The widespread application of sophisticated structural health monitoring systems in civil infrastructures produces a large volume of data. As a result, the analysis and mining of structural health ...monitoring data have become hot research topics in the field of civil engineering. However, the harsh environment of civil structures causes the data measured by structural health monitoring systems to be contaminated by multiple anomalies, which seriously affect the data analysis results. This is one of the main barriers to automatic real-time warning, because it is difficult to distinguish the anomalies caused by structural damage from those related to incorrect data. Existing methods for data cleansing mainly focus on noise filtering, whereas the detection of incorrect data requires expertise and is very time-consuming. Inspired by the real-world manual inspection process, this article proposes a computer vision and deep learning–based data anomaly detection method. In particular, the framework of the proposed method includes two steps: data conversion by data visualization, and the construction and training of deep neural networks for anomaly classification. This process imitates human biological vision and logical thinking. In the data visualization step, the time series signals are transformed into image vectors that are plotted piecewise in grayscale images. In the second step, a training dataset consisting of randomly selected and manually labeled image vectors is input into a deep neural network or a cluster of deep neural networks, which are trained via techniques termed stacked autoencoders and greedy layer-wise training. The trained deep neural networks can be used to detect potential anomalies in large amounts of unchecked structural health monitoring data. To illustrate the training procedure and validate the performance of the proposed method, acceleration data from the structural health monitoring system of a real long-span bridge in China are employed. The results show that the multi-pattern anomalies of the data can be automatically detected with high accuracy.
► LDS–VSLLME–GC–MS was applied to the determination of PEs. ► Low density and less toxic organic solvent toluene was used as extractant. ► Good extraction solvent dispersion was achieved by ...relatively less toxic surfactant combined with vortex agitation. ► Fast extraction time (within 1min) and high extraction efficiency with good enrichment factors up to 290 were achieved. ► The low-density organic solvent collection procedure was simple and fast.
For the first time, a novel low-density solvent-based vortex-assisted surfactant-enhanced-emulsification liquid–liquid microextraction (LDS–VSLLME) was developed for the fast, simple and efficient determination of six phthalate esters (PEs) in bottled water samples followed by gas chromatography–mass spectrometry (GC–MS). In the extraction procedure, the aqueous sample solution was injected into a mixture of extraction solvent (toluene) and surfactant (cetyltrimethyl ammonium bromide), which were placed in a glass tube with conical bottom, to form an emulsion by the assistance of vortex agitation. After extraction and phase separation by centrifugation, and removal of the spent sample, the toluene extract was collected and analyzed by GC–MS. The addition of surfactant enhanced the dispersion of extraction solvent in aqueous sample and was also favorable for the mass transfer of the analytes from the aqueous sample to the extraction solvent. Moreover, using a relatively less toxic surfactant as the emulsifier agent overcame the disadvantages of traditional organic dispersive solvents that are usually highly toxic and expensive and might conceivably decrease extraction efficiency to some extent since they are not as effective as surfactants themselves in generating an emulsion. With the aid of surfactant and vortex agitation to achieve good organic extraction solvent dispersion, extraction equilibrium was achieved within 1min, indicating it was a fast sample preparation technique. Another prominent feature of the method was the simple procedure to collect a less dense than water solvent by a microsyringe. After extraction and phase separation, the aqueous sample was removed using a 5-mL syringe, thus leaving behind the extract, which was retrieved easily. This novel method simplifies the use of low-density solvents in DLLME. Under the optimized conditions, the proposed method provided good linearity in the range of 0.05–25μg/L, low limits of detection (8–25ng/L) and good enrichment factors up to 290. The proposed method was successfully applied to the extraction of PEs in bottled water samples as a fast, efficient, and convenient method.
A review of carbon-based thermal interface materials: mechanisms, thermal measurements and thermal properties.
Display omitted
•Various carbon-based thermal interface materials were reviewed, ranging ...from one-dimensional carbon nanotubes, two-dimensional graphene films, to three-dimensional graphene foams, graphene aerogels, vertical graphene and other 3D graphene.•Thermal measurements for bulk and nanoscale materials were discussed to offer a guidance for improving the precision of the results.•The challenges for carbon-based thermal interface materials were discussed, including how to decrease the overall thermal resistance of thermal interface materials, the potential applications from micro to macro devices and the prerequisites for industrial application.
With the development of electronic technologies, electronic devices become smaller, while their power density increases dramatically. The resulting excessive heat requires excellent heat dissipation to ensure great performance of the devices. A good thermal interface material (TIM), with excellent bulk thermal conductivity and proper elastic modulus, which can fill the gap between contact surfaces, is of great importance to improve overall performance of thermal management in the electronic devices. Carbon-based materials, such as carbon nanotubes (CNTs) and graphene (Gr), have attracted great attentions, due to their intrinsic high thermal conductivity. In this paper, carbon-based TIMs are reviewed, as well as the thermal conducting mechanisms and techniques to measure thermal properties for materials. The unique three-dimensional network of 3D-Gr provides not only high thermal conductivity, but also excellent mechanical properties, which makes it more competitive as TIM than CNTs and Gr. Furthermore, there is currently no universal characterization techniques, which are suitable to measure thermal properties of all TIMs. Hence, special attention must be paid to select a proper technique based on the measuring principle, in order to obtain accurate results. An outlook of the future challenges of the thermal interface materials is proposed at the end of the paper.
The geometrical and topological configurations of particles have great influences on their surrounding pore tortuosity and the permeability of granular‐porous media. In this work, we develop a ...relaxation iteration scheme to create random dense packings of different anisotropic‐shaped spheroidal particles with monodispersity and polydispersity in sizes, which manifest the effects of particle shape, fineness, and size distribution on the random packing fraction of particles. Subsequently, we propose a direction‐guided rapidly exploring random tree (DGRRT) algorithm to probe the geometrical tortuosity of complex pore space interstitial to spheroidal particles. A non‐linear pore tortuosity prediction model that relies on the specific surface area and packing fraction of particles, is developed to suit for polydisperse and monodisperse spheroidal particle systems. We further investigate the permeability of granular‐porous media through the lattice Boltzmann method (LBM) of fluid flow. These proposed methods can accurately predict the tortuosity and permeability by comparing against available experimental, theoretical, and numerical results reported in literature. Moreover, the effects of particle packing fraction (i.e., porosity), shape, fineness, and size distribution on the pore tortuosity and permeability of granular‐porous media are evaluated. The results reveal that these microstructural configurations have important influences on the permeability and tortuosity. Our results give an intrinsic interplay between the geometrical tortuosity and permeability of monodisperse and polydisperse particle systems, which have implications for a broad range of scientific disciplines, including the properties of rocks, sandstones and soils, and the design of ultra‐high performance concrete.
Plain Language Summary
Random packing of spheroidal particles arises in a variety of problems, involving precipitation of salt crystals during CO2 sequestration in rock and intrusion of fresh water in aquifers by saline water. The packing properties are also crucial to explore the effects of particle configurations on the pore tortuosity interstitial to particles and the permeability of porous media. This contribution develops a relaxation iteration scheme to realize random dense packings of monodisperse and polydisperse spheroidal particles. The maximum random packing fraction approaches 0.844 that is the highest packing fraction of spheroidal particles yet simulated. Subsequently, a direction‐guided rapidly exploring random tree algorithm is proposed to obtain the pore tortuosity. A non‐linear tortuosity model that relies on the specific surface area and packing fraction of particles, is derived. Moreover, the lattice Boltzmann method is presented to explore the effective permeability of spheroidal particle packings. The results elucidated that the particle shape, packing fraction, size distribution, and non‐uniformity coefficient have significant effects on the permeability of systems studied. Our numerical models and findings can provide insights on designing the geometrical configurations of particles for regulating their surrounding pore tortuosity, and further for optimizing the physical and mechanical properties of porous media in practice.
Key Points
Relaxation iteration scheme generates random dense packings of spheroids with monodispersity and polydispersity in sizes
An accurate and efficient direction‐guided rapidly exploring random tree algorithm and a non‐linear model are developed for the pore tortuosity around mono‐/poly‐disperse spheroids
Effects of particle shape, fineness, and particle size distribution on the particle packing fraction, pore tortuosity and permeability are quantitatively evaluated
A superhydrophilic and underwater superoleophobic PVDF membrane (PVDFAH) has been prepared by surface-coating of a hydrogel onto the membrane surface, and its superior performance for oil/water ...emulsion separation has been demonstrated. The coated hydrogel was constructed by an interfacial polymerization based on the thiol-epoxy reaction of pentaerythritol tetrakis (3-mercaptopropionate) (PETMP) with diethylene glycol diglycidyl ether (PEGDGE) and simultaneously tethered on an alkaline-treated commercial PVDF membrane surface via the thio-ene reaction. The PVDFAH membranes can be fabricated in a few minutes under mild conditions and show superhydrophilic and underwater superoleophobic properties for a series of organic solvents. Energy dispersive X-ray (EDX) analysis shows that the hydrogel coating was efficient throughout the pore lumen. The membrane shows superior oil/water emulsion separation performance, including high water permeation, quantitative oil rejection, and robust antifouling performance in a series oil/water emulsions, including that prepared from crude oil. In addition, a 24 h Soxhlet-extraction experiment with ethanol/water solution (50:50, v/v) was conducted to test the tethered hydrogel stability. We see that the membrane maintained the water contact angle below 5°, indicating the covalent tethering stability. This technique shows great promise for scalable fabrication of membrane materials for handling practical oil emulsion purification.
Intelligent transportation is an indispensable part of the smart city and the primary development direction of the future transportation systems. Vehicle detection and recognition, which is one of ...the most important aspects of intelligent transportation, plays a very important role in various areas of our daily life; one such important area is criminal investigation. In the fine-grained vehicle type detection and recognition, several difficult issues such as problems in data acquisition and tagging, dramatic variance in the data of different vehicle types, and challenges in identifying vehicles of the same brand with highly similar appearances remain unsolved. For the problems of data acquisition and tagging, this paper presents a strategy for automatic data acquisition and tagging based on object detection that can label the vehicle images efficiently while rapidly acquiring all types of fine-grained models. Considering the problem of data imbalance in the training process, this paper proposes a Faster-RCNN based data equalizing strategy (Faster-BRCNN), thereby improving the performance of object detection. In view of the severe information attenuation caused by the feature information transfer obstruct between layers in the traditional deep learning network, the lack of mutual dependency of these features, and the inability of the network to focus on the important region and characteristics, we propose an intensive dense attention network (DA-Net). Through its intensive connection and attention unit, we enhance the model's detection ability. The proposed method achieves mAP of 94.5% and 95.8% in the Stanford Cars and FZU Cars datasets, respectively, thereby verifying its effectiveness.
Abstract
Titanium implants have been widely used in bone tissue engineering for decades. However, orthopedic implant-associated infections increase the risk of implant failure and even lead to ...amputation in severe cases. Although TiO
2
has photocatalytic activity to produce reactive oxygen species (ROS), the recombination of generated electrons and holes limits its antibacterial ability. Here, we describe a graphdiyne (GDY) composite TiO
2
nanofiber that combats implant infections through enhanced photocatalysis and prolonged antibacterial ability. In addition, GDY-modified TiO
2
nanofibers exert superior biocompatibility and osteoinductive abilities for cell adhesion and differentiation, thus contributing to the bone tissue regeneration process in drug-resistant bacteria-induced implant infection.
Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for ...enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.
Extracellular vesicles (EVs) are involved in the regulation of cell physiological activity and the reconstruction of extracellular environment. Matrix vesicles (MVs) are a type of EVs released by ...bone-related functional cells, and they participate in the regulation of cell mineralization. Here, we report bioinspired MVs embedded with black phosphorus (BP) and functionalized with cell-specific aptamer (denoted as Apt-bioinspired MVs) for stimulating biomineralization. The aptamer can direct bioinspired MVs to targeted cells, and the increasing concentration of inorganic phosphate originating from BP can facilitate cell biomineralization. The photothermal effect of the Apt-bioinspired MVs can also promote the biomineralization process by stimulating the upregulated expression of heat shock proteins and alkaline phosphatase. In addition, the Apt-bioinspired MVs display outstanding bone regeneration performance. Our strategy provides a method for designing bionic tools to study the mechanisms of biological processes and advance the development of medical engineering.
PDF
