石家庄经济学院

Reconstructing causal dynamic networks from multivariate time series is a foundational problem in complex systems science. Yet, the key scientific issue is not simply causality detection, but causal interpretability. Interpretable causality is the foundation for testable mechanistic hypotheses, transferable forecasting, and principled decision-making for intervention and control. In real-world complex systems, causal inference is often compromised by noise, missing data, high dimensionality, nonlinearity, time delays, heterogeneity, and partial observability. Classic approaches to interpretable causality yield explicit, inspectable quantities such as causal graphs, coefficients, and governing equations. However, this methodological shift has heightened expectations: AI-extended approaches are increasingly required to recover explicit causal mechanisms rather than opaque predictive dependencies, thereby preserving interpretability. This review summarizes four classic methods and their AI-extended counterparts based on time series data, including Granger frameworks, information-theoretic measures, nonlinear state–space/manifold reconstruction, and mechanistic differential-equation learning. Next, we elucidate their motivations, core principles, the origins of interpretability, and the assumptions required for meaningful conclusions. Finally, we highlight representative applications across climate studies, neuroscience, epidemiology, finance, social science, ecology and molecular biology, followed by a discussion of comparative analysis, open challenges, and future research directions.

Mineral prospectivity mapping (MPM), which aims to identify areas with high mineral potential, has recently attracted greater attention owing to the application of deep learning algorithms. Mineralization is a coupled event arising from multiple geological processes within specific favorable spaces and generally exhibits multiple stages and clustering distributions. Therefore, fully incorporating both the mineralization processes and their spatial distribution offers significant advantages for improving MPM. However, most existing deep learning-based MPM models tend to emphasize either mineralization processes or mineralization spatial distribution, rather than integrating both. This limitation is particularly evident in 3D MPM. This paper presents a process–space dual-branch transformer with gating fusion (PSDBTG) model that considers processes and spatial distribution of mineralization, enabling the comprehensive utilization of process- and space-related features for 3D MPM. The process-based branch utilizes evidence layers that represent the mineralization processes or exploration criteria to capture the process-related features. The space-based branch exploits the interactions among neighboring voxels to obtain local spatial features related to orebody distribution. Subsequently, a gating module is employed to adaptively integrate process- and space-related features with different emphases from the two branches. Here, the PSDBTG model was applied to conduct 3D MPM in the Lannigou gold district, China. The comparative experiments demonstrated that the PSDBTG model, which incorporates both processes and spatial distribution of mineralization, outperformed the models that consider only a single type of information in terms of prediction performance. Moreover, the gating module exhibited superiority over other feature fusion methods by adaptively regulating negative constraints derived from mineralization processes. This paper proposes a novel and transparent framework for embedding geological prior knowledge into data-driven MPM, which allows model predictions to explicitly incorporate mineralization process constraints, contributing to improved interpretability and reliability.

The utilization of marine resources has become a strategic priority of global significance. Offshore platforms and offshore wind turbines are critical components of offshore energy development systems. To address the complexity, randomness, and uncertainty inherent in the marine environment and to ensure the safety of offshore installations during service, structural analysis and optimization of their foundations are essential. Jacket foundations, which provide high rigidity and stability, are suitable for shallow water and have emerged as the preferred foundation type for deepwater offshore installations. This study systematically reviews recent advances in theoretical modeling, numerical simulation, and experimental validation of structural response analyses and optimization methodologies for jacket foundations under complex marine conditions. Driven by diverse engineering requirements, researchers have proposed various structural optimization strategies. Existing efforts have primarily focused on structural topology, lightweight design, and performance-based optimization. Furthermore, this review identifies key technical challenges and outlines future research directions for optimizing offshore jacket foundations in ocean engineering.

The Silk Road traversed diverse and challenging landscapes, with one of its most characteristic stretches spanning the extremely arid Tarim Basin. The meandering paths of trade routes gradually developed from ancient corridors of diffusion into organised and government-regulated commerce by roughly two millennia ago, institutions that remained in use until the sixteenth century. Our understanding of what caused the reduction of Silk Road trade remains limited. Conventional theories tend to take socioeconomic perspectives, particularly relying on political transformations in the Eurasian heartland and a redirection towards maritime routes. Here, we examined a potential role for hydro-environmental factors in the fall of the long-distance trade networks by compiling a unique landscape-scale dataset, comprising 164 radiocarbon dates of in situ plant remains from along palaeochannels and 254 recruitment ages of living trees in the Tarim Basin. Using Bayesian modelling, we produce a novel proxy for the time that the rivers dried up with high spatiotemporal resolution, and integrate it with palaeohydrological records from adjacent regions. Given the hyper-aridity, riparian forests became desiccated across the region as their glacial-melt streams stopped flowing, leaving a direct record of the death of river branches. We conclude that the decline of core routes of the Silk Road coincided with significant decreases in streamflow across northwest China, especially during a low-flow period around 1500 to 1650 CE in the Tarim Basin. We suggest that this dry phase was significant enough to cause the abandonment of many central trading routes, contributing to what may have been a complex set of compounding variables.

This study presents petrological, geochemical and geochronological data from the Cangjinyu mafic-ultramafic complex in the Beishan Terrane, northwestern China, aiming to clarify the origin of its Ni-Cu sulphide mineralisation and the regional tectonic evolution. LA-ICP-MS zircon U-Pb dating of olivine gabbro and gabbro reveals crystallisation ages of 278 and 276 Ma, respectively, indicating the emplacement timing of the complex. Re-Os isotopic dating of pentlandite gives an isochron age of 277 Ma, consistent with the host rock ages, suggesting a magmatic sulphide liquid immiscibility origin for the deposit. Geochemical analyses show that the rocks have tholeiitic affinities, low SiO2 (41.33-50.79 wt.%), enriched heavy rare earth elements and depletions in high-field-strength elements, with negative Nb-Ta-Ti anomalies typical of arc-related magmatism. High La/Nb (2.40-7.37) and low La/Ba (0.03-0.13) ratios indicate a subduction-modified lithospheric mantle source metasomatised by fluids from ancient subducted plates. The complex formed in a post-subduction extensional setting after the closure of the Paleo-Asian Ocean, with magmatic evolution driven by fractional crystallisation and crustal contamination (1%-10%), leading to sulphide saturation and mineralisation.