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.
The digitalization of power networks and the integration of distributed generation have increased protection complexity in underground transmission systems, where non-stationary transient faults challenge conventional detection methods. This study presents a multiscale db4 Discrete Wavelet Transform (DWT) energy representation combined with a hybrid Lazy Rule-Guided classifier for fast underground fault detection under Supervisory Control and Data Acquisition (SCADA) latency constraints. Three phase and ground currents from a simulated 12-class underground feeder are denoised, decomposed into level-3 wavelet bands, and aggregated into multiscale energy vectors. The proposed approach is evaluated against classical, metaheuristic, and deep learning benchmarks. It achieves 0.9106 accuracy and 0.9084 macro-F1 with an inference latency of 0.0035 ms per sample. It achieves performance comparable to Random Forest (RF) with 103 - 104 times lower latency than Convolutional Neural Networks (CNN) and Transformer-based models. Robustness analysis under 10 dB additive white Gaussian noise shows limited performance degradation of <= 7% over classification accuracy. The resulting interpretable decision rules support real-time, physics-informed underground cable protection and practical SCADA deployment. With future extension to field SCADA, Phasor Measurement Units (PMU) data, and evaluation under a broader range of noise levels, the model could be deployed in power systems' real-time protection.
Multi-image steganography is one of the important branches in protecting information. However, the steganographic or hiding capacity is greatly limited by the visual security. In this paper, we propose a new strategy named maximum energy block to identify the optimal hiding region of cover carrier, where multiple secret images can be hidden while causing an extremely minimal change of visual perception. This concept is enlightened by the fact that the human visual system is not sensitive to the grayscale variation in the areas of high energy and the edges or textures of complex. To furtherly minimize the visual impact of secret image on cover carrier and greatly extend the available steganographic space, a cover carrier of color video and the discrete wavelet transform are adopted. Thereby, one can screen out the high-frequency component of the highest energy block in video frames as the best-of-breed area for steganography, such that greatly increasing the difficulties of forensic. Meanwhile, multiple secret images are also compressed using 2D compressive sensing and then embedded into specified channels of different video frames based on a SHA256-based mapping, respectively. This operation can not only reduce the amount of hiding data and improve the steganographic capacity, but also bring about more safety and higher visual security. The secret images can be extracted and reconstructed accurately in the inverse process using correct keys. Experimental results verify the effectiveness of our method.
Unraveling the mechanisms of organic matter (OM) enrichment in lacustrine rift basins is critical for optimizing shale oil exploration in continental sedimentary systems. However, these mechanisms remain poorly understood due to complex interactions among paleoenvironmental factors. The Gubei area, a typical Cenozoic continental rift sag in the Bohai Bay Basin (eastern China), contains abundant organic-rich shale in the lower sub-section of the third section of Shahejie Formation (Es3x). This study integrates petrological, geochemical, and sedimentological analyses to reconstruct the paleoenvironmental framework of the Es3x shales and to investigate the characteristics and mechanisms of OM enrichment. Results show that the Es3x shales are characterized by mixed and quartz-feldspar lithofacies, with calcite (31.33%), quartz (25.7%), and clay minerals (17.2%) as major components. High paleoproductivity proxies and geochemical indices (C-value, CIA) indicate elevated primary productivity under a semi-arid to arid climate transition. Paleosalinity and water depth reconstructions suggest brackish to saline lacustrine settings with a shallow-to-deep lake evolution, while redox-sensitive trace elements indicate predominantly anoxic to euxinic bottom waters favorable for OM preservation. Correlation analyses demonstrate positive relationships between TOC and paleoproductivity, whereas detrital influx shows a negative correlation. Quantitative assessments indicate that primary productivity and redox conditions are the dominant direct controls on OM enrichment, with paleoclimate exerting indirect modulation through its influence on salinity, detrital dilution, and paleo-water depth. This study elucidates the coupled tectonic–climatic–paleolimnological processes on OM accumulation in continental rift basins, providing a robust framework for evaluating shale oil prospects in analogous settings.
An energy-optimized exponential scalar auxiliary variable (EOP-ESAV) method for the Ginzburg-Landau equation (GLE) with a relaxation step in exponential scalar auxiliary variable (ESAV) schemes is proposed. The EOP-ESAV method is an explicit linear approach that allows for high-order, computationally efficient schemes. Furthermore, this approach retains the convergence order of the ESAV schemes while achieving smaller numerical errors. Most notably, we rigorously prove that the proposed method maintains a modified energy dissipation law that optimally approximates the original energy within the relaxation framework. Extensive numerical experiments are conducted to demonstrate notable improvements of the EOP-ESAV method in accuracy and numerical stability. The EOP-ESAV schemes are also applied on the simulation of vortex dynamics in GLEs with various setups, studying vortex dipole interactions and lattice motion patterns.
