Charge-parity (CP) violation in the tau-charm energy region is a promising area for sensitive tests of Standard Model (SM) predictions and searches for new, beyond the SM physics. A future Tau-Charm Facility that operates at center-of-mass energies between 2.0 and 7.0 GeV, with a peak luminosity of 0.5×1035 cm−2 s−1, would provide huge numbers of hadrons and tau (τ) leptons that are produced in low-background environments and with well understood kinematic properties. In this report, prospects for unique studies of CP violation in the decay of charmed hadrons, and in the production and decay of hyperons and τ leptons at a next-generation tau-charm facility are discussed. In addition, opportunities for improved tests of CPT invariance test in K0−K̄0 mixing are presented.
Existing legal benchmarks for Large Language Models (LLMs) primarily focus on final judgment outcomes or isolated subtasks, making it difficult to systematically evaluate the multi-step reasoning processes and reasoning quality required in real-world legal decision-making. To address this gap, we introduce MSLR, a Chinese benchmark for multi-step legal reasoning, which models and evaluates complete judicial reasoning trajectories based on the IRAC (Issue-Rule-Application-Conclusion) framework. MSLR comprises 1389 Chinese insider trading judgments issued between 2005 and 2024, with approximately 60,000 step-level reasoning paths annotated through a combination of human and automated methods, averaging 43 intermediate reasoning steps per case. Based on MSLR, we adopt two evaluation metrics, IRAC Recall and LLM Score, to provide a fine-grained assessment of legal reasoning capabilities. We systematically evaluate 19 mainstream general-purpose, legal-domain, and reasoning-oriented models. The results show that current models exhibit overall limited performance on multi-step legal reasoning tasks; even the best-performing o1-mini model fails to exceed 75% IRAC Recall. In addition, we report the performance of mainstream models on structured annotation tasks and legal judgment reasoning tasks. The MSLR dataset and benchmark construction code are publicly available at https://github.com/yuwenhan07/MSLR-Bench.
The high-voltage spinel LiNi0.5 Mn1.5 O4 (LNMO) is a promising cobalt-free cathode with high specific energy, attributed to its high operating voltage (approximately 4.7 V). However, its practical durability is limited by irreversible transition-metal migration, spinel-to-rock-salt phase reconstruction, highvoltage interfacial side reactions, and transition-metal dissolution. Herein, we report a multi-elementdoped, cation-disordered spinel LiNi0.4 Mn1.4 Fe0.05 Cr0.05 Cu0.05 Nb0.05 O4 (HELNMO) synthesized via a solidstate method. Rietveld refinement confirms a single-phase Fd 3 m structure with a slightly expanded lattice parameter (8.1857 A & ring; for HELNMO versus 8.1776 A & ring; for LNMO), and energy-dispersive X-ray spectroscopy mapping reveals a homogeneous distribution of the dopant elements. Structural and spectroscopic characterizations consistently confirm enhanced cation disorder in HELNMO, which contributes to favorable electrochemical performance by delivering approximately 130 mAh g-1 at 0.1 C and exhibiting markedly improved cycling stability, retaining 92 % of its initial capacity after 10 0 0 cycles at 2 C between 3.5 and 4.9 V. In-situ X-ray diffraction reveals smoother, more continuous lattice evolution of HELNMO than LNMO during (de)lithiation, consistent with a more homogeneous reaction pathway. Post-cycling analyses corroborate suppressed surface rock-salt reconstruction, reduced impedance growth, and alleviated Mn/Ni dissolution. Collectively, multi-element doping coupled with disorder engineering can effectively mitigate transition-metal migration and high-voltage interfacial degradation, providing a practical strategy for durable, high-energy spinel cathodes. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
False Data Injection Attacks (FDIA) pose a serious threat to smart grid security due to their strong stealthiness. Existing methods often process spatial and temporal features in isolation or only achieve single detection functionality, making it difficult to accurately pinpoint attack sources and thus insufficient for proactive defense. To overcome these challenges, this paper proposes a novel approach that integrates Sparrow Search Algorithm (SSA) optimization with a dual-channel Convolutional Neural Network (CNN)-Transformer architecture. The main innovation of this study lies in the integration of SSA-based parameter optimization with the CNN-Transformer architecture for feature extraction, enabling unified detection and localization of FDIA: the CNN branch captures local spatiotemporal anomaly patterns induced by attacks in node measurement data, while the transformer branch explicitly models global dependencies across all nodes in both spatial and temporal dimensions via a multi-head self-attention mechanism. The SSA is introduced to adaptively optimize key hyperparameters, enhancing the model's generalization ability across power grids of different scales. Experimental results on the IEEE 14-bus and 118-bus systems demonstrate that the proposed model significantly outperforms the baseline model across multiple metrics, including Accuracy, Precision, Recall, and F1-score. Specifically, Accuracy can be improved by 0.45% to 6.86%, Precision by 0.52% to 15.21%, Recall by 1.21% to 19.12%, and F1-score by 1.13% to 17.38%.
In recent years, photocatalytic reduction of CO2 using renewable solar energy has emerged as a promising technology for addressing the energy crisis. To achieve efficient CO2 photoreduction, enhancing the sunlight absorption capacity and charge separation efficiency of g-C3N4-based photocatalysts was crucial. Hydrothermal preparation of dual Z-scheme Cu2-xS/g-C3N4/NiAl-LDH composite catalysts was used in this study. The optimized Cu2-xS/g-C3N4/NiAl-LDH photocatalyst demonstrated CH4 and CO yields of 28.13 and 4.47 & micro;mol g-1h-1, respectively. Notably, the CH4 yield was 12.5 times higher than that of the LDH monomer, and the selectivity of CH4 was as high as 96.2%. The promotion mechanism could be attributed to the photothermal effect of composite catalyst and the augmented light absorption, as well as the efficient internal charge transfer originated from the bi-heterostructure. Consequently, combining Cu2-xS/g-C3N4/NiAl-LDH and photothermal effects, this photocatalyst design provides insightful information for creating full-spectrum responsive photocatalysts for CO2 reduction.
