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A data-driven heuristic for the dynamic vehicle routing problem with multiple soft time windows
Full text linkIn the Dynamic Vehicle Routing Problem with Multiple Soft Time Windows (Dynamic VRPMSTW), customer requests arrive in real time and must be scheduled within flexible service intervals. This problem is complicated by operational constraints, such as vehicle capacities, travel durations, and heterogeneous fleets, which make it difficult for classical optimization methods to adapt quickly to changing conditions. Following recent trends in contextual optimization, we propose a Data-Driven Dynamic Heuristic that integrates Artificial Neural Networks for predicting travel times and demands into a Dynamic Hybrid Adaptive Large Neighborhood Search (DD-Dynamic HALNS). Using cluster assignment and genetic crossover operators, the method generates high-quality initial solutions and continuously re-optimizes them as new requests emerge, ensuring adaptability and service reliability. The effectiveness of the proposed method is evaluated on real-world logistics data and benchmark instances. Results from real-world delivery operations demonstrate an average distance reduction of 11.6% compared with the current solution, with further improvements up to 15.5% when a 10-minute time window flexibility is introduced. These findings highlight the practical benefits of integrating predictive analytics with heuristic optimization, leading to improved cost efficiency, reduced operational constraints, and enhanced service reliability.</p
Ten questions on indoor greening and environmental quality
Full text linkWhile outdoor urban greening is recognised for its benefits, indoor green infrastructure (iGI) in shaping indoor environmental quality (IEQ) - including air quality, thermal comfort, and bioaerosols - remains underexplored. This ten-question paper identifies key challenges, opportunities, and research gaps in the iGI-IEQ nexus, organised under 10 questions across five thematic clusters: (1) biophysical and technical performance; (2) ecological and microbiological dynamics; (3) human health and wellbeing; (4) equity, access, and socio-economic factors; and (5) implementation and systems integration. Findings indicate that iGI can improve air quality, regulate humidity, and enhance thermal comfort. However, its performance depends strongly on plant density, species selection, and ventilation. Most evidence comes from controlled settings. iGI may offer positive psychological and cognitive benefits, and can reduce health inequalities through affordable indoor interventions. However, significant data scarcity exists for long-term field studies, indoor microbial ecosystem effects, and socio-economic accessibility. Widespread adoption of iGI requires quantification of proven benefit conditions, followed by overcoming technical, operational, and regulatory barriers via adaptive design, digital monitoring, and interdisciplinary collaboration. As a culminating synthesis, this study introduces a newly developed comprehensive matrix that classifies twenty-six indoor greening types across twenty IEQ parameters, incorporating an assessment of current data confidence. This matrix lays a foundational framework for informed decision-making and design guidance. This review offers evidence-based insights for researchers, policymakers, and practitioners to effectively leverage iGI where suitable, in creating healthier, climate-resilient residential and commercial buildings, addressing both immediate IEQ challenges and supporting long-term sustainability objectives.</p
What are the experiences of fulfilling Care Act 2014 duties for social workers in mental health settings?
No full textTowards a Patient Specific Computational Model of Coronary Flow, Myocardial Perfusion, and Ventricular Mechanics
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Investigation of Non-Inductive Bifilar Pancake SFCL Losses in Electric Aircraft Cryogenic Propulsion
Full text linkSuperconducting powertrain is critical for nextgeneration electric aircraft, allowing for lossless power transmission and significantly increasing efficiency, performance, and range. A superconducting fault current limiter (SFCL) is a device on the DC side that uses superconducting materials to control excessive current during faults, thereby protecting the powertrain and enhancing system stability. Among several superconducting fault current limiters (SFCLs), resistive SFCLs (R-SFCLs) have benefits such as compactness, lightweight, high reliability, and a fail-safe nature. When subjected to alternating magnetic fields or currents, superconducting materials exhibit hysteresis, eddy currents, and flux flow effects, which lead to energy dissipation and reduced efficiency. This also applies to RSFCLs operating under normal conditions in the superconducting state, where AC losses occur and require appropriate thermal management to mitigate heat generation and prevent unexpected quenching. DC current ripple and its harmonic contents in an R-SFCL cause additional losses, increasing joule heating and potentially driving the superconductor out of its superconducting state, reducing its efficiency and fault-limiting capability. This research was conducted from two perspectives: theoretical calculations and experiments. The objective is to study the losses in the R-SFCL within the superconducting system, including ripple and harmonic effects, and to determine the design parameters of the R-SFCL cooling system to minimise thermal impacts
The dynamic effects of becoming disabled on work, wages and wellbeing in the UK from 1991 to 2018
Full text linkOver recent decades it has consistently been shown that disabled adults in the UK fare worse in the labour market and have lower levels of wellbeing than non-disabled adults. However, this is in part due to the selection into dis-ability of those with existing socio-economic disadvantages. In this article, we use panel data from the combined British Household Panel Survey and Understanding Society, covering the 27-years from 1991 to 2018, to distinguish between the effect of selection, the effect of dis-ability onset and the effect of dis-ability duration on a range of labour market and wellbeing outcomes. We show that there is important selection both into dis-ability and into longer experience of dis-ability on the basis of observable characteristics. We also show the importance of controlling for time-invariant unobservable individual characteristics that similarly affect selection into dis-ability and duration of dis-ability. Even after controlling for both forms of selection we find significant negative effects of dis-ability onset and duration, and offer policy solutions to address them
Vortex-carrying solitary gravity waves of large amplitude
Full text linkIn this paper, we study two-dimensional traveling waves in finite-depth water that are acted upon solely by gravity. We prove that, for any supercritical Froude number (non-dimensionalized wave speed), there exists a continuous one-parameter family C of solitary waves in equilibrium with a submerged point vortex. This family bifurcates from an irrotational uniform flow, and, at least for large Froude numbers, extends up to the development of a surface singularity or blowup of the circulation. These are the first rigorously constructed gravity wave-borne point vortices without surface tension, and notably our formulation allows the free surface to be overhanging. We also provide a numerical bifurcation study of traveling periodic gravity waves with submerged point vortices, which strongly suggests that some of these waves indeed overturn. Finally, we prove that at generic solutions on C—including those that are large amplitude or even overhanging—the point vortex can be desingularized to obtain solitary waves with a submerged hollow vortex. Physically, these can be thought of as traveling waves carrying spinning bubbles of air.</p
Coherent forecasts for tourism demand with automated immutability constraints
Full text linkThis study tackles key challenges in tourism demand forecasting within a hierarchical time series framework. To ensure coherence across aggregation levels and improve forecasting performance, we incorporate immutability constraints that preserve forecasts for strategically important nodes. Two automated selection methods are proposed to identify such nodes: (i) a clustering-based approach that ensures dispersion across levels, and (ii) a penalized optimization approach that selects immutable nodes based on data-driven criteria. Through Monte Carlo simulations, and two empirical applications, we demonstrate that the proposed methods improve forecast accuracy, robustness and flexibility while preserving interpretability. The framework is model-agnostic with respect to base forecasts and provides tourism managers with a scalable, data-driven tool to focus on critical segments, improve resource allocation, and support strategic planning in tourism management
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