Two Distinctive Processes for Abnormal Spring to Summer Transition over the South China Sea

The period from April to June signifies the transition from spring to summer over the South China Sea (SCS). The present study documents two distinct processes for abnormal spring to summer transition over the SCS. One process is related to large-scale sea surface temperature (SST) anomalies in the tropical Indo-Pacific region. During spring of La Nina decaying years, negative SST anomalies in the equatorial central Pacific (ECP) and the southwestern tropical Indian Ocean (TIO) coexist with positive SST anomalies in the tropical western North Pacific. Negative ECP SST anomalies force an anomalous Walker circulation, negative southwestern TIO SST anomalies induce anomalous cross-equatorial flows from there, and positive tropical western North Pacific SST anomalies produce a Rossby wave-type response to the west. Together, they contribute to enhanced convection and an anomalous lower-level cyclone over the SCS, leading to an advanced transition to summer there. The other process is related to regional air-sea interactions around the Maritime Continent. Preceding positive ECP SST anomalies induce anomalous descent around the Maritime Continent, leading to SST increase in the SCS and southeast TIO. An enhanced convection region moves eastward over the south TIO during spring and reaches the area northwest of Australia in May. This enhances descent over the SCS via an anomalous cross-equatorial overturning circulation and contributes to further warming in the SCS. The SST warming in turn induces convection over the SCS, leading to an accelerated transition to summer. Analysis shows that the above two processes are equally important during 1979-2015

Swings in Crude Oil Valuations: Analyzing Their Bearing on China’s Stock Market Returns amid the COVID-19 Pandemic Upheaval

The advent of the COVID-19 pandemic has markedly affected energy valuations and financial markets. As such, this article aims to scrutinize the dynamic interplay between stock market returns and crude oil prices, with a particular focus on China, factoring in the second-moment effect of volatility spillover. Employing an EGARCH process to model the leverage impact on returns’ volatility, the analysis utilizes daily data spanning from January 30, 2020, to August 30, 2022, and incorporates causality-in-mean and variance assessments. Empirical findings indicate that the QDII-LOF benchmark, representing oil prices, exerts a substantial influence on stock market returns. Nevertheless, the complete sample reveals no discernible spillover effects attributable to oil price fluctuations. These insights imply that the Chinese government’s actions should carefully weigh the ramifications of spillovers. Concurrently, investors are advised to attentively monitor the crude oil market when making portfolio allocation decisions

Optimal maintenance policy for multi-component systems under Markovian environment changes

In this paper, we study multi-component systems, which environmental conditions and opportunistic maintenance (OM) involve. Environmental conditions will exert an influence on deterioration processes of the components in the system. For each component, the worse the environmental conditions are, the faster its deterioration speed is. We want to determine when to preventively maintain each component under such environmental influence. Our purpose is to minimize its long-run average maintenance cost. We decompose such a multi-component system into mutually influential single-component systems, and formulate the maintenance problem of each component as a Markov decision process (MDP). Under some reasonable assumptions, we prove the existence of the optimal (nr, Nr) type policy for each component. A policy iteration method is used to calculate its optimal maintenance policy. Based on the method, we develop an iterative approximation algorithm to obtain an acceptable maintenance policy for a multi-component system. Numerical examples find that environmental conditions and OM pose significant effects on a maintenance policy.close0

Modified iterative aggregation procedure for maintenance optimisation of multi-component systems with failure interaction

This paper studies maintenance policies for multi-component systems which have failure interaction among their components. Component failure might accelerate deterioration processes or induce instantaneous failures of the remaining components. We formulate this maintenance problem as a Markov decision process (MDP) with an objective of minimising a total discounted maintenance cost. However, the action set and state space in MDP exponentially grow as the number of components increases. This makes traditional approaches computationally intractable. To deal with this curse of dimensionality, a modified iterative aggregation procedure (MIAP) is proposed. We mathematically prove that iterations in MIAP guarantee the convergence and the policy obtained is optimal. Numerical case studies find that failure interaction should not be ignored in a maintenance policy decision making and the proposed MIAP is faster and requires less computational memory size than that of linear programming.close2