It is more and more acknowledged that land-use/cover dynamic change has become a key subject urgently to be dealt with in the study of global environmental change. Supported by the Landsat TM digital images, spatial patterns and temporal variation of land-use change during 1995 –2000 are studied in the paper. According to the land-use dynamic degree model, supported by the 1km GRID data of land-use change and the comprehensive characters of physical, economic and social features, a dynamic regionalization of land-use change is designed to disclose the spatial pattern of land-use change processes. Generally speaking, in the traditional agricultural zones, e.g., Huang-Huai-Hai Plains, Yangtze River Delta and Sichuan Basin, the built-up and residential areas occupy a great proportion of arable land, and in the interlock area of farming and pasturing of northern China and the oases agricultural zones, the reclamation of arable land is conspicuously driven by changes of production conditions, economic benefits and climatic conditions. The implementation of “returning arable land into woodland or grassland” policies has won initial success in some areas, but it is too early to say that the trend of deforestation has been effectively reversed across China. In this paper, the division of dynamic regionalization of land-use change is designed, for the sake of revealing the temporal and spatial features of land-use change and laying the foundation for the study of regional scale land-use changes. Moreover, an integrated study, including studies of spatial pattern and temporal process of land-use change, is carried out in this paper, which is an interesting try on the comparative studies of spatial pattern on change process and the change process of spatial pattern of land-use change.
This study examines the impact of TFP shock, world interest rate shock, domestic deposit rate shock, and fiscal policy shock on China's macroeconomic variables—such as capital credit scale and output growth rate—under the framework of endogenous and exogenous capital controls. Quantitative analysis reveals that an increase in the world interest rates has a negative impact on the domestic credit market, leading to a simultaneous decline in both household savings and capital inflows. Endogenous capital control can mitigate the adverse effect, playing a macroprudential role, whereas exogenous capital controls tend to amplify the negative shock. Expansionary fiscal policy through tax cutting proves effective in stimulating output growth rate. When facing economic downturns, priority should be given to implementing proactive fiscal measures, complemented by appropriate monetary easing with endogenous capital controls, to achieve output growth with less fluctuations.
Economic growth, development, planning, Economic history and conditions
Abstract Background Recent studies emphasize the significance of copper dyshomeostasis in neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, thereby highlighting the role of copper in neurotoxicity. Cuproptosis, a novel mechanism of copper-dependent cell death, remains underexplored, particularly concerning environmental pollutants like polystyrene nanoplastics (PS-NPs). While PS-NPs are recognized for inducing neurotoxicity through various forms of cell death, including apoptosis and ferroptosis, their potential to trigger neuronal cuproptosis has not yet been investigated. This study aims to determine whether exposure to PS-NPs induces neurotoxicity via cuproptosis and to explore the preliminary molecular mechanisms involved, thereby addressing this significant knowledge gap. Methods Seven-week-old male C57BL/6 mice were exposed to PS-NPs at dose of 12.5 mg/kg, and were co-treated with the antioxidant N-acetylcysteine (NAC). Complementary in vitro experiments were conducted using SH-SY5Y neuronal cells exposed to PS-NPs at a concentration of 0.75 mg/mL, with interventions that included the copper chelator tetrathiomolybdate (TTM), NAC, and the MAPK inhibitor PD98059. Results Exposure to PS-NPs significantly increased cerebral copper accumulation (P < 0.05) and induced cuproptosis, characterized by lipid-acylated DLAT oligomerization, dysregulation of cuproptosis regulators (FDX1, LIAS, HSP70), and mitochondrial damage. In murine models, PS-NPs elicited neurotoxicity, as evidenced by neuronal loss, decreased Nissl body density, impaired synaptic plasticity, and suppressed oxidative stress markers (GSH, SOD, Nrf2), alongside activation of the ERK-MAPK pathway, ultimately resulting in deficits in learning and memory. Treatment with NAC alleviated these adverse effects. In SH-SY5Y cells, exposure to PS-NPs resulted in reduced cell viability (p < 0.01), an effect that was mitigated by TTM. Furthermore, NAC and PD98059 were found to reverse elevated copper levels, cuproptosis markers, and mitochondrial anomalies (p < 0.05). Conclusion This study presents preliminary evidence indicating that PS-NPs may induce neuronal cuproptosis, potentially through the oxidative stress-mediated activation of the ERK-MAPK pathway, which contributes to cognitive dysfunction in mice. These findings provide insights into the potential mechanisms underlying PS-NPs neurotoxicity and highlight possible therapeutic targets, such as copper chelation or MAPK inhibition, for mitigating the neurological risks associated with nanoplastic exposure, pending further validation in human-relevant models.
The landing gear system is a major aircraft subsystem that must withstand extreme forces during ground maneuvers and absorb vibrations. While traditional systems perform well under normal conditions, their efficiency drops under varying landing and runway scenarios. This study addresses this issue by simultaneously optimizing controller coefficients, parameters of a nonlinear hydraulic actuator integrated into the traditional shock absorber, and a vibration absorber using a bee-inspired multi-objective algorithm. To demonstrate adaptability, the paper includes sensitivity analysis for three-point landings affected by added payload and touchdown speed, and robustness analysis for one- and two-point landings under emergency wind conditions. The dynamic flight equations of an Airbus A320-200 during landing are derived and solved numerically. Results show that the active shock absorber system, optimized via two bee-based algorithms, outperforms the passive system in reducing bounce and pitch displacements and momenta, suspension travel, and impact force in both time and frequency domains. This leads to significantly improved passenger comfort and potentially longer structural fatigue life, demonstrating industrial applicability.
Island endemism is shaped by complex interactions among environmental, ecological, and evolutionary factors, yet the relative contributions of topography, climate, and land cover remain incompletely quantified. We investigated the drivers of endemic plant richness across Crete, a Mediterranean biodiversity hotspot, using spatially explicit data on species distributions, topographic complexity, climatic variability, land cover, and soil characteristics. Artificial Neural Network models, a machine learning tool, were employed to assess the relative importance of these predictors and to identify hotspots of endemism. We found that total species richness, elevation range, and climatic variability were the strongest predictors of endemic richness, reflecting the role of biodiversity, topographic heterogeneity, and climatic gradients in generating diverse habitats and micro-refugia that promote speciation and buffer extinction risk. Endemic hotspots only partially overlapped with areas of high total species richness, indicating that total species richness was the optimal from the ones examined, yet an imperfect surrogate. These environmentally heterogeneous areas also provide critical ecosystem services, including soil stabilization, pollination, and cultural value, which are increasingly threatened by tourism, renewable energy development, land-use change, and climate impacts. Our findings underscore the importance of prioritizing mountainous and climatically variable regions in conservation planning, integrating ecosystem service considerations, and accounting for within-island spatial heterogeneity. By explicitly linking the environmental drivers of endemism to both biodiversity patterns and ecosystem function, this study provides a framework for evidence-based conservation planning in Crete and other Mediterranean islands with similar geological and biogeographic contexts.
Luigi Russo, Antonietta Sorriso, Silvia Liberata Ullo
et al.
Land Cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformer-based Swin-Unet architecture with seasonal synthesized spatio-temporal images has been employed to classify LC types using spatio-temporal features extracted from Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data, organized into seasonal clusters. The study focuses on three distinct regions - Amazonia, Africa, and Siberia - and evaluates the model performance across diverse ecoregions within these areas. By utilizing seasonal feature sequences instead of dense temporal sequences, notable performance improvements have been achieved, especially in regions with temporal data gaps like Siberia, where S1 data distribution is uneven and non-uniform. The results demonstrate the effectiveness and the generalization capabilities of the proposed methodology in achieving high overall accuracy (O.A.) values, even in regions with limited training data.
Kazuki Numazato, Keiichiro Kan, Masaki Kitagawa
et al.
Flapping-wing drones have attracted significant attention due to their biomimetic flight. They are considered more human-friendly due to their characteristics such as low noise and flexible wings, making them suitable for human-drone interactions. However, few studies have explored the practical interaction between humans and flapping-wing drones. On establishing a physical interaction system with flapping-wing drones, we can acquire inspirations from falconers who guide birds of prey to land on their arms. This interaction interprets the human body as a dynamic landing platform, which can be utilized in various scenarios such as crowded or spatially constrained environments. Thus, in this study, we propose a falconry-like interaction system in which a flapping-wing drone performs a palm landing motion on a human hand. To achieve a safe approach toward humans, we design a trajectory planning method that considers both physical and psychological factors of the human safety such as the drone's velocity and distance from the user. We use a commercial flapping platform with our implemented motion planning and conduct experiments to evaluate the palm landing performance and safety. The results demonstrate that our approach enables safe and smooth hand landing interactions. To the best of our knowledge, it is the first time to achieve a contact-based interaction between flapping-wing drones and humans.
Rayees Ahmed, Taha Shamim, Joshal Kumar Bansal
et al.
Climate change poses significant challenges to the Himalayas, a region characterised by its fragile ecosystems and vulnerable communities dependent on environmental resources. Accurate climate data are crucial for understanding regional climatic variations and assessing climate change impacts, particularly in areas with limited observational networks. This study represents a pioneering effort in evaluating climatic fluctuations in the Jhelum basin, located in the North Western Himalayas, by utilising a diverse range of gridded meteorological datasets (APHRODITE, CHIRPS, CRU, and IMDAA) alongside observed climate data from the Indian Meteorological Department. The primary goal is to identify the most effective gridded climate data product for regions with limited data and to explore the potential of combining gridded data sets with observed data to understand climatic variability. Findings indicate a consistent upward trend in temperature across all datasets, with varying rates of increase. CRU records a rise of 1 °C in Tmax and 1.6 °C in Tmin, while APHRODITE shows a Tmean increase of approximately 1 °C. IMDAA reports increases in Tmax and Tmin. Observed mean annual Tmax and Tmin show net increases of 1 °C and 0.6 °C, respectively. Regarding precipitation, all datasets except IMDAA exhibit an increasing trend, contrary to observed data, which decreases from 1266 mm to 1068 mm over 40 years. CHIRPS, CRU, and APHRODITE display increasing trends, while IMDAA aligns closely with observed data but tends to overestimate precipitation by about 30%. Our research identifies IMDAA as the most suitable gridded climate data for the Jhelum basin in the North-western Himalayas. Despite some discrepancies in precipitation trends, IMDAA closely aligns with observed data, providing valuable insights for scholars and policymakers navigating climate data uncertainties in complex environments. Our findings contribute to informed decision-making and effective climate change mitigation strategies in the region.