Objective: Marijuana is a prevalent substance used among young adults and has serious psychosocial and health-related consequences. Thus, identifying factors associated with marijuana use is critical. The current stud...Objective: Marijuana is a prevalent substance used among young adults and has serious psychosocial and health-related consequences. Thus, identifying factors associated with marijuana use is critical. The current study aimed to examine personality factors and health risk behaviors associated with marijuana use. Methods: We administered an online survey to six colleges in the Southeast. Overall, we recruited 24,055 college students, yielding 4840 responses (20.1% response rate), with complete data from 4,401 students. Results: Current (past 30 day) marijuana use was reported by 13.8% of our sample. Users either reported infrequent use of marijuana (i.e., between 1 and 5 days;52.3%) or very frequent use of marijuana (i.e. ,between 26 and 30 days;18.2%). Mutlivariate analyses modeling correlates of marijuana use (Nagelkerke R2 = 0.323) indicated that significant factors included being younger (p < 0.001), being male (p = 0.002), being Black (p = 0.002), attending a four-year college (p = 0.005), being a nondaily (p < 0.001) or daily smoker (p < 0.001) vs. a nonsmoker, other tobacco use (p < 0.001), greater alcohol use (p < 0.001), greater perceived stress (p = 0.009), higher levels of sensation seeking (<0.001) and openness to experiences (p = 0.02), and lower levels of agreeableness (p = 0.01) and conscientiousness (p < 0.001). Conclusions: Identifying risk factors related to marijuana use is critical in developing interventions targeting both use and prevention. Moreover, understanding different college settings and the contextual factors associated with greater marijuana use is critical.展开更多
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.Data competitions have a rich history in plant phenotyping,and new outdoor fi...Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.Data competitions have a rich history in plant phenotyping,and new outdoor field datasets have the potential to embrace solutions across research and commercial applications.We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions.We analyze the winning challenge solutions in terms of their robustness when applied to new datasets.We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.展开更多
The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an ass...The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat heads.We now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.展开更多
Ammonia is the second most produced chemical worldwide that makes up 80%of nitrogen-based fertilisers,which have supported approximately 27%of the world’s population over the last century.The Haber–Bosch process,wh...Ammonia is the second most produced chemical worldwide that makes up 80%of nitrogen-based fertilisers,which have supported approximately 27%of the world’s population over the last century.The Haber–Bosch process,which is the main process for producing ammonia,is extremely energy intensive and consumes around 1%of the world’s energy.Additionally,it requires hydrogen gas as a reactant that is produced via steam reforming which emits carbon dioxide as a by-product.Over 500 million tonnes of ammonia are produced per year via industrial processes which required 3–5%of total natural gas consumption worldwide and also accounted for 2%global energy usage.Therefore,more sustainable processes,such as electrocatalysis and photocatalysis,using electrons and the transfer of protons has been investigated.This review covers the most state-of-the-art technologies used to produce ammonia via electrocatalysis and photocatalysis by comparing different electrolyte systems and electrocatalysts as well as discussing issues with these methods and possible solutions.In addition,substantial improvements to electrocatalysts and photocatalysts as well as methods to prevent both the promotion of the hydrogen evolution reaction and the decomposition of ammonia at higher temperatures are reviewed.Challenges and perspectives are discussed.展开更多
文摘Objective: Marijuana is a prevalent substance used among young adults and has serious psychosocial and health-related consequences. Thus, identifying factors associated with marijuana use is critical. The current study aimed to examine personality factors and health risk behaviors associated with marijuana use. Methods: We administered an online survey to six colleges in the Southeast. Overall, we recruited 24,055 college students, yielding 4840 responses (20.1% response rate), with complete data from 4,401 students. Results: Current (past 30 day) marijuana use was reported by 13.8% of our sample. Users either reported infrequent use of marijuana (i.e., between 1 and 5 days;52.3%) or very frequent use of marijuana (i.e. ,between 26 and 30 days;18.2%). Mutlivariate analyses modeling correlates of marijuana use (Nagelkerke R2 = 0.323) indicated that significant factors included being younger (p < 0.001), being male (p = 0.002), being Black (p = 0.002), attending a four-year college (p = 0.005), being a nondaily (p < 0.001) or daily smoker (p < 0.001) vs. a nonsmoker, other tobacco use (p < 0.001), greater alcohol use (p < 0.001), greater perceived stress (p = 0.009), higher levels of sensation seeking (<0.001) and openness to experiences (p = 0.02), and lower levels of agreeableness (p = 0.01) and conscientiousness (p < 0.001). Conclusions: Identifying risk factors related to marijuana use is critical in developing interventions targeting both use and prevention. Moreover, understanding different college settings and the contextual factors associated with greater marijuana use is critical.
基金support from ANRT for the CIFRE grant of E.D.,cofunded by Arvalispartly supported by several projects,including:Canada:The Canada First Research Excellence Fund and the Global Institute Food Security,University of Saskatchewan supported the organization of the competition.+2 种基金rance:PIA#Digitag Institut Convergences Agriculture Numérique,Hiphen sup-ported the organization of the competition and the Agence Nationale de la Recherche projects ANR-11-INBS-0012(Phenome)Japan:Kubota supported the organization of the competitionAustralia:Grains Research and Development Corporation(UOQ2002-008RTX Machine learning applied to high-throughput feature extraction from imagery to map spatial variability and UOQ2003-011RTX INVITA-A technol-ogy and analytics platform for improving variety selection)supported competition and data provision/discussions.
文摘Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.Data competitions have a rich history in plant phenotyping,and new outdoor field datasets have the potential to embrace solutions across research and commercial applications.We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions.We analyze the winning challenge solutions in terms of their robustness when applied to new datasets.We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.
基金the French National Research Agency under the Investments for the Future Program,referred as ANR-16-CONV-0004 PIA#Digitag.Institut Convergences Agriculture Numérique,Hiphen supported the organization of the competition.Japan:Kubota supported the organization of the competi-tion.Australia:Grains Research and Development Corpora-tion(UOQ2002-008RTX machine learning applied to high-throughput feature extraction from imagery to map spatial variability and UOQ2003-011RTX INVITA-a technology and analytics platform for improving variety selection)sup-ported competition.
文摘The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat heads.We now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.
文摘Ammonia is the second most produced chemical worldwide that makes up 80%of nitrogen-based fertilisers,which have supported approximately 27%of the world’s population over the last century.The Haber–Bosch process,which is the main process for producing ammonia,is extremely energy intensive and consumes around 1%of the world’s energy.Additionally,it requires hydrogen gas as a reactant that is produced via steam reforming which emits carbon dioxide as a by-product.Over 500 million tonnes of ammonia are produced per year via industrial processes which required 3–5%of total natural gas consumption worldwide and also accounted for 2%global energy usage.Therefore,more sustainable processes,such as electrocatalysis and photocatalysis,using electrons and the transfer of protons has been investigated.This review covers the most state-of-the-art technologies used to produce ammonia via electrocatalysis and photocatalysis by comparing different electrolyte systems and electrocatalysts as well as discussing issues with these methods and possible solutions.In addition,substantial improvements to electrocatalysts and photocatalysts as well as methods to prevent both the promotion of the hydrogen evolution reaction and the decomposition of ammonia at higher temperatures are reviewed.Challenges and perspectives are discussed.
