Search Results (115)

The growing integration of renewable energy sources (RESs) into modern power systems calls for enhanced flexibility and control mechanisms. Conventional demand response (DR) strategies, such as price-based and incentive-driven methods, often encounter challenges that limit their effectiveness. This paper proposes a novel DR approach grounded in Customer Directrix Load (CDL) and formulated through Stackelberg game theory. A bilevel optimization framework is established, with air conditioning (AC) systems and electric vehicles (EVs) serving as the main DR participants. The problem is addressed using a genetic algorithm. Simulation studies on a modified IEEE 33-bus distribution system reveal that the proposed strategy significantly improves RES accommodation, reduces power curtailment, and yields mutual benefits for both system operators and end users. The findings highlight the potential of the CDL-based DR mechanism in enhancing operational efficiency and encouraging proactive consumer involvement. Full article

Under the “Dual Carbon” strategy, collaborative governance of port atmospheric pollutants and carbon emissions is critical for low-carbon transformation. Focusing on Ningbo-Zhoushan Port (48% regional ship emissions), this study examines government, port enterprises, and public interactions. A tripartite evolutionary game model with numerical simulation reveals dynamic patterns and key factors. The results show the following: (1) A substitution effect exists between government incentive costs and penalty intensity—increased environmental governance budgets reduce the probability of government incentives, whereas higher public reporting rewards accelerate corporate emission reduction convergence. (2) Public supervision exhibits cyclical fluctuations due to conflicts between individual rationality and collective interests, with excessive reporting rewards potentially triggering free-rider behavior. (3) The system exhibits two stable equilibria: a low-efficiency equilibrium (0,0,0) and a high-efficiency equilibrium (1,1,1). The latter requires policy cost compensation, corporate emission reduction gains exceeding investments, and a supervision benefit–cost ratio greater than 1. Accordingly, the study proposes a three-dimensional “Incentive–Constraint–Collaboration” governance strategy, recommending floating penalty mechanisms, green financial instrument innovation, and community supervision network optimization to balance environmental benefits with fiscal sustainability. This research provides a dynamic decision-making framework for multi-agent collaborative emission reduction in ports, offering both methodological innovation and practical guidance value. Full article

With the increasing air traffic flow, the workload of air traffic controllers is also growing, and their proficiency directly impacts civil aviation safety and efficiency. To address the lack of clear training objectives and inconsistent evaluation methods in the initial controller training at the Southwest Air Traffic Management Bureau, this study aimed to develop and validate a core competency model for initial air traffic controllers. Referencing ICAO Document 10056, the study first defined core competencies. Subsequently, using job analysis, the behavioral event interview (BEI) method, and expert panels, a core competency model tailored to the training objectives of the Southwest ATMB was constructed. The key findings of this research include: first, the defined structure of the developed model, comprising seven competency dimensions, 21 elements, and 26 observable behaviors (OBs); second, the determination of combined weights for each dimension and indicator using questionnaire surveys, the Analytic Hierarchy Process (AHP), and the Entropy Weight Method; and third, the successful application and validation of the model. Specifically, in its application, the weighted TOPSIS method was employed to evaluate trainees in a specific group. This not only provided a ranking of trainee abilities but also facilitated in-depth analysis through radar charts of competency dimensions and box plots of OB items. These application results demonstrate the model’s effectiveness and practicality. Full article

Short-term control measures are often implemented during major events to improve air quality and protect public health. In preparation for the 11th National Traditional Games of Ethnic Minorities of China (denoted as “NMG”), held from 8 to 16 September 2019 in Zhengzhou, China, the authorities introduced several air pollution control measures, including traffic restrictions and dust control. In the study presented herein, we applied automated machine learning-based weather normalisation combined with an augmented synthetic control method (ASCM) to evaluate the effectiveness of these interventions. Our results show that the impacts of the NMG control measures were not uniform, varying significantly across pollutants and monitoring stations. On average, nitrogen dioxide (NO₂) concentrations decreased by 8.6% and those of coarse particles (PM₁₀) decreased by 3.0%. However, the interventions had little overall effect on fine particles (PM_2.5), despite clear reductions observed at the traffic site, where NO₂ and PM_2.5 levels decreased by 7.2 and 5.2 μg m⁻³, respectively. These reductions accounted for 56.3% of the NMG policy’s effect on NO₂ concentration and 73.2% of its effect on PM_2.5 concentration at the traffic site. Notably, the control measures led to an increase in ozone (O₃) concentrations. Our results demonstrate the moderate effect of the short-term NMG intervention, emphasising the necessity for holistic strategies that address pollutant interactions, such as nitrogen oxides (NO_X) and volatile organic compounds (VOCs), as well as location-specific variability to achieve sustained air quality improvements. Full article

In response to the International Maritime Organization’s (IMO) 2020 sulfur cap and China’s stricter emission control policies, this study investigates the strategic interaction between port authorities and shipowners concerning air pollution emissions from ships in port areas. Using supervisory game theory, we construct a model that captures the cost–benefit trade-offs between inspection efforts by regulators and compliance behavior by ship operators. Empirical data from Guangzhou Port in 2020—including government inspection costs, fuel substitution costs, subsidy schemes, and fine levels—are incorporated into the model to simulate equilibrium outcomes. Results indicate that while the current level of inspection has a significant deterrent effect, the probability of full compliance remains low at 34.36%, highlighting the importance of a balanced regulatory approach combining inspection, fines, and subsidies. Policy implications suggest that increased financial incentives and stronger penalties can reduce both regulatory costs and non-compliance risks. This study contributes to the literature on maritime environmental governance by providing a quantitative supervisory framework grounded in real-world port data. Full article

Air pollution poses significant threats to public health and ecological sustainability, necessitating precise air quality prediction to facilitate timely preventive measures and policymaking. Although Long Short-Term Memory (LSTM) networks demonstrate effectiveness in air quality prediction, their performance critically depends on appropriate hyperparameter configuration. Traditional manual parameter tuning methods prove inefficient and prone to suboptimal solutions. While conventional swarm intelligence algorithms have been proved to be effective in optimizing the hyperparameters of LSTM models, they still face challenges in prediction accuracy and model generalizability. To address these limitations, this study proposes an improved chaotic game optimization (ICGO) algorithm incorporating multiple improvement strategies, subsequently developing an ICGO-LSTM hybrid model for Chengdu’s air quality prediction. The experimental validation comprises two phases: First, comprehensive benchmarking on 23 mathematical functions reveals that the proposed ICGO algorithm achieves superior mean values across all test functions and optimal variance metrics in 22 functions, demonstrating enhanced global convergence capability and algorithmic robustness. Second, comparative analysis with seven swarm-optimized LSTM models and six machine learning benchmarks on Chengdu’s air quality dataset shows the ICGO-LSTM model’s superior performance. Extensive evaluations show that the proposed model achieves minimal error metrics, MAE = 3.2865, MAPE = 0.720%, and RMSE = 4.8089, along with an exceptional coefficient of determination (R² = 0.98512). These results indicate that the proposed ICGO-LSTM model significantly outperforms comparative models in predictive accuracy and reliability, suggesting substantial practical implications for urban environmental management. Full article

Targeting the autonomous decision-making problem of unmanned combat aerial vehicles (UCAVs) in a two-versus-one (2v1) within-visual-range (WVR) air combat scenario, this paper proposes a maneuver decision-making method based on tactical coordination. First, a coordinated situation assessment model is designed, which subdivides the air combat situation into optimization-driven and tactical coordinated situations. The former combines missile attack zone calculation and trajectory prediction to optimize the control quantity of a single aircraft, while the latter uses fuzzy logic to analyze the overall situation of the three aircraft to drive tactical selection. Second, a decision-making model based on a hierarchical expert system is constructed, establishing a hierarchical decision-making framework with a UCAV-coordinated combat knowledge base. The coordinated situation assessment results are used to match corresponding tactics and maneuver control quantities. Finally, an improved particle swarm optimization algorithm (I-PSO) is proposed, which enhances the optimization ability and real-time performance through the design of local social factor iterative components and adaptive adjustment of inertia weights. Air combat simulations in four different scenarios verify the effectiveness and superiority of the proposed decision-making method. The results show that the method can achieve autonomous decision making in dynamic air combat. Compared with decision-making methods based on optimization algorithms and differential games, the win rate is increased by about 17% and 18%, respectively, and the single-step decision-making time is less than 0.02 s, demonstrating high real-time performance and win rate. This research provides new ideas and methods for the autonomous decision making of UCAVs in complex air combat scenarios. Full article

Excessively pursuing the comfort of the indoor environment in buildings may increase the energy consumption of operating equipment. A non-cooperative game strategy to solve the above-mentioned problem is proposed in this paper, in which multi-comfort and economic objectives are treated as equal virtual gamers. Firstly, several kinds of electrical equipment in buildings are modeled. Secondly, a visual comfort index is established by measuring the approach, followed by the construction of multi-dimensional comfort expression, including thermal, water, and air quality in indoor environments. Then, based on game theory, the non-cooperative game model of a single entity is built by using economic and multi-comfort objectives as virtual players to avoid subjectivity in multi-objective optimization. To ensure the existence of a Nash equilibrium, the Nikaido–Isoda function is employed to reformulate the payoff function, with strategy spaces allocated based on power differences. Finally, the optimization strategy is solved by using a particle swarm optimization algorithm. The simulation results show that the proposed solution increased comfort by 31.45% and reduced economic costs by 3.89% in comparison to the multi-objective optimization algorithm. Full article

Given the significant impact of air pollution on global health, the continuous and precise monitoring of air quality in all populated environments is crucial. Unfortunately, even in the most developed economies, current air quality monitoring networks are largely inadequate. The high cost of monitoring stations has been identified as a key barrier to widespread coverage, making cost-effective air quality monitoring devices a potential game changer. However, the accuracy of the measurements obtained from low-cost sensors is affected by many factors, including gas cross-sensitivity, environmental conditions, and production inconsistencies. Fortunately, machine learning models can capture complex interdependent relationships in sensor responses and thus can enhance their readings and sensor accuracy. After gathering measurements from cost-effective air pollution monitoring devices placed alongside a reference station, the data were used to train such models. Assessments of their performance showed that models tailored to individual sensor units greatly improved measurement accuracy, boosting their correlation with reference-grade instruments by up to 10%. Nonetheless, this research also revealed that inconsistencies in the performance of similar sensor units can prevent the creation of a unified correction model for a given sensor type. Full article

This paper examines control and cooperation mechanisms for trans-regional air pollution using differential game theory. This study focuses on analyzing pollution control pathways in regions characterized by asymmetric economic development. Three models are constructed: the Nash non-cooperative game, the pollution control cost compensation mechanism, and the collaborative cooperation mechanism. These models are used to investigate optimal pollution control strategies for various regions. The findings indicate that the collaborative cooperation model substantially reduces pollution emissions and enhances overall benefits. Additionally, the pollution control cost compensation mechanism alleviates the burden of pollution control on less developed regions. Numerical analysis confirms the effectiveness of the proposed models and offers theoretical foundations and policy recommendations for regional cooperation in pollution prevention. Full article

As the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is proposed. First, important parameters, such as the aircraft in the scenario, are modeled and abstracted into a multi-objective optimization problem. Next, the problem is adapted into a single-objective optimization problem using hierarchical analysis and linear weighting. Finally, considering a problem where the convergence of the particle swarm optimization (PSO) is not enough to meet the demands of a particular scenario, the PW-PSO algorithm is proposed, introducing position weight information and optimizing the speed update strategy. To verify the effectiveness of the optimization, a 6v6 aircraft gaming simulation example is provided for comparison, and the experimental results show that the convergence speed of the optimized PW-PSO algorithm is 56.34% higher than that of the traditional PSO; therefore, the algorithm can improve the speed of decision-making while meeting the performance requirements. Full article

In the field of real-time autonomous decision-making for Unmanned Combat Aerial Vehicles (UCAVs), reinforcement learning is widely used to enhance their decision-making capabilities in high-dimensional spaces. These enhanced capabilities allow UCAVs to better respond to the maneuvers of various opponents, with the win rate often serving as the primary optimization metric. However, relying solely on the terminal outcome of victory or defeat as the optimization target, but without incorporating additional rewards throughout the process, poses significant challenges for reinforcement learning due to the sparse reward structure inherent in these scenarios. While algorithms enhanced with densely distributed artificial rewards show potential, they risk deviating from the primary objectives. To address these challenges, we introduce a novel approach: the homotopy-based soft actor–critic (HSAC) method. This technique gradually transitions from auxiliary tasks enriched with artificial rewards to the main task characterized by sparse rewards through homotopic paths. We demonstrate the consistent convergence of the HSAC method and its effectiveness through deployment in two distinct scenarios within a 3D air combat game simulation: attacking horizontally flying UCAVs and a combat scenario involving two UCAVs. Our experimental results reveal that HSAC significantly outperforms traditional algorithms, which rely solely on using sparse rewards or those supplemented with artificially aided rewards. Full article

Meteorological factors are the key drivers of air pollution. Stable weather conditions, the boundary layer height, and temperature inversion significantly influence the dispersion of particulate matter, which is also associated with the aerodynamic properties of particles. However, limited studies have been conducted on this topic in northeast China. This study investigates the influence of meteorological factors on PM_2.5 pollution under cold weather conditions, employing both qualitative and quantitative methods. The key meteorological factors considered include temperature difference, relative humidity, wind speed and direction, the boundary layer height (BLH), and temperature inversion. The stable weather index (SWI) is introduced as a quantitative measure of the stability of weather based on data from the last five winters in a typical megacity of northeast China. The monthly PM_2.5 concentrations recorded during the last five Februarys ranged from 59.79 μg/m³ to 85.68 μg/m³, with the highest daily concentration reaching 417 μg/m³. A new parameter, ‘temperature difference (ΔT)’, is defined in this study as the difference in temperature between two consecutive days, calculated by subtracting the previous day’s temperature from the current day’s. The temperature differences were found to have a significantly positive correlation with the differences in PM_2.5 concentrations (p < 0.01). The results showed that PM_2.5 pollution was associated with increased temperature, higher relative humidity, and lower wind speed, or any combination of these factors. The SWI explained 65% and 64% of the variances in air quality index (AQI) and PM_2.5 pollution, respectively. When the predicted SWI exceeds 10, the likelihood of particle pollution increases. A lower BLH, in conjunction with a thicker inversion layer, contributes to the formation of severe particle pollution. In the early stages of a winter pollution episode in Harbin, the temperature inversion layer thickened and intensified, with the inversion top height reaching approximately 200 m. The boundary layer remained below 200 m, resulting in a poor vertical dispersion condition. PM_2.5 pollution, therefore, is influenced by the combined effects of multiple meteorological factors. Our study quantitatively analyzed the characteristics of weather conditions and their impacts on air quality, which could provide scientific evidence for air pollution prediction and assist in making specific policy interventions, particularly for the upcoming ninth Asian Winter Games in Harbin in February 2025. Full article

The impact of climate change on nature-based tourism is gaining significance. This study evaluated the impacts of climate change and tourism stakeholders’ perspectives on the subject in the Maasai Mara National Reserve and World Heritage Site. Surveys and interviews were used to collect data. The main climate-related threats to tourism were heavy rain, floods, and extreme droughts. These events adversely impacted infrastructure, such as roads, bridges, and accommodation facilities, and outdoor tourism activities, such as game viewing, cultural tours, birdwatching, and hot air ballooning. They also exacerbated human–wildlife conflicts. The key challenges identified in dealing with impacts were poor planning, non-prioritizing climate change as a threat, a lack of expertise, inadequate research, and a lack of internal early warning systems. The key recommendations included prioritization of climate change planning, development of internal early warning systems, and building resilience toward climate-related disasters. This study contributes to practice by making recommendations for management and other stakeholders. It also extends the discussions of climate change and tourism to wildlife tourism destinations in Africa. Full article

State-of-the-art object detection models need large and diverse datasets for training. As these are hard to acquire for many practical applications, training images from simulation environments gain more and more attention. A problem arises as deep learning models trained on simulation images usually have problems generalizing to real-world images shown by a sharp performance drop. Definite reasons and influences for this performance drop are not yet found. While previous work mostly investigated the influence of the data as well as the use of domain adaptation, this work provides a novel perspective by investigating the influence of the object detection model itself. Against this background, first, a corresponding measure called sim-to-real generalizability is defined, comprising the capability of an object detection model to generalize from simulation training images to real-world evaluation images. Second, 12 different deep learning-based object detection models are trained and their sim-to-real generalizability is evaluated. The models are trained with a variation of hyperparameters resulting in a total of 144 trained and evaluated versions. The results show a clear influence of the feature extractor and offer further insights and correlations. They open up future research on investigating influences on the sim-to-real generalizability of deep learning-based object detection models as well as on developing feature extractors that have better sim-to-real generalizability capabilities. Full article