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In this paper, it is pointed out that the descriptions of alloy phase structures are dependent on structural unit sequence. In the systematic science of alloys (SSA), the alloy phase structures are described by means of the symmetry element sequence combining with characteristic atom sequence. It is named the characteristic atom arranging structure, which can display the characteristic atoms at the lattice sites and the micro-inhomogeneity, besides the symmetry. Each characteristic atom has its own characters: neighboring configuration, potential energy, volume and electronic structure. The micro-inhomogeneity of alloy phases can be described by concentrations and short-range ordered parameters of characteristic atoms. The differences between the electronic structures of alloy phases and electronic structures of characteristic atoms in the alloy phases are also discussed.

By one-atom theory, the electronic structure of pure Al metal with f. c. c. structure has been determined to be [Ne](3sc)1.8790 (3pc)0.4982 (3sf+3pf)0.6228. According to this electronic structure, the potential curve, lattice constant, cohesive energy, elastisity, and the temperature dependence of the linear thermal expansion coefficients have been calculated. The electronic structures and characteristic properties of Al metals with b. c. c., h. c. p. structures and liquid have been studied. It is argued that the pure Al metal with f. c. c. structure can exist naturally, but with b. c. c. and h. c. p. structures cannot.

Egocentric video-language pretraining has significantly advanced video representation learning. Humans perceive and interact with a fully 3D world, developing spatial awareness that extends beyond text-based understanding. However, most previous works learn from 1D text or 2D visual cues, such as bounding boxes, which inherently lack 3D understanding. To bridge this gap, we introduce EgoDTM, an Egocentric Depth- and Text-aware Model, jointly trained through large-scale 3D-aware video pretraining and video-text contrastive learning. EgoDTM incorporates a lightweight 3D-aware decoder to efficiently learn 3D-awareness from pseudo depth maps generated by depth estimation models. To further facilitate 3D-aware video pretraining, we enrich the original brief captions with hand-object visual cues by organically combining several foundation models. Extensive experiments demonstrate EgoDTM's superior performance across diverse downstream tasks, highlighting its superior 3D-aware visual understanding. Code: https://github.com/xuboshen/EgoDTM.

In this study, two “on–off” probes (BF2-cur-Ben and BF2-cur-But) recognizing acetylcholinesterase (AChE) were designed and synthesized. The obtained probes can achieve recognition of AChE with good selectivity and pH-independence with a linear range of 0.5~7 U/mL and 0.5~25 U/mL respectively. BF2-cur-Ben has a lower limit of detection (LOD) (0.031 U/mL), higher enzyme affinity (Km = 16 ± 1.6 μM), and higher inhibitor sensitivity. A responsive mechanism of the probes for AChE was proposed based on HPLC and mass spectra (MS) experiments, as well as calculations. In molecular simulation, BF2-cur-Ben forms more hydrogen bonds (seven, while BF2-cur-But has only four) and thus has a more stable enzyme affinity, which is mirrored by the results of the comparison of Km values. These two probes could enable recognition of intracellular AChE and probe BF2-cur-Ben has superior cell membrane penetration due to its higher log p value. These probes can monitor the overexpression of AChE during apoptosis of lung cancer cells. The ability of BF2-cur-Ben to monitor AChE in vivo was confirmed by a zebrafish experiment.

Arterial stiffness (AS), an independent cardiovascular risk factor, is elevated in individuals with overweight or obesity. Exercise is a key non-pharmacological intervention, yet the comparative efficacy and optimal dose across modalities remain unclear. This study aimed to evaluate the effects of different exercise types and doses on AS in overweight and obese populations. A systematic search of PubMed, Web of Science, EBSCO, Cochrane Library, and Embase identified randomized controlled trials (RCTs) published up to August 2025. Bayesian network meta-analysis and dose-response analysis were conducted using Stata 17.0 and R software. 40 RCTs (n = 2,064) were included, covering five exercise modalities: aerobic exercise (AE), resistance training (RT), combined AE and RT (CT), high-intensity interval training (HIIT), and HIIT + RT. Compared with controls, AE (SMD = - 0.81), RT (-0.48), HIIT (-0.87), and HIIT + RT (-0.95) significantly reduced arterial stiffness, whereas CT showed a borderline reduction (SMD = - 0.39, 95% CI: - 0.79 to 0.00). HIIT + RT ranked highest in efficacy, followed by HIIT, AE, and RT, with CT ranked lowest. Bayesian dose-response analysis demonstrated a nonlinear L-shaped association, with a minimum effective dose of 92 MET-min/week and the overall benefit approaching a plateau at approximately 670 MET-min/week. Modality-specific plateau ranges were approximately 720 MET-min/week for AE, 390 MET-min/week for HIIT, and 550 MET-min/week for HIIT + RT. No significant dose-response relationship was identified for RT or CT. Exercise improves arterial stiffness in individuals with overweight or obesity. AE, HIIT, and HIIT + RT showed favorable effects, although the apparent superiority of HIIT + RT should be interpreted cautiously because the available evidence remains limited. The findings support the use of modality-specific dose ranges, rather than a single overall dose estimate, to inform individualized exercise prescription. Further large-scale direct randomized controlled trials are needed to confirm these findings.