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A significant negative relationship is found between the summer mean north Indian Ocean sea surface temperature (SST) and East Asian surface temperature anomalies. However, the relationship is distinctively different for each month and shows a time-lagged relation rather than a simultaneous one. The north Indian Ocean warming in June is responsible for significant cold anomalies over the region of the Korean Peninsula and Japan (KJ region) that peak in July, exhibiting a 1-month leading role. The SST increase is closely associated with enhanced convective activity in the region in June, but the relationship between SST and resultant precipitation is substantially weakened afterward. This dependency of the precipitation sensitivity on SST anomalies is related to the climatological evolution of SST. The relatively low background SST due to the strengthening of southwesterly monsoons in the late summer can weaken the sensitivity of the precipitation to SST anomalies, yet the background SST in June is strong enough to maintain an increased sensitivity of precipitation. Thus, the Indian Ocean warming in June effectively drives atmospheric Kelvin waves that propagate into the equatorial western Pacific. In the western North Pacific (WNP), the resultant Kelvin wave–induced Ekman divergence triggers suppressed convection and anticyclonic anomalies. The WNP suppressed convection and anticyclonic anomalies move slowly northeastward until they are located near 20°N through the local air–sea interaction, and act as a source of the Pacific–Japan teleconnection. This teleconnection pathway brings clod surface anomalies to the KJ region due to the cyclonic circulation that causes the radiative and horizontal advection.

Potassium-competitive acid blockers (P-CABs) can be used to eradicate Helicobacter pylori infection. We aimed to evaluate the impact of treatment duration (7 vs 14 days) on successful H. pylori eradication with P-CAB-based triple therapy in Korea, where clarithromycin resistance rate is high.We retrospectively reviewed the data of patients who received first-line treatment for H. pylori infection with tegoprazan-based triple therapy (50 mg tegoprazan + 1000 mg amoxicillin + 500 mg clarithromycin twice daily for 1 or 2 weeks). The primary endpoint was the eradication rate in intention-to-treat (ITT) analysis.Of the 948 patients included in the study, 435 and 513 received 7-day and 14-day tegoprazan-based triple therapy, respectively. The eradication rate was higher in the 14-day therapy group than in the 7-day therapy group (ITT, 63.9%; 95% confidence interval [CI], 59.3-68.3%] vs 78.6% [95% CI, 74.9-81.9%], respectively, P 0.001; per-protocol, 70.5% [95% CI, 65.8-74.8%] vs 85.1% [81.7-88.1%], respectively, P 0.001). Overall adverse event rates did not differ between the two groups. Although six patients in the 14-day treatment group discontinued the prescribed medications due to adverse events, four of them (67%) discontinued the medication within 4 days.The 14-day tegoprazan-based triple therapy showed a superior eradication rate and acceptable adverse events compared with the 7-day tegoprazan-based triple therapy. A 14-day treatment regimen may be required when H. pylori infection is treated with tegoprazan-based triple therapy in regions with high clarithromycin resistance.

The Madden–Julian oscillation (MJO) is closely related to the intraseasonal variability of surface temperature in East Asia. It has been shown that significant cold surface temperature anomalies are observed in East Asia during MJO phase 3. However, the cooling tendency develops prior to phase 3, suggesting that the cold surface anomalies in East Asia are a delayed and accumulated response to the MJO forcings prior to phase 3. Here, using a thermodynamic equation, it is shown that both meridional advection and adiabatic cooling terms associated with the MJO flow are the dominant contributors to the cooling tendency. The meridional cold advection initially manifests in East Asia in the form of northerly wind anomalies in the eastern part of anticyclonic circulation anomalies that are centered over eastern Europe and develop before the establishment of the cold anomalies. It is suggested that the enhanced convection in the western North Pacific Ocean is responsible for the anomalous anticyclonic flow over eastern Europe with about a 10-day lag via a meridionally propagating Rossby wave train. Further, cooling by the vertical wind component in East Asia is a result of adiabatic cooling interpreted as a reversed local overturning circulation, with downward motion in the tropics and upward motion in the subtropics. This anomalous meridional overturning circulation process initiated from suppressed convection spanning the tropical Indian Ocean to East Asia also takes about 10 days. Therefore, both the Rossby wave propagation and a local overturning circulation induced by the tropical convections play an important role in driving the lagged response of cold surface anomalies in East Asia. Interestingly, these tropical convection forcings are similar to the typical dipole pattern in convection during MJO phase 7, with suppressed Indian Ocean convection and enhanced western North Pacific convection. This implies that the dipole convective forcing during MJO phase 7 possibly leads to the cold anomalies in East Asia following phase 3.

Effective life-cycle management of deteriorating structures should be based on an accurate and reliable damage propagation prediction model under uncertainty. Appropriate updating with inspected in...

Demand response (DR) aims at improving the reliability and efficiency of the power grids by shaping the power demand over time. Given that building energy consumption constitutes a significant portion of the overall grid load, building energy management is a critical component for the DR portfolio. In this study, DR control policies for lighting and air-conditioner systems for the individual spaces in buildings are proposed. The policies are designed to achieve the energy reduction amount specified in the DR request while minimizing the user discomfort. A significant challenge is to cope with the uncertainty of various environmental factors such as the solar illuminance and ambient temperature, as well as the psycho-economic factors such as the energy usage preferences of the occupants. We employ a data-driven machine learning approach to tackle this challenge. Our novel idea is to take advantage of the structural similarity of the control policies across the spaces in a lifelong multi-task learning framework. To accommodate significant nonlinearity in efficient policies, a kernel-based learning approach is pursued. The dual decomposition method is employed to relax the constraint coupled across the spaces, which allows solving the overall learning problem via a series of unconstrained subproblems. The efficacy of the proposed method is verified by numerical experiments based on semi-real data sets.

Improving the condition and safety of deteriorating bridges usually requires large amount of investments. According to ASCE (2009), more than 26% of the bridges in the United States are structurally deficient, and $17 billion (in 2006 dollars) annual investments are is required over the next 50 years to eliminate all existing and arising bridge deficiencies over this time interval. Bridge managers are usually faced with the problem of allocating limited financial resources in a cost-effective manner to maintain adequate functionality of deteriorating bridges (Liu and Fran gopol 2005a). Life-cycle analysis has been well recognized as a significant tool to maximize the cost-effectiveness of improving bridge condition and safety in addition to extending service life. Therefore, understanding of life-cycle analysis is necessary and essential for bridge engineers and managers. This chapter presents general concepts of bridge life-cycle performance analysis and optimization. To understand the structural performance prediction for life-cycle analysis under uncertainty, critical deterioration mechanisms for bridges, such as corrosion and fatigue, and reliability concepts are discussed. Furthermore, effects of inspection, monitoring, and maintenance on bridge life-cycle performance are presented. Considering these effects, several approaches for life-cycle optimization including optimum inspection and maintenance planning are introduced. General concepts of optimal management using multiobjective optimization and their applications are also provided. 18

The East Asian countries have experienced heavy rainfalls in boreal summer 2020. Here, we investigate the dynamical processes driving the East Asian rainfall extremes during July and August. The Indian Ocean basin warming in June can be responsible for the anticyclonic anomalies in the western North Pacific (WNP), which contribute to the zonally-elongated rainfalls in East Asia during July through an atmospheric Rossby wave train. In August, the East Asian rainfall increase is also related to the anticyclonic anomalies in the subtropical WNP, although it is located further north. It is suggested that the north tropical Atlantic warming in June partly contributes to the subtropical WNP rainfall decrease in August through a subtropical teleconnection. The rainfall decrease in the subtropical WNP region during August drives the local anticyclonic anomalies that cause the rainfall increase in East Asia. The tropical Indian Ocean anomalously warmed in June and the subtropical WNP rainfall decreased in August 2020, which played a role in modulating the WNP anticyclonic anomalies. Therefore, the record-breaking rainfalls in East Asia occurred during July and August 2020 can potentially be explained by the teleconnections induced by the tropical origins, such as tropical Indian Ocean warming and subtropical WNP rainfall decrease.

Over the past decades, significant advances have been accomplished in developing SHM techniques to detect the existing damages in deteriorating structures and maintenance techniques to extend the service life of these structures. The application of SHM can lead to more accurate damage detection. By using the information obtained from SHM, the uncertainties associated with structural performance assessment and prediction can be reduced. If the advanced SHM techniques are optimally integrated in life-cycle management, the efficiency and effectiveness of service life management of deteriorating structures can be maximized. In this paper, a computational platform for optimum monitoring planning based on multi-objective optimization (MOPT) and decision making is presented. The main components integrated in this computational platform are (a) formulation of objectives for optimum monitoring planning; (b) MOPT and decision making for application of the best monitoring plan; and (c) updating the damage propagation and structural performance prediction. The objectives for optimum monitoring planning are formulated considering the availability of monitoring data, damage detection, maintenance, service life and life-cycle cost. Through the MOPT and decision making, the best monitoring plan is determined. The updating process integrates the information obtained from monitoring to improve the accuracy and reduce the uncertainty associated with the damage occurrence and propagation prediction and monitoring planning.

In order to ensure structural safety, deteriorating bridges should be maintained above their target reliability level. This level affects the optimum maintenance interventions during the s...

Probabilistic service life management of a deteriorating structure subjected to fatigue is a systemic process of assessing and predicting its structural performance, and establishing the optimum inspection and repair plans under uncertainty. In order to consider multiple objectives for optimum inspection and repair planning simultaneously, it is necessary to apply the multi-objective probabilistic optimization process (MOPOP) approach. In general, an increase in the number of objectives that requires probabilistic simulations for fatigue initiation and propagation results in high computational cost and difficulty in the visualization of Pareto optimal solutions and decision making to select the best solutions. Consequently, efficient decision making for multi-objective inspection planning of fatigue-sensitive structures is necessary. This paper deals with such a decision making framework for probabilistic fatigue inspection planning based on multi-objective optimization. The multiple objectives are converted into a single objective by using weights of the objectives, and as a result, a single optimum solution for inspection planning is obtained. The final decision making is performed by identifying the essential objectives and selecting the best Pareto optimal solution. Furthermore, the computational efficiency of the decision making and the effect of the uncertainties associated with fatigue crack initiation and propagation on the decision making for optimum inspection planning are investigated.