Search Results (2,743)

This study integrates LA-ICP-MS zircon U–Pb ages and the first zircon trace element data from metasedimentary and metaigneous rocks of the Nyong Complex (NyC) in the NW Congo Craton, southern Cameroon, to constrain its petrogenesis, tectonic setting, and crustal evolution. Chondrite-normalized REE patterns show strong HREE enrichment, depleted LREE–MREE, and pronounced positive Ce and negative Eu anomalies, indicating a magmatic origin for the zircons. Trace element signatures suggest that the zircons derived from continental crustal magmas generated under variable oxidation conditions in a long-lived arc-related tectonic environment. Detrital zircon ages range from Archean to Paleoproterozoic, with five major age peaks at 2885 ± 8 Ma, 2775 ± 6 Ma, 2654 ± 7 Ma, 2469 ± 11 Ma, and 2316 ± 11 Ma. These ages correspond to major magmatic and metamorphic events recognized in both the Congo and São Francisco cratons. The preservation of felsic continental crust between 2.9 and 2.2 Ga in the NyC and the Borborema Province (NE Brazil) likely records a critical transition in Earth’s geodynamic regime, marked by enhanced consumption and recycling of mafic crust during Proterozoic accretion compared to the late Archean. This transition reflects the onset of modern-style plate tectonics, enabling craton stabilization and contributing to the assembly of the Nuna/Columbia supercontinent. The NyC is thus interpreted as part of the Trans-Amazonian belt, analogous to that in NE Brazil, and formed during the collision between the Congo and São Francisco cratons. Full article

Peen forming is a dieless manufacturing process used to shape large, thin aerospace panels through controlled shot impacts that induce residual stresses and curvature. Despite long-standing industrial use, process monitoring still depends largely on indirect proxies such as Almen intensity and coverage, limiting spatially resolved deformation assessment and hindering closed-loop control. In parallel, vision-based artificial intelligence (AI) has enabled adaptive monitoring and feedback in smart-manufacturing domains such as welding, additive manufacturing, and sheet forming. This review examines how such sensing and learning strategies can be transferred to adaptive peening forming. We compare six vision sensing modalities and assess major AI model families for surface mapping, temporal prediction, robustness, and deployment maturity. The synthesis shows that progress is primarily constrained by limited validated datasets, harsh in-cabinet sensing conditions, scarce closed-loop demonstrations, and weak validation on curved aerospace geometries. We conclude that the sensing and AI foundations for adaptive peen forming are already emerging, but industrial translation now depends on stronger experimental validation, standardized benchmarking, robust multi-sensor integration, and edge-capable feedback pipelines. Full article

A systematic assessment of the human settlement suitability (HSS) and its structural resilience in metropolitan areas, from a spatial network perspective, is essential for understanding the spatial organization and evolutionary mechanisms of regional human settlement systems. It also supports the high-quality development of metropolitan areas. This study considers the Shenyang Metropolitan Area as the research object and constructs a comprehensive evaluation model of HSS from two dimensions: natural environmental suitability (NES) and human environmental suitability (HES). This study systematically analyzes the spatial distribution pattern of HSS, characteristics of its spatial association network, and its structural resilience, by integrating a modified gravity model, social network analysis (SNA), and structural resilience measurement methods. The results indicate that NES exhibits a high-west to low-east gradient, with high-value areas primarily located in peripheral regions with better ecological conditions. HES reveals a pronounced core–periphery structure, with high suitability concentrated in core cities and their adjacent suburban areas. Under the combined influence of NES and HES, the HSS forms a layered differentiation pattern dominated by core cities. The spatial association network of HSS has an overall low density and displays the coexistence of a core–periphery structure and proximity dependence, in which the HES network demonstrates strong cross-node transmission capacity, while the NES network is significantly constrained by geographical proximity. The structural resilience of the network is characterized by a moderate hierarchy, predominantly homophilic matching, limited transmission efficiency, and pronounced spatial differentiation in aggregation, indicating an overall pattern of highly connected cores with low aggregation and moderately or weakly connected nodes with high aggregation. The findings provide a scientific basis for optimizing the human settlements and enhancing regional resilience governance in metropolitan areas, while offering a novel analytical perspective for research on human settlement systems. Full article

Bismuth oxychloride (BiOCl) has garnered significant research interest owing to its non-toxicity, affordability, and distinct layered structure. Although BiOCl possesses promising photocatalytic potential, its large band gap and rapid photocarrier recombination restrict its practical use. In this work, a natural tourmaline mineral was effectively integrated with BiOCl to form a composite (TBO). Comprehensive characterization and photocatalytic assessments revealed that the intrinsic electric field of tourmaline notably strengthened both the adsorption capacity and the light-driven catalytic efficiency of BiOCl. Under visible-light irradiation, ofloxacin (OFX, 10 ppm) was eliminated by approximately 98% within 60 min. The apparent reaction rate constant (k) of TBO was 0.0407 min⁻¹, which was approximately 184.8 and 2.26 times those of tourmaline alone and pristine BiOCl, respectively. Furthermore, both the visible-light absorption and the separation efficiency of photogenerated electron–hole pairs were significantly enhanced. After evaluating its behavior under various simulated natural environmental conditions, TBO displayed strong potential for practical application. Reactive species trapping and analysis identified singlet oxygen (¹O₂) and superoxide radicals (·O₂⁻) as the primary active species in photocatalysis. Moreover, the degradation route of ofloxacin and the toxicity of its intermediates were systematically examined. These findings offer meaningful guidance for improving photocatalytic materials by utilizing naturally occurring minerals. Full article

Zinc oxide (ZnO), silver (Ag) and titanium dioxide (TiO₂) nanoparticles (NPs), are three of the most widely manufactured NPs, while composite NPs have gained popularity due to their enhanced properties. NP release in environmental matrices increases chances of bioavailability and subsequent impact on human health. The current study focuses on manufacturing, characterization and cyto-genotoxic assessment of Ag, ZnO/Ag, TiO₂ and TiO₂/Ag NPs with and without humic acids (HAs), aiming for a holistic approach that leads to a comprehensive profile of said NPs. It entails (a) the synthesis of the aforementioned NPs via single-nozzle Flame Spray Pyrolysis (SN-FSP); (b) the characterization of NPs (in powder form and in dispersion media) using Powder X-ray Diffraction (PXRD), Transmission Electron Microscopy (TEM) and Dynamic Light Scattering (DLS); and (c) the assessment of their genotoxicity and cytotoxicity against human lymphocytes in presence of two HAs, thus simulating actual environmental conditions, and without HAs, through the cytokinesis block micronucleus assay (CBMN) with cytochalasin-B. No genotoxicity was observed in any case, whereas cytotoxicity induction varied depending on the NP and the presence or absence of the two HAs. Therefore, it is indispensable to evaluate the toxic profile of NPs considering different environmental scenarios, while conducting an integrated characterization of NPs. Full article

Urban heritage structures are most commonly understood as memorial artifacts, tourism assets, or redevelopment resources. While this common view acknowledges cultural and economic value, it overlooks a deeper function of heritage within the long evolution of human settlements. This paper advances a counter thesis: in addition to its historic contingencies and power relationships—which are real, but only part of the picture—urban heritage embodies valuable but often hidden intelligence that is highly relevant to contemporary urban challenges. Specifically, heritage environments encode useful structured information about spatial configurations that have gained adaptive value over time in a process known as stigmergy. Drawing on complexity science, network theory, the mathematics of symmetry, and theories of extended cognition, the paper argues that enduring urban forms persist not only for symbolic or historical reasons, but because they embed structural properties conducive to resilience, legibility, social interaction, climatic adaptation, and human well-being. Recurring characteristics include fine-grained network connectivity, fractal scaling hierarchies, organized symmetry, articulated thresholds, and biophilic integration. Evidence from environmental psychology, public health, and urban morphology suggests that such properties correlate with reduced stress, increased walkability, stronger social capital, and improved ecological performance. The paper proposes a methodological framework—what we call the Adaptive Patterns Model—for identifying, evaluating, and translating this embedded intelligence into contemporary regeneration practice. The Model is presented as a four-phase, conceptually synthesized framework—integrating insights from complexity science and stigmergy, urban morphological analysis, and pattern-language methodology—comprising documentation, pattern extraction, encoding, and performance correlation. It concludes by challenging a still-prevalent assumption that contemporary conditions invalidate accumulated spatial knowledge. Instead, urban heritage is understood as adaptive capital within an ongoing evolutionary process, offering a structurally grounded foundation for resilient urban transformation. Full article

Search coil magnetometers (SCMs) are widely used in space science missions to measure time-varying magnetic fields. However, conventional SCM designs often increase sensor mass and electronic power consumption in order to meet mission-specific sensitivity requirements. This study presents the design and ground-based test results of a space search coil magnetometer (SSCM) concept aimed at reducing sensor mass and electronic power consumption while maintaining practical system operability for platform-constrained missions. Mass reduction was achieved by adopting a rolling-sheet core configuration. In addition, printed circuit board (PCB)-based interconnections between segmented windings were implemented to improve the reproducibility of assembly and mechanical robustness without additional structural complexity. Power reduction was achieved by employing an application-specific integrated circuit (ASIC)-based sensor amplifier and a compact control electronic unit implemented as a modular stack with a 1U CubeSat standard board form factor. Performance tests confirmed the stable operation of the integrated sensor–electronics chain over the target measurement band. The system-level noise-equivalent magnetic induction (NEMI) measured under laboratory conditions was 33 fT/√Hz at 1 kHz. Environmental tests including vibration and thermal cycling were performed to further verify the structural safety and functional stability of the sensor assembly under space-relevant conditions. The proposed SSCM architecture provides a practical approach for implementing low-mass and low-power magnetic field instruments for platform-constrained space missions. Full article

The purpose of this paper is to present the development of an engineering level code for calculating hypersonic aerodynamics and convective heating, HI-Mach. Novel to this paper are the use of analytic methods for streamline tracing and the direct differentiation of geometric sensitivities for both forces and heat load. Independent panel inclination methods calculate the pressure distribution on the surface of a hypersonic vehicle. Normal shock relations provide the thermodynamic state on each panel. Streamlines are integrated using closed-form streamline equations. Flat plate formulas corrected for compressibility calculate the skin friction coefficient and acreage heat flux on each panel. Formulas for heating on stagnation points and lines, including effects of ellipticity and sweep, are used to calculate stagnation region heating. A method for obtaining the sensitivities of a quantity of interest with respect to the geometry in a hypersonic panel code is described. These are obtained using direct analytical derivatives. The approach is precise and has been thoroughly tested against finite differencing. HI-Mach provides results orders of magnitude faster than would be obtained by CFD. Results from HI-Mach are analyzed and compared to experimental results for the HL-20 lifting body geometry. For the aerodynamic characteristics, HI-Mach predicted force coefficients within 12% of experimental results at $M\infty =4.5$ and 21% at $M_{\infty }=1.6$. Heating results for the HL-20 match experimental and CFD results to within 20% over a wide range of operating conditions. Full article

To evaluate the coupled deformation of existing shield tunnels induced by multi-segment excavations with isolation piles, this study develops an integrated analytical framework combining a Kerr three-parameter foundation-plate model with a three-dimensional image-source solution. A closed-form expression for the soil displacement field is first derived by incorporating layered soil conditions, staged excavation, and associated spatial effects. The soil–pile interaction of isolation piles is then modeled using the Kerr foundation, and the flexural response is obtained through variational formulation and finite-difference discretization. These responses are sequentially propagated through the excavation stages, enabling the superposition of multi-pit effects on the final retaining-wall deformation. The image-source method and a volume-equivalent transformation are further used to convert wall deformation into an additional stress field acting on the tunnel, which is ultimately coupled with a tunnel–soil deformation–coordination model to compute horizontal tunnel displacements. This unified workflow establishes a continuous mechanical transfer chain—from excavation-induced soil loss to isolation-pile bending and finally tunnel deformation. Parametric analyses show that lateral displacement of the retaining structure is jointly governed by wall bending and pit-bottom uplift, producing a right-skewed “S-shaped’’ profile. The bending-moment peak shifts toward earlier-excavated zones, indicating a memory effect of excavation sequencing. Two engineering cases verify that the proposed method accurately reproduces the magnitude and depth of measured wall deflections, while predicted tunnel displacements show a near-Gaussian pattern with high accuracy near the peak. The analytical framework provides a robust theoretical basis for optimizing pit segmentation and excavation sequencing adjacent to shield tunnels. Full article

This study proposes a novel parameterized hollow spiral lattice (HSL) structure designed for additive manufacturing (AM). The structure is composed of two right-handed and two left-handed spiral members. Its unit cell is formed by sweeping a circular ring cross-section along a cylindrical helical path, creating a porous topology that integrates continuous flow channels with structural load-bearing capability. An analytical model correlating key design parameters, including spiral radius, helix angle, and tube inner/outer diameters, with the structural relative density is established. Considering the manufacturability constraints of Laser Powder Bed Fusion (LPBF), an adaptive parametric design framework is developed to simultaneously optimize the geometry, relative density, and process feasibility. Ti6Al4V HSL samples were fabricated using LPBF. Their thermo–mechanical performance was systematically characterized through Computational Fluid Dynamics (CFD) simulations, Finite Element Analysis (FEA), and quasi-static compression experiments. Thermal analysis under internal and internal–external flow conditions reveals that the centrifugal force induced by the spiral geometry generates Dean vortices. This enhances momentum exchange between the central mainstream and near-wall fluid, significantly improving radial mixing, promoting temperature uniformity, and effectively suppressing local hot spots. Mechanically, the HSL exhibits significantly superior specific strength and stiffness compared to traditional body-centered cubic (BCC) and diamond lattices, approaching the performance of cubic topology, thus demonstrating outstanding lightweight load-bearing potential. The developed HSL structure presents a promising innovative design strategy for next-generation applications requiring integrated thermal management and structural load-bearing functions. Full article

Accurate prediction of transient melt pool thermal fields in Laser Powder Bed Fusion (LPBF) is essential for understanding melt pool geometry and defect formation mechanisms, yet conventional finite element methods (FEM) impose prohibitive computational costs for parametric process exploration. A variational physics-informed neural network (VPINN) framework is presented for 3D transient thermal modeling of a GH3536 single-track LPBF scan. The framework incorporates a continuously differentiable Goldak double-ellipsoid moving heat source, temperature-dependent thermophysical property surrogates, and an effective heat-capacity treatment of latent heat associated with solid–liquid phase change and vaporization. These components are embedded in a weak-form residual-minimization scheme with octree-adaptive domain decomposition, hierarchical Legendre test functions, and sequential sliding-window time marching. Effective absorptivity is inferred jointly with the network parameters, using sparse experimental melt pool profiles as supervision. Within a parametric study covering laser powers from 100 to 140 W and scan speeds from 1000 to 1500 mm/s, the predicted melt pool width, depth, and aspect ratio agree closely with FEM benchmarks and cross-sectional optical micrograph measurements across both supervised and held-out interpolation conditions, with total relative L₂ nodal temperature errors ranging from 3.23% to 6.75%. Following a one-time offline training investment of 15,323 s that simultaneously resolves the full parametric space, surrogate inference reduces per-condition query time from 3000–4000 s (FEM) to merely 4–5 s, delivering a speedup of two to three orders of magnitude and making the framework increasingly cost-effective for high-throughput parametric studies and digital-twin integration as the number of queried conditions grows. Full article

Tension-type headache (TTH) is the most prevalent primary headache disorder worldwide, contributing substantially to individual disability and global socioeconomic burden. Despite its high prevalence, TTH remains clinically heterogeneous, with episodic and chronic forms influenced by the dynamic interplay of peripheral, central, psychosocial, and lifestyle-related mechanisms. Peripheral musculoskeletal factors, including craniocervical muscle alterations and myofascial trigger points, interact with central sensitization processes, while psychosocial stressors, coping strategies, and lifestyle habits such as sleep and physical activity modulate pain perception and chronification risk. Current approaches often address these domains in isolation, limiting therapeutic effectiveness and the understanding of interindividual variability. This narrative review critically synthesizes evidence on the multifactorial determinants of TTH, providing an integrative conceptual framework. We systematically searched PubMed, Scopus, and Web of Science for articles published between 2010 and 2025, including conceptually or methodologically foundational studies outside this range. Relevant studies were selected based on predefined inclusion criteria and synthesized narratively to highlight key mechanisms and contributing factors. The proposed model emphasizes multidimensional assessment, incorporating peripheral musculoskeletal evaluation, central pain modulation, psychosocial profiling, and lifestyle factors, thereby providing a conceptual basis for future personalized management approaches. Recognizing TTH as a dynamic, multidimensional condition may inform clinical assessment and patient-centered interventions, while also highlighting key gaps for future longitudinal and multimodal research aimed at validating the framework and improving individualized therapeutic strategies. The evidence presented is primarily narrative and observational, and clinical applicability should be confirmed in future studies. Full article

This study addresses energy-response prediction in sheet metal forming under small-data conditions, where conventional simulation-based approaches are computationally expensive and data acquisition is limited. We propose an Energy-Informed Neural Network (EINN) framework that integrates energy consistency constraints and a Fenchel–Young duality-based loss to enforce physically consistent learning without relying on explicit governing equations. Using a dataset generated from 54 finite element simulations across 18 materials and three friction conditions, the proposed model demonstrates significant performance improvements. Specifically, EINN achieves an RMSE of 0.0096, MAE of 0.0065, and R² of 0.9778, corresponding to approximately a 48% reduction in RMSE compared to the best baseline model. Compared to an energy-constrained neural network without the Fenchel–Young term, prediction error is reduced by approximately 50% with substantially improved stability. These results indicate that embedding energy-consistent dual structures enhances both prediction accuracy and robustness, providing a practical surrogate modeling approach for process optimization in sheet metal forming under limited data availability. Full article

The low-carbon transition of industrial parks is driving an increasing demand for advanced energy systems. Integrated energy supply systems (IESSs), which couple multiple energy forms, offer a critical pathway to alleviate the high-carbon intensity of energy structures and supply–demand imbalances in industrial parks by enhancing energy efficiency and reducing carbon emissions. The rapid advancement of energy storage technologies, multi-energy system modeling, and advanced energy management strategies has further propelled the research and application of IESSs. This review comprehensively delineates the distinctions between IESSs and traditional energy systems, highlighting the architecture and operational characteristics of IESSs to elucidate the impacts of multi-energy coupling and source–grid–load–storage interactions. We examine existing equipment and system modeling approaches and load modeling methods, and discuss modeling techniques for variable operating conditions. We analyze operational optimization methods for IESSs under deterministic, multi-time-scale, and uncertain conditions, and investigate the utilization mechanisms of flexibility resources across source–grid–load–storage links to illustrate how system flexibility supports dynamic supply–demand coordination. The review also identifies emerging trends in AI-driven IESS operation, highlighting the integration of physics-informed modeling, large language models, and multi-agent systems. This review establishes a unified analytical perspective for flexible supply–demand matching within IESSs, offering theoretical support for the development of future low-carbon industrial energy systems. Full article

For the complex problems of multi-aircraft cooperative game guidance with No-Fly Zone (NFZ) avoidance and cross-task constraint propagation, a deep deterministic policy gradient algorithm with temporal awareness and priority cooperative optimization (TP-MADDPG) is proposed. Based on the three-body cooperative guidance, a new coupled guidance task is formed by adding the NFZ avoidance constraint. At the same time, considering the constraint compatibility problem in dynamic task switching, the cooperative aircraft are modeled as independent agents with differentiated policy networks. First, a nonlinear kinematic model of the three-body game constructed by Evader–Pursuer–Defender is established. And four complex constraint conditions, namely homing guidance, NFZ avoidance, collision avoidance, and cooperative guidance, are modeled separately. Secondly, the Long Short-Term Memory-based (LSTM) Actor–Critic framework is proposed to dynamically capture the evolution patterns of adversarial scenarios by mining hidden correlations in historical state-action sequences. This enables smooth policy transitions between the cooperative guidance phase and subsequent homing guidance phase, effectively addressing the challenges of environmental non-stationarity and temporal task dependencies. Then, a priority-driven adaptive sampling mechanism is proposed along with a heterogeneous roles cooperative reward function to specifically address credit assignment imbalance and sparse reward problems, respectively. The sampling mechanism capitalizes on the efficient retrieval properties of SumTree data structures while integrating bias correction techniques to expedite policy gradient convergence. The reward function utilizes the reward shaping method to formulate cooperative reward components that explicitly capture behavioral correlations among agents. Finally, simulations show that the proposed method significantly outperforms multi-agent reinforcement learning baselines, effectively improving the performance of cooperative game guidance under complex constraints. Full article