The development of digital technology has brought about a substantial evolution in the multimedia field.The use of generative technologies to produce digital multimedia material is one of the newer developments in thi...The development of digital technology has brought about a substantial evolution in the multimedia field.The use of generative technologies to produce digital multimedia material is one of the newer developments in this field.The“Digital Generative Multimedia Tool Theory”(DGMTT)is therefore presented in this theoretical postulation by Timothy Ekeledirichukwu Onyejelem and Eric Msughter Aondover.It discusses and describes the principles behind the development and deployment of generative tools in multimedia creation.The DGMTT offers an all-encompassing structure for comprehending and evaluating the fundamentals and consequences of generative tools in the production of multimedia content.It provides information about the creation and use of these instruments,thereby promoting developments in the digital media industry.These tools create dynamic and interactive multimedia content by utilizing machine learning,artificial intelligence,and algorithms.This theory emphasizes how crucial it is to comprehend the fundamental ideas and principles of generative tools in order to use them efficiently when creating digital media content.A wide range of industries,including journalism,advertising,entertainment,education,and the arts,can benefit from the practical use of DGMTT.It gives artists the ability to use generative technologies to create unique and customized multimedia content for its viewers.展开更多
The characteristics and generation mechanism of（Ti,Nb,V）（C,N） precipitates larger than 2 μm in Nb-containing H13 bar steel were studied. The results show that two types of（Ti,Nb,V）（C,N） phases exist—a Ti-V-r...The characteristics and generation mechanism of（Ti,Nb,V）（C,N） precipitates larger than 2 μm in Nb-containing H13 bar steel were studied. The results show that two types of（Ti,Nb,V）（C,N） phases exist—a Ti-V-rich one and an Nb-rich one—in the form of single or complex precipitates. The sizes of the single Ti-V-rich（Ti,Nb,V）（C,N） precipitates are mostly within 5 to 10 μm, whereas the sizes of the single Nb-rich precipitates are mostly 2–5 μm. The complex precipitates are larger and contain an inner Ti-V-rich layer and an outer Nb-rich layer. The compositional distribution of（Ti,Nb,V）（C,N） is concentrated. The average composition of the single Ti-V-rich phase is（Ti_（0.511）V_（0.356）Nb_（0.133））（CxNy）, whereas that for the single Nb-rich phase is（Ti_（0.061）V_（0.263）Nb_（0.676））（C_xN_y）. The calculation results based on the Scheil–Gulliver model in the Thermo-Calc software combining with the thermal stability experiments show that the large phases precipitate during the solidification process. With the development of solidification, the Ti-V-rich phase precipitates first and becomes homogeneous during the subsequent temperature reduction and heat treatment processes. The Nb-rich phase appears later.展开更多
A middle size experiental wave generator has been implemented is the Ujikawa Open Laboratory, Disaster Prevention Research Institute, Kyoto University. The generator is composed of a pistontype wave maker, a head stra...A middle size experiental wave generator has been implemented is the Ujikawa Open Laboratory, Disaster Prevention Research Institute, Kyoto University. The generator is composed of a pistontype wave maker, a head strage water tank and a current generator to mainly reproduce long waves like tsunami and storm surge. The paper desribes several experimental series to predict the applicability of the generator to model tests. The three operating sysemes are capable to be controlled in one operating sysytem and start time is contorolled separately according with the target tsunami and storm surge profiles. A sharp tsunami profile is reproduced in adjusting the start timing of piston type wave maker and opening gates of head storage tunk. Any type of tsunami waves are reproduced in the generator and it becomes a storong tool to predict the effective of＂resiliency＂ of hardwares.展开更多
1.Introduction Generative artificial intelligence(GenAI)tools are increasingly developed and deployed with limited consideration for their social and environmental impacts(SEIs).This is partly because not all impacts ...1.Introduction Generative artificial intelligence(GenAI)tools are increasingly developed and deployed with limited consideration for their social and environmental impacts(SEIs).This is partly because not all impacts are ostensible and quantifiable,and it is unclear who should steward the calculation and mitigation of SEIs,as well as which mechanisms or pressure points should be used to ensure they are addressed.Here,we show a preliminary list of focus areas to discuss SEIs of GenAI and stress challenges involved in identifying and quantifying these.Of note,we exclude the consequences of using GenAI(e.g.,potential biases,privacy concerns,dual-use,and accountability)because these are context-specific and vary significantly depending on the utilized GenAI,the user,and the use cases.Development and deployment phases are selected for their substantial impacts on resources and social dynamics.We explore SEIs in relation to the required(1)hardware and(2)training and development(Fig.1).展开更多
文摘The development of digital technology has brought about a substantial evolution in the multimedia field.The use of generative technologies to produce digital multimedia material is one of the newer developments in this field.The“Digital Generative Multimedia Tool Theory”(DGMTT)is therefore presented in this theoretical postulation by Timothy Ekeledirichukwu Onyejelem and Eric Msughter Aondover.It discusses and describes the principles behind the development and deployment of generative tools in multimedia creation.The DGMTT offers an all-encompassing structure for comprehending and evaluating the fundamentals and consequences of generative tools in the production of multimedia content.It provides information about the creation and use of these instruments,thereby promoting developments in the digital media industry.These tools create dynamic and interactive multimedia content by utilizing machine learning,artificial intelligence,and algorithms.This theory emphasizes how crucial it is to comprehend the fundamental ideas and principles of generative tools in order to use them efficiently when creating digital media content.A wide range of industries,including journalism,advertising,entertainment,education,and the arts,can benefit from the practical use of DGMTT.It gives artists the ability to use generative technologies to create unique and customized multimedia content for its viewers.
文摘The characteristics and generation mechanism of（Ti,Nb,V）（C,N） precipitates larger than 2 μm in Nb-containing H13 bar steel were studied. The results show that two types of（Ti,Nb,V）（C,N） phases exist—a Ti-V-rich one and an Nb-rich one—in the form of single or complex precipitates. The sizes of the single Ti-V-rich（Ti,Nb,V）（C,N） precipitates are mostly within 5 to 10 μm, whereas the sizes of the single Nb-rich precipitates are mostly 2–5 μm. The complex precipitates are larger and contain an inner Ti-V-rich layer and an outer Nb-rich layer. The compositional distribution of（Ti,Nb,V）（C,N） is concentrated. The average composition of the single Ti-V-rich phase is（Ti_（0.511）V_（0.356）Nb_（0.133））（CxNy）, whereas that for the single Nb-rich phase is（Ti_（0.061）V_（0.263）Nb_（0.676））（C_xN_y）. The calculation results based on the Scheil–Gulliver model in the Thermo-Calc software combining with the thermal stability experiments show that the large phases precipitate during the solidification process. With the development of solidification, the Ti-V-rich phase precipitates first and becomes homogeneous during the subsequent temperature reduction and heat treatment processes. The Nb-rich phase appears later.
文摘A middle size experiental wave generator has been implemented is the Ujikawa Open Laboratory, Disaster Prevention Research Institute, Kyoto University. The generator is composed of a pistontype wave maker, a head strage water tank and a current generator to mainly reproduce long waves like tsunami and storm surge. The paper desribes several experimental series to predict the applicability of the generator to model tests. The three operating sysemes are capable to be controlled in one operating sysytem and start time is contorolled separately according with the target tsunami and storm surge profiles. A sharp tsunami profile is reproduced in adjusting the start timing of piston type wave maker and opening gates of head storage tunk. Any type of tsunami waves are reproduced in the generator and it becomes a storong tool to predict the effective of＂resiliency＂ of hardwares.
基金originated through the 2023e2024 Biomedical Data Science Innovation Lab(BDSIL,NIGMS grant No.R25GM139080)“Building Partnerships for Generative AI Training in Biomedical and Clinical Research.”Mohammad Hosseini was supported by the NIH National Center for Advancing Translational Sciences(NCATS,UM1TR005121).
文摘1.Introduction Generative artificial intelligence(GenAI)tools are increasingly developed and deployed with limited consideration for their social and environmental impacts(SEIs).This is partly because not all impacts are ostensible and quantifiable,and it is unclear who should steward the calculation and mitigation of SEIs,as well as which mechanisms or pressure points should be used to ensure they are addressed.Here,we show a preliminary list of focus areas to discuss SEIs of GenAI and stress challenges involved in identifying and quantifying these.Of note,we exclude the consequences of using GenAI(e.g.,potential biases,privacy concerns,dual-use,and accountability)because these are context-specific and vary significantly depending on the utilized GenAI,the user,and the use cases.Development and deployment phases are selected for their substantial impacts on resources and social dynamics.We explore SEIs in relation to the required(1)hardware and(2)training and development(Fig.1).
