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European Union legislation obliges natural gas transmission system operators to publish key transport indicators on a transparency platform operated by the European Network of Gas Transmission System Operators for Gas (ENTSOG). Gas Infrastructure Europe (GIE) maintains similar transparency initiatives, providing data on the use of liquefied natural gas (LNG) terminals as well as underground gas storage facilities. These platforms have become a useful source of information for researchers, practitioners, and policy makers that is available to the general public at no cost. We demonstrate that these data platforms essentially represent an archive of the European natural gas system and how the security of supply situation of Europe can be monitored in almost real time by analyzing the evolution of flow patterns on a daily basis as well as longer time horizons. In particular, we show how the system has reacted to infrastructure expansions, disruptions, and an evolving geopolitical context.

Several technological challenges delay the adoption of electrified powertrains in the heavy-duty transport sector. For fuel-cell hybrid electric trucks, key issues include slow cold start, reduced cooling power during high ambient temperatures, and uncertainties regarding durability. In addition, the engineers must handle the complexity of the system. In this article, a Matlab/Simulink library is introduced, which has been developed to aid engineers in the design and optimization of energy management systems and strategies of this complex system that consider mechanical, electrochemical, and thermal energy flows. The library is introduced through five example vehicle models, and through case studies that highlight the various kinds of analysis that can be performed using the provided models. All library code is open source, open for commercial use, and runs in Matlab/Simulink without any need for external libraries.

The rapid increase in electric vehicles (EVs) and installed photovoltaic systems (PV) has resulted in new challenges for electric systems, e.g., voltage variations in low-voltage grids. Grid owners cannot directly control the power consumption of the end consumers. However, by the design of transparent tariffs, economic incentives are introduced for the end consumers to adjust their EV charging patterns. In this work, the main objective is to design a time-of-use pricing tariff to reduce the voltage variations in a low-voltage grid when introducing PVs and EVs with smart charging. Data from an existing low-voltage grid and hourly data from household power consumption, together with models of PV and EV charging, are used to simulate the voltage fluctuations based on the modified electric consumption. The results show that a time-of-use pricing tariff taking into consideration maximum peak power is important to reduce grid voltage variations. Another observation is that the use of economic incentives, such as subsidies when selling power from the household, combined with V2G technology can be economical for households but increases the voltage variations in the grid.

Individual differences (IDs) have long been considered one of the most important factors explaining variable rates and outcomes in second language acquisition (Dewaele, 2013). While traditional operationalizations of IDs have, explicitly or implicitly, assumed that IDs are static traits that are stable through time, more recent research inspired by complex dynamic systems theory (Larsen-Freeman, 1997, 2020) demonstrates that many IDs are dynamic and variable through time and across contexts, a theme echoed throughout the current issue. This study reports the initial semester of a diachronic project investigating the dynamicity of four learner IDs: motivation, personality, learning and cognitive styles, and working memory. In the initial semester, data from 323 participants in their first year of university-level Spanish were collected and analyzed to determine what type of variability may be present across learners with respect to the four IDs studied at one time point and to discern possible learner profiles in the data or patterns via which the data may be otherwise meaningfully described. The results revealed four types of learner profiles present in the dataset.

A straightforward technique to explore the "total effects" of neighborhoods on health outcomes is to compare the degree of similarity of outcomes of neighbors with those of non-neighbors. Several issues arise in interpreting these estimates around spatial and temporal definitions of "neighbors" and life course mobility patterns. Indeed, much work uses "cross- sectional neighbors," which makes the interpretation of the estimates unclear because they combine short-term effects (for movers) and long-term effects (for stayers). This paper contributes to the literature by assessing the importance of measuring neighbor mobility as well as neighborhood selection. Using the Panel Study of Income Dynamics, we examine the extent to which having longitudinal measures of "neighbors" shapes estimates of neighborhood effects, and also use a negative test of neighborhood effects to assess the importance of neighborhood selection. Specifically, we estimate similarity in self-rated health of adults over 30 years old who live in the same county over various periods of time and find that "cross-sectional" neighbor definitions may understate neighborhood effect estimates by as much as 35%. However, when we contrast these health estimates with contemporaneous neighborhood "effects" on completed education, we find that much of the "understated" effects on health are likely related to selection effects rather than causal effects of neighborhoods.

The increasing complexity and size of cyber-physical systems (e.g., aircraft, manufacturing processes, and power generation plants) is making it hard to develop centralized diagnosers that are reliable and efficient. In addition, advances in networking technology, along with the availability of inexpensive sensors and processors, are causing a shift in focus from centralized to more distributed diagnosers. This paper develops two structural approaches for distributed fault detection and isolation. The first method uses redundant equation sets for residual generation, referred to as minimal structurally-over-determined sets, and the second is based on the original model equations. We compare the diagnosis performance of the two algorithms and clarify the pros and cons of each method. A case study is used to demonstrate the two methods, and the results are discussed together with directions for future work.