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Sulforaphane (SFN) which is abundant in broccoli florets, seeds, and sprouts has been reported to have beneficial effects on attenuating metabolic diseases, such as antiobesity, antidiabetes, and antioxidative activities. However, the effects of SFN on the regulation of type II diabetes through easing nonalcoholic fatty liver (NAFLD) and repairing pancreas tissue are rarely reported. In this study, we found that the administration with different dosages of SFN was able to increase serum insulin level, enhance HOMA‐β index, decrease fasting blood glucose and serum total cholesterol, triglyceride, low‐density lipoprotein (LDL‐C), fibroblast growth factor21 (FGF21) levels, ease NAFLD level, and repair the pancreas tissue. In addition, SFN was able to increase liver antioxidant capacities. In particular, high (10 mg/kg) dosage of SFN exerted a significant beneficial effect for decreasing serum lipopolysaccharide levels. Furthermore, the administration of SFN could also decrease the relative abundance of Allobaculum at the genus level. Low dosage (2 mg/kg) of SFN could increase the relative abundance of Bacteroidetes and decrease the relative abundance of Firmicutes at the phylum level. Overall, our results showed that SFN exerted its antidiabetic effect through easing NAFLD and repairing pancreas tissue in association with modulation of gut microbiota. The ease of NAFLD by SFN was accompanied by enhancing liver antioxidant abilities and improving FGF21 resistance. Our results showed that SFN exerted its antidiabetic effect through easing NAFLD and repairing pancreas tissue in association with modulation of gut microbiota. The ease of NAFLD by SFN was accompanied by enhancing liver antioxidant abilities and improving FGF21 resistance.

Riveted workpieces are widely used in manufacturing; however, current inspection sensors are mainly limited in nondestructive testing and obtaining the high-accuracy dimension automatically is difficult. We developed a 3-D sensor for rivet inspection using fringe projection profilometry (FPP) with texture constraint. We used multi-intensity high dynamic range (HDR) FPP method to address the varying reflectance of the metal surface then utilized an additional constraint calculated from the fused HDR texture to compensate for the artifacts caused by phase mixture around the stepwise edge. By combining the 2-D contours and 3-D FPP data, rivets can be easily segmented, and the edge points can be further refined for diameter measurement. We tested the performance on a sample of riveted aluminum frame and evaluated the accuracy using standard objects. Experiments show that denser 3-D data of a riveted metal workpiece can be acquired with high accuracy. Compared with the traditional FPP method, the diameter measurement accuracy can be improved by 50%.

In recent years, perovskite solar cells (PSCs) have experienced rapid development and have presented an excellent commercial prospect as the PSCs are made from raw materials that are readily and cheaply available depending on simple manufacturing techniques. However, the commercial production and utilization of PSCs remain immature, leading to substantial efforts needed to boost the development of scalable fabrication of PSCs, pilot scale tests, and the establishment of industrial production lines. In this way, the PSCs are expected to be successfully popularized from the laboratory to the photovoltaic market. In this review, the history of power conversion efficiency (PCE) for laboratory-scale PSCs is firstly introduced, and then some methods for maintaining high PCE in the upscaling process is displayed. The achievements in the stability and environmental friendliness of PSCs are also summarized because they are also of significance for commercialization. Finally, this review evaluates the commercialization prospects of PSCs from the economic view and provides a short outlook.

Highlights A highly aligned and compact MXene film for ultra‑broadband and ultra-high electromagnetic interference shielding was fabricated by continuous centrifugal spraying strategy. The film exhibits an exceptional electromagnetic interference shielding effectiveness (EMI SE) of 45 dB, specific shielding effectiveness of 1.545 × 10 6  dB cm 2  g⁻ 1 in gigahertz band (8.2–40 GHz), and EMI SE of 59 dB in terahertz band (0.2–1.6 THz). The film exhibits an ultra-low infrared emissivity of 0.1 in the infrared band (2.5–16.0 μm) and an efficient electrothermal performance. With the rapid development of electronic detective techniques, there is an urgent need for broadband (from microwave to infrared) stealth of aerospace equipment. However, achieving effective broadband stealth primarily relies on the composite of multi-layer coatings of different materials, while realizing broadband stealth with a single material remains a significant challenge. Herein, we reported a highly compact MXene film with aligned nanosheets through a continuous centrifugal spraying strategy. The film exhibits an exceptional electromagnetic interference shielding effectiveness of 45 dB in gigahertz band (8.2–40 GHz) and 59 dB in terahertz band (0.2–1.6 THz) at a thickness of 2.25 μm, owing to the high conductivity (1.03 × 10 6 S m −1 ). Moreover, exceptionally high specific shielding effectiveness of 1.545 × 10 6  dB cm 2  g⁻ 1 has been demonstrated by the film, which is the highest value reported for shielding films. Additionally, the film exhibits an ultra-low infrared emissivity of 0.1 in the wide-range infrared band (2.5–16.0 μm), indicating its excellent infrared stealth performance for day-/nighttime outdoor environments. Moreover, the film demonstrates efficient electrothermal performance, including a high saturated temperature (over 120 °C at 1.0 V), a high heating rate (4.4 °C s −1 at 1.0 V), and a stable and uniform heating distribution. Therefore, this work provides a promising strategy for protecting equipment from multispectral electromagnetic interference and inhibiting infrared detection.

Sweet cherries ( L.) are highly valued for their taste and nutrients but are prone to decay due to their delicate skin and high respiration rate. Traditional chemical preservatives have drawbacks like residues and resistance, prompting the search for natural alternatives. Phenylactic acid (PLA) has shown promise due to its antibacterial and antioxidant properties, making it a potential natural preservative to extend cherry shelf life. 'Stella' sweet cherries were treated with varying concentrations of PLA (0, 2, 4, 8, 16 mmol·L ) and stored at 4°C. Key quality indicators, including firmness, total acidity, total soluble solids, weight loss, decay index, and antioxidant activity, were assessed over time. Additionally, HPLC, GC-MS, GC-IMS, colony counts, in vivo inhibition analyses were conducted to evaluate phenolic content, aroma compounds and antifungal ability. PLA at 8 mmol·L effectively maintained cherry quality by reducing weight loss and decay of cherries, delaying the decline of firmness, while enhancing antioxidant capacity, flavor stability, and antifungal ability. Higher concentrations (16 mmol·L ) provided stronger antimicrobial effects but caused slight surface wrinkling. Thus, 8 mmol·L was optimal, balancing preservation and appearance, making it a promising natural preservative for extending cherry shelf life.

Conventional computer systems based on the Von Neumann architecture rely on silicon transistors with binary states for information representation and processing. However, exploiting emerging materials’ intrinsic physical properties and dynamic behaviors offers a promising pathway for developing next-generation brain-inspired neuromorphic hardware. Here, we introduce a stable and controllable photoelectricity-induced halide-ion segregation effect in epitaxially grown mixed-halide perovskite CsPbBr 1.5 I 1.5 microwire networks on mica, as confirmed by various in-situ measurements. The dynamic segregation and recovery processes show the reconfigurable, self-powered photoresponse, enabling non-volatile light information storage and precise modulation of optoelectronic properties. Furthermore, our microwire array successfully addressed a typical graphical neural network problem and an image restoration task without external circuits, underscoring the potential of in-material dynamics to achieve highly parallel and energy-efficient physical computing in the post-Moore era. This study demonstrates stable photoelectricity-induced halide segregation in epitaxial mixed-halide perovskite microwire networks on mica, verified by in-situ observations. Dynamic segregation-recovery processes and reconfigurable self-powered photoresponse reveal physical computing potential.

Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi‐component systems from a high‐dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi‐component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1‐ω)(Ba0.61Ca0.28Sr0.11TiO3)‐ω(BaTi0.888Zr0.0616Sn0.0028Hf0.0476O3) with a relatively high transition temperature and triple point, as well as the NiTi‐based pseudo‐binary phase diagram (1‐ω)(Ti0.309Ni0.485Hf0.20Zr0.006)‐ω(Ti0.309Ni0.485Hf0.07Zr0.068Nb0.068) designed for high transition temperature (ω ⩽ 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today's computational methods. A machine learning‐based approach is proposed to predict all possible phase diagrams of a given multi‐component system from a high‐dimensional virtual space. By quickly screening the space, a specific phase diagram with given design criteria can be constructed. Bayesian experimental design is then employed to refine the phase diagram with as few experiments as possible.

Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for sensitive industrial data processing. In contrast to existing work that treats scheduling and privacy as separate concerns, this paper proposes a unified polymorphic heterogeneous security architecture that integrates hybrid event–time triggered scheduling with adaptive privacy-preserving arbitration, specifically designed to address the unique challenges of cloud–edge collaboration ICSs where both security resilience and privacy preservation are paramount requirements. The architecture introduces three key innovations: (1) a hybrid event–time triggered scheduling algorithm with credibility assessment and heterogeneity metrics to mitigate common-mode escape scenarios, (2) an adaptive privacy budget allocation mechanism that balances privacy protection effectiveness with system availability based on attack activity levels, and (3) a unified framework that organically integrates privacy-preserving arbitration with heterogeneous redundancy management. Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (10−2 probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments.

As cyber threats continue to grow in complexity and persistence, resilience has become a critical requirement for cyber–physical systems (CPSs). Resilience quantitative assessment is essential for supporting secure system design and ensuring reliable operation. Although various methods have been proposed for evaluating CPS resilience, major challenges remain in accurately modeling the interaction between cyber and physical domains and in providing structured guidance for resilience-oriented design. This study proposes an integrated CPS resilience assessment framework that combines cyber-layer anomaly modeling based on Markov chains with mathematical modeling of performance degradation and recovery in the physical domain. The framework establishes a structured evaluation process through parameter normalization and cyber–physical coupling, enabling the generation of resilience curves that clearly represent system performance changes under adverse conditions. A case study involving an industrial controller equipped with a diversity-redundancy architecture is conducted to demonstrate the applicability of the proposed method. Modeling and simulation results indicate that the framework effectively reveals key resilience characteristics and supports performance-informed design optimization.

Tin‐based perovskite solar cells (PSCs) are promising environmentally friendly alternatives to their lead‐based counterparts, yet they currently suffer from much lower device performance. Due to variations in the chemical properties of lead (II) and tin (II) ions, similar treatments may yield distinct effects resulting from differences in underlying mechanisms. In this work, a surface treatment on tin‐based perovskite is conducted with a commonly employed ligand, iso‐butylammonium iodide (iso‐BAI). Unlike the passivation effects previously observed in lead‐based perovskites, such treatment leads to the recrystallization of the surface, driven by the higher solubility of tin‐based perovskite in common solvents. By carefully designing the solvent composition, the perovskite surface is effectively modified while preserving the integrity of the bulk. The treatment led to enhanced surface crystallinity, reduced surface strain and defects, and improved charge transport. Consequently, the best‐performing power conversion efficiency of FASnI3 PSCs increases from 11.8% to 14.2%. This work not only distinguishes the mechanism of surface treatments in tin‐based perovskites from that of lead‐based counterparts, but also underscores the critical role in designing tailor‐made strategies for fabricating efficient tin‐based PSCs. The surface recrystallization triggered by iso‐BAI is identified as a distinct mechanism in tin‐based perovskites, compared to the lead‐based counterparts. GIWAXS results suggest that the defective tin perovskite surfaces are dissolved by the moderate solvent, followed by a guided recrystallization with the assistance of iso‐BAI, resulting in a promising power conversion efficiency of 14.2%.