Claykehoe1378

The main synapomorphy for Aculeata is the sting apparatus, which allows the female to envenom potential prey or hosts. The sting is the modified ovipositor which is not used for laying eggs anymore. Here, we explore the morphology of the sting apparatus within the families of Chrysidoidea. 27 skeletal structures were recognized, including three (dp1vf, dorsal projection of first valvifer; ppa, projection of posterior area of the second valvifer; vl9, ventral lap of tergite 9) that have not been observed previously, and 13 pairs of muscles, including four (superior dorsal T9-2vf (M5); inferior dorsal T9-2vf (M6); postero-lateral T9-2vf/mbr (M9); anterolateral 2vf/bl-2vv/fu (M11)) that have not been observed previously. Very conserved morphological patterns were observed; character support in the sting apparatus was found at the subfamily level, and within three families at the genus level. In addition, we describe the variation within the sting apparatus structures and musculature, propose evolutionary hypotheses about the function and evolution of the structures, and summarize phylogenetic conclusions for Chrysidoidea.There has been no scarcity in the literature of suggested antecedents of employee safety behavior, and this paper brings together the disaggregated antecedents of safety behavior in the construction field. In total, 101 eligible empirical articles are obtained. check details Bibliometric and context analyses are combined to identify the influential journals, scholars, keywords, use of theory, research methods, and countries or regions of the empirical samples. The 83 factors that are identified are divided into five groups, namely (a) individual characteristics, (b) workgroup interactions, (c) work and workplace design, (d) project management and organization, and (e) family, industry, and society. This indicates that the causes of safety behavior are manifold. Various factors from different systems likely work in concert to create situations in which an individual chooses to comply with safety rules and participate voluntarily in safety activities. Given this, we propose that safety behavior is only an ostensible symptom of more complex "The Self-Work-Home-Industry/Society" systems and establish a safety behavior antecedent analysis and classification model. Based on this model, we develop a resource flow model, illustrating why, how, and when the flow of resources between the five systems-namely the self system, work system, home system, work-home interface system, and industry/society system-either promotes or inhibits safety behavior. The safety behavior antecedent analysis and classification model and resource flow model are based mainly on bioecological system theory and resources theories. Avenues for future theoretical development and method designs are suggested based on the reviewed findings and the two conceptual models. The intention with this systematic review together with the two integrated conceptual models is to advance theoretical thinking on how safety behavior can be promoted, or instead, inhibited.In recent years, globally quantile-based model (e.g. quantile regression) and spatially conditional mean models (e.g. geographically weighted regression) have been widely and commonly employed in macro-level safety analysis. The former ones assume that the model coefficients are fixed over space, while the latter ones only represent the entire distribution of variable effects by a single concentrated trend. However, the influence of crash related factors on the distribution of crash frequency is observed to vary over space and across different quantiles. Therefore, a geographically weighted Poisson quantile regression (GWPQR) model is employed to investigate the spatial heterogeneity of variable effects crossing different quantiles. Five categories, including exposure, socio-economic, transportation, network and land use were selected to estimate the spatial effects on crash frequency. In the case study, vehicle related crashes collected in New York City were used to validate the predicted performance of the proposed models. The results show that the GWPQR outperforms the NB, QR and GWNBR for modeling the skewed distribution, reconstructing the crash distribution and capturing the unobserved spatial heterogeneity. Additionally, the significant coefficients are further used to classify all 21 variables into key, important and general parts. Then we discuss how these factors affects the regional crashes over space and distribution of crash frequency. This study confirms that the influencing factors have varying effects on different quantiles of distribution and on different regions, which could be helpful to provide support for making safety countermeasures and policies at urban regional level.This study applies a simulation-based traffic conflict technique to evaluate the hypothesis that sun glare under upper vents exerts negative impacts on traffic safety in urban tunnels. A modified cellular automata (CA) model is applied to simulate the deceleration behavior due to sun glare (DBSG) in real traffic. And the model is calibrated and validated against the empirical data. Conflict occurrences are generated through simulating vehicular interactions based on this model. Simulation experiments are conducted with different density and illuminance to evaluate the safety impacts of sun glare. Comparison of simulated conflict occurrences shows that rear-end conflicts occur more frequently as illuminance and density get higher. And the impacts of sun glare are more obvious on weak conflicts in moderate-density flow and more severe conflicts in high-density flows, respectively. To alleviate the negative impacts of sun glare, a sunshade system is designed based on the quantitative results.Benefiting from the rapid development of communication and intelligent vehicle technology in recent years, most traffic information is capable of being collected, processed, and transmitted to each vehicle through a connected and automated vehicles (CAVs) system. To meet the higher requirements of driving safety in CAVs environment, it is necessary to develop more effective safety evaluation indicators that combine all the traffic information received by the vehicle. To this end, this study proposes a novel methodology for risk perception and warning strategy based on safety potential field model to minimize driving risk in the CAVs environment. A dynamic safety potential field model was constructed to describe the spatial distribution of driving risk encountered by vehicles. This safety potential field model can comprehensively consider the impact of various types of traffic information on driving risk. And then, a novel driving risk indicator, named potential field indicator (PFI), was established to evaluate the level of driving risk.