Probabilistic Gridded Seasonal Sea Ice Presence Forecasting using Sequence to Sequence Learning

Accurate and timely forecasts of sea ice conditions are crucial for safe shipping operations in the Canadian Arctic and other ice-infested waters. Given the recent observations on the declining trend of Arctic sea ice extent over the past decades due to global warming, machine learning (ML) approaches are deployed to provide accurate short-term to long-term forecasting. This study unlike previous ML approaches in the sea-ice forecasting domain provides a daily spatial map of the probability of ice in the study domain up to 90 days of lead time. The predictions are further used to predict freeze-up/breakup dates and show their capability to capture these events within a valid time period (7 days) at specific locations of interest to communities.

Approximating Correlation Matrices Using Stochastic Lie Group Methods

Specifying time-dependent correlation matrices is a problem that occurs in several important areas of finance and risk management. The goal of this work is to tackle this problem by applying techniques of geometric integration in financial mathematics, i.e., to combine two fields of numerical mathematics that have not been studied yet jointly. Based on isospectral flows we create valid time-dependent correlation matrices, so called correlation flows, by solving a stochastic differential equation (SDE) that evolves in the special orthogonal group. Since the geometric structure of the special orthogonal group needs to be preserved we use stochastic Lie group integrators to solve this SDE. An application example is presented to illustrate this novel methodology.

A new methodology to create valid time-dependent correlation matrices via isospectral flows

In many areas of finance and of risk management it is interesting to know how to specify time-dependent correlation matrices. In this work we propose a new methodology to create valid time-dependent instantaneous correlation matrices, which we called correlation flows. In our methodology one needs only an initial correlation matrix to create these correlation flows based on isospectral flows. The tendency of the time-dependent matrices can be controlled by requirements. An application example is presented to illustrate our methodology.

Sustained Release Technology and Its Application in Environmental Remediation: A Review

Sustained release technology is a class of technology characterized by slowly-releasing specific active substances into a target medium to keep a certain concentration in the system within valid time. As a new of type technology, it has been extensively applied to medicine, chemical engineering, agriculture, environmental protection, etc. The principles and classification of sustained release technologies, as well as typical preparation methods of sustained release agents, were summarized in this paper; by introducing applied research progress of sustained release technologies into environmental fields like rainwater purification, sewage/drinking water treatment, and soil and atmosphere remediation, application features of these sustained release technologies were evaluated, and their application prospect in environmental remediation, especially in water treatment, was predicted.

Degradation Behavior In Vitro of Carbon Nanotubes (CNTs)/Poly(lactic acid) (PLA) Composite Suture

Poly(lactic acid) (PLA) suture can be absorbed by the human body, and so have wide applications in modern surgery operations. The degradation period of PLA suture is expected to meet with the healing time of different types of wounds. In order to control the degradation period of the PLA suture, the carbon nanotubes (CNTs) were composited with PLA suture, and the degradation experiment in vitro was performed on sutures. The structure and properties of sutures during degradation, such as surface morphology, breaking strength, elongation, mass and chemical structure, were tracked and analyzed. The results indicated that the degradation brought about surface defects and resulted in 13.5 weeks for the strength valid time of the original PLA suture. By contrast, the strength valid time of the CNTs/PLA suture was increased to 26.6 weeks. Whilst the toughness of both the pure PLA and CNTs/PLA sutures decreased rapidly and almost disappeared after 3 to 4 weeks of degradation. The mass loss demonstrated that the time required for complete degradation of the two sutures was obviously different, the pure PLA suture 49 weeks, while CNTs/PLA sutures 63 to 73 weeks. The research proved that CNTs delayed PLA degradation and prolonged its strength valid time in degradation.

On the Use of Cost-Effective Valid-Time-Shifting (VTS) Method to Increase Ensemble Size in the GFS Hybrid 4DEnVar System

Valid-time-shifting (VTS) ensembles, either in the form of full ensemble members (VTSM) or ensemble perturbations (VTSP), were investigated as inexpensive means to increase ensemble size in the NCEP Global Forecast System (GFS) hybrid four-dimensional ensemble–variational (4DEnVar) data assimilation system. VTSM is designed to sample timing and/or phase errors, while VTSP can eliminate spurious covariances through temporal smoothing. When applying a shifting time interval (τ = 1, 2, or 3 h), VTSM and VTSP triple the baseline background ensemble size from 80 (ENS80) to 240 (ENS240) in the EnVar variational update, where the overall cost is only increased by 23%–27%, depending on the selected τ. Experiments during a 10-week summer period show the best-performing VTSP with τ = 2 h improves global temperature and wind forecasts out to 5 days over ENS80. This could be attributed to the improved background ensemble distribution, ensemble correlation accuracy, and increased effective rank in the populated background ensemble. VTSM generally degrades global forecasts in the troposphere. Improved global forecasts above 100 hPa by VTSM may benefit from the increased spread that alleviates the underdispersiveness of the original background ensemble at such levels. Both VTSM and VTSP improve tropical cyclone track forecasts over ENS80. Although VTSM and VTSP are much less expensive than directly running a 240-member background ensemble, owing to the improved ensemble covariances, the best-performing VTSP with τ = 1 h performs comparably or only slightly worse than ENS240. The best-performing VTSM with τ = 3 h even shows more accurate track forecasts than ENS240, likely contributed to by its better sampling of timing and/or phase errors for cases with small ensemble track spread.

Treatment of terminal cardiovascular disease

This chapter is an introduction to the key components of the palliative treatment of terminal cardiovascular disease. While definitions of the term ‘terminal’ may vary, a person who may die within 6–12 months could be considered to be approaching the terminal stage of their illness, while those expected to die within a number of days or hours would be considered to be at the terminal stage. Either situation is a valid time to implement palliative treatment, but considering end-of-life care earlier provides opportunities that may otherwise be missed.