Comprehensive Scenario Research Articles

Automated Visceral Adipose Tissue Segmentation and Quantification from Abdominal MRI using an Enhanced U-Net and Region-growing

The study focuses on enhancing the accuracy and reliability of visceral adipose tissue (VAT) segmentation and quantification from abdominal MRI images. Accurate segmentation of VAT is crucial for assessing obesity-related health risks, as traditional methods struggle with irregular shapes and varying intensities. The research utilizes a methodology consisting of three key modules: homomorphic filtering for intensity inhomogeneity correction, a U-Net architecture with attention mechanisms for primary segmentation, and a region-growing algorithm for refining segmentation. Homomorphic filtering effectively separates bias fields, enhancing image quality by transforming multiplicative artifacts into additive ones and removing them with high-pass filtering. This process ensures precise segmentation by maintaining high-frequency anatomical details. The U-Net model incorporates attention mechanisms and skip connections to focus on VAT regions, utilizing both local and global image contexts.The Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge dataset and the Cancer Imaging Archive (TCIA) dataset are used to train and evaluate the model. It achieves a Dice Similarity Coefficient (DSC) of up to 0.985 on the CHAOS dataset and 0.972 on the TCIA dataset, outperforming existing methods in terms of segmentation accuracy. The region-growing algorithm further refines the segmentation by expanding VAT regions from high-confidence seed points, ensuring accurate boundary delineation and reducing noise. The study's results, evaluated using k-fold cross-validation, show that the proposed methodology significantly improves VAT segmentation efficiency, achieving a median DSC of 0.96 for the CHAOS dataset and 0.95 for the TCIA dataset in the most comprehensive experimental scenario. Comparative analysis indicates that the proposed approach outperforms other models, with higher sensitivity and specificity values, highlighting its potential for clinical applications in obesity management..

Transferable situation recognition system for scenario-independent context-aware surgical assistance systems: a proof of concept.

Surgical interventions and the intraoperative environment can vary greatly. A system that reliably recognizes the situation in the operating room should therefore be flexibly applicable to different surgical settings. To achieve this, transferability should be focused during system design and development. In this paper, we demonstrated the feasibility of a transferable, scenario-independent situation recognition system (SRS) by the definition and evaluation based on non-functional requirements. Based on a high-level concept for a transferable SRS, a proof of concept implementation was demonstrated using scenarios. The architecture was evaluated with a focus on non-functional requirements of compatibility, maintainability, and portability. Moreover, transferability aspects beyond the requirements, such as the effort to cover new scenarios, were discussed in a subsequent argumentative evaluation. The evaluation demonstrated the development of an SRS that can be applied to various scenarios. Furthermore, the investigation of the transferability to other settings highlighted the system's characteristics regarding configurability, interchangeability, and expandability. The components can be optimized step by step to realize a versatile and efficient situation recognition that can be easily adapted to different scenarios. The prototype provides a framework for scenario-independent situation recognition, suggesting greater applicability and transferability to different surgical settings. For the transfer into clinical routine, the system's modules need to be evolved, further transferability challenges be addressed, and comprehensive scenarios be integrated.

Dental Implants and Orthodontic Mini-Screws in a Patient with Undiagnosed Von Willebrand’s Disease: A Case Report

Dental Implants and Orthodontic Mini-Screws in a Patient with Undiagnosed Von Willebrand’s Disease: A Case Report

Advancing autonomous vehicle safety assessment: A novel methodology for moving from functional to concrete scenarios using kinetic 3D-LiDAR and SHAP

The commercialization of autonomous driving systems is gaining momentum, yet ensuring safety remains an ongoing challenge. To address these safety concerns, autonomous vehicle safety assessment employs a scenario-based approach, which can be categorized into knowledge-based and data-driven methods. In this study, we propose a framework that extends existing data-driven approaches by addressing three critical limitations: data, method, and scenario development. Using actual driving data, including 3D LiDAR Point Cloud Data (3D-LiDAR PCD), we extracted kinetic properties such as vehicle speed, acceleration, and detected critical accident triggered vehicle (ATV). Subsequently, we employed SHAP (SHapley Additive exPlanations) to assess the importance of kinetic properties and set criteria for selecting the configuration value into scenario. Specifically, we used SHAP value to determine the optimal configuration values for each variable in concrete scenario. This study presents a comprehensive scenario development framework that not only overcomes data limitations but also provides a methodological foundation for developing scenarios that accurately reflect real world. It offers an innovative approach to addressing safety concerns in the commercialization of autonomous driving systems.

Dynamic response characteristics of molten salt solar power tower plant under rapid load changes

Utilizing molten salt STP plants in grid peak-shaving endeavors is poised to become increasingly pivotal in the forthcoming energy landscape. Investigating the dynamic response characteristics of molten salt STP systems during rapid load fluctuation is paramount for their efficient integration into the grid. In this study, a dynamic simulation model employing a lumped parameters method is present, alongside the proposition of two distinct control strategies tailored to regulate water level and load fluctuation. Moreover, to assess the resilience of STP systems to actual disturbances, three comprehensive closed-loop disturbance scenarios encompassing variations in molten salt temperature, feed water temperature, and main steam valve opening degree are conducted. Subsequently, a three-element water level control strategy is implemented to mitigate feedwater pressure and false-water level effects, and the power generation of the unit is controlled by adjusting the molten salt flow rate. Furthermore, the dynamic response characteristics of STP units during load reduction phases under varying ramp rates (3%, 6%, and 10% Pe/min) employing the two aforementioned control strategies are scrutinized, the delay effects of heat transfer and phase change processes are analyzed, and the adjustment process can be completed within 120 to 280 s, with a maximum water level overshoot of 2.5 mm. The results underscore the efficacy of the combined three-element water level control and load control strategy in ensuring safe and flexible operation during load regulation processes. Additionally, the main factors affecting the change in drum water level and the intricate coupling relationships among critical parameters are analyzed. Ultimately, these findings furnish essential guidelines for optimizing control strategy design and enhancing the flexibility of molten salt STP systems in the context of grid integration and peak load management.

A Stochastic Approach to the Power Requirements of the Electric Vehicle Charging Infrastructure: The Case of Spain

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total.

Assessment and Multiscenario Simulation of Land Use and Ecosystem Services Interactions in Inner Mongolia

ABSTRACTBalancing land development and ecological protection poses significant challenges for sustainable development in arid and semi‐arid regions like Inner Mongolia. This study uses the integrated valuation of ecosystem services (ESs) trade‐offs model and patch‐generated land‐use simulation model to analyze the impacts of land‐use/cover (LULC) changes on ESs from 2005 to 2020 and simulate scenarios for 2035. The results show that (1) ecological governance efforts improved the comprehensive ecological index (CEI), habitat quality, and soil conservation capacity but led to a decrease in the annual water yield (AWY) in the eastern region. Additionally, the conversion of grassland and forest land to construction land and farmland caused slight declines in carbon sequestration and increased nitrogen export. (2) The external driving factors of different ESs vary, and the internal relationships within ESs have also changed. Notably, changes in LULC transformed the synergy between AWY and habitat quality into a trade‐off relationship after 2010 due to increased forest and grassland. (3) Among the five land‐use scenarios, the comprehensive development scenario combined with zonal management strategies can achieve the sustainable development goals of economic growth, ecological protection, and food security. This study clarifies the internal and external factors affecting ESs in Inner Mongolia and provides a scientific basis for future LULC management and ecological governance policies. It fills a gap in the spatiotemporal simulation of ESs under multiple scenarios in this region. These findings offer valuable references for promoting sustainable development in similar arid and semi‐arid regions, highlighting the importance of integrating multi‐objective strategies and adaptive management practices to balance ecological and economic objectives.

Assessing and mitigating building fire risk based on the scenario clusters in Bangladesh

The increasing occurrence of fire hazards in residential buildings in Bangladesh has led to severe property damage, physical injuries, and fatalities, underscoring a critical research gap in effective fire risk management. This study aims to address this gap by identifying key fire risks and proposing mitigation strategies using BIM technology. The research employs a comprehensive survey and scenario clustering methodology to identify these risks and assess the efficacy of various fire-resistant materials. Data collection involved questionnaires distributed to 200 respondents, including building owners, occupants, engineers, and firefighters. These clusters are based on the metrics of fatality numbers and property damage to quantify fire risk. Findings highlight the significant role of wall and ceiling lining materials (RII = 0.919) in contributing to fire risk. Further analysis was conducted using a residential building model developed in Autodesk Revit and simulated in Pyrosim software, assessing various lining materials. Quantitative results demonstrated that fire brick is the most effective material, exhibiting the lowest heat release rate (968 kW/m2), minimum adiabatic surface temperature (28°C), minimum heat transfer coefficient (1.75 W/m2/K), and lowest surface burning rate (7.5e−08 Kg/m2/s). These results suggest that fire brick is the optimal choice for enhancing fire resistance in residential buildings.

Construction Path of "Zero Carbon" Power Plant Based on the LEAP Model

As the power industry is the primary carbon emission industry, the research on the construction path of "zero-carbon" power plants against the background of the "dual-carbon" goal must be strengthened. Considering a state-owned power generation enterprise as an example, based on the carbon emissions of the power plant in recent years, the LEAP model was constructed by combining its energy structure and geographical and climatic conditions and the baseline, energy structure adjustment, technological progress, and comprehensive scenarios were set up. The energy consumption demand under each scenario was analyzed and the future carbon emissions under each scenario were predicted. The results showed that in 2060, the total carbon emissions from the power generation sector under the technological progress and energy structure adjustment scenarios decrease by 54.55% and 75.97% compared with those in the baseline scenario, respectively, which demonstrated the large potential for carbon emission reduction from clean energy substitution and that the flexibility transformation of thermal power units and the upgrading and replacement of ultra-supercritical generating units could reduce coal consumption and decrease carbon emissions, whereas the development of CCUS technology was significant, and the construction of CCUS projects was a necessary condition for realizing carbon neutrality of power plants while retaining a certain scale of thermal power generation. Under a comprehensive scenario, "zero carbon" emissions from power plants could be realized around 2056. The results of the study provide ideas for the construction of "zero carbon" power plants.

Land Cover Simulation and Carbon Storage Assessment in Daqing City based on FLUS-InVEST Model

Considering Daqing City as the research area, the impact of land cover change on carbon storage in the future was discussed, and the hot spots of carbon sequestration capacity were identified. The future land use simulation （FLUS） model was used to simulate the land cover pattern of a natural succession scenario, ecological protection scenario, urban development scenario, and comprehensive development scenario in 2030, and the integrated valuation of ecosystem services and trade-offs （InVEST） model was combined to estimate carbon storage in 2010, 2020, and 2030. Finally, the hot spot analysis tool was used to identify the cold hot spots of carbon sequestration capacity. The results showed the following： ① From 2010 to 2020, the area of cultivated land, water, and artificial surface increased, whereas the area of other land cover types decreased, and the total carbon storage decreased by 8.6×105 t. ② The land cover change of the natural succession scenario and urban development scenario in 2030 was similar to that of 2010-2020, with carbon storage decreasing by 1.16×106 t and 1.20×106 t, respectively. The carbon storage of the comprehensive development scenario decreased by 1.00×106 t compared with that in 2020, and carbon storage of the ecological protection scenario was 5.677 7×108 t, which increased by 2.53×106 t compared with that in 2020. ③ The conversion of grassland and wetland to cultivated land was the main cause of carbon storage loss, and the main contributor of carbon storage in the ecological protection scenarios was wetland. ④ The hot spots of carbon sequestration capacity were mainly located in the wetland area, and the cold spots were mainly distributed in the central part of Daqing City. The carbon sequestration capacity of cultivated land was not significant. According to the research results, to realize the urban transformation of Daqing City, we should insist on returning farmland to forest and grass, increase the intensity of returning moisture, improve the utilization rate of urban land, and increase green infrastructure in the main urban area.

Spatiotemporal Evolution and Simulation Prediction of Ecosystem Carbon Storage in the Yellow River Basin Before and After the Grain for Green Project

Under the background of "dual carbon", the impact of the implementation of the Grain for Green project on the carbon storage of the ecosystem in the Yellow River Basin must be explored, which can serve as an important reference for improving the policy implementation of the new round of the Grain for Green project and improving the carbon sink capacity of the ecosystem in the Yellow River Basin. In this study, 1990, before the implementation of the project, was selected as the starting year of the research period, and 2020, after the implementation of the two rounds of the project, was selected as the end year of the research period. Based on the ecosystem type data from 1990 to 2020, the InVEST model was used to calculate the soil carbon pool, underground carbon pool, below carbon pool, dead organic matter carbon pool, and total carbon storage of ecosystems in the Yellow River Basin and the area where the project was implemented from 1990 to 2020. The results showed that： ① From 1990 to 2020, the area of forest ecosystem in the Yellow River Basin expanded by 26 610.06 km2, and the area of farmland decreased by 46 849.06 km2 after the implementation of two rounds of the project. Spatially, the upper reaches of the Yellow River were dominated by grassland and other ecosystems； the middle reaches of the Yellow River were dominated by farmland, forest, and grassland ecosystems； and the lower reaches of the Yellow River were dominated by farmland ecosystems. ② From 1990 to 2020, the carbon storage in the project implementation area showed a fluctuating and increasing trend, and the total carbon storage reached a peak （219.47×108 t） in 2009 and decreased to 218.59×108 t in 2020 due to the decrease of grassland ecosystem from 2010 to 2020. Spatially, the high-value areas of carbon storage were distributed in Aba Tibetan and Qiang Autonomous Prefecture of Sichuan Province and the southern tip of Gansu Province in the upper reaches of the forest and grass accumulation and in the whole of Shanxi Province and the central and southern parts of Shaanxi Province in the middle reaches. Shangluo City in Shaanxi Province and Alxa League in Inner Mongolia Autonomous Region were prefecture-level cities with the highest and lowest average carbon density. ③ In 2035, the carbon storage loss of the natural development scenario was predicted to be 0.83×108 t, and the other three scenarios would increase this loss. Under the moderate farmland return scenario, the Yellow River Basin ecosystem had the strongest carbon sequestration capacity, and the predicted carbon storage would increase by 2.72×108 t compared with that in 2020, and the deep farmland return scenario was the comprehensive optimal scenario. Therefore, in the future, the Yellow River Basin could refer to the deep farmland return scenario to optimize and adjust the implementation plan of the Grain for Green project, and the predicted value of carbon storage can provide some data support for achieving the dual carbon goal.

Achieving deep transport energy demand reductions in the United Kingdom

The transport sector is a crucial yet challenging area to decarbonize, given its heavy reliance on fossil fuel usage, carbon-intensive infrastructure and car-centric lifestyles. It remains the largest contributor to local air pollution in cities yet has the potential to improve people's physical and mental health. This research investigated the potential contribution of transport energy demand reduction to climate change mitigation and improving public health. Using a comprehensive bottom-up modelling framework, the Transport Energy and Air pollution Model (TEAM), this study provides an integrated assessment of the impacts of deep mobility-related energy demand reductions, including lifecycle carbon emissions, local air pollution and health impacts. Using a sociotechnical scenario approach and the UK as a case study, this research reveals that energy demand reductions of up to 61 % by 2050 compared to baseline levels are achievable and can enhance citizens' quality of life. Business as usual approaches which rely on a technical transition miss the legislated carbon budgets and result in higher energy demand in 2050. More comprehensive scenarios deliver a reduction of up to 72 % in total lifecycle carbon emissions by 2050, with approximately half of the reduction achieved through mode shifting and avoiding travel, while the other half comes from vehicle energy efficiency, electrification, and downsizing of the vehicle fleets. The research shows that it can lead to significant co-benefits such as improved local air pollution and public health. The feasibility and practicality of policy measures and integrated strategies identified for achieving deep transport-energy demand reductions are discussed.

CNN-GRU-Attention Neural Networks for Carbon Emission Prediction of Transportation in Jiangsu Province

With the increasing energy use and carbon emissions in the transportation industry, its impact on the greenhouse effect is gradually being recognized. Therefore, this study aims to explore the achievement of carbon emission peak and carbon neutrality in transportation through prediction. The research employs a deep learning model, the CNN-GRU-Attention model, to predict carbon emissions in the transportation industry in Jiangsu, China. We select influencing factors through an extended STIRPAT model coupled with Lasso regression, and construct the CNN-GRU-Attention traffic carbon emission prediction model according to data indicators from 1995 to 2021. The model predicts carbon emissions from the transportation industry in Jiangsu Province between 2022 and 2035 under six distinct scenarios and proposes corresponding emission reduction strategies. The results show that the model in this study has higher prediction accuracy compared with other models, with a mean absolute error (MAE) of 0.061582, root mean square error (RMSE) of 0.085025, and R2 of 0.91609 on the test set. Scenario-based predictions reveal that emission peak in the transportation industry in Jiangsu Province can be achieved under the clean development and comprehensive low-carbon scenarios, with technological innovation being the primary driver of low-carbon emission reductions. This study provides a novel approach for forecasting carbon emissions from the transportation industry and explores the implementation path of emission peak through this method.

The Effect of Partial Shadings on the Output Power of the Photovoltaic Modules Connected with Different Current and Voltage Characteristics

A mismatch in the output power of photovoltaic (PV) modules in a PV array can occur due to partial shading or a module replacement. Substitution of a module in a PV array with a new one might lead to different current and voltage characteristics between the new and existing modules and result in power losses. The amount of power loss might be increased more if the PV array experiences partial shading. For this reason, this study aims to investigate the effect of partial shadings on the power output of photovoltaic arrays with different current and voltage characteristics. The PV array under test consists of 25 units of solar modules with a total cross-tied (TCT) configuration. There are five shading conditions applied to the test PV array, i.e., the short narrow (ShN), the short wide (ShW), the long narrow (LnN), the long wide (LnW), and the diagonal. A magic square method is applied to reduce the power loss when the PV modules experience partial shading conditions. The results show that the power loss due to partial shadings, either on all identical modules or partially identical, is the same. The most significant power loss occurs in the long comprehensive shading scenario, where 80% of the modules experience shading, which is 41.30%.

Leveraging Large-scale AI Models for Transforming China's Marine Industry: A TOE Framework Analysis

As an important driving force leading a new round of scientific and technological revolution and industrial change, artificial intelligence is becoming a hot spot. Promoting the deep integration of "artificial intelligence + ocean" will help build a world-class Marine artificial intelligence industry cluster with global competitiveness. How to effectively apply artificial intelligence to the Marine industry to solve the problems of resource redundancy and information island caused by industrial agglomeration is urgent for innovation and breakthrough. In this paper, TOE and modified T-TOE framework are used to analyze the above problems, and the following conclusions are drawn: 1) From the perspective of technical factors, intelligent information technology plays an important role in promoting the construction of large-scale models at the level of hardware and software; From the perspective of organizational factors, enterprise digital transformation and open sharing platform play an important role in supporting organization construction. From the perspective of environmental factors, policy support and market orientation play an important role in encouraging the construction of large models. 2) From the perspective of T-T, the breakthrough and application of technology need to balance the contradiction between development and conflict; From the perspective of T-O, intelligent supply-demand matching and transition resistance have both positive and negative effects; From the perspective of T-E, there is a problem of actively promoting multidimensional interactive innovation and achieving a balance. Relevant studies provide new ideas for the comprehensive development scenario of Marine industry and inject new impetus into the development of Marine industry.

Medium and long-term energy demand forecasts by sectors in China under the goal of “carbon peaking & carbon neutrality”: Based on the LEAP-China model

Energy is a critical material foundation for sustainable economic and social development and national security, and it is of great significance to explore China's medium and long-term energy demand to realize the “dual-carbon” goal. Based on the current state of economic and social development and energy consumption, the LEAP-CHINA model is constructed to create baseline scenario, structural adjustment scenario, technology abatement scenario, and comprehensive scenario to forecast China's total energy demand, end-use industry subsectors, end-use energy subvarieties, processing and conversion sectors from 2022 to 2060. The results show: (1) China's total energy demand showed a “rapid increase before 2039, a slow increase between 2039 and 2049, and a gentle decrease after peaking in 2049". (2) The contribution rate of energy conservation and emission reduction was comprehensive scenario > technology abatement scenario > industrial adjustment scenario > baseline scenario > energy adjustment scenario. (3) Industry was the largest energy demand industry, and the rest of the industries' energy demand share was decreasing. (4) There is a definite trend towards power system decarbonization and energy system electrification, with non-fossil energy power generation anticipated to increase to 78%–82 % in 2060. Finally, it proposes countermeasures for China's energy development in the medium and long-term.

Study on Optimization of Land Use Structure in Fujian Province Based on Low-Carbon Perspective

Carbon peaking and carbon neutrality strategies are pivotal in addressing climate change. Optimizing land use structure is a fundamental approach to achieving low-carbon development within a given territory. This study focuses on Fujian Province as the research subject, predicting carbon emissions for the next decade by analyzing the correlation between land use types and carbon emissions using the gray model. This analysis is based on land use panel data spanning from 2007 to 2021. The study applies the FLUS-Markov model to simulate Fujian’s land use in 2030. A multi-objective optimization model is developed from a low-carbon perspective, integrating carbon emissions, economic, and ecological factors. The study explores land use under three scenarios: natural development scenario (NS), low carbon scenario (LCS), and comprehensive scenario (CS). Findings highlight the relationship between land use-related carbon emissions, urbanization, and relevant policies in Fujian. The FLUS-Markov simulations suggest that under the NS scenario, carbon emissions in 2030 will reach 77.829 million tons, an increase of 11.013 million tons from 2020. In contrast, the LCS demonstrates that optimizing land use structures can effectively balance carbon reduction, economic growth, and ecological preservation. Under the CS, 2030 emissions could be reduced by 7.2854 million tons while maintaining economic and ecological benefits. Despite variations in construction land expansion across these scenarios, all follow a “one belt, one core” development pattern. The study concludes with policy recommendations, including industrial layout optimization and clean energy promotion. These findings support the alignment of land use optimization with Fujian’s future development needs, offering guidance for land-use planning and policies focused on low-carbon objectives.

A data-driven framework for enhancing coastal flood resilience in resource-crunched developing nations

A comprehensive Flood Resilient Scenario Model ‘FReSMo’ employs a data-driven, evidence-based approach for assessing climate-induced flood risk and validating the efficacy of mangroves (as context-specific adaptation measure) in reducing residential building damage. Based on an improvised Source-Pathway-Receptor-Consequence-Evidence concept, FReSMo employs a three-step analysis. First, hazard mapping estimates coastal flood extents for various return period under different Shared Socioeconomic Pathways. Second, the model maps the exposure of residential buildings to these flood extents by projecting built-up area for 2050 using the FUTURES model, based on physiographic, socio-demographic, and economic parameters. Finally, a data-driven probabilistic damage model is applied to estimate the built-up area damage for a 100-year coastal flood event (SSP-2.6). The pre-and post-adaptation damage estimate demonstrates the efficacity of NBS (mangroves) in reducing coastal flood risk. A 100 m mangrove patch on the Sagar coastline reduced the building damage cost by 70% for a 48-hr and 75% for a 24-hr flood. Considering a plantation cost for 6.2 km2, the total benefit, despite persistent losses, was a 222% return on investment. FReSMo transcends conventional risk assessment frameworks by offering a comprehensive approach for evaluating the cost-effectiveness of adaptation investments in developing countries, making it an invaluable tool in the face of climate change.

Techno-economic assessment and transient modeling of a solar-based multi-generation system for sustainable/clean coastal urban development

To ensure the health of vulnerable coastal ecosystems, a transition to sustainable energy solutions is essential. Environmentally friendly systems powered by renewable sources offer not only a reduction in pollution but also the adaptability needed for a flexible and resilient energy future. This study proposes and comprehensively evaluates an integrated solar-based system designed to meet the daily needs of coastal cities. The proposed system incorporates key components such as dual-loop power cycles, parabolic trough solar collectors, liquefied natural gas (LNG) regasification, reverse osmosis, and proton exchange membrane electrolysis. To optimize energy utilization, the inclusion of a thermoelectric generator (TEG) is considered, harnessing the thermal gradient among the LNG stream and the power cycle fluid. We conduct transient modeling, incorporating comprehensive scenarios that account for both thermal and economic aspects. The performance evaluation of the system focuses specifically on coastal regions, with San Francisco serving as a case study. The dynamic simulation results demonstrate the capability of the integrated system in fulfilling the urban needs for one year, delivering 1,134,207 cubic meters of potable water and generating 11,306 MWh of electricity. Financial analysis reveals that the solar unit accounts for over 46 % of the total cost, with an hourly cost rate of $69.61. The levelized cost of electricity is predicted at 4.61 cents/kWh, while the levelized cost of water is calculated at 30.54 cents/m3. These findings provide valuable insights into the cost-effectiveness and competitive advantage of the system in terms of energy and water production.

Game-Theoretic Adversarial Interaction-Based Critical Scenario Generation for Autonomous Vehicles

Ensuring safety and efficiency in the rapidly advancing autonomous vehicle (AV) industry presents a significant engineering challenge. Comprehensive performance evaluations and critical scenario testing are essential for identifying situations that AVs cannot handle. Thus, generating critical scenarios is a key problem in AV testing system design. This paper proposes a game-theoretic adversarial interaction method to efficiently generate critical scenarios that challenge AV systems. Initial motion prediction for adversarial and surrounding vehicles is based on kinematic models and road constraints, establishing interaction action spaces to determine possible driving domains. A novel evaluation approach combines reachability sets with adversarial intensity to assess collision risks and adversarial strength for any state, used to solve behavior values for each interaction action state. Further, equilibrium action strategies for the vehicles are derived using Stackelberg game theory, yielding optimal actions considering adversarial interactions in the current traffic environment. Simulation results show that the adversarial scenarios generated by this method significantly increase incident rates by 158% to 1313% compared to natural driving scenarios, while ride comfort and driving efficiency decrease, and risk significantly increases. These findings provide critical insights for model improvement and demonstrate the proposed method’s suitability for assessing AV performance in dynamic traffic environments.