Shenyang Jianzhu University

With the rapid development of the Internet of Everything (IoE), the number of smart devices connected to the Internet is increasing, resulting in large-scale data, which has caused problems such as bandwidth load, slow response speed, poor security, and poor privacy in traditional cloud computing models. Traditional cloud computing is no longer sufficient to support the diverse needs of today's intelligent society for data processing, so edge computing technologies have emerged. It is a new computing paradigm for performing calculations at the edge of the network. Unlike cloud computing, it emphasizes closer to the user and closer to the source of the data. At the edge of the network, it is lightweight for local, small-scale data storage and processing. This article mainly reviews the related research and results of edge computing. First, it summarizes the concept of edge computing and compares it with cloud computing. Then summarize the architecture of edge computing, keyword technology, security and privacy protection, and finally summarize the applications of edge computing.

Abstract Cell adhesion is a basic requirement for anchorage-dependent cells to survive on the matrix. It is the first step in a series of cell activities, such as cell diffusion, migration, proliferation, and differentiation. In vivo , cells are surrounded by extracellular matrix (ECM), whose physical and biochemical properties and micromorphology may affect and regulate the function and behavior of cells, causing cell reactions. Cell adhesion is also the basis of communication between cells and the external environment and plays an important role in tissue development. Therefore, the significance of studying cell adhesion in vitro has become increasingly prominent. For instance, in the field of tissue engineering and regenerative medicine, researchers have used artificial surfaces of different materials to simulate the properties of natural ECM, aiming to regulate the behavior of cell adhesion. Understanding the factors that affect cell behavior and how to control cell behavior, including cell adhesion, orientation, migration, and differentiation on artificial surfaces, is essential for materials and life sciences, such as advanced biomedical engineering and tissue engineering. This article reviews various factors affecting cell adhesion as well as the methods and materials often used in investigating cell adhesion.

At early ages of concrete structures, strength monitoring is important to determine the structures' readiness for service. Piezoelectric-based strength monitoring methods provide an innovative experimental approach to conduct concrete strength monitoring at early ages. In this paper, piezoelectric transducers in the form of 'smart aggregates' are embedded into the concrete specimen during casting. Piezoceramic materials can be used as actuators to generate high frequency vibrating waves, which propagate within concrete structures; meanwhile, they can also be used as sensors to detect the waves. The smart aggregate is a one cubic inch, pre-cast concrete block with a wired, embedded PZT (lead zirconate titanate, a type of piezoceramic) patch. The strength development of concrete structures is monitored by observing the development of harmonic response amplitude from the embedded piezoelectric sensor at early ages. From experimental results, the amplitude of the harmonic response decreases with increasing concrete strength. The concrete strength increases at a fast rate during the first few days and at a decreasing rate after the first week. Concordantly, the amplitude of the harmonic response from the piezoelectric sensor drops rapidly for the first week and continues to drop slowly as hydration proceeds, matching the development of the concrete strength at early ages. Concrete is heterogeneous and anisotropic, which makes it difficult to analyze mathematically. Fuzzy logic has the advantage of conducting analysis without requiring a mathematical model. In this paper, a fuzzy logic system is trained to correlate the harmonic amplitude with the concrete strength based on the experimental data. The experimental results show that the concrete strength estimated by the trained fuzzy correlation system matches the experimental strength data. The proposed piezoelectric-based monitoring method has the potential to be applied to strength monitoring of concrete structures at early ages.

Abstract Excessive emissions of greenhouse gases — of which carbon dioxide is the most significant component, are regarded as the primary reason for increased concentration of atmospheric carbon dioxide and global warming. Terrestrial vegetation sequesters 112–169 PgC (1PgC = 10 15 g carbon) each year, which plays a vital role in global carbon recycling. Vegetation carbon sequestration varies under different land management practices. Here we propose an integrated method to assess how much more carbon can be sequestered by vegetation if optimal land management practices get implemented. The proposed method combines remotely sensed time-series of net primary productivity datasets, segmented landscape-vegetation-soil zones, and distance-constrained zonal analysis. We find that the global land vegetation can sequester an extra of 13.74 PgC per year if location-specific optimal land management practices are taken and half of the extra clusters in ~15% of vegetated areas. The finding suggests optimizing land management is a promising way to mitigate climate changes.

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network.

As recently demonstrated, after programming, thermo-responsive shape memorypolymers can exhibit the multi-shape memory effect (SME) upon heating. In addition, it is confirmed that the temperature corresponding to the maximum recovery stress in constrained recovery is roughly the temperature at which pre-deformation is conducted, a phenomenon known as the temperature memory effect (TME). In this paper, we propose a framework to investigate the underlying mechanisms behind both effects and provide the conditions for the TME. According to this framework, we can achieve fully controllable shape recovery following a very complicated sequence in a continuous manner.

Since the late 1980s, additive manufacturing (AM), commonly known as three-dimensional (3D) printing, has been gradually popularized. However, the microstructures fabricated using 3D printing is static. To overcome this challenge, four-dimensional (4D) printing which defined as fabricating a complex spontaneous structure that changes with time respond in an intended manner to external stimuli. 4D printing originates in 3D printing, but beyond 3D printing. Although 4D printing is mainly based on 3D printing and become an branch of additive manufacturing, the fabricated objects are no longer static and can be transformed into complex structures by changing the size, shape, property and functionality under external stimuli, which makes 3D printing alive. Herein, recent major progresses in 4D printing are reviewed, including AM technologies for 4D printing, stimulation method, materials and applications. In addition, the current challenges and future prospects of 4D printing were highlighted.

Shear thickening fluids (STFs) are a new type of nanosuspension, which are formed by dispersing micro and nanoparticles in a dispersant. STFs are easily deformed under the action of a low shear rate. However, they instantly transform into a hard solid-like state at a high shear rate. After the removal of the impact force, STFs revert to their original liquid state. During this process, STFs absorb a significant amount of impact energy. Hence, they can be employed as a buffer and for vibration reduction. In this study, a comprehensive review of existing literature on STFs is presented. First, the basic properties, classification, and rheological mechanism evolution of STFs are discussed. The factors influencing the shear thickening behavior of these fluids are then reviewed. Subsequently, several computational models of the STF are discussed because the underlying mechanism of STF is still unclear, and to date, there is a paucity of good computational models. Finally, the research progress of composites based on STF in the fields of stab and spike resistance and low- and high-velocity impacts, and the use of STF as a new energy dissipation medium in the fields of explosion resistance, vibration control, adaptive structure, and industrial polishing are summarized.

The world is rich in marine resources, and the use of seawater, sea sand and coral instead of fresh water, river sand and gravel can solve problems such as the scarcity of traditional materials for marine engineering construction. Additionally, fibre-reinforced polymer (FRP) bars have demonstrated excellent corrosion resistance performance, which can effectively solve the problem of the corrosion of steel in harsh marine environments. To study the bond performance between FRP bars and sea sand coral concrete (SSCC), 72 specimens of direct Pull-out were designed, and relevant tests were carried out to explore the effects of fibre types, bar diameters, bond lengths and SSCC strength grades. The results show that the bond strength between carbon fibre-reinforced polymer (CFRP) bars and SSCC was higher than that of basalt fibre-reinforced polymer (BFRP) bars and glass fibre-reinforced polymer (GFRP) bars. The splitting damage pattern occurred in most of the specimens; the bond strength between FRP bars and SSCC decreased with increasing diameter and bond length of FRP bars but increased with increasing SSCC strength grade. As a result, by fitting the bond-slip curves obtained from the tests, the bond-slip constitutive relationship between FRP bars and SSCC specimens was obtained, which clearly and precisely represents the bond failure process of SSCC with FRP bar reinforcement.

Nowadays, oily wastewater and spilled oil have caused great threats on both ecosystem and human life. To address these severe problems, considerable efforts have been possessed on developing novel oil/water separation materials. The porous oil‐absorbent materials, especially the porous polydimethylsiloxane (PDMS) with excellent properties of easy fabrication and inherent hydrophobicity, have attracted tremendous attentions from worldwide. The conventional methods using salt or sugar as sacrificial template and water as solvent have been widely adopted to fabricate the porous PDMS sponge. Due to the inherent hydrophobicity of PDMS, the solvent of water hardly penetrates into the inside of PDMS, which results in the difficult and incomplete remove of the hard template. In this contribution, the 3D interconnected porous PDMS sponge is facilely prepared by utilizing a modified technique with the citric acid monohydrate as hard template and ethanol as solvent. The proposed approach is capable of removing the hard template efficiently and thoroughly, which demonstrates promising utilizations in practical applications.

With the innovation of power market and the development of energy intelligent technology, load forecasting technology as an important direction of power system development plays an important role in power system planning. Aiming at the problem of insufficient feature extraction and low prediction accuracy, a short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price is proposed in this paper. Firstly, the maximum information coefficient method is used to analyze the correlation between electricity price and load. The historical load, real-time electricity price, weather and other factors are constructed in the form of continuous feature maps as input. Secondly, the Convolutional Neural Network (CNN) is used to cascade the shallower and deeper feature from four different scales. Feature vectors of different scales are fused as the input of Long Short Term Memory (LSTM) network , and LSTM network is used for short-term load forecasting. Finally, the proposed method is used to predict the real load data of a city in Liaoning Province. The experimental results show that the proposed method has higher prediction accuracy than the standard LSTM model, Support Vector Machine (SVM) model, Random Forest (RF) model and Auto Regressive Integrated Moving Average (ARIMA) model. Besides, the prediction results show that this study has high application value and provides a new way for the development of power load forecasting.

The detection of obstructive sleep apnea (OSA) based on single-lead electrocardiogram (ECG) is better suited to the noninvasive needs and hardware conditions of wearable mobile devices. From previous ECG-based OSA detection methods, we can find that deep learning methods have shown great potential and advantages. However, due to the nonstationarity of sympathetic nerve signals and the complex characteristics of heart rate variability (HRV), the neural network under a single scale cannot effectively capture the features of HRV. In this study, an OSA detection method based on a multiscale dilation attention 1-D convolutional neural network (MSDA-1DCNN) and a weighted-loss time-dependent (WLTD) classification model were proposed. The introduction of dilated convolution effectively balanced the relationship between model parameters and performance. Attention mechanism technology modified the multiscale features after fusion and improved the weight of features under important channels. In the final classification part of the network, the combination of weighted cross-entropy loss function and hidden Markov model effectively alleviated the problem of data imbalance and improved the classification accuracy of the classifier. In segment identification, the accuracy, sensitivity, and specificity of the proposed method are 89.4%, 89.8%, and 89.1%, respectively; as for individual identification, the accuracy of that achieved 100%. The results demonstrated that the method proposed in this study can identify sleep apnea accurately.

The detection of gas molecules is critical for environmental monitoring, chemical process control, agriculture, and medical applications. Therefore, gas sensors and electronic noses (e-nose) are widely studied by researchers all over the world. Graphene has been considered to be a promising gas detection material due to its special electronic properties, which are strongly influenced by the adsorption of extrinsic molecules. Doping of metal oxides and nanometal particles has also been extensively studied and their electrical property is highly sensitive to the properties of absorbed gases. Carbon nanotubes (CNTs) are expensive but have advantages of high sensitivity, good reversibility, and nice stability. Several research groups have studied the mixed structure of the above three materials with their derivatives blended, which show improved gas sensing capabilities. This review summarizes the state-of-the-art progresses in the research on gas sensors and e-nose, based on graphene, metal oxide, and CNTs.

The rapid development of micromanipulation technologies has opened exciting new opportunities for the actuation, selection and assembly of a variety of non-biological and biological nano/micro-objects for applications ranging from microfabrication, cell analysis, tissue engineering, biochemical sensing, to nano/micro-machines. To date, a variety of precise, flexible and high-throughput manipulation techniques have been developed based on different physical fields. Among them, optoelectronic tweezers (OET) is a state-of-art technique that combines light stimuli with electric field together by leveraging the photoconductive effect of semiconductor materials. Herein, the behavior of micro-objects can be directly controlled by inducing the change of electric fields on demand in an optical manner. Relying on this light-induced electrokinetic effect, OET offers tremendous advantages in micromanipulation such as programmability, flexibility, versatility, high-throughput and ease of integration with other characterization systems, thus showing impressive performance compared to those of many other manipulation techniques. A lot of research on OET have been reported in recent years and the technology has developed rapidly in various fields of science and engineering. This work provides a comprehensive review of the OET technology, including its working mechanisms, experimental setups, applications in non-biological and biological scenarios, technology commercialization and future perspectives.

Precise patterning and controllable assembly of graphene into 3D architectures for flexible micro-supercapacitors was achieved by a printing assembly approach.

This research focuses on a desensitization method to develop a wide-range FBG sensor for extra-large strain monitoring, which is an essential requirement in large scale infrastructures or for some special occasions. Under appropriate hypotheses, the strain transfer distribution of wide-range FBG sensor based on the shear-lag theory is conducted to improve the accuracy of extra-large strain measurements. It is also discussed how the elastic modulus of adhesive layer affects the strain transfer rate. Two prototypes in different monitoring ranges are designed and fabricated by two layers of steel pipe encapsulation. The presented theoretical model is verified by experimental results. Moreover, it is demonstrated that experimentation in regards to the calibration of the wide-range FBG sensor, improved the amplification coefficient up to 2.08 times and 3.88 times, respectively. The static errors are both calculated and analyzed in this experiment. The wide-range FBG strain sensor shows favourable linearity and stability, which is an excellent property of sensors for extra-large strain monitoring.

Abstract Supercapacitor has gained significant attention due to its fast charging/discharging speed, high power density and long-term cycling stability in contrast to traditional batteries. In this review, state-of-the-art achievements on supercapacitor electrode based on carbon materials is summarized. In all-carbon composite materials part, various carbon materials including graphene, carbon nanotube, carbon foam and carbon cloth are composited to fabricate larger specific surface area and higher electrical conductivity electrodes. However, obstacles of low power density as well as low cycling life still remain to be addressed. In metal-oxide composites part, carbon nanotube, graphene, carbon fiber fabric and hollow carbon nanofibers combine with MnO 2 respectively, which significantly address drawbacks of all-carbon material electrodes. Additionally, TiO 2 is incorporated into graphene electrode to overcome the low mechanical flexibility of graphene. In organic active compounds part, conducting polymers are employed to combinate with carbon materials to fabricate high specific capacitance, long-term thermal stability and outstanding electroconductivity flexible textile supercapacitors. In each part, innovation, fabrication process and performance of the resulting composites are demonstrated. Finally, future directions that could enhance the performance of supercapacitors are discussed.

In this paper, a smart aggregate-based approach is proposed for the structural health monitoring of a concrete shear wall structure. The piezoceramic-based smart aggregates were distributed in predetermined locations prior to the casting of the concrete structure to form an active-sensing system for the health monitoring purpose. To evaluate the damage in different areas, the concrete shear wall was sectioned into sub-domains and a wavelet-packet-based damage index matrix is proposed to evaluate the health status in these sections. A cyclic loading procedure was applied to gradually fail the concrete shear wall and the proposed structural health monitoring approach was used to perform structural health monitoring during this loading procedure. The experimental results have shown that the proposed smart aggregate-based approach effectively evaluated the damage status in different areas and detected the precautionary point to predict the structural failure. The proposed approach has the potential to be applied to the structural health monitoring of large-scale concrete shear wall structures.

Abstract Nanoscience is a booming field incorporating some of the most fundamental questions concerning structure, function, and applications. The cutting-edge research in nanoscience requires access to advanced techniques and instrumentation capable of approaching these unanswered questions. Over the past few decades, atomic force microscopy (AFM) has been developed as a powerful platform, which enables in situ characterization of topological structures, local physical properties, and even manipulating samples at nanometer scale. Currently, an imaging mode called PeakForce Tapping (PFT) has attracted more and more attention due to its advantages of nondestructive characterization, high-resolution imaging, and concurrent quantitative property mapping. In this review, the origin, principle, and advantages of PFT on nanoscience are introduced in detail. Three typical applications of this technique, including high-resolution imaging of soft samples in liquid environment, quantitative nanomechanical property mapping, and electrical/electrochemical property measurement will be reviewed comprehensively. The future trends of PFT technique development will be discussed as well.

This paper establishes a bio-economic singular Markovian jump model by considering the price of the commodity as a Markov chain. The controller is designed for this system such that its biomass achieves the specified range with the least cost in a finite-time. Firstly, this system is described by Takagi-Sugeno fuzzy model. Secondly, a new design method of fuzzy state-feedback controllers is presented to ensure not only the regularity, nonimpulse, and stochastic singular finite-time boundedness of this kind of systems, but also an upper bound achieved for the cost function in the form of strict linear matrix inequalities. Finally, two examples including a practical example of eel seedling breeding are given to illustrate the merit and usability of the approach proposed in this paper.