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In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system's ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas. [ABSTRACT FROM AUTHOR]

The prediction of surrounding agent trajectories in heterogeneous traffic environments remains a challenging task for autonomous driving due to several critical issues, such as understanding social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories in accordance with real‐world rules, all of which hinder prediction accuracy. To address these issues, a new multimodal trajectory prediction framework based on the transformer network is presented in this study. A hierarchical‐structured context‐aware module, inspired by human perceptual logic, is proposed to capture contextual information within the scene. An efficient linear global attention mechanism is also proposed to reduce the computation and memory load of the transformer framework. Additionally, this study introduces a novel auxiliary loss to penalize infeasible off‐road predictions. Empirical results on the Lyft l5kit data set demonstrate the state‐of‐the‐art performance of the proposed model, which substantially enhances the accuracy and feasibility of prediction outcomes. The proposed model also possesses a unique feature, effectively dealing with missing input observations. This study underscores the importance of comprehending social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories adhering to real‐world rules in autonomous driving. [ABSTRACT FROM AUTHOR]

• Performance fundamentals established for thermal storage charged and discharged by heat pump. • Greatest peak demand savings when thermal storage temperature is far from application temperature. • Greatest energy savings when thermal storage temperature is near application temperature. • Results were obtained via analytical derivations and a R410A vapor compression system model. A heat pump (HP) moves heat from a low-temperature source to a high-temperature sink with an input of energy. Often, one temperature body fluctuates with time (e.g., diurnal ambient temperature), causing the HP efficiency to vary. Integrating thermal energy storage (TES) into a HP system adds a third temperature body, enabling the HP to be advantageously coupled to any two: the application, the ambient, or the TES at strategic times. Although TES integration with HPs is an important emerging technology to lower energy consumption and decrease energy demand during critical times, the favorable circumstances for TES integration are poorly understood. This paper establishes the energy reduction and demand reduction potential of TES-integrated HPs with both analytical and numerical HP models. All possible temperature arrangements are considered for HP-TES systems with two fixed temperature bodies (application and TES) and one variable temperature (ambient). Results show that overall energy savings are most attainable when the TES temperature is near the application temperature, whereas a large temperature difference between the TES and the application leads to the most peak demand reduction. The potential for overall energy savings increases as the magnitude of ambient temperature fluctuations increases. [ABSTRACT FROM AUTHOR]

Capturing TC intensity change remains a great challenge for most state‐of‐the‐art operational forecasting systems. Recent studies found TC intensity forecasts are sensitive to three‐dimensional ocean dynamics and air‐sea interface processes beneath extreme winds. By performing a series of numerical simulations based on hierarchical atmosphere‐wave‐ocean (AWO) coupling configurations, we showed how atmosphere‐ocean and atmosphere‐sea wave coupling can affect the intensity of super typhoon Mangkhut (2018). The AWO coupled model can simulate TC‐related strong winds, oceanic cold wake, and wind waves with high fidelity. With atmosphere‐ocean (AO) coupling implemented, the simulated maximum surface wind speed is reduced by 7 m s−1 compared to the atmosphere‐only run, due to TC‐induced oceanic cold wakes in the former experiment. In the fully coupled AWO simulations, the wind speed deficit can be completely compensated by the wave‐air coupling effect. Further analyses showed that, in the AWO experiment, two mechanisms contribute to the improvement of TC intensity. First, in the high wind scenario (>28 m s−1), the surface drag coefficient reaches an asymptotic level, assisting extreme wind speed to be maintained within the eyewall. Second, the wind speed distribution is modulated and becomes broader; higher wind within the TC area helps to offset the negative effect due to leveling off of the heat exchange coefficient as wind speed increases. Overall, the simulated TC in the AWO run can extract 8–9% more total heat energy from the ocean to maintain its strength, compared to that from the AO experiment. Key Points: Regional atmosphere‐wave‐ocean (AWO) coupled models have been customized to study the super typhoon Mangkhut (2018)Atmosphere‐ocean (AO) coupling reduces TC's surface maximum wind, but AWO coupling can completely compensate that deficitSurface drag coefficient leveling off and broader wind speed distribution function help to maintain the TC intensity [ABSTRACT FROM AUTHOR]

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]

This study examines the climate response to a sea surface temperature (SST) warming imposed over the southwest tropical Indian Ocean (TIO) in a coupled ocean–atmosphere model. The results indicate that the southwest TIO SST warming can remotely modulate the atmospheric circulation over the western North Pacific Ocean (WNP) via interbasin air–sea interaction during early boreal summer. The southwest TIO SST warming induces a "C shaped" wind response with northeasterly and northwesterly anomalies over the north and south TIO, respectively. The northeasterly wind anomalies contribute to the north TIO SST warming via a positive wind–evaporation–SST (WES) feedback after the Asian summer monsoon onset. In June, the easterly wind response extends into the WNP, inducing an SST cooling by WES feedback on the background trade winds. Both the north TIO SST warming and the WNP SST cooling contribute to an anomalous anticyclonic circulation (AAC) over the WNP. The north TIO SST warming, WNP SST cooling, and AAC constitute an interbasin coupled mode called the Indo-western Pacific Ocean capacitor (IPOC), and the southwest TIO SST warming could be a trigger for IPOC. While the summertime southwest TIO SST warming is often associated with antecedent El Niño, the warming in 2020 seems to be related to extreme Indian Ocean dipole during the autumn of 2019. The strong southwest TIO SST warming seems to partly explain the strong summer AAC of 2020 over the WNP even without a strong antecedent El Niño. [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]

Changes in the tropical atmospheric overturning circulation can strongly influence the global weather pattern via affecting the location, extent, and strength of tropical convective heating. However, due to limitations in obtaining reliable global data before the satellite era, there are still uncertainties on long‐term changes in the tropical overturning features, such as the Walker Circulation. By analysing the 20th century reanalysis products, ship‐based observations and gauge‐based land precipitation data, robust intensification of convection is found over the western equatorial Pacific and Maritime Continent (WEP‐MC; 90°E–150°E/15°S–15°N), where rainfall and total cloud cover increased by 15–20% in boreal spring during 1901–2010. The signal is noticeably seasonally dependent, and both rainfall and cloud cover show consistent and significant increasing trends only in the boreal spring season. General circulation model (GCM) experiments were conducted using two different reconstructed SST data sets as forcing; results from both runs indicate springtime intensification of WEP‐MC convection, even with relatively uniform SST warming. Further, numerical experiments using a simplified model show that the Pacific trade wind can be accelerated by intensifying cumulus heating; however, this can be sensitive to the location of convection, and only occurs if the latter is within ±5° in latitude along the equator. Our findings suggest that the secular change in the Walker Circulation is potentially discovered from the current reanalysis and observational data sets if "regional‐up" approaches are applied. [ABSTRACT FROM AUTHOR]

Changes in the tropical atmospheric overturning circulation can strongly influence the global weather pattern via affecting the location, extent, and strength of tropical convective heating. However, due to limitations in obtaining reliable global data before the satellite era, there are still uncertainties on long‐term changes in the tropical overturning features, such as the Walker Circulation. By analysing the 20th century reanalysis products, ship‐based observations and gauge‐based land precipitation data, robust intensification of convection is found over the western equatorial Pacific and Maritime Continent (WEP‐MC; 90°E–150°E/15°S–15°N), where rainfall and total cloud cover increased by 15–20% in boreal spring during 1901–2010. The signal is noticeably seasonally dependent, and both rainfall and cloud cover show consistent and significant increasing trends only in the boreal spring season. General circulation model (GCM) experiments were conducted using two different reconstructed SST data sets as forcing; results from both runs indicate springtime intensification of WEP‐MC convection, even with relatively uniform SST warming. Further, numerical experiments using a simplified model show that the Pacific trade wind can be accelerated by intensifying cumulus heating; however, this can be sensitive to the location of convection, and only occurs if the latter is within ±5° in latitude along the equator. Our findings suggest that the secular change in the Walker Circulation is potentially discovered from the current reanalysis and observational data sets if "regional‐up" approaches are applied. [ABSTRACT FROM AUTHOR]

As autonomous vehicles (AVs) and human-driven vehicles (HVs) are expected to share the road for the foreseeable future, understanding how to improve the stability of mixed-autonomy platoons is crucial. This paper introduces a novel adaptive control strategy tailored for a specific platoon configuration termed as “1+n+1\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$1+n+1$$\end{document}”, consisting of a leading AV, n intervening vehicles, and a trailing AV. Utilizing vehicle-to-vehicle communication, the trailing AV adapts to real-time traffic states, thereby promoting overall platoon stability. Our analysis, grounded in Lyapunov theory, demonstrates that stabilizing only the trailing vehicle is sufficient to ensure the entire system reaches a stable state. To mitigate the negative effects of sensor and actuator uncertainties, we also introduce a corrective signal framework capable of nullifying adverse inputs. Numerical experiments validate the effectiveness of the proposed strategy in platoon control, which can also be adapted to other platoon configurations. Additionally, the performance of this strategy on macroscopic traffic flow is explored, suggesting substantial potential throughput increases compared with the benchmark strategy of Cooperative Adaptive Cruise Control (CACC). [ABSTRACT FROM AUTHOR]

It is of practical importance to extract and predict taxi hotspots in urban traffic networks. However, the extraction of taxi hotspot is generally influenced by multiple sources of dependence, which has not been well recognized in the existing literature. This study aims to investigate how the integration of clustering models and deep learning approaches can learn and extract the network-wide taxi hotspots in both temporal and spatial dimensions. A density based spatiotemporal clustering algorithm with noise (DBSTCAN) was established to extract the historical taxi hotspots, which changed with time. A conditional generative adversarial network with long short-term memory structure (LSTM-CGAN) model was proposed for taxi hotspot prediction, which is capable of capturing the spatial and temporal variations of taxi hotspots simultaneously. Specifically, the DBSTCAN was applied to process the large-scaled geo-coded taxi pickup data into time-varying historical hotspot information. The proposed LSTM-CGAN model was then trained by the network-wide hotspot data. As illustrated in the numerical tests, it was found that the proposed LSTM-CGAN model provided comparable results with different model layouts and model with 4 LSTM layers in both generator and discriminator performed best. Moreover, four typical prediction approaches were selected to compare with the proposed LSTM-CGAN model. The comparative analyses showed that the proposed LSTM-CGAN model reduced the false positive rate from 5.20% to 0.56% and the false negative rate from 63.50% to 39.93%, and improved the section consistency from 41.78% to 73.43% and the area under the curve from 0.670 to 0.969. The comparison results indicated that the proposed LSTM-CGAN model outperformed all these benchmark methods and demonstrated great potential to enable many shared mobility applications. [ABSTRACT FROM AUTHOR]

The Cuk dual resonance core (DRC) realizes overlapped dual resonance to regulate its output voltage. However, the DRC configuration was only applied to the Fibonacci switched-capacitor converter (SCC), series–parallel SCC, and voltage doubler. In this paper, the Cuk DRC is modified and then is extended to the Dickson SCC to form a multi-resonance-core-based (MRC) Dickson resonant switched-capacitor converter. All charging and discharging loops achieve soft charging, and so the inherent capacitor charge sharing loss and the high current transient spikes are eliminated. The modular configuration makes the MRC Dickson easy to be extended to a higher voltage level. The on-time fixed frequency modulation is adopted, all transistors being turned on with zero-current-switching (ZCS) operation and all diodes being turned off with ZCS operation. Most importantly, the conversion ratio of the MRC Dickson can be regulated continuously, widely, and efficiently even at light load condition, which is a huge advantage over the traditional Dickson SCC with limited control headroom. A comprehensive analysis of the operation principle, voltage-gain curve, component stress analysis, and output characteristic is provided. The converter operation principle and regulation capability were verified by both simulation and experiment. [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]

Extensive research has improved our understanding and forecast of the occurrence, evolution and global impacts of the El Niño–Southern Oscillation (ENSO). However, ENSO changes as the global climate warms up and it exhibits different characteristics and climate impacts in the twenty-first century from the twentieth century. Climate models project that ENSO will also change in the warming future and have not reached an agreement about the flavor, as to the intensity and the frequency, of future ENSO conditions. This article presents the conventional view of ENSO properties, dynamics and teleconnections, and reviews the emerging understanding of the diversity and associated climate impacts of ENSO. It also reviews the results from investigations into the possible changes in ENSO under the future global-warming scenarios. [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]

In this study, El Niño events are classified as long El Niño (LE) events and short El Niño (SE) events based on their durations, and the characteristics of the early stages of these events are investigated. Results indicate that LE events tend to start earlier compared to SE events, initiating in boreal spring and peaking in winter. Their early occurrence is attributed to the western equatorial Pacific (WEP) sea surface wind anomalies that benefit the eastward propagation of warmwater by forcing the downwellingKelvin waves. It is also found that the wind anomalies are potentially induced by the convection anomalies over the WEP in spring. Experiments with a fully coupled climate model forced by convection heating anomalies over theWEP show that El Niño events become stronger and longer after introducing anomalous convection heating. The convection anomalies induce an extensive anomalous westerly belt over the WEP, which charges El Niño by eastward-propagating Kelvin waves. Moreover, induced by the anomalously northward-shifted ITCZ heating and the suppressed heating over the Maritime Continent, the equatorially asymmetric westerly belt reduces the meridional shear of mean easterly wind in the lower latitudes, which maintains an anomalous equatorward Sverdrup transport and in turn prolongs the persistence of ElNiño events.Acase study of the 2015/16 superElNiño and a regression study by using a rainfall index in critical regions support the above results. [ABSTRACT FROM AUTHOR]

Recent worldwide heatwaves have shattered temperature records in many regions. In this study, we applied a dynamical downscaling method on the high-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-1-2-HR) to obtain projections of the summer thermal environments and heatwaves in the Pearl River Delta (PRD) considering three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the middle and late twenty-first century. Results indicated that relative to the temperatures in the 2010s, the mean increases in the summer (June–September) daytime and nighttime temperatures in the 2040s will be 0.7°–0.8°C and 0.9°–1.1°C, respectively. In the 2090s, the mean difference will be 0.5°–3.1°C and 0.7°–3.4°C, respectively. SSP1-2.6 is the only scenario in which the temperatures in the 2090s are expected to be lower than those in the 2040s. When compared with those in the 2010s, hot extremes are expected to be more frequent, intense, extensive, and longer-lasting in the future in the SSP2-4.5 and SSP5-8.5 scenarios. In the 2010s, a heatwave occurred in the PRD lasted for 6 days on average, with a mean daily maximum temperature of 34.4°C. In the 2040s, the heatwave duration and intensity are expected to increase by 2–3 days and 0.2°–0.4°C in all three scenarios. In the 2090s, these values will become 23 days and 36.0°C in SSP5-8.5. Moreover, a 10-yr extreme high temperature in the 2010s is expected to occur at a monthly frequency from June to September in the 2090s. Significance Statement: Pearl River Delta (PRD) has been experiencing record-shattering heatwaves in recent years. This study aims to investigate the future trends of summer heatwaves in the PRD by modeling three future scenarios including a sustainable scenario, an intermediate scenario, and a worst-case scenario. Except for the sustainable scenario, summer temperatures in the intermediate and worst-case scenarios will keep increasing, and heatwaves will become more frequent, intense, extensive, and longer-lasting. In the worst-case scenario, extreme heat events that occurred once in 10 years in the 2010s will shorten to once a month in the 2090s. A better understanding of heatwave trends will benefit implementing climate mitigation methods, urban planning, and improving social infrastructure. [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]

A generic evacuation model is developed based on the behavioral characteristics of passengers during bus fires. Inspired by hydrodynamic mechanisms, the proposed particle-based force-driven model is built using force analysis of evacuees and analogs the movement of people as fluid motion. Several modules are developed to characterize evacuees' preferences for varying exits, aggregated behavior of groups, and effects of driver. The Smoothed Particle Hydrodynamics (SPH) methods are applied to solve the proposed model using smoothed kernel functions. Experiments are conducted to calibrate and validate the proposed model based on observed evacuees' behavioral characteristics, such as evacuation time, evacuation speed under different scenarios in terms of bus capacity, and the interaction between evacuees. The results of the study will contribute to a better understanding of bus evacuation options, improved design of bus vehicles to cope with internal fires, and the development of appropriate policies and regulations for bus passenger evacuation in response to fires. The proposed method can also be readily applied to other related fields with minor modifications. • A force-driven human motion model is proposed. • Heterogeneous effects of different evacuees are considered. • The model is calibrated with real-world experiments. [ABSTRACT FROM AUTHOR]

Most state-of-the-art climate models substantially underestimate the near-surface air temperature (SAT) over the Tibetan Plateau (TP), especially for the cold season. While previous studies have attributed this cold bias to local factors such as the elevation difference, coarse resolution, and excessive snow cover, this investigation addresses the potential contributions of the systematic bias of tropical sea surface temperature (SST) to the TP cold bias. Experiments with the NCAR Community Atmosphere Model demonstrate that tropical SST bias results in an apparent cold bias over the TP, especially in boreal winter, and explains about 40% of the deviation in multi-model mean SAT over the TP relative to station observations. Forced by the tropical SST bias, heat flux exhibits an anomalous divergence over the plateau, causing a cooling center in the mid- and lower-troposphere over the TP. This atmospheric cooling in turn leads to a reduction of the downward longwave radiative fluxes reaching the surface, less energy supply, and thus a cold bias over the TP. [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]

The Tibetan Plateau (TP) exerts significant influences on Earth's climate, and it is commonly accepted that the plateau enhances the intensity of the Asian summer monsoon (ASM). However, since the TP is located in the subtropics and its climate responses consist of both tropical and extratropical characteristics, a natural question to ask is how the TP would affect the ASM if it were shifted to different latitudes. A series of experiments with a state-of-the-art Earth system model demonstrates that the current location of the TP is not optimal for intensifying the ASM. When the TP is moved southward, the tropical South Asian monsoon (SAM) intensifies, associated with strengthened thermally driven atmospheric circulation, while the subtropical East Asian monsoon (EAM) weakens. When the TP is located in higher-than-current latitudes, on the other hand, the SAM weakens and the EAM intensifies. In particular, when the TP shifts northward by 8° of latitude, the Asian continent witnesses the heaviest summer monsoon rainfall. Changes in the meridional location of the plateau cause substantial differences in atmospheric circulation and water vapor transport, and thus in monsoon rainfall. Significance Statement: The existence of the Tibetan Plateau (TP) enhances the Asian summer monsoon; however, the optimal positions of the TP for affecting the monsoon and its various components are unknown. This study shows that the different TP locations exert different influences on the monsoon. When the TP is shifted southward, the South Asian monsoon intensifies while the East and Southeast Asian monsoons weaken. When the TP is shifted northward, the South Asian monsoon weakens constantly while the East and Southeast Asian monsoons strengthen before they become weaker when the plateau is shifted by 12° of latitude. Much of the Asian continent would witness the heaviest monsoon rainfall when the TP is shifted northward by 8° of latitude. [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]

Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post‐processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post‐processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation‐triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations. Plain Language Summary: Numerical weather prediction models depend on certain empirical formulations known as parameterizations to estimate precipitation. However, these methods often fall short due to the intricate dynamics of rainfall, which involves numerous small‐scale interactions that these models are unable to fully capture. To counteract these limitations, our study deploys a form of machine learning known as deep learning (DL) to predict precipitation. This DL model utilizes fundamental weather variables derived from NWP models to make its estimations, serving as a remedy for the inherent weaknesses of traditional models caused by the uncertainties in their parameterization schemes. The implementation of our DL model resulted in a significant enhancement in rainfall prediction accuracy, particularly in the case of extreme precipitation events. This suggests that the application of machine learning strategies could be a promising approach to improve the reliability of rainfall forecasts, a crucial element for effective weather prediction and water resource management. Key Points: We employ a transformer based deep learning model to improve the accuracy of precipitation estimation in numerical weather prediction modelsVarious training strategies were implemented to manage the highly skewed precipitation data leading to improvements in heavy rainfall eventsEvaluation conducted with multiple metrics including skill score, quantile and spatial distribution as well as two case studies [ABSTRACT FROM AUTHOR]

Background: Studies have shown that CCR7, an important inflammatory factor, can promote the proliferation and metastasis of oral squamous cell carcinoma (OSCC), but its role in the tumor microenvironment (TME) remains unclear. This paper explores the role of CCR7 in the TME of OSCC. Methods: In this work, we constructed CCR7 gene knockout mice and OSCC mouse models. Single-cell RNA sequencing (scRNA-seq) and bioinformatics were used to analyze the differences in the OSCC microenvironment between three CCR7 gene knockout mice (KO) and three wild-type mice (WT). Immunohistochemistry, immunofluorescence staining, and flow cytometry were used to analyze the expression of key genes in significantly different cell types between the KO and WT groups. An in vitro experiment was used to verify the effect of CCR7 on M2 macrophage polarization. Results: In the mouse OSCC models, the tumor growth rate in the KO group was significantly lower than that in the WT group. Eight main cell types (including tumor cells, fibroblasts, macrophages, granulocytes, T cells, endothelial cells, monocytes, and B cells) were identified by Seurat analysis. The scRNA-seq results showed that the proportion of tumor cells was lower, but the proportion of inflammatory cells was significantly higher in the KO group than in the WT group. CellPhoneDB analysis results indicated a strong interaction relationship between tumor cells and macrophages, T cells, fibroblasts, and endothelial cells. Functional enrichment results indicated that the expression level of the Dusp1 gene in the KO group was generally higher than that in the WT group in various cell types. Macrophage subclustering results indicated that the proportion of M2 macrophages in the KO group was lower than that in the WT group. In vitro experimental results showed that CCR7 can promote M2 macrophage polarization, thus promoting the proliferation, invasion and migration of OSCC cells. Conclusions: CCR7 gene knockout can significantly inhibit the growth of mouse oral squamous cell carcinoma by promoting the polarization of M2 macrophages. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated that atmospheric diabatic heating over the Tibetan Plateau (TP) exerts significant influences on the "upstream" climate of the Atlantic-African-European sector in boreal summer. Using the NCAR Community Earth System Model, this study demonstrates that the TP-induced change in North Atlantic sea surface temperature (SST) including evident warming over the mid-latitude North Atlantic and cooling over the south can in turn modulate the above TP impact. Compared with the TP heating experiment without Atlantic SST variation, anomalous wave train pattern appears with north-northeastward downstream influences when the change in Atlantic SST is considered. The wave train pattern is characterized by three positive centers over the North Atlantic, the Arctic Ocean, and east of Japan, and four negative centers over northeastern North America, North Europe, the mid-Atlantic, and the northwestern Pacific. It intensifies the northern portions of the TP-induced tropospheric anticyclones over the extratropical North Atlantic and the cyclones over northeastern North America and North Europe. Correspondingly, precipitation decreases over the northwestern Atlantic but increases over northeastern North America and North Europe. Due to the easterly anomalies on the southern side of the weakened thermal low over subtropical Africa, precipitation over the Sahel decreases, indicating a weakening of TP-induced precipitation dipole over the tropical eastern Atlantic and West Africa when the Atlantic SST influence is considered. Overall, the modulation of Atlantic SST variation accounts for above 20 percent of the upstream climate signals induced by the TP thermal effect. [ABSTRACT FROM AUTHOR]

The South China Sea (SCS) summer monsoon onset has been regarded as the beginning of the East Asian summer monsoon. In this study, we investigated the impacts of the SCS monsoon onset on the transition from the spring persistent rainfall to the summer Meiyu in the middle and lower reaches of the Yangtze River basin (MLYZB). It is found that rainfall in the MLYZB reduces after the SCS monsoon onset. This reduction in rainfall persists until the onset of the Meiyu and is accompanied by a weakening of southwesterlies to the south of the MLYZB. These features exist in both climatology and interannual variability. Rainfall increases significantly over the SCS and the subtropical western North Pacific after the SCS monsoon onset. The latent heating of the increased rainfall can excite an anomalous cyclone over the western North Pacific, which weakens the mean southwesterlies to the south of the MLYZB and decreases water vapor entering the MLYZB. It also generates descending motion over southeastern China. Thus, the SCS monsoon onset could suppress rainfall over the MLYZB by the latent heating induced changes in circulation. Compared to increased rainfall over the SCS, the latent heating of increase rainfall over the subtropical western North Pacific plays a more important role in the reduction of rainfall over the MLYZB. As the SCS monsoon onset affects the timing of the reduction of rainfall in the MLYZB, an early SCS monsoon onset is accompanied by below-normal May rainfall in the MLYZB, while a late SCS monsoon onset is accompanied by above-normal May rainfall. [ABSTRACT FROM AUTHOR]

We conduct several experiments using a fully-coupled climate model to understand the role of Tibetan Plateau (TP) surface heating in the climate variations over West Asia, South Europe, North Africa, and the North Atlantic during summer. Emphasis has been placed on the physical processes and responsible mechanisms that involve the shift of the Hadley cell and the important features of rotational and divergent response of the atmosphere to the TP heating. The relative importance of the TP to the Asian continent is also analyzed. A heating of the TP surface leads to local increases in tropospheric temperature and the thickness of the air column due to the so-called air pumping effect. In the upper troposphere, the South Asian high intensifies and extends westward. To the west of TP, especially in West Asia, South Europe, North Africa, and the North Atlantic, distinguished Rossby wave responses to the TP heating occur with anomalous high pressure and uniform warming in the entire troposphere. Correspondingly, descending motions intensify and precipitation decreases. However, the tropical Sahel rainfall increases because of a northward shift of the Atlantic intertropical convergence zone and the anomalous westerlies due to the weakening of the southeastern portion of the Atlantic subtropical high. These effects of the TP heating explain a remarkable portion of the effects by the Asian continent heating. In addition, the impacts of different magnitudes of TP surface heating are also discussed. [ABSTRACT FROM AUTHOR]

• A thermoelectric (TE) liquid-to-liquid heat pump (HP) model was developed. • Variation of properties with temperature has minor impact on performance. • TE surface temperatures have major impact on the HP capacity and efficiency. • Coefficient of performance uncertainty decreased by up to 95% from the new approach. Thermoelectric (TE) heat pumps (TEHPs) are advantageous for heating and cooling in various applications because of their modularity and simple design. A TEHP system includes the TE modules with p- and n-type materials bonded to substrates, plus heat exchangers, thermal interfaces to the heat exchangers, and heat transfer fluids. Although modeling an individual TE module has been extensively studied, limited studies have reported performance at the larger system-level. Furthermore, no prior study has addressed the impact of temperature-dependent TE material properties (e.g., electric resistivity, thermal conductivity, and Seebeck coefficient) on overall heat-pump-system-level performance. This work presents a mathematical model for TEHP system performance based on Goldsmid's approach for TE material performance, "effective" TE material properties, Gnielinski's correlation for convective heat transfer, and thermal balance theory for a heat exchange network. This combined approach provides an accurate model of the liquid-to-liquid TEHP system. Three different approaches—one empirical, one based on the manufacturer's specifications, and one drawn from the literature—were then used to determine values for TE material properties. The first two methods treated properties as constants, while the last approach treated properties as surface-temperature-based functions. Finally, experimental TEHP data was used to validate the models, all with relative absolute deviations of approximately 10% when predicting heating capacity and 10%–25% when forecasting cooling capacity up to a 30 K surface temperature lift. The results demonstrated that, at the TEHP system level, the TE material properties could be treated as constants, avoiding solver iterations and reducing the performance uncertainty by up to 95%. [ABSTRACT FROM AUTHOR]

Deep-learning frameworks can effectively forecast the air pollution data for individual stations by decoding time series data. However, most of the existing time-series-based deep-learning models use offline spatial interpolation strategies and thus cannot reliably project the station-based forecast to the spatial region of interest. In this study, the station-based long short-term memory (LSTM) technique was extended for spatial air quality forecasting by combining a novel deep-learning layer, termed the broadcasting layer, which incorporates a learnable weight decay parameter designed for point-to-area extension. Unlike most existing deep-learning-based methods that isolate the interpolation from the model training process, the proposed end-to-end LSTM broadcasting framework can consider the temporal characteristics of the time series and spatial relationships among different stations. To validate the proposed deep-learning framework, PM 2.5 and O 3 forecasts for the next 48 h were obtained using 3D chemical transport model simulation results and ground observation data as the inputs. The root mean square error associated with the proposed framework was 40 % and 20 % lower than those of the Weather Research and Forecasting–Community Multiscale Air Quality model and an offline combination of the deep-learning and spatial interpolation methods, respectively. The novel LSTM broadcasting framework can be extended for air pollution forecasting in other regions of interest. [ABSTRACT FROM AUTHOR]

The response of monsoon precipitation to global warming, which is one of the most significant climate change signals at the earth's surface, exhibits very distinct regional features, especially over the South China Sea (SCS) and adjacent regions in boreal summer. To understand the possible atmospheric dynamics in these specific regions under the global warming background, changes in atmospheric heating and their possible influences on Asian summer climate are investigated by both observational diagnosis and numerical simulations. Results indicate that heating in the middle troposphere has intensified in the SCS and western Pacific regions in boreal summer, accompanied by increased precipitation, cloud cover, and lower-tropospheric convergence and decreased sea level pressure. Sensitivity experiments show that middle and upper tropospheric heating causes an east-west feedback pattern between SCS and western Pacific and continental South Asia, which strengthens the South Asian High in the upper troposphere and moist convergence in the lower troposphere, consequently forcing a descending motion and adiabatic warming over continental South Asia. When air-sea interaction is considered, the simulation results are overall more similar to observations, and in particular the bias of precipitation over the Indian Ocean simulated by AGCMs has been reduced. The result highlights the important role of air-sea interaction in understanding the changes in Asian climate. [ABSTRACT FROM AUTHOR]

The Maritime Continent (MC) is regarded as an important heat source region and the impact of MC heating on the West Antarctic climate which is largely controlled by the Amundsen Sea Low (ASL) is unknown. This study is focused on the impact of intensified convective heating over the MC on the variation of the ASL in the austral fall. It is observed that enhanced MC heating is linked to deepening of the eastern ASL, a feature reproduced successfully by a model experiment in which additional heating is introduced to the MC. The intensified MC heating generates anticyclonic anomalies that result in a Rossby wave source in the upper troposphere to the south of MC. Then the Rossby wave originated from the new source propagates southeastward and deepens the eastern ASL. Additional model experiments prove that the response of ASL to MC heating is stronger than that to central Pacific cooling. Plain Language Summary: West Antarctica has experienced the strongest atmospheric warming in the Southern Hemisphere and the atmospheric circulation here is largely controlled by the Amundsen Sea Low (ASL) that resides along most of the western Antarctic Peninsula coast. Tropical heating is important for modulating the variations of ASL but the complicated mechanism between the tropics and the Antarctica is still unclear. Recent studies found that the atmospheric circulation over West Antarctica is affected by anomalous heating over the central Pacific (CP) that usually occurs simultaneously with the heating over the Maritime Continent (MC), and the impact of the MC heating on West Antarctic climate is unknown. This study is focused on the impact of intensified heating over the MC on the variation of ASL in austral fall. Both observations and model experiments prove that MC heating influences the ASL through Rossby wave trains while the CP cooling exerts an impact on the ASL through the westerly jet stream. The response of ASL to MC heating is stronger than that to CP cooling in austral fall in model simulations. This study enhances our understanding of how tropical heatings in different locations influence the West Antarctic climate. Key Points: Maritime Continent (MC) variability replaces the dominant role of central Pacific (CP) variability in modulating the variations of Amundsen Sea Low (ASL) in March‐April‐MayMC heating influences the ASL through Rossby wave trains while the CP cooling exerts an impact on the ASL through the westerly jet streamThe response of ASL to MC heating is stronger than that to CP cooling [ABSTRACT FROM AUTHOR]

Atmospheric diabatic heating, a major driving force of atmospheric circulation over the tropics, is strongly confined to the tropical western North Pacific (TWNP) region, with the global warmest sea surface temperature (SST). The changes in diabatic heating over the TWNP, which exert great impacts on the global climate system, have recently exhibited a noticeable seasonal dependence with a remarkable increase in boreal spring. In this study, we applied observations, reanalysis data, and numerical experiments to investigate the causes of the seasonality in heating changes. Results show that in boreal spring convection is more sensitive to the TWNP SST, leading to a more significant enhancement of deep convection, although the increase in the SST is nearly the same as that in the other seasons. In the non-spring seasons, the enhanced convection due to increased local SST is suppressed by the anomalous anticyclonic wind shear over the TWNP, generated by the easterly wind anomalies induced by the tropical Indian Ocean (TIO) warming via the Kevin waves. However, the TIO warming does not show any suppressing effect in spring because it is much weaker than that in the other seasons and thus the warming itself cannot induce sufficient convective heating anomalies to excite the Kelvin waves. [ABSTRACT FROM AUTHOR]

Cities face twin challenges of changing climate and urban heat island (UHI) effect, with the environmental implications of urbanization still unclear. This study employs the Weather Research and Forecasting (WRF) model to examine the impacts of urbanization in the Greater Bay Area (GBA) during warm and cold seasons in past (2000) and near-future (2030) scenarios [i.e., under shared socioeconomic pathways (SSP): SSP1-26, SSP2-45, and SSP5-85]. Datasets from the World Urban Database and Access Portal Tools (WUDAPT) were incorporated into the WRF model to improve the representation of urbanization effects on weather patterns. Our findings indicate that urban expansion significantly increases urban temperatures and decreases wind speed across selected climate change scenarios. The spatially averaged urban temperatures in the fine resolution domain for 2030 could rise by 0.9 °C (warm season) and 2.4 °C (cold season), respectively, and urban wind speed decreases by ∼ 3 m/s under the SSP5-85 scenario. In cold season, the UHI effect could last over 20 h, while urban area may experience cooler nighttime temperatures than rural areas in warm season. Moreover, the future scenario predicts higher daytime O 3 levels due to the warming effects of climate change and urbanization, but lower nighttime levels in warm season, attributed to intensified south-easterly sea breeze and background winds, when compared to the past scenario. This study highlights the importance of incorporating urbanization and climate change in future urban atmospheric environment studies, and underscores that urban climate change adaptation and mitigation should consider extra impacts on built-environment caused by urbanization. • Climate change and urbanization impacts on meteorological and O 3 fields were explored in GBA. • Urbanization and climate change are expected to raised temperatures by 0.9 °C in warm season over the study area. • Urbanization and climate change are expected to raised temperatures by 2.4 °C in cold season over the study area. • Urbanization is expected to reduce wind speed by 3 m/s and intensify sea breeze penetration over the study area. [ABSTRACT FROM AUTHOR]

It is known that the Tibetan Plateau (TP) can weaken the transient eddies (TEs) transported along the westerly jet stream. This study investigates the effects of the persistently suppressed TEs by the TP on the East Asian summer monsoon and the associated mechanisms using the NCAR Community Earth System Model. A nudging method is used to modify the suppression of the TEs without changing the steady dynamic and thermodynamic effects of the TP. The suppressed TEs by the TP weaken the East Asian westerly jet stream through the weakened poleward TE vorticity flux. On the one hand, the weakened jet stream leads to less (more) rainfall in northern (southern) East Asia by inducing anomalous moisture convergence, midtropospheric warm advection, and upper-level divergence, particularly in early summer when the eastward propagation of TE suppression by the TP is strong. On the other hand, the precipitation anomalies can shift the East Asian westerly jet stream southward and promote the moisture convergence in southern East Asia through latent heat release. Therefore, the persistent suppression of the TEs leads to a southward shift of the East Asian rain belt by a convective feedback, as it was previously found that the steady thermodynamic and dynamic forcings of the TP favored a northward shift of the rain belt. This study suggests that the anomalously weak TEs can lead to a rainfall change (more in the south, less in the north) over East Asia. [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]