Using the newly available 13-year Argo float data for the period 2004-2016, a three-dimensional temperature-salinity (T-S) correlation field is constructed for the global upper ocean. It is revealed that the layer-averaged correlation between T and S time series has a significant peak (~ 0.7) at a depth of approximately 300 m, suggesting that the density-compensated thermohaline covariation may lead to a maximum T-S coupling in the vicinity of the pycnocline. This argument is supported by subsequent findings that the spatial distributions of temperature and salinity have excellent consistencies in terms of both climatology and seasonality around this depth, which we call a "T-S mirror layer". Since mass, heat and salt are mainly transported by dynamic events and processes, dominant currents and prevailing eddies in the pycnocline zone are found to determine the fundamental patterns of the global T/S climatologies which are characterized by a unified pattern of six well-defined warm/salty pools with collocated centroids and a clear west preference. Our results also suggest that the climatological ocean circulations transport heat/salt as a conveyor belt, meanwhile heat and salt parcels are discretely transported by numerous migrating mesoscale eddies, the combined effects of which lead to the formation of six well-defined "warm/ salty pools" in the western subtropical oceans with a core depth of ~ 300 m at the "mirror layer".

New products are highly valued by manufacturers and retailers due to their vital role in revenue generation. Product life cycle (PLC) curves often vary by their shapes and are complicated by promotional activities that induce spiky and irregular behaviors. We collaborate with JD.com to develop a flexible PLC curve forecasting framework based on Bayesian functional regression that accounts for useful covariate information, including product attributes and promotion. The functional model treats PLC curves as target variables and includes both scalar and functional predictors, capturing time‐varying promotional activities. Harnessing the power of basis function transformation, the developed model can effectively characterize the local features and temporal evolution of sales curves. Our Bayesian framework can generate initial curve forecasts before the product launch and update the forecasts dynamically as new sales data are collected. We validate the superior performance of our method through extensive numerical experiments using three real‐world data sets. Our forecasting framework reduces the forecasting error by 5.35%–30.76% over JD.com's current model and outperforms alternative models significantly. Furthermore, the estimated promotion effect function provides useful insights into how promotional activities interact with sales curves.

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In the subway tunnel construction. Studying the disturbance effects of construction on the surface and underground pipelines requires not only pre-estimation through theoretical and experimental analysis. And more importantly, dynamic forecasting is required through information obtained from the site during the construction process. Dynamic forecasting is of great significance in engineering. Based on the monitoring and analysis of the construction site of the underground excavation section of Shijiazhuang Metro Line 1, this paper studies the settlement curves of surface settlement and underground pipelines. By analyzing a large number of measured data, the empirical parameters applicable to Shijiazhuang area are summarized..

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