Scarboroughhagan4039

Unintentional injuries are the leading cause of death for young children and many result from them doing injury-risk behaviors in the home. 3,4-Dichlorophenyl isothiocyanate nmr There are a number of questionnaire measures of injury-risk behaviors for children 2 years and older, but none that apply during infancy. The current study addressed this gap. Parents completed the new Infant/Toddler-Injury Behavior Questionnaire when infants were pre-mobile (sitting independently) and mobile (walking independently), with diary measures of injuries and risk behaviors taken continuously throughout this period. Validated questionnaire measures of chaos and routines in the home were also completed. The IT-IBQ showed positive associations with injuries, risk behaviors, and degree of chaos in the home, and was negatively associated with family routines. The results provide evidence for criterion validity and suggest that the new measure holds promise as one that can aid in identifying infants who are likely to engage in injury-risk behaviors.Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1% compared to a baseline model using non-telematics predictors. The proposed model is superior to models based on a receiver operating characteristic curve criterion, with 5.3% and 31.8% improvement in prediction precision. The results confirm that the optimal thresholds for ACC, DEC and LAT are sensitive to the decision rules, especially when predicting a small percentage of high-risk drivers. This study demonstrates the value of kinematic driving behavior in crash risk prediction and the necessity for a systematic approach for extracting prediction features. The proposed method can benefit broad applications, including fleet safety management, use-based insurance, driver behavior intervention, as well as connected-vehicle safety technology development.Safety performance functions (SPFs) are the main building blocks in understanding the relationships between crash risk factors and crash frequencies. Many research efforts have focused on high-volume roadways that typically experience more crashes. A few studies have documented SPFs for non-federal aid system (NFAS) roads including rural minor collectors, rural local roads, and urban local roads. NFAS roads are characterized by unique features such as lower speeds, and shorter segment lengths, and they usually experience fewer crashes given the low exposure of these roads. As a result, there is a clear need to investigate the associated safety issues of NFAS roadways and generate distinct SPFs for them. The main objective of this study is to bridge the gap in the literature and develop SPFs for NFAS roads. This study examined the application of traditional negative binomial and zero-favored negative binomial models (i.e., negative binomial-Lindley). Both groups of models were formulated by different variance and dispersion structures. Using crash, roadway inventory, and traffic volume data from 2014 to 2018 in Virginia, the results showed that the NB-L models perform better than the traditional NB models. Furthermore, an appropriate variance structure along with a reasonably chosen dispersion function can further improve the model performance.The phytohormone producing Streptomyces rosealbus MTTC 12,951 (S.R) and green microalga Chlorella vulgaris MSU-AGM 14 (C.V) were cultivated in co-culture system to evaluate exogenous hormonal activity. Biosynthesis of indole-3-acetic acid (IAA) and their precursors were quantitatively evaluated by employing High Performance Liquid Chromatography (HPLC). The concentration of IAA (0.72 ± 0.02 µg mL-1) was observed to be elevated in co-cultivation system due to symbiotic interaction between Streptomyces and microalgae. In exchange, microalgae produced adequate volume of tryptophan (Trp) to induce IAA biosynthesis. The Trp stress in late exponential phase encouraged lipid accumulation (175 ± 10 mg g-1). The bioflocculation property of microalgae ensures potential and economic viable harvesting process by reducing 148% input energy compared to conventional method. The overall results evidenced that C.V co-cultivation with S.R exhibits promotional behavior and serves as a promising cultivation process for microalgae in terms of cost efficiency and energy conservation.The effect of CO2 enrichment in sewage sludge anaerobic digestion (AD) as a potential strategy to improve the biogas yield was assessed at increasing organic loading rates (OLR). Effects on process performance and resilience were evaluated in long-term continuous AD experiments at lab-scale. The specific methane production (SMP) was sustainably enhanced in the test digester compared to a control at elevated OLRs, reaching an increase of 6 ± 12% on average at the highest OLR tested (4.0 kgVS/(m3·d)). The reduction of CO2 via homoacetogenesis, facilitating acetoclastic CH4 formation is proposed as the dominant conversion pathway. Results suggest that sufficient load of easily degradable substances is a prerequisite for intrinsic formation of the reduction equivalent H2 via acidogenesis. The enhanced resilience of the process under CO2-enriched conditions in response to acid accumulation further qualifies this approach as a viable option for improving AD performance by converting a waste stream into a valuable product.