In railway planning, effective disruption management tools can limit the impact of a disruption. A key challenge is that disruption durations are typically uncertain, leading to inefficiencies such as unnecessary shunting, seat-shortages, end-of-day inventory mismatches, and trip cancellations. To address this challenge, we propose two different approaches for incorporating robustness in real-time rolling stock rescheduling under uncertain disruption duration, an aspect that is typically assumed to be known in existing rescheduling models.The first approach, Light trip robustness (LTR) requires that a pre-specified percentage of critical trips are robust with respect to their incoming composition across different disruption durations. The second approach, Strict composition robustness (STR), ensures feasibility across all possible disruption durations without further changes to compositions or shunting operations. Both methods extend a well-known rolling stock rescheduling model by explicitly accounting for multiple possible disruption durations.We develop an online evaluation framework and conduct a computational study using real-world data from the Dutch railway network. Our results demonstrate that explicitly accounting for disruption duration uncertainty can significantly reduce rescheduling costs when disruptions last longer than initially expected. For instance, the STR model achieves up to 21% lower total costs compared to models that do not consider robustness, while the LTR model offers flexibility and performs well under moderate uncertainty. However, these benefits can come at a cost when the initially expected disruption duration is accurate. Overall, our results show that robust rolling stock rescheduling can significantly improve operational resilience and reduces the need for reactive updates. The appropriate level of robustness should be chosen based on the likelihood of disruption duration extensions.
Recent research has highlighted how visual narrative sequences involve similar structures and processing as syntax in language, yet no work has asked whether these structures may interact with each other. Here we examine whether the syntactic word orders and/or headedness of languages influence the orders of particular reversible constructions in visual narrative sequencing (POV, refiners). We first compared data from two corpora of annotated comics (TINTIN Corpus: 761 comics, 144 countries, 51 languages; Visual Language Research Corpus: 376 comics, 10 countries, 9 languages) to data about syntactic structure from multiple different language corpora (Grambank, WALS, Universal Dependencies). Across analyses, syntactic headedness of the languages of the comics aligned with ordering of constructions in visual narratives. These differences extended beyond the systematic diversity in visual narrative constructions across comics’ styles. Next, reanalysis of behavioral data from Cohn, van Middelaar, et al. (2024) showed that the syntactic structure of participants’ langauges affected their preferences for the ordering of visual narrative constructions. Altogether, this combination of corpus and experimental findings suggest that the headedness of syntactic structures in language can permeate the sequencing of visual narratives.
Open-Source Software (OSS) is a cornerstone of modern software development, yet evaluating the trajectory of individual projects throughout their development lifecycle remains a challenge. This paper develops an exploratory classification framework for examining how OSS projects evolve along two interrelated dimensions: innovation intensity and contribution sustainability. Building on contemporary OSS research on governance and contributor dynamics, we analyze 1785 OSS projects initiated by Microsoft, Google, and Apache on GitHub. Our findings reveal that contribution sustainability is primarily driven by the resilience and engagement of the contributor base, rather than innovation alone. We further highlight the nuanced role of the first release in shaping early project dynamics, the evolutionary patterns observed in OSS projects, and the contrasting associations of for-profit and non-profit sponsorship with contribution sustainability and contributor retention. By synthesizing insights from OSS literature, this research seeks to offer theoretical contributions and practical strategies for project owners seeking to sustain innovation in OSS communities.
Although employees often have limited control over task assignments, schedules, or collaboration partners, they may still have discretion in how they carry out tasks. Building on Job Demands-Resources theory, we integrate information-processing perspectives of play to propose that playful work design – proactively adding elements of fun and competition to routine activities – can be useful even in tightly structured jobs by increasing activated positive affect and reducing exhaustion. We tested this proposition in a 20-day diary study among 57 Norwegian naval cadets, yielding 1,046 day-level observations, using multilevel structural equation modeling. On days when cadets designed their work playfully, they reported more activated positive affect and less exhaustion. Combining designing fun with designing competition amplified these benefits. Thus, small discretionary choices in how employees approach work may produce immediate well-being gains in low-discretion roles and offer a micro-level pathway through which play at work can support performance.
This paper examines the dynamic relationship between real commodity price changes and interest rate changes during the U.S. Federal Reserve’s quantitative easing (QE) and COVID-19-era policies. Employing wavelet coherency and phase difference analysis over the period from January 2003 to December 2025, we provide a time–frequency characterisation of how these variables co-move under unconventional monetary regimes. We find robust short-term positive correlations between interest rate changes and real price changes across metals, energy, agriculture, and food commodities during both the QE and COVID-19 periods. Precious metals exhibit negative correlations during the subprime crisis, reflecting demand for value preservation. Critically, real price changes in hard-to-store commodities frequently lead interest rate changes, consistent with commodity prices anticipating monetary policy actions. To distinguish inflation-related channels from broader confounders, we implement partial wavelet coherency conditioned on money growth and core inflation. The significant attenuation of correlations under these conditions supports the quantity theory of money and the Fisher effect, indicating that the observed co-movements are substantially linked to inflation expectations. Our findings carry a direct policy implication: when the zero lower bound stifles conventional interest rate channels, commodity prices act as high-frequency barometers of evolving inflation expectations. This necessitates that policymakers treat energy, metals, and food markets as primary leading indicators during episodes of unconventional monetary policy. Complementing this policy insight, we introduce a wavelet-based methodology that significantly advances the literature by exposing the nonlinear, time-varying dynamics that standard econometric approaches systematically suppress.
