There has been some good news, and some bad news in the controlled fusion community recently. The good news is that the Lawrence Livermore National Laboratory (LLNL) has recently produced a burning plasma. It succeede...There has been some good news, and some bad news in the controlled fusion community recently. The good news is that the Lawrence Livermore National Laboratory (LLNL) has recently produced a burning plasma. It succeeded on several of its shots where ~1.5 - 2 megajoules from its laser (National Ignition Facility, or NIF) has generated ~1.3 - 3 megajoules of fusion products. The highest ratio of fusion energy to laser energy it achieved, defined as its Q, was 1.5 at the time of this writing. While LLNL is sponsored by nuclear stockpile stewardship, this author sees a likely path from their result to fusion for energy for the world, a path using a very different laser and a very different target configuration. The bad news is that the International Tokamak Experimental Reactor (ITER) has continued to stumble on more and more delays and cost overruns, as its capital cost has mushroomed from ~$5 billion to ~ $25 B. This paper argues that the American fusion effort, for energy for the civilian economy, should switch its emphasis not only from magnetic fusion to inertial fusion but should also take much more seriously fusion breeding. Over the next few decades, the world might well be setting up more and more thermal nuclear reactors, and these might need fuel which only fusion breeders can supply. In other words, fusion should begin to color outside the lines.展开更多
Study Objective: The purpose of the study is to present independent laboratory testing for a novel technology in air and on surfaces. Since 2020, public health goals have focused on improving indoor air quality. This ...Study Objective: The purpose of the study is to present independent laboratory testing for a novel technology in air and on surfaces. Since 2020, public health goals have focused on improving indoor air quality. This includes protection from airborne pathogens, such as tuberculosis, RSV, SARS-CoV-2, common cold or influenza viruses, measles, and others. Engineering controls are highly effective at reducing hazardous pathogens found in indoor air and from recontamination of surfaces. This occurs from a continuous cycle of settling of small, sustained airborne pathogens, which may become dehumidified, becoming airborne again, carried by room air currents around indoor spaces, then repeating the cycle. Methods: The novel technology utilizes a catalytic process to produce safe levels of hydrogen peroxide gas that are effective in reducing pathogens in the air and on surfaces. Air testing was performed with the MS2 bacteriophage, the test organism for ASHRAE standard 241, and methicillin-Resistant Staphylococcus aureus (MRSA). Surface testing was performed with SARS-COV-2 (Coronavirus COVID-19) and H1N1 (Influenza). Typical ventilation and filtration does not effectively remove disbursed pathogens from the entire facility, due to inconsistent air circulation and surface deposits of pathogens. Results: MS2 was reduced by 99.9%;MRSA was reduced by 99.9%;SARS-CoV-2 was reduced by 99.9%;H1N1 was reduced by 99.9%. Conclusion: This novel catalytic converter reduces a variety of pathogens in the air (99%) and on surfaces (99%), by actively disinfecting with the introduction of gaseous hydrogen peroxide. This active disinfection provides a strong solution for protecting the entire facility and its occupants.展开更多
This study presents results from sentiment analysis of Dynamic message sign (DMS) message content, focusing on messages that include numbers of road fatalities. As a traffic management tool, DMS plays a role in influe...This study presents results from sentiment analysis of Dynamic message sign (DMS) message content, focusing on messages that include numbers of road fatalities. As a traffic management tool, DMS plays a role in influencing driver behavior and assisting transportation agencies in achieving safe and efficient traffic movement. However, the psychological and behavioral effects of displaying fatality numbers on DMS remain poorly understood;hence, it is important to know the potential impacts of displaying such messages. The Iowa Department of Transportation displays the number of fatalities on a first screen, followed by a supplemental message hoping to promote safe driving;an example is “19 TRAFFIC DEATHS THIS YEAR IF YOU HAVE A SUPER BOWL DON’T DRIVE HIGH.” We employ natural language processing to decode the sentiment and undertone of the supplementary message and investigate how they influence driving speeds. According to the results of a mixed effect model, drivers reduced speeds marginally upon encountering DMS fatality text with a positive sentiment with a neutral undertone. This category had the largest associated amount of speed reduction, while messages with negative sentiment with a negative undertone had the second largest amount of speed reduction, greater than other combinations, including positive sentiment with a positive undertone.展开更多
In this paper, we explore the classification of vibration modes generated by handwriting on an optical desk using deep learning architectures. Three deep learning models—Long Short-Term Memory (LSTM) networks with at...In this paper, we explore the classification of vibration modes generated by handwriting on an optical desk using deep learning architectures. Three deep learning models—Long Short-Term Memory (LSTM) networks with attention mechanism, Video Vision Transformer (ViViT), and Long-term Recurrent Convolutional Network (LRCN)—were evaluated to determine the most effective method for analyzing time series patterns generated by a Michelson interferometer. The interferometer was used to detect vibration modes created by handwriting, capturing time-series data from the diffraction patterns. Among these models, the LSTM-Attention network achieved the highest validation accuracy, reaching up to 92%, outperforming both ViViT and LRCN. These findings highlight the potential of deep learning in material science for detecting and classifying vibration patterns. The powerful performance of the LSTM-Attention model suggests that it could be applied to similar classification tasks in related fields.展开更多
文摘There has been some good news, and some bad news in the controlled fusion community recently. The good news is that the Lawrence Livermore National Laboratory (LLNL) has recently produced a burning plasma. It succeeded on several of its shots where ~1.5 - 2 megajoules from its laser (National Ignition Facility, or NIF) has generated ~1.3 - 3 megajoules of fusion products. The highest ratio of fusion energy to laser energy it achieved, defined as its Q, was 1.5 at the time of this writing. While LLNL is sponsored by nuclear stockpile stewardship, this author sees a likely path from their result to fusion for energy for the world, a path using a very different laser and a very different target configuration. The bad news is that the International Tokamak Experimental Reactor (ITER) has continued to stumble on more and more delays and cost overruns, as its capital cost has mushroomed from ~$5 billion to ~ $25 B. This paper argues that the American fusion effort, for energy for the civilian economy, should switch its emphasis not only from magnetic fusion to inertial fusion but should also take much more seriously fusion breeding. Over the next few decades, the world might well be setting up more and more thermal nuclear reactors, and these might need fuel which only fusion breeders can supply. In other words, fusion should begin to color outside the lines.
文摘Study Objective: The purpose of the study is to present independent laboratory testing for a novel technology in air and on surfaces. Since 2020, public health goals have focused on improving indoor air quality. This includes protection from airborne pathogens, such as tuberculosis, RSV, SARS-CoV-2, common cold or influenza viruses, measles, and others. Engineering controls are highly effective at reducing hazardous pathogens found in indoor air and from recontamination of surfaces. This occurs from a continuous cycle of settling of small, sustained airborne pathogens, which may become dehumidified, becoming airborne again, carried by room air currents around indoor spaces, then repeating the cycle. Methods: The novel technology utilizes a catalytic process to produce safe levels of hydrogen peroxide gas that are effective in reducing pathogens in the air and on surfaces. Air testing was performed with the MS2 bacteriophage, the test organism for ASHRAE standard 241, and methicillin-Resistant Staphylococcus aureus (MRSA). Surface testing was performed with SARS-COV-2 (Coronavirus COVID-19) and H1N1 (Influenza). Typical ventilation and filtration does not effectively remove disbursed pathogens from the entire facility, due to inconsistent air circulation and surface deposits of pathogens. Results: MS2 was reduced by 99.9%;MRSA was reduced by 99.9%;SARS-CoV-2 was reduced by 99.9%;H1N1 was reduced by 99.9%. Conclusion: This novel catalytic converter reduces a variety of pathogens in the air (99%) and on surfaces (99%), by actively disinfecting with the introduction of gaseous hydrogen peroxide. This active disinfection provides a strong solution for protecting the entire facility and its occupants.
文摘This study presents results from sentiment analysis of Dynamic message sign (DMS) message content, focusing on messages that include numbers of road fatalities. As a traffic management tool, DMS plays a role in influencing driver behavior and assisting transportation agencies in achieving safe and efficient traffic movement. However, the psychological and behavioral effects of displaying fatality numbers on DMS remain poorly understood;hence, it is important to know the potential impacts of displaying such messages. The Iowa Department of Transportation displays the number of fatalities on a first screen, followed by a supplemental message hoping to promote safe driving;an example is “19 TRAFFIC DEATHS THIS YEAR IF YOU HAVE A SUPER BOWL DON’T DRIVE HIGH.” We employ natural language processing to decode the sentiment and undertone of the supplementary message and investigate how they influence driving speeds. According to the results of a mixed effect model, drivers reduced speeds marginally upon encountering DMS fatality text with a positive sentiment with a neutral undertone. This category had the largest associated amount of speed reduction, while messages with negative sentiment with a negative undertone had the second largest amount of speed reduction, greater than other combinations, including positive sentiment with a positive undertone.
文摘In this paper, we explore the classification of vibration modes generated by handwriting on an optical desk using deep learning architectures. Three deep learning models—Long Short-Term Memory (LSTM) networks with attention mechanism, Video Vision Transformer (ViViT), and Long-term Recurrent Convolutional Network (LRCN)—were evaluated to determine the most effective method for analyzing time series patterns generated by a Michelson interferometer. The interferometer was used to detect vibration modes created by handwriting, capturing time-series data from the diffraction patterns. Among these models, the LSTM-Attention network achieved the highest validation accuracy, reaching up to 92%, outperforming both ViViT and LRCN. These findings highlight the potential of deep learning in material science for detecting and classifying vibration patterns. The powerful performance of the LSTM-Attention model suggests that it could be applied to similar classification tasks in related fields.
