Revisiting ENSO impacts on the Indian Ocean SST based on a combined linear regression method

The El Nino-Southern Oscillation(ENSO)has great impacts on the Indian Ocean sea surface temperature(SST).In fact,two major modes of the Indian Ocean SST namely the Indian Ocean Basin(IOB)and the Indian Ocean Dipole(IOD)modes,exerting strong influences on the Indian Ocean rim countries,are both influenced by the ENSO.Based on a combined linear regression method,this study quantifies the ENSO impacts on the IOB and the IOD during ENSO concurrent,developing,and decaying stages.After removing the ENSO impacts,the spring peak of the IOB disappears along with significant decrease in number of events,while the number of events is only slightly reduced and the autumn peak remains for the IOD.By isolating the ENSO impacts during each stage,this study reveals that the leading impacts of ENSO contribute to the IOD development,while the delayed impacts facilitate the IOD phase switch and prompt the IOB development.Besides,the decadal variations of ENSO impacts are various during each stage and over different regions.These imply that merely removing the concurrent ENSO impacts would not be sufficient to investigate intrinsic climate variability of the Indian Ocean,and the present method may be useful to study climate variabilities independent of ENSO.

Optimization of synergistic drug combinations of different sparganins against blood stasis syndrome

Background:Blood stasis syndrome is one of the major syndromes in the development of chronic conditions in traditional Chinese medicine.Blood stasis syndrome is closely related to microcirculation dysfunction and thrombosis.Trigonaceae,as a representative traditional Chinese medicine for promoting blood circulation and removing blood stasis,there are many studies on its anticoagulant,antiplatelet aggregation and antithrombotic effects.Methods:Optimization of sparganin compounds,comprising sparganin A,sparganin B,and sparganin C for the characterization of drug pair effects against blood stasis syndrome was carried out.Ternary quadratic regression orthogonal combination design was carried out in a mouse model of blood stasis syndrome.The concentrations of thromboxane B2,plasminogen activator inhibitor 1,fibrinogen,and endothelin 1 were evaluated by enzyme linked immunosorbent assay.Thymus,hepatic,and spleen indices were also assessed.The outcomes of the evaluations aided in identifying optimum parameter values for the most favorable precipitation against blood stasis syndrome.Subsequently,Cytoscape 3.7.1 was used for network pharmacology to predict the key targets of sparganins and disease.Results:After sparganin A,sparganin B,and sparganin C treatment,hepatic had a downward trend,while spleen indices and thymus had an upward trend.Compared with the model group,thromboxane B2,endothelin 1,plasminogen activator inhibitor 1,and fibrinogen were decreased(P<0.05).The blood stasis syndrome model regression equation was extremely significant,and the correlation coefficient of R was 0.939,indicating that sparganin A,sparganin B and sparganin C were positively correlated at all levels.According to the dosage of these three factors and test results,the equation was fitted and the maximum values of the three sparganin in the corresponding dose range were obtained as follows:A=0.2982 mg/10g,B=0.1438 mg/10g,C=0.0417 mg/10g.Thus,the optimum parameters were found to be 0.298:0.144:0.042 for synergistic drug combinations of sparganin compounds sparganin A,sparganin B,and sparganin C in blood stasis syndrome.The possible key targets of sparganins included matrix metalloproteinase 9,plasminogen,proto-oncogene tyrosine-protein kinase src,and akt serine/threonine kinase 1 in blood stasis syndrome.Conclusion:Our study for the first time found two novel cyclic peptide compounds sparganin B and sparganin C have an anti-blood stasis effect,and speculated sparganins key targets included matrix metalloproteinase 9,plasminogen,proto-oncogene tyrosine-protein kinase src,and akt serine/threonine kinase 1 in blood stasis syndrome.

Optimization design of resistance spot welding parameters of magnesium alloy

By means of the quadratic regression combination design process, the regression equations of nugget diameter and tensile shear load of spot welded joint were established. Effects of welding parameters on the nugget diameter and the tensile shear load were investigated. The results show that effect of welding current on nugget diameter is the most evident. And higher welding current will result in bigger nugget diameter. Besides, interaction effect of electrode force and welding current on tensile shear load is the most evident compared with others. The optimum welding parameters corresponding to the maximum of tensile shear load have been obtained by programming using Matlab software, which is 4, 7 kN electrode force, 28 kA welding current and 4 cycle welding time. Under the condition of the optimum welding parameters, the joint having no visible defects can be obtained, nugget diameter and tensile shear load being 6. 8 mm and 3 256 N, respectively.

Effects of Mixed Fertilizer Applications on Hay Yield of Alfalfa

[ Objective] To study the effects of combined application of different elements on alfalfa hay yield. [ Medthod] Potassium sulfate, ammonium chloride, superphosphate and magnesium oxide were applied to alfalfa field in the experiment, and quaternary quadratic regression orthogonal combination was used to examine the effects of the experiment factors on hay yield of alfalfa. [ Result] The order of targets based on the effects on hay yield is as follows： potassium sulfate 〉 ammonium chloride 〉 magnesium oxide 〉 superphosphate. Among them, the potassium sulfate had a significant impact on alfalfa hay yield. The interaction between potassium sulfate and magnesium oxide also had a significant impact on alfalfa hay yield. [ Conclusion] The optimal cultivation measures for alfalfa hay yield of more than 19 224.6 kg/hm^2 are as follows： potassium sulfate, 163.253 - 181.679 kg/hm^2 ; ammonium chloride, 155.877 - 174.258 kg/hm^2 ; superphosphate, 566.177 - 632.924 kg/hm^2 ; and magnesium oxide, 30.060 - 33.628 kg/hm^2.

Effects Analysis of Mg^2＋, dNTPs and Taq DNA Polymerase on SSR-PCR System of Pear

Effects of Mg^2＋, NTPs and Taq DNA polymerase in pear SSR-PCR system were analyzed by quadratic regressive orthogonal rotational combinational design. Results showed： absolute IOD （Integrated OD of each band） value of target band reduced with concentration rising of Mg^2＋ and Taq DNA polymerase, but heightened with concentration rising of dNTPs. The decay rate of absolute IOD value increased progressively with rising Mg^2＋ concentration, decreased gradually with rising Taq DNA polymerase concentration; the rising speed would be slower than the dNTPs increase. Absolute IOD value would reduce with concentration rising of dNTPs at a low level of Mg^2＋ concentration. Conversely it would rise with the increase of dNTPs while high Mg^2＋ concentration. Absolute IOD value would generally rise with concentration rising of Taq DNA polymerase while low Mg^2＋ concentration. On the contrary it would reduce with concentration rising of Taq DNA polymerase while high Mg^2＋ concentration.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.