We demonstrate that the one-dimensional shallow water moment equations satisfy an auxiliary entropy conservation law, where the entropy function corresponds to the total energy. Additionally, we show that the classical Newtonian slip friction and Manning friction terms are entropy dissipative with respect to the developed entropy variables. The results from the continuous entropy analysis are used to construct an entropy stable and well-balanced nodal discontinuous Galerkin spectral element method for the spatial approximation. Key to ensure the entropy stability of the scheme is the derivation of entropy conservative numerical fluxes that satisfy a discrete version of the entropy flux compatibility condition. Finally, numerical examples demonstrate the performance of the scheme and validate the theoretical results.
Background Metacognitive therapy (MCT; Wells, 2009) is a transdiagnostic alternative to cognitive-behavioural therapy (CBT) for adult depression. Prior meta-analyses reported large effects but combined within- and between-group designs, mixed anxiety and depression samples, and did not apply contemporary small-sample meta-analytic methods. Several large multi-site randomised controlled trials (RCTs) published after 2018 have not been incorporated into a focused, methodologically up-to-date synthesis. Objective This study had two complementary aims: (a) to appraise the existing meta-analytic evidence base on MCT for depression through an umbrella review (with AMSTAR-2 quality appraisal); and (b) to synthesize the efficacy of MCT for adult depression using strictly between-group post-treatment comparisons from RCTs reporting BDI or BDI-II as the primary outcome, applying methods appropriate for small-k meta-analyses. Methods Reporting followed the 2020 PRISMA statement. We searched MEDLINE, PsycINFO, CENTRAL, and Embase through May 2026. Three peer-reviewed meta-analyses or systematic reviews were formally appraised in the umbrella review component; one additional non-peer-reviewed preprint was considered contextually. For the meta-analysis component, six RCTs (N = 641 randomised; 546 contributing to the analyses) met all inclusion criteria. Standardized mean differences (Hedges’ g) at post-treatment were synthesised using a random-effects model with restricted maximum likelihood (REML) estimation and Knapp-Hartung adjustment. We pre-specified moderator analyses for control type, researcher allegiance, and MCT version; conducted formal influence diagnostics (DFFITS, Cook’s distance); and reported a 95% prediction interval alongside the 95% confidence interval. Four sensitivity analyses examined inclusion of additional depression measures, exclusion of model-implied SDs, exclusion of influential observations, and long-term follow-up. Results The umbrella review identified three peer-reviewed meta-analyses or systematic reviews (Normann et al., 2014, Normann and Morina, 2018, Zanini et al., 2024), each with at least one critical methodological limitation (mixing within- and between-group estimates; no prediction interval; no formal allegiance moderator analysis); AMSTAR-2 ratings ranged from low to moderate confidence. The meta-analytic update found that MCT produced a statistically significant reduction in post-treatment depressive symptoms (Hedges’ g = 0.87, 95% CI [0.05, 1.68], p = 0.041). Heterogeneity was considerable (I2 = 89.4%, τ2 = 0.47); the 95% prediction interval [-1.23, 2.96] extended into negative values. Control type explained 61.3% of between-study variance: against passive controls g = 1.42 (k = 3), against active comparators g = 0.41 (k = 3). One trial (Hagen et al., 2017) was identified as a single influential observation (DFFITS = 1.59, Cook’s D = 0.64); its exclusion attenuated the pooled estimate to g = 0.61 (95% CI [0.17, 1.06]). At 6–12 month follow-up, no significant pooled effect was detected (g = 0.56, p = 0.154, k = 4). Formal tests detected no funnel-plot asymmetry, although the statistical power of these tests is low with k = 6 trials. Conclusion The available RCT evidence is consistent with a beneficial effect of MCT for adult depression in the short term, but the magnitude of this effect varies substantially with the comparator, depends heavily on a single influential trial, and is not clearly maintained at follow-up. Independent, adequately powered RCTs using active control comparators are needed.
Data centres have moved from the background of the digital economy to the foreground of energy, climate, industrial and national-security politics. Artificial intelligence is intensifying this shift through growing demand for compute, electricity, cooling, land, hardware and critical materials. Yet governance remains largely organised around facility-level metrics that treat data centres as bounded technical assets rather than strategic infrastructure embedded in grids, water systems, supply chains, communities and security architectures. This article develops the CLOUDS framework—Coherence, Lifecycle materiality, Operational locality, Utility burden, Digital sovereignty, and Social licence and public purpose—as a diagnostic architecture for evaluating data centres as system-shaping infrastructure. It also develops capacity extraction: the process through which heterogeneous inputs such as electricity, water, land, permits, institutional trust and community tolerance are rendered as a single quantity called capacity and converted into compute whose higher-value layers may remain controlled elsewhere. The framework is developed through an integrative review and tiered empirical design spanning Chile, Australia, Querétaro, Ireland and Singapore. It is proposed for testing rather than presented as validated. Two findings are central. First, instrument incoherence can arise without regulatory capture when no institution can govern trade-offs across electricity, water, planning and public value. Second, hosting is not sovereignty: physical location alone creates no assured claim on compute access, workload priority, crisis continuity or higher-value layers. The policy implication is strategic infrastructure bargaining over both system capacity and the institutions governing its conversion into compute.
Securing curb space near a delivery location is a major source of uncertainty for urban commercial vehicle drivers. While tour planning tools increasingly account for time-dependent traffic conditions, they typically ignore parking delays, even though evidence shows they vary systematically across locations and times of day. This paper empirically examines how expected parking delays affect urban delivery tour planning. Using GPS data from real-world delivery operations, we estimate time- and location-specific parking delays and incorporate them into a Time-Dependent Traveling Salesman Problem with Time Windows. We compare conventional parking-blind planning, which considers only travel times, with parking-aware planning, which additionally accounts for expected parking delays. The results show that parking-blind planning substantially underestimates tour duration and overestimates schedule feasibility. When realistic parking conditions are considered, parking delays accounted for about 33% of total delivery tour duration and generate substantial increases in lateness and time-window violations. Parking-aware planning reduces tour duration by 3.4% on average (17.4 min), with some instances obtaining total tour duration reductions of more than 10%, by trading slightly longer driving times for lower parking delays, while reducing time spent in parking delays by 11% on average. More importantly, it substantially improves service reliability, reducing average lateness and greatly decreasing the frequency of time-window violations. These improvements are achieved primarily through small adjustments in stop sequencing and service timing that shift deliveries toward periods with lower expected parking delays.
Digital transformation has become a central topic in management research over the past decades. This systematic review of 109studies from 2000 to 2025 clarifies the role of organizational design in digital transformation. Synthesizing insights from the literature, we identify eight organizational design dimensions and integrate them into a framework of three interdependent stages of digital transformation. The Align stage connects digital leadership, digital governance, and digital culture; the Execute stage mobilizes digital strategy, digital organization, as well as digital resources and capabilities; and the Transform stage establishes digital operating and business models. Our analysis further reveals a distinct tradeoff at each stage: balancing control and autonomy during alignment, stability and adaptability during execution, and optimization and reconfiguration during transformation. We build on these insights to develop a research agenda and derive implications for managerial practice.
