Search Results (171)

Anthropogenic alterations of the land surface through activities such as agriculture, forestry and urban development represent important human-induced forcings on the Earth’s climate system. This study, conducted in the framework of the UpClim project, employs the non-hydrostatic WRF-ARW v4.5.1 model forced by ERA5 reanalysis data to assess the impact of land use changes (LUCs) on the European climate. The study aims to quantify the effects of LUCs over the EURO-CORDEX domain at 0.11° resolution during 1980–1985 by comparing simulations with transient land use forcing against a control run with static land use. Full article

Against the global backdrop of energy transition, the precise assessment of urban rooftop photovoltaic (PV) system capacity is recognized as crucial for optimizing the energy structure and enhancing the sustainable utilization efficiency of spatial resources. Publicly available aerial imagery is characterized by non-orthorectified issues; direct utilization is known to lead to geometric distortions in rooftop PV and errors in capacity prediction. To address this, a dual-optimization framework is proposed in this study, integrating monocular vision-based 3D reconstruction with a lightweight linear model. Leveraging the orthogonal characteristics of building structures, camera self-calibration and 3D reconstruction are achieved through geometric constraints imposed by vanishing points. Scale distortion is suppressed via the incorporation of a multi-dimensional geometric constraint error control strategy. Concurrently, a linear capacity-area model is constructed, thereby simplifying the complexity inherent in traditional multi-parameter fitting. Utilizing drone oblique photography and Google Earth public imagery, 3D reconstruction was performed for 20 PV-equipped buildings in Wuhan City. Two buildings possessing high-precision field survey data were selected as typical experimental subjects for validation. The results demonstrate that the 3D reconstruction method reduced the mean absolute percentage error (MAPE)—used here as an estimator of measurement uncertainty—of PV area identification from 10.58% (achieved by the 2D method) to 3.47%, while the coefficient of determination (R²) for the capacity model reached 0.9548. These results suggest that this methodology can provide effective technical support for low-cost, high-precision urban rooftop PV resource surveys. It has the potential to significantly enhance the reliability of energy planning data, thereby contributing to the efficient development of urban spatial resources and the achievement of sustainable energy transition goals. Full article

Carbon dioxide is naturally present in the Earth’s atmosphere and plays a role in regulating and balancing the planet’s temperature. However, due to various human activities, the amount of carbon dioxide is increasing beyond safe limits, disrupting the Earth’s natural temperature regulation system. Today, CO₂ is the most prevalent greenhouse gas; as its concentration rises, significant climate change occurs. Therefore, there is a need to utilize anthropogenically released carbon dioxide in valuable fuels, such as formic acid (HCOOH). Single-atom catalysts are widely used, where a single metal atom is anchored on a surface to catalyze chemical reactions. In this study, we investigated the potential of Cu@Phosphorene as a single-atom catalyst (SAC) for CO₂ reduction using quantum chemical calculations. All computations for Cu@Phosphorene were performed using density functional theory (DFT). Mechanistic studies were conducted for both bimolecular and termolecular pathways. The bimolecular mechanism involves one CO₂ and one H₂ molecule adsorbing on the surface, while the termolecular mechanism involves two CO₂ molecules adsorbing first, followed by H₂. Results indicate that the termolecular mechanism is preferred for formic acid formation due to its lower activation energy. Further analysis included charge transfer assessment via NBO, and interactions between the substrate, phosphorene, and the Cu atom were confirmed using quantum theory of atoms in molecules (QTAIM) and non-covalent interactions (NCI) analysis. Ab initio molecular dynamics (AIMD) calculations examined the temperature stability of the catalytic complex. Overall, Cu@Phosphorene appears to be an effective catalyst for converting CO₂ to formic acid and remains stable at higher temperatures, supporting efforts to mitigate climate change. Full article

The spatial extent of the global land monsoon (GLM), known as the global land monsoon area, is a fundamental climate characteristic with significant socio-ecological implications. While the influence of natural external forcing on GLM intensity during the last millennium (950–1850) is becoming increasingly understood, the responses of the GLM area remain less explored. This study investigates the forced interdecadal variability in the GLM area using the Community Earth System Model Ensemble, focusing on two key drivers: global mean surface temperature (GMST) changes and variations in the tropical Pacific temperature gradient (TPTG). Our analysis reveals that these drivers explain approximately 33% of forced GLM area variance. Global cooling (Cool-GMST) and weakened Pacific gradients (Weak-TPTG) induce significant area contractions of −0.37% and −0.74%, respectively. Most notably, the response to compound forcing is highly non-linear. Concurrent episodes of strong cooling and Weak-TPTG induce a substantially amplified GLM area reduction of −1.37%, far exceeding the linear sum of the individual driver effects. This non-linear amplification, driven by synergistic decreases in both APR and SPF, challenges the conventional assumptions used to model and attribute monsoon boundary changes. This discovery of a non-linear threshold-dependent behavior in the monsoon’s spatial extent, which contrasts with the more linear response of monsoon intensity, is a key finding of our study. This distinction is critical for interpreting paleoclimate records, and serves as a strong indication that future climate projections must account for such non-linearities to avoid underestimating the risk of abrupt monsoon boundary shifts under combined natural and anthropogenic stressors. Full article

Effective energy management in Low Earth Orbit satellites is critical, as inefficient energy management can significantly affect mission objectives. The dynamic and harsh space environment further complicates the development of effective energy management strategies. To address these challenges, we propose a Deep Reinforcement Learning approach using Deep-Q Network to develop an adaptive energy management framework for Low Earth Orbit satellites. Compared to traditional techniques, the proposed solution autonomously learns from environmental interaction, offering robustness to uncertainty and online adaptability. It adjusts to changing conditions without manual retraining, making it well-suited for handling modeling uncertainties and non-stationary dynamics typical of space operations. Training is conducted using a realistic satellite electric power system model with accurate component parameters and single-orbit power profiles derived from real space missions. Numerical simulations validate the controller performance across diverse scenarios, including multi-orbit settings, demonstrating superior adaptability and efficiency compared to conventional Maximum Power Point Tracking methods. Full article

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

In this paper, we present a system-level model of a digital multibeam antenna designed for Low Earth Orbit satellite communications operating in the Ka-band. We initially develop a suitable array topology, which is based on a thinned lattice, then adopt it as the foundation for evaluating its performance within a digital beamforming architecture. This architecture is implemented in a system-level simulator to evaluate the performance of the transmitter and receiver chains. This study advances the analysis of the digital antennas by incorporating both the RF front-end and digital sections non-idealities into a digital-twin framework. This approach enhances the designer’s ability to optimize the system with a holistic approach and provides insights into how various impairments affect the transmitter and receiver performance, identifying the subsystems’ parameter limits. To achieve this, we analyze several subsystems’ parameters and impairments, assessing their effects on both the antenna radiation and quality of the transmitted and received signals in a real applicative context. The results of this study reveal the sensitivity of the system to the impairments and suggest strategies to trade them off, emphasizing the importance of selecting appropriate subsystem features to optimize overall system performance. Full article

Bacteriophages (phages), the most abundant biological entities on Earth, have long served as both model systems and therapeutic tools. Recent advances in synthetic biology and genetic engineering have revolutionized the capacity to tailor phages with enhanced functionality beyond their natural capabilities. This review outlines the current landscape of synthetic and functional engineering of phages, encompassing both in-vivo and in-vitro strategies. We describe in-vivo approaches such as phage recombineering systems, CRISPR-Cas-assisted editing, and bacterial retron-based methods, as well as synthetic assembly platforms including yeast-based artificial chromosomes, Gibson, Golden Gate, and iPac assemblies. In addition, we explore in-vitro rebooting using TXTL (transcription–translation) systems, which offer a flexible alternative to cell-based rebooting but are less effective for large genomes or structurally complex phages. Special focus is given to the design of customized phages for targeted applications, including host range expansion via receptor-binding protein modifications, delivery of antimicrobial proteins or CRISPR payloads, and the construction of biocontained, non-replicative capsid systems for safe clinical use. Through illustrative examples, we highlight how these technologies enable the transformation of phages into programmable bactericidal agents, precision diagnostic tools, and drug delivery vehicles. Together, these advances establish a powerful foundation for next-generation antimicrobial platforms and synthetic microbiology. Full article

Cigarette smoking exposes individuals to numerous toxic substances, including heavy metals. Smokers are at risk due to the accumulation of these substances in various tissues. Objective: To compare the concentrations of 41 elements in 11 brain regions, the spinal cord, the bronchial, the lungs, and the liver in smokers (n = 11) and non-smokers (n = 17). Elemental composition was determined by ICP-MS after wet digestion in a microwave system. The following toxic elements were detected at levels of µg/g w.w.: Al, Cd, Pb, Ba, As, Ni, and Tl. Significantly higher concentrations of Al were detected in bronchial and lung, and more Pb, Tl, and rare earth elements were detected in the liver of smokers compared to non-smokers. In addition, smokers had significantly lower concentrations of essential elements involved in antioxidant defense, such as Cu, in liver tissue (p = 0.033). The brain and spinal cord in smokers and non-smokers were similar in terms of chemical composition, except the insula, where smokers had greater Al accumulation (p = 0.030), the precentral gyrus, where higher amounts of As, Cd, and Mn were detected, and the septal nucleus accumbens, which preferentially accumulated Cd in smokers; however, the p-values indicate that these differences were not statistically significant. Most brain areas of smokers were characterized by higher Na content (p < 0.05). These findings prove the long-term effects of smoking, demonstrating the bioaccumulation of toxic elements, the increased levels of rare earth elements in the liver, decreased levels of elements involved in the body’s antioxidant defense, and disruption of sodium homeostasis in the brain of smokers. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

Geothermal systems play a crucial role in understanding Earth’s heat dynamics. The Yunkai Uplift in southern China exemplifies a geothermally rich region characterized by ancient lithologies and high heat flow. This study investigates the geochemical characteristics of geothermal waters in the Yunkai Uplift. Both geothermal and non-thermal water samples were collected along the Xinyi–Lianjiang (XL) Fault Zone and the Cenxi–Luchuan (CL) Fault Zone flanking the core of the Yunkai Mountains. Analytical techniques were applied to examine major ions, trace elements, and dissolved CO₂ and H₂, as well as isotopic characteristics of O, H, Sr, C, and He in water samples, allowing for an investigation of geothermal reservoir temperatures, circulation depths, and mixing processes. The findings indicate that most geothermal waters are influenced by water–rock interactions primarily dominated by granites. The region’s diverse lithologies, change from ancient Caledonian granites and medium–high-grade metamorphic rocks in the central hinterland (XL Fault Zone) to low-grade metamorphic rocks and sedimentary rocks in the western margin (CL Fault Zone). The chemical compositions of geothermal waters are influenced through mixing contacts between diverse rocks of varying ages, leading to distinct geochemical characteristics. Notably, δ¹³C_CO2 values reveal that while some samples exhibit significant contributions from metamorphic CO₂ sources, others are characterized by organic CO₂ origins. Regional heat flow results from the upwelling of mantle magma, supplemented by radioactive heat generated from crustal granites. Isotopic evidence from δ²H and δ¹⁸O indicates that the geothermal waters originate from atmospheric sources, recharged by precipitation in the northern Yunkai Mountains. After infiltrating to specific depths, meteoric waters are heated to temperatures ranging from about 76.4 °C to 178.5 °C before ascending through the XL and CL Fault Zones under buoyancy forces. During their upward migration, geothermal waters undergo significant mixing with cold groundwater (54–92%) in shallow strata. As part of the western boundary of the Yunkai Uplift, the CL Fault Zone may extend deeper into the crust or even interact with the upper mantle but exhibits weaker hydrothermal activities than the XL Fault Zone. The XL Fault Zone, however, is enriched with highly heat-generating granites, is subjected more to both the thermal and mechanical influences of upwelling mantle magma, resulting in a higher heat flow and tension effect, and is more conducive to the formation of geothermal waters. Our findings underscore the role of geotectonic processes, lithological variation, and fault zone activity in shaping the genesis and evolution of geothermal waters in the Yunkai Uplift. Full article

With the advancement of 5G-Advanced Non-Terrestrial Network (5G-A NTN) mobile communication technologies, direct satellite connectivity for mobile devices has been increasingly adopted. In the highly dynamic environment of low-Earth-orbit (LEO) satellite communications, the synchronization of satellite–ground signals remains a critical challenge. In this study, a Doppler frequency-shift estimation method applicable to high-mobility LEO scenarios is proposed, without reliance on the Global Navigation Satellite System (GNSS). Rapid access to satellite systems by mobile devices is enabled without the need for additional time–frequency synchronization infrastructure. The generation mechanism of satellite–ground Doppler frequency shifts is analyzed, and a relationship between satellite velocity and beam-pointing direction is established. Based on this relationship, a Doppler frequency-shift estimation method, referred to as DFS-BP (Doppler frequency-shift estimation using beam pointing), is developed. The effects of Earth’s latitude and satellite orbital inclination are systematically investigated and optimized. Through simulation, the estimation performance under varying minimum satellite elevation angles and terminal geographic locations is evaluated. The algorithm may provide a novel solution for Doppler frequency-shift compensation in Non-Terrestrial Networks (NTNs). Full article

Aluminum (Al) is one of the most abundant metals on Earth and is well known as an environmental neurotoxic agent in the pathogenesis of Alzheimer’s disease. Aluminum toxicity is associated with oxidative stress, reduction of antioxidant enzymes, and disruption of the balance of cellular metals, such as iron (Fe), calcium (Ca), and copper (Cu), which causes structural and functional changes in the nervous tissue of the brain or peripheral nervous system. The intake of functional foods, rich in antioxidants, such as quercetin, may be beneficial in combating oxidative stress and neurodegenerative changes in the brain. The aim of this study was to provide deeper insight into the cellular and molecular neuroprotective effects of quercetin in regulating amyloid-beta (Aβ) accumulation, tau pathology, and neuroinflammation in the Al/D-galactose-induced rat model (Al/D-gal) of AD. The results showed that quercetin successfully modulated the impaired homeostatic and neuropathological consequences of aluminum chloride and D-galactose administration over 28 days: it directly protected neurons by regulating the level of oxidative stress and antioxidants, reduced Aβ aggregation by inhibiting the activity of acetylcholinesterase (AChE), increased the survival, growth, and differentiation of nerve cells by maintaining the level of brain-derived neurotrophic factor (BDNF), and regulated microglial immunoreactivity and neuroinflammation by reducing the level of proinflammatory cytokines. The multiple effects confirm that quercetin can be applied as an alternative non-pharmaceutical approach in reducing Al-induced neurotoxicity and maintaining adaptive homeostasis, which consequently affects the functioning of the central nervous system and the whole organism. Full article

Understanding Earth as a complex, dynamic, and interconnected system is crucial to addressing the contemporary environmental challenges intensified in the Anthropocene. This article reviews foundational Earth System Science (ESS) developments, emphasizing its transdisciplinary nature and highlighting how it has evolved to address critical issues like climate change, biodiversity loss, and sustainability. Concurrently, Earth System Education (ESE) has emerged as an educational approach to foster holistic a understanding, environmental insights, and geoethical values among citizens. Integrating geoethics into ESE equips citizens with scientific knowledge and the ethical reasoning necessary for responsible decision making and proactive engagement in sustainability efforts. This article identifies system thinking and environmental insight as the key competencies that enable individuals to appreciate the interconnectedness of Earth’s subsystems and humanity’s role within this complex framework. This study advocates for embedding a holistic and geoethical view of the Earth system into formal and non-formal education, promoting inclusive, participatory, and action-oriented learning experiences. This educational shift is essential for empowering citizens to effectively address the environmental, social, and economic dimensions of sustainability, thereby fostering resilient, informed, and ethically responsible global citizenship in the Anthropocene. Full article

Rare earth-doped upconversion nanoparticles (UCNPs) can convert low-energy photons (NIRs) into high-energy photons (visible light), offering advantages such as low background signal, good stability, and excellent biocompatibility. However, exploring a strategy to combine the advantages of high efficiency, low cost, and easy fabrication of a plasmonics–UCNPs system is still a challenge. Here, we reported a metal–dielectric–metal (MDM)-type plasmonic platform based on the aluminum metasurface, which can efficiently enhance the luminescence intensity of magnetic and non-magnetic rare earth-doped UCNPs. Attributed to the strong local field effect of the nanocavities formed by the aluminum anti-transmission layer at the bottom, the fluorescence of the two types of UCNPs in such a platform can be enhanced by over 1000 folds compared with that in the conventional substrate. It is found that the deposited UCNPs amount and the aluminum pillar size can both impact the enhancement. We confirmed that the constructed MDM nanocavities could enhance and regulate the local field strength, and the optimum enhancement can be achieved by choosing proper parameters. All these findings provide an efficient way of exploring the plasmon-enhanced UCNPs luminescence system with low cost, high efficiency, and easy fabrication and can be promising in the fields of biosensing and photovoltaic devices. Full article