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• VR effectiveness in construction safety training and education (CSTE) was determined. • Measures of VR effectiveness in CSTE were categorized into five categories. • VR significantly improved construction workers' behavior, skills, and experience. • Training context and work experience significantly affected VR effectiveness in CSTE. Introduction : Virtual reality (VR) gains attention in construction safety training because it allows users to simulate real activities without the risks of real activities. However, a literary work comprehensively describing the effectiveness of VR in construction safety training and education (CSTE) is lacking. Method: This study provides a systematic review of the research related to VR applications for CSTE over the past decade using meta -analysis techniques. Standardized mean differences between traditional training methods and VR training were grouped by measurement. Potential moderators possibly affecting the effectiveness of VR in CSTE were analyzed. Results: Results showed that VR is significantly more effective in construction training and education than traditional methods. The effectiveness of VR was 0.593, 0.432, and 0.777 higher than that of traditional methods for behaviors, skills, and experience measurements, respectively. The training context and mean work experience of trainees were two important moderators that significantly affected the effectiveness of VR in CSTE (p < 0.001). Practical Applications: The presented results suggested the need for targeted development and management of VR technology in the construction industry and the early promotion of VR for general safety training among young, inexperienced construction workers. [ABSTRACT FROM AUTHOR]

Vehicle rear-end collisions are primarily caused by tight car following in a continuous traffic flow, as well as a driver's incorrect perception of the traffic environment ahead and delayed response. To facilitate an investigation pertaining to rear-end collision mechanisms and accurately measure the risk, the concept of a vehicle group is introduced herein. A risk measurement model for a vehicle group (RMVG) based on temporal and spatial similarities is proposed. First, vehicles are categorized based on their temporal and spatial similarities. Risk measurement metrics are defined based on the traffic composition, movement state, and conflict extent. Subsequently, vehicle group risk identification and risk measurement models based on an isolation forest are established. the rear-end collision risk of the vehicle groups is analyzed both qualitatively and quantitatively. Finally, the RMVG is tested using the vehicle trajectory data set of Longpan South Road, Nanjing City, Jiangsu Province, China, and the results are compared with those of a support vector machine and local outlier factor. The results show that the accuracy of the RMVG is higher than those of other models: its accuracy rate and specificity are 95.68% and 88.89%, respectively, whereas its false alarm rate is only 3.47%. [ABSTRACT FROM AUTHOR]

• Accessibility, vulnerability, and socioeconomic interaction are integrated for RNs. • The RN in underdeveloped region is optimized for regional coordinated development. • A two-stage bi-level programming model for comprehensive efficiency is proposed. Previous studies on performance assessment and optimization of road networks (RNs) have focused on either normal performance related to accessibility or extreme performance related to vulnerability. However, these two aspects are rarely considered together, and the impact of socioeconomic interaction has not been addressed. Therefore, the RN may be unable to adapt to the construction of future resilient infrastructure, leading to unstable and inefficient operations. Besides, the neglect of the role and development of RNs in underdeveloped regions can lead to unbalanced regional development. This study develops an optimization model of RNs in underdeveloped regions for comprehensive efficiency to solve the regional coordinated development problem. Specifically, a quantitative assessment method of the comprehensive efficiency integrating accessibility, vulnerability, and socioeconomic interaction is proposed to measure the performance. Then, a two-stage bi-level programming model for underdeveloped regions focusing on optimizing comprehensive efficiency is proposed. Finally, a case is studied to verify the model's effectiveness and enlighten the assessment and planning of RNs. Results show that compared with the original RN regarding invulnerability, reachable time, and socioeconomic interaction, optimization results of Stage 1 are optimized by 14.81 %, 31.62 %, and 0.03 %, respectively, and Stage 2 are optimized by 44.64 %, 42.87 %, and 57.71 %, respectively. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced polarization-independent two-layer five-port grating with covering layer is proposed. The rigorous coupled-wave analysis (RCWA) is used to predict grating parameters. In addition, the total efficiency of the polarization-independent five-port grating with covering layer can exceed 80% with the good uniformity of 1.97% for TE and TM polarization. The inherent coupling mechanism and the electric field energy distribution of the gratings are explained well under TE and TM polarization by the simplified mode method (SMM) and the finite element method (FEM). According to the reported five-port gratings, the proposed transmission five-port grating with a covering layer has good uniformity for TE and TM polarization. Moreover, the grating can be protected and the groove of the grating can be kept clean by adding a covering layer during the actual fabrication. At the same time, the fabrication errors of the two-layer five-port diffraction grating are further considered. Therefore, the numerically simulated five-port grating with covering layer has a wide application prospect in precision displacement measurement and holographic projection imaging. [ABSTRACT FROM AUTHOR]

• This paper examines freeway crash injury severity incorporating real-time weather data. • Spatial multinomial logit model was proposed to analyze freeway injury severity. • Risk factors such as vertical grade, visibility, EMS response time, and vehicle type are significant in the model. • Corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. Introduction: It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, "adverse weather," which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study. Methods: Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models. Result: The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade. [ABSTRACT FROM AUTHOR]

Because of the complex topography in mountainous areas, road traffic risk factors like sharp turns, continuous long downhills, multi-tunnel sections and dangerous roadside environment are very common. The objective of this study was to analyze how accurately do the drivers perceive hazardous degrees of the above four mountainous highway traffic risk factors by making a comparison between subjective risk and objective risk. The results of subjective/perceived risk were based on a self-completion questionnaire survey in a sample of the drivers. 12,866 cases of mountainous highway crashes in Yunnan from 2015 to 2017 were used to establish the "objective/statistical risk" for various risk factors. The results show that as far as differences in hazardous degrees between different mountainous highway traffic risk factors are concerned, the comparisons show high correlations between statistical risk and perceived risk. Both the relative values of statistical risk and perceived risk corresponding to "multi-tunnel sections" are the highest, and the two relative values corresponding to "continuous long downhills" are the lowest. The factors of age, gender, crash experience and the injuries in the crashes of the drivers are proved to have effects on the corresponding perceived risk according to the investigation results. The elderly over 65 years old have much less insufficient perception of the mountainous highway traffic risk factors compared with the objective risk they are facing. There are great differences in statistical and perceived risk between female and male drivers who are between 18 and 24 years old, and the differences in other age groups are not as great as this age group. Drivers recovered from injuries perceived much higher hazardous degrees of all the studied four mountainous highway traffic risk factors than the uninjured drivers in the crashes. The results of this research can provide references for the drivers to modify their driving behaviors to be more appropriate to the circumstances according to the risk they perceive during their driving on the mountainous highways. [ABSTRACT FROM AUTHOR]

Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate relationships between mountainous highway single-vehicle traffic accident and route familiarity. Route familiarity is defined by the spatial distance between the accident occurring site and driver's residence in this research. Logistic regression analysis was used to study the relationships. The investigation results show that the odds of finding familiar driver involved in mountainous highway single-vehicle traffic accident is higher: in accidents that occur in the evening and at night; in accidents that occur on the normal sections of the highways; in accidents that occurred when it is sunny; in accidents that the lighting condition is dark without road lights; in the accidents that occurred on the road with road width ≥ 9 m; on the highways that the speed limits are higher than 100 km/h; in the accidents that female drivers are involved. And the factors of evening, night, run-off-road, collision with the fixed object, tunnel, sharp turn, rainy, foggy, dark without light, road width < 7 m, young driver are associated with higher odds of having unfamiliar drivers involved. These research results can help alleviate mountainous highway single-vehicle traffic accidents and develop effective countermeasures and proper policies for mountainous highway traffic safety to some extent. [ABSTRACT FROM AUTHOR]

According to the accident analysis of vehicles in the curve, the skidding, rollover, and lateral drift of vehicles are determined as means to evaluate the lateral stability of vehicles. The utility truck of rear-wheel drive (RWD) is researched, which is high accident rate. Human-vehicle-road simulation models are established by CarSim. Through the orthogonal experiment method, the effects of different road geometries, speed, and interaction factors between road geometries on vehicle lateral stability are studied. In this paper, skidding risk of the vehicle is characterized by the Side-way Force Coefficient (SFC). Rollover risk of the vehicle is characterized by lateral acceleration and the load transfer ratio. Lateral drift risk of the vehicle is characterized by the sideslip angle of wheels. The results of orthogonal analysis reveal that the maximum tire-road friction coefficient and speed are highly significant in skidding of the vehicle. The effects of the combination of horizontal alignment and superelevation on vehicle skidding are important. The effects of horizontal alignment and speed on vehicle rollover risk are highly significant. The effects of superelevation on vehicle rollover risk are significant. The effects of the interaction of horizontal alignment and superelevation are also important on vehicles' rollover risk. The speed and the maximum tire-road friction coefficient have highly significant effect on the vehicle's lateral drift. The superelevation has a significant effect on the vehicle's lateral drift. The effects of the interaction of horizontal alignment and superelevation and longitudinal slope are also important on the lateral drift of the vehicle. [ABSTRACT FROM AUTHOR]

This study presents a joint analysis of daytime and nighttime crash frequencies at the zone level with consideration of spatial correlations. Crash data from 131 traffic analysis zones in Hong Kong in 2011 are investigated. A Bayesian bivariate conditional autoregressive model is proposed to establish links between crash frequencies and traffic attributes, road network characteristics, and land use patterns. The proposed model allows not only for the distinct heterogeneous and spatial effects of each dependent variable, but also for the correlations between them. The parameter estimates indicate that more daytime and nighttime crashes are associated with more vehicle hours traveled and with networks that have greater global integration. Average speed alone has a significant negative effect on daytime crashes. The crash risk in commercial and other areas is lower than that in residential areas, but the crash risk in areas of mixed residential and commercial use is higher. Meanwhile, significant spatial autocorrelation emerges across zones and explains 46.7% and 48.2% extra-Poisson variations for daytime and nighttime crash frequencies, respectively. High positive correlations are found in both heterogeneous and spatial effects. These findings, together with its better performance on model fit than the univariate counterparts, demonstrate the strength of the proposed model. [ABSTRACT FROM AUTHOR]

The aim of this paper was to systematically obtain the poverty reduction effects and diversified poverty alleviation paths of road infrastructure, to assist in the planning of transport poverty alleviation and rural revitalization in the concentrated contiguous poverty-stricken areas. The measurement methods for impact effects and paths of transport poverty alleviation have been scientifically proposed based on methods of transportation economics and transportation geography. Firstly, an overview of the interactive mechanism and processes by which road infrastructure investment influences poverty reduction has been offered, and the characteristics of poverty space and spatial coupling result of road infrastructure have been systematically obtained. The results show that about 70% of the district counties' road infrastructure and poverty rate are in a state of spatial coupling imbalance; the coordinated synchronous type is mainly distributed along the road network. Secondly, the structural equation model system has been formulated with variables that reflect transportation input in adjacent geographical units to consider spatial spillover effects. The results show that the direct poverty reduction effect of road infrastructure (0.105) is much lower than the indirect poverty reduction effect (0.830). Thirdly, empirical analysis at regional level of the concentrated contiguous poverty-stricken areas in China has been conducted. As a result, while addressing the limitations of previous studies, the poverty alleviation path that has been proposed also aims to catalyze actions to reduce the transport-related exclusion in poverty-stricken areas caused by the lack of access to basic facilities. [ABSTRACT FROM AUTHOR]

In road traffic crashes, although rollover crashes account for a relatively low proportion, those result in a high fatality rate. The present study performed random parameters ordered logit models to examine risk factors as well as their heterogeneous effects on driver injury severity in single-vehicle passenger car and SUV rollover crashes. Crash data for the empirical analysis were extracted from Texas Crash Record Information System (CRIS) database during the year 2016. Model estimation results show that six variables (male drivers, drivers' age, airbag deployment, failure to drive in single lane, speed limit, and rural area) were found to produce normally distributed parameters in passenger car model, while nine parameters (male drivers, safety belt use, airbag deployment, drug or alcohol use, failure to drive in single lane, improper evasive action, vehicle model year, friday, and rural area) in SUV model were found to be normally distributed. Several other factors with fixed parameters were found to be associated with driver injury severity in single-vehicle passenger car or SUV rollover crashes, most notably: ejection or partial ejection, turning left, intersection, August, adverse weather conditions, and night with light. These variables were significant in both models; most variables have stronger effects on nonincapacitating injury and serious injury outcomes in SUV than in passenger car rollover crashes. These findings provide a deep insight into causality nature and factor involved in driver injury severity in single-vehicle passenger car and SUV rollover crashes and are also helpful for transport agencies to determine appropriate countermeasures aimed at mitigating injuries sustained by drivers in single-vehicle rollover crashes. [ABSTRACT FROM AUTHOR]

In order to identify crash-prone sections of the highways in mountainous areas professionally and exclusively, the common phenomena of "sharp turns", "continuous long downhill", "multiple tunnels", "dangerous roadside environment", and complex and changeable meteorological environment, which all imply risk factors of the mountainous highways, are comprehensively considered. Utilizing the improved classical coupling model, the coupling mechanism of the risk factors is revealed, and the coupling model of the traffic risk factors is constructed, by which the coupling degrees of multi-risk factors couplings are calculated respectively. Based on the coupling degrees of the above factors, the concept of vehicle operation risk index (VORI) of the mountainous highway is introduced and its numerical value is quantified as the basis for identifying the crash-prone sections. The 21 km of Songming to Huize section of the Songdai Highway in the Yunnan Province of China is selected as an example, and the good applicability of the identification model is verified. [ABSTRACT FROM AUTHOR]

Poly(lactic acid) (PLA) biocomposite with 1 wt % microcrystalline cellulose (MCC) addition were prepared by melt‐mixed method using torque rheometer and annealed under different isothermal conditions. The morphology, crystallization behaviors, thermal and dynamic‐mechanical properties were evaluated by techniques of polarized optical microscopy (POM), differential scanning calorimetry (DSC), X‐ray diffraction (XRD) and dynamic mechanical analysis (DMA). Nucleus density, crystallization rate and crystallinity (Xc) of PLA matrix were significantly increased by addition of MCC. It was found that MCC could improve the activity of PLA segments at low crystallization temperature (Tc), and the stability of segments in crystal lattice was also enhanced at high Tc. DSC and XRD analysis supported that MCC contributed to the transformation of α' form into α form in PLA during annealing. The temperature range in which α crystal form appeared for pure PLA was 125 to 135 °C and it was enlarged from 105 to 145 °C for PLA with MCC addition. The Avrami exponent (n) and crystallization rate constant (k) for MCC/PLA decreased and increased respectively, supporting that the time dimension in nucleation mode was inhibited and crystallization kinetics was improved. MCC/PLA showed an increased stiffness and the highest value of storage modulus (E') was 52 GPa at Tc of 125 °C, which is basically consistent with the condition of maximum Xc. DMA analysis supported that at high Tc, the addition of MCC greatly increased the crystallization ability of PLA and the elasticity and toughness were also improved. [ABSTRACT FROM AUTHOR]

The final structure of molten syndiotactic polypropylene (sPP) sheared under different conditions was investigated by synchrotron small-angle x-ray scattering (SAXS) and wide-angle x-ray diffraction (WAXD) techniques to elucidate the shear effects on sPP crystalline structure. The results obtained from the WAXD show that there is no variation on crystalline form but a little difference on the orientation of the 200 reflection. The SAXS data indicate that the lamellar thickness and long period have not been affected by shear but the lamellar orientation is dependent on shear. The experimental data of sPP crystallization from sheared melt may indicate a mesophase structure that is crucial to the shear effects on the final polymer multiscale crystalline structures. [ABSTRACT FROM AUTHOR]

This study sets out to investigate the effects of traffic composition on freeway crash frequency by injury severity. A crash dataset collected from Kaiyang Freeway, China, is adopted for the empirical analysis, where vehicles are divided into five categories and crashes are classified into no injury and injury levels. In consideration of correlated spatial effects between adjacent segments, a Bayesian multivariate conditional autoregressive model is proposed to link no-injury and injury crash frequencies to the risk factors, including the percentages of different vehicle categories, daily vehicle kilometers traveled (DVKT), and roadway geometry. The model estimation results show that, compared to Category 5 vehicles (e.g., heavy truck), larger percentages of Categories 1 (e.g., passenger car) and 3 (e.g., medium truck) vehicles would lead to less no-injury crashes and more injury crashes. DVKT, horizontal curvature, and vertical grade are also found to be associated with no-injury and/or injury crash frequencies. The significant heterogeneous and spatial effects for no-injury and injury crashes justify the applicability of the proposed model. The findings are helpful to understand the relationship between traffic composition and freeway safety and to provide suggestions for designing strategies of vehicle safety improvement. [ABSTRACT FROM AUTHOR]

In this paper, the appearing probability of truck is introduced and an extended car-following model is presented to analyze the traffic flow based on the consideration of driver's characteristics, under honk environment. The stability condition of this proposed model is obtained through linear stability analysis. In order to study the evolution properties of traffic wave near the critical point, the mKdV equation is derived by the reductive perturbation method. The results show that the traffic flow will become more disorder for the larger appearing probability of truck. Besides, the appearance of leading truck affects not only the stability of traffic flow, but also the effect of other aspects on traffic flow, such as: driver's reaction and honk effect. The effects of them on traffic flow are closely correlated with the appearing probability of truck. Finally, the numerical simulations under the periodic boundary condition are carried out to verify the proposed model. And they are consistent with the theoretical findings. [ABSTRACT FROM AUTHOR]

This study proposed a non-lane-discipline-based car-following model by synthetically considering the visual angles and the timid/aggressive characteristics of drivers under honk environment. We firstly derived the neutral stability condition by the linear stability theory. It showed that the parameters related to visual angles and driving characteristics of drivers under honk environment all have significant impact on the stability of non-lane-discipline traffic flow. For better understanding the inner mechanism among these factors, we further analyzed how each parameter affects the traffic flow and gained further insight into how the visual angles information influences other parameters and then influences the non-lane-discipline traffic flow under honk environment. And the results showed that the other aspects such as driving characteristics of drivers or honk effect are all interacted with the “Visual-Angle Factor”. And the effect of visual angle is not just to say simply it has larger stable region or not as the existing studies. Finally, to verify the proposed model, we carried out the numerical simulation under the periodic boundary condition. And the results of numerical simulation are agreed well with the theoretical findings. [ABSTRACT FROM AUTHOR]

• Injury severity of multi-vehicle truck crashes on mountainous freeways was studied in China. • Correlated random parameter logit model is used to estimate the covariance of random parameters and to avoid the data from heterogeneity. • The combination of curve and slope, which is the focus of our study, has a significant impact of injury severity of truck crashes. • Truck crashes occurring on the gravel pavement increase the probability of medium and severe injury. • Rollover positively impact injury severity of truck crashes. Many studies examine the road characteristics that impact the severity of truck crash accidents. However, some only analyze the effect of curves or slopes separately, ignoring their combination. Therefore, there are nine types of the combination of curve and slope in this study. The combination of curve and slope factor that affected the injury severity of truck crashes on mountainous freeways was examined using a correlated random parameter logit model. This method is applied to evaluate the correlation between the random parameters and those that exhibit unobserved heterogeneity. Also, the multinomial logit model and traditional random parameter logit model are used. The study's data were collected from multi-vehicle truck crashes on mountainous freeways in China. The results showed that the correlated random parameters logit model was better than the others. In addition, they demonstrated a correlation between the random parameters. Based on the estimation coefficients and marginal effects, the combination of curve and slope has a great influence on the injury severity of truck crashes. The main finding is that curve with medium radius and medium slope will significantly increase the probability of medium severity comparing to curve with high radius and flat slope. On the other hand, the injury severity of truck accidents was significantly impacted by crash type, vehicle type, surface condition, time of day, season, lighting condition, pavement type, and guardrail. Variables such as sideswipe, head-on, medium trucks, morning, dawn or dusk and summertime reduced the probability of truck crashes. Rollover, winter, gravel, and guardrail variables increased the risk of truck crashes. Correlations were also discovered between a rollover and dry surface condition and rollover and gravel pavement type. The research findings will help traffic officials determine effective countermeasures to decrease the severity of truck crashes on mountainous freeways. [ABSTRACT FROM AUTHOR]

This study develops a Bayesian spatial random parameters Tobit model to analyze crash rates on road segments, in which both spatial correlation between adjacent sites and unobserved heterogeneity across observations are accounted for. The crash-rate data for a three-year period on road segments within a road network in Florida, are collected to compare the performance of the proposed model with that of a (fixed parameters) Tobit model and a spatial (fixed parameters) Tobit model in the Bayesian context. Significant spatial effect is found in both spatial models and the results of Deviance Information Criteria (DIC) show that the inclusion of spatial correlation in the Tobit regression considerably improves model fit, which indicates the reasonableness of considering cross-segment spatial correlation. The spatial random parameters Tobit regression has lower DIC value than does the spatial Tobit regression, suggesting that accommodating the unobserved heterogeneity is able to further improve model fit when the spatial correlation has been considered. Moreover, the random parameters Tobit model provides a more comprehensive understanding of the effect of speed limit on crash rates than does its fixed parameters counterpart, which suggests that it could be considered as a good alternative for crash rate analysis. [ABSTRACT FROM AUTHOR]

In this study, a multivariate random-parameters Tobit model is proposed for the analysis of crash rates by injury severity. In the model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed model is compared with a multivariate (fixed-parameters) Tobit model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002–2006). The multivariate random-parameters Tobit model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R 2 , while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters model. Thus, the random-parameters Tobit model, which provides a more comprehensive understanding of the factors’ effects on crash rates by injury severity, is superior to the multivariate Tobit model and should be considered a good alternative for traffic safety analysis. [ABSTRACT FROM AUTHOR]

Introduction This study sets out to investigate the interactive effect on injury severity of driver-vehicle units in two-vehicle crashes. Method A Bayesian hierarchical ordered logit model is proposed to relate the variation and correlation of injury severity of drivers involved in two-vehicle crashes to the factors of both driver-vehicle units and the crash configurations. A total of 6417 crash records with 12,834 vehicles involved in Florida are used for model calibration. Results The results show that older, female and not-at-fault drivers and those without use of safety equipment are more likely to be injured but less likely to injure the drivers in the other vehicles. New vehicles and lower speed ratios are associated with lower injury degree of both drivers involved. Compared with automobiles, vans, pick-ups, light trucks, median trucks, and heavy trucks possess better self-protection and stronger aggressivity. The points of impact closer to the driver's seat in general indicate a higher risk to the own drivers while engine cover and vehicle rear are the least hazardous to other drivers. Head-on crashes are significantly more severe than angle and rear-end crashes. We found that more severe crashes occurred on roadways than on shoulders or safety zones. Conclusions Based on these results, some suggestions for traffic safety education, enforcement and engineering are made. Moreover, significant within-crash correlation is found in the crash data, which demonstrates the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

• A novel model to model, measure, and control cascading failures of multi-modal public transit network is proposed. • Three types of failure load dynamic redistributions (FLDRs) are designed considering domain knowledge of transportation engineering. • This model has a unique advantage of calculation efficiency based on complex networks. Previous studies had focused on cascading failures of urban rail transit network (URTN) or bus transit network (BTN) while ignoring their coupling performances against cascading failures. Particularly, when considering the embedded effect of the other transit modes network (OTN, i.e., private cars and taxis sharing road infrastructures with BTN), cascading failures of multi-modal public transit network (MPTN) exhibit complex coupling dynamics caused by the intertwining and coupling effect of three types of failure load dynamic redistributions (FLDRs in a single layer or between layers). This work proposes a cascading reliability model to model, measure, and control this special type of cascading failures, which provides a novel perspective to test the entire network reliability with a unique advantage of calculation efficiency based on complex networks. Then, a case study concerning the cascading failures of an MPTN under impacts of a large passenger flow from a large-scale integrated passenger hub station is conducted. Results show that the proposed model has good adaptability, and its 5 / 6 control parameters have a different but effective correlation with cascading reliability and can realize the controllability of cascading reliability. Furthermore, the proposed multiple evaluation indicators of cascading reliability realize the diversity of measuring perspectives and demonstrate good mutual verification performances. [ABSTRACT FROM AUTHOR]

Inspired by the bio-oxygen oxidation/reduction processes of hemoglobin, iron-based transition metal-like enzyme catalysts have been explored as oxygen reduction reaction (ORR) electrocatalysts. We synthesized a chlorine-coordinated monatomic iron material (FeN4Cl-SAzyme) via a high temperature pyrolysis method as a catalyst for the ORR. The half-wave potential (E1/2) was 0.885 V, which exceeded those of Pt/C and the other FeN4X-SAzyme (X = F, Br, I) catalysts. Furthermore, through density functional theory (DFT) calculations, we systematically explored the better performance reason of FeN4Cl-SAzyme. This work offers a promising approach toward high-performance single atom electrocatalysts. [ABSTRACT FROM AUTHOR]

Crystallization behavior of sheared polymer melts of a series of poly(ethylene-co-octene)s with different octene content was investigated by different scanning calorimetry (DSC), wide angle X-ray diffraction (WAXD) and synchrotron small-angle X-ray scattering (SR-SAXS) techniques. The DSC results indicated that the hexyl branches content had dramatic effects on the thermal properties and crystallinity of polyethylene. It was also found that shear had no obvious effects on the size of either crystallite or lamellae. However, both crystallite and lamellae were oriented by shearing, especially for the lamellae. All obtained results indicate that the initial states of the polymer melt play an important role in affecting the crystallization behaviors. The difference of the shear-induced crystalline structure evolution and the orientation between crystallite and lamellae support the preordered mesomorphic phase of flexible polymer crystallization process proposed by Strobl. [ABSTRACT FROM AUTHOR]

Background: Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images. Methods: A dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience. Results: The AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland–Altman analysis further confirmed the high agreement between the model and experienced sonographers. Conclusion: We developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects. [ABSTRACT FROM AUTHOR]

Risky lane change behavior of drivers normally will pose some negative impacts on traffic safety. To ensure a lane change safe and prevent potential accidents, it is important to recognize some lane-changing conditions with potential risks in advance. Despite the researches on traffic safety assessment have been developed for decades, most of the existing researches are mainly interesting in how to estimate the overall safety of the lane-changing process based on historical data. These methods tend to ignore the interactive impacts between lane-changing vehicle and its surrounding vehicles, and have the disadvantages of the long-term evaluation period and single evaluation index. To address these gaps, this study presents a temporal and spatial risk estimation (TSRE) to recognize lane-changing risk in real-time. However, this study concentrates on the instantaneous risk for a lane change event, considering temporal and spatial dimensions for the current lane change circumstance on the highway. After processing the realistic vehicle trajectory dataset, this study extracted 1444 groups of lane change samples, and then incorporates the temporal risk level (TRL) and spatial risk level (SRL) into a comprehensive risk index by applying fault tree analysis. Furthermore, SRL and comprehensive risk index are both used to determine whether the traffic condition of a lane change is safe, and it can effectively overcome the conventional recognition defects that existed in other methods. To achieve a better evaluation effect, the sensitivity tests of recognition accuracy for various risk threshold combinations were carried out. Ultimately, experimental results showed that the proposed TSRE model achieved 97.45 %, 97.79 %, 84.1 % and 85.05 % accuracy rate in terms of classifying the risky lane change samples, traffic conflicts, risky frames and safe frames, when an appropriate risk threshold combination was selected. This encouraging finding can provide the basis for algorithm design of the lane change warning system for connected vehicles. [ABSTRACT FROM AUTHOR]

• Vulnerability assessment and visualization of a large-scale bus transit network. • Distinguishing the infrastructure disruption and the route service disruption. • A mesoscopic study perspective based on a complex network approach. Having a vulnerability assessment of a large-scale bus transit network (LBTN), an essential part of urban vulnerability assessment, is indispensable for developing urban transportation sustainability. The complex network approach is usually used to realize this goal because it focuses on a mesoscopic perspective and has a unique advantage of calculation efficiency in large-scale networks. However, the vulnerability assessment of LBTNs is usually tested based on node attacks or edge attacks rather than route attacks because of the widely existing "route collinear problem", which makes the infrastructure disruption and the route service disruption indistinguishable. This paper establishes a novel vulnerability assessment and visualization framework under the perspective of route service disruption. This framework provides a novel perspective for understanding complex systems, lays the foundation for LBTN risk identification, and significantly lowers the availability barrier of the required data, thereby extending the vulnerability study framework based on a complex network approach, from statistical physics to engineering. [ABSTRACT FROM AUTHOR]

• Differences in injuries of familiar and unfamiliar drivers in single-vehicle crashes on mountainous highways are studied. • Using data from Yunnan Province of China, random-effects generalized ordered probit models are applied. • Model estimations show that determinants of injury-severity vary considerably between familiar and unfamiliar drivers. • The study provides insights on injury prevention of familiar and unfamiliar drivers. Mountainous highways suffer from high crash rates and fatality rates in many countries, and single-vehicle crashes are overrepresented along mountainous highways. Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single-vehicle crashes. Based on 3037 cases of mountainous highway single-vehicle crashes from 2015 to 2017, the characteristics related to crash, environment, vehicle and driver are included. Random-effects generalized ordered probit (REGOP) models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single-vehicle crashes on the mountainous highways, given that the single-vehicle crashes had occurred. The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers, and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers. These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers. The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single-vehicle crashes. These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single-vehicle crashes to some extent. Based on the results of this study, some potential countermeasures can be proposed to minimize the risk of single-vehicle crashes on different mountainous highways, including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers. [ABSTRACT FROM AUTHOR]

Given the potential difference in drivers' route familiarity, this study aims to investigate the respective factors contributing to local-vehicle and non-local-vehicle crashes on freeway. Crash data from Kaiyang Freeway in Guangdong Province, China in 2014 are collected for the investigation, where the crashes with all involved vehicles registered in the province are labeled as local-vehicle crashes while others are labeled as non-local-vehicle crashes. Bayesian spatial models are developed to build local-vehicle and non-local-vehicle crash frequencies' relationships with traffic characteristics, roadway design, and weather condition. The parameter estimation results indicate that the factors including daily vehicle kilometers traveled, vertical grade, air temperature, and precipitation, impact local-vehicle and non-local-vehicle crash frequencies in the same directions. The proportion of vehicles in Class 1 (e.g., passenger car) impacts the two type crash frequencies in different directions. The proportion of vehicles in Class 3 (e.g., medium-sized bus and truck) and horizontal curvature have significant effects on local-vehicle crashes only. The results are indicative of developing countermeasures from the perspectives of roadway geometry design and driving assistance apps, which aims to reduce freeway crash occurrence. [ABSTRACT FROM AUTHOR]

• The interaction effects of roadway and weather attributes on freeway crashes are investigated. • A Bayesian spatio-temporal model is proposed for the investigation. • The results indicate significant interactions between several roadway and weather attributes. • The proposed model outperforms a random effect model and a spatial model. This study attempts to examine the main and interaction effects of roadway and weather conditions on crash incidence, using the comprehensive crash, traffic and weather data from the Kaiyang Freeway in China in 2014. The dependent variable is monthly crash count on a roadway segment (with homogeneous horizontal and vertical profiles). A Bayesian spatio-temporal model is proposed to measure the association between crash frequency and possible risk factors including traffic composition, presence of curve and slope, weather conditions, and their interactions. The proposed model can also accommodate the unstructured random effect, and spatio-temporal correlation and interactions. Results of parameter estimation indicate that the interactions between wind speed and slope, between precipitation and curve, and between visibility and slope are significantly correlated to the increase in the freeway crash risk, while the interaction between precipitation and slope is significantly correlated to the reduction in the freeway crash risk, respectively. These findings are indicative of the design and implementation of real-time traffic management and control measures, e.g. variable message sign, that could mitigate the crash risk under the adverse weather conditions. [ABSTRACT FROM AUTHOR]

Objective: To develop and investigate a deep learning model with data integration of ultrasound contrast-enhanced micro-flow (CEMF) cines, B-mode images, and patients' clinical parameters to improve the diagnosis of significant liver fibrosis (≥ F2) in patients with chronic hepatitis B (CHB). Methods: Of 682 CHB patients who underwent ultrasound and histopathological examinations between October 2016 and May 2020, 218 subjects were included in this retrospective study. We devised a data integration-based deep learning (DIDL) model for assessing ≥ F2 in CHB patients. The model contained three convolutional neural network branches to automatically extract features from ultrasound CEMF cines, B-mode images, and clinical data. The extracted features were fused at the backend of the model for decision-making. The diagnostic performance was evaluated across fivefold cross-validation and compared against the other methods in terms of the area under the receiver operating characteristic curve (AUC), with histopathological results as the reference standard. Results: The mean AUC achieved by the DIDL model was 0.901 [95% CI, 0.857–0.939], which was significantly higher than those of the comparative methods, including the models trained by using only CEMF cines (0.850 [0.794–0.893]), B-mode images (0.813 [0.754–0.862]), or clinical data (0.757 [0.694–0.812]), as well as the conventional TIC method (0.752 [0.689–0.808]), APRI (0.792 [0.734–0.845]), FIB-4 (0.776 [0.714–0.829]), and visual assessments of two radiologists (0.812 [0.754–0.862], and 0.800 [0.739–0.849]), all ps < 0.01, DeLong test. Conclusion: The DIDL model with data integration of ultrasound CEMF cines, B-mode images, and clinical parameters showed promising performance in diagnosing significant liver fibrosis for CHB patients. Key Points: • The combined use of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical data in a deep learning model has potential to improve the diagnosis of significant liver fibrosis. • The deep learning model with the fusion of features extracted from multimodality data outperformed the conventional methods including mono-modality data-based models, the time-intensity curve-based recognizer, fibrosis biomarkers, and visual assessments by experienced radiologists. • The interpretation of the feature attention maps in the deep learning model may help radiologists get better understanding of liver fibrosis-related features and hence potentially enhancing their diagnostic capacities. [ABSTRACT FROM AUTHOR]

The special properties of ionic liquids are due to the simultaneous presence of Coulombic and van der Waals forces. Evaluation of their interaction strengths is a basic work for explaining the macroscopic physical properties of ionic liquids, understanding the molecular dynamics and microstructure of ionic liquids, and assessing the binding force induced by ions in biological systems. In this paper, a modified Coulomb's law was constructed to calculate the average electrostatic force in ionic liquids through treating an ionic liquid molecule as two charges separated by some organic groups and taking these organic groups as an organic molecule. The modified Coulomb's law was verified with the surface tension data of 117 non-ionic liquids and 153 ionic liquids. According to the modified Coulomb's law, the average strengths of the electrostatic and van der Waals interactions between a pair of cation-anion in the 153 ionic liquids were almost equal, which were about 20 pN. [ABSTRACT FROM AUTHOR]

Highlights • A spatial generalized ordered logit model is proposed to analyze freeway crash severity. • A crash dataset from Kaiyang Freeway, China is collected for the empirical analysis. • The spatial model is found outperform its aspatial counterpart. • Contributing factors are identified and their marginal effects are illustrated. • Countermeasures related to education, enforcement, engineering, and emergency are suggested. Abstract This study develops a Bayesian spatial generalized ordered logit model with conditional autoregressive priors to examine severity of freeway crashes. Our model can simultaneously account for the ordered nature in discrete crash severity levels and the spatial correlation among adjacent crashes without fixing the thresholds between crash severity levels. The crash data from Kaiyang Freeway, China in 2014 are collected for the analysis, where crash severity levels are defined considering the combination of injury severity, financial loss, and numbers of injuries and deaths. We calibrate the proposed spatial model and compare it with a traditional generalized ordered logit model via Bayesian inference. The superiority of the spatial model is indicated by its better model fit and the statistical significance of the spatial term. Estimation results show that driver type, season, traffic volume and composition, response time for emergency medical services, and crash type have significant effects on crash severity propensity. In addition, vehicle type, season, time of day, weather condition, vertical grade, bridge, traffic volume and composition, and crash type have significant impacts on the threshold between median and severe crash levels. The average marginal effects of the contributing factors on each crash severity level are also calculated. Based on the estimation results, several countermeasures regarding driver education, traffic rule enforcement, vehicle and roadway engineering, and emergency services are proposed to mitigate freeway crash severity. [ABSTRACT FROM AUTHOR]

Acoustic radiation force impulse (ARFI) elastography is a non-invasive method for performing liver assessment via liver shear wave velocity (SWV) measurements. The aim of this study was to evaluate the performance of the ARFI technique in the diagnosis of nonalcoholic steatohepatitis (NASH) and fibrosis and to investigate the effect of steatosis and inflammation on liver fibrosis SWV measurements in a rat model of nonalcoholic fatty liver disease (NAFLD). The ex vivo right liver lobes from 110 rats were processed and embedded in a fabricated gelatin phantom, and the other lobes were used for histologic assessment. The SWV induced by acoustic radiation force was derived to evaluate liver stiffness. The experimental results showed that the liver SWV value could be used to differentiate non-NASH rats from NASH-presenting rats and NASH from cirrhosis, and these comparisons showed areas under the receiver operating characteristic curves (AUROC) of 0.951 and 0.980, respectively. The diagnostic performances of ARFI elastography in predicting severe fibrosis ( F  ≥ 3) and cirrhosis ( F  ≥ 4) showed AUROC values of 0.997 and 0.993, respectively. In rats with mild fibrosis (F0–F1), severe steatosis had a significant effect on the mean SWV values. In rats with significant fibrosis (F2–F4), severe lobular inflammation had significant effects on the mean SWV values. Our findings indicate that ARFI elastography is a promising method for differentiating non-NASH rats from NASH rats and for staging hepatic fibrosis in NASH. The presence of severe steatosis and severe lobular inflammation are significant factors for evaluating fibrosis stages. [ABSTRACT FROM AUTHOR]

During the past two decades, tissue elasticity has been extensively studied and has been used in clinical disease diagnosis. But biological soft tissues are viscoelastic in nature. Therefore, they should be simultaneously characterized in terms of elasticity and viscosity. In addition, the mechanical properties of soft tissues are temperature dependent. However, how the temperature influences the shear wave dispersion and the viscoelasticity of soft tissue are still unclear. The aim of this study is to compare viscoelasticity of fat emulsion phantom with different temperature using acoustic radiation force elasticity imaging method. In our experiment, we produced four proportions of ultrasonic phantom by adding fat emulsion gelatin. Through adjusting the component of the fat emulsion, we change the viscoelasticity of the ultrasonic phantom. We used verasonics system to gather data and voigt model to fit the elasticity and viscosity value of the ultrasonic phantom we made. The influence of temperature to the ultrasonic phantom also measured in our study. The results show that the addition of fat emulsion to the phantom can increase the viscosity of the phantom, and the shear wave phase velocity decreases gradually at each frequency with the temperature increases, which provides a new material for the production of viscoelastic phantom. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the utility of acoustic radiation force impulse (ARFI) elastography for assessing hepatic fibrosis stage and non-alcoholic fatty liver disease (NAFLD) severity, as well as the relationship among hepatic histologic changes using shear wave velocity (SWV). Animal models with various degrees of NAFLD were established in 110 rats. The right liver lobe was processed and embedded in a fabricated gelatin solution (porcine skin). Liver mechanics were measured using SWV induced by acoustic radiation force. Among the histologic findings, liver elasticity could be used to differentiate normal rats from rats with simple steatosis (SS) as well as distinguish SS from non-alcoholic steatohepatitis (NASH), with areas under the receiver operating characteristic curves (AUROC) of 0.963 (95% confidence interval = 0.871-0.973) and 0.882 (95% confidence interval = 0.807-0.956), respectively. For NAFLD rats, the diagnostic performance of ARFI elastography in predicting significant fibrosis (F ≥ 2) had an AUROC of 0.963. For evaluating steatosis severity, we found a progressive increase in ARFI velocity proportional to steatotic severity in NAFLD rat models, but we observed no significant differences for steatotic severity after excluding the rats with fibrosis. ARFI elastography may be used to differentiate among degrees of severity of NAFLD and hepatic fibrotic stages in NAFLD rat models. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) is one of the most common liver diseases in developed countries. Accurate, noninvasive tests for diagnosing NAFLD are urgently needed. The goals of this study were to evaluate the utility of acoustic radiation force impulse (ARFI) elastography for determining the severity grade of steatosis in rat livers, and to investigate the changes in various histologic and biochemical characteristics. Steatosis was induced in the livers of 57 rats by gavage feeding of a high fat emulsion; 12 rats received a standard diet only and served as controls. Liver mechanics were measured ex vivo using shear wave velocity (SWV) induced by acoustic radiation force. The measured mean values of liver SWV ranged from 1.33 to 3.85 m/s for different grades of steatosis. The area under the receiver operative characteristic curve (⩾S1) was equal to 0.82 (95% CI = 0.69, 0.96) between the steatosis group and the normal group, and the optimal cutoff value was 2.59 with sensitivity of 88% and specificity of 76%. However, there are no significant differences in SWV measurements between the steatosis grades. SWV values did not correlate with the early grade of inflammation. In conclusion, ARFI elastography is a promising method for differentiating normal rat liver from rat liver with steatosis, but it cannot reliably predict the grade of steatosis in rat livers. The early grade of inflammation activity did not significantly affect the SWV measurements. [ABSTRACT FROM AUTHOR]

Poly(lactic acid) (PLA) is a bio-based and compostable thermoplastic polyester that has rapidly evolved into a competitive commodity material over the last decade. One key bottleneck in expanding the field of application of PLA is the control of its structure and properties. Therefore, in situ investigations under cooling are necessary for understanding the relationship between them. The most intriguing feature of a supercooled liquid is its dramatic rise in viscosity as it is cooled toward the glass transition temperature (Tg) though accompanied by very little change in the structural features observable by typical X-ray experiments. The deformation behaviors and structure evolution of glassy PLA during uniaxially stretching below Tg were investigated in situ by synchrotron small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) techniques. The stretched samples were measured by differential scanning calorimetry (DSC). The obtained results showed that the deformation and yield stress of glassy PLA are strongly dependent on the stretching temperatures together with the transition from mesophase to mesocrystal and the formation of cavities. With the increase in drawing temperature, the onset of the mesocrystal formation is delayed to a higher strain value, whereas corresponding to the same critical orientation degree of amorphous chains (fam≈ 0.45). The DSC results indicated that the post-Tg endothermic peak corresponding to the melting of mesocrystal appears and shifts to a higher temperature with increasing stretching temperature, followed by the down-shifts (to a lower temperature) of the exothermic peak of cold crystallization of PLA. The appearance of a small exothermic peak just before the melting peak related to the transition of the α′ to α crystal implies the formation of an α′ crystal during cold crystallization in the drawn PLA samples. The structure evolution of glassy PLA stretched below Tg was discussed in details. [ABSTRACT FROM AUTHOR]

The shearing effects of sheared polymer melts on their finally formed crystalline structures of poly(l-lactide) (PLLA) were investigated by means of small angle X-ray scattering (SAXS) and wide angle X-ray diffraction (WAXD). The results of WAXD prove that shear has no effects on the crystal structure of PLLA. The SAXS results demonstrate that both of the long period and the thickness of crystalline lamellae increase with rising shear rates when vertical to the shear direction, but remains constant when being parallel to the shear direction. The structural changes for samples prepared with different shear temperature or under the same shear strain but different shear rate were investigated. The mesophase of polymer melts and shearing effects on their pre-ordered phase turned out to be the key factor affecting the crystal structure of PLLA under different shearing conditions. [ABSTRACT FROM AUTHOR]

• A Bayesian multivariate spatio-temporal interaction model-based approach is proposed on roadway traffic safety. • The spatio-temporal correlations and interactions are incorporated into the crash frequency model. • The overall goodness-of-fit performance is improved. • Significant differences are found between the proposed approach and the naïve ranking approach. • A Bayesian approach based on the multivariate Poisson-lognormal model is provided. Ranking sites with promise is an essential step for cost-effective engineering improvement on roadway traffic safety. This study proposes a Bayesian multivariate spatio-temporal interaction model based approach for ranking sites. The severity-weighted crash frequency and crash rate are used as the decision parameters. The posterior expected rank and posterior mean of the decision parameters are adopted as the statistical criteria. The proposed approach is applied to rank road segments on Kaiyang Freeway in China, which is conducted via programming in the freeware WinBUGS. The results of Bayesian estimation and assessment indicate that incorporating spatio-temporal correlations and interactions into the crash frequency model significantly improves the overall goodness-of-fit performance and affects the identified crash-contributing factors and the estimated safety effects for each severity level. With respect to the ranking results, significant differences are found between those generated from the proposed approach and those generated from the naïve ranking approach and a Bayesian approach based on the multivariate Poisson-lognormal model. Besides, the ranks under the posterior mean criterion are found generally consistent with those under the posterior expected rank criterion. [ABSTRACT FROM AUTHOR]

Intra- and inter-molecular interactions play an important role in polymer chain dynamics. Polymer crystallization behavior and crystalline structure are affected by the chain dynamics. The enantiomer containing PLLA and PDLA is considered as an important nucleation for PLA crystallization since it was found. The mixtures containing PDLA and stereocomplex (SC) crystal were prepared via different solvents or without any solvent. The effect of the stereocomplex promoted homopolymer crystallization and the non-isothermal crystallization behaviors of the prepared mixtures were characterized by DSC. The DSC results showed that the crystallization temperature of the homopolymer in the PDLA/SC mixtures increases with the stereocomplex contents as cooling from 200 °C at 10 °C/min. The increased crystallization temperature reduces with decreasing of cooling rate, and the increasing is even negligible when the cooling rate is 1 °C/min or lower. The promoted crystallization behavior could be influenced by melting temperature and melting time. FTIR results indicated the CH 3 ⋯O interactions between the enantiomers of polylactide is related to the solvents with different solubility parameters for sample preparations. The stereocomplex effect on the PDLA crystallization in the mixtures was discussed based on the molecular interactions between enantiomers and chain dynamics in presence of obstacles. [Display omitted] • The crystallization temperature of homopolymer and mixture is almost same when the cooling rate is 1 °C/min. • The promoted crystallization behavior of homopolymer would be reduced at higher melting temperature and longer melting time. • The red shift of the CH 3 and C O stretching bands of the PLA enantiomers mixed in solvent was observed by IR. • The CH 3 ⋯O interactions between the enantiomers is related to the solvents for sample preparations. [ABSTRACT FROM AUTHOR]

Dynamics of polymer chains not only is a fundamental bridge between the structures and properties of polymers but also plays a crucial role in the formation of polymer structures. The slow crystallization caused low crystallinity of Poly (lactic acid) (PLA) is the bottleneck for its applications. The chain dynamics affected memory for recrystallization morphology and behavior of poly (d -lactic acid) (PDLA) were observed by POM and characterized by DSC. The POM images showed that the PDLA could outline the morphology of original banded spherulites after recrystallization from melting of the original spherulites. The re-formed crystal inherited the crystalline location from the original crystalline morphology, forming a circle ring morphology that is following up the initial banded spherulites, even though the circle ring was constructed of smaller spherulites. DSC results showed various crystallization behaviors of PLA recrystallization with different thermal history. The influence of heating temperature, keeping time, and cooling rate on the recrystallization morphology were investigated and discussed. It was proposed that the adjustment and change of PDLA chain conformation is more difficult in various states between the crystalline and melt than that of other polymers, which relates to the strength of melt and entropy. Image 1 • The dynamics affected relaxation and entropy of strong PLA chain. • PDLA recrystallization could outline the morphology of original banded spherulites. • The dynamics affected memory for PDLA crystallization is following the Adam-Gibbs approach. [ABSTRACT FROM AUTHOR]

The morphologies and structures of crystalline polymers depend on the crystallization condition, such as the quenching rate, crystallization temperature, pressure, and molecular weight, tacticity, branching, topology and so on. The morphologies and growth of poly (d -lactic acid) (PDLA) spherulites were observed by POM when the samples isothermally crystallized at various temperatures after cooling from 200 °C to 250 °C. The crystallization behavior of PDLA was characterized by DSC measurements at various temperatures and it is not consistent with the kinetics of spherulite growth, which is due to the difference between the characterized partial and total crystallization behavior. It was found that the formation of the banded spherulites of PDLA is not only dependent on the crystallization temperatures, but also on the melting temperatures. Light fluctuation of the POM images was investigated during heating and it was found that the dark bands or black bands disappeared earlier than the bright ones during heating of the banded spherulites. The observed melting phenomenon implies mutiple lamellar stacking model inside the spherulites. The formation condition of the banded spherulites morphology was discussed based on the crystallization rate and diffusion rate. The proposed mechanism would be helpful to understand the structure and formation of PDLA ring-banded spherulites. Image 1 • The formation of the banded spherulites of PDLA is not only dependent on T c , but also on T h. • The dark bands disappeared earlier than the bright ones during heating of the banded spherulites. • In PDLA melt-crystallization, banded spherulites can formed at T c when V c > V d > V dc. [ABSTRACT FROM AUTHOR]