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Constructing ordered hierarchical porous electrocatalysts while maintaining their high efficiency and stability for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is highly desirable but still remains challenging. Herein, hierarchical tri-metal-based hollow nano/micro rod-on-rod array structures, including hydroxides and phosphates, are vertically grown on Cu foam by the cation-exchanging and anion exchanging approaches. Such ordered porous architecture, providing multi metal center, regular interconnection of macropore channel, large electrochemical surface area, small charge-transfer resistance, efficient mass transport and electron transfer for overall water splitting in alkaline media. These obtained hydroxide OER catalysts (CoMn LDH@Cu(OH)2/CF) and phosphide HER catalysts (CoMnP@Cu3P/CF) exhibited low onset potential, low Tafel slope, and prominent durability. Such excellent performance, ordered porous structure and simple preparation methods indicate that this study provides a new strategy for assembling promising catalysts for water splitting and other applications.

This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.

Strain coupling with ferromagnetism and ferroelectricity plays an important role in the development of multiferroic metal-organic frameworks (MOFs) with strong magnetoelectric coupling, but the underlying mechanisms have not been well understood. Strain coupling and dynamic relaxation in multiferroic MOF with perovskite structure [(CH3)2NH2][Mn(HCOO)3] were investigated using X-ray diffraction (XRD), Raman spectroscopy, Infra-red (IR) spectroscopy, differential scanning calorimetry (DSC), magnetic measurements and dynamic mechanical analysis (DMA). DSC results showed peaks at 183 K and 190 K at the rate of 5 K/min during cooling and heating processes, respectively. Magnetic measurements showed magnetic transition at ∼8.5 K at the heating rate of 2 K/min. Temperature and frequency dependences of elastic properties studied by DMA at frequencies of 0.5 Hz–10 Hz between 140 K and 300 K at heating rate of 2 K/min indicated that the minimum in storage modulus and the maximum in loss modulus and loss factor occurred near 190 K. The peak height of loss modulus and loss factor decreased at higher frequency, and the peak temperature was independent of frequency, showing the features of first-order phase transition. Near 190 K, paraelectric to ferroelectric phase transition triggered by disorder–order transition of alkylammonium cations located in the framework cavities occurred accompanied by the structural phase transition from rhombohedral space group R3-c to monoclinic space group Cc. The elastic anomalies and large energy loss near 190 K were associated with the coupling of the local strain with the freezing of dimethylammonium cation and the freezing of twin walls. Keywords: Metal-organic framework (MOF), Phase transition, Dynamic mechanical analysis (DMA), Elastic modulus, Energy loss

Damage detection is very important to ensure the safety of carbon fiber reinforced plastics (CFRP) structure. In this paper, a damage detection method of composite structure based on fiber Bragg grating sensors and principal component analysis was proposed. Firstly, the non-damaged CFRP structure was excited by active excitation, and its dynamic response signal was monitored by FBG sensors. The FBG sensors were attached to the surface of the CFRP structure. And then, the frequency response was extracted as the damage feature by using the Fourier transform method. After that, principal component analysis was used to reduce the dimension of damage feature. The principal component model of the non-damaged state only based on the non-damaged state samples was established and the reconstruction error threshold was determined as the index of damage detection. Thus, structural damage can be detected by determining if the reconstruction error of the new sample was greater than the reconstruction error threshold. Finally, the damage detection system of CFRP structure was constructed to validate the proposed method. The experimental results showed that the method proposed in this paper can accurately identify whether the structure was damaged only based on the non-damaged state samples. This paper provided a feasible method for the damage detection of CFRP structure.

The assessment of structural damage is of great significance for ensuring the service safety of carbon fiber reinforced plastics (CFRP) structures. In this paper, the damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression was studied. The structural dynamic response signals were detected by fiber Bragg grating sensors. Then, the Fourier transform was used to extract the dynamic characteristics of the structure as the damage feature, and the damage feature dimensionality was reduced by using the RReliefF algorithm. On this basis, the damage degree prediction model of CFRP structure based on epsilon-support vector regression was established. Finally, the method proposed in this paper was experimentally verified. The results showed that the epsilon-support vector regression model can accurately predict the damage degree of unknown samples, and the absolute relative error of 27 experiments was less than 10% for 30 testing experiments. This paper provided a feasible method for predicting the damage degree of CFRP structures.

China’s metro construction accidents have occurred frequently due to the lack of effective safety risk control in the context of large-scale construction. Specifically, the safety risk control for China’s metro construction mainly includes pre-construction risk identification and risk warning in construction. However, it is actually a “black box” in China. Based on Building Information Model (BIM) platform, this paper presents the Safety risk identification system (SRIS) and early warning system (SREWS) for China’s metro construction. Methodologies including the modelling process, methods and key technologies for SRIS and SREWS are discussed in detail. The SRIS constructs risk identification knowledge database, designs retrieval matching algorithm and calculates the risk confidence level. The SREWS adopts data fusion, BIM and modern information technology to monitor the safety status. Discussions are drawn and the paper concludes by summarizing the main scientific contribution.

A highly challenging problem that has been plaguing regulators is the inaccurate collection and sharing of safety risks for underground construction. In order to establish a mechanism that could achieve timely and accurate recognition at the pre-construction stage, Building information model (BIM) is adopted as the risk recognition platform. This paper provides the automated safety risk recognition process based on BIM which is generally composed of three parts. The first part is to build the risk database. By means of knowledge structuralizing, questionnaires, depth interviews and group decision-making, explicit and tacit knowledge source are acquired. The safety risk knowledge source is divided into three categories. SQL database is used to express the safety risk knowledge, and all safety risks are stored in the BIM-cloud. The second part is to analyse the relation between engineering information and safety risks. Risk-related engineering information is extracted from BIM models. Backus–Naur form is used to describe the syntax of languages used in computing. A mapping table of engineering information and safety risks is established. The third part concerns the automated safety risk recognition mechanism in the BIM platform. The safety risk recognition mechanism is expressed as “If e, then h (CF (h, e), λ)”. The confidence level as the link is adopted to reveal the mechanism. Finally, a case about flowing sand risk at the foundation pit bottom is conducted. The reasoning process and recognition results are demonstrated. The paper concludes by summarizing the main scientific contribution and giving direction to future research in this field.

Leukemia remains a fatal disease for most patients and novel therapeutic strategies are urgently needed. Aberrant DNA methylation is an epigenetic modification that is important in the initiation and progression of leukemia. Here, we demonstrated NCL/miR-221/NFκB/DNMT1 axis as a new molecular pathway promoting aggressive acute myeloid leukemia (AML) leukemogenesis and successfully designed and prepared a nuclear localization signal (NLS) peptide-targeted gold nanoparticles with co-loaded anti-221 and AS1411 (NPsN-AS1411/a221), which can specifically target NCL/miR-221/NFκB/DNMT1 signaling pathway in AML. NPsN-AS1411/a221 synergistically abrogate endogenous miR-221 promoting cancerous growth by inhibiting the expression of p27Kip1 suppressor gene, as well as effectively deregulate the DNMT1 expression through NFκB signaling which led to a reduction of global DNA methylation and the restoration of tumor suppressor p15INK4B via its promoter DNA hypomethylation. Functionally, NPsN-AS1411/a221 remarkably blockage leukemia proliferation and clonogenic potential in NCL/miR-221/NFκB/DNMT1 positive AML cell lines. More importantly, NPsN-AS1411/a221 cooperatively extend the overall survival, lower the white blood cells, reverse splenomegaly, inhibit blasts in bone marrow and metastatic to lung in a preclinical AML animal model. Altogether, our studies provide a proof of concept for multiple-functional drug delivery system that based on the specific gene network involved in tumor growth, and highlight the clinical potential of NCL/miR-221/NFκB/DNMT1-targeted AML nanotherapy.

The single-step prediction of burning zone temperature plays an important role in the safety and stability control of cement rotary kiln. This is because, the abnormal temperature events can be found as early as possible and the operator can take effective emergency measures in time. In this paper, the burning zone temperature single-step prediction method based on real-time wavelet filtering and kernel extreme learning machine is studied. Firstly, the visual inspection device is used to detect the burning zone temperature. And then, the amplitude limited filtering method is used to weaken the effects of temperature anomalies. On this basis, the real-time filtering of the burning zone temperature is realized by combining the sliding time window and wavelet filtering method. After that, the single-step prediction of burning zone temperature is realized by combining the sliding time window and kernel extreme learning machine method. At last, the burning zone temperature prediction method is validated. The minimum root mean squared error of the 5 consecutive days is 0 . 4259 ° C . The single average running time of model training and prediction of kernel extreme learning machine is much less than support vector regression, which is very helpful for the online prediction of burning zone temperature. The result shows that the burning zone temperature single-step prediction method proposed in this paper is feasible and effective.

In order to deeply understand the combustion process of marine low speed two-stroke dual fuel engine under the background of “carbon neutrality”, MAN B&W 6S50ME-C-GI was taken as the research object, combustion processes of the full diesel (FD) and diesel ignited natural gas (DING) at 100 % load were tested and calculated. The results show that the total combustion duration period (CDP) under DING is 15.5°CA shorter than that under FD. The slow combustion period (SCP) and post combustion period (PCP) under DING were 1.75°CA and 15°CA lower than those under FD, respectively. At the same combustion time, the local high temperature region under DING is reduced by an average of 8 % compared with FD. The radial flame propagation velocity under DING and FD showed a “single peak” law, and the axial flame propagation velocity shows a “fluctuation” law. The difference is that the radial flame propagation velocity under FD shows attenuation to zero twice at 8°CA ATDC and 14°CA ATDC, and the flame propagation velocity of natural gas combustion is faster than that of diesel combustion. The maximum radial flame propagation velocity under DING is 2.5 m/s faster than that under FD. The maximum axial flame propagation velocity under DING is 0.7 m/s faster than that under FD.

The damage assessment of Carbon Fiber Reinforced Plastics (CFRP) laminate structure is very important to ensure the safety of the structure in service. Machine learning is a research hotspot in damage assessment of CFRP structures. However, there are problems such as noise interference, small number of training samples, and few damage types of training samples. In order to solve the above problems, the damage assessment method of CFRP laminate based on Synchrosqueezed Wavelet Transform and ensemble Principal Component Analysis was proposed. First, the damage feature extraction method based on Synchrosqueezed Wavelet Transform (SWT) and Singular Spectrum Analysis (SSA) was proposed to reduce the feature dimension and achieve effective extraction of damage features. Then, the damage assessment method based on ensemble Principal Component Analysis (PCA) was proposed to realize the damage assessment of CFRP structure. Finally, the simulation experiment was designed to verify the damage assessment results in the case of noise interference, small number of training samples, and few damage types of training samples. The results showed that the damage assessment method of CFRP laminate structure based on SWT, SSA and ensemble PCA was feasible. This paper provided a feasible method for evaluating the damage degree of CFRP laminate structure.

The accurate recognition of the burning state of cement rotary kiln is of great significance to improve the qualified rate of clinker quality, ensure the safe operation of rotary kiln and reduce the production coal consumption of enterprises. In this paper, a recognition method of the burning state of cement rotary kiln based on Otsu-Kmeans flame image segmentation and support vector machine (SVM) was proposed. Firstly, the flame image of the clinker burning core region in the rotary kiln was obtained by using advanced vision detection technology, and then the Otsu-Kmeans image segmentation method was used to realize the effective segmentation of the target region of a flame image. The target region of flame image includes radiation region, flame burning region and high-temperature region. On this basis, 10 feature parameters of the target region were extracted as state recognition features. Then, with the one-minute statistical average value of the extracted feature parameters as input, and the three typical burning states of the rotary kiln as output, an SVM-based recognition model for the burning state of the rotary kiln was established. Finally, the established SVM state recognition model was used to classify the flame image, and the two parameters in the model were optimized by the K-fold cross-validation K − C V algorithm. The results show that for 30 sets of test samples, the SVM-based burning state recognition model can correctly identify 28 sets of samples, which provided a feasible method for the recognition of the burning state of cement rotary kilns.

The trend analysis of the temperature change of burning zone is very important for the normal production of cement clinker. In this paper, empirical mode decomposition (EMD) combined with the least square method is researched to realize the trend extraction and identification of cement burning zone flame temperature. Firstly, the temperature data is decomposed by EMD to obtain every intrinsic mode function (IMF) and the remainder. Then, the least square method is used to fit every IMF and the remainder. With the fitting error as the judgment criterion, the trend of the signal is extracted as the combination of the IMF components with less fitting error and the remainder. Further, the first derivative of the fitting function is used to determine the fundamental change in the trend of the signal, and the trend identification result is obtained. At last, the trend extraction and identification method is validated on the burning zone temperature data obtained from a cement company. The results show that the trend extraction and identification of the burning zone temperature based on EMD and least square is effective. More importantly, the method proposed in this paper can automatically determine the number of EMD decomposition layers. It does not have the problem of artificial selection of basis function and decomposition layers, and does not require empirical knowledge and complex experimental procedures for extracting trend item of burning zone temperature. This paper provides a feasible method for the extraction and identification of the burning zone temperature.

Background The management of obstructive left colon cancer (OLCC) remains debatable with the single-stage procedure of primary colonic anastomosis after cancer resection and on-table intracolonic lavage now being supported. Patients and Methods Patients with acute OLCC who were admitted between January 2008 and January 2015 were distributed into 5 different groups. Group ICI underwent emergency laparotomy for primary anastomosis following colonic resection and intraoperative colonic lavage; Group HP underwent emergency Hartmann's Procedure; Group CON consisted of patients treated by conservative management with subsequent elective open cancer resection; Group COL were colostomy patients; and Group INT consisted of patients who had interventional radiology followed by open elective colon cancer resection. The demographics of the patients and comorbidity, intraoperative data, and postoperative data were collected, with P Results There were 4 deaths in 138 cases (2.90%). There was only 1 patient who had anastomotic leakage (5.56%) in Group ICI, compared with none in Group HP and Group COL, 1 case in Group INT (7.69%), and 2 cases in Group CON (6.06%) ( P > .05). Group INT and Group CON, when compared to the three surgical groups, Groups ICI, Group COL, and Group HP, individually, were statistically significant for the duration of surgery ( P Conclusions Primary anastomosis following colonic resection after irrigation can be safely performed in selected patients, with the necessary surgical expertise, with no increased risk in mortality, anastomotic leakage, and other postoperative complications.

Benggang, a fragmented erosion landform, is the most serious threat to the sustainable development of ecosystem and economy in southern China. However, few studies have been conducted on variations and conditions of the geo-environmental factors that influence the susceptibility of Benggang. To address this challenge, the spatial susceptibility of Benggang was investigated using three data-driven methods (multinomial logistic regression, MLR; random forest, RF; multilayer perceptron, MLP) in Fujian province, and the dominant driving forces to Benggang susceptibility were identified by Boruta algorithm, while the contribution of each factor’s class to Benggang occurrence was determined by frequency ratio (FR). Herein, thirteen conditioning factors with relate to geomorphology, climate, vegetation, and land use was employed. Among these factors, the most noticeable contributions to Benggang occurrence at a provincial scale were from rainfall erosivity, soil moisture, and NDVI with their corresponding critical ranges of 7475 ∼ 8349 MJ mm(ha ha)−1, 0.268 ∼ 0.297 m3 m-3, and 0.58 ∼ 0.68. According to the area under the success rate and prediction curves (AUC) for the evaluation of susceptibility modelling, the averaged training accuracies for susceptibility levels were 85.70 %, 99.99 % and 99.23 %, while the prediction accuracies were 85.79 %, 99.17 % and 97.07 % for MLR, RF and MLP, respectively. In general, RF with its AUC value, exceeding 98% at each susceptibility level for both success and prediction curve, was especially suitable for the assessment of the regional Benggang susceptibility. Furthermore, the methodological framework of this study could be implanted in Benggang erosion regions with similar conditions and data availabilities, whereas different data mining techniques and condition factors should be considered at different scales in our future work.

Low energy impact damage identification of carbon fiber reinforced plastics (CFRP) structure is of great significance to ensure the safe service of the structure. In this paper, the low energy impact damage identification method of CFRP structure by using fiber Bragg grating (FBG) sensor, continuous wavelet transform and probabilistic neural network (PNN) was studied. Firstly, the CFRP structure with low energy impact damage was actively excited, and the structural dynamic response signals were accurately detected by FBG sensors. Then, the dynamic response signals were subjected to continuous wavelet transform, and their wavelet time-frequency diagrams were extracted as the structural damage feature. On this basis, with damage feature as input and low energy impact damage as output, the damage identification model based on PNN was established. Finally, the experimental system was constructed to verify the feasibility of the method proposed in this paper. The results showed that for 12 groups of test samples, the damage identification model based on PNN can correctly identified 11 groups. This paper provided a feasible method for the low energy impact damage identification of CFRP structure.

In this study, gum arabic (GA)-based nanofibres were prepared and characterized in combination with pullulan (PUL) by electrospinning for the first time, and they served as the wall material for Lactobacillus encapsulation. The presence of GA in hybrid solutions resulted in increased conductivity, reduced surface tension and lower viscosity (P

To achieve a drug delivery system combining the programmable long circulation and targeting ability, surface engineering nanoparticles (NPs), having a sandwich structure consisting of a long circulating outmost layer, a targeting middle layer and a hydrophobic innermost core were constructed by mixing a matrix metalloproteinase MMP2 and MMP9-sensitive copolymers (mPEG-Pep-PCL) and folate receptor targeted copolymers (FA-PEG-PCL). Their physiochemical traits including morphology, particle size, drug loading content, and in vitro release profiles were studied. In vitro studies validated that the inhibition efficiency of tumor cells was effectively correlated with NP concentrations. Furthermore, The PEG layer would detach from the NPs due to the up-regulated extracellular MMP2 and MMP9 in tumors, resulting in the exposure of folate to enhance the cellular internalization via folate receptor mediated endocytosis, which accelerated the release rate of CPT in vivo. The antitumor efficacy, tumor targeting ability and bio-distribution of the NPs were examined in a B16 melanoma cells xenograft mouse model. These NPs showed improved tumor target ability and enhanced aggregation of camptothecin (CPT) in tumor site and prominent suppression of tumor growth. Thus this mPEG-Pep-PCL@FA-PEG-PCL core-shell structure NP could be a better candidate for the tumor specific delivery of hydrophobic drug.

In the present study, mechanical blends of AlCuFeB quasicrystal and tin bronze powders were deposited by cold spray process to obtain metal matrix composites (MMCs) reinforced with quasicrystalline particulates. The influences of the incorporation of quasicrystal particles on the particle deposition behavior, microstructure and microhardness of the composite coatings were investigated. In order to evaluate the influence of reinforcing quasicrystal phase on the tribological behavior of the coatings, ball-on-disc sliding tribological tests were conducted in an ambient condition. The results showed that the incorporation of quasicrystal particles reduced the porosity and increased the microhardness of the composite coatings. At the same time, a reduction of the friction coefficient and an increase of wear rate were found. Wear mechanisms were discussed and correlated to the microstructure and microhardness of coatings.

Surface modification of PCL–PEG–PCL nanoparticles (NPs) with β-cyclodextrin (β-CD) and other functional groups could enhance the therapeutic and targeting characteristics of NPs. In this report, β-CD grafted poly(acrylic acid) (PCDAA) polymer was synthesized by radical polymerization, and then embedded on the surface of polycaprolactone–poly(ethylene glycol) (PCL–PEG–PCL) NPs through host–guest interaction and hydrogen bonding between oxygen of PEG and carboxyl group of PCDAA. These PCDAA embedded PCL–PEG–PCL (PCDAA@PCL–PEG–PCL) NPs showed a spherical core–shell structure and their size was less than 200 nm. The drug loading experiments described that these NPs had high drug loading capacity for PTX compared to the original PCL–PEG–PCL NPs. It is likely that hydrophobic cavities of β-CD also encapsulated some amount of drug along with its inner core which could enhance the drug loading capacity of these NPs. Moreover, PTX was released smoothly without remarkable initial burst release during the in vitro drug release experiments i.e. only 20% drug was released in first 12 h, which could be attributed to its effective encapsulation of drug inside the core and in outer cavities of β-CD. The blank PCDAA@PCL–PEG–PCL NPs showed little cytotoxicity against HepG2 cells, but after loading PTX, they displayed significant cytotoxicity to the same cell line. Moreover, the surface engineering stratagem of many functional groups originating from PCDAA on the surface of PCL–PEG–PCL NPs, offers the ability to facilely conjugate fluorescent dyes or targeting ligands, which broad their application in biomedical field. This facile stratagem to modify polymeric nanoparticles can be extended to other particle systems.

Up to now, more and more people use Internet storage services as a new way of sharing. File sharing by a distributed storage system is quite different from a specific sharing application like BitTorrent. And as large file sharing becomes popular, the data transmission rate takes the place of the response delay to be the major factor influencing user experience. We present the design and implementation of a distributed storage system named as Granary in this paper, which provides reliable data storage and sharing service to cyber users. Granary uses a specific DHT(Distributed Hash Table) layer to store file meta-data and employs a raw data storage scheme to scatter large data. We introduce its adaptive DHT recovery algorithm in this paper which assures the availability and consistency of meta-data with small bandwidth consumption and improved throughout. The replication strategies which are used to accelerate file sharing with low bandwidth consumptions are further discussed. Experimental results show that these methods offer a reliable and efficient data storage and sharing speed with network bandwidth costs less than conventional policies.

We propose a supervised term weighting scheme for sentiment analysis.This scheme use ITD and ITS to measure the importance of a term in sentiment analysis.Experiment results on three real datasets show that our scheme outperforms state-of-the-art unsupervised approaches. Term weighting is a strategy that assigns weights to terms to improve the performance of sentiment analysis and other text mining tasks. In this paper, we propose a supervised term weighting scheme based on two basic factors: Importance of a term in a document (ITD) and importance of a term for expressing sentiment (ITS), to improve the performance of analysis. For ITD, we explore three definitions based on term frequency. Then, seven statistical functions are employed to learn the ITS of each term from training documents with category labels. Compared with the previous unsupervised term weighting schemes originated from information retrieval, our scheme can make full use of the available labeling information to assign appropriate weights to terms. We have experimentally evaluated the proposed method against the state-of-the-art method. The experimental results show that our method outperforms the method and produce the best accuracy on two of three data sets.

a Distributed Control System(DCS) of cement raw materials is presented for the vertical roller mill by the application of ABB's IndustrialIT. The results demonstrate the practical application of the system's utility and effectiveness and reliability.

The serious environmental pollution and the energy crisis all over the world have caused the development of lower pollution and lower energy consumption marine diesel engine to become a major research goal. In order to obtain the performance and emission of marine diesel engine on natural gas/diesel, a comparative analysis was carried out between the pure diesel mode and natural gas/diesel (dual fuel) mode at turbulence intensity, combustion process, rate of heat release and emissions. The performance of different fuel modes was studied through numerical analysis and experiment. The results showed that marine diesel engine, in natural gas/diesel mode, can get a lower turbulent kinetic energy, temperature reduction of about 400K, ignition advance, soot emission reduction by 75%, reduction of CO2 emission by 35% and NOx emission reduction by 80%.

A polypiperazine amide (PA) composite membrane was prepared by interfacial polymerization using a trimesoyl hexane solution as the oil phase and a piperazine aqueous solution as the water phase on a polysulfone ultrafiltration substrate. The surface was inspected by attenuated total reflection–Fourier transform infrared spectroscopy, showing that a polyamide layer was formed. The molecular structure of polypiperazine amide was simulated using Materials Studio as a plane molecule with hexagon rings of 1.5 nm in diameter of the inscribed circle. Incorporating the result of wide angle X-ray diffraction pattern, the supermolecular structure of the PA was predicted as an irregular laminated structure. This model appears good according to the performance of the membrane. Also investigated was the morphology of the membrane by scanning electron microscope, showing a dense layer of 300 to 400 nm on the porous substrate. The composite membrane obtained is a high-performance nanofiltration membrane.