Recently, developing multi-UAVs to cooperatively pursue a fast-moving target has become a research hotspot in the current world. Although deep reinforcement learning (DRL) has made a lot of achievements in the UAV pursuit game, there are still some problems such as high-dimensional parameter space, the ease of falling into local optimization, the long training time, and the low task success rate. To solve the above-mentioned issues, we propose an improved twin delayed deep deterministic policy gradient algorithm combining the genetic algorithm and maximum mean discrepancy method (GM-TD3) for multi-UAV cooperative pursuit of high-speed targets. Firstly, this paper combines GA-based evolutionary strategies with TD3 to generate action networks. Then, in order to avoid local optimization in the algorithm training process, the maximum mean difference (MMD) method is used to increase the diversity of the policy population in the updating process of the population parameters. Finally, by setting the sensitivity weights of the genetic memory buffer of UAV individuals, the mutation operator is improved to enhance the stability of the algorithm. In addition, this paper designs a hybrid reward function to accelerate the convergence speed of training. Through simulation experiments, we have verified that the training efficiency of the improved algorithm has been greatly improved, which can achieve faster convergence; the successful rate of the task has reached 95%, and further validated UAVs can better cooperate to complete the pursuit game task.

With the rapid advancement of intelligent technologies and network environments, the efficient and accurate handling of uncertain decision-making information has become an urgent challenge. Traditional methods often struggle to process complex and incomplete information, especially in cyber defense. To address this, we introduce discrete multi-dimensional Z-numbers (MZs) as a mathematical tool for modeling uncertainty and reliability in network defense decisions. This paper proposes a synthesis method for MZs, enabling the integration of multi-source information while considering both uncertainty and reliability. By leveraging a hidden probability model, we extend MZs into multi-dimensional Z⁺-numbers, enhancing their expressiveness in handling uncertainty. Furthermore, we define utility functions based on MZs and develop a multi-attribute group decision-making framework tailored for network defense. This approach offers a novel perspective for designing strategies against highly adaptive and covert cyberattacks. The proposed method is validated through a case study on the network security assessment of an intelligent logistics company. Results demonstrate significant improvements in the accuracy and efficiency of decision-making, highlighting the method’s advantages and broad potential in cyber defense. Beyond logistics, this integrated MZ-based decision framework provides an adaptable and intelligent tool for strengthening network security defenses.

Traditional analytical design methods for photonic devices often suffer from high computational complexity, limited analytical solutions, and substantial time requirements, while relying heavily on prior experience and template libraries. Such limitations hinder the flexible realization of non-standard device architectures. In this work, we propose an adjoint-based inverse design strategy to optimize high-efficiency micro-nano photonic power splitters. A 30 × 30 circular-hole array with a Y-shaped configuration was designed on a silicon-on-insulator platform. Iterative optimization yielded a precise 1:1 power-splitting ratio with an additional loss of only 1.12 dB. Simulations indicate favorable optical performance over an approximately 100 nm wavelength range, demonstrating suitability for high-density photonic integrated circuits. Furthermore, a systematic analysis of key design parameters was conducted, and an innovative linear bias mechanism was introduced, enhancing both transmission efficiency and fabrication-oriented implementation. This work demonstrates the potential of inverse design in micro-nano photonic devices, offering a compact and efficient solution for photonic integrated circuits.

Mineral prospectivity mapping (MPM) increasingly integrates geological, geochemical, geophysical, remote-sensing, and structural evidence through machine-learning workflows. Predictive accuracy alone, however, does not establish that a prospectivity map is geologically credible or useful for exploration decisions. This critical narrative review examines explainable artificial intelligence (XAI) for MPM through the connections among model behavior, mineral-system knowledge, sampling, spatial validation, uncertainty, and field evidence. We distinguish methods demonstrated in representative MPM studies from general explanation tools and proposed applications. Study-level comparisons show that SHAP and permutation-based attribution can support evidence-layer auditing and target interpretation, while their meaning depends on correlated predictors, label construction, and evaluation design. Spatially separated evaluation tests a different generalization problem from random splitting; neither replaces newly acquired field evidence. Geological plausibility, model faithfulness, explanation stability, and decision utility therefore require separate assessment. We synthesize practical pathways for geological knowledge embedding and three-dimensional modeling, identify limits in current graph explanations and uncertainty reporting, and propose a minimum reporting checklist. Future priorities include geospatial foundation models, source-traceable language tools, three-dimensional prospectivity and four-dimensional extensions incorporating geological time, knowledge-guided hypothesis generation, integrated exploration systems, and field-based evaluation of explanations.

Urban soil contamination poses persistent risks to redevelopment, public health, and ecological restoration, yet actionable evidence is scattered across site investigation reports, monitoring databases, and regulatory documents. Existing decision-support tools often depend on manual searches and provide limited structured reasoning. This study develops a domain knowledge graph (KG) and a KG-powered question-answering (KBQA) system for urban soil management to organize multi-source evidence and deliver precise, auditable answers to parcel- and pollutant-specific queries. The approach (1) defines an urban soil ontology covering parcels, land uses, pollutants, measurements, pathways, and regulatory thresholds; (2) extracts and links entities and relations from textual and tabular sources; (3) constructs a graph database with provenance; and (4) implements a KBQA pipeline that maps natural-language questions to constrained graph queries and verbalizes results with citations. The resulting system supports source identification, land-use-specific exceedance checks, affected-parcel listing, and remediation reference retrieval. Experiments on a curated QA set and a South China case study show higher answer accuracy and lower latency than text-only baselines, while consistently returning traceable evidence and reducing cross-document lookup effort. Compared to text-only RAG baselines, the KG-powered system achieved a 0.14 improvement in Exact Match scores (e.g., 0.81 vs. 0.58 for Threshold tasks) and maintained a competitive median latency of 0.75 s. The pipeline utilizes a 13B-parameter instruction-tuned LLM. The ontology, schema, benchmark QA sets, and sample queries are publicly released to support transfer to other regions.