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We estimate the impact of the extensity of macroprudential policies on the correlation of the policy interest rates between the center economies (CEs, i.e., the U.S., Japan, and the Euro area), and the peripheral economies (PHs). We find a more extensive implementation of macroprudential policies would lead PHs to (re)gain monetary independence from the CEs when the CEs implement expansionary monetary policy; when PHs run current account deficit; when they hold lower levels of international reserves; when their financial markets are relatively closed; when they are experiencing an increase in net portfolio flows; and when they are experiencing credit expansion.

This paper investigates Rodrik’s political-economy trilemma: policy makers face a trade-off of choosing two out of three policy goals or governance styles, namely, (hyper-) globalization, national sovereignty, and democracy. We develop a set of indexes that measure the extent of attainment of the three factors for 139 countries in the period of 1975–2016. Using these indexes, we examine the validity of the hypothesis of the political-economy trilemma by testing whether the three trilemma variables are linearly related. We find that, for industrialized countries, there is a linear relationship between globalization and national sovereignty (i.e., a dilemma), and that for developing countries, all three indexes are linearly correlated (i.e., a trilemma). We also investigate whether and how three political-economic factors affect the degree of political and financial stability. The results indicate that more democratic industrialized countries tend to experience more political instability while developing countries tend to be able to stabilize their politics if they are more democratic. The lower level of national sovereignty an industrialized country attains, the more stable its political situation tends to be, while a higher level of sovereignty helps a developing country to stabilize its politics. Globalization brings about political stability for both groups of countries. Furthermore, more globalized countries, whether industrial or developing, tend to experience more financial stability. Future data will allow us to test the possibility of regime changes associated with the post-2016 dynamics.

The wound healing process attempts to restore the integrity and function of the injured tissue. Additionally, proinflammatory cytokines, growth factors, and oxidative stress play important roles in wound healing. The aim of this study was to determine whether hydrogen-rich water intake induces the activation of the Nrf2/antioxidant defense pathway in rat palatal tissue, thereby reducing systemic oxidative stress and proinflammatory cytokine levels and promoting healing-associated genes. A circular excisional wound was created in the oral palatal region, and the wound healing process was observed. The rats were divided into two experimental groups in which either hydrogen-rich water or distilled water was consumed. In the drinking hydrogen-rich water, the palatal wound healing process was accelerated compared to that in the control group. As molecular hydrogen upregulated the Nrf2 pathway, systemic oxidative stresses were decreased by the activation of antioxidant activity. Furthermore, hydrogen-rich water intake reduced proinflammatory cytokine levels and promoted the expression of healing-associated factors in rat palatal tissue. In conclusion, hydrogen-rich water intake exhibited multiple beneficial effects through activation of the Nrf2/antioxidant defense pathway. The results of this study support the hypothesis that oral administration of hydrogen-rich water benefits the wound healing process by decreasing oxidative stress and inflammatory responses.

The dCache storage system at Brookhaven National Laboratory (BNL) serves as a critical cache for the ATLAS collaboration, enabling efficient access to petabytes of data located on tape, remote repositories, and cold storage. Effective cache management is vital to minimize access latency, particularly as operators have observed persistent high-demand datasets that warrant prolonged retention (“pinning”) in disk cache. This study evaluates machine learning (ML) techniques to automate dataset pinning decisions by predicting future access patterns. Our models, which integrate temporal trends and request-specific features, achieve predictive errors significantly below the inherent variability of dataset access patterns. We further explore dynamic updates to these predictions using real-time dCache access logs, enabling adaptive pinning strategies for high-priority datasets. Ongoing work focuses on validating system-wide performance gains under realistic user workloads, with the goal of optimizing resource utilization for large-scale scientific data infrastructures.

This paper overviews different exit strategies for the U.S. from the debt-overhang, and analyses their implications for emerging markets and global stability. These strategies are discussed in the context of the debates about secular-stagnation versus debt-overhang, the fiscal theory of the price level, the size of fiscal multipliers, prospects for a multipolar currency system, and historical case studies. We conclude that the reallocation of U.S. fiscal efforts towards infrastructure investment aiming at boosting growth, followed by a gradual tax increase, aiming at reaching a modest primary fiscal surplus over time are akin to an upfront investment in greater long-term global stability. Such a trajectory may solidify the viability and credibility of the U.S. dollar as a global anchor, thereby stabilizing Emerging Markets economies and global growth.

We consider the classical power management problem: There is a system or “device” which has two states—ON and OFF—and one has to develop a control algorithm for changing between these states as to minimize cost (energy or some other hybrid cost) when given a sequence of service requests. We analyze this problem in terms of online competitive analysis to obtain worst-case guarantees. Although an optimal 2-competitive algorithm exists, that algorithm does not result in good performance in many practical situations, especially in case the device is not used frequently. To take the frequency of device usage into account, we construct an algorithm based on the concept of “slackness degree”. Then by relaxing the worst-case competitive ratio of our online algorithm to 2+ε, where ε is an arbitrary small constant, we make the algorithm flexible to slackness. The algorithm thus automatically tunes itself to slackness degree and gives better performance than the optimal 2-competitive algorithm for real world inputs. In addition to worst-case competitive ratio analysis, a queueing model analysis is given and computer simulations are reported, confirming that the performance of the algorithm is high. We show how the approach can be generalized to a situation where the system has a number of intermediate states. Our model can be used to facilitate renewable energy integration into the electrical grid and we highlight that an online competitive approach can yield techniques for grid resiliency.

The storage management system dCache acts as a disk cache for high-energy physics (HEP) data from the US ATLAS community. Since its disk capacity is considerably smaller than the total volume of ATLAS data, a heuristic is needed to determine what data should be kept on disks. An effective heuristic would be to keep the data files that are expected to be heavily accessed in the near future. Through a careful study of access statistics, we find a few most popular datasets are accessed much more frequently than others, even though these popular datasets change over time. If we could predict the near-term popularity of datasets, we could pin the most popular ones in the disk cache to prevent their accidental removal and guarantee their availability. To predict a dataset popularity, we present several methods for forecasting the number of times a dataset will be accessed in the next day. Test results show that these methods could predict the next-day access counts of popular datasets reliably. This observation is confirmed with dCache logs from two separate time ranges.

We study constant-time approximation algorithms for bounded-degree graphs, which run in time independent of the number of vertices $n$. We present an algorithm that decides whether a vertex is contained in a some fixed maximal independent set with expected query complexity $O(d^2)$, where $d$ is the degree bound. Using this algorithm, we show constant-time approximation algorithms with certain multiplicative error and additive error $\\epsilon n$ for many other problems, e.g., the maximum matching problem, the minimum vertex cover problem, and the minimum set cover problem, that run exponentially faster than existing algorithms with respect to $d$ and $\\frac{1}{\\epsilon}$. Our approximation algorithm for the maximum matching problem can be transformed to a two-sided error tester for the property of having a perfect matching. On the contrary, we show that every one-sided error tester for the property requires at least $\\Omega(n)$ queries. [PUBLICATION ABSTRACT]

The Worldwide Large Hadron Collider Computing Grid (WLCG) community’s deployment of dual-stack IPv6/IPv4 on its worldwide storage infrastructure has been very successful. Dual-stack is not, however, a viable longterm solution; the HEPiX IPv6 Working Group has focused on studying where and why IPv4 is still being used, and how to flip such traffic to IPv6. The agreed end goal is to turn IPv4 off and run IPv6-only over the wide-area network to simplify both operations and security management. This paper reports our work since the CHEP2023 conference. Firstly, we present our campaign to deploy IPv6 on CPU services and Worker Nodes, with a deadline of end of June 2024. Then, the WLCG Data Challenge (DC24) performed in February 2024 was an excellent opportunity to observe the percentage of data transfers carried by IPv6. We observed the predominance of IPv6 in data transfers during DC24 and were able to understand yet more reasons for the use of IPv4 and areas for remedial action. The paper ends with the working group’s plans for moving WLCG to “IPv6- only”. One aspect of this is the possible automated use of IPv6-only clients configured with a customer-side translator, or CLAT, together with a deployment of NAT64 using what is often known as “IPv6-Mostly”, enabling IPv6-only sites to connect to non-WLCG IPv4-only services.

A power-down system has an on-state, an off-state, and a finite or infinite number of intermediate states. In the off-state, the system uses no energy and in the on-state energy it is used fully. Intermediate states consume only some fraction of energy but switching back to the on-state comes at a cost. Previous work has mainly focused on asymptotic results for systems with a large number of states. In contrast, the authors study problems with a few states as well as systems with one continuous state. Such systems play a role in energy-efficiency for information technology but are especially important in the management of renewable energy. The authors analyze power-down problems in the framework of online competitive analysis as to obtain performance guarantees in the absence of reliable forecasting. In a discrete case, the authors give detailed results for the case of three and five states, which corresponds to a system with on-off states and three additional intermediate states “power save”, “suspend”, and “hibernate”. The authors use a novel balancing technique to obtain optimally competitive solutions. With this, the authors show that the overall best competitive ratio for three-state systems is 9 5 and the authors obtain optimal ratios for various five state systems. For the continuous case, the authors develop various strategies, namely linear, optimal-following, progressive and exponential. The authors show that the best competitive strategies are those that follow the offline schedule in an accelerated manner. Strategy “progressive” consistently produces competitive ratios significantly better than 2.