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The ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering-type scanning near-field optical microscopy (s-SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s-SNOM, together with scanning probe research in general, can benefit in many ways from artificial-intelligence (AI) and machine-learning (ML) algorithms. Augmented with AI- and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.

Accidental trauma is always accompanied by tissue defects with noncompressible hemorrhages. Deformable and tough biomaterials are desirable for self‐compressible hemostasis, although their preparation is challenging. Here, a critical‐size calcium phosphate (C–CaP) with polymer‐like high deformability and mineral‐like high bending strength is prepared, which is hybridized with silk fibroin (SF) to fabricate a hemostatic sponge (SF‐C). C–CaP endows SF‐C with an ultrahigh elastic modulus (36.0 kPa) among analogous materials, leading to an ultrafast water absorption rate (1.2 g cm−3 s−1) and space‐filling rate (200.0%/s). The deformability of the SF‐C sponge further enables self‐adaptability to defects with micrometre‐scale precision. Consequently, SF‐C achieves rapid hemostasis in both damaged soft tissues and hard tissues in rats, resulting in less blood loss and a shorter hemostatic time than those of clinical materials. This study provides a promising hemostatic material for the treatment of hemorrhages in defective tissues and expands the current knowledge by revealing the critical‐sized ionic structure and unique mechanics of C–CaP. [ABSTRACT FROM AUTHOR]

In modern manufacturing, the prediction of the remaining useful life (RUL) of computer numerical control (CNC) milling cutters is crucial for improving production efficiency and product quality. This study proposes a hybrid CNN-LSTM-Attention-PSA model that combines convolutional neural networks (CNN), long short-term memory (LSTM) networks, and attention mechanisms to predict the RUL of CNC milling cutters. The model integrates cutting force, vibration, and current signals for multi-channel feature extraction during cutter wear. The model's hyperparameters are optimized using a PID-based search algorithm (PSA), and comparative experiments were conducted with different predictive models. The experimental results demonstrate the proposed model's superior performance compared to CNN, LSTM, and hybrid CNN-LSTM models, achieving an R2 score of 99.42% and reducing MAE, RMSE, and MAPE by significant margins. The results validate that the proposed method has significant reference and practical value for RUL prediction research of CNC milling cutters. [ABSTRACT FROM AUTHOR]

Industrial control system(ICS) is widely used in water treatment, power, oil, transportation, military industry and other industries. The safety and stability are related to the normal operation of key national infrastructures. ICS are facing with serious security threats with the advancement of digital production and intelligent manufacturing. This paper proposes and designs a set of in-depth ICS security protection system by discussing the typical attack methods and the weaknesses of existing protection measures，and elaborates its technical focus from five aspects: physical security, data security, network security, host and application security, and control security. And then a comprehensive security protection platform is built on the basis of a trusted platform, forming a set of adaptive, closed-loop, security models and mechanisms that can self-defense and recover, maintain the safety of the ICS in three dimensions.It is helpful for security and stability operation of ICS.

Author(s): Zhaoming Liu [sup.1] , Changyu Shao [sup.1] , Biao Jin [sup.1] , Zhisen Zhang [sup.2] , Yueqi Zhao [sup.1] , Xurong Xu [sup.3] , Ruikang Tang [sup.1] [sup.4] Author [...], Inorganic materials have essential roles in society, including in building construction, optical devices, mechanical engineering and as biomaterials.sup.1-4. However, the manufacture of inorganic materials is limited by classical crystallization.sup.5, which often produces powders rather than monoliths with continuous structures. Several precursors that enable non-classical crystallization--such as pre-nucleation clusters.sup.6-8, dense liquid droplets.sup.9,10, polymer-induced liquid precursor phases.sup.11-13 and nanoparticles.sup.14--have been proposed to improve the construction of inorganic materials, but the large-scale application of these precursors in monolith preparations is limited by availability and by practical considerations. Inspired by the processability of polymeric materials that can be manufactured by crosslinking monomers or oligomers.sup.15, here we demonstrate the construction of continuously structured inorganic materials by crosslinking ionic oligomers. Using calcium carbonate as a model, we obtain a large quantity of its oligomers (CaCO.sub.3).sub.n with controllable molecular weights, in which triethylamine acts as a capping agent to stabilize the oligomers. The removal of triethylamine initiates crosslinking of the (CaCO.sub.3).sub.n oligomers, and thus the rapid construction of pure monolithic calcium carbonate and even single crystals with a continuous internal structure. The fluid-like behaviour of the oligomer precursor enables it to be readily processed or moulded into shapes, even for materials with structural complexity and variable morphologies. The material construction strategy that we introduce here arises from a fusion of classic inorganic and polymer chemistry, and uses the same cross-linking process for the manufacture the materials. The crosslinking of oligomeric precursors, controlled by a capping agent, enables the production of moulded crystalline calcium carbonate with continuous structures.

PtII based organometallic photosensitizers (PSs) have emerged as novel potent photodynamic inactivation (PDI) reagents through their enhanced intersystem crossing (ISC) processes. Currently, few PtII PSs have been investigated as antibacterial materials, with relatively poor performances reported and with structure‐activity relationships not well described. Herein, a pair of configurational isomers are reported of Bis‐BODIPY (4,4‐difluoro‐boradizaindacene) embedded PtII PSs. The cis‐isomer (cis‐BBP) displayed enhanced 1O2 generation and better bacterial membrane anchoring capability as compared to the trans‐isomer (trans‐BBP). The effective PDI concentrations (efficiency > 99.9%) for cis‐BBP in Acinetobacter baumannii (multi‐drug resistant (MDR)) and Staphylococcus aureus are 400 nM (12 J cm−2) and 100 nM (18 J cm−2), respectively; corresponding concentrations and light doses for trans‐BBP in the two bacteria are 2.50 µM (30 J cm−2) and 1.50 µM (18 J cm−2), respectively. The 50% and 90% minimum inhibitory concentration (MIC50 and MIC90) ratio of trans‐BBP to cis‐BBP is 22.22 and 24.02 in A. baumannii (MDR); 21.29 and 22.36 in methicillin resistant S. aureus (MRSA), respectively. Furthermore, cis‐BBP displays superior in vivo antibacterial performance, with acceptable dark and photoinduced cytotoxicity. These results demonstrate cis‐BBP is a robust light‐assisted antibacterial reagent at sub‐micromolecular concentrations. More importantly, configuration of PtII PSs should be an important issue to be considered in further PDI reagents design. [ABSTRACT FROM AUTHOR]

Precise morphology acquisition for the variable wing leading edge is essential for its bio-inspired adaptive control. Therefore, this study proposes a morphological reconstruction method for the variable wing leading edge, utilizing the node curvature vectors-based curvature propagation method (NCV-CPM). By establishing a strain–arc curvature function, the method fundamentally mitigates the impact of surface curvature angle on curvature computation accuracy at sensing points. We introduce a technique that uses high-order curvature fitting functions to determine the curvature vectors of arc segment nodes. This method reduces cumulative errors in curvature computation linked to the linear interpolation-based curvature propagation method (LI-CPM) at unattached sensor positions. Integrating curvature–strain functions aids in wing leading-edge strain field reconstruction, supporting structural health monitoring. Additionally, a particle swarm algorithm optimizes the sensing point distribution, reducing network complexity. This study demonstrates significantly enhanced morphological reconstruction accuracy compared to those obtained with conventional LI-CPM. [ABSTRACT FROM AUTHOR]

The lack of dimensionality of ocean observations makes it difficult to utilize multi-scale data assimilation to correct model errors with limited observations. Since satellite observations can provide high-resolution and time-continuous sea surface information, this study utilizes sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) anomalies to invert the vertical temperature and salinity fields and thus realize multi-scale data assimilation in the three-dimensional space. We propose a temperature and salinity inversion network (TSI-Net) for reconstructing the mapping of the sea surface's spatial distribution features to vertical structural features to obtain pseudo-observed fields. In this study, measured satellite remote-sensing data and temperature and salinity profiles are used to correct the model errors in the waters around the China Sea. The sensitivity analysis shows that the multi-component inversion can better fit the temperature field relationship, with a correlation coefficient of about 0.87. The results of the assimilation experiments show that the analytical field obtained by assimilating the pseudo-observed field is more consistent with the target field in terms of the spatial distribution characteristics. [ABSTRACT FROM AUTHOR]

Magnesium slag is a type of industrial solid waste produced during the production of magnesium metal. In order to gain a deeper understanding of the structure of magnesium slag, the composition and microstructure of magnesium slag were investigated by using characterization methods such as X-ray fluorescence, particle size analysis, X-ray diffraction, Fourier transform infrared spectroscopy and scanning electron microscopy. In addition, the state of Si occurrence in magnesium slag was analyzed using a solid-state nuclear magnetic resonance technique in comparison with granulated blast furnace slag. An inductively coupled plasma-optical emission spectrometer and scanning electron microscope with energy dispersive X-ray spectroscopy were used to characterize their cementitious behavior. The results show that the chemical composition of magnesium slag mainly includes 54.71% CaO, 28.66% SiO2 and 11.82% MgO, and the content of Al2O3 is much lower than that of granulated blast furnace slag. Compared to granulated blast furnace slag, magnesium slag has a larger relative bridging oxygen number and higher [SiO4] polymerization degree. The cementitious activity of magnesium slag is lower compared to that of granulated blast furnace slag, but it can replace part of the cement to obtain higher compressive strength. Maximum compressive strength can be obtained when the amount of magnesium slag replacing cement is 20%, where the 28-day compressive strength can be up to 45.48 MPa. This work provides a relatively comprehensive analysis of the structural characteristics and cementitious behavior of magnesium slag, which is conducive to the promotion of magnesium slag utilization. [ABSTRACT FROM AUTHOR]