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While our understanding of pH dynamics has strongly progressed for open-ocean regions, for marginal seas such as the East China Sea (ECS) shelf progress has been constrained by limited observations and complex interactions between biological, physical and chemical processes. Seawater pH is a very valuable oceanographic variable but not always measured using high-quality instrumentation and according to standard practices. In order to predict total-scale pH (pHT) and enhance our understanding of the seasonal variability of pHT on the ECS shelf, an artificial neural network (ANN) model was developed using 11 cruise datasets from 2013 to 2017 with coincident observations of pHT, temperature (T), salinity (S), dissolved oxygen (DO), nitrate (N), phosphate (P) and silicate (Si) together with sampling position and time. The reliability of the ANN model was evaluated using independent observations from three cruises in 2018, and it showed a root mean square error accuracy of 0.04. The ANN model responded to T and DO errors in a positive way and S errors in a negative way, and the ANN model was most sensitive to S errors, followed by DO and T errors. Monthly water column pHT for the period 2000–2016 was retrieved using T, S, DO, N, P and Si from the Changjiang biology Finite-Volume Coastal Ocean Model (FVCOM). The agreement is good here in winter, while the reduced performance in summer can be attributed in large part to limitations of the Changjiang biology FVCOM in simulating summertime input variables.

This study proposes a methodology for helical mill-grinding of tiny internal threads made of hard brittle materials such as SiCp/Al composites. The methodology uses the helical mill-grinding method incorporating with a diamond form-grinding wheel. A mathematical model is established to predict thread form errors and provide a rational range of wheel parameters, such as variation of tool profile angle Δα and ratio of the wheel diameter to the thread major diameter η. Based on the methodology, a grinding wheel is developed for processing the M2 internal threads in a validation experiment. The study demonstrates that an M2 internal thread made of the SiCp/Al composite of 45% SiC volume fraction is successfully machined in 5 min with pitch error

While our understanding of pH dynamics has strongly progressed for open ocean regions, for marginal seas such as the East China Sea (ECS) progress has been constrained by limited observations and complex interactions between biological, physical, and chemical processes. Seawater pH is a very valuable oceanographic variable but not always measured using high quality instrumentation and according to standard practices. In order to predict water column total scale pH (pHT) and enhance our understanding of the seasonal variability of pHT on the ECS shelf, an artificial neural network (ANN) model was developed using 11 cruise datasets from 2013 to 2017 with coincident observations of pHT, temperature (T), salinity (S), dissolved oxygen (DO), nitrate (N), phosphate (P) and silicate (Si) together with sampling position and time. The reliability of the ANN model was evaluated using independent observations from 3 cruises in 2018, and showed a root mean square error accuracy of 0.04. A weight analysis of the ANN model variables suggested that DO, S, T were the most important predictor variables. Monthly water column pHT for the period 2000-2016 was retrieved using T, S, DO, N, P, and Si from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM).