present that the MS-electret is an outstanding candidate for effective skin scar inhibition with a battery-free,physiological electrical microenvironment,and noninvasive treatment that allows it to prevent external infection.
