@misc{KimCraigKingetal., author = {Kim, Samuel S. and Craig, Wendy M. and King, Nathan and Bilz, Ludwig and Cosma, Alina and Molcho, Michal and Qirjako, Gentiana and Matos, Margarida G. de and Augustine, Lilly and Šmigelskas, Kastytis and Pickett, William}, title = {Bullying, Mental Health, and the Moderating Role of Supportive Adults: A Cross-National Analysis of Adolescents in 45 Countries}, series = {International Journal of Public Health}, volume = {67}, journal = {International Journal of Public Health}, issn = {1661-8564}, doi = {10.3389/ijph.2022.1604264}, pages = {10}, abstract = {Objectives: Relationships with supportive adults during adolescence may be a protective factor that lowers the risks associated with bullying. The current study aimed to examine the moderating role of supportive adults in the associations between bullying involvement (in-person and cyber) and mental health problems (psychological symptoms and low life satisfaction). Methods: Data from 45 countries and regions taking part in the 2017/18 Health Behaviour in School-Aged Children study (N = 230,757) were used. Multivariable Poisson regression models were used to estimate relative risks of bullying on mental health. Effect estimates were compared across the number of supportive adults to examine a possible cumulative protective effect of relationships with supportive adults. Results: Bullying involvement was consistently associated with poor mental health across the 45 countries. Risk of mental health problems associated with bullying involvement was greatest among students reporting relationships with multiple supportive adults. This was true for all indicators of bullying involvement. Conclusion: Bullying remains a prevalent and harmful experience for youth worldwide. Merely having supportive adults is not sufficient in protecting youth from experiencing the mental health risks associated with bullying.}, language = {en} } @misc{KimWebberAdikuetal., author = {Kim, Yean-Uk and Webber, Heidi and Adiku, Samuel G.K. and N{\´o}ia J{\´u}nior, Rog{\´e}rio de S. and Deswarte, Jean-Charles and Asseng, Senthold and Ewert, Frank}, title = {Mechanisms and modelling approaches for excessive rainfall stress on cereals: waterlogging, submergence, lodging, pests and diseases}, series = {Agricultural and Forest Meteorology}, volume = {344}, journal = {Agricultural and Forest Meteorology}, publisher = {Elsevier BV}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2023.109819}, pages = {13}, abstract = {As the intensity and frequency of extreme weather events are projected to increase under climate change, assessing their impact on cropping systems and exploring feasible adaptation options is increasingly critical. Process-based crop models (PBCMs), which are widely used in climate change impact assessments, have improved in simulating the impacts of major extreme weather events such as heatwaves and droughts but still fail to reproduce low crop yields under wet conditions. Here, we provide an overview of yield-loss mechanisms of excessive rainfall in cereals (i.e., waterlogging, submergence, lodging, pests and diseases) and associated modelling approaches with the aim of guiding PBCM improvements. Some PBCMs simulate waterlogging and ponding environments, but few capture aeration stresses on crop growth. Lodging is often neglected by PBCMs; however, some stand-alone mechanistic lodging models exist, which can potentially be incorporated into PBCMs. Some frameworks link process-based epidemic and crop models with consideration of different damage mechanisms. However, the lack of data to calibrate and evaluate these model functions limit the use of such frameworks. In order to generate data for model improvement and close knowledge gaps, targeted experiments on damage mechanisms of waterlogging, submergence, pests and diseases are required. However, consideration of all damage mechanisms in PBCM may result in excessively complex models with a large number of parameters, increasing model uncertainty. Modular frameworks could assist in selecting necessary mechanisms and lead to appropriate model structures and complexity that fit a specific research question. Lastly, there are potential synergies between PBCMs, statistical models, and remotely sensed data that could improve the prediction accuracy and understanding of current PBCMs' shortcomings.}, language = {en} }