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One significant part of Electroencephalography (EEG) signal classification is data preprocessing. The traditional methods are hard to remove the noise and restore the original signal. In this paper, a novel method based on fuzzy c-means clustering is proposed for EEG data preprocessing. This novel method can make up for the lack of traditional methods and can evaluate whether a certain stage of data meets the requirements. After excluding the data that does not meet the requirements, the model classification effect has been significantly improved. The proposed method has achieved a good performance across the data from the BCI competition IV dataset I.

Aiming at the problem of limited resources and operational time combat air defense combat, naval combat formation analysis dynamic resources scheduling needs, on the basis of the establishment of ship dynamic scheduling model of warship formation combat resources dynamic combat formation resource scheduling, the operational resources and operational objectives, relationship between reasonable allocation of resources to play the warship formation combat operations the maximum efficiency. The design of three kinds of air defense command models: centralized platform free attack mode, command mode, request attack mode of air defense strategy, put forward the dynamic scheduling hybrid algorithm for solving the model, according to the naval fleet air defense combat scenario example for simulation. The simulation results show that the air defense strategy with the request attack mode can have effects on multi-target attacks under the shared combat situation, and the hybrid algorithm based on combat resource dynamic scheduling can meet the real-time operational requirements.

The $\beta$ -delayed particle emission from 21Mg was studied at the Radioactive Ion Beam Line in Lanzhou (RIBLL1). Silicon array and high-purity germanium (HPGe) detectors were used to detect the charged particles and $\gamma$ -rays emitted from the decay. Half-life time of 21Mg was determined to be 121.9(6) ms. New levels (5.639(26) MeV and 6.644(34) MeV) in the nucleus 21Na were observed. An improved proton spectrum coincident with $\gamma$ -rays at 1.633 MeV was obtained. A new partial decay scheme was constructed based on the present measurement.

To solve the problem of America huge infrastructure investment demand and its government limited ability, this paper combines both advantages of $1 trillion government investment plan and $10 trillion private investment plan to produce a new version. With the help of policy innovation, America only needs to add $250 billion government debt in the first two years,that can drive $10 trillion private investment to construct 20 thousand km high speed railways and 8 super cities infrastructures in 8 years. It would deliver more than 10 million jobs, create several trillions dollars of tax, double the social wealth and economic growth, and make America enter a period of reducing debt for the first time and enter a new era of the traffic revolution marked by time, speed and efficiency. This conception would be expected to become an American Bill in view of its practical significance. If passed, it will rewrite the history.

This paper presents a bearing fault diagnosis algorithm based on human self regulating particle swarm optimization and stochastic resonance. The algorithm first sets up the objective function according to the stochastic resonance structural parameters a and b. Then the self regulating particle swarm optimization algorithm is used to optimize the objective function, and the optimized optimal values a and b are substituted into the system of variable scale stochastic resonance. Finally, the measured bearing vibration signal to noise reduction and spectrum analysis to find the bearing fault characteristic frequency. Experimental results show that this method is effective in bearing fault diagnosis.

The 60 MN build-up force transfer system (60 MN FTS) was established at 2014 in Fujian Province Institute of Metrology (FJIM), China, which is used to transfer or calibrate large force over 20 MN, and to do inter-comparison of force standards. It had been used to compare large force between 60 MN BM, FJIM and 30 MN HM, NPL, UK [1][2][3]. The paper described principle, construction and main specifications of the 60 MN FTS.

Premise of the Study Microsatellite primers were developed in Prunus mongolica (Rosaceae), a relict flora endemic in arid areas of the Asian interior, to investigate the genetic diversity, phylogeography, population structure, and history of the species. Methods and Results Fifty‐one microsatellite loci, including di‐, tri‐, and tetranucelotide repeats, were identified using transcriptome sequencing and bioinformatic screening. The number of alleles ranged from seven to 11 and the levels of observed and expected heterozygosity ranged from 0.545 to 1.000 and 0.600 to 0.989, respectively. Most of the primers also amplified in a group of congeneric species (P. triloba, P. davidiana, P. persica, P. cerasifera, and P. serrulata). Conclusions This set of microsatellite loci is useful for studying the genetic diversity of P. mongolica. In addition, they can also be used to investigate the population structure, phylogeography, and landscape genetic patterns of congeneric species.

Acetohydroxyacid synthase (AHAS) catalyzes the first essential biosynthetic step of branched-chain amino acids and is a biologically safe target against Mycobacterium tuberculosis (MTB). In our previous research, we used virtual screening to identify some novel AHAS inhibitors as potent antituberculosis agents. In this study, we synthesized twenty-four additional quinazolinone benzoates and explored their antitubercular activity. Five of these compounds displayed significant MTB-AHAS inhibition and their IC50 values were determined to be in the range of 6.50 μM-12.08 μM. Importantly, these compounds also exhibited strong in vitro activity (MICs in the range of 2.5-10 mg/L) and intracellular activity against clinically isolated extensively drug-resistant strains of M. tuberculosis. Taken together, these results indicated that the quinazolinone benzoate compounds should be regarded as promising lead compounds for the development of potent antituberculosis drugs with a novel mode of action.

1. Why animals mate multiple times, owing to the lack of immediate fitness benefits, presents an intriguing problem for evolutionary biologists. Yet, the profusion of this behaviour suggests it must be maintained by natural selection via increased performance. 2. The possible benefits of multiple mating using the leaf beetles Ophraella communa LeSage, the biological control agent of the invasive common ragweed Ambrosia artemisiifolia L., were studied and the fitness consequences of single, twice, three, four, and unrestricted mating events were assessed. 3. Overall, it was observed that the number of copulation events was positively associated with fitness parameters of the insects. Insects performed the best under unrestricted mating regimes, with average increases of 48% in longevity, 75% in fecundity, and 55% in egg hatch rate. In addition, females that experienced unrestricted access to mates maintained very high viability over their entire reproductive lives. Nevertheless, insects also performed better when allowed to mate four times compared with once or twice. 4. The present findings thus support the hypothesis that multiple-mating behaviour is maintained owing to increased fitness benefits in the current and the next generation. Selection for re-mating is, therefore, expected in field populations, which is likely to happen naturally owing to the aggregate lifestyle of O. communa.

Relying on the construction of a deep underground reservoir, vibration effects of ground were studied from two aspects: the peak value of vibration velocity and the basic frequency of blasting seismic waves, and were compared with the blasting vibration effect on the flat terrain surface. Research shows that blasting seismic wave’ impact on the surrounding building is relatively small because of its high frequency for the blasting excavation of underground reservoir. The frequency distribution in vertical and horizontal direction with scaled distance is roughly the same. And the peak value of vibration velocity in vertical direction is greater than that in horizontal direction, so blasting vibration effect in vertical direction is bigger than that in horizontal direction.

Both the plant parameter4 and delay time of the system may vary in the time-varying system, which makes system identification relatively complex. In this paper, a new system identification algorithm based on subspace method and cross-correlation function is proposed to identify system parameter and time-delay simultaneously. The time delay is identified with the calculation of the cross-correlation function between pre-processed input data that processed through initial plant parameters and original output data. Then, the time delay in the output data can be deleted with the time delay identified before and the parameters can be achieved by subspace identification algorithm. Therefore, the process parameters and time delay identification can be performed online by through above two alternative operations. The validity of the proposed identification algorithm is verified by the simulation study.

Fault isolation is an essential step in multivariate statistical process monitoring, which discovers the cause of the detected fault. The common adopted techniques for fault isolation, such as contribution plots, reconstruction analysis, branch and bound, etc., often suffer from the problems of the smearing effect, insufficient historical fault data, and heavy computational burden. In this paper, the nonnegative garrote (NNG) estimator is utilized for multivariate statistical fault isolation, which solves the problems of the existing methods. The proposed method is based on the finding that there is equivalence between fault isolation and variable selection in discriminant analysis. Different from the existing methods, the NNG-based fault isolation method does not suggest a single set of faulty variables. Instead, it provides a sequence of process variables according to their responsibilities for the detected fault. Such results are informative for diagnosing the root cause of the fault.

Pulsed thermography (PT) is a widely used non-destructive testing (NDT) technique for defect detection in carbon fiber reinforced polymers (CFRP) structures. Thermographic signal reconstruction (TSR) is often employed to process thermographic data due to its excellent performance in enhancing the contrast between defects and background. However, TSR does not reduce the noise of thermographic data effectively. To solve this problem, this thesis proposes an image denoising method based on wavelet transform. The experiment results show that dealt with wavelet transform the characteristics of the defective regions are more clearly compared with previous images. And the signal-to-noise ratio (SNR) values are improved obviously at the same time.

In industrial process, some important variables such as quality index, efficiency index and concentration of product components are difficult or even impossible to be measured directly due to the limitation of technology. This phenomenon leads to few labeled data and plenty of unlabeled data. Traditional identification method for controlled auto regressive (CAR) model usually cannot deal with unlabeled training data. As a result, these traditional identification methods may receive poor identification precision or even cannot work entirely. To solve the problems above, this paper proposes a new identification method based on deep learning (DL). Firstly, the CAR model is transformed into finite impulse response (FIR) model solve the problem of lack of autoregressive part; Secondly, autoencoder of deep learning make full use of unlabeled data to pretrain the model; Thirdly, small amount of label data is used for fine-tuning. As a semi-supervised learning method, deep learning can be able to extract more information from unlabeled data than traditional supervised learning method. The results show that the proposed method can acquire higher identification accuracy than BP neural network.

Based on the actual data analysis, it is found in this paper that the supplementary air flow rate in the CDQ operation didn't follow the variation of the discharge rate of incandescent coke well, which results in the concentration increase of combustible gas in the exhaust gas and the decrease of economic efficiency. The correlation analysis results show that the introduced derived variables are more useful than some plain variables for the purpose of prediction. Next, to handle the contradiction between the steam productivity and the coke burning loss, a new economic efficiency index is introduced by synthesizing the two competing aspects. A kind of soft sensor of economic efficiency is put forward by combining nonnegative garrote (NNG) variable selection algorithm with the autoregressive integrated moving average (ARIMA) model, which gives a good solution to the economic efficiency real-time prediction problem of CDQ system. Then, the implementation of model-based optimization is studied based on the actual operation data. The results show that there exists large room for economic efficiency promotion.

How to operate a BFG/coal co-firing boiler in high efficiency is challenging for a gas/solid multi-fuel combustion system. Taking operation data from a real boiler, this study proposes an operation optimization strategy of BFG/coal co-firing boiler based on deep learning. Firstly, the thermal efficiency model is constructed based on deep learning with all the actual sampling data, which outperform the other modeling methods. Then, the high efficiency data being selected as labeled data, a deep learning-based model of air flow is constructed. It needs to be emphasized that, to take full advantage of all available information and improve the model accuracy, all the labeled and unlabeled data are utilized in auto-coder learning of the air flow modeling. Finally, the constructed air flow model is used for the operation optimization and the thermal efficiency model is used to evaluate the level efficiency improvement. The high accuracy of the proposed modeling approaches makes the implementation of the operation optimization strategy readily practicable.

The charge transfer processes in collisions of B4+(1s) ions with H(1s) atoms are investigated by using the quantum-mechanical molecular orbital close-coupling method with electron translational effects included in the impact energy region of 10-5 − 20 keV/u. Molecular data with high accuracy are calculated using ab initio method. Our calculations clarified the controversy in the total and state-selective cross sections at low energies. The treatment of the core electron will influence the accuracy of the calculated molecular structure and then sensitively influence the charge transfer cross sections in the low energy region. The rotational couplings play an important role in the state-selective cross sections at energies above 50 eV/u, but weakly influence the total cross sections for this collision system.

Physical potential economic model is to use the physical motion phenomenon in the nature as the physical model of financial market. It deeply reveals the natural motion property in the world economy, it applies the physical scientific achievements to the financial sector, obtains a good academic support for the quantitative analysis, and also provides a quantitative basis to make business investment decisions and formulate national policies. Physical potential economic model first proposes the financial investment potential, argues that the pursuit of potential energy maximum is the decision target of financial investments, and thus creates a potential investment concept based on multi-factor analysis; potential investment also integrates with securities, futures, exchange rates and other different types of financial data into one for comprehensive analysis. It makes all the previous analysis concept absorption and utilization information fold increase, and thus judges all previous ambiguous and unclear phenomenon.

Fault isolation is essential to fault monitoring, which can be used to detect the cause of the fault. Commonly used methods include contribution plots, LASSO, Nonnegative garrote, construction-based methods, branch and bound algorithm (B & B), etc. However, these existing methods have shortcomings limiting their implementation when there exist vertical outliers and leverage points, Therefore, to further improve the fault prediction accuracy, this paper present a strategy based on robust nonnegative garrote (R-NNG) variable selection algorithm, which is proved to be robust to outliers in the TE process.

Multivariate fault isolation is a critical step for monitoring industrial chemical and biological processes. For some complex cases with strong correlation variables which commonly exist in the industry process, conventional methods may perform poorly. Therefore, to further improve the fault prediction accuracy, a fault isolation method based on the ridge nonnegative garrote variable selection algorithm (R-NNG) was proposed in this dissertation, it transformed the multivariate fault isolation problem into a variable selection problem in discriminant analysis, which is proven to be capable for handling strongly correlated variables by the application to the benchmark Tennessee Eastman (TE) process.

With the development in virtual reality technology, it has been applied in kinds of fields, e.g. education and engineering. This article introduces the concept and basic features of virtual reality, the application of VR in education and engineering, the building procedures and the functions of the virtual system with the case of the virtual Storage Cavern system. The system is consisted of four parts which enables students to understand and grasp the basic operation, maintenance, and standard accident responses more effectively than other teaching methods.

Due to the internal structure of red sandstone is loose, the influence factors of acoustic velocity are variety, it is necessary to make a quantitatively research on the influence factors. With the aid of the ultrasonic testing for the rock, p-wave velocity of red sandstone decreased with the increase of porosity under the condition of no coupling medium, when the porosity exceeded a certain value, its internal pores and fractures had a great influence on wave velocity. When the density of red sandstone was greater than 1.82 g/cm3, wave velocity changed little, while the density was less than 1.82 g/cm3, wave velocity decreased rapidly. Under water-solid coupling condition, the change of wave velocity and porosity had no obvious regularity, part of the specimens’ wave velocity decreased a bit, while some specimens’ wave velocity were slightly higher, but the amplitude were small; Under oil-solid coupling condition, longitudinal wave velocity changed significantly, the biggest percentage of increase was 46.3%, the average was 33%.

Using finite element software ANSYS/LS-DYNA, dynamic response of pile-soil-structure interaction (PSSI) system under blasting load was analyzed, and the effects of blasting load on surrounding buildings foundation and superstructure were studied. This can provide a reference for structural and seismic reinforcement under blast loading. The results show that the every floor’ acceleration of superstructure of PSSI system along the vertical direction is usually larger than the value along the horizontal direction under horizontal explosion load. Peak-acceleration along the vertical direction decreases from the lowest layer to the top and peak-acceleration along the horizontal direction decreases and then increases with the increase of layer. The top peak-shear stress of piles is larger than the tip of piles, and the distribution laws are inverted triangles. The maximum values of peak of pile-soil contact pressure are located on the top of the piles. With little change in height and contact pressure plummeted. Contact pressure increases more near the tip of the piles.

This paper proposes an automatic control strategy of the strip width in cold rolling process. The prediction model for the exit strip width of each stand is established. In addition to the strip width variation of the incoming hot-rolled material between the measured and the pre-set values, the variations of the rolling force, tension, bending force and the roll shift amount of each stand are utilized to model and forecast the stand exit width deformation. Then, the model can be used to determine the compensation values of the different profile adjustment mechanism and the exit strip width can be controlled in high accuracy. In this way, the effect of the strip width variation of the incoming hot-rolled material and the fluctuation of the stand rolling process parameters on the exit strip width can be eliminated. Moreover, the feed-forward control of the strip width can reduce the incidence of abnormal situation, such as the strip breakage and off-tracking ,and improve the stability of the cold rolling process.

This paper firstly analyzes the problems that exist in the cold rolling of steel industry. Secondly, a new approach is proposed to temper and straighten the strip steel based on the temper leveling mill, shortly, TLM. The required high quality of the wave, surface and mechanical properties can be achieved only with TLM. TLM holds the functions of both temper machine and straightening mill and can produce good quality strip steel, no only the waviness, mechanical properties, but also the surface quality. The usage of TLM can lower the cost of the production of tempering, straightening and equipment investment.Finally, the experiments are done and verify the validity of the approach.

Hubei Shiyan is the largest city in the source of South to North Water Diversion Project in China. The City District has 100 km of river, along cross-strait with high-rise buildings, dense population, and coverged sewage straight flow to the river. In our paper, the design of "Ming change dark" river closure project can fundamental solve the city environmental pollution problems, improve the source water quality without any menace from the "rear", and make Shiyan's green economy grow rapidly. On the other hand, this "city agriculture" new thinking will completely change the serious situation of arable land area which is decreasing year by year in the world.

A novel method based onEmpirical Mode Decomposition(EMD) is approached to process the geometry signal. The main idea is to decompose the signal into some different detail components called Intrinsic Mode Function (IMF). The key steps are as follows: First, the signal is spherical parameterization; Second it is transformed into the plane signal and sampled regularly; Third, the translated signal is processed as an image using Bid-Empirical Mode Decomposition, getting several image IMFs; Finally, invert mapping these IMFs to geometry signal and getting the geometry signal’s IMFs.We demonstrate the power of the algorithms through a number of application examples including de-noising and enhancement.

The theoretical analysis and numerical simulation of electron beam generation in foil-less diode are presented. The theories of OAL, CL, and equilibrium beam model are briefly compared. All the theoretical models can give a relatively accurate solution of electron gun, and the equilibrium beam model is more appreciated. A PIC simulation tool UNIPIC is introduced and is used to analyze the behaviors of a foil-less diode. Under the conditions of 180 kV incident voltage wave and 2.5 T axial magnetic fields, we obtained the 250 kV, 1.5 kA output beam current which can be used in high power microwave generators.

New micromachining techniques now provide the opportunity to fabricate vacuum electron devices with dimensions suitable for operation in the terahertz region of the electromagnetic spectrum. In this paper, results of theoretical and numerical simulation studies of a MW-class, large diameter terahertz backward wave oscillator are presented. A novel full electromagnetic particle-in-cell simulation tool UNIPIC is also introduced. With this method, we studied the device at the frequency of 0.14 THz. It is found that the steady-state, single-frequency operation in this oversized situation could be realized by the property of surface wave near the upper cutoff area.

Stent implantation is a non-surgical method to treat the coronary artery disease that can support vessel walls and reduce the risk of heart attack. In this paper, undertaking the 316L stainless steel as a medical stent material, a certain coronary stent is utilized to study its mechanical characteristics by the finite element analysis as it is deployed and implanted in a vessel containing a plaque, and try to reach to a model close to a real condition of stent implantation. The result shows that the width size of stent rods is greater than its thickness size in the capability of resisting deformation of configuration, and then displacement changes of the coronary stent on the axial direction should be keeping a close eye in their expansion process.

In November 2011, the renal sphaerosporosis was found in cage-cultured orange-spotted grouper Epinephelus coioides in a fish farm in Guangdong, China. The infected fish exhibited emaciation, anaemia, anorexia, renomegaly, slight splenomegaly, and occasionally skin ulcer. The cumulative mortality reached at 50% to 80% within 2 weeks, when water temperature ranged from 21 degrees C to 27 degrees C. The renal tubules were almost completely occluded by sporogonic pseudoplasmodia of a myxosporean, and mature spores were found in the lumen. Combined with morphological, histopathological, and molecular analyses, Sphaerospora epinepheli was suggested as the etiological agent. This is the first report of mortality case of cultured E. coioides associated with S. epinepheli.

This paper proposes a data-driven adaptive modeling approach to investigate the performance and optimal operation of a cooling tower for energy conservation. To achieve this aim, the cooling tower process was first characterized by an adaptive model with nonnegative garrote (NNG) variable selection procedure, which ensured a compact and robust input–output relation. Owing to the high accuracy of the obtained model, implementing the optimal operation strategy for energy saving became readily practicable. Subsequently, on the basis of the statistical results of NNG variable selection, the effects of ambient air temperature and humidity on the cooling capacity of the tower were investigated by principal component analysis (PCA). Finally, the optimal strategy of fan operation was proposed and its implementation was virtually studied based on data from the actual operation of a cooling tower, which showed that there was considerable room for energy conservation. This is the first attempt to use the NNG variable selection method for developing model for cooling tower and to propose a model-based control scheme for operating a cooling tower.

The aim of this study is to investigate the effects of hypoxia inducible factor-2α (HIF-2α) and Notch3 on CoCl

NiFe bimetallic catalyst shows an excellent activity and selectivity for the hydrodeoxygenation (HDO) of three typical model compounds of bio-oil. The conversion of furfuryl alcohol, benzene alcohol and ethyl oenanthate is 100, 95.48 and 97.89% at 400 °C and the yield to 2-methylfuran, toluene and heptane is 98.85, 93.49 and 96.11% at 0.1 ml/min flow speed and atmospheric pressure. It indicates that the major reaction pathway is the cleavage of C–O rather than C–C. After the catalytic HDO of bio-oil over NiFe/Al2O3 catalyst, the heating value changes from 37.8 to 43.9 MJ/kg, the pH changes from 6.65 to 7.50.

Maximum-likelihood (ML) and its relaxation algorithm are discussed which are used to identify the mathematics model of an Underwater Vehicle (UV). With trial data of zigzag tests, the hydrodynamic derivatives of the UV were estimated, and the better astringency of the relaxation algorithm can be acquired from the contrast between the two methods. A simulation environment based on these parameters is established to verify the validity and effect of these methods. The result shows the model is credible and the methods are very useful for the research of maneuverability and intelligent control of underwater vehicles.

Up to now, some technologies of neural networks are developed to solve the non-linearity of research objects and the adaptive control is implemented in many engineering fields, and some good results are achieved. Though the learning mechanism of neural networks is really unknowable, the importance of study ratio is widely realized, and some methods on modification of study ratio are provided. Improving the stability and increasing the convergent rate of networks by defining a good form of study ratio is the main target. A new algorithm named least disturbance BP algorithm is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an underwater vehicle. The good performance of the algorithm and the controller is demonstrated by the experimental results.

Distinctions between supported Au and Pt catalysts on TiO2(110) for CO oxidation have been investigated by means of density functional theory calculations. Our study shows that the following factors determine the obvious differences between two kinds of catalysts for CO oxidation: (1) The adsorption strength of Au11 is much weaker than that of Pt11 on TiO2(110), but both are strongly dependent on the surface properties of TiO2. The addition of Pt increases the interaction between the alloyed cluster and TiO2 support. (2) O2 can adsorb only on the interfacial site between Au and TiO2(110), whereas O2 can adsorb on both the interfacial and metal sites of supported Pt nanoparticles. (3) CO is directly activated by the adsorbed molecular oxygen on the interfacial site of Au11/TiO2(110)_OH. While on Pt11/TiO2(110)_OH, the main reaction pathway is the dissociated oxygen reacting with CO. Once a Pt ensemble is formed on Au clusters (such as Au8Pt3/TiO2(110)_OH), both of the reaction mechanisms work.

This paper presents the design scheme of monitoring management and fault diagnosis of open CNC system based on Internet, the system hardware structure introduces network technology three-tier monitoring mode based on field control layer and process monitoring control layer and remote fault diagnosis layer, the system software design includes CNC system management and monitoring and fault diagnosis. The paper integrates condition monitoring and fault diagnosis and expert system technology into CNC system monitoring and management, and analyzes the requirements of distributed numerical control system, and details description system model of remote monitoring and fault diagnosis system. The practice has proved that the system design of open remote monitoring and fault diagnosis is practicality and feasibility.

With the rapid development of wind power in recent years, the wind power industry has gradually shifted from infrastructure construction to equipment operation and maintenance. The importance of the operation and maintenance of the wind turbine is increasingly apparent. A new vibration monitoring mode of wind turbine, called distribution acquisition-Centralized analysis, has been established by full investigation and study. This mode involves information technology, electronics technology and automation control technology. It is based on the mechanical movement principle and the principles of mechanics analysis. Throughout the one-year application of this vibration monitoring mode in wind farms, it is obseraved that: this model can grasp the operational status of the key equipment of the wind turbine, and effectively identify potential failures in a timely manner. This mode reduces costs of maintenance, improves equipment reliability and extends equipments operation time.

The traditional heritage corridor is considered as one strategy of regional heritage preservation which linked with urban green space system. However, heritage corridor is seen as one landscape with heritage preservation value and closely bound up with human activities and social conversations which related to the heritage corridor. In this paper, taking the region of Nanjing Qinhuai River from Shuixi Gate to Hanzhong Gate as an example, we divided the evaluation system into two aspects, elements of landscape and ways of observation, to discuss the interaction and relationship between the visual experiences of different human activities and the landscape quality of heritage corridor. Using qualitative study as its primary means while quantitative study as auxiliary one, we attempted to establish one quantitative visual evaluation system under the influence of multi-factor interactive model. With the system, we did the visual evaluation for that region considering different motion ways, which differs from the past single-factor qualitative research, for the aim of facilitating the landscape quality quantification.

Brick-concrete structures and common buildings are destroyed in strong earthquakes. Based on physical mechanics, a pattern contrast illustrates three special designs of building structure---removal of basal markets and underground garages, use of corrugated beams, adoption of trapezoid bars---that may effectively avoid damages and casualties.

It is one of large copper mine which discoveried newly in the middle of arc-magma-metallogenic belt in Xiongcun Cu (Au) deposits, Xietongmen county, Tibet. In order to analyze deposits model, the methods adopted includes that arrangement of existing geophysical and geochemical exploration results, familiar with the former research results, and based on the collection of data, studying comprehensively the geological characteristics of deposits, discussing the deposits genesis. It found that metal mineral combination about the Cu (Au) ore body is pyrite + chalcopyrite + molybdenite (less) + magnetite (less), and transit to a peripheral area of pyrite + chalcopyrite + magnetite, to sphalerite + chalcopyrite + pyrite + lead ore (less) from inside to outside in porphyries body in Xiongcun by studying, which reflected the rock permeability and diffusion about the hydrothermal system of ore-bearing porphyry, all this caused by different types of mineralization process due to temperature and pressure reduction. Elements in the vertical direction with zoning characteristics, that is Cu, Au, Ag, As, Sb, (Bi) - Co, Ni - Mo - Mn - Ba - Pb, Zn, Cd, Bi, (Sb) from the center to outward. The elements anomalies and combination is the important geochemical markers to find and evaluate this kind of deposits. Xiongcun Cu (Au) deposits belong to low-grade porphyry type Cu (Au) deposits which related to emplaced hornblende quartz diorite late Jurassic, and is series of island arc type porphyry copper metallogenic of the Gangdese terrane subduction. Deficiency is a lack of data, it is very difficult to distinguish which kind of exploration method played a key role, the division includes both the exploration methods and working train of thought, prospecting cognition. One deposits is likely to belong to this category, and may belong to the other because of the mutual cross.

Graphene oxide (GO), prepared from oxidation of graphite powders using a modified Hummers method, exhibits a promising catalytic activity and a high selectivity for the solvent-free catalytic dehydrative etherification of benzyl alcohol (BA). A maximum yield (85.4%) of dibenzyl ether (DE) was achieved at 150 °C for 24 h when the BA/GO ration was 20 ml/g under solvent-free condition. This discovery provided a new insight into the development of GO as a carbocatalysts for a variety of applications in carbocatalysis.

In this study, a soft-sensor modeling algorithm with adaptive partial least squares nonnegative garrote is developed by incorporating nonstationary disturbance. The approach is capable of monitoring the stationary and nonstationary behaviors of the process dynamics. The procedure of adaptive variable selection ensures that a compact and robust input–output relation is obtained online. Hence, in addition to simply tracking prediction, the model can be used for the detection of structural model change and the emergence of disturbance. The advantages of the proposed method are demonstrated with a simulation example and two industrial applications to predict the temperature of a blast furnace hearth wall and to estimate impurity composition of a distillation column.

This article is based on TengChong County municipal sewage treatment plant supporting pipe network project, monitored and analyze the vibration influence on the historic building taiji bridage by the Pipe-jacking blasting construction. Via regression analysis of velocity in vertical direction of measuring points on the basis of monitoring data, obtained the attenuation laws toward taij bridage direction. The vibration signals were decomposed into IMF components by using HilbertHuang transform (HHT),analyzed the several IMF components could be acquired the timefrequency characteristics of the vibration signals. These will provide references to the similar projects.

This study is aimed to utilize the operation data to build a physical-meaningful and precise-enough model to assist the operation of a cooling tower. To do so, this work introduces a dimensionless index, which can describe the cooling capability of a cooling tower in terms of effective power utilization. In the first phase of this study, principal component analysis, one of factor analysis methods, is used to investigate effects of ambient air temperature and relative humidity on the cooling capability of a cooling tower. Based on the proposed cooling capability index, the operation data are partitioned into different groups by the fuzzy c-mean clustering algorithm. The resulted groups are distinctly categorized by the conditions of ambient air temperature and relative humidity. In the second phase of the study, data within the same mode of a set of fans are partitioned by the fuzzy c-mean clustering algorithm. The resulted groups of data are then modeled by linear regression. The acquired multiple models are highly accurate in predicting the output temperature of cooling water from the cooling tower. The acquired models assist the operator to accurately select the proper fan mode when process conditions, e.g., cooling loading, or environment conditions, e.g., ambient air temperature, change. It results in electricity saving. This study is concluded by the presentation of a discrete model-based approach to determine the fan mode. The application to a real cooling tower in an iron and steel plant is promising in saving electricity consumed by the fan set.

Even though ratio of air to fuel is the major factor in determining the thermal efficiency of a boiler, how to optimize the ratio in the real operation is still more like art than science, especially for a gas/solid multi-fuel combustion system. This study, taking operation data from a real gas/solid fuels co-firing boiler, is aimed to develop the thermal efficiency model. To cope with the complexity of combustion mechanism in a co-firing boiler, this study proposed a two-tier approach to modeling thermal efficiency. The first tier is to transform the plant input variables into the derived variables, which are physically and statistically meaningful to the model-building. In the second tier, this study proposed an adaptive modeling approach by employing nonnegative garrote variable selection and auto-regression integrated moving average correction. Both approaches are first time proposed in building the thermal efficiency model for boilers. The prediction error of boiler thermal efficiency made by the acquired model reaches less than 0.03%. The high accuracy of the proposed modeling approaches makes the implementation of the model-based control of ratio of air to fuel for improving boiler’s efficiency readily practicable.

The gas-water relations of low-permeability tight sand gas reservoir are complex and not necessarily linked to the structural relief, phenomenon that water is distributed in high structural position while gas in low structural position is common what makes it difficult to make a refined description of gas-water relations and distribution low. The article takes Zizhou gas field for example, establishes 4 gas-water relations modes of single well to analyze some profiles where there is abnormal phenomenon water up gas down, the fact is that water and gas are distributed in different sand bodies and Zizhou Gas Field is not a deep basin gas reservoir. The research shows the phenomenon that water up gas down does not exist, the gas-water relations are normal. The research results have certain significance for the determination of gas-water distribution law.