Lumbar and neck injuries of occupants in different reclining postures.

PURPOSE: With the increasing level of automation in automobiles, the advent of autonomous vehicles has reduced the tendency of drivers and passengers to focus on the task of driving. The increasing automation in automobiles reduced the drivers' and passengers' focus on driving, which allowed occupants to choose a more relaxed and comfortable sitting position. Meanwhile, the occupant's sitting position went from a frontal, upright position to a more relaxed and reclined one, which resulted in the existing restraint systems can not to keep occupants safe and secure. This study aimed to determine the effects of different reclining states on occupants' lumbar and neck injuries. METHODS: This is an original research on the field of automotive safety engineering. Occupants in different initial seating positions (25°, 35°, 45°, and 55°) were adapted to changes in seat back angle and restraint systems and placed in the same frontal impact environment. Neck injury indexes, lumbar axial compression force and acceleration, as well as occupant dynamic response during the impact, were compared in different seating positions. The injury response and kinematic characteristics of occupants in different reclining positions were analyzed by the control variable method. RESULTS: As the sitting angle increased, the occupant's head acceleration decreased, and the forward-lean angle decreased. Occupants in the standard sitting position had the greatest neck injury, with an Nij of 0.95, and were susceptible to abbreviated injury scale 2+ cervical medullary injuries. As the seatback angle increased, the geometric position of the lumbar spine tended to be horizontal, and the impact load transmitted greater forces to the lumbar spine. The occupant's lumbar injury was greatest in the lying position, with a peak axial compression force on the lumbar region of 5.5 KN, which was 2.3 KN greater than in the standard sitting position. CONCLUSION: The study of occupant lumbar and neck injuries based on different recline states can provide a theoretical basis for optimizing lumbar evaluation indexes, which is conducive to the understanding of the lumbar injury mechanism and the comprehensive consideration of occupant safety protection.

How does new energy demonstration city pilot policy affect carbon dioxide emissions? Evidence from a quasi-natural experiment in China.

Energy transition policies are of great significance in adjusting the structure of energy supply and demand and coping with climate change. The new energy demonstration city pilot (NEDCP) policy, as an important pilot project in China's energy transition process, lacks a scientific assessment of the carbon reduction effect of the NEDCP policy and an in-depth explanation of the mechanism of the NEDCP. Based on panel data of 209 Chinese cities at the prefectural and higher levels from 2007 to 2019, this study takes the NEDCP policy as a quasi-natural experiment, using a difference-in-differences model combined with firm-level data to identify the impact of the NEDCP policy on urban carbon dioxide (CO2) emissions. This study analyzes the impact of heterogeneity of urban characteristics on the policy effect from multiple perspectives, and further investigates its mechanism. The conclusions are shown in the following aspects. (1) The implementation of the NEDCP policy decreases urban CO2 emissions significantly. Meanwhile, a series of robustness tests, including the instrumental variables method, propensity score matching difference-in-differences method, placebo test, exclusion of policy interference test, and machine learning method, support this conclusion. (2) The NEDCP policy achieves carbon reduction effects mainly through scale and structure effects. (3) The results of the heterogeneity test show that the NEDCP policy is more effective in cities with higher administrative levels, energy-demanding cities, cities in the southeast of Hu-line, and cities with a higher degree of nationalization. Therefore, the Chinese government should summarize the implementation experience of the NEDCP policy and expand its scope of application. The evaluation of the NEDCP policy in China has important reference value for the energy transition of other developing countries.

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest.

Accurate crash frequency prediction is critical for proactive safety management. The emerging connected vehicles technology provides us with a wealth of vehicular motion data, which enables a better connection between crash frequency and driving behaviors. However, appropriately dealing with the spatial dependence of crash frequency and multitudinous driving features has been a difficult but critical challenge in the prediction process. To this end, this study aims to investigate a new Artificial Intelligence technique called Geographical Random Forest (GRF) that can address spatial heterogeneity and retain all potential predictors. By harnessing more than 2.2 billion high-resolution connected vehicle Basic Safety Message (BSM) observations from the Safety Pilot Model Deployment in Ann Arbor, MI, 30 indicators of driving volatility are extracted, including speed, longitudinal and lateral acceleration, and yaw rate. The developed GRF was implemented to predict rear-end crash frequency at intersections. The results show that: 1) rear-end crashes are more likely to happen at intersections connecting minor roads compared to major roads; 2) a higher number of hard acceleration and deceleration events beyond two standard deviations in the longitudinal direction is a leading indicator of rear-end crashes; 3) the optimal GRF significantly outperforms Global Random Forest, with a 9% lower test error and a substantially better fit; and 4) geographical visualization of variable importance highlights the presence of spatial non-stationarity. The proposed framework can proactively identify at-risk intersections and alert drivers when leading indicators of driving volatility tend to worsen.

Deep learning enabled reflective coded aperture snapshot spectral imaging.

Coded aperture snapshot spectral imaging (CASSI) can acquire rich spatial and spectral information at ultra-high speed, which shows extensive application prospects. CASSI innovatively employed the idea of compressive sensing to capture the spatial-spectral data cube using a monochromatic detector and used reconstruction algorithms to recover the desired spatial-spectral information. Based on the optical design, CASSI currently has two different implementations: single-disperser (SD) CASSI and dual-disperser (DD) CASSI. However, SD-CASSI has poor spatial resolution naturally while DD-CASSI increases size and cost because of the extra prism. In this work, we propose a deep learning-enabled reflective coded aperture snapshot spectral imaging (R-CASSI) system, which uses a mask and a beam splitter to receive the reflected light by utilizing the reflection of the mask. The optical path design of R-CASSI makes the optical system compact, using only one prism as two dispersers. Furthermore, an encoder-decoder structure with 3D convolution kernels is built for the reconstruction, dubbed U-net-3D. The designed U-net-3D network achieves both spatial and spectral consistency, leading to state-of-the-art reconstruction results. The real data is released and can serve as a benchmark dataset to test new reconstruction algorithms.

The impact of internal and external green supply chain management activities on performance improvement: evidence from the automobile industry.

Although the significance of green supply chain management activities on corporation sustainable development has been highlighted in the literature, the potential inter-dependencies between these activities and corporation performance have not been investigated by considering cooperation moderation. The purpose of this research is to examine internal and external Green Supply Chain Management (GSCM) activity on automobile performance: Environmental performance, operational performance, positive economic performance, and negative economic performance. Using survey data from foreign-owned company A with 117 manufacturing firms and domestic company B with 94 manufacturing firms, a significant relationship between GSCM and corporation performance has been found. For foreign-owned automobile companies, internal organizational activity has the greatest influence on operational performance, and promotes corporate value and corporation with outside. However, for domestic automobile companies with the advanced green concept, Eco-design exerts the biggest effect on environmental performance, and also brought a negative economic impact on corporate performance. Moreover, although both corporate social responsibility (CSR) and creating shared value (CSV) promotion could influence the corporation with consumers and suppliers, CSV promotion has a more positive influence on cooperation with consumers than that CSR. These findings have important implications for designing GSCM strategic plans for the automotive industry in developing countries.

Visualization and Analysis of Air Pollution and Human Health Based on Cluster Analysis: A Bibliometric Review from 2001 to 2021.

Bibliometric techniques and social network analysis are employed in this study to evaluate 14,955 papers on air pollution and health that were published from 2001 to 2021. To track the research hotspots, the principle of machine learning is applied in this study to divide 10,212 records of keywords into 96 clusters through OmniViz software. Our findings highlight strong research interests and the practical need to control air pollution to improve human health, as evidenced by an annual growth rate of over 15.8% in the related publications. The cluster analysis showed that clusters C22 (exposure, model, mortality) and C8 (health, environment, risk) are the most popular topics in this field of research. Furthermore, we develop co-occurrence networks based on the cluster analysis results in which a more specific keyword classification was obtained. These key areas include: "Air pollutant source", "Exposure-Response relationship", "Public & Occupational Health", and so on. Future research hotspots are analyzed through characteristics of the cluster groups, including the advancement of health risk assessment techniques, an interdisciplinary approach to quantifying human exposure to air pollution, and strategies in health risk assessment.

Computing Free Energies with Fluctuation Relations on Quantum Computers.

As a central thermodynamic property, free energy enables the calculation of virtually any equilibrium property of a physical system, allowing for the construction of phase diagrams and predictions about transport, chemical reactions, and biological processes. Thus, methods for efficiently computing free energies, which in general is a difficult problem, are of great interest to broad areas of physics and the natural sciences. The majority of techniques for computing free energies target classical systems, leaving the computation of free energies in quantum systems less explored. Recently developed fluctuation relations enable the computation of free energy differences in quantum systems from an ensemble of dynamic simulations. While performing such simulations is exponentially hard on classical computers, quantum computers can efficiently simulate the dynamics of quantum systems. Here, we present an algorithm utilizing a fluctuation relation known as the Jarzynski equality to approximate free energy differences of quantum systems on a quantum computer. We discuss under which conditions our approximation becomes exact, and under which conditions it serves as a strict upper bound. Furthermore, we successfully demonstrate a proof of concept of our algorithm using the transverse field Ising model on a real quantum processor. As quantum hardware continues to improve, we anticipate that our algorithm will enable computation of free energy differences for a wide range of quantum systems useful across the natural sciences.

The Interaction of Facial Expression and Donor-Recipient Eye Contact in Donation Intentions: Based on the Intensity of Emotion.

Both happy and sad facial expressions of recipients are frequently used in charity advertisements. However, the relative effectiveness of these two types of facial expressions has been found paradoxical in the past. In this study, we examine when happy facial expression can more effectively increase donation intentions of consumers and when vice versa. Specially, we propose that eye contact between a donor and a potential recipient may moderate the relative effectiveness of happy and sad facial expressions, and further explain the interaction effect from the perspective of emotional intensity. Results from two experiments suggest that, when donor-recipient eye contact is present, consumers tend to have stronger emotional intensity, and, in turn, show higher donation intentions when the recipient is with a happy rather than sad facial expression. In contrast, when the eye contact is absent, consumers may show stronger emotional intensity and donation intentions toward the charity advertisement with a recipient showing sad rather than happy expression.

An Examination of Factors Contributing to the Acceptance of Online Health Misinformation.

This study examined factors including health-related anxiety, preexisting misinformation beliefs, and repeated exposure contributing to individuals' acceptance of health misinformation. Through a large-scale online survey, this study found that health-related anxiety was positively associated with health misinformation acceptance. Preexisting misinformation beliefs, as well as repeated exposure to health misinformation, were both positively associated with health misinformation acceptance. The results also showed that demographic variables were significantly associated with health misinformation acceptance. In general, females accepted more health misinformation compared to males. Participants' age was negatively associated with health misinformation acceptance. Participants' education level and income were both negatively associated with their acceptance of health misinformation.

Lactobacillus rhamnosus JYLR-005 Prevents Thiram-Induced Tibial Dyschondroplasia by Enhancing Bone-Related Growth Performance in Chickens.

Tibial dyschondroplasia (TD) is a leg disorder caused by the abnormal development of the tibia in fast-growing poultry. Lactobacillus rhamnosus (L. rhamnosus) strains have been reported to have effects on increasing bone growth and improving osteoporosis in animals. However, whether L. rhamnosus JYLR-005 can improve bone growth in TD chickens remains unclear. In this study, we noted that L. rhamnosus JYLR-005 could not reduce the suppression of the production performance of TD broilers (p > 0.05) but had a slight protective effect on the broiler survival rate (χ2 = 5.571, p = 0.062). However, for thiram-induced TD broiler chickens, L. rhamnosus JYLR-005 could promote tibia growth by increasing tibia-related parameters, including the tibia weight (day 11, p = 0.040), tibia length (day 15, p = 0.013), and tibia mean diameter (day 15, p = 0.035). Moreover, L. rhamnosus JYLR-005 supplementation improved the normal growth and development of the tibial growth plate by maintaining the morphological structure of the chondrocytes and restored the balance of calcium and phosphorus. Taken together, these findings provide a proof of principle that L. rhamnosus JYLR-005 may represent a therapeutic strategy to treat leg disease in chickens.

Social Bots' Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter.

During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity-positive or negative-for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans' emotions, rather than to trigger humans' amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

Good News or Bad News? How Message Framing Influences Consumers' Willingness to Buy Green Products.

Despite the growing social interest in green products, companies often find it difficult to find effective strategies to induce consumers to purchase green products or engage in other environmentally friendly behaviors. To address this situation, we examined the favorable or unfavorable effects of positive and negative message frames on consumers' willingness to consume green products in different psychological distance contexts. Through two Studies, we found that the positive information framework played a more pronounced role in context when consumers were in closer spatial distances. More importantly, we found that the emotional factors of fear and hope were intrinsic causes of this phenomenon. Correspondingly, the negative information framework played a better facilitating role in context with farther spatial distance, while shame and pride were the emotions responsible for this effect. Finally, we discuss the theoretical and managerial implications of our work, as well as its limitations and future research directions.

Point Pair Feature-Based Pose Estimation with Multiple Edge Appearance Models (PPF-MEAM) for Robotic Bin Picking.

Automation of the bin picking task with robots entails the key step of pose estimation, which identifies and locates objects so that the robot can pick and manipulate the object in an accurate and reliable way. This paper proposes a novel point pair feature-based descriptor named Boundary-to-Boundary-using-Tangent-Line (B2B-TL) to estimate the pose of industrial parts including some parts whose point clouds lack key details, for example, the point cloud of the ridges of a part. The proposed descriptor utilizes the 3D point cloud data and 2D image data of the scene simultaneously, and the 2D image data could compensate the missing key details of the point cloud. Based on the descriptor B2B-TL, Multiple Edge Appearance Models (MEAM), a method using multiple models to describe the target object, is proposed to increase the recognition rate and reduce the computation time. A novel pipeline of an online computation process is presented to take advantage of B2B-TL and MEAM. Our algorithm is evaluated against synthetic and real scenes and implemented in a bin picking system. The experimental results show that our method is sufficiently accurate for a robot to grasp industrial parts and is fast enough to be used in a real factory environment.

On-chip Microscopy Using Random Phase Mask Scheme.

In this study, a simple and novel phase-retrieval scheme is implemented using multi-angle illumination to enhance the resolution of lensless microscopy. A random-phase mask (from 0 to 2π) precedes the sample to encode the information at the sensor plane. The sample is illuminated with multiple angles that are symmetrical along the optical axis of the system. The system is initially calibrated while recording the images without any sample at the corresponding multi angles. The two types of image are mutually subtracted, and the resultant images are summed at the sensor plane and backpropagated to the sample plane. The final image is free of the twin-image effect, and has a high signal-to-noise ratio owing to the multi angles of the illumination scheme. This scheme gives a resolution of ~4 micron for a large field-of-view (~15 mm2). The scheme is useful for robust imaging owing to the fast phase-retrieval method, and it enables a straightforward analytical reconstruction instead of using complicated iterative algorithms in a lensless microscopic setup.

Lensless imaging through multiple phase patterns illumination.

A stable optical system is required to acquire a high-quality image. A motionless lensless setup is designated to obtain high-resolution and large field of view images. The sample is sequentially illuminated with multiple random phase patterns, and the recorded images are subtracted from the system calibration images correspondingly. The resultant images are propagated to the sample plane. The summation of all images yields a final image with resolution of ∼4 µm, field of view of ∼15 mm2, and better signal-to-noise ratio. This technique provides a compact, stable, and cost-effective optical system.