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Systematic and scientific assessments on heavy metal pollution are greatly important to protecting the coastal eco-environment. In this paper, the spatial distribution, pollution degree, ecological toxicity and possible sources of eight heavy metal elements collected from the 126 marine and 715 terrestrial surface sediments surrounding Dingzi Bay were analyzed by obtaining concentration measurements. The results revealed that the concentrations of heavy metals followed a pattern: inner bay > terrestrial areas > outer bay. Cr, Ni, Cu, Zn, Cd, Pb, and Hg were found to accumulate in fine particles (<0.063 mm), while As showed an association with specific particles (>0.25 mm and <0.016 mm). Spatial pollution patterns varied from non-polluted to low pollution levels overall, with localized contamination by individual elements. In addition to natural sources, four types of anthropogenic pollution were identified in the marine and terrestrial settings. Agricultural pollution, characterized by As predominance, exerted profound effects on both terrestrial and marine environments. Industrial pollution, featuring Hg dominance, was widespread in land environment and predominantly linked to atmospheric deposition. Traffic pollution, marked by elevated Pb and Cd, was concentrated around factories and densely populated areas. Maritime pollution, comprising Hg, Cr, Cd, and Zn, primarily occurred in the nearshore areas outside the bay. The findings of this study provide scientific data to the authorities in charge of sustainable coastal zone management in the South Yellow Sea. [ABSTRACT FROM AUTHOR]

Leaf spot disease is an extremely common disease in the growth process of maize in Northern China and its degree of harm is quite significant. Therefore, the rapid and accurate identification of maize leaf spot disease is crucial for reducing economic losses in maize. In complex field environments, traditional identification methods are susceptible to subjective interference and cannot quickly and accurately identify leaf spot disease through color or shape features. We present an advanced disease identification method utilizing YOLOv8. This method utilizes actual field images of diseased corn leaves to construct a dataset and accurately labels the diseased leaves in these images, thereby achieving rapid and accurate identification of target diseases in complex field environments. We have improved the model based on YOLOv8 by adding Slim-neck modules and GAM attention modules and introducing them to enhance the model's ability to identify maize leaf spot disease. The enhanced YOLOv8 model achieved a precision (P) of 95.18%, a recall (R) of 89.11%, an average recognition accuracy (mAP50) of 94.65%, and an mAP50-95 of 71.62%, respectively. Compared to the original YOLOv8 model, the enhanced model showcased enhancements of 3.79%, 4.65%, 3.56%, and 7.3% in precision (P), recall (R), average recognition accuracy (mAP50), and mAP50-95, respectively. The model can effectively identify leaf spot disease and accurately calibrate its location. Under the same experimental conditions, we compared the improved model with the YOLOv3, YOLOv5, YOLOv6, Faster R-CNN, and SSD models. The results show that the improved model not only enhances performance, but also reduces parameter complexity and simplifies the network structure. The results indicated that the improved model enhanced performance, while reducing experimental time. Hence, the enhanced method proposed in this study, based on YOLOv8, exhibits the capability to identify maize leaf spot disease in intricate field environments, offering robust technical support for agricultural production. [ABSTRACT FROM AUTHOR]

This paper uses nonparametric causality-in-quantiles tests to examine the asymmetric effects of climate risk perception (CRP) on the thermal and coking coal futures high-frequency returns and volatilities. The results show that CRP significantly impacts the dynamic high-frequency returns of the coal futures market, with volatility indicators exhibiting asymmetry at different percentiles and being more pronounced in a downward market. The influence of CRP on dynamic coal futures mainly transmits through continuous components, while its impact on coking coal futures primarily transmits through jump parts. Additionally, the positive and negative volatilities of coal futures are asymmetrically affected by CRP. By incorporating the climate risk perception factor, investors can better predict price fluctuations in the coal market. This study provides an important supplement to the theory of pricing climate risks, and it is beneficial for formulating financial policies related to climate risk management and promoting the sustainable development of the coal industry. [ABSTRACT FROM AUTHOR]

This study investigates the spillover effects among partisan conflict, national security policy uncertainty and tourism (i.e., tourist arrivals, exports, and stock) in the U.S. by using the TVP-VAR-based connectedness measures. Specifically, we discuss the association strength, spillover direction and dynamic linkages among the three under this framework. The results show that partisan conflict and national security policy uncertainty are net transmitters of spillovers to tourism, and those effects are more potent for inbound tourism demand than tourism stock performance. Moreover, the magnitude of spillovers among the three is time-varying and increases significantly in times of crisis, especially during the 9/11 attacks, the global financial crisis and the COVID-19 pandemic. Our results have important implications for tourism managers to develop sustainable development strategies to buffer or adapt to the uncertainty impact. [ABSTRACT FROM AUTHOR]

This study investigates the time-varying effects of three types of oil price shocks (oil demand, supply, and risk shock) on exchange rates by applying the time-varying parameter structural vector autoregression stochastic volatility (TVP-SVAR-SV) model. Through examining the impulse response of exchange rates to oil price shocks at different lag periods and time points, this paper contributes to the existing literature on the dynamic relationship between oil shocks and exchange rates. From the response at different lag periods, we find that oil price shocks have a significant time-varying impact on the exchange rate, among which oil demand shock has the most significant effect. The response of the exchange rate market to oil price shock shows an obvious short-term time-varying effect and is positive and negative alternately, with a certain periodicity. From the response at different time points, the time-varying effect of oil price shock on exchange rates is related to external shock, and is more intense during the global economic and political turmoil. This is the first empirical study using a novel method to examine the time-varying effects of oil price shock from different sources on exchange rates, providing investors and policy makers assistance to manage foreign exchange during global turmoil periods. [ABSTRACT FROM AUTHOR]

China has a wetland area of 53.42 million hectares, the fourth largest in the world; it includes all types of wetlands defined by the Ramsar Convention and has a carbon sink capacity of more than 1.71 million metric tons per year. Inland wetlands in China are mainly distributed in 10 major catchments, among which the Yellow River, the Yangtze River, the rivers in the northwest, and the rivers in the northeast each have more than 8 million hectares of wetlands. There are 4220 species of plants and 4015 species of animals in China's wetland ecosystem. The wetland resources that have been developed and utilized include edible products, reeds for paper making, peat for fertilizer, fuel for power generation, and chemical, pharmaceutical, ceramic, and building materials. However, wetland areas in China have shrunk by about 54% since 1980. In recent years, China's central government has set great store by Chinese wetlands, and although 49.03% of wetland area is now officially protected, many issues have confounded the implementation of that protection. It is imperative that knowledge gained from scientific research be used to formulate a sound wetland protection and management plan that takes into consideration social, economic, and ecological issues in a way that facilitates the sustainable use of wetland resources and informs decision-makers of the paths that must be followed to achieve that goal. [ABSTRACT FROM AUTHOR]