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The Split Hopkinson pressure bar (SHPB) is widely used for characterizing the mechanical behavior of materials at high strain rates. One of the most challenging factors is achieving constant strain rate (CSR) loading of the specimen at a certain strain rate. Obtaining the effective incident pulse based on the experimental material for achieving CSR loading remains unresolved. This research focuses on obtaining the proper incident pulse for achieving constant strain rate loading using the pulse-shaping technique. A parameterized objective incident model in terms of the strain rate and quasi-static (or dynamic stress–strain) behavior of the material is established utilizing the three-wave method. Experimental pulses that closely resemble the desired objective pulses can be generated by adjusting parameters such as the geometry of the shaper, the shaper material, striker velocities, and the length of the striker according to the pulse-shaping model. The model is applied to the design of the incident pulse for B4CP/2024Al composite material, and the dynamic stress–strain curves at different strain rates are obtained under CSRs. This model provides effective guidance for selecting an appropriate shaper and achieving CSR loading in SHPB tests.

Camellia oleifera is a woody oil crop with the highest oil yield and the largest cultivation area in China, and C. oleifera seed oil is a high-quality edible oil recommended by the Food and Agriculture Organization of the United Nations. The objectives of this study were to investigate the variation in fruit yield traits and seed chemical compositions of wild C. oleifera in China and to identify the differences between wild C. oleifera and cultivated varieties. In this study, we collected wild C. oleifera samples from 13 sites covering the main distribution areas of wild C. oleifera to comprehensively evaluate 25 quantitative traits of wild C. oleifera fruit and seed chemical compositions and collected data of 10 quantitative traits from 434 cultivated varieties for a comparative analysis of the differences between wild and cultivars. The results showed that the coefficients of variation of the 25 quantitative traits of wild C. oleifera ranged from 2.605% to 156.641%, with an average of 38.569%. The phenotypic differentiation coefficients ranged from 25.003% to 99.911%, with an average of 77.894%. The Shannon–Wiener index (H’) ranged from 0.195 to 1.681. Based on the results of principal component analysis (PCA) and phenotypic differentiation coefficients, 10 traits differed significantly between wild C. oleifera and cultivated varieties, while the differentiation coefficients (VST) for fresh fruit weight, oleic acid, unsaturated fatty acids, stearic acid, and saturated fatty acids were more than 95%, of which fresh fruit weight and oleic acid content were potential domestication traits of C. oleifera. The results of this study can contribute to the efficient excavation and utilization of wild C. oleifera genetic resources for C. oleifera breeding.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid beta (Aβ) plaques in the brain. Aβ1–42 is the main component of Aβ plaque, which is toxic to neuronal cells. Si nanowires (Si NWs) have the advantages of small particle size, high specific surface area, and good biocompatibility, and have potential application prospects in suppressing Aβ aggregation. In this study, we employed the vapor–liquid–solid (VLS) growth mechanism to grow Si NWs using Au nanoparticles as catalysts in a plasma-enhanced chemical vapor deposition (PECVD) system. Subsequently, these Si NWs were transferred to a phosphoric acid buffer solution (PBS). We found that Si NWs significantly reduced cell death in PC12 cells (rat adrenal pheochromocytoma cells) induced by Aβ1–42 oligomers via double staining with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and fluorescein diacetate/propyl iodide (FDA/PI). Most importantly, pre-incubated Si NWs largely prevented Aβ1–42 oligomer-induced PC12 cell death, suggesting that Si NWs exerts an anti-Aβ neuroprotective effect by inhibiting Aβ aggregation. The analysis of Fourier Transform Infrared (FTIR) results demonstrates that Si NWs reduce the toxicity of fibrils and oligomers by intervening in the formation of β-sheet structures, thereby protecting the viability of nerve cells. Our findings suggest that Si NWs may be a potential therapeutic agent for AD by protecting neuronal cells from the toxicity of Aβ1–42.

Aluminum matrix composites (AMMCs) have demonstrated substantial potential in the realm of armor protection due to their favorable properties, including low density, high specific stiffness, and high specific strength. These composites are widely employed as structural components and frequently encounter high strain rate loading conditions, including explosions and penetrations during service. And it is crucial to note that under dynamic conditions, these composites exhibit distinct mechanical properties and failure mechanisms compared to static conditions. Therefore, a thorough investigation into the dynamic mechanical behavior of aluminum matrix composites and precise constitutive equations are imperative to advance their application in armor protection. This review aims to explore the mechanical properties, strengthening the mechanism and deformation damage mechanism of AMMCs under high strain rate. To facilitate a comprehensive understanding, various constitutive equations are explored, including phenomenological constitutive equations, those with physical significance, and those based on artificial neural networks. This article provides a critical review of the reported work in this field, aiming to analyze the main challenges and future development directions of aluminum matrix composites in the field of protection.

In this study, Al-B4C/Al laminated composites with high interlayer bonding strength were fabricated by integrated hot-pressed sintering accompanied with hot rolling. The mechanical properties and interface behavior of the Al-B4C/Al laminated composites were investigated under quasi-static and impact loading. The results show that the Al-B4C/Al laminated composites obtain a high interface bonding strength, because no interlayer delamination occurs even after fractures under quasi-static and impact loads. The Al-B4C/Al laminated composites exhibit a better comprehensive mechanical performance, and the fracture can be delayed due to the high bonding strength interface. Moreover, laminated composites can absorb more impact energy than the monolithic material under impact loading due to the stress transition and relaxation.

Ceramic panel collapse will easily lead to the failure of traditional targets. One strategy to solve this problem is to use separate ceramic units as armor panels. Based on this idea, we propose an aluminum matrix composite using pressure infiltration, containing an array of ceramic balls, the reinforcement of which consists of centimeter-scale SiC balls and micron-scale B4C particles. Three different array layouts were designed and fabricated: compact balls in the front panel (F-C), non-compact balls in the front panel (F-NC), and compact balls inside the target (I-C). The penetration resistance properties were tested using a 12.7 mm armor-piercing incendiary (API). The results show that there are no significant internal defects, and the ceramic balls are well-bonded with the matrix composite. The F-NC structure behaves the best penetration resistance with minimal overall damage; the I-C structure has a large area of spalling and the most serious damage. Finite element simulation reveals that the ceramic balls play a major role in projectile erosion; in the non-compact structure, the composite materials between the ceramic balls can effectively disperse the stress, thereby avoiding the damage caused by direct contact between ceramic balls and improving the efficiency of ceramic ball erosion projectiles. Furthermore, it is essential to have a certain thickness of supporting materials to prevent spalling failure caused by stress wave transmission during penetration. This multi-scale composite exhibits excellent ballistic performance, providing valuable insights for developing anti-penetration composite armor in future applications.

Three-dimensional LiDAR systems that capture point cloud data enable the simultaneous acquisition of spatial geometry and multi-wavelength intensity information, thereby paving the way for three-dimensional point cloud recognition and processing. However, due to the irregular distribution, low resolution of point clouds, and limited spatial recognition accuracy in complex environments, inherent errors occur in classifying and segmenting the acquired target information. Conversely, two-dimensional visible light images provide real-color information, enabling the distinction of object contours and fine details, thus yielding clear, high-resolution images when desired. The integration of two-dimensional information with point clouds offers complementary advantages. In this paper, we present the incorporation of two-dimensional information to form a multi-modal representation. From this, we extract local features to establish three-dimensional geometric relationships and two-dimensional color relationships. We introduce a novel network model, termed MInet (Multi-Information net), which effectively captures features relating to both two-dimensional color and three-dimensional pose information. This enhanced network model improves feature saliency, thereby facilitating superior segmentation and recognition tasks. We evaluate our MInet architecture using the ShapeNet and ThreeDMatch datasets for point cloud segmentation, and the Stanford dataset for object recognition. The robust results, coupled with quantitative and qualitative experiments, demonstrate the superior performance of our proposed method in point cloud segmentation and object recognition tasks.

Demand fluctuations and uncertainty bring challenges to inventory management, and intermittent demand patterns increase the risk of inventory backlogs and raise inventory holding costs. In previous studies on inventory routing problems, different variants have been proposed to cope with complicated industrial scenarios. However, there are few studies on inventory routing problems with intermittent demand patterns. To solve this problem, we introduce a lateral transshipment strategy and build a single-product multi-period inventory routing mixed integer programming model to reduce customers’ inventory backlogs, balance regional inventory, reduce inventory holding costs, and improve inventory management efficiency. Furthermore, we design an adaptive large-neighborhood search algorithm with new operators to improve the solving efficiency. The experimental results show that an appropriate transshipment price can reduce the share of distribution costs. Another finding is that higher-capacity vehicles lead to higher revenue. Our findings not only expand the scope of the IRP domain but also provide actionable management insights for business practitioners.

Accurate sales forecasting can provide a scientific basis for the management decisions of enterprises. We proposed the xDeepFM-LSTM combined forecasting model for the characteristics of sales data of apparel retail enterprises. We first used the Extreme Deep Factorization Machine (xDeepFM) model to explore the correlation between the sales influencing features as much as possible, and then modeled the sales prediction. Next, we used the Long Short-Term Memory (LSTM) model for residual correction to improve the accuracy of the prediction model. We then designed and implemented comparison experiments between the combined xDeepFM-LSTM forecasting model and other forecasting models. The experimental results show that the forecasting performance of xDeepFM-LSTM is significantly better than other forecasting models. Compared with the xDeepFM forecasting model, the combined forecasting model has a higher optimization rate, which provides a scientific basis for apparel companies to make adjustments to adjust their demand plans.

This study proposes a digital twin (DT) application framework that integrates deep reinforcement learning (DRL) algorithms for the dynamic scheduling of crane transportation in workshops. DT is used to construct the connection between the workshop service system, logical simulation environment, 3D visualization model and physical workshop, and DRL is used to support the core decision in scheduling. First, the dynamic scheduling problem of crane transportation is constructed as a Markov decision process (MDP), and the corresponding double deep Q-network (DDQN) is designed to interact with the logic simulation environment to complete the offline training of the algorithm. Second, the trained DDQN is embedded into the DT framework, and then connected with the physical workshop and the workshop service system to realize online dynamic crane scheduling based on the real-time states of the workshop. Finally, case studies of crane scheduling under dynamic job arrival and equipment failure scenarios are presented to demonstrate the effectiveness of the proposed framework. The numerical analysis shows that the proposed method is superior to the traditional dynamic scheduling method, and it is also suitable for large-scale problems.

The optoelectronic properties of layered α-MoO3 are greatly limited due to its wide band gap and low carrier concentration. The insertion of hydrogen (H) can effectively tune the band structure and carrier concentration of MoO3. Herein, first-principles calculations were performed to unravel the physical mechanism of a H-doped α-MoO3 system. We found that the modulation of the electronic structure of H-doped MoO3 depends on the doping concentration and position of the H atoms. It was found that the band gap decreases at 8% doping concentration due to the strong coupling between Mo-4d and O-2p orbits when H atoms are inserted into the interlayer. More interestingly, the band gap decreases to an extreme due to the Mo-4d orbit when all the H atoms are inserted into the intralayer only, which has a remarkable effect on light absorption. Our research provides a comprehensive theoretical discussion on the mechanism of H-doped α-MoO3 from the doping positions and doping concentrations, and offers useful strategies on doping modulation of the photoelectric properties of layered transition metal oxides.

The contribution of rheological properties and viscoelasticity of the interfacial adsorbed layer to the emulsification mechanism of enzymatic modified sugar beet pectin (SBP) was studied. The component content of each enzymatic modified pectin was lower than that of untreated SBP. Protein and ferulic acid decreased from 5.52% and 1.08% to 0.54% and 0.13%, respectively, resulting in a decrease in thermal stability, apparent viscosity, and molecular weight (Mw). The dynamic interfacial rheological properties showed that the interfacial pressure and modulus (E) decreased significantly with the decrease of functional groups (especially proteins), which also led to the bimodal distribution of particle size. These results indicated that the superior emulsification property of SBP is mainly determined by proteins, followed by ferulic acid, and the existence of other functional groups also promotes the emulsification property of SBP.

Determining the complex relationships between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has been proven to be capable of effectively detecting the statistical patterns of epistasis, although classification accuracy is required for this approach. The imbalanced dataset can cause seriously negative effects on classification accuracy. Moreover, MDR methods cannot quantitatively assess the disease risk of genotype combinations. Hence, we introduce a novel weighted risk score-based multifactor dimensionality reduction (WRSMDR) method that uses the Bayesian posterior probability of polymorphism combinations as a new quantitative measure of disease risk. First, we compared the WRSMDR to the MDR method in simulated datasets. Our results showed that the WRSMDR method had reasonable power to identify high-order gene-gene interactions, and it was more effective than MDR at detecting four-locus models. Moreover, WRSMDR reveals more information regarding the effect of genotype combination on the disease risk, and the result was easier to determine and apply than with MDR. Finally, we applied WRSMDR to a nasopharyngeal carcinoma (NPC) case-control study and identified a statistically significant high-order interaction among three polymorphisms: rs2860580, rs11865086 and rs2305806.

Resistive random access memory (RRAM) devices with Ni/AlOx/Pt-structure were manufactured by deposition of a solution-based aluminum oxide (AlOx) dielectric layer which was subsequently annealed at temperatures from 200 °C to 300 °C, in increments of 25 °C. The devices displayed typical bipolar resistive switching characteristics. Investigations were carried out on the effect of different annealing temperatures for associated RRAM devices to show that performance was correlated with changes of hydroxyl group concentration in the AlOx thin films. The annealing temperature of 250 °C was found to be optimal for the dielectric layer, exhibiting superior performance of the RRAM devices with the lowest operation voltage (104), the narrowest resistance distribution, the longest retention time (>104 s) and the most endurance cycles (>150).