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This study delves into the factors that contribute to the severity of single-vehicle crashes, focusing on enhancing both computational speed and model robustness. Utilizing a mixed logit model with heterogeneity in means and variances, we offer a comprehensive understanding of the complexities surrounding crash severity. The analysis is grounded in a dataset of 39,788 crash records from the UK's STATS19 database, which includes variables such as road type, speed limits, and lighting conditions. A comparative evaluation of estimation methods, including pseudo-random, Halton, and scrambled and randomized Halton sequences, demonstrates the superior performance of the latter. Specifically, our estimation approach excels in goodness-of-fit, as measured by ρ 2 , and in minimizing the Akaike Information Criterion (AIC), all while optimizing computational resources like run time and memory usage. This strategic efficiency enables more thorough and credible analyses, rendering our model a robust tool for understanding crash severity. Policymakers and researchers will find this study valuable for crafting data-driven interventions aimed at reducing road crash severity. • Mixed logit model with heterogeneity in means and variance for crash analysis. • Scrambled and random Halton sequences optimize estimation accuracy. • Study achieves robustness and efficiency in crash severity modeling. [ABSTRACT FROM AUTHOR]

• A systematic treatment of airflow configurations for SSLC. • A comprehensive enumeration for vapor compression-based systems is identified. • Limited to one sensible and one latent coil, only one unique SSLC system exists. • Analytical solution and simulation results show 14.8% efficiency improvement. Separate sensible and latent cooling (SSLC) is a technology with efficiency and comfort advantages over conventional cooling systems used for space conditioning of buildings. Using multiple cooling processes at different temperatures allows SSLC to save energy by raising the evaporation temperature of the sensible cooling process. In this paper, all possible airflow configurations of SSLC systems are enumerated under the following constraints: exactly two heat exchangers are used, and air is recirculated to the conditioned space (no exhaust or outdoor air treatment). Seven designs are identified, with varying free operating variables, and each is modeled. Analysis reveals that several configurations are equivalent, and there is only one unique basic airflow SSLC configuration: the one with the sensible and latent heat exchangers placed in series. The efficiency of the SSLC system is compared against that of the conventional system. Under standard conditions, an SSLC system can improve the coefficient of performance by 14.8%. In addition to the numerical simulation, the optimal operating condition of the basic air configuration of the SSLC system is derived analytically. The basic SSLC system is shown to offer the highest performance improvement when the outdoor temperature is relatively cool and the space sensible heat ratio is high. [ABSTRACT FROM AUTHOR]

• New approach to design refrigerant mixture composition and heat exchanger circuitry. • Multi-objective formulation improves energy efficiency, fluid flammability and GWP. • New approach yields greater performance improvement than mixture-only optimization. • Case study shows 5.9% EER improvement, 48.6% flammability reduction with GWP of 268. The recently introduced hydrofluoroolefin (HFO) refrigerants, including R1234yf and R1234ze(E), have significantly lower global warming potentials (GWPs) than traditional hydrofluorocarbon (HFC) refrigerants like R410A. However, prior tests show that direct drop-in of pure R1234yf or R1234ze(E) into equipment designed for R410A results in a decrease in heat exchanger capacity and the system coefficient of performance. The primary reason is the lower in-tube heat transfer performance of R1234yf and R1234ze(E) compared with that of R410A. To address this issue, previous studies have mixed the mildly flammable HFC R32 with HFOs to improve system performance, with HFC R125 also added to suppress flammability. Previous studies selected compositions based on simple cycle analyses and did not consider modifications of the heat exchanger circuitry configuration to adapt to the new refrigerants. This study presents a novel multi-objective optimization approach to design a refrigerant composition that maximizes energy efficiency within flammability and GWP limits. The approach in this work simultaneously optimizes mixture composition and heat exchanger circuitry configuration. A case study on a rooftop unit indicates that, compared with mixture-only optimization, simultaneous optimization of mixture and heat exchanger circuitry yields a 5.9% improvement in cycle efficiency and a 48.6% reduction in refrigerant flammability with a GWP of 268. Circuitry optimization using refrigerants with different temperature glides shows that the larger the temperature glide is, the larger EER improvement is obtained. The results show that zeotropic blends with a large temperature glide are more sensitive to the refrigerant circuitry than pure refrigerants and may suffer significant performance degradation with subpar heat exchanger circuitry design. The proposed optimization approach is generally applicable to mixtures with any number of components. Using this approach to design a HVAC system can yield higher system efficiency within flammability and GWP constraints. [ABSTRACT FROM AUTHOR]

• We have developed a hierarchical approach to optimize evacuation management strategies under tsunami disasters. • On the first layer, the management objective is to minimize the evacuation time for all individual evacuees. • On the second layer, the optimization objective aims to minimize the total evacuation time of all the evacuees. • A heuristic solution was developed to provide coordinated vehicle routing guidance for evacuees. An optimal traffic evacuation strategy is of practical importance to minimize potential devastating damages caused by tsunamis. In this study, we developed a tsunami-induced, network-wide traffic evacuation optimization strategy. A hierarchical evacuation structure is established to guide vehicle routing decisions. At the first stage of evacuation, the optimization objective is to minimize the maximum evacuation time for all the individual evacuees from affected areas to temporary shelter zones to satisfy their primary safety needs within the shortest time periods. At the second stage of evacuation, the optimization objective aims to minimize the total evacuation time of all the evacuees from temporary shelter zones to safe zones. The lexicographic minimax optimization and Tabu search techniques are adopted to obtain a unique solution. Numerical examples are conducted to demonstrate the effectiveness of the developed model based on a real transportation network in Honolulu, Hawaii. The proposed evacuation strategy contributes to the state of the art by innovatively balancing the evacuation safety priority and the evacuation efficiency in the hierarchical evacuation structure. The research findings are helpful for decision makers to develop optimal tsunami-induced, network-wide traffic evacuation strategies. [ABSTRACT FROM AUTHOR]

• A Bayesian vector auto-regression data analytics approach is proposed. • A Gibbs sampler is used to conduct Bayesian inference for model parameters and unobserved variables. • The model has a fairly superior fit accuracy, and is able to capture the unobserved heterogeneity in the dataset. Traffic collision data are always collected in irregularly spaced and mixed frequency. Conventional treatment on these kinds of data, for instance, aggregating the high-frequency data into the lower frequency, can lead to the loss of relevant information of high-frequency data, and introduce potential temporal instabilities. A novel Bayesian vector autoregression approach is proposed to address this problem. An unevenly-spaced traffic collision data with missing values, containing all collisions in different severities that occurred on the state highways in Washington State from January 2006 to December 2016, is selected in this study the impacts of transportation-, weather- and socioeconomic-related characteristics on traffic collisions. A Gibbs sampler is used to conduct Bayesian inference for model parameters and unobserved high-frequency variables. Results show that the model has a fairly superior fit accuracy, and is able to capture the unobserved heterogeneity in the dataset. The proposed VAR also demonstrates better performance than other missing value imputation techniques, including linear regression, predictive mean matching, k-nearest neighbors, and random forests. This study provides potential in the guidance of model construction that considers the mixed-time-series nature of data. [ABSTRACT FROM AUTHOR]

• This study examines driver injury severities in single-vehicle crashes. • Latent class analysis and mixed logit model are combined and developed. • This study provides insights on injury prevention in single-vehicle crashes. The single-vehicle crash has been recognized as a critical crash type due to its high fatality rate. In this study, a two-year crash dataset including all single-vehicle crashes in New Mexico is adopted to analyze the impact of contributing factors on driver injury severity. In order to capture the across-class heterogeneous effects, a latent class approach is designed to classify the whole dataset by maximizing the homogeneous effects within each cluster. The mixed logit model is subsequently developed on each cluster to account for the within-class unobserved heterogeneity and to further analyze the dataset. According to the estimation results, several variables including overturn, fixed object , and snowing , are found to be normally distributed in the observations in the overall sample, indicating there exist some heterogeneous effects in the dataset. Some fixed parameters, including rural , wet , overtaking , seatbelt used , 65 years old or older , etc., are also found to significantly influence driver injury severity. This study provides an insightful understanding of the impacts of these variables on driver injury severity in single-vehicle crashes, and a beneficial reference for developing effective countermeasures and strategies for mitigating driver injury severity. [ABSTRACT FROM AUTHOR]

• 3D CAV trajectories are formulated in the combined temporal-spatial dimensions. • A DFROC algorithm is developed to minimize average control delays at intersections. • Simulation models are developed to examine the proposed algorithm. • The proposed DFROC algorithm is superior to the existing control algorithms. Traffic congestion has become a serious issue worldwide due to the rapid increase in population and traffic demands. Advances in connected automated vehicle (CAV) technology demonstrate the potential to improve traffic mobility and safety performance at intersections. An advanced intersection control system is proposed in this study to coordinate vehicle trajectories and ensure safety and operation efficiency at intersections. A temporal-spatial dimension extension-based trajectory coordination model is developed by formulating all possible trajectories of vehicles at the intersection. Correspondingly, according to the trajectory coordination model, two signal-free control algorithms, including the priority-based algorithm and the Discrete Forward-Rolling Optimal Control (DFROC) algorithm are proposed in this study to manage vehicles at the intersection. These two algorithms, together with the FCFS policy, are compared with the conventional signal control method in a SUMO-based simulation platform. Experimental results indicate that the proposed algorithms outperform the signal control method in terms of reducing total traffic delays at intersections and increasing intersection capacity and operation efficiency. [ABSTRACT FROM AUTHOR]

• A novel integer permutation-based Genetic Algorithm (IPGA) for heat exchanger circuitry optimization is presented. • Novel chromosome representation and genetic operators lead to improved convergence. • Used exhaustive search to verify that IPGA can find optimal or near-optimal designs. • Compared to other methods in the literature, IPGA finds better designs for the same problems. The performance of tube-fin heat exchangers is strongly influenced by the refrigerant circuitry, i.e., the refrigerant flow path along different tubes. Since for a given number of tubes, the number of possible circuitries is exponentially large, neither exhaustive search nor traditional optimization algorithms can be used to optimize the circuitry. Researchers previously used Evolutionary Algorithms (EAs) coupled with a learning module or other heuristic algorithm to solve this problem, but there is no guarantee that the resulting circuitry can be manufactured in a cost-effective manner. In this paper, we present a novel integer permutation-based Genetic Algorithm (IPGA) for optimizing circuitry with manufacturability and operating constraints. The novel genetic operators are designed such that all chromosomes generated by IPGA can be mapped to a valid circuitry. As a result, the proposed approach can explore the solution space more efficiently than conventional GA. A constraint-dominated sorting technique is used in the fitness assignment stage to handle manufacturability constraints. An exhaustive search on a small heat exchanger proves IPGA can find optimal or near-optimal circuitries using relatively small population size and low number of iterations. The analyses of four case studies show that IPGA can find circuitry designs with capacities superior to those designed based on engineers' experience and meanwhile guarantee good manufacturability. Overall, a 2.4–14.6% increase in heat exchange capacity is observed by applying IPGA to an A-shaped indoor unit. Comparison with other optimization methods in literature shows that IPGA finds better designs exhibiting higher capacity, lower pressure drop and better manufacturability. [ABSTRACT FROM AUTHOR]

Highlights • This paper examines driver injury severities in rain-related single-vehicle crashes. • Mixed logit model and latent class model are both developed and compared. • Pseudo-elasticity analysis approach is proposed in this study. • This study provides insights on casualties and injury prevention. Abstract Due to limited visibility and low skid resistance on road surface, single-vehicle crashes under rain conditions, especially those occurred in rural areas, are more likely to result in driver incapacitating injuries and fatalities. A three-year crash dataset including all rural single-vehicle crashes under rain conditions from 2012 to 2014 in four South Central states, i.e., Texas, Arkansas, Oklahoma, and Louisiana, are selected in this paper to analyze the impact factors on driver injury severity. The mixed logit model (MLM) and the latent class model (LCM) are developed on the same dataset. Several parsimony indices, e.g., AIC and BIC, and as well as McFadden pseudo r-squared, are calculated for all the models to evaluate their respective performance. Results show that choosing the uniform distribution as the prior for random parameters could better improve the goodness-of-fit of the MLM than using normal and lognormal distributions. In addition, the two-class LCM also shows superiority when compared to three- and four-class LCMs. Finally, a careful comparison between these two models is conducted, and the results indicate that the LCM has a slightly better performance in analyzing the aforementioned dataset in this study. Model estimation results show that curve , on grade , signal control , multiple lanes , pickup , straight , drug/alcohol impaired , and seat belt not used can significantly increase the probability of incapacitating injuries and fatalities for drivers in the two models. On the other hand, wet , male , semi-trailer , and young can significantly decrease the probability of incapacitating injuries and fatalities for drivers. This study provides an insightful understanding of the effects of these attributes on rural single-vehicle crashes under rain conditions and beneficial references for developing effective countermeasures for severe injury prevention. [ABSTRACT FROM AUTHOR]

Highlights • This study examines driver injury severities in intersection-related crashes. • A hybrid method of cluster analysis and hierarchical Bayesian model is developed. • Risk compensation, unobserved heterogeneity, and data structure are all considered. • This study provides insights on injury prevention in intersection-related crashes. Abstract Traffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns in intersection-related crashes based on two-year crash data in New Mexico. Three clusters are defined by K-means cluster analysis based on weather and roadway environmental conditions in order to reveal drivers’ risk compensation instability under diverse external environment. Hierarchical Bayesian random intercept models are developed for each of the three clusters as well as the whole dataset to identify the contributing factors on multilevel driver injury outcomes: property damage only (Level I), complaint of injury and visible injury (Level II), and incapacitating injury and fatality (Level III). Model comparison with an ordinary multinomial logistic model omitting crash data hierarchical features and cross-level interactions verifies the suitability and effectiveness of the proposed hybrid approach. Results show that a number of crash-level variables (time period, weather, light condition, area, and road grade), vehicle/driver-level variables (traffic controls, vehicle action, vehicle type, seatbelt used, driver age, drug/alcohol impaired, and driver age) along with some cross-level interactions (i.e., left turn and night, drug and dark) impose significantly influence driver injury severity. This study provides insightful understandings of the effects of these variables on driver injury severity in intersection-related crashes and beneficial references for developing effective countermeasures for severe crash prevention. [ABSTRACT FROM AUTHOR]

• Proposed Bayesian robit model for outlier handling in crash data. • Replaced logit/probit link with heavy-tailed Student's t distribution. • Improved posterior estimation with sandwich algorithm and data augmentation. • Tested on tunnel crash dataset, showing superior efficiency and robustness. Traffic crash datasets are often marred by the presence of anomalous data points, commonly referred to as outliers. These outliers can have a profound impact on the results obtained through the application of traditional methods such as logit and probit models, commonly used in the domain of traffic safety analysis, resulting in biased and unreliable estimates. To mitigate this issue, this study introduces a robust Bayesian regression approach, the robit model, which utilizes a heavy-tailed Student's t distribution to replace the link function of these thin-tailed distributions, effectively reducing the influence of outliers on the analysis. Furthermore, a sandwich algorithm based on data augmentation is proposed to enhance the estimation efficiency of posteriors. The proposed model is rigorously tested using a dataset of tunnel crashes, and the results demonstrate its efficiency, robustness, and superior performance compared to traditional methods. The study also reveals that several factors such as night and speeding have a significant impact on the injury severity of tunnel crashes. This research provides a comprehensive understanding of the outliers treatment methods in traffic safety studies and offers valuable recommendations for the development of appropriate countermeasures to effectively prevent severe injuries in tunnel crashes. [ABSTRACT FROM AUTHOR]

While the bus is generally considered to be a relatively safe means of transportation, the property losses and casualties caused by bus accidents, especially fatal ones, are far from negligible. The reasons for a driver to incur fatalities are different in each case, and it is essential to discover the underlying risk factors of bus fatality severity for different types of drivers in order to improve bus safety. The current study investigates the underlying risk factors of fatal bus accident severity to different types of drivers in the U.S. by estimating an ordered logistic model. Data for the analysis are retrieved from the Buses Involved in Fatal Accidents (BIFA) database from the USA for the years 2006–2010. Accidents are divided into three levels by counting their equivalent fatalities, and the drivers are classified into three clusters by the K-means cluster analysis. The analysis shows that some risk factors have the same impact on different types of drivers, they are: (a) season; (b) day of week; (c) time period; (d) number of vehicles involved; (e) land use; (f) manner of collision; (g) speed limit; (h) snow or ice surface condition; (i) school bus; (j) bus type and seating capacity; (k) driver's age; (l) driver's gender; (m) risky behaviors; and (n) restraint system. Results also show that some risk factors only have impact on the “young and elder drivers with history of traffic violations”, they are: (a) section type; (b) number of lanes per direction; (c) roadway profile; (d) wet road surface; and (e) cyclist–bus accident. Notably, history of traffic violations has different impact on different types of bus drivers. [ABSTRACT FROM AUTHOR]

• An integrated LSTM-WRF-CMAQ model was proposed to further improve the forecast of PM 2.5 and O 3 in the future 48 h. • The reduction of RMSE reached more than 30% for both PM 2.5 and O 3 forecast. • A spatial correction method was introduced to extend the improvement effect to the whole Greater Bay Area. Air pollution is a major impediment to the sustainable development of cities and society. Governed by emission characteristics and meteorological conditions, the formation and destruction of fine particulate matter (PM 2.5) and ozone (O 3) are complicated, and accurate predictions of the concentrations of these two major secondary atmospheric pollutants remain challenging. In this study, by combining meteorological and air pollutant data from ground observations and the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations, a deep learning model structure based on long short-term memory layers (LSTM) was developed and applied to predict the PM 2.5 and O 3 concentrations in the future 48 h period. The forecasting improvement was extended to the whole Greater Bay Area by introducing a spatial correction (SC) method to the CMAQ simulation results. Compared with the original CMAQ forecast, the new method gained a 26% reduction in mean absolute error (MAE) and a 33% reduction in root mean square error (RMSE), respectively, in terms of PM 2.5 ; it also achieved a 40% reduction in MAE and a 34% reduction in RMSE in terms of O 3. SC method, applied to the whole GBA region, also reduced the overall MAE and RMSE by 10% and 17% in terms of PM 2.5 and by 31% and 25% in terms of O 3 , respectively. Using an AI approach, our study provides new perspectives for further improving air quality forecasting from both temporal and spatial perspectives, thus increasing the smartness and resilience of the cities and promoting environmentally sustainable development in the area. [ABSTRACT FROM AUTHOR]

Mitogen-activated protein kinases (MAPKs) require MAPK phosphatases (MKPs) for deactivation of MAPK intracellular signaling. MKP-1 (encoded by Dusp1) is a key negative regulator of MAPKs and prior reports have indicated that MKP-1 regulates oral cancer-associated inflammation and leukocyte infiltration.Objective: To determine the significance of myeloid-based expression of MKP-1 in oral cancer.Methods: The Cancer Genome Atlas (TCGA) was used to address DUSP1 expression in oral squamous cell carcinoma (OSCC). Syngeneic and carcinogen-induced mouse models using global and myeloid-specific Dusp-1 deficient mice with immunophenotypic, histologic, and transcriptomic analyses and in vitro migration assays.Results: Data from TCGA indicates the DUSP1 expression is inversely related to oral cancer burden and nodal involvement. Using murine models of OSCC, the role of MKP-1 signaling in tumor associated macrophages (TAMs) was assessed. Dusp1-deficient mice had increased tumor burden and TAM infiltrate with increased M2 macrophage polarization. Transcriptomic signatures of TAMs from Dusp1-deficent mice indicated a pro-metastatic phenotype as well as concomitant differences in myeloid-associated genes, cytokine/chemokine signaling, and Notch signaling consistent with tumor progression. In vitro and in vivo assays revealed mouse OSCC cells had a higher migration rate using TAM cell-free supernatant from Dusp1 deficiency mice compared to controls with enhanced regional cervical lymph node metastasis, respectively. To validate TAM studies using implantable mouse models, an OSCC progression model with conditional myeloid-specific Dusp-1 deficient mice demonstrated enhanced OSCC disease progression, characterized by advanced onset, histological stage, and tumor burden.Conclusion: Myeloid-based Dusp1-deficiency increases OSCC burden and metastasis through alteration in TAM recruitment, gene profile, and polarity suggesting that MKP-1 could be a viable target to reprogram TAM to limit local/regional OSCC extension. [ABSTRACT FROM AUTHOR]

• Distinct Bi 2 Te 3 family materials show substantial variations in module modeling. • Current models, vendor dataset, and test results reveal gaps in TE properties. • TE module parameter changes must be considered for temperature lifts above 15 K. • TE module parameter shifts cause up to 40 % cooling capacity errors. Thermoelectric (TE) cooling has experienced rapid advancements with the foundational understanding of TE materials. TE modules, compact and lightweight devices, have become the prevalent approach for implementing TE technologies. Accurately quantifying TE physical parameters (Seebeck coefficient α , thermal conductivity κ , and thermal resistance ρ) is challenging due to the dynamic temperature changes in operation. Furthermore, extracting lumped property parameters is crucial for designing energy systems using TE modules. Existing research has several limitations, such as lack of comparative analysis between prevalent formulae, reliance on potentially inaccurate vendor-supplied data, disregard for fundamental assumptions, and absence of empirical measurements. This study addresses these gaps by conducting TE material characterization, comparing three existing formulae using vendor datasheets, designing a laboratory test facility for model validation and refinement, and outlining a structured data extraction procedure. The study's novelty lies in multiple key contributions: (1) a detailed comparative analysis of existing formulae for extracting TE property parameter; (2) executing experimental work in a laboratory setting to validate the model and elucidate its limitations; (3) highlighting potential risks; (4) clarifying possible assumptions from both material and engineering perspectives; and (5) considering temperature differential impacts. This comprehensive approach addresses the current research gaps and provides valuable insights into the design and application of TE modules in various energy systems. [ABSTRACT FROM AUTHOR]

Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste. This paper proposes a novel multi-agent reinforcement learning method, named KS-DDPG (K nowledge S haring D eep D eterministic P olicy G radient) to achieve optimal control by enhancing the cooperation between traffic signals. By introducing the knowledge-sharing enabled communication protocol, each agent can access to the collective representation of the traffic environment collected by all agents. The proposed method is evaluated through two experiments respectively using synthetic and real-world datasets. The comparison with state-of-the-art reinforcement learning-based and conventional transportation methods demonstrate the proposed KS-DDPG has significant efficiency in controlling large-scale transportation networks and coping with fluctuations in traffic flow. In addition, the introduced communication mechanism has also been proven to speed up the convergence of the model without significantly increasing the computational burden. • A multi-agent reinforcement learning for adaptive signal control. • Agents can communicate with others through knowledge-sharing protocol. • Proposed algorithm achieves consistent improvements over baselines. [ABSTRACT FROM AUTHOR]

Oral squamous cell carcinomas (OSCC) constitute over 95% of all head and neck malignancies. As a key component of the tumor microenvironment (TME), chronic inflammation contributes towards the development, progression, and regional metastasis of OSCC. Tumor associated macrophages (TAMs) associated with OSSC promote tumorigenesis through the production of cytokines and pro-inflammatory factors that are critical role in the various steps of malignant transformation, including tumor growth, survival, invasion, angiogenesis, and metastasis. The mitogen-activated protein kinases (MAPKs) can regulate inflammation along with a wide range of cellular processes including cell metabolism, proliferation, motility, apoptosis, survival, differentiation and play a crucial role in cell growth and survival in physiological and pathological processes including innate and adaptive immune responses. Dual specificity MAPK phosphatases (MKPs) deactivates MAPKs. MKPs are considered as an important feedback control mechanism that limits MAPK signaling and subsequent target gene expression. This review outlines the role of MKP-1, the founding member of the MKP family, in OSCC and the TME. Herein, we summarize recent progress in understanding the regulation of p38 MAPK/MKP-1 signaling pathways via TAM-related immune responses in OSCC development, progression and treatment outcomes. [ABSTRACT FROM AUTHOR]

Land surface parameters play a crucial role in simulations of weather and air quality. This study investigated the impact of underlying surface parameters on meteorological fields and pollutant concentrations in the Greater Bay Area (GBA), China, during warm and cold season. Updated land surface parameters, namely land use type, leaf area index, vegetation fraction, albedo, and roughness length, were incorporated to improve simulations. The model showed 7%–14% improvements (in terms of the index of agreement) in wind speed simulations, whereas temperature and relative humidity were not sensitive to the adoption of updated parameters. Moreover, compared with the model with default parameters, the model with updated natural underlying surface parameters showed a 2–3 m/s reduction in cross-border wind speed during cold season in the GBA. The model also showed a 1–2 m/s increase in the wind speed in urban areas, owing to thermal contrast. During periods in which clean air came from the north, the reduced wind speed hindered ozone removal in the GBA. The use of updated underlying surface parameters enhanced the urban heat island effect by ∼2 K and increased the wind speed over urban areas by ∼2 m/s. The intensified urban heat island circulation generated strong updrafts and intensified sea breezes, promoting the penetration of sea breezes inland. Additionally, the intensified sea breeze and urban heat island circulation contributed to a ∼10-ppb reduction in ozone concentrations. Hence, incorporating the latest land surface parameters improved the simulation of meteorological fields and influenced cross-border pollutant transportation. This study highlights the importance of considering underlying surface characteristics in regional modelling and provides insights for air pollutant management. • Latest land surface parameters are incorporated into WRF-CAMx for model improvement. • These updated inputs provide improved meteorological simulation, especially on wind speed. • The local circulations and intense updraft induced by UHI modulate the transportation of O 3. [ABSTRACT FROM AUTHOR]

• This paper examines driver injury severities in snow-related single-vehicle crashes. • Mixed logit model is developed in different years. • Pseudo-elasticity analysis approach is applied. • The results show temporal instability in model specification. • The study provides insights on casualties and injury prevention. Snowy weather is consistently considered as a hazardous factor due to its potential leading to severe fatal crashes. A seven-year crash dataset including rural highway single vehicle crashes from 2010 to 2016 in Washington State is applied in the present study. Pseudo elasticity analysis is conducted to investigate significant impact factors and the temporal stability of model specifications is tested via a likelihood ratio test. The proposed model based on the seven-year dataset is able to capture the individual-specific heterogeneity across crash records for four significant factors, i.e., surface ice, male, and airbag combine deployment for minor injury, and male for serious injury and fatality. Their estimated parameters were found to be normal distribution instead of fixed value over the observations. Other significant impact factors with fixed effects are: inroad object, animal, overturn, surface wet, surface snow, unusual horizontal design, medium and high speed limits, driver age, impaired condition, no belt usage, vehicle type, airbag deployment. Especially, when compared to significant factors for crashes under other weather conditions, male indicator and impaired condition show significant higher effects in snow-related crashes. The results of temporal stability test show that the model specification is generally not temporally stable for driver injury severity model based on the years of crash data that were used, especially for longer period (more than 3-year dataset). Models that allow the explanatory variables to track temporal heterogeneity, are of great interest and can be explored in future research. [ABSTRACT FROM AUTHOR]

• Assessing landslide-induced roadway network vulnerability and accessibility. • Vulnerability analysis is conducted by integrating static and dynamic factors. • The analytical hierarchy process model is developed to assess landslide likelihood. • A generic Vulnerability Index (VI) for each roadway link in the network. As one of the devastating natural disasters, landslide may induce significant losses of properties and lives area-wide, and generate dramatic damages to transportation network infrastructure. Accessing the impacts of landslide-induced disruptions to roadway infrastructure can be extremely difficult due to the complexity of involved impact factors and uncertainties of vulnerability related events. In this study, a data-driven approach is developed to assess landslide-induced transportation roadway network vulnerability and accessibility. The vulnerability analysis is conducted by integrating a series of static and dynamic factors to reflect the landslide likelihood and the consequences of network accessibility disruptions. The analytical hierarchy process (AHP) model was developed to assess and map the landslide likelihood. A generic vulnerability index (VI) was calculated for each roadway link in the network to identify critical links. Spatial distributions of landslide likelihood, consequences of network disruptions, and network vulnerability degrees were fused and analyzed. The roadway network on Oahu Island in Hawaii is utilized to demonstrate the effectiveness of the proposed approach with all the geo-coded information for its network vulnerability analysis induced by area-wide landslides. Specifically, the study area was classified into five categories of landslide likelihood: very high, high, moderate, low, and stable. About 34% of the study area was assigned as the high or very high categories. The results of network vulnerability analyses highlighted the importance of three highway segments tunnel through the Ko'olau Range from leeward to windward, connecting Honolulu to the windward coast including the Pali highway segment, Likelike highway segment, and Interstate H-3 highway segment. The proposed network vulnerability analysis method provides a new perspective to examine the vulnerability and accessibility of the roadway network impacted by landslides. [ABSTRACT FROM AUTHOR]

Southeast Asia (SEA) is one of the world's major sources of biomass burning (BB). In this study, the recently released Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) two-way coupled model was used with the Global Fire Emissions Database Version 4, to investigate the effect of BB emissions on the meteorology and air quality over SEA in March and April 2015. The results show that the model performance was improved by considering BB emissions. When BB pollutants reach the free troposphere, they can be transported by westerly and southwesterly winds to downstream regions. The contributions of BB were up to 48.4 and 35.5 ppbv to surface O 3 concentrations and 251.0 and 104.4 μg/m3 to surface PM 2.5 concentrations in March and April, respectively. The BB emission caused aerosol direct radiative effect (DRE) on the monthly mean clear-sky downward shortwave flux at the ground surface to decrease by −65.4 and −33.6 W/m2 in March and April, respectively. The surface temperature decreased over the land (by a maximum of −0.24 °C in March) and increased over the sea, while the temperature at higher levels tended to increase (to a maximum of 0.12 °C in March). The BB aerosol DRE caused the planetary boundary layer height (PBLH) to decrease by a maximum of −91.2 m in March. Surface O 3 concentrations decreased generally owing to variations in the shortwave flux and temperature. Moreover, the decreased PBLH worsened the diffusion condition within the PBL but also caused larger amounts of BB emissions to penetrate the free atmosphere. This led to a decrease in surface PM 2.5 concentrations in southern Indochina and an increase in Myanmar. These findings highlight the key effects of BB emissions on local and downwind meteorology and air quality over SEA and demonstrate the practical applications of the WRF-CMAQ coupled model. • The BB contributed up to 48.4 and 35.5 ppbv to O 3 and 251.0 and 104.4 μg/m3 to PM 2.5 in March and April, respectively. • The BB aerosol DRE caused ground downward shortwave to decrease by −65.4 and −33.6 W/m2 in March and April, respectively. • The surface temperature decreased over the land, while the temperature at higher levels increased due to aerosol DRE. • The BB aerosol DRE caused the PBLH to decrease by a maximum of −91.2 m in March. • The surface O 3 decreased by −1.7 ppbv, and the surface PM 2.5 varied within −6.6−18.5 μg/m3 in March due to BB aerosol DRE. [ABSTRACT FROM AUTHOR]

Highlights: [•] DES inhibits 16HBE cells proliferation in a dose- and time-dependent manner. [•] Sublethal dose of DES accelerates G1/S transition and arrests S and G2/M progression. [•] Activation of DNA damage checkpoint is responsible for DES-induced S and G2/M arrest. [•] Lethal dose of DES induces apoptosis through evoking apoptosis programs. [•] Down-regulation of p53 increases DES-induced apoptosis in 16HBE cells. [ABSTRACT FROM AUTHOR]

Abstract: In this study, the human bronchial epithelial cells (16HBE) were treated five times with 10μM benzo(a)pyrene (BaP), followed by 20 passages culture, and the in vitro BaP-induced malignant transformation of 16HBE cells was established. Five colonies in soft agarose were then amplified and donated as T-16HBE-C1∼5 cells, respectively. T-16HBE-C1∼5 cells can form tumors subcutaneously in nude mice. Histopathological changes in the tumors indicated nests growth, high nuclear-cytoplasmic ratios, coarse and clumped chromatin, numerous and distinctly atypical mitoses, cell necrosis and surrounding normal adipose, muscle and connective tissue immersed. In addition, lung metastasis was observed in nude mice in T-16HBE-C1, 3 and 4 groups. In vitro cell migration assay results indicated that T-16HBE-C2∼5 cells showed much lower migration capabilities than 16HBE cells. Western blotting analysis showed that the expressions of p53 and p-Akt (Ser473) in T-16HBE-C1∼5 cells were significant higher than those in 16HBE cells. Our results demonstrated that BaP could induce the malignant transformation of 16HBE cells, and p53 and p-Akt (Ser473) might play crucial roles in BaP-induced carcinogenesis. The five monoclonal cell lines (T-16HBE-C1∼5) with different migration capabilities could be used as research models for further understanding the mechanisms of BaP-induced carcinogenesis and cell migration. [Copyright &y& Elsevier]

Reperfusion therapy is the most common and effective treatment against ischemic heart disease (IHD), but the process inflicts massive ischemia/reperfusion (I/R) injury for which no treatment exists. Notably, reperfusion after ischemia causes ischemia/reperfusion injury (IR injury) and the "no-reflow" phenomenon seriously affecting the therapeutic effects in clinical practice. The principle purpose of this study is to validate the effect of hydrogen gas on IHD and further explore the mechanism of hydrogen gas in alleviating myocardial I/R injury and no-reflow phenomenon. The rat model of myocardial ischemia-reperfusion was well established. Myocardial infarct size was evaluated by TTC & Evans blue staining. The no-reflow area and the cardiac function were assessed by thioflavin-S staining and echocardiography respectively. Microstructure and mitochondria of myocardial tissue were assessed by transmission electron microscope. Western blot and immunohistochemistry were used to evaluate the expression of NLRP3 mediated pyroptosis related proteins. The 8-OHdG, MDA and serum total ROS were used to evaluate the degree of oxidative stress. The myocardial infarct size, no-reflow area, cardiac function, microstructure and mitochondrial morphology of I/R model rats were significantly improved after hydrogen inhalation. In addition, the expression of 8-OHdG, MDA, ROS and NLRP3 mediated pyroptosis related proteins were significantly decreased. We found that oxidative stress and NLRP3 mediated pyroptosis are the important mechanisms for hydrogen to alleviate myocardial I/R injury, and we also confirmed that hydrogen can significantly improve no reflow phenomenon caused by ischemia-reperfusion. [ABSTRACT FROM AUTHOR]

Recent studies have focused on the chemistry of tropospheric halogen species which are able to deplete tropospheric ozone (O 3). In this study, the effect of bromine and iodine chemistry on tropospheric O 3 within the annual cycle in Asia-Pacific is investigated using the CMAQ model with the newly embedded bromine and iodine chemistry and a blended and customized emission inventory considering marine halogen emission. Results indicate that the vertical profiles of bromine and iodine species show distinct features over land/ocean and daytime/nighttime, related to natural and anthropogenic emission distributions and photochemical reactions. The halogen-mediated O 3 loss has a strong seasonal cycle, and reaches a maximum of −15.9 ppbv (−44.3%) over the ocean and −13.4 ppbv (−38.9%) over continental Asia among the four seasons. Changes in solar radiation, dominant wind direction, and nearshore chlorophyll-a accumulation all contribute to these seasonal differences. Based on the distances to the nearest coastline, the onshore and offshore features of tropospheric O 3 loss caused by bromine and iodine chemistry are studied. Across a coastline-centric 400-km-wide belt from onshore to offshore, averaged maximum gradient of O 3 loss reaches 1.1 ppbv/100 km at surface level, while planetary boundary layer (PBL) column mean of O 3 loss is more moderate, being approximately 0.7 ppbv/100 km. Relative high halogen can be found over Tibetan Plateau (TP) and the largest O 3 loss (approximately 4–5 ppbv) in the PBL can be found between the western boundary of the domain and the TP. Halogens originating from marine sources can potentially affect O 3 concentration transported from the stratosphere over the TP region. As part of efforts to improve our understanding of the effect of bromine and iodine chemistry on tropospheric O 3 , we call for more models and monitoring studies on halogen chemistry and be considered further in air pollution prevention and control policy. • The vertical profiles of bromine and iodine species show distinct features over land/ocean and daytime/nighttime. • The halogen-mediated O 3 loss reaches a maximum of −15.9 ppbv (−44.3%) over ocean and −13.4 ppbv (−38.9%) over continental Asia. • Across a coastline-centric 400-km-wide belt, averaged maximum gradient of O 3 loss reaches 1.1 ppbv/100 km at surface level. • The horizontal advection of free tropospheric air from nearshore western boundary may contribute to the O 3 loss over the TP. [ABSTRACT FROM AUTHOR]