Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users.

Due to the escalating occurrence and high casualty rates of accidents involving Electric Two-Wheelers (E2Ws), it has become a major safety concern on the roads. Additionally, with the widespread adoption of current autonomous driving technology, a greater challenge has arisen for the safety of vulnerable road participants. Most existing trajectory planning methods primarily focus on the safety, comfort, and dynamics of autonomous vehicles themselves, often overlooking the protection of vulnerable road users (VRUs), typically E2W riders. This paper aims to investigate the kinematic response of E2Ws in vehicle collisions, including the 15 ms Head Injury Criterion (HIC15). It analyzes the impact of key collision parameters on head injuries, establishes injury prediction models for anticipated scenarios, and proposes a trajectory planning framework for autonomous vehicles based on predicting head injuries of VRUs. Firstly, a multi-rigid-body model of two-wheeler-vehicle collision was established based on a real accident database, incorporating four critical collision parameters (initial collision velocity, initial collision position, and collision angle). The accuracy of the multi-rigid-body model was validated through verifications with real fatal accidents to parameterize the collision scenario. Secondly, a large-scale effective crash dataset has been established by the multi-parameterized crash simulation automation framework combined with Monte Carlo sampling algorithm. The training and testing of the injury prediction model were implemented based on the MLP + XGBoost regression algorithm on this dataset to explore the potential relationship between the head injuries of the E2W riders and the crash variables. Finally, based on the proposed injury prediction model, this paper generated a trajectory planning framework for autonomous vehicles based on head collision injury prediction for VRUs, aiming to achieve a fair distribution of collision risks among road users. The accident reconstruction results show that the maximum error in the final relative positions of the E2W, the car, and the E2W rider compared to the real accident scene is 11 %, demonstrating the reliability of the reconstructed model. The injury prediction results indicate that the MLP + XGBoost regression prediction model used in this article achieved an R2 of 0.92 on the test set. Additionally, the effectiveness and feasibility of the proposed trajectory planning algorithm were validated in a manually designed autonomous driving traffic flow scenario.

Spontaneous In-Source Fragmentation Reaction Mechanism and Highly Sensitive Analysis of Dicofol by Electrospray Ionization Mass Spectrometry.

Although dicofol has been widely banned all over the world as a kind of organochlorine contaminant, it still exists in the environment. This study developed a high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS) detection technique for dicofol, an environmental pollutant, for the first time using in-source fragmentation. The results confirmed that m/z 251 was the only precursor ion of dicofol after in-source fragmentation, and m/z 139 and m/z 111 were reasonable product ions. The main factors triggering the in-source fragmentation were the H+ content and solution conductivity when dicofol entered the mass spectrometer. Density functional theory can be used to analyze and interpret the mechanism of dicofol fragmentation reaction in ESI source. Dicofol reduced the molecular energy from 8.8 ± 0.05 kcal/mol to 1.0 ± 0.05 kcal/mol, indicating that the internal energy release from high to low was the key driving force of in-source fragmentation. A method based on HPLC-MS/MS was developed to analyze dicofol residues in environmental water. The LOQ was 0.1 µg/L, which was better than the previous GC or GC-MS methods. This study not only proposed an HPLC-MS/MS analysis method for dicofol for the first time but also explained the in-source fragmentation mechanism of compounds in ESI source, which has positive significance for the study of compounds with unconventional mass spectrometry behavior in the field of organic pollutant analysis and metabonomics.

Highly-Selective Analytical Strategy for 90 Pesticides and Metabolites Residues in Fish and Shrimp Samples.

The analysis of pesticide residues in aquatic products is challenging due to low residue levels and the complex matrix interference. In this study, we developed a simple, fast method for the trace analysis of 90 pesticides and metabolites in aquatic products. The analytes covered a wide polarity range with log Kow (log octanol-water partition coefficient) ranging from -1.2 to 6.37. Grass carp (Ctenopharyngodon idellus) and prawn (Penaeus chinensis) samples were chosen to validate the quantification method. The samples were extracted by 0.2% formic-acetonitrile, cleaned by solid-phase extraction (PRiME HLB), and analyzed by high performance liquid chromatography-tandem mass spectrometry. The results showed good linearities for the analytes and were observed in the range of 0.05-50 µg/L. The recoveries of the method were within 50.4-118.6%, with the relative standard deviations being lower than 20%. The limits of quantifications (LOQs) of the method were in the range of 0.05-5.0 µg/kg, which were superior to values compared with other research. The developed method was applied to detect pesticide residues in prawn samples from eastern coastal areas of China. Three herbicide residues of diuron, prometryn, and atrazine were detected in prawn samples. The method was sensitive and efficient, which is of significance in expanding the screening scope and improving the quantitative analysis efficiency in aquatic products.

An Integrated Strategy for Purification by Combining Solid-Phase Extraction with Dispersive-Solid-Phase Extraction for Detecting 22 Pesticides and Metabolite Residues in Fish.

A robust isotope-labeled internal standard method was established for the detection of 22 pesticides and metabolite residues in four kinds of fish; two were from freshwater fish, and two were from marine fish. Pesticides with wide application possibilities in rice in China, strong leaching to water, or high bioconcentration factors (BCF) in fish were selected. The samples were extracted with 1% acetic acid-99% acetonitrile. The extracts were first purified by solid-phase extraction (PEP-plus), cleaned with dispersive-solid-phase extraction (PSA and C18), and finally analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results showed that good linearities for the target compounds were observed in the range of 0.1-100 ng/mL, and the correlation coefficient (R2) of each compound was greater than 0.99. The recoveries of the method were within 70-120% with RSDs <20% at three different spiked concentration levels (0.5, 5, and 100 ng/g). The quantitative limit of the method was 0.5-5 ng/g. The method is shown to be sensitive and accurate and can meet the demands for the quantitative analysis of pesticides in fish.

Comparison of Urban and Rural Physical Activity and Outdoor Play Environments of Childcare Centers and Family Childcare Homes.

The purpose of this study was to examine the physical activity environment in childcare programs across type (childcare centers [CCCs] and family childcare homes [FCCHs]) and geographic location (urban and rural) as assessed by physical activity best practices according to the Go Nutrition and Physical Activity Self-assessment in Child Care. Results showed CCCs compared with FCCHs reported higher achievement of best practices. Further, urban childcare programs (CCCs and FCCHs) reported higher achievement of best practices in comparison to rural childcare programs. There is a need to deliver targeted interventions that promote children's physical activity in FCCHs and CCCs in rural areas.

Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breeds.

BACKGROUND: SNPs are informative to estimate genomic breed composition (GBC) of individual animals, but selected SNPs for this purpose were not made available in the commercial bovine SNP chips prior to the present study. The primary objective of the present study was to select five common SNP panels for estimating GBC of individual animals initially involving 10 cattle breeds (two dairy breeds and eight beef breeds). The performance of the five common SNP panels was evaluated based on admixture model and linear regression model, respectively. Finally, the downstream implication of GBC on genomic prediction accuracies was investigated and discussed in a Santa Gertrudis cattle population. RESULTS: There were 15,708 common SNPs across five currently-available commercial bovine SNP chips. From this set, four subsets (1,000, 3,000, 5,000, and 10,000 SNPs) were selected by maximizing average Euclidean distance (AED) of SNP allelic frequencies among the ten cattle breeds. For 198 animals presented as Akaushi, estimated GBC of the Akaushi breed (GBCA) based on the admixture model agreed very well among the five SNP panels, identifying 166 animals with GBCA = 1. Using the same SNP panels, the linear regression approach reported fewer animals with GBCA = 1. Nevertheless, estimated GBCA using both models were highly correlated (r = 0.953 to 0.992). In the genomic prediction of a Santa Gertrudis population (and crosses), the results showed that the predictability of molecular breeding values using SNP effects obtained from 1,225 animals with no less than 0.90 GBC of Santa Gertrudis (GBCSG) decreased on crossbred animals with lower GBCSG. CONCLUSIONS: Of the two statistical models used to compute GBC, the admixture model gave more consistent results among the five selected SNP panels than the linear regression model. The availability of these common SNP panels facilitates identification and estimation of breed compositions using currently-available bovine SNP chips. In view of utility, the 1 K panel is the most cost effective and it is convenient to be included as add-on content in future development of bovine SNP chips, whereas the 10 K and 16 K SNP panels can be more resourceful if used independently for imputation to intermediate or high-density genotypes.

Improving the nutrition and screen time environment through self-assessment in family childcare homes in Nebraska.

OBJECTIVE: To determine if family childcare homes (FCCH) in Nebraska meet best practices for nutrition and screen time, and if focusing on nutrition and screen time policies and practices improves the FCCH environment. DESIGN: A pre-post evaluation was conducted using the Go Nutrition and Physical Activity Self-Assessment for Childcare (Go NAP SACC). SETTING: FCCH in Nebraska, USA. SUBJECTS: FCCH enrolled in the Child and Adult Care Food Program (CACFP; n 208) participated in a pre-post evaluation using Go NAP SACC. RESULTS: At baseline, all FCCH met the minimum childcare standards for fifty-four of fifty-six practices in nutrition and screen time. After the intervention, FCCH demonstrated significant improvement in fourteen of the forty-four Child Nutrition items and eleven of the twelve Screen Time items. However, FCCH providers did not meet best practices at post-intervention. Lowest scores were found in serving meals family-style, promoting visible support for healthy eating, planned nutrition education and written policy on child nutrition. For screen time, lowest scores were reported on the availability of television, offering families education on screen time and having a written policy on screen time. CONCLUSIONS: FCCH in Nebraska were able to strengthen their policies and practices after utilizing Go NAP SACC. Continued professional development and participation in targeted interventions may assist programmes in sustaining improved practices and policies. Considering the varying standards and policies surrounding FCCH, future studies comparing the current findings with childcare centres and non-CACFP programmes are warranted.

Improving the Physical Activity and Outdoor Play Environment of Family Child Care Homes in Nebraska Through Go Nutrition and Physical Activity Self-Assessment for Child Care.

BACKGROUND: The purpose of this study was to determine if the Go Nutrition and Physical Activity Self-Assessment in Child Care (Go NAP SACC) intervention was effective in improving best practices in the areas of infant and child physical activity and outdoor play and learning in family child care homes (FCCHs) in Nebraska. METHODS: FCCHs (n = 201) participated in a pre-post evaluation using the Infant and Child Physical Activity and Outdoor Play and Learning assessments from the Go NAP SACC validated measure to assess compliance with best practices. RESULTS: At post, FCCHs demonstrated significant differences in 85% of the Infant and Child Physical Activity items (17 of 20) and 80% of the Outdoor Play and Learning items (12 of 15). Significant differences in best practices between urban and rural FCCH providers were also found. CONCLUSION: Go NAP SACC appears to be an effective intervention in Nebraska as, after participation in the initiative, providers were improving child care physical activity best practices. Additional research is needed to objectively determine if these changes resulted in objective improvements in children's physical activity levels. Further, efforts are needed to develop and/or identify geographic-specific resources for continued improvement.