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Bone defect is a common clinical symptom which can arise from various causes. Currently, bone tissue engineering has demonstrated positive therapeutic effects for bone defect repair by using seeding cells such as mesenchymal stem cells and precursor cells. N-acetylcysteine (NAC) is a stable, safe and highly bioavailable antioxidant that shows promising prospects in bone tissue engineering due to the ability to attenuate oxidative stress and enhance the osteogenic potential and immune regulatory function of cells. This review systematically introduces the antioxidant mechanism of NAC, analyzes the advancements in NAC-related research involving mesenchymal stem cells, precursor cells, innate immune cells and animal models, discusses its function using the classic oral microenvironment as an example, and places particular emphasis on the innovative applications of NAC-modified tissue engineering biomaterials. Finally, current limitations and future prospects are proposed, with the aim of providing inspiration for targeted readers in the field.

Abstract Developing strain sensors with both high sensitivity and high linearity has always been the goal of researchers. Compared to resistive strain sensors, capacitive strain sensors have incomparable linearity advantages, but have always been limited by low sensitivity. Here, we report a gradient stiffness sliding design strategy that addresses this problem, significantly improving sensitivity while maintaining high linearity. By controlling the distribution of the locally enhanced electric field and the heterogeneous deformation of the substrate, a strain sensor with excellent performance is successfully prepared, exhibiting a giant gauge factor (9.1 × 106) and linearity (R 2 = 0.9997) over the entire sensing range, together with almost no hysteresis and fast response time (17 ms). The gradient stiffness sliding design is a general strategy expected to be applied to other types of sensors to achieve ultra-high sensitivity and ultra-high linearity at the same time.

Abstract The main issue limiting the performance of the solar evaporators is that water-thermal management is difficult to coordinate. Herein, we achieve integrated water-thermal management by designing hierarchical MXene-reduced graphene oxide sponges with anisotropic thermal conductivity and axial-directional water conveyance channels. The reduced graphene oxide acts as the sponge framework and carbon source for in situ synthesis of MXenes on the surface. The axial-oriented framework supports the structure and provides fast water transmission channels to the air-water interface. Meanwhile, the MXene nanosheets are vertically aligned on the framework surface, making the radial thermal conductivity of the sponges much greater than the axial one, which suppresses heat loss in the axial direction. The material exhibits an evaporation rate of 2.35 kg m−2 h−1 under one sunlight and maintains 85 % energy efficiency under weak sunlight (0.5-sun). Furthermore, the sponge shows a long working life with 96 % evaporation rate retention after a 30-day-sustained operation.

In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models.

A novel graphene-coated nanocrystalline ceramic particle, iron-based composite inoculant was developed in this study to optimize the as-cast microstructure and mechanical properties of W18Cr4V high-speed steel (HSS). The effects of the composite inoculant on the microstructure, crystal structure, and mechanical properties of HSS were analyzed using transmission electron microscopy, scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffraction. The (002-) and (020) crystal planes of the Fe3C and Cr7C3 phases, respectively, were collinear at two points in the reciprocal space, indicating a coherent relationship between the Fe3C and Cr7C3 phases in the tempered modified HSS. This contributed to an improved non-uniform nucleation rate and refining of the HSS grains. The mechanical properties of the modified steel exhibited a general improvement. Specifically, the modification treatment enhanced the hardness of HSS from HRC 63.2 to 66.4 and the impact toughness by 48.3%.

Mimicking the photosynthesis of green plants to combine water oxidation with CO2 reduction is of great significance for solving energy and environmental crises. In this context, a trinuclear nickel complex, [NiII3(paoH)6(PhPO3)2]·2ClO4 (1), with a novel structure has been constructed with PhPO32− (phenylphosphonate) and paoH (2-pyridine formaldehyde oxime) ligands and possesses a reflection symmetry with a mirror plane revealed by single-crystal X-ray diffraction. Bulk electrocatalysis demonstrates that complex 1 can homogeneously catalyze water oxidation and CO2 reduction simultaneously. It can catalyze water oxidation at a near-neutral condition of pH = 7.45 with a high TOF of 12.2 s−1, and the Faraday efficiency is as high as 95%. Meanwhile, it also exhibits high electrocatalytic activity for CO2 reduction towards CO with a TOF of 7.84 s−1 in DMF solution. The excellent electrocatalytic performance of the water oxidation and CO2 reduction of complex 1 could be attributed to the two unique µ3-PhPO32− bridges as the crucial factor for stabilizing the trinuclear molecule as well as the proton transformation during the catalytic process, while the oxime groups modulate the electronic structure of the metal centers via π back-bonding. Therefore, apart from the cooperation effect of the three Ni centers for catalysis, simultaneously, the two kinds of ligands in complex 1 can also synergistically coordinate the central metal, thereby significantly promoting its catalytic performance. Complex 1 represents the first nickel molecular electrocatalyst for both water oxidation and CO2 reduction. The findings in this work open an avenue for designing efficient molecular electrocatalysts with peculiar ligands.

Abstract Background Due to the high nursing pressure of patients with cerebral hemorrhage and the general shortage of clinical nurses, nursing support workers often participate in clinical nursing work, but the influence of nursing support workers' participation on the negative emotion, quality of life and life satisfaction of patients with intracerebral hemorrhage is unknown. Methods This quasi-experimental study was conducted with a pretest–posttest design. A total of 181 ICH patients admitted to our hospital from January 2022 to April 2022 were enrolled, including 81 patients receiving conventional care (CG control group) and 80 patients receiving nursing support worker participation (RG research group). All patients were recorded with self-perceived Burden Scale (SPBS), Hamilton Depression Scale (HAMD), Quality of Life Scale (SF-36), Somatic Self rating Scale (SSS), Patient self-care ability assessment scale (Barthel) and Satisfaction with life scale (SWLS) scores. Results Patients with high negative emotion were more willing to participate in clinical nursing work (p

The discrete element method (DEM) serves as a robust tool for simulating the mechanical behavior of granular materials. The accuracy of the DEM simulation is determined by the calculation of contact forces between elements. Compared to spherical elements, the contact modes of polyhedral elements are more complex. In this work, the contact force between polyhedral elements under different contact modes was investigated by experiment. Based on the experimental results, the normal stiffness coefficient in the Cundall’s contact model was modified. The improved contact force model was then applied in the DEM simulation and validated by means of comparison with the results of packing experiments. The research results demonstrate that the improved contact force model can be effectively applied to the simulation of different contact modes between polyhedral elements. The results of the packing experiment highly coincide with the results of the DEM simulation, which confirms the accuracy and reliability of the improved contact force model.

Granular materials are widely prevalent in nature and industry, and the discrete element method (DEM) is the most effective method for the mechanical analysis of granular materials. Because of the difficulty inherent in friction coefficient determination, the friction behavior of granular geomaterials has always been a difficult problem in DEM. In order to study the variation rules of the sliding friction coefficients in DEM, an experiment designed to determine the sliding friction coefficient between particles with different shapes and different contact forms and an experiment designed to determine the sliding friction coefficient between particles and different boundaries were proposed, and the experimental results were fitted. In order to verify the reliability of the experiments, the fitting results were embedded into DEM software, and several packing experiments and simulation experiments were carried out and compared. The simulated results are detailed and discussed with reference to the experimental results as part of this paper. The results indicate that the simulation results are highly consistent with the experimental results, and the sliding friction coefficient obtained by the proposed experiment is in accordance with that determined theoretically. The sliding friction coefficient determined by fitting according to the experimental results can determine the sliding friction coefficient in DEM simulation and improve the accuracy of simulation results.

Abstract The normal contact force determines the behavior of a particle system. To investigate the normal contact force in a one‐dimensional sphere chain subjected to impact load, by comparing the simulation results of the existing typical normal contact force models embedded in the discrete element program, an improved normal contact force model was proposed in this paper. The improved model consists of two parts: the Cundall model for loading and the Daniel model for unloading. Moreover, a systematic test was designed to verify the accuracy and applicability of the improved model. The results showed that the calculated contact force curves agree well with the experimental results. Furthermore, the improved model is implemented in the solution algorithm without need for complex numerical methods and parameters fitting, leading to more efficient simulations.

The nature of the fracture and fragmentation processes in concrete medium under blast loading is the transformation of the medium from continuum to discontinuity. Coupled with the significant rate correlation of concrete medium, its mechanical behavior presents a high degree of complexity. When tackling this problem, the finite element method (FEM) frequently encounters problems such as grid distortion and even negative volume, whereas the material point method (MPM) can efficiently avoid these problems. Furthermore, the original Holmquist-Johnson-Cook (HJC) model does not take the segmented characteristics of the calculation function for the dynamic increasing factor into consideration. As a result, in this article, first, the calculation function for the dynamic increasing factor in the HJC model was modified by the Split-Hopkinson Pressure Bar (SHPB) experiment, and an improved HJC model was proposed; second, an MPM simulation program was developed, and the improved HJC concrete model was embedded into the simulation program; and finally, the simulation program was verified by numerical examples, and the results show that the developed simulation program can better simulate the fracture and fragmentation process of the concrete medium under blast loading, especially the pulverization characteristics of the medium in the near zone of the load.

MXene-based fibers are expected to have the hopeful potential for manufacturing multifunctional fabrics with permeability, lightweight, remarkable electrical conductivity, and outstanding mechanical properties. However, the weak interaction between fibers limits the performance of the fabrics, and researchers are still seeking a suitable solution and easily scalable production methods. Here a green and novel wet-assembly approach is proposed for producing non-woven MXene fiber fabrics (MFFs) constructed by random-oriented and well-connected interfused MXene fibers. The as-prepared fabrics are porous and lightweight while exhibiting high electrical conductivity (708 S cm−1). A maximum specific electrical conductivity is achieved with a relatively low density (0.24 g cm−3), even superior to individual MXene fibers. Moreover, the MFFs exhibit outstanding electromagnetic interference (EMI) shielding effectiveness of 75.0 dB at a thickness of 107 μm, exceptional Joule heating (up to 370 °C at a voltage of 3.5 V), and excellent photothermal conversion abilities, showing their great potential as high-performance and multifunctional fabrics in EMI shielding, wearable intelligent garments, and personal heating applications.

Face alignment is widely used in high-level face analysis applications, such as human activity recognition and human–computer interaction. However, most existing models involve a large number of parameters and are computationally inefficient in practical applications. In this paper, we aim to build a lightweight facial landmark detector by proposing a network-level architecture-slimming method. Concretely, we introduce a selective feature fusion mechanism to quantify and prune redundant transformation and aggregation operations in a high-resolution supernetwork. Moreover, we develop a triple knowledge distillation scheme to further refine a slimmed network, where two peer student networks could learn the implicit landmark distributions from each other while absorbing the knowledge from a teacher network. Extensive experiments on challenging benchmarks, including 300W, COFW, and WFLW, demonstrate that our approach achieves competitive performance with a better trade-off between the number of parameters (0.98 M–1.32 M) and the number of floating-point operations (0.59 G–0.6 G) when compared to recent state-of-the-art methods.

Image deraining is increasingly critical in the domain of computer vision. However, there is a lack of fast deraining algorithms for multiple images without temporal and spatial features. To fill this gap, an efficient image-deraining algorithm based on dual adjacent indexing and multi-deraining layers is proposed to increase deraining efficiency. The deraining operation is based on two proposals: the dual adjacent method and the joint training method based on multi-deraining layers. The dual adjacent structure indexes pixels from adjacent features of the previous layer to merge with features produced by deraining layers, and the merged features are reshaped to prepare for the loss computation. Joint training method is based on multi-deraining layers, which utilise the pixelshuffle operation to prepare various deraining features for the multi-loss functions. Multi-loss functions jointly compute the structural similarity by loss calculation based on reshaped and deraining features. The features produced by the four deraining layers are concatenated in the channel dimension to obtain the total structural similarity and mean square error. During the experiments, the proposed deraining model is relatively efficient in primary rain datasets, reaching more than 200 fps, and maintains relatively impressive results in single and crossing datasets, demonstrating that our deraining model reaches one of the most advanced ranks in the domain of rain-removing.

Glyamifop (R&D code: FG001), (R)-(2-(4-(6-chlorobenzoxazol-2-oxy) phenoxy) propionyl) glycine ethyl ester is a newly developed aryloxyphenoxypropionate (HRAC Group 1) herbicide for weed control in paddy fields. This work determined the effect of Glyamifop on weeds and its safety for rice in the glasshouse. Glyamifop controlled the common gramineous weeds in paddy fields at 100 g a.i. ha−1: the fresh weight inhibition rates of Echinochloa crus-galli, Leptochloa chinensis, Setaria viridis, Eragrostis japonica, Digitaria sanguinalis and Panicum bisulcatum were all above 90%. It has almost no inhibitory effect on broad-leaved and cyperaceae weeds, such as Eclipta prostrata and Cyperus iria. Glyamifop inhibited cyhalofop-butyl-resistant L. chinensis, penoxsulam-resistant E. crus-galli and quinclorac-resistant E. crusgalli var. zelayensis by 100%, 99.98% and 96.37%, respectively, at 100 g a.i. ha−1, based on the fresh weight. The selectivity index of Glyamifop foliage treatment in the rice varieties japonica ‘Huaidao 5’, indica ‘Xiangliangyou 900’ and glutinous ‘Zhennuo 29’ was 5.93, 6.81 and 4.91, respectively; therefore, Glyamifop is safe for the 3 different rice varieties. Fresh weight rice inhibition rates were 7.18%, 2.99% and 7.93% at the 2.5-, 3.5- and 5.5-leaf stage, respectively, and the selectivity index was 5.18, 6.04 and 7.93, respectively, indicating that Glyamifop was safe for rice at these leaf stages. L. chinensis ACCase activity decreased with increasing Glyamifop concentration, and the inhibitory effect was similar to that of cyhalofop acid; this confirmed that Glyamifop is an ACCase inhibitor. In conclusion, Glyamifop has potential for the management of gramineous weeds as it has good activity against weeds that are resistant to common herbicides in paddy fields.

To study the vibration response of the layered medium under impact loading, single-layer concrete slabs, multilayer concrete slabs, and multilayer concrete slabs with a cemented filling layer were used as the working media to simulate the layered medium. A large number of impact loading tests were carried out by using a simple drop hammer device designed by us. The experimental results indicate that, under the impact load, the vibration response of the surface of the medium decreases with the increase in the distance to the impact source, showing the law of fast attenuation near field and slow attenuation far field, and the vibration energy moves to the low-frequency component; the vibration response increases with the increase in the impact energy, and the difference in the vibration response caused by the impact energy decreases as the distance increases; the vibration response is negatively correlated with the thickness of the dielectric layer, and the divergence of vibration response caused by impact energy decreases with the increase in the thickness of the dielectric layer. Due to the existence of the free surface and bedding, the vibration response of the layered medium surface increases with the increase in the number of layers and the vibration velocity response increases with the increase in the distance to the impact source when it is close to the free surface and far from the vibration source. For the filling of the cemented layer, the vibration response of the layered concrete slab becomes more complex under impact loading, showing obvious disorder. At the same time, this paper also used the dimensional analysis method to establish the calculation model of the peak response of vibration velocity of layered media under the impact load, which provided an idea for determining the peak response of vibration velocity of the layered media.

In blasting operation, some undesirable impacts, such as fly-rock, fragmentation, and back break, are induced. If the blasting design is not optimized, these mentioned impacts would reduce the blasting efficiency. To improve and optimize the blast design, blasting effect evaluation is essential. Due to the complexity of interactions among blasting parameters, empirical methods may not be appropriate for blast design optimization. A two-level mathematical model based on fuzzy mathematics, is proposed in this work. In total, 11 typical parameters were chosen and classified into three groups. The blasting effect is evaluated from three aspects, and then the comprehensive evaluation is given. A blasting effect evaluation system was developed based on the mentioned method on the platform of VC++. Some other techniques, such as image processing, were integrated into the system, which allowed for obtaining all of the parameters rapidly and conveniently. The system was applied in practical bench blast engineering. The results obtained from the system can provide effective information for the optimization of the next blast design.