Pub Date : 2024-05-10DOI: 10.1038/s44287-024-00056-3
Mario Lanza, Naomi Godfrey, Victor Zhirnov
The way in which researchers, scientists and engineers apply for jobs is very inefficient. Creating free online databases of candidates with filtering, ranking and video features could help to maximize reach and identify the most suitable person for each job offer much faster.
{"title":"Solving the problem of hiring in STEM","authors":"Mario Lanza, Naomi Godfrey, Victor Zhirnov","doi":"10.1038/s44287-024-00056-3","DOIUrl":"10.1038/s44287-024-00056-3","url":null,"abstract":"The way in which researchers, scientists and engineers apply for jobs is very inefficient. Creating free online databases of candidates with filtering, ranking and video features could help to maximize reach and identify the most suitable person for each job offer much faster.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 6","pages":"352-353"},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-07DOI: 10.1038/s44287-024-00044-7
Bin Luo, A. R. Will-Cole, Cunzheng Dong, Yifan He, Xiaxin Liu, Hwaider Lin, Rui Huang, Xiaoling Shi, Michael McConney, Michael Page, Mohan Sanghadasa, Ramamoorthy Ramesh, Nian X. Sun
The internet of things (IoT) has revolutionized society by creating a network of interconnected devices with sensors, processing ability and software for data exchange. However, the expansion of IoT places undue strain on energy resources. Thus, the development of low-power components is critical. Moreover, the demand for IoT has opened new markets for wearable technologies, necessitating innovations towards miniaturization. This rapid growth introduces further challenges in communication and environmental adaptability. Magnetoelectric (ME) microelectromechanical and nanoelectromechanical systems (M/NEMS) introduce unparalleled properties to reshape the IoT landscape. ME M/NEMS enable a 100,000× reduction in wavelength, resulting in reduced size and weight, and provide multifunctionality, such as simultaneous sensing, data transmission and wireless power transfer. With renewed interest in ME M/NEMS platforms, several disruptive technologies have emerged ranging from ultra-compact radiofrequency front-ends to quantum sensing, computing and communication networks. This Review delves into ME materials, ME composites and ME M/NEMS for IoT functions, including logic memory; magnetic sensing; wireless power transfer; ultra-compact antennas; power, radiofrequency and microwave electronics; and communication systems. Magnetoelectric (ME) microelectromechanical and nanoelectromechanical systems (M/NEMS) are vital for addressing the challenges of the internet of things (IoT) networks in size, energy efficiency and communication. This Review delves into ME materials and M/NEMS for IoT applications, such as sensing and communication technologies.
物联网（IoT）通过创建一个由具有传感器、处理能力和数据交换软件的互联设备组成的网络，给社会带来了革命性的变化。然而，物联网的扩展给能源资源带来了过大的压力。因此，开发低功耗元件至关重要。此外，物联网的需求也为可穿戴技术开辟了新的市场，因此有必要向微型化方向创新。这种快速增长为通信和环境适应性带来了更多挑战。磁电（ME）微机电和纳米机电系统（M/NEMS）具有无与伦比的特性，将重塑物联网的格局。ME M/NEMS 可使波长减少 100,000 倍，从而减小尺寸和重量，并提供多功能性，如同时传感、数据传输和无线功率传输。随着人们对 ME M/NEMS 平台的重新关注，出现了从超小型射频前端到量子传感、计算和通信网络等多项颠覆性技术。本综述深入探讨了用于物联网功能的 ME 材料、ME 复合材料和 ME M/NEMS，包括逻辑存储器、磁感应、无线电力传输、超小型天线、电源、射频和微波电子设备以及通信系统。
{"title":"Magnetoelectric microelectromechanical and nanoelectromechanical systems for the IoT","authors":"Bin Luo, A. R. Will-Cole, Cunzheng Dong, Yifan He, Xiaxin Liu, Hwaider Lin, Rui Huang, Xiaoling Shi, Michael McConney, Michael Page, Mohan Sanghadasa, Ramamoorthy Ramesh, Nian X. Sun","doi":"10.1038/s44287-024-00044-7","DOIUrl":"10.1038/s44287-024-00044-7","url":null,"abstract":"The internet of things (IoT) has revolutionized society by creating a network of interconnected devices with sensors, processing ability and software for data exchange. However, the expansion of IoT places undue strain on energy resources. Thus, the development of low-power components is critical. Moreover, the demand for IoT has opened new markets for wearable technologies, necessitating innovations towards miniaturization. This rapid growth introduces further challenges in communication and environmental adaptability. Magnetoelectric (ME) microelectromechanical and nanoelectromechanical systems (M/NEMS) introduce unparalleled properties to reshape the IoT landscape. ME M/NEMS enable a 100,000× reduction in wavelength, resulting in reduced size and weight, and provide multifunctionality, such as simultaneous sensing, data transmission and wireless power transfer. With renewed interest in ME M/NEMS platforms, several disruptive technologies have emerged ranging from ultra-compact radiofrequency front-ends to quantum sensing, computing and communication networks. This Review delves into ME materials, ME composites and ME M/NEMS for IoT functions, including logic memory; magnetic sensing; wireless power transfer; ultra-compact antennas; power, radiofrequency and microwave electronics; and communication systems. Magnetoelectric (ME) microelectromechanical and nanoelectromechanical systems (M/NEMS) are vital for addressing the challenges of the internet of things (IoT) networks in size, energy efficiency and communication. This Review delves into ME materials and M/NEMS for IoT applications, such as sensing and communication technologies.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 5","pages":"317-334"},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44287-024-00044-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140888598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-02DOI: 10.1038/s44287-024-00058-1
Lishu Wu
An article in IEEE Journal on Selected Areas in Communications proposes algorithmic solutions to dynamically optimize MIMO waveforms to minimize or eliminate interference in autonomous machine-to-machine communications.
电气和电子工程师学会通信选区期刊》（IEEE Journal on Selected Areas in Communications）上的一篇文章提出了动态优化多输入多输出（MIMO）波形的算法解决方案，以尽量减少或消除自主机器对机器通信中的干扰。
{"title":"Autonomous interference-avoiding machine-to-machine communications","authors":"Lishu Wu","doi":"10.1038/s44287-024-00058-1","DOIUrl":"10.1038/s44287-024-00058-1","url":null,"abstract":"An article in IEEE Journal on Selected Areas in Communications proposes algorithmic solutions to dynamically optimize MIMO waveforms to minimize or eliminate interference in autonomous machine-to-machine communications.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 5","pages":"285-285"},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1038/s44287-024-00054-5
Silvia Conti
An article in Nature Machine Intelligence presents a neural signal-based speech decoding framework comprising interchangeable architectures for the electrocorticography decoder and a differentiable speech synthesizer.
Pub Date : 2024-04-30DOI: 10.1038/s44287-024-00051-8
Martina Gschwendtner, Henning Soller, Sheila Zingg
Quantum computing can benefit from the advancements made in artificial intelligence (AI) holistically across the tech stack — AI may even unlock completely new ways of using quantum computers. Simultaneously, AI can benefit from quantum computing leveraging the expected future compute and memory power.
{"title":"Combining quantum and AI for the next superpower","authors":"Martina Gschwendtner, Henning Soller, Sheila Zingg","doi":"10.1038/s44287-024-00051-8","DOIUrl":"10.1038/s44287-024-00051-8","url":null,"abstract":"Quantum computing can benefit from the advancements made in artificial intelligence (AI) holistically across the tech stack — AI may even unlock completely new ways of using quantum computers. Simultaneously, AI can benefit from quantum computing leveraging the expected future compute and memory power.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 6","pages":"350-351"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140832897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.1038/s44287-024-00045-6
Senfeng Zeng, Chunsen Liu, Peng Zhou
The miniaturization of metal–oxide–semiconductor field-effect transistors (MOSFETs) has been the driving force behind the development of integrated circuits over the past 60 years; however, owing to short channel effect, reducing the gate length of MOSFETs to sub-10 nm represents a fundamental challenge. Two-dimensional materials (2DMs) with atomic scale thicknesses and non-dangling bonds interface enable sub-10 nm scale length, making them suitable candidates for advanced tech nodes beyond sub-3 nm. Although the performance metrics of a single 2DMs transistor have equalled or surpassed those of silicon, leaving no doubt about the potential of 2DMs at the laboratory level, the way of moving 2DMs from ‘lab to fab’ remains unclear. In this Review, we analyse the similarities and differences between 2DMs MOSFETs and silicon MOSFETs in the integrated circuits engineering process; we present potential solutions for channel, contact and dielectric engineering using 2DM to address the scaling challenges faced by a silicon-based device at the advanced tech node. Finally, we summarize the challenges in translating the performance of individual 2DMs devices into large-scale integrated circuits, including large-scale and stable transfer technology, high-quality material synthesis with controllable layers. Once these technical issues are properly solved, 2DMs can take full advantage of their properties at a farther scaling. This Review systematically compares 2DMs and silicon metal–oxide–semiconductor field-effect transistors technologies in the integrated circuits engineering process and presents potential solutions for channel, contact and dielectric engineering using 2DM to address the scaling challenges faced by a silicon-based device at the advanced tech node.
{"title":"Transistor engineering based on 2D materials in the post-silicon era","authors":"Senfeng Zeng, Chunsen Liu, Peng Zhou","doi":"10.1038/s44287-024-00045-6","DOIUrl":"10.1038/s44287-024-00045-6","url":null,"abstract":"The miniaturization of metal–oxide–semiconductor field-effect transistors (MOSFETs) has been the driving force behind the development of integrated circuits over the past 60 years; however, owing to short channel effect, reducing the gate length of MOSFETs to sub-10 nm represents a fundamental challenge. Two-dimensional materials (2DMs) with atomic scale thicknesses and non-dangling bonds interface enable sub-10 nm scale length, making them suitable candidates for advanced tech nodes beyond sub-3 nm. Although the performance metrics of a single 2DMs transistor have equalled or surpassed those of silicon, leaving no doubt about the potential of 2DMs at the laboratory level, the way of moving 2DMs from ‘lab to fab’ remains unclear. In this Review, we analyse the similarities and differences between 2DMs MOSFETs and silicon MOSFETs in the integrated circuits engineering process; we present potential solutions for channel, contact and dielectric engineering using 2DM to address the scaling challenges faced by a silicon-based device at the advanced tech node. Finally, we summarize the challenges in translating the performance of individual 2DMs devices into large-scale integrated circuits, including large-scale and stable transfer technology, high-quality material synthesis with controllable layers. Once these technical issues are properly solved, 2DMs can take full advantage of their properties at a farther scaling. This Review systematically compares 2DMs and silicon metal–oxide–semiconductor field-effect transistors technologies in the integrated circuits engineering process and presents potential solutions for channel, contact and dielectric engineering using 2DM to address the scaling challenges faced by a silicon-based device at the advanced tech node.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 5","pages":"335-348"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44287-024-00045-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.1038/s44287-024-00038-5
Darsith Jayachandran, Najam U Sakib, Saptarshi Das
The adoption of three-dimensional (3D) integration has revolutionized NAND flash memory technology, and a similar transformative potential exists for logic circuits, by stacking transistors into the third dimension. This pivotal shift towards 3D integration of logic arrives on the heels of substantial improvements in silicon device structures and their subsequent scaling in size and performance. Yet, advanced scaling requires ultrathin semiconducting channels, which are difficult to achieve using silicon. In this context, field-effect transistors based on two-dimensional (2D) semiconductors have drawn notable attention owing to their atomically thin nature and impressive performance milestones. In addition, 2D materials offer a broader spectrum of functionalities — such as optical, chemical and biological sensing — that extends their utility beyond simple ‘more Moore’ dimensional scaling and enables the development of ‘more than Moore’ technologies. Thus, 3D integration of 2D electronics could bring us unanticipated discoveries, leading to sustainable and energy-efficient computing systems. In this Review, we explore the progress, challenges and future opportunities for 3D integration of 2D electronics. Since the most advanced nodes in silicon are reaching the limits of planar integration, 2D materials could help to advance the semiconductor industry. With the potential for use in multifunctional chips, 2D materials offer combined logic, memory and sensing in integrated 3D chips.
{"title":"3D integration of 2D electronics","authors":"Darsith Jayachandran, Najam U Sakib, Saptarshi Das","doi":"10.1038/s44287-024-00038-5","DOIUrl":"10.1038/s44287-024-00038-5","url":null,"abstract":"The adoption of three-dimensional (3D) integration has revolutionized NAND flash memory technology, and a similar transformative potential exists for logic circuits, by stacking transistors into the third dimension. This pivotal shift towards 3D integration of logic arrives on the heels of substantial improvements in silicon device structures and their subsequent scaling in size and performance. Yet, advanced scaling requires ultrathin semiconducting channels, which are difficult to achieve using silicon. In this context, field-effect transistors based on two-dimensional (2D) semiconductors have drawn notable attention owing to their atomically thin nature and impressive performance milestones. In addition, 2D materials offer a broader spectrum of functionalities — such as optical, chemical and biological sensing — that extends their utility beyond simple ‘more Moore’ dimensional scaling and enables the development of ‘more than Moore’ technologies. Thus, 3D integration of 2D electronics could bring us unanticipated discoveries, leading to sustainable and energy-efficient computing systems. In this Review, we explore the progress, challenges and future opportunities for 3D integration of 2D electronics. Since the most advanced nodes in silicon are reaching the limits of planar integration, 2D materials could help to advance the semiconductor industry. With the potential for use in multifunctional chips, 2D materials offer combined logic, memory and sensing in integrated 3D chips.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 5","pages":"300-316"},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44287-024-00038-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140658861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-23DOI: 10.1038/s44287-024-00037-6
Yi Huang, Takashi Ando, Abu Sebastian, Meng-Fan Chang, J. Joshua Yang, Qiangfei Xia
Satisfying the rapid evolution of artificial intelligence (AI) algorithms requires exponential growth in computing resources, which, in turn, presents huge challenges for deploying AI models on hardware. Memristor-based hardware accelerators provide a promising solution to the energy efficiency and latency issues in large AI model deployments. The non-volatility of memristive devices facilitates in-memory computing, in which computing occurs within memory cells where data are stored. This approach eliminates the constant data shuttling between the processing and memory units found in the von Neumann architecture, resulting in substantial time and energy savings. The recent surge of research and development in this field indicates a pivotal transition of memristor technology from proof-of-concept demonstrations to commercial products that accelerate AI models across various applications. In this Review, we survey the latest progress in memristive crossbar arrays, peripheral circuits, architectures, hardware–software co-designs and system implementations for memristor-based hardware accelerators. We discuss how these research efforts bridge the gap between memristive devices and energy-efficient accelerators for AI. Finally, we summarize the key remaining issues and propose potential pathways to future hardware accelerators with low latency and high energy efficiency, emphasizing the technology scale-up and commercialization for large-scale AI applications. This Review summarizes latest advancements in memristor-based hardware accelerators, an energy-efficient solution for computing-intensive artificial intelligence algorithms, covering crossbar arrays, peripheral circuits, architectures and software–hardware co-designs. It analyses challenges and pathways for the transition of memristor technology to commercial products.
{"title":"Memristor-based hardware accelerators for artificial intelligence","authors":"Yi Huang, Takashi Ando, Abu Sebastian, Meng-Fan Chang, J. Joshua Yang, Qiangfei Xia","doi":"10.1038/s44287-024-00037-6","DOIUrl":"10.1038/s44287-024-00037-6","url":null,"abstract":"Satisfying the rapid evolution of artificial intelligence (AI) algorithms requires exponential growth in computing resources, which, in turn, presents huge challenges for deploying AI models on hardware. Memristor-based hardware accelerators provide a promising solution to the energy efficiency and latency issues in large AI model deployments. The non-volatility of memristive devices facilitates in-memory computing, in which computing occurs within memory cells where data are stored. This approach eliminates the constant data shuttling between the processing and memory units found in the von Neumann architecture, resulting in substantial time and energy savings. The recent surge of research and development in this field indicates a pivotal transition of memristor technology from proof-of-concept demonstrations to commercial products that accelerate AI models across various applications. In this Review, we survey the latest progress in memristive crossbar arrays, peripheral circuits, architectures, hardware–software co-designs and system implementations for memristor-based hardware accelerators. We discuss how these research efforts bridge the gap between memristive devices and energy-efficient accelerators for AI. Finally, we summarize the key remaining issues and propose potential pathways to future hardware accelerators with low latency and high energy efficiency, emphasizing the technology scale-up and commercialization for large-scale AI applications. This Review summarizes latest advancements in memristor-based hardware accelerators, an energy-efficient solution for computing-intensive artificial intelligence algorithms, covering crossbar arrays, peripheral circuits, architectures and software–hardware co-designs. It analyses challenges and pathways for the transition of memristor technology to commercial products.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 5","pages":"286-299"},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140671041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1038/s44287-024-00049-2
Melanie E. Moses, Sonia M. Gipson Rankin
Bias and distrust in medicine have been perpetuated by the misuse of medical equations, algorithms and devices. Artificial intelligence (AI) can exacerbate these problems. However, AI also has potential to detect, mitigate and remedy the harmful effects of bias to build trust and improve healthcare for everyone.
{"title":"Medical artificial intelligence should do no harm","authors":"Melanie E. Moses, Sonia M. Gipson Rankin","doi":"10.1038/s44287-024-00049-2","DOIUrl":"10.1038/s44287-024-00049-2","url":null,"abstract":"Bias and distrust in medicine have been perpetuated by the misuse of medical equations, algorithms and devices. Artificial intelligence (AI) can exacerbate these problems. However, AI also has potential to detect, mitigate and remedy the harmful effects of bias to build trust and improve healthcare for everyone.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 5","pages":"280-281"},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.1038/s44287-024-00046-5
In the spirit of promoting gender equality, Sony, in partnership with Nature, has launched the ‘Sony Women in Technology Award’ to recognize and celebrate the remarkable women spearheading advancements in STEM.
{"title":"Promoting women in tech","authors":"","doi":"10.1038/s44287-024-00046-5","DOIUrl":"10.1038/s44287-024-00046-5","url":null,"abstract":"In the spirit of promoting gender equality, Sony, in partnership with Nature, has launched the ‘Sony Women in Technology Award’ to recognize and celebrate the remarkable women spearheading advancements in STEM.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"1 4","pages":"209-209"},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44287-024-00046-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140559863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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