Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, t...Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, the currently existing models are based on rather local data and, thus, there is a lack of predictive tools to monitor mushroom yields on larger scales.Results: This work presents the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms and related ecosystem services in Pinus sylvestris and Pinus pinaster stands in northern Spain, using a long-term dataset suitable to account for the combined effect of meteorological conditions and stand structure.Models were fitted for the following groups of fungi separately: all ectomycorrhizal mushrooms, edible mushrooms and marketed mushrooms. Our results show the influence of the weather variables(mainly precipitation) on mushroom yields as well as the relevance of the basal area of the forest stand that follows a right-skewed unimodal curve with maximum predicted yields at stand basal areas of 30–40 m2·ha-1.Conclusion: These models are the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms in Pinus sylvestris and Pinus pinaster stands in northern Spain, being of the highest resolution developed to date and enable predictions of mushrooms productivity by taking into account weather conditions and forests’ location, composition and structure.展开更多
In this article we propose a novel hurdle negative binomial (HNB) regression combined with a distributed lag nonlinear model (DLNM) to model weather factors’ impact on heat related illness (HRI) in Singapore. AIC cri...In this article we propose a novel hurdle negative binomial (HNB) regression combined with a distributed lag nonlinear model (DLNM) to model weather factors’ impact on heat related illness (HRI) in Singapore. AIC criterion is adopted to help select proper combination of weather variables and check their lagged effect as well as nonlinear effect. The process of model selection and validation is demonstrated. It is observed that the predicted occurrence rate is close to the observed one. The proposed combined model can be used to predict HRI cases for mitigating HRI occurrences and provide inputs for related public health policy considering climate change impact.展开更多
Many construction projects are met with stringent timelines or the threat of exorbitant liquidated damages. In addition, construction schedulers are frequently forced to incorporate aggressive schedule compression tec...Many construction projects are met with stringent timelines or the threat of exorbitant liquidated damages. In addition, construction schedulers are frequently forced to incorporate aggressive schedule compression techniques. As already discussed by previous researchers, these schedule compression techniques have direct impacts on project productivity and quality defects.Researchers have also pointed out that schedule compression will affect safety incidents such as Occupational Safety & Health Administration recordable injuries and near misses over long project durations. However, most of the existing studies treated safety as a subcategory of project productivity and project quality, and minimal research has been done to directly quantify the effect of schedule compression on safety at the project level.Therefore, in this research, we conducted a survey and statistical analysis to investigate the relationship between schedule compression and safety in construction projects.We interviewed various members of the Houston construction community from both industrial and non-industrial roles. Statistical analysis was used to identify factors that have significant impacts on the occurrence of safety incidents at an industry specific level.展开更多
基金partially supported by the Spanish Ministry of Science,Innovation and Universities(grant number RTI2018-099315-A-I00)by the Spanish Ministry of Economy and Competitivity(MINECO)(Grant number AGL2015–66001-C3)+1 种基金by the Cost action FP1203:European Non-Wood Forest Products Networkby the European project Star Tree–Multipurpose trees and non-wood forest products(Grant number 311919)a Serra-Húnter Fellowship provided by the Generalitat of Catalunya
文摘Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, the currently existing models are based on rather local data and, thus, there is a lack of predictive tools to monitor mushroom yields on larger scales.Results: This work presents the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms and related ecosystem services in Pinus sylvestris and Pinus pinaster stands in northern Spain, using a long-term dataset suitable to account for the combined effect of meteorological conditions and stand structure.Models were fitted for the following groups of fungi separately: all ectomycorrhizal mushrooms, edible mushrooms and marketed mushrooms. Our results show the influence of the weather variables(mainly precipitation) on mushroom yields as well as the relevance of the basal area of the forest stand that follows a right-skewed unimodal curve with maximum predicted yields at stand basal areas of 30–40 m2·ha-1.Conclusion: These models are the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms in Pinus sylvestris and Pinus pinaster stands in northern Spain, being of the highest resolution developed to date and enable predictions of mushrooms productivity by taking into account weather conditions and forests’ location, composition and structure.
文摘In this article we propose a novel hurdle negative binomial (HNB) regression combined with a distributed lag nonlinear model (DLNM) to model weather factors’ impact on heat related illness (HRI) in Singapore. AIC criterion is adopted to help select proper combination of weather variables and check their lagged effect as well as nonlinear effect. The process of model selection and validation is demonstrated. It is observed that the predicted occurrence rate is close to the observed one. The proposed combined model can be used to predict HRI cases for mitigating HRI occurrences and provide inputs for related public health policy considering climate change impact.
文摘Many construction projects are met with stringent timelines or the threat of exorbitant liquidated damages. In addition, construction schedulers are frequently forced to incorporate aggressive schedule compression techniques. As already discussed by previous researchers, these schedule compression techniques have direct impacts on project productivity and quality defects.Researchers have also pointed out that schedule compression will affect safety incidents such as Occupational Safety & Health Administration recordable injuries and near misses over long project durations. However, most of the existing studies treated safety as a subcategory of project productivity and project quality, and minimal research has been done to directly quantify the effect of schedule compression on safety at the project level.Therefore, in this research, we conducted a survey and statistical analysis to investigate the relationship between schedule compression and safety in construction projects.We interviewed various members of the Houston construction community from both industrial and non-industrial roles. Statistical analysis was used to identify factors that have significant impacts on the occurrence of safety incidents at an industry specific level.
