Lead exposure estimation through a physiologically based toxicokinetic model using human biomonitoring data and comparison with scenario-based exposure assessment: A case study in Korean adults.

Pb toxicity is linked to cardiovascular and nephrotoxicity issues. Exposure to this heavy metal can occur through food and drinking water. Therefore, this study aimed to evaluate Pb exposure and assess health risks in Korean adults using a physiologically based toxicokinetic (PBTK) model. Human blood Pb concentrations were monitored using the Korean National Environmental Health Survey (KoNEHS) Cycle 4. The average Pb exposure in Korean adults was 0.520 µg/kg bw/day. The PBTK results were compared with scenario-based results from the 2021 risk assessment report of five heavy metals, including Pb, conducted by the MFDS. Exposure determined through reverse dosimetry was approximately two times higher than scenario-based exposure (0.264 µg/kg bw/day). The higher exposure levels obtained during PBTK analysis may be attributed to sustained exposure within historically more contaminated living environments and the long half-life of Pb. These findings suggest that the PBTK-based method can quantify aggregated exposure levels in the body over time, potentially serving as a complementary tool to address the constraints of scenario-based assessment methods for integrated risk assessment. Moreover, this model is convenient and cost-effective compared with scenario-based exposure estimation. These findings can facilitate the application of model for tracking continuous national changes in hazardous substance levels.

The impacts of thermocouple insulation failure on the accuracy of temperature measurement data in forensic fire-death scenarios-Part I: Physical disintegration.

Thermocouples are utilized to monitor a wide range of temperatures in industrial applications. They are also used in both fire and forensic science research to measure temperatures of fires and of materials exposed to fire. Taking accurate temperature measurements during forensic fire-death scenarios is very difficult due to direct fire exposure to thermocouples, shrinkage and destruction of tissues, and movements from pyre collapse and pugilistic posturing of human donors. This two-part study investigates the impacts on the accuracy of temperature data if the selected thermocouples are unable to withstand fire exposure. Part I (this article) provides an overview of thermocouple theory along with evidence of the physical deterioration that occurs when glass fiber-insulated thermocouple wires are overheated by exposure to fire-level temperatures in a muffle furnace. This study verified that insulation overheating causes embrittlement and disintegration, which can cause the indicated temperature to reflect a new location of measurement located far away from the original measuring junction at the thermocouple tip. Part II will discuss the measurement errors that occurred due to low electrical resistance of insulation when three different thermocouple models were passed through fire-level temperatures to measure an ice bath at a constant temperature of 0°C.

The impacts of thermocouple insulation failure on the accuracy of temperature measurement data in forensic fire-death scenarios-Part II: Low electrical resistance and contamination.

Part II of this two-part article investigates the impact of thermocouple insulation failure on temperature measurement data in forensic fire-death scenarios. Two different models of glass fiber-insulated thermocouple wires (GG-K-24-SLE and HH-K-24 from Omega Engineering) were passed through a ceramic kiln at temperatures up to 1093°C to measure an ice bath at a constant 0°C. In a separate experiment, the same two models of thermocouple wire plus a BLMI-XL-K-18U-120 mineral-insulated metal-sheathed thermocouple probe were passed through a wood pallet fire to measure an ice bath. In the ceramic kiln, the effect on measurement errors was determined for short vs. long exposure lengths and clean insulation vs. insulation contaminated with pork fat. Glass fiber-insulated thermocouple wires showed severe failure in both experiments, with errors ranging from -270°C to almost 2200°C. The metal-sheathed probe showed no evidence of insulation failure and continued to accurately measure the ice bath temperature within expected margins of error around 0°C. This study highlights how exposure of inadequate thermocouples to fire-level temperatures produces severe errors in temperature data. Consequently, it will not be possible to use this data to draw any accurate conclusions about the effects of fire exposure to human donors or animal proxies.

Pontederia crassipes utilization for dual phytoremediation and adsorption in greywater treatment: a techno-economic and sustainable approach.

While phytoremediation has been widely employed for greywater treatment, this system suffers from the transfer of considerable amounts of surfactants to the aquatic environment through partially treated effluent and/or exhausted plant disposal. Hence, this study focuses on greywater phytoremediation followed by recycling the spent plant for preparing an adsorbent material used as post-treatment. P. crassipes was used to operate a phytoremediation unit under 23 °C, 60% relative humidity, plant density (5-30 g/L), dilution (0-50%), pH (4-10), and retention time (3-15 days). The optimum condition was 12.7 g/L density, 34.0% dilution, pH 8.4, and 13 days, giving chemical oxygen demand (COD), surfactant, and NH4-N removal efficiencies of 94.62%, 90.45%, and 88.09%, respectively. The exhausted plant was then thermally treated at 550 °C and 40 min to obtain biochar used as adsorbent to treat the phytoremediation effluent. The optimum adsorption process was biochar dosage of 1.51 g/L, pH of 2.1, and 137 min, providing a surfactant removal efficiency of 92.56%. The final discharge of this phytoremediation/adsorption combined process contained 8.30 mg/L COD, 0.23 mg/L surfactant, and 0.94 mg/L NH4+-N. Interestingly, this approach could be economically feasible with a payback period of 6.5 years, 14 USD net present value, and 8.6% internal rate of return.

The research succeeded in treating greywater by phytoremediation followed by recycling the exhausted P. crassipes plant to prepare an adsorbent material used in the post-treatment phase, giving an economically feasible scenario with 6.5-year payback period.

Simulation dataset of annual crop growth, GHG emission and SOC stock dynamics under current and projected climate conditions for major crops with current and reduced fertiliser inputs in Southwest, England.

For mitigating the unintended environmental impacts associated with intensive farming across the world, it is crucial to understand the complex impacts of potential reductions in fertiliser use on multiple ecosystem services, including crop production, GHG emissions and changes in soil organic carbon (SOC) stocks. Using site specific spatial data and information, a novel integrated modelling approach using established agroecosystem models (SPACSYS and RothC) was implemented to evaluate the impacts of various fertiliser reductions (10 %, 30 % and 50 %) under current / baseline and projected (RCP2.6, RCP4.5 and RCP8.5) climate scenarios in a study catchment in southwest England. 48 unique combinations of soil types, climate conditions and fertiliser inputs were evaluated for five major arable crops (winter wheat, maize, winter barley, spring barley, winter oilseed rape) plus ryegrass. Modelled annual estimates of crop yields and biomass, emissions of gases with warming potentials (nitrous oxide, methane, carbon) and SOC stocks in the topsoil (0-30 cm) were tabulated for all combinations considered. These simulated data series could be further analysed to evaluate inter-annual variations and their implications for climate resilience and combined with additional data to quantify nutrient use efficiency and undertake cost- benefit analysis, and to contribute to inter-regional comparisons of fertiliser management at broad scale.

Study on factors influencing carbon dioxide emissions and carbon peak heterogenous pathways in Chinese provinces.

Implementing a Carbon Peak Action Plan at the regional level requires comprehensive consideration of the developmental heterogeneity among different provinces, which is an effective pathway for China to realize the goal of carbon peak by 2030. However, there is currently no clear provincial roadmap for carbon peak, and existing studies on carbon peak pathways inadequately address provincial heterogeneity. Therefore, this paper employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to decompose assess 8 factors influencing carbon emissions of 30 provinces. According to scenario analysis, the paper explores the differentiated pathways for provincial carbon peaks based on policy expectation indicators (including population, economy, and urbanization rate) and comprises policy control indicators (including the energy structure, energy efficiency, industrial structure, transportation structure, and innovation input). The results indicate that population, per capita GDP, urbanization rate, and innovation input are the primary factors for influencing (negatively) the growth of carbon emissions. In contrast, the optimization and upgrading of the industrial structure, energy intensity, energy structure, and transportation structure have mitigating effects on carbon emissions, especially for the first two factors. The forecasting results reveal that robust regulations of the energy and industry can effectively accelerate carbon peak at a reduced magnitude. If developed at BAU, China cannot achieve carbon peak by 2030, continuing an upward trend. However, by maximizing the adjustment strength of energy and industrial transformation within the scope of provincial capabilities, China could achieve carbon peak as early as 2025, with a peak of 12.069 billion tons. In this scenario, 24 provinces could achieve carbon peak before 2030. Overall, this study suggests the feasibility of differentiated pathway to achieve carbon peaks in China, exploring the carbon peak potential and paths of 30 provinces, and identifying provinces where carbon peak is more challenging. It also provides a reference for the design of carbon peak roadmaps at both provincial and national levels and offers targeted recommendations for the implementation of differentiated policy strategies for the government.

[Impact of Land Use Change on Carbon Storage in Urban Agglomerations in the Guanzhong Plain].

It is important to study the impact of land use change on terrestrial ecosystem carbon stocks in urban agglomerations for the optimization of land use structure and sustainable development in urban agglomerations. Based on the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and trade-offs (InVEST) model, a simulation was developed that predicted the land use change and carbon stock of the Guanzhong Plain urban agglomeration in 2040 under different scenarios and further analyzed the impact of land use change on carbon stock. The results showed that:① The land use types of the Guanzhong Plain urban agglomeration were mainly cultivated land, forest land, and grassland, which accounted for more than 90 % of the total study area. ② From 2000 to 2020, the carbon stock in the Guanzhong Plain showed a continuous downward trend, with cropland, woodland, and grassland being the main sources of carbon stock in the Guanzhong Plain, and the overall carbon stock declined by 15.12×106 t, with the spatial distribution presenting the distribution characteristics of "high in the north and south and low in the middle." ③ By 2040, the carbon stock would decrease the most under the urban development scenario, with a total reduction of 27.08×106 t, and the least under the ecological development scenario, with a total reduction of 4.14×106t. The research results can provide data support for the high-quality development and rational land use planning of the Guanzhong Plain urban agglomeration.

[Spatio-temporal Evolution and Prediction of Carbon Storage in Huaibei City Based on InVEST-PLUS Model].

This study aimed to investigate the impact of spatiotemporal changes in land use on ecosystem carbon storage. The study analyzed the spatiotemporal changes in carbon storage in the study area based on land use data from five periods (1985, 1995, 2005, 2015, and 2020) using the InVEST model. The PLUS model was used to predict land use changes in the study area under four different scenarios (natural development, farmland protection, ecological protection, and double protection of farmland and ecology) in 2035, and the ecosystem carbon storage under different scenarios was estimated. The results of the study indicated that the farmland in the area under investigation had been decreasing consistently from 1985 to 2020, with a more rapid rate of change observed between 2015 and 2020. During this period, the overall dynamic attitude towards land use reached 34.62 %. Additionally, the carbon storage in the area showed a decreasing trend over the years, with a decrease of 1.55×105 t from 1985 to 2020. Between 2005 and 2015, the carbon storage showed a decrease of 1.22×105 t, with an average annual decrease of 1.22×104 t. The areas with higher carbon storage were located in the eastern part of the study area, whereas areas with lower carbon storage were found in the central and northwestern parts. Although the proportion of carbon storage in farmland decreased from 66.89 % to 57.73 %, farmland remained the most important carbon pool in the study area. The conversion of other land use types to grassland and forestland was advantageous for increasing ecosystem carbon storage. Finally, the study projected that by 2035, the carbon storage in the natural development scenario, the farmland protection scenario, the ecological protection scenario, and the dual protection scenario would be 81.77×105, 82.45×105, 82.82×105, and 82.51×105 t, respectively.

[Spatiotemporal Simulation and Prediction of Soil Organic Carbon Density in Gannan Grassland Under Future Climate Scenarios].

To study the temporal and spatial distribution characteristics of soil organic carbon density in grassland and explore the relationship between organic carbon density and influencing factors is of great significance to the management and maintenance of grassland ecosystems in Gannan Autonomous Prefecture, which is conducive to realizing the goal of "double carbon," promoting carbon sink, and mitigating climate change. Taking Gannan Tibetan Autonomous Prefecture of Gansu Province as the research object, based on data from two CMIP6 future climate scenarios (SSP126 and SSP585), the CENTURY model was used to simulate and predict the temporal and spatial changes in soil organic carbon density in grassland of Gannan during 2023-2100. The main conclusions were as follows:① From 2023 to 2100, total organic carbon density, slow organic carbon density, and inert organic carbon density all showed a downward trend, whereas active organic carbon density fluctuated first and then increased. Meanwhile, the total organic carbon density, active organic carbon density, slow organic carbon density, and inert organic carbon density under the SSP585 scenario were higher than those under the SSP126 scenario. ② Mann-Kendall mutation analysis showed that the abrupt change in the difference of soil total organic carbon density (Δsomtc) occurred in 2030. The abrupt change in the difference of soil active carbon density (Δsom1c) occurred in 2027. ③ During the study period, the average soil organic carbon density of Gannan grassland was 7 505.69 g·m-2 under the SSP126 scenario and 7 551.87 g·m-2 under the SSP585 scenario. Gannan grassland soil organic carbon density was higher in the west and lower in the east, and the coefficient of variation was relatively stable. ④ The results of partial correlation analysis showed that precipitation was positively correlated with soil organic carbon density, whereas temperature was significantly negatively correlated with soil organic carbon density under future climate scenarios. ⑤ The results of the Theil-Sen Median trend analysis and Mann-Kendall test showed that under the two climate scenarios, the soil organic carbon density in Gannan showed an overall downward trend, in which Luqu County showed the fastest downward trend and Dibe County showed the slowest.

[Multi Scenario Carbon Peak Prediction and Emission Reduction Path Analysis of Xi'an Hi-tech Zone].

The fifth session of the 13th National People's Congress proposed to be committed to promoting carbon peaking and carbon neutrality, promoting the comprehensive green and low-carbon transformation of the economy and society and achieving high-quality development. As an important scientific and technological innovation and industrial cluster in Shaanxi Province, the economic development of the Xi'an Hi-tech Zone largely relies on energy consumption, making the task of carbon reduction particularly challenging. Firstly, taking the Xi'an Hi-tech Zone as the research object, through systematic accounting of carbon emissions within the park, we analyzed the current carbon emission status of enterprises in different energy types and industries. Then, using the Kaya model, multiple independent carbon peak scenarios were set up to predict the total carbon emissions and peak time under different scenarios. Finally, based on the development characteristics of the Xi'an Hi-tech Zone, we scientifically selected corresponding carbon emission reduction paths and provided reasonable emission reduction suggestions. The results showed that the proportion of carbon emissions consumed by electricity was currently the highest, and the share was increasing yearly. Industrial carbon emissions had always been dominant, and the development of the tertiary industry was becoming increasingly prosperous. In the scenario prediction, the carbon emission factor scenario, energy intensity scenario, and economic level scenario could reach the carbon peak by 2030. Among them, the economic development level had the greatest impact on the peak and time of the future carbon peak in the Xi'an Hi-tech Zone, whereas the industrial structure scenario, energy source structure scenario, and population size scenario had no peak before 2030. The future emission reduction path mainly started from decarbonization of the power sector, stable and high-quality economic development, green upgrading of energy and industrial structure, and building a green transportation system. This can reserve more preparation time for achieving carbon neutrality and provide decision-making reference for the low-carbon development of industrial parks in China.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

Advancing sustainability in the steel industry: the key role of the triple helix sectors.

The steel industry, crucial to the global economy, grapples with critical sustainable challenges, including high energy consumption, greenhouse gas emissions, and non-renewable resource utilization, making sustainability imperative for upholding its economic role without compromising the planet or societal well-being. This study proposes a framework aimed at advancing sustainability in the steel industry through the articulation of the triple helix sectors (university, industry, and government). Based on the integrative review scientific method, systematic selection, interpretation, and synthesis of information from various sources were carried out to map a technical-scientific scenario of sustainability in the steel industry. This scenario informed benchmarking which, in light of the scientific theory and the authors' expertise, enabled the proposition of customized actions aimed at the triple helix actors. The main theoretical-scientific contribution lies in deepening and expanding the knowledge that connects sustainability to the steel industry, thus reinforcing the basis for future research and empirical studies. As for the managerial-applied contribution, this work can guide universities in developing sustainable projects and establishing industrial partnerships; steel companies benefit from the best practices and technologies, while also achieving regulatory compliance; and governments can promote public policies that boost sustainability in the steel sector.

The 2024 French guidelines for scenario design in simulation-based education: manikin-based immersive simulation, simulated participant-based immersive simulation and procedural simulation.

BACKGROUND: Simulation-based education in healthcare encompasses a wide array of modalities aimed at providing realistic clinical experiences supported by meticulously designed scenarios. The French-speaking Society for Simulation in Healthcare (SoFraSimS) has developed guidelines to assist educators in the design of scenarios for manikin- or simulated participant- based immersive simulation and procedural simulation, the three mainly used modalities. METHODS: After establishing a French-speaking group of experts within the SoFraSimS network, we performed an extensive literature review with theory-informed practices and personal experiences. We used this approach identify the essential criteria for practice-based scenario design within the three simulation modalities. RESULTS: We present three comprehensive templates for creating innovative scenarios and simulation sessions, each tailored to the specific characteristics of a simulation modality. The SoFraSimS templates include five sections distributed between the three modalities. The first section contextualizes the scenario by describing the practicalities of the setting, the instructors and learners, and its connection to the educational program. The second section outlines the learning objectives. The third lists all the elements necessary during the preparation phase, describing the educational method used for procedural simulation (such as demonstration, discovery, mastery learning, and deliberate practice). The fourth section addresses the simulation phase, detailing the behaviors the instructor aims to analyze, the embedded triggers, and the anticipated impact on simulation proceedings (natural feedback). This ensures maximum control over the learning experience. Finally, the fifth section compiles elements for post-simulation modifications to enhance future iterations. CONCLUSION: We trust that these guidelines will prove valuable to educators seeking to implement simulation-based education and contribute to the standardization of scenarios for healthcare students and professionals. This standardization aims to facilitate communication, comparison of practices and collaboration across different learning and healthcare institutions.

'What this article adds'1. The SoFraSimS provides guidelines to facilitate the development of simulation-based activities.2. These guidelines are theory-informed as well as evidence and experience-based.3. A detailed approach to writing a complete activity or scenario for procedural and immersive simulation including manikins or simulated participants is provided (the 'SoFraSimS templates').4. This work aims at standardizing practices and exchanging scenarios between simulation centers.

Climate and land use changes impact the trajectories of ecosystem service bundles in an urban agglomeration: Intricate interaction trends and driver identification under SSP-RCP scenarios.

The delivery of ecosystem services (ESs), particularly in urban agglomerations, faces substantial threats from impending future climate change and human activity. Assessing ES bundles (ESBs) is critical to understanding the spatial allocation and interactions between multiple ESs. However, dynamic projections of ESBs under various future scenarios are still lacking, and their underlying driving mechanisms have received insufficient attention. This study examined the Beijing-Tianjin-Hebei urban agglomeration and proposed a framework that integrates patch-generating land use simulation into three shared socioeconomic pathway (SSP) scenarios and clustering analysis to assess spatiotemporal variations in seven ESs and ESBs from 1990 to 2050. The spatial trajectories of ESBs were analyzed to identify fluctuating regions susceptible to SSP scenarios. The results indicated that (1) different scenarios exhibited different loss rates of regulating and supporting services, where the mitigation of degradation was most significant under SSP126. The comprehensive ES value was highest under SSP245. (2) Bundles 1 and 2 (dominated by regulating and supporting services) had the largest total proportion under SSP126 (51.92 %). The largest total proportion of Bundles 4 and 5 occurred under SSP585 (48.96 %), with the highest provisioning services. The SSP126 scenario was projected to have the least ESB fluctuation at the grid scale, while the most occurred under SSP585. (3) Notably, synergies between regulating/supporting services were weaker under SSP126 than under either SSP245 or SSP585, while trade-offs between water yield and non-provisioning services were strongest. (4) Forestland and grassland proportions significantly affected carbon sequestration and habitat quality. Climatic factors (precipitation and temperature) acted as the dominant drivers of provisioning services, particularly water yield. Our findings advocate spatial strategies for future regional ES management to address upcoming risks.

Assessing freedom from chronic wasting disease in semi-domesticated reindeer in Norway and Sweden.

Establishing freedom from disease is a key component of surveillance and may have direct consequences for trade and economy. Transboundary populations pose challenges in terms of variable legislation, efforts, and data availability between countries, often limiting surveillance efficiency. Chronic wasting disease (CWD) is a contagious prion disease of cervids. The long incubation period and slow initial epidemic growth make it notoriously difficult to detect CWD in the early phase of an epidemic. The recent emergence of CWD in wild reindeer in Norway poses a threat to approximately 250,000 semi-domesticated reindeer in Norway and 250,000 in Sweden, including transboundary populations. Here, we provide a first analysis of surveillance data (2016-2022) from all reindeer districts in Norway and Sweden to determine the probability of freedom from CWD infection. During the six years, 6017 semi-domesticated reindeer were tested in Sweden and 51,974 in Norway. Most samples came from healthy slaughtered animals (low risk). Reindeer use large and remote areas and (high risk) samples from fallen stock and animals with clinical signs were difficult to obtain. A scenario tree model was run for seven different set of values for the input parameters (design prevalence within and between districts, probability of introduction, and relative risks) to determine the effect on surveillance sensitivity. At the national level, the mean probability of disease freedom was 59.0 % in Sweden and 87.0 % in Norway by 2021. The most marked effect on sensitivity was varying the design prevalence both within and between districts. Uncertainty about relative risk ratios affected sensitivity for Sweden more than for Norway, due to the higher proportion of animals in the high-risk group in the former (13.8 % vs. 2.1 %, respectively). A probability of disease freedom of 90 % or higher was reached in 8.2 % of the 49 districts in Sweden and 43.5 % of the 46 districts in Norway for a design prevalence of 0.5 %. The probability of freedom remained below 60 % in 29 districts (59.2 %) in Sweden and 10 districts (21.7 %) in Norway. At the national level, only Norway had a sufficiently large number of samples to reach a probability of more than 95 % of disease freedom within a period of 10 years. Our cross-border assessment forms an important knowledge base for designing future surveillance efforts depending on the spatial pattern of prevalence of CWD and risk of spread.

Developing a dynamic life cycle assessment framework for buildings through integrating building information modeling and building energy modeling program.

The construction and building sector is one of the largest contributors to the global carbon emissions. Therefore, it is imperative to accurately assess the carbon emissions of buildings throughout the life cycle. Many studies conducted life cycle assessment (LCA) of buildings to evaluate carbon emissions. However, due to the lack of dynamic data, most studies adopted the static LCA methodology, which neglected the dynamic variations during life cycle stages of a building. Unlike previous studies that collected static data from questionnaires and documents, the present study aims to establish a novel dynamic life cycle assessment (D-LCA) framework for buildings by incorporating the building information modeling (BIM) and the building energy modeling program (BEMP) into the static LCA. First, a static LCA is established as the baseline scenario that covers the "cradle-to-grave" life cycle stages. A BIM model is established using Revit to obtain the inventory of building materials. The Designer Simulation Toolkit (DeST) is used as a BEMP to simulate the operating energy consumption of the studied building, taking into account changes in energy mix, climate change, and occupant behavior. At the same time, the DeST results are further used as a data input for dynamic scenarios. The D-LCA framework is applied to a high-rise commercial building in China. This study found that the difference between static and dynamic scenarios was up to 66.7 %, mainly reflected in the dynamic energy consumption during the operation phase, indicating the inaccuracy of traditional static LCA. Therefore, a D-LCA by integrating BIM and BEMP can facilitate dynamic modeling and improve the accuracy and reliability of LCA for buildings.

Multiple scenario land use simulation based on a coupled MOGA-PLUS model: a case of the Yellow River Basin.

Land use changes have profoundly influenced global environmental dynamics. The Yellow River (YR), as the world's fifth-longest river, significantly contributes to regional social and economic growth due to its extensive drainage area, making it a key global player. To ensure ecological stability and coordinate land use demand, modeling the future land allocation patterns of the Yellow River Basin (YRB) will assist in striking a balance between land use functions and the optimization of its spatial design, particularly in water and sand management. In this research, we used a multi-objective genetic algorithm (MOGA) with the PLUS model to simulate several different futures for the YRB's land use between 1990 and 2020 and predict its spatial pattern in 2030. An analysis of the spatiotemporal evolution of land use changes in the YRB indicated that construction land expansion is the primary driver of landscape pattern and structure changes and ecological degradation, with climate change also contributing to the expansion of the watershed area. On the other hand, the multi-scenario simulation, constrained by specific targets, revealed that economic development was mainly reflected in land expansion for construction. At the same time, grassland and woodland were essential pillars to support the region's ecological health, and increasing the development of unused land emerged as a potential pathway towards sustainable development in the region. This study could be used as a template for the long-term growth of other large river basins by elucidating the impacts of human activities on land use and rationalizing land resource allocation under various policy constraints.

Vehicle Lane-Changing scenario generation using time-series generative adversarial networks with an Adaptative parameter optimization strategy.

Connected and automated vehicles (CAVs) hold promise for enhancing transportation safety and efficiency. However, their large-scale deployment necessitates rigorous testing across diverse driving scenarios to ensure safety performance. In order to address two challenges of test scenario diversity and comprehensive evaluation, this study proposes a vehicle lane-changing scenario generation method based on a time-series generative adversarial network (TimeGAN) with an adaptive parameter optimization strategy (APOS). With just 13.3% of parameter combinations tested, we successfully trained a satisfactory TimeGAN and generate a substantial number of lane-changing scenarios. Then, the generated scenarios were evaluated for diversity, fidelity, and utility, demonstrating their effectiveness in capturing a wide range of driving situations. Furthermore, we employed a Lane-Changing Risk Index (LCRI) to identify the rare adversarial cases in scenarios. Compared to real scenarios, our approach generates 27 times more adversarial cases with 1.8 times higher average risk, highlighting its potential for uncovering critical safety vulnerabilities. This study paves the way for more comprehensive and effective CAV testing, ultimately contributing to safer and more reliable autonomous driving technologies.

Exploring master scenarios for autonomous driving tests from police-reported historical crash data using an adaptive search sampling framework.

Crash scenario-based testing is crucial for assessing autonomous driving safety. However, existing studies on scenario generation tend to prioritize concrete scenarios for direct testing, neglecting the construction of fundamentally functional scenarios with a broader range. Police-reported historical crash data is a valuable supplement, yet detecting all potential crash scenarios is laborious. In order to address this issue, this study proposes an adaptive search sampling framework based on deep generative model and surrogate model (SM) to extract master scenario samples from police-reported historical crash data. The framework starts with selecting representative samples from the full crash dataset as initial master scenario samples using various sampling techniques. Evaluation indexes are then constructed, and derived scenario samples are synthesized using the deep generative model. To enhance efficiency, an SM is established to replace the generative model's training and data generation process. Based on the SM, an adaptive search sampling method is developed, which iteratively adjusts the sampling strategy using the Similarity Score to achieve comprehensive sampling. Experimental results demonstrate the notable advantage of the adaptive search sampling method over other sampling methods. Furthermore, statistical analysis and visualization assessments confirm the effectiveness and accuracy of the proposed method.

A Dynamic Traffic Light Control Algorithm to Mitigate Traffic Congestion in Metropolitan Areas.

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing.