Study on Artificial Propagation Techniques for Glyptosternum maculatum in Tibet

This study aimed to explore artificial propagation techniques for Glyptosternum maculatum that is endemic in the Yarlung Zangbo River. Total 86 female fish in weight of 0.065-0.250 kg were selected, and the average weight of every 15 fish was 0.30 kg. Oxytocic drugs were injected into the base of pectoral fins of the female fish. The results showed that the total amount of female fish with artificial insemination was 59. The gloss weight of the fertilized eggs was 897.5 g with total amount of 57 440, thus the average fecundity was 1 194 eggs/fish. The fecundity showed a positive correlation with body weight （P〈0.01）. The average induction ratio and fertilization rate were 69% and 73.5%, respectively. The optimum water temperature for hatching of the fertilized eggs was 13-14 ℃ with dissolved oxygen of 6.0- 7.2 mg/L. The accumulated temperature for embryonic development of G. maculatum ranged from 2 592 to 2 916℃·h. This set of completely artificial propagation techniques has a very important significance for the artificial breeding of G. maculatum.

The Preliminary Study on Artificial Propagation and Fry Rearing of Misgurnus anguillicaudatus

[Objective] The aim was to seek the optimal choice of hormones,induced temperature,and initial feed for cultivating loach.[Method] Different hormones were injected to Ioaches for artificial reproduction in water at different temperatures.During cultivation,varied initial feeds were put in to explore the optimal choice of hormones,temperature and initial feed.[Result] According to the test,fecundity of female loach is directly proportional to gonad maturity,namely,the higher gonad maturity,the more fecundity.For example,for a female loach whose maturity coefficient was 24％,absolute and relative fecundity rates were 62 142 and 990.In contrast,for a female whose maturity coefficient was 9％,the rates were 18 207 and 367,respectively.The induced effect differs upon hormones and the test demonstrated that LRH-A2 is better in improving fertility rate compared with HCG.For example,the fertility rate in the group with HCG was 78.84 and in the group with LRH-A2 was 83.04％.It is notable that the mixture of the two at a certain ratio would enhance induced effect and the fertility rate can be as high as 89.17％.With temperature in a certain range,the higher temperature,the better induced effect.The test indicated that water temperature at （30±0.5） ℃ is an optimal one and the effect horizon is 6-8 h.In group 4,induced rate was 93.33％ and the fertility rate was 89.26％.The research indicated that the survival rate was the highest if wheel animalcule,dominated by Brachionus calyciflorus and Brachionus angularis,was taken as an initial feed,followed by artemia nauplii and corpuscule.In addition,loach fry in the group with yolk and wheel animalcule grew about 1.21 times than the group with only yolk,1.04 times than the group with only fairy shrimp,and 1.1 times than the group with fairy shrimp.[Conclusion] The research provides scientific references for scale loach farming.

ARTIFICIAL PROPAGATION AND BREEDING OF MARINE FISH IN CHINA

Since the 1990s, artificial propagation and breeding technique of marine fish in China have developed by way of increasing species and fry numbers, with special stress laid on valuable species. Large quantities of artificial fry can meet the needs of both marine cage culture and pond culture for most species. Experimental results obtained by scientists have been put into use in actual production. Fish fry production has entered a period of sustainable development. So far, at least 44 species (21 families) of marine fish have been successfully bred in China. The artificial fry number of large yellow croaker (Pseudosciaena crocea) exceeded 300 million in 1999. The species whose artificial fry numbers have each surpassed 10 million annually are red drum (Sciaenops ocellatus), Japanese seabass (Lateolabrax japonicus), cuneate drum (Nibea miichthioides), spring spawning red seabream (Pagrosomus major) and threebanded sweetlip (Plectorhynchus cinctus). Millions of artificial fry are bred annually in the species of black porgy (Sparus macrocephalus), Russell’s snapper (Lutjanus russelli), javelin grunt (Pomadasys hasta), miiuy croaker (Miichthys miiuy) and skewband grunt (Hapalogenys nitens). The fish in the family Sciaenidae are the main species in artificial propagation and breeding. Some problems and prospects on marine fish culture and stock enhancement are also discussed and some proposals for sustainable development are put forward in this article.

Application of the back-error propagation artificial neural network(BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

In blasting operation,the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak.Therefore,predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome.Since many parameters affect the blasting results in a complicated mechanism,employment of robust methods such as artificial neural network may be very useful.In this regard,this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran.Back propagation neural network（BPNN） and radial basis function neural network（RBFNN） are adopted for the simulation.Also,regression analysis is performed between independent and dependent variables.For the BPNN modeling,a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN,architecture 636-2 with spread factor of 0.79 provides maximum prediction aptitude.Performance comparison of the developed models is fulfilled using value account for（VAF）,root mean square error（RMSE）,determination coefficient（R2） and maximum relative error（MRE）.As such,it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error.Also,sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak,respectively.On the other hand,for both of the outputs,specific charge is the least effective parameter.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.

Performance prediction of gravity concentrator by using artificial neural network-a case study

In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.

Artificial boundary conditions for Euler–Bernoulli beam equation

In a semi-discretized Euler-Bernoulli beam equa- tion, the non-nearest neighboring interaction and large span of temporal scales for wave propagations pose challenges to the effectiveness and stability for artificial boundary treat- ments. With the discrete equation regarded as an atomic lattice with a three-atom potential, two accurate artificial boundary conditions are first derived here. Reflection co- efficient and numerical tests illustrate the capability of the proposed methods. In particular, the time history treatment gives an exact boundary condition, yet with sensitivity to nu- merical implementations. The ALEX （almost EXact） bound- ary condition is numerically more effective.

Studies on the Construction Parameter of an Artificial Occluded Cell for In-situ Inspection of the Propagation Rate of Localized Corrosion

An artificial localized corrosion system is assembled and some parameters related to the localized corrosion in active dissolution state (i.e., non-passive state) have been studied. The results showed that the developed electrochemical system can satisfactorily imitate a naturally formed localized corrosion and the coupling current can indicate the maximum localized propagating rate. In this artificial system, the anodic dissolution reaction followed the auto-catalytic mechanism. The localized corrosion current density was dependent on the area ratio R of the cathode to the occluded anode. While R was equal to or more than 6, the coupling current reached at a maximum value and did not alter with the increase in R-value. Therefore, R=7 is chosen as one of these optimum parameters used in constructing the system, with which the biggest galvanic current might be obtained. In contrast, the thickness of the polymer filler separating the occluded anode area from the bulk electrolyte solution and the volume of the occluded anode area did not affect the corrosion current obviously. They might affect the response time to approach a steady state.

Microstructure quantification of Cu-4.7Sn alloys prepared by two-phase zone continuous casting and a BP artificial neural network model for microstructure prediction

Microstructures of Cu-4.7Sn(%) alloys prepared by two-phase zone continuous casting(TZCC)technology contain large columnar grains and small grains.A compound grain structure,composed of a large columnar grain and at least one small grain within it,is observed and called as grain-covered grains(GCGs).Distribution of small grains,their numbers and sizes as well as numbers and sizes of columnar grains were characterized quantitatively by metallographic microscope.Back propagation(BP) artificial neural network was employed to build a model to predict microstructures produced by different processing parameters.Inputs of the model are five processing parameters,which are temperatures of melt,mold and cooling water,speed of TZCC,and cooling distance.Outputs of the model are nine microstructure quantities,which are numbers of small grains within columnar grains,at the boundaries of the columnar grains,or at the surface of the alloy,the maximum and the minimum numbers of small grains within a columnar grain,numbers of columnar grains with or without small grains,and sizes of small grains and columnar grains.The model yields precise prediction,which lays foundation for controlling microstructures of alloys prepared by TZCC.