Human RAN blocking peptide

Chlorantranilipmle (CAP) is extensively used for rice pest management. Lack of information on the role of standing water and amount and timing of rainfall on CAP dissipation in rice ecosystem could hamper its prospective use. Present study was performed to investigate the effects of different water regimes (saturated, 5 and 10 cm standing water) and simulated rainfall (40 and 100 mm occurred at 4, 8 and 24 h after CAP application) on leaching, surface runoff and dissipation of CAP into components of rice ecosystem. The results showed highest concentration of CAP residues in soil and plant under saturated condition followed by 5 and 10 cm standing water conditions. Whereas, the highest concentration of CAP in leachates was detected under 10 cm standing water (12.19 ng mL(-1)). The results revealed large amount of leaching (21.99 ng mL(-1)) and surface runoff (42.25 ng mL(-1)) losses of CAP when 100 mm rainfall occurred at 4 h after pesticide application. The total quantity of CAP residues in soil and plant was highest when rainfall occurred at 24 h after pesticide application under both the rainfall amounts. Water stagnation and high intensity rainfall occurred shortly after pesticide application will contribute to pesticide loss to non-target sites through surface run-off and leaching. There will be less pesticide available in soil for plant uptake which may not be sufficient to kill the target organisms.

In this paper, we develop two adaptive approaches for detecting the signal source in a bivariate process when a shift occurs in the location vector or the scale matrix or both. The proposed method capitalises the notion of Sklar's principle of expressing any multivariate joint distribution in terms of univariate marginal-distribution functions and a copula, which represents the dependence structure between the variables. Motivated by this, we recommend monitoring the two marginal distributions and the copula function simultaneously using appropriate nonparametric (distribution-free) test statistics. At each stage of Phase-II monitoring, we adopt the permutation method for computing the individual p-values and derive the plotting statistics of our proposed schemes combining suitable transforms of the three p-values of the component testing. We establish the in-control robustness of the proposed surveillance plans and compare them with two competitors in terms of run length properties. Performance of the proposed schemes in detecting a correct out-of-control signal is as good or better than some existing charting schemes for bivariate process monitoring. The novelty of our proposed technique lies in the fact that it indigenously helps in identifying the component(s) responsible for the signal, which is not straightforward with the traditional schemes for surveillance of a bivariate process. Numerical results substantiate that the proposed procedure performs significantly better than its competitors in many cases. Also, we investigate the percentage of correct diagnosis of a signal via the proposed charting schemes. Nowadays, in monitoring and control of smooth service operations, the use of quality monitoring has increased than ever before, but the problem and data structures become more complicated in the Industry 4.0 era. We analyse two real case studies, one in the context of monitoring the response time and service quality in a call centre and the other related to the inspection of product quality, to illustrate the application of the proposed schemes. (C) 2020 Elsevier B.V. All rights reserved.