Offshore wind turbines (WTs) are crucial in offshore wind energy development. However, the dynamic responses of WTs are subject to significant uncertainties which are usually not properly considered. ...To the end, this paper proposes an efficient method for quantifying the uncertainties in WTs' dynamic responses based on cumulative distribution function (CDF)-manifold learning. First, a probabilistic model is developed to represent the environmental parameters and sampling for aerodynamic-hydraulic-servo-elastic simulations. Then, the CDF is obtained by statistically analyzing the simulated data. To tackle the higher dimensionality resulting from discretizing the CDF, a manifold learning-based approach is subsequently proposed to reduce its dimensionality and obtain a manifold space. Furthermore, a mapping relation is established between the environmental parameters and the low-dimensional data to efficiently obtain the response CDF under different environmental parameters, leading to the construction of a probability box (P-box) model. To demonstrate the effectiveness of the proposed method, the National Renewable Energy Laboratory (NREL) 5 MW offshore WT on an Offshore Code Comparison Collaboration (OC3) monopile is selected as a case study and analyzed accordingly. The results show P-box models of seven WT responses and validate the effectiveness of the proposed method.
Human fingers exhibit both high sensitivity and wide tactile range. The finger skin structures are designed to display gradient microstructures and compressibility. Inspired by the gradient ...mechanical Young's modulus distribution, an electric‐field‐induced cationic crosslinker migration strategy is demonstrated to prepare gradient ionogels. Due to the gradient of the crosslinkers, the ionogels exhibit more than four orders of magnitude difference between the anode and the cathode side, enabling gradient ionogel‐based flexible iontronic sensors having high‐sensitivity and broader‐range detection (from 3 × 102 to 2.5 × 106 Pa) simultaneously. Moreover, owing to the remarkable properties of the gradient ionogels, the flexible iontronic sensors also show good long‐time stability (even after 10 000 cycles loadings) and excellent performance over a wide temperature range (from −108 to 300 °C). The flexible iontronic sensors are further integrated on soft grips, exhibiting remarkable performance under various conditions. These attractive features demonstrate that gradient ionogels will be promising candidates for smart sensor applications in complex and extreme conditions.
Inspired by gradient modulus distribution of human fingers, gradient ionogels are prepared by an electric‐field‐induced cationic crosslinkers migration strategy. The gradient of the modulus distribution enables the gradient‐ionogel‐based flexible sensors to demonstrate high sensitivity and broader‐range detection simultaneously. Moreover, the flexible iontronic sensors also show good longtime stability and excellent performance over a wide temperature range.
Abstract Photothermal elastomers are recognized as smart flexible materials that can rapidly and effectively convert light energy into heat energy. However, there has been a lack of adequate focus on ...tackling the sustainability challenges of photothermal elastomers, particularly in terms of material selection, the integration of complex functionalities, and final disposal. A fully bio‐derived photothermal elastomer (BPTE) produced through a simple and chemical‐free approach is introduced, utilizing alkali lignin, lipoic acid, and phytic acid as bio‐derived feedstocks. The BPTE exhibits an adaptive polymeric network crosslinked by dynamic covalent disulfide bonds and multiple hydrogen bonds, endowing it with dual‐mode photothermal conversion capability, robustness, stretchability, rapidly self‐healing property, hydrophobicity, swelling resistance, self‐adhesion, full recyclability, and degradability. The BPTE is further demonstrated as a next‐generation solution for photothermal generators, light‐driven actuators, photothermal antibacterial dressings, and photothermal fibers. The versatility of BPTE opens avenues for innovative smart devices and systems with significant potential in energy conversion, soft robotics, medical treatment, and smart clothing. With outstanding photothermal performances, full recyclability, and biodegradability, these fully bio‐based elastomers present an attractive prospect for the development of the advanced smart photothermal products.
Polymer blending is an efficient way to obtain extraordinary polymeric materials. However, once permanently cross‐linked thermosets are involved in blending, there are challenges in designing and ...optimizing the structures and interfacial compatibility of blends. Vitrimer with dynamic covalent polymer networks provides an innovative opportunity for blending thermoplastics and thermosets. Herein, a reactive blending strategy is proposed to develop thermoplastic‐thermoset blend with enhanced compatibility on the basis of dynamic covalent chemistry. Specifically, polybutylene terephthalate (PBT) and polymerized epoxy vitrimer can be directly melt blended to obtain tough and thermostable blends with desirable microstructures and interfacial interaction. Bond exchange facilitates the grafting of PBT and epoxy vitrimer chains, thus enhancing the interfacial compatibility and thermal stability of blends. The obtained blend balances the strength and stretchability of PBT and epoxy vitrimer, resulting in enhanced toughness. This work offers a new way of designing and fabricating new polymeric materials by blending thermoplastics and thermosets. It also suggests a facile direction towards upcycling thermoplastics and thermosets.
Conventionally cross‐linked polymer networks known as thermosets have posed great challenges to polymer blending in designing and optimizing microstructures and interfacial compatibility. Based on dynamic covalent chemistry, thermoplastic polybutylene terephthalate (PBT) and epoxy vitrimer are reactive‐blended to produce tough and thermostable blends with designed networks and microstructures. This work offers an innovative opportunity to fabricate new polymeric materials.
Front Cover: Article 2200972 by Tiejun Wang and co‐workers proposes a reactive blending strategy to develop thermoplastic‐thermoset blends with enhanced compatibility based on dynamic bond exchange ...reactions at the interface. This work offers an innovative way to design and fabricate new polymeric materials by blending thermoplastics and thermosets. It also suggests a facile direction towards upcycling thermoplastics and thermosets.
Measuring the process parameters of multiphase flow has remained a widely discussed topic within the field of multiphase flow. The measurement of phase fraction of oil-water two-phase flow with ...ultrasound attenuation is very difficult due to the nonlinear response to the measuring parameters, and the main attenuation mechanisms are influenced by ultrasonic frequency and particle size distribution. Therefore, the ultrasound propagation at various phase fractions and distributions are investigated by numerical simulations. The relationship between ultrasonic attenuation coefficient and oil phase fraction is analyzed. A simulation model which can reflect the ultrasonic attenuation mechanisms in the phase inversion process is obtained. The oil-water two-phase flow experiments verified the simulation results.
This study presents a 3D pick-rock contact calculation method for conical picks, aiming to develop a predictive method with high accuracy and lithological tolerance for peak cutting force (PCF). The ...method is based on the projection profile method and D. L. Sikarskie stress distribution function. By integrating Griffith’s theory with rock damage constitutive model, the energy relationship between the rock fracturing process and crack propagation process is analyzed. Furthermore, in order to accurately correct the PCF, the energy correction function (C-Kf) is proposed to calculate the damage intensity index (Ke), which accounts for the relationship between rock brittleness and rock damage elastic–plastic energy. To validate the method, it is compared with full-scale cutting tests and three existing models, and statistical analysis confirms its high lithological tolerance and accuracy, the present model has the highest R2 of 0.90404, which is at least 12.5% higher relative to the mainstream models. Moreover, incorporating Ke into the method further enhances its predictive capability.
One of the most challenging task in face recognition is to identify people with varied poses. Namely, the test faces have significantly different poses compared with the registered faces. In this ...paper, we propose a high-level feature learning scheme to extract pose-invariant identity feature for face recognition. First, we build a single-hidden-layer neural network with sparse constraint, to extract pose-invariant feature in a supervised fashion. Second, we further enhance the discriminative capability of the proposed feature by using multiple random faces as the target values for multiple encoders. By enforcing the target values to be unique for input faces over different poses, the learned high-level feature that is represented by the neurons in the hidden layer is pose free and only relevant to the identity information. Finally, we conduct face identification on CMU Multi-PIE, and verification on Labeled Faces in the Wild (LFW) databases, where identification rank-1 accuracy and face verification accuracy with ROC curve are reported. These experiments demonstrate that our model is superior to other state-of-the-art approaches on handling pose variations.
Learning discriminant face representation for pose-invariant face recognition has been identified as a critical issue in visual learning systems. The challenge lies in the drastic changes of facial ...appearances between the test face and the registered face. To that end, we propose a high-level feature learning framework called "collaborative random faces (RFs)-guided encoders" toward this problem. The contributions of this paper are three fold. First, we propose a novel supervised autoencoder that is able to capture the high-level identity feature despite of pose variations. Second, we enrich the identity features by replacing the target values of conventional autoencoders with random signals (RFs in this paper), which are unique for each subject under different poses. Third, we further improve the performance of the framework by incorporating deep convolutional neural network facial descriptors and linking discriminative identity features from different RFs for the augmented identity features. Finally, we conduct face identification experiments on Multi-PIE database, and face verification experiments on labeled faces in the wild and YouTube Face databases, where face recognition rate and verification accuracy with Receiver Operating Characteristic curves are rendered. In addition, discussions of model parameters and connections with the existing methods are provided. These experiments demonstrate that our learning system works fairly well on handling pose variations.
BACKGROUND Cancer-associated fibroblasts (CAFs) are key factors in malignant tumor initiation, progression, and metastasis. However, the effect of CAFs autophagy on triple-negative breast cancer ...(TNBC) cells is not clear. In this study, the growth effect of TNBC cells regulated by CAFs autophagy was evaluated. MATERIAL AND METHODS CAFs were obtained from invasive TNBC tumors and identified by Western blot and immunofluorescence staining assay. CAFs were co-cultured with TNBC cells, and migration and invasion were evaluated by Matrigel-coated Transwell and Transwell inserts. TNBC cells growth was detected by MTT assay, and epithelial-mesenchymal transition (EMT) regulated by CAFs was evaluated by Western blot assay. RESULTS CAFs were identified by the high expression of α-smooth muscle actin (α-SMA) protein. Autophagy-relevant Beclin 1 and LC3-II/I protein conversion levels in CAFs were higher than those in NFs (P<0.05). TNBC cells migration, invasion, and proliferation levels were significantly improved in the CAFs-conditioned medium (CAFs-CM) group, compared with the other 3 groups (P<0.05). TNBC cells vimentin and N-cadherin protein levels were upregulated and E-cadherin protein level was downregulated in the CAFs-CM group compared with the control group (P<0.05). Further study indicated b-catenin and P-GSK-3β protein levels, which are the key proteins in the Wnt/β-catenin pathway, were upregulated in the CAFs-CM group compared with the control group (P<0.05). CONCLUSIONS Our data demonstrated CAFs autophagy can enhance TNBC cell migration, invasion, and proliferation, and CAFs autophagy can induce TNBC cells to engage in the EMT process through the Wnt/β-catenin pathway.
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