Search Results (59)

Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article

Battery electric vehicles (BEVs) are seeing widespread adoption globally due to technological improvements, lower manufacturing costs, and supportive policies aimed at reducing greenhouse gas emissions. Governments have introduced incentives such as purchase subsidies and investments in charging infrastructure, while automakers continue to broaden their electric vehicle portfolios. Although BEVs show high overall safety performance comparable to internal combustion engine vehicles (ICEVs), they also raise distinct safety challenges that merit policy attention. This review synthesizes the current literature on safety concerns associated with BEVs, with particular attention to fire risks, vehicle weight, low-speed noise levels, and unique driving characteristics. Fire safety remains a significant issue, as lithium-ion battery fires, although less frequent than those in ICEVs, tend to be more severe and difficult to manage. Strategies such as improved thermal management, fire enclosures, and standardized response protocols are essential. BEVs are typically heavier than ICEVs, affecting crash outcomes and braking performance. These risks are especially important for interactions with pedestrians and smaller vehicles. Quiet operation at low speeds can also reduce pedestrian awareness, prompting regulations for vehicle sound alerts. Together, these issues highlight the need for policies that address both emerging safety risks and the evolving nature of BEV technology. Full article

Accurate extraction of plastic greenhouses from high-resolution remote sensing imagery is essential for agricultural resource management and facility-based crop monitoring. However, the dense spatial distribution, irregular morphology, and complex background interference of greenhouses often limit the effectiveness of conventional segmentation methods. This study proposes a deep learning framework that integrates a multi-scale Transformer-based decoder with a Swin-UNet architecture to improve feature representation and extraction accuracy. To enhance geometric consistency, a post-processing strategy is introduced, combining connected component analysis and morphological operations to suppress noise and refine boundary shapes. Using GF-2 satellite imagery over Weifang City, China, the model achieved a recall of 92.44%, precision of 91.47%, intersection-over-union of 85.13%, and F1-score of 91.95%. In addition to instance-level extraction, spatial distribution and statistical analysis were performed across administrative divisions, revealing regional disparities in protected agriculture development. The proposed approach offers a practical solution for greenhouse mapping and supports broader applications in land use monitoring, agricultural policy enforcement, and resource inventory. Full article

Unmanned aerial vehicle (UAV) swarms are widely applied in various fields, including military and civilian domains. However, due to the scarcity of spectrum resources, UAV swarm clustering technology has emerged as an effective method for achieving spectrum sharing among UAV swarms. This paper introduces a distributed heterogeneous multi-agent deep reinforcement learning algorithm, named HMDRL-UC, which is specifically designed to address the cluster-based spectrum sharing problem in heterogeneous UAV swarms. Heterogeneous UAV swarms consist of two types of UAVs: cluster head (CH) and cluster member (CM). Each UAV is equipped with an intelligent agent to execute the deep reinforcement learning (DRL) algorithm. Correspondingly, the HMDRL-UC consists of two parts: multi-agent proximal policy optimization for cluster head (MAPPO-H) and independent proximal policy optimization for cluster member (IPPO-M). The MAPPO-H enables the CHs to decide cluster selection and moving position, while CMs utilize IPPO-M to cluster autonomously under the condition of certain partial channel distribution information (CDI). Adequate experimental evidence has confirmed that the HMDRL-UC algorithm proposed in this paper is not only capable of managing dynamic drone swarm scenarios in the presence of partial CDI, but also has a clear advantage over the other existing three algorithms in terms of average throughput, intra-cluster communication delay, and minimum signal-to-noise ratio (SNR). Full article

This study investigated the influence of stochastic fluctuations on financial system stability by analyzing both ordinary and fractional-order financial models under noise. The ordinary financial system experiences perturbations due to bounded random disturbances, whereas the fractional-order counterpart models memory-dependent behaviors by incorporating fractional Gaussian noise (FGN) characterized by a Hurst parameter that governs long-term correlations. This study used data generated through MATLAB simulations based on standard financial models from the literature. Numerical simulations compared system behavior in deterministic and noisy environments. The results reveal that ordinary systems experience transient fluctuations, quickly returning to a stable state, whereas fractional systems exhibit persistent deviations due to historical dependencies. This highlights the fundamental difference between integer-order and fractional-order derivatives in financial modeling. Our key findings indicate that noise significantly impacts interest rates, investment needs, price indices, and profit margins, with the fractional system displaying higher sensitivity to external shocks. These insights emphasize the necessity of incorporating memory effects in financial modeling to improve accuracy in predicting market behavior. The study further underscores the importance of adaptive monetary policies and risk management strategies to mitigate financial instability. Future research should explore hybrid models combining short-term stability with long-term memory effects for enhanced financial forecasting and stability analysis. Full article

This literature review examines the relationship between the urban environment and human health, focusing on five key parameters: air pollution, extreme temperatures, noise, green spaces, and urban mobility. A systematic review was conducted using indexed scientific databases (Scopus, Web of Science, and PubMed) and technical reports, following predefined search terms and exclusion criteria. A total of 131 publications were selected and analyzed. The study highlights the negative health effects of air pollution, heat, and noise—particularly on the respiratory, cardiovascular, nervous, and reproductive systems—especially in vulnerable populations including older adults, children, pregnant women, individuals with chronic illnesses, and those living in socioeconomically disadvantaged areas. In contrast, green spaces and sustainable mobility have shown beneficial impacts, including improvements in mental health, increased physical activity, and indirect benefits as they contribute to reducing air pollution, urban heat, and noise. Among all parameters, air pollution emerges as the most extensively studied and regulated, while significant research gaps persist in the fields of urban mobility and noise pollution. Furthermore, regulatory development remains limited across all parameters analyzed, highlighting the need for more comprehensive and consistent policy frameworks. Based on the evidence, three key urban strategies are proposed: renaturalizing cities, promoting sustainable mobility, and implementing data-driven management and educational tools. These actions are essential to create healthier, more resilient, and sustainable urban environments. Full article

Illegal, unreported, and unregulated (IUU) fishing significantly threatens marine ecosystems, disrupts the ecological balance of the oceans, and poses serious challenges to global fisheries management. This contribution presents the efficacy of China’s summer fishing moratorium using BeiDou vessel monitoring system (VMS) data from 2805 fishing vessels in the East China Sea and Yellow Sea, integrated with a deep learning framework for spatiotemporal analysis. A preprocessing protocol addressing multidimensional noise in raw VMS datasets was developed, incorporating velocity normalization and gap filling to ensure data reliability. The CNN-BiLSTM hybrid model emerged as optimal for fishing behavior classification, achieving 89.98% accuracy and an 87.72% F1 score through synergistic spatiotemporal feature extraction. Spatial analysis revealed significant policy-driven reductions in fishing intensity during the moratorium (May–August), with hotspot areas suppressed to sporadic coastal distributions. However, concentrated vessel activity in Zhejiang’s nearshore waters suggested potential illegal fishing. Post-moratorium, fishing hotspots expanded explosively, peaking in October and clustering in Yushan, Zhoushan, and Yangtze River estuary fishing grounds. Quarterly patterns identified autumn–winter 2021 as peak fishing seasons, with hotspots covering >80% of East China Sea grounds. The framework enables real-time fishing state detection and adaptive spatial management via dynamic closure policies. The findings underscore the need for strengthened surveillance during moratoriums and post-ban catch regulation to mitigate overfishing risks. Full article

High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal distance and orientation. This paper introduces a novel Reinforcement Learning (RL) approach to optimize inspection trajectories for profilometric sensors based on the boustrophedon scanning method. The RL model dynamically adjusts sensor position and tilt to ensure consistent profile distribution and high-quality scanning. We use a simulated environment replicating real-world conditions, including sensor noise and surface irregularities, to plan trajectories offline using CAD models. Key contributions include designing a state space, action space, and reward function tailored for profilometric sensor inspection. The Proximal Policy Optimization (PPO) algorithm trains the RL agent to optimize these trajectories effectively. Validation involves testing the model on various parts in simulation and performing real-world inspection with a UR3e robotic arm, demonstrating the approach’s practicality and effectiveness. Full article

This study investigates visitor soundscape perceptions in Queulat National Park (QNP), Chile, to inform biodiversity conservation strategies amid rising anthropogenic pressures. By analyzing responses at two sites—Lagoon and Overlook—during peak tourist periods, this research examines how visitor experiences align with protected area management goals. A one-minute listening exercise was followed up by a survey to gather data on perceived sounds and their appeal. The results highlight the importance of involving visitors in monitoring acoustic environments, providing managers with insights into visitor-perceived soundscape dynamics. Unique QNP ecosystem characteristics emerged, with visitors identifying anthrophonic sounds as problematic, especially at the Lagoon site. Perceptions aligned with management concerns about noise impacts from congestion, showing visitors can discern when soundscapes diverge from protected area objectives. These findings underscore the need to integrate visitor engagement into acoustic monitoring to enhance biodiversity conservation. This study advocates ongoing sound level monitoring, protective policies, and tools derived from visitor input. It promotes protected areas as educational venues in order to deepen connections with local environments through sound recognition and calls for signage to inform visitors about noise impacts. Future research should continue to explore these strategies and the potential of visitor soundscape perceptions to reshape conservation strategies and support biodiversity preservation. Full article

In today’s data–driven world, the explosive growth of network traffic often leads to network congestion, which seriously affects service performance and user experience. Network traffic scheduling is one of the key technologies to deal with congestion problems. Traditional traffic scheduling methods often rely on static rules or pre–defined policies, which make it difficult to cope with dynamically changing network traffic patterns. Additionally, the inability to efficiently manage tail contributors that disproportionately contribute to traffic can further exacerbate congestion issues. In this paper, we propose ESTS, an efficient and secure traffic scheduling based on private sketch, capable of identifying tail contributors to adjust routing and prevent congestion. The key idea is to develop a randomized admission ($RA$) structure, linking two count–mean–min (CMM) sketches. The first CMM sketch records cold items, while the second, following the $RA$ structure, stores hot items with high frequency. Moreover, considering that tail contributors may leak private information, we incorporate Gaussian noise uniformly into the CMM sketch and $RA$ structure. Experimental evaluations on real and synthetic datasets demonstrate that ESTS significantly improves the accuracy of feature distribution estimation and privacy preservation. Compared to baseline methods, the ESTS framework achieves a 25% reduction in average relative error and a 30% improvement in tail contributor identification accuracy. These results underline the framework’s efficiency and reliability. Full article

This study aims to explore the practice of Christian ocean stewardship on Taiwan’s marine wind farm policy, with a particular focus on the critically endangered Taiwanese humpback dolphins (Sousa chinensis taiwanensis). Marine wind farms, while integral to the shift toward renewable energy, present complex ethical challenges due to their adverse environmental impacts—particularly noise pollution, which poses a serious threat to vulnerable marine species. International laws have underscored the importance of preventing marine noise pollution. Although Taiwan has relevant laws and policies, their implementation and supervision in preventing marine noise pollution are inadequate. This study critically examines the anthropocentric frameworks that currently dominate Taiwan’s marine development policies, arguing that they inadequately address the moral obligations humans have toward the broader ecosystem. Through a theological reflection grounded in Christian stewardship ethics, this research advocates for a shift away from human-centered environmental policies towards a more holistic ethic that acknowledges the intrinsic value of all creation. It emphasizes that ethical stewardship requires not merely reducing harm but actively participating in the restoration and protection of ecosystems, thus extending beyond utilitarian considerations of human benefit. The plight of the Taiwanese humpback dolphin serves as a case study for exploring these ethical tensions, highlighting how the energy transition can inadvertently contribute to biodiversity loss if not approached with caution and moral responsibility. Building on this, this study proposed four key principles to guide future marine development. These principles advocate for respecting nature, responsible management, continuous innovation, and social participation and transparency. This approach not only helps guide Taiwan’s marine policies but also provides new perspectives and practical approaches for applying Christian ethics in the field of marine environmental protection. Full article

The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers. Full article

In this study, the global land surface albedo namely GAC43 was retrieved for the years 1979 to 2020 using Advanced Very High Resolution Radiometer (AVHRR) global area coverage (GAC) data onboard National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. We provide a comprehensive retrieval process of the GAC43 albedo, followed by a comprehensive assessment against in situ measurements and three widely used satellite-based albedo products, the third edition of the CM SAF cLoud, Albedo and surface RAdiation (CLARA-A3), the Copernicus Climate Change Service (C3S) albedo product, and MODIS BRDF/albedo product (MCD43). Our quantitative evaluations indicate that GAC43 demonstrates the best stability, with a linear trend of ±0.002 per decade at nearly all pseudo invariant calibration sites (PICS) from 1982 to 2020. In contrast, CLARA-A3 exhibits significant noise before the 2000s due to the limited availability of observations, while C3S shows substantial biases during the same period due to imperfect sensors intercalibrations. Extensive validation at globally distributed homogeneous sites shows that GAC43 has comparable accuracy to C3S, with an overall RMSE of approximately 0.03, but a smaller positive bias of 0.012. Comparatively, MCD43C3 shows the lowest RMSE (~0.023) and minimal bias, while CLARA-A3 displays the highest RMSE (~0.042) and bias (0.02). Furthermore, GAC43, CLARA-A3, and C3S exhibit overestimation in forests, with positive biases exceeding 0.023 and RMSEs of at least 0.028. In contrast, MCD43C3 shows negligible bias and a smaller RMSE of 0.015. For grasslands and shrublands, GAC43 and MCD43C3 demonstrate comparable estimation uncertainties of approximately 0.023, with close positive biases near 0.09, whereas C3S and CLARA-A3 exhibit higher RMSEs and biases exceeding 0.032 and 0.022, respectively. All four albedo products show significant RMSEs around 0.035 over croplands but achieve the highest estimation accuracy better than 0.020 over deserts. It is worth noting that significant biases are typically attributed to insufficient spatial representativeness of the measurement sites. Globally, GAC43 and C3S exhibit similar spatial distribution patterns across most land surface conditions, including an overestimation compared to MCD43C3 and an underestimation compared to CLARA-A3 in forested areas. In addition, GAC43, C3S, and CLARA-A3 estimate higher albedo values than MCD43C3 in low-vegetation regions, such as croplands, grasslands, savannas, and woody savannas. Besides the fact that the new GAC43 product shows the best stability covering the last 40 years, one has to consider the higher proportion of backup inversions before 2000. Overall, GAC43 offers a promising long-term and consistent albedo with good accuracy for future studies such as global climate change, energy balance, and land management policy. Full article

The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making. Full article

Urban development and population growth present new challenges for developing urban public spaces (UPS) in cities. The significance of cities as centers of integration of different socio-cultural groups is growing. Taking into account the diversity of needs and expectations of sensory-sensitive groups (women) as residents and users of urban public spaces is a key task facing modern city managers. Women’s public participation is relevant and important, because recognizing their needs and taking them into account in urban spatial policy contributes to creating cities tailored to the needs and expectations of residents and users, according to the principle of “everyone-important”. This article has goals for the identification of factors that reduce the safety and comfort of women’s activities and the most important architectural features UPS. The results of the research indicate that the determinants that reduce the friendliness of safety and comfort in UPS are a lack of lighting, lack of esthetics of space development, lack of benches and toilets, and street noise. A space tailored to women’s needs and expectations should be well-lit with a level walking and sidewalk surface and include architectural infrastructure elements such as adequate municipal sanitation (toilets), benches and urban furniture, with landscaped green space (squares, flowerbeds, trees), which is in line with the principles of universal design. This study’s results found that such factors can help create safe, egalitarian, and inclusive cities. They complete the research gap in the field of sustainable urban development and are key to developing gender-equitable urban planning and urban development policy practices. Full article