Search Results (896)

The high costs and time-consuming nature of space exploration missions are among the major barriers to studying deep space. The lack of samples and limited information make such studies challenging, highlighting the need for innovative solutions, including advanced data-mining techniques and tools such as geochemical modeling, as strategies for overcoming challenges in data scarcity. Geochemical modeling is a powerful tool for understanding the processes that govern the composition and distribution of elements and compounds in a system. In cosmology, space geochemical modeling could support cosmochemistry by simulating the evolution of the atmospheres, crusts, and interiors of astronomical objects and predicting the geochemical conditions of their surfaces or subsurfaces. This study uniquely focuses on the geochemical modeling of celestial bodies beyond Mars, fills a significant gap in the literature, and provides a vision of what has been done by analyzing, categorizing, and providing the critical points of these research objectives, exploring geochemical modeling aspects, and outcomes. To systematically trace the intellectual structure of this field, this study follows the PRISMA guidelines for systematic reviews. It includes a structured screening process that uses bibliographic methods to identify relevant studies. To this end, we developed the Custom Bibliometric Analyses Toolkit (CBAT), which includes modules for keyword extraction, targeted thematic mapping, and visual network representation. This toolkit enables the precise identification and analysis of relevant studies, providing a robust methodological framework for future research. Europa, Titan, and Enceladus are among the most studied celestial bodies, with spectrometry and thermodynamic models as the most prevalent methods, supported by tools such as FREZCHEM, PHREEQC, and CHNOSZ. By exploring geochemical modeling solutions, our systematic review serves to inform future exploration of distant celestial bodies and assist in ambitious questions such as habitability and the potential for extraterrestrial life in the outer solar system. Full article

Characterization of shale pore architecture forms the scientific basis for effective shale gas exploitation. Deep LMX FM shale from the Luzhou area was analyzed using SANS, LTPA, XRD, and SEM. This study quantitatively characterized the pore structure, focusing on closed-pore development and connectivity, and explored lithological controls. Pore-size distribution shows that micropores and small mesopores dominate the pore volume, with an average median pore diameter of 5.17 nm. Closed pores are abundant, indicated by a high average closed-pore ratio of 28.98%, reflecting generally poor connectivity. Pores smaller than 5 nm contribute 88.12% of the total SSA. Both pore volume and SSA correlate positively with TOC. In organic-rich and moderately organic-rich siliceous shales, these parameters also correlate positively with quartz content. In contrast, for organic-rich mixed shales, they correlate positively with clay mineral content. Among the lithofacies, organic-rich siliceous shale possesses relatively larger pore volume and SSA, along with better pore connectivity, making it the most favorable reservoir facies. Based on pore-structure characteristics and the regional structural setting, we recommend adopting close-spacing hydraulic fracturing with reduced cluster spacing in structurally stable areas to enhance stimulation. In structurally complex areas, engineering designs should prioritize risk mitigation to ensure operational success. Full article

To address the issues of parameter setting, reliance on human experience, and the limitations of traditional model-driven control methods in handling complex nonlinear dynamics in the tin smelting industrial process, this paper proposes a data-driven control approach based on improved deep reinforcement learning (RL). Aiming to reduce the tin entrainment rate in smelting slag and CO emissions in exhaust gas, we construct a data-driven environment model with an 8-dimensional state space (including furnace temperature, pressure, gas composition, etc.) and an 8-dimensional action space (including lance parameters such as material flow, oxygen content, backpressure, etc.). We innovatively design a Dual-Action Discriminative Deep Deterministic Policy Gradient (DADDPG) algorithm. This method employs an online Actor network to simultaneously generate deterministic and exploratory random actions, with the Critic network selecting high-value actions for execution, consistently enhancing policy exploration efficiency. Combined with a composite reward function (integrating real-time Sn/CO content, their variations, and continuous penalty mechanisms for safety constraints), the approach achieves multi-objective dynamic optimization. Experiments based on real tin smelting production line data validate the environment model, with results demonstrating that the tin content in slag is reduced to between 3.5% and 4%, and CO content in exhaust gas is decreased to between 2000 and 2700 ppm. Full article

This study focuses on the transfer of architectural facade styles. Using the node-based visual deep learning platform ComfyUI, the system integrates the Flux Redux and Flux Depth models to establish a modular workflow. This workflow achieved style transfer of building facades guided by deep perception, encompassing key stages such as style feature extraction, depth information extraction, positive prompt input, and style image generation. The core innovation of this study lies in two aspects: Methodologically, a modular low-code visual workflow has been established. Through the coordinated operation of different modules, it ensures the visual stability of architectural forms during style conversion. In response to the novel challenges posed by generative AI in altering architectural forms, the evaluation framework innovatively introduces a “semantic inheritance degree” assessment system. This elevates the evaluation perspective beyond traditional “geometric similarity” to a new level of “semantic and imagery inheritance.” It should be clarified that the framework proposed by this research primarily provides innovative tools for architectural education, early design exploration, and visualization analysis. This workflow introduces an efficient “style-space” cognitive and generative tool for teaching architectural design. Students can use this tool to rapidly conduct comparative experiments to generate multiple stylistic facades, intuitively grasping the intrinsic relationships among different styles and architectural volumes/spatial structures. This approach encourages bold formal exploration and deepens understanding of architectural formal language. Full article

Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting purely data-driven approaches. In this work, we develop a deep-learning Fourier Neural Operator (FNO) as a fast, physics-consistent surrogate for one-dimensional shallow-water debris-flow simulations and demonstrate its application to the Rendinara–Morino system in central Italy. A validated finite-volume solver, equipped with HLLC and Rusanov fluxes, hydrostatic reconstruction, Voellmy-type basal friction, and robust wet–dry treatment, is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the $(x,t)$ domain is trained to learn the full solution operator, mapping topography, rheological parameters, and initial conditions directly to $h(x,t)$ and $u(x,t)$, thereby acting as a site-specific digital twin of the numerical solver. On a held-out validation set, the surrogate achieves mean relative $L^2$ errors of about 6–$7\%$ for flow depth and 10–$15\%$ for velocity, and it generalizes to an unseen longitudinal profile with comparable accuracy. We further show that targeted reweighting of the training objective significantly improves the prediction of the velocity field without degrading depth accuracy, reducing the velocity error on the unseen profile by more than a factor of two. Finally, the FNO provides speed-ups of approximately $36\times$ with respect to the reference solver at inference time. These results demonstrate that combining physics-based synthetic data with operator-learning architectures enables the construction of accurate, computationally efficient, and site-adapted surrogates for debris-flow hazard analysis in data-scarce environments. Full article

Accurate gas analysis plays a critical role in aerospace missions, including spacecraft safety assurance, crew health monitoring, and deep-space scientific exploration. Although conventional gas chromatography (GC) techniques are well established, their large size, high power consumption, and long analysis time limit their applicability in modern aerospace missions that require miniaturized, low-power, and highly integrated analytical systems. The development of microelectromechanical systems (MEMS) technology provides an effective pathway for the miniaturization of gas chromatography. MEMS-based micro gas chromatography columns enable the integration of meter-scale separation channels onto centimeter-scale chips through micro- and nanofabrication techniques, significantly reducing system volume and power consumption while improving analysis speed and integration capability. Compared with conventional GC systems, MEMS µGC exhibits clear advantages in size, weight, energy efficiency, and response time. This review systematically summarizes the fundamentals, structural designs, fabrication processes, and stationary phase preparation of MEMS micro gas chromatography columns. Representative aerospace application cases along with related experimental and engineering validation studies are highlighted; we re-evaluate these systems using Technology Readiness Levels (TRL) to distinguish flight heritage from concept demonstrations and propose a standardized validation roadmap for environmental reliability. In addition, key technical challenges for aerospace deployment are discussed. This work aims to provide a useful reference for the development of aerospace gas analysis systems and the engineering application of MEMS-based technologies. Full article

As critical platforms for long-endurance ocean exploration, unmanned underwater vehicles (AUVs) play an increasingly vital role in marine surveying and resident observation. However, in extreme deep-sea environments, their energy systems face severe constraints imposed by hydrostatic pressure and thermodynamic conflicts within confined spaces. Therefore, developing energy technologies with high energy density, intrinsic safety, and high-pressure adaptability is of paramount importance. This paper provides a comprehensive review of the multi-physics coupling issues in deep-sea energy systems and the research progress of current mainstream deep-sea energy technologies. Based on energy sources and conversion principles, existing technological paths are categorized into four classes, with a detailed assessment of their performance and bottlenecks in deep-sea environments. Finally, the paper outlines key future development directions for deep-sea energy systems to provide reference for subsequent research. Full article

With the continuous expansion of high-density urban forms, residents’ opportunities for daily contact with natural environments have been increasingly reduced, making the equity of urban green space allocation a critical challenge for sustainable urban development. Existing studies have largely focused on green space quantity or accessibility at single time points, lacking systematic investigations into the spatiotemporal evolution of green space exposure (GSE) and its equity from the perspective of residents’ actual environmental experiences. GSE refers to the integrated level of residents’ contact with urban green spaces during daily activities across multiple dimensions, including visual exposure, physical accessibility, and spatial distribution, emphasizing the relationship between green space provision and lived environmental experience. Based on this framework, this study takes the central urban area of Hangzhou as the study area and integrates multi-temporal remote sensing imagery with large-scale street view data. A deep learning–based approach is developed to identify green space exposure, combined with spatial statistical methods and equity measurement models to systematically analyze the spatiotemporal patterns and evolution of GSE and its equity from 2013 to 2023. The results show that (1) GSE in Hangzhou increased significantly over the study period, with accessibility exhibiting the most pronounced improvement. However, these improvements were mainly concentrated in peripheral areas, while changes in the urban core remained relatively limited, revealing clear spatial heterogeneity. (2) Although overall GSE equity showed a gradual improvement, pronounced mismatches between low exposure and high demand persisted in densely populated areas, particularly in older urban districts and parts of newly developed residential areas. (3) The spatial patterns and evolutionary trajectories of equity varied significantly across different GSE dimensions. Composite inequity characterized by “low visibility–low accessibility” formed stable clusters within the urban core. This study further explores the mechanisms underlying green space exposure inequity from the perspectives of urban renewal patterns, land-use intensity, and population concentration. By constructing a multi-dimensional and temporally explicit analytical framework for assessing GSE equity, this research provides empirical evidence and decision-making references for refined green space management and inclusive, sustainable urban planning in high-density cities. Full article

The Cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on time, access and operational procedures. This study applies a confined-space UAV equipped with LiDAR-based SLAM navigation to document and assess the stability of the vertical rock wall leading to “La Hoya” Hall, a structurally sensitive sector of the cave. Twelve autonomous and assisted flights were conducted, generating dense LiDAR point clouds and video sequences processed through videogrammetry to produce high-resolution 3D meshes. A Mask R-CNN deep learning model was trained on manually segmented images to explore automated crack detection under variable illumination and viewing conditions. The results reveal active fractures, overhanging blocks and sediment accumulations located on inaccessible ledges, demonstrating the capacity of UAV-SLAM workflows to overcome the limitations of traditional surveys in confined subterranean environments. All datasets were integrated into the DiGHER digital twin platform, enabling traceable storage, multitemporal comparison, and collaborative annotation. Overall, the study demonstrates the feasibility of combining UAV-based SLAM mapping, videogrammetry and deep learning segmentation as a reproducible baseline workflow to inform preventive conservation and future multitemporal monitoring in Paleolithic caves and similarly constrained cultural heritage contexts. Full article

Artificial intelligence is reshaping approaches to architectural heritage conservation by enabling a deeper understanding of how people perceive and experience historic built environments. This study employs deep learning and large language models (LLMs) to explore public perceptions of the Qinghefang Historical and Cultural District in Hangzhou, illustrating how AI-driven analytics can inform intelligent heritage management and architectural revitalization. Large-scale public online reviews were processed through BERTopic-based clustering to extract thematic structures of experience, while interpretive synthesis was refined using an LLM to identify core perceptual dimensions including Hangzhou Housing & Residential Choice, Hangzhou Urban Tourism & Culture, Hangzhou Food & Dining, and Qinghefang Culture & Creative. Sentiment polarity and emotional intensity were quantified using a fine-tuned BERT model, revealing distinct affective and perceptual patterns across the district’s architectural and cultural spaces. The results demonstrate that AI-based textual analytics can effectively decode human–heritage interactions, offering actionable insights for data-informed conservation, visitors’ experience optimization, and sustainable management of historic districts. This research contributes to the emerging field of AI-driven innovation in architectural heritage by bridging computational intelligence and heritage conservation practice. Full article

Recent progress in Reinforcement Learning (RL), especially deep RL, has created new possibilities for autonomous control in complex and uncertain environments. This study explores these possibilities through a practical approach, implementing an RL agent on a custom-built CubeSat. The CubeSat, equipped with a reaction wheel for active attitude control, serves as a physical testbed for validating RL-based strategies. To mimic space-like conditions, the CubeSat was placed on a custom air-bearing platform that allows near-frictionless rotation along a single axis, simulating microgravity. Unlike simulation-only research, this work showcases real-time hardware-level implementation of a Double Deep Q-Network (DDQN) controller. The DDQN agent receives real system state data and outputs control commands to orient the CubeSat via its reaction wheel. For comparison, a traditional PID controller was also tested under identical conditions. Both controllers were evaluated based on response time, accuracy, and resilience to disturbances. The DDQN outperformed the PID, showing better adaptability and control. This research demonstrates the successful integration of RL into real aerospace hardware, bridging the gap between theoretical algorithms and practical space applications through a hands-on CubeSat platform. Full article

Gravity waves (GWs) are an important dynamic process in the planetary atmosphere. They are typically excited by convection, topography, or other sources from the lower atmosphere and propagate upwards. The GWs have a significant effect on the global atmospheric circulation on Mars. However, the lack of high-resolution data from previous observations has resulted in an insufficient understanding of GWs in the Martian atmosphere, particularly in terms of its global distribution and long-term evolution characteristics at different altitudes. Based on multiple years of Mars Climate Sounder (MCS) limb observations on board the Mars Reconnaissance Orbiter (MRO), we conducted a detailed study of the global distribution, seasonal and interannual variations in Martian atmospheric GWs with vertical wavelengths ranging from 9 to 15 km at three different altitude ranges, i.e., the low-altitude range of 200–20 Pa (Lp, ~10–30 km), the mid-altitude range of 20–2 Pa (Mp, ~30–50 km), and the high-altitude range of 2–0.2 Pa (Hp, ~50–70 km). The results indicate complex regional and north–south differences, as well as night–day variations, in the spatial distribution of GWs. Particularly, a three-wave structure of the GW activity is observed over mountainous regions in the mid-to-low latitudes of the Northern Hemisphere. The peak longitude range of this structure closely matches the mountainous terrain. In addition, our results reveal the presence of bands of GW aggregations in the mid- to-high latitudes of the Northern Hemisphere in the Mp and Hp layers, which may be caused by the instability of the polar jet. There are also obvious seasonal and interannual variations in GW activities, which are related to topography, polar jets, and large dust storms. The interannual variations in GWs imply that, in addition to the well-known large seasonal dust storms, complex interannual variations in atmospheric activity over the polar jets and in the complex topography at mid-to-low latitudes on Mars may also exist, which deserve further studies in the future. Full article

Future lunar south pole missions face dual challenges of highly variable illumination and rugged terrain that directly constrain rover mobility and energy sustainability. To address these issues, this study proposes a dynamic illumination-constrained spatio-temporal A* (DIC3D-A*) path-planning algorithm that jointly optimizes terrain safety and illumination continuity in polar environments. Using high-resolution digital elevation model data from the Lunar Reconnaissance Orbiter Laser Altimeter, a 1300 m × 1300 m terrain model with 5 m/pixel spatial resolution was constructed. Hourly solar visibility for November–December 2026 was computed based on planetary ephemerides to generate a dynamic illumination dataset. The algorithm integrates slope, distance, and illumination into a unified heuristic cost function, performing a time-dependent search in a 3D spatiotemporal state space. Simulation results show that, compared with conventional A* algorithms considering only terrain or distance, the DIC3D-A* algorithm improves CSDV by 106.1% and 115.1%, respectively. Moreover, relative to illumination-based A* algorithms, it reduces the average terrain roughness index by 17.2%, while achieving shorter path length and faster computation than both the Rapidly-exploring Random Tree Star and Deep Q-Network baselines. These results demonstrate that dynamic illumination is the dominant environmental factor affecting lunar polar rover traversal and that DIC3D-A* provides an efficient, energy-aware framework for illumination-adaptive navigation in upcoming missions such as Chang’E-7. Full article

Spontaneous living spaces are public activity venues within cities that emerge through residents’ autonomous creation and informal planning. Although these spaces may appear disorganized, they serve vital functions: fostering social interaction, enhancing community vitality, improving spatial adaptability, and increasing life satisfaction. However, research on the formation mechanisms, structural logic, resident satisfaction, and the impact of spontaneous living spaces on community vitality is limited, and there is a lack of robust research methodologies. This study aims to explore the formation mechanisms of spontaneous living spaces within historic cultural districts and their influence on community vitality. Using Wuhan’s Tanhualin National Historic and Cultural District as a case study, this research innovatively combines the Mask R-CNN deep learning model with a Random Forest regression model. The Mask R-CNN model was employed to accurately identify and perform pixel-level segmentation of 1249 spontaneous living spaces. Combined with questionnaire surveys and the Random Forest model, this study reveals non-linear relationships between key factors such as community vitality, resident satisfaction with various types of spontaneous living spaces, and crowd density. The findings show that spontaneous living spaces effectively address residents’ unmet needs for emotional connection and dynamic lifestyles—needs often overlooked by official residential planning. This research provides a reliable technical framework and quantitative decision support for regulating the formation of spontaneous living spaces, thereby enhancing residents’ quality of life and urban vitality while preserving historical character. Full article

The moldic pore-vuggy reservoirs of the Ma5₄-Ma5₁ sub-member in the Majiagou Formation, central Ordos Basin, are key targets for deep natural gas exploration, yet the alteration mechanisms and controlling factors of burial-stage pressure-released water karstification remain unclear. Herein, an integrated methodology encompassing core observation, thin-section analysis, and geochemical testing was adopted to systematically clarify the development characteristics and multi-factor coupling control mechanisms of this karst process. Results show that burial-stage pressure-released water karst is dominated by overprinting on pre-existing syndepositional and supergene pore networks, forming complex reservoir spaces via synergistic selective dissolution. The development of preferential dissolution zones is jointly controlled by differential compaction of the weathering crust, permeability heterogeneity of the overlying strata and weathered crust, and diagenetic fluid properties. After the supergene diagenetic stage, differential tectonic deformation and burial compaction induced overpressure in pore fluids, which drove acidic pressure-released water to migrate along high-permeability pathways such as the “sandstone windows” overlying the Ordovician weathering crust. These fluids preferentially dissolved high-permeability moldic pore-vuggy dolomites in paleo-karst platforms and steep slope zones, whereas tight micritic dolomites served as effective barriers. The acidic environment sustained by organic acids and H₂S in pressure-released water promoted carbonate dissolution, and carbon-oxygen isotopes as well as pyrite δ³⁴S values verify that the fluids were derived from mudstone compaction. This study reveals that the distribution of high-quality reservoirs is jointly determined by the synergistic preservation of moldic pore-vuggy systems in paleo-karst platforms and steep slopes and directional alteration of pressure-released water along preferential pathways, providing crucial geological guidance for the evaluation of deep carbonate reservoirs. Full article