Classical experimental observation process, even in highly ideal operative controlled condition, like the one achieved in current, most sophisticated and advanced experimental laboratories like CERN, can capture just a small fraction only of overall ideally available information from unique experiment. A number of recent reports in the peer-reviewed literature have discussed irreproducibility of results in biomedical research. Some of these articles suggest that the inability of independent research laboratories to replicate published results has a negative impact on the development of, and confidence in, the biomedical research enterprise. Furthermore, poor reporting of health research is a serious and widespread issue, distorting evidence, limiting its transfer into practice, and providing an unreliable basis for clinical decisions and further research. A series of papers published by the Lancet in January 2014 highlighted the problems of waste in biomedical research and the myriad of issues that can disrupt completion and use of high quality research. To get more resilient data and to achive higher result reproducibility, we present an adaptive and learning system reference architecture for anticipatory smart sensing system interface. To design, analyse and test system properties, a simulation environment has been programmed in MATLAB language, called VEDA®. In this way, it is possible to verify and validate through numerical computation the behavior of all subsystems that compose the final combined overall system. Due to its intrinsic self-adapting and self-scaling relativity properties, this system approach can be applied at any system scale: from single quantum system application development to full system governance strategic assessment policies and beyond. The present paper is a relevant contribute towards a new General Theory of Systems to show how homeostatic operating equilibria can emerge out of a self-organizing landscape of self-structuring attractor points.
More effective biomedical experimentation data by CICT advanced ontological uncertainty management techniques
FIORINI, RODOLFO
2015-01-01
Abstract
Classical experimental observation process, even in highly ideal operative controlled condition, like the one achieved in current, most sophisticated and advanced experimental laboratories like CERN, can capture just a small fraction only of overall ideally available information from unique experiment. A number of recent reports in the peer-reviewed literature have discussed irreproducibility of results in biomedical research. Some of these articles suggest that the inability of independent research laboratories to replicate published results has a negative impact on the development of, and confidence in, the biomedical research enterprise. Furthermore, poor reporting of health research is a serious and widespread issue, distorting evidence, limiting its transfer into practice, and providing an unreliable basis for clinical decisions and further research. A series of papers published by the Lancet in January 2014 highlighted the problems of waste in biomedical research and the myriad of issues that can disrupt completion and use of high quality research. To get more resilient data and to achive higher result reproducibility, we present an adaptive and learning system reference architecture for anticipatory smart sensing system interface. To design, analyse and test system properties, a simulation environment has been programmed in MATLAB language, called VEDA®. In this way, it is possible to verify and validate through numerical computation the behavior of all subsystems that compose the final combined overall system. Due to its intrinsic self-adapting and self-scaling relativity properties, this system approach can be applied at any system scale: from single quantum system application development to full system governance strategic assessment policies and beyond. The present paper is a relevant contribute towards a new General Theory of Systems to show how homeostatic operating equilibria can emerge out of a self-organizing landscape of self-structuring attractor points.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
