Parallelized Research Articles

Long term follow-up of patients with patent ductus arteriosus after transcatheter closure.

To investigate the concurrent, long-term, and future adverse events and assess the trend of adverse events in pediatric patients with patent ductus arteriosus (PDA) after transcatheter closure. A total of 1590 patients underwent transcatheter PDA closure were enrolled, including 465 patients (median age = 22 months) in the training group and 1125 patients in the validation group. Logistic regression analysis was used to assess independent risk factors associated with concurrent adverse events after closure. The chi-square test was used to evaluate the 5-year follow-up trend. Multivariable logistic regression analysis indicated that low age, female, and high pulmonary end diameter were independent risk factors for concurrent adverse events after closure. For patients without concurrent adverse events and for those who with concurrent adverse events but return to normal, the Chi-square test showed no abnormal results at the 5-year follow-up. Furthermore, the follow-up data of the validation group were not significantly different from those of the training group. The value of long-term follow-up of children may be limited for those who did not have a concurrent adverse event after closure nor for those who had a concurrent adverse event after closure but returned to normal during the 5-year follow-up period. Follow-up monitoring of adverse events tended to be recommended in pediatric patients with PDA after transcatheter closure. However, follow-up in these pediatric patients is expensive and there is a risk of sedation for echocardiography examination frequently. ·Patients who had no concurrent adverse events after closure did not show any abnormality at 5-year follow-up. ·Most of the patients who had concurrent adverse events after closure returned to normal at 5-year follow-up. The value of long-term follow-up may be limited for the above patients after transcatheter PDA closure.

Design complexity based flexible order dispatching for additive manufacturing production

Understanding design complexity for additive manufacturing (AM) is essential in AM production planning since conventional make-to-order production for individual AM orders of complex designs can amplify operational uncertainty in an entire AM production system. As a response, this study aims not only to demonstrate the impact of design complexity on AM production but also to propose a novel order dispatching approach based on design complexity that mitigates operational uncertainty in an AM production system. First, a design complexity measure was developed using an information theoretic approach. Next, a discrete-event simulation model to represent an AM production system consisting of parallel AM machines for jet-engine bracket designs was built to identify the impact of design complexity on average order lead time and total production cost through regressions. Finally, a flexible order dispatching rule that reflects operational attitudes toward design complexity was proposed to determine part-processing priorities by tracking both part- and system-level design complexity states in a centralized queue for AM production. The proposed dispatching rule was compared with relevant static dispatching rules to assess its performance in operational efficiency under varied attitudes toward design complexity. The findings from this study clearly showed the negative impact of design complexity on operational performance for AM production. Moreover, the proposed dispatching rule resulted in lead time reduction and balanced lead time performance in AM production against alternative static dispatching strategies. This study demonstrates the importance of design complexity-based flexible operations to properly handle latent uncertainties in an AM production system.

Novel integer L-shaped method for parallel machine scheduling problem under uncertain sequence-dependent setups

We study scheduling problems on unrelated parallel machines with uncertainty in job processing and sequence-dependent setup times. We first formulate this problem as a two-stage stochastic program using a scenario-based approach, in which the first-stage decisions involve job-machine assignments, and the second-stage deals with job sequencing. We then propose an innovative modified integer L-shaped method, uniquely designed to decompose the primary problem into independent subproblems at both the scenario and machine levels. We have augmented this method with a specialized integer optimality cut, which is further strengthened using a sequential lifting procedure. To broaden the applicability of our model and solution, we have adapted it to a distributionally robust optimization (DRO) framework under Wasserstein ambiguity. This process involves reformulating the DRO model into a mixed-integer linear program (MILP) using conic duality theory. Additionally, we integrate a distribution separation program to define the worst-case probability distribution during each iteration of the proposed integer L-shaped method, enhancing the computational speed for solving the DRO model. We conducted a series of numerical experiments to illustrate the effectiveness and scalability of the proposed solution methodology, which demonstrated highly promising results. Our innovative decomposition algorithms showed superior performance, achieving solution times that are significantly faster (up to two orders of magnitude) compared to the traditional integer L-shaped method for the stochastic scheduling model and the direct MILP reformulation of the DRO model. These results were obtained for problem instances solved to the exactness within the time limit. Moreover, the solution derived from the DRO model exhibited greater robustness in out-of-sample analysis with simulated data compared to the solution obtained from the stochastic model.

Large-scale dynamic surgical scheduling under uncertainty by hierarchical reinforcement learning

Dynamic surgical scheduling within a workday is a complicated decision-making process. The critical challenge is that the actual duration of surgery and the arrival process of emergency patients are uncertain and unknown in advance. In this work, we propose a two-level dynamic scheduling framework based on hierarchical reinforcement learning to solve dynamic surgical scheduling problems considering both elective and emergency patients. Specifically, with the realisation of uncertainty, the upper-level agent (UA) dynamically decides whether to trigger rescheduling to optimise the workday total cost. The lower-level agent (LA) aims at obtaining subscheduling solutions when rescheduling is triggered. The subproblem at the LA can be formulated as a mixed integer programming model, which can be generalised to unrelated parallel machine scheduling with machine eligibility restrictions and sequence- and machine-dependent setup times. Such problems can be solved in small-scale cases and suffers the combinatorial explosion in large scale cases. To address this issue, we propose a heuristic method that is built upon deep reinforcement learning to obtain high-quality solutions. We conduct extensive simulation experiments with real data to test the effective of our framework. The results for different scenarios show that our proposed framework outperforms existing methods in terms of overall performance and has strong generalisation ability.

Variable neighbourhood search for parallel machine scheduling with a single loading server: a truck-scheduling perspective

ABSTRACT In this paper, we study a class of parallel machine scheduling problem with a single loading and multiple unloading servers. We show an application of this problem in a truck-scheduling context. We design a mixed-integer-programming formulation and use it to solve instances of various sizes with different characteristics. The obtained results reveal that a state-of-the-art commercial solver is only able to tackle instances of limited size. To solve larger instances, a general variable neighborhood search is proposed. Our comprehensive computational experiments demonstrate how the proposed metaheuristic can provide high-quality solutions in reasonable computing time (a few seconds). This type of performance is particularly relevant when considering operational problems that must be tackled on a daily basis. Finally, a sensitivity analysis is performed with respect to the number of trucks.

Fast approximation of the traveling salesman problem shortest route by rectangular cell clustering pattern to parallelize solving

A method of quickly obtaining an approximate solution to the traveling salesman problem (TSP) is suggested, where a dramatic computational speedup is guaranteed. The initial TSP is broken into open-loop TSPs by using a clustering method. The clustering method is based on either imposing a rectangular lattice on the nodes or dividing the dataset iteratively until the open-loop TSPs become sufficiently small. The open-loop TSPs are independent and so they can be solved in parallel without synchronization, whichever the solver is. Then the open-loop subroutes are assembled into an approximately shortest route of the initial TSP via the shortest connections. The assemblage pattern is a symmetric rectangular closed-loop serpentine. The iterative clustering can use the rectangular assembling approach as well. Alternatively, the iterative clustering can use the centroid TSP assembling approach, which requires solving a supplementary closed-loop TSP whose nodes are centroids of the open-loop-TSP clusters. Based on results of numerical simulation, it is ascertained that both the iterative clustering and rectangular cell clustering pattern are roughly equally accurate, but the latter is way more computationally efficient on squarish datasets. Fast approximation of the TSP shortest route serves as an upper bound. In addition, the route can be studied to find its bottlenecks, which subsequently are separated and another bunch of open-loop TSPs is approximately solved. For big-sized TSPs, where the balance of the accuracy loss and computational time is uncertain, the rectangular cell clustering pattern allows obtaining fast solution approximations on multitudinous non-synchronized parallel processor cores.

A note on scheduling identical parallel machines with preemptions and setup times

We correct, extend and improve recent algorithmic and computational complexity results for the identical parallel machine scheduling problem with preemptions and setup times preceding uninterrupted job parts. The objective is to minimise the makespan. Our results include a proof of strong NP-hardness for an arbitrary number of machines, and a pseudo-polynomial algorithm and an FPTAS for the two machines case.

A novel technique for leaf disease classification using Legion Kernels with parallel support vector machine (LK-PSVM) and fuzzy C means image segmentation

Detection of plant disease and classificationare being investigated in many parts of the worldto save precious medical plants from becoming extinct.Major problem in this task, include the lack of advanced and technology driven solution. Manual identification is often time-consuming and prone to inaccuracies. Therefore, there is an urgent need for an automated and efficient method that can accurately identify and classify plant diseases. This article focuses on detecting the disease through classificationthrough a new technique using leaf images for automatic classification. This paper proposes a novel segmentation technique using Fuzzy C means and Particle Swarm Optimization for effective segmentation of leaf images and feature extraction that can help in classification of disease.The approach emphasizes on the integration of techniques such as image processing, segmentation and feature extraction and finally the classification, which offers a comprehensive solution for the disease detection. The work leverages on the advantages of Legion Kernels and Parallal support vector Machine (LK-PSVM) clubbed with fuzzy C means Image segmentation to offer a framework that can handle diverse leaf images and which can effectively differentiate the type of the disease.The proposed method LK-PSVM combined with Fuzzy C means presents a novel approach that is significantly deviated from the conventional methods of leaf disease classification.The proposed wok brings an integrated framework which can synergistically combine the Legion Kernels with the PSVM technique coupled with Fuzzy C Means Image segmentation which can handle the issue of overlapped data sets and support vector machines are used to handle the situation where the number of dimensions are more than the number of samples, which is more probable in the classification problem under consideration.By integrating these components, the proposed method achieves more accuracy and robustness when compared to the existing methods in the literature. The segmentation is carried out using PSO after pre-processing of images. The Gaussian functions are used to eliminate the background subtraction. Different features of the images are then computed. A total of 55,400 images were used for the experiment consisting of various plants’ leaves spreading across 38 labels. A classifier is then proposed using Machine learning methods for the detection of disease in apple fruit leaves. The experiments prove that the proposed method have high degree of classification accuracy when compared to existing methods. The proposed method not only cater to the need in terms of accuracy but also making it scalable for different types of leaves.

Appraising Linguistic and Reading Impairments in Kannada-Speaking Persons With Central Alexia.

Alexia is an acquired condition resulting in impaired abilities to comprehend and/or read aloud written scripts secondary to lesions in the brain involved in reading processes. Just as how linguistic aspects are multi-faceted in persons with aphasia (PWAs), the reading impairments also vary extensively across each PWA depending on the type and nature of the language deficits. Each language has its unique linguistic properties. Whether the impairment is in the spoken form or written form, the dissociations in both aspects across the linguistic distinctions are paramount. Given the diverse orthographic features of Kannada, alongside inquiries into the impairment in oral language skills among individuals with acquired reading difficulties, the study emphasizes distinguishing the concurrent language processes associated with reading impairment. The study aimed to explore the relationship between spoken language and reading abilities in Kannada-speaking post-stroke survivors with acquired alexia. The study recruited 15 Kannada-speaking adults with complaints of reading impairment post-ictus through convenient sampling. The enrolled participants included both males and females with a mean age of 42.2 years (SD=15.58; range=20-68) with 16 years of formal education. The study performed three primary investigations; a) linguistic, b) oral reading, and c) reading comprehension. Linguistic tasks entailed semantics, syntax, and phonological tasks. Oral reading entailed real word and non-word reading tasks. Reading comprehension tasks entailed single-word level, sentence-level, and paragraph-level tasks. Results of overall domain performance across linguistics, oral reading, and reading comprehension revealed superior performance in linguistics (M=71.77, SD=19.18) followed by reading comprehension (M=70.55, SD=24.10) and oral reading (M=41.55, SD=24.66), which was significant (p<0.05). Performance in phonology was weakest (M=58.06, SD=12.44) compared to syntax (M=71.13) and semantics (M=85.33) on comparing PWAs' abilities within the linguistic domain. Reading abilities were compared across oral reading and reading comprehension, which significantly varied (p<0.05), and reading comprehension abilities were superior (M=72.65, SD=24.10). Task-specific variabilities were significant (p<0.05) in both oral reading and reading comprehension, wherein, real word reading (M=58.22) and comprehension at a single level (M=80.22) were better performed. Results of correlation analysis revealed semantics (r=0.494, p<0.05) and phonology (r=0.428, p>0.05) were highly positively correlated to oral reading abilities. Syntax (r=0.412, p>0.05), and semantics (r=0.377, p>0.05) were strongly positively correlated to reading comprehension abilities. The study has convincingly shown that performance on reading-related tasks reflects the functionality of central semantic, phonological, and syntactic processing elements. The literate adults routinely interact with both spoken and written language and a comprehensive assessment framework of language processing must encompass both modalities (linguistic and reading) for individuals with alexia, specific to Indian languages. Owing to the transparent writing system of the Kannada language, a multimodal phonological and lexical-based reading treatment may prove to be beneficial in remediating reading impairments among adult post-stroke survivors.

Use of Infliximab in the Treatment of Immune Checkpoint Inhibitors-Related Myocarditis: A Systematic Review of Case Reports

Immune-mediated myocarditis is uncommon, progresses rapidly and has high mortality rate. Infliximab has been used in practice, particularly in steroid refractory cases, but evidence supporting the use is still lacking. This review aims to provide an overview of infliximab use in managing immune-related myocarditis. A systematic search was conducted using the PubMed and Cochrane Library databases as well as manual searching of bibliographies, from inception to 1 October 2021. Eligible studies were selected by the inclusion criteria. All the included studies were assessed for methodological quality using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Case Reports. There were 13 articles with total 14 reported cases of immune-related myocarditis treated with infliximab. Most patients developed myocarditis with high severity grade, concurrent immune-related adverse events (irAEs), particularly neuromuscular irAEs and multiple potentially fatal complications. Most patients received single dose of infliximab, whereas few received multiple doses. Four patients improved clinically or biochemically, but 10 patients did not improve or deteriorated clinically with the addition of infliximab. Half of the reported cases did not survive due to myocarditis and its complications. Conflicting results were observed when infliximab was used in patients with heart failure and higher dose did not appear to be beneficial in these patients. Concomitant irAEs, high severity grade and occurrence of potentially fatal complications indicate the need to escalate the treatment by using additional immunosuppressive agents such as infliximab. However, there is no standardisation on the infliximab treatment regimen, outcomes have been inconsistent and safety data is limited.

Non-identical parallel machines batch processing problem to minimize the makespan: Models and algorithms

This paper studies a parallel heterogeneous machine batching and scheduling problem in which weighted jobs are first batched, and the batches are then assigned and sequenced on machines of varying capacities. The duration of a batch is the longest time needed to process a job, and the objective is that of minimizing the makespan, or the sum of the batches durations on the machine finishing last. The authors develop polynomial-size mathematical formulations and a variable neighborhood search metaheuristic. Extensive computational results suggest that a flow-based formulation outperforms a compact formulation, despite its larger number of variables. The metaheuristic is capable of producing high-quality solutions within a limited computing time.

Synchronisation of operations and maintenance in distributed unrelated parallel machine systems: a hierarchical optimisation framework

ABSTRACT Synchronisation of operations and maintenance means that production and maintenance departments work together effectively to achieve integrated optimisation of production scheduling and maintenance activities. This paper addresses such an integrated optimisation problem towards distributed unrelated parallel machine systems, where deteriorating processing time and machine failures are considered. For the trade-off between production and maintenance to obtain a minimal makespan in the worst-case scenario, a hierarchical optimisation framework with reinforcement learning-based encoder and knowledge-based intelligent optimiser (HOFRK) is developed. Experimental results show that HOFRK can be treated as an excellent optimiser for enterprise information systems to solve the proposed problem.

Parallel virus machines

Within the branch of Natural Computing, several models or types of computation arise, some of them are well-established such as neural networks, evolutionary computing or membrane computing, while others have yet to be studied and developed. One such model is virus machines, which draws inspiration from the replication and transmission mechanisms of viruses. This model has been successfully applied to mathematical problems, supported by its robust formal structure and the verification of various parallel virus machines. Parallelism has been one of the milestones in several branches of Natural Computing. This paper presents a novel extension: parallel virus machines. Furthermore, several semantics are studied to fix possible ambiguities related to this new variant. Finally, a comparison with well-established neural-like systems, called spiking neural P systems, is discussed.

A mathematical model and solution algorithms for optimising system-level configurations of reconfigurable single part flow lines

This study addresses a system-level configuration selection problem for reconfigurable single part flow lines (R-SPFLs) with parallel identical flexible machines at each stage. The problem is to determine the number of stages, the number of machines of a certain type at each stage and the assignment of operations to each stage in order to satisfy the demand of a part type. Precedence relations among the operations and space limitations are also considered. For the objective of minimising the sum of machine purchase and operation processing costs, a nonlinear integer programming model is developed that gives the optimal solutions. Then, due to the problem's complexity, a basic variable neighbourhood search algorithm is proposed that constructs an initial solution using a greedy-type heuristic and improves it using a shaking and a local search improvement methods. Moreover, the basic algorithm is extended to a general one that uses a variable neighbourhood descent method. Computational results show that the two algorithms outperform the previous one significantly in both solution quality and computation times, and the general algorithm improves the basic one significantly. Finally, the applicability of the variable neighbourhood search algorithms is shown by reporting a brief case study.

The Parallel Compact Object CALculator: An Efficient General Relativistic Initial Data Solver for Compact Objects

Every numerical general relativistic investigation starts from the solution of the initial value equations at a given time. Astrophysically relevant initial values for different systems lead to distinct sets of equations that obey specific assumptions tied to the particular problem. Therefore, a robust and efficient solver for a variety of strongly gravitating sources is needed. In this work, we present the OpenMP version of the Compact Object CALculator (COCAL) on shared memory processors. We performed extensive profiling of the core COCAL modules in order to identify bottlenecks in efficiency, which we addressed. Using modest resources, the new parallel code achieves speedups of approximately one order of magnitude relative to the original serial COCAL code, which is crucial for parameter studies of computationally expensive systems such as magnetized neutron stars, as well as its further development towards more realistic scenarios. As a novel example of our new code, we compute a binary quark system where each companion has a dimensionless spin of 0.43 aligned with the orbital angular momentum.

Fair scheduling on parallel batch machines with non-identical capacities

The fairness of jobs from agents is considered in an advanced manufacturing factory and the fair scheduling of jobs on parallel batch machines is studied for two competing agents. Each agent and the system aim to minimize the makespan of their own jobs and the weighted sum makespans of the two agents, respectively. A modified full-batch longest processing time (MFBLPT) algorithm is proposed to minimize the makespan, and a Kalai–Smorodinsky fairness (KSF) algorithm is proposed to obtain fair schedules. These algorithms are analysed based on worst-case ratios, and the results show that these ratios are related to the workloads and importance of agents. Computational experiments are conducted to compare the performance of algorithm KSF with that of machine-centric and agent-centric algorithms. The results show that the pursuance of fairness leads to interest losses of a system, and that a proper preference of an agent leads to good performance of algorithm KSF.

Combinatorial optimization methods for yarn dyeing planning

AbstractManaging yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.

A K-means-Teaching Learning based optimization algorithm for parallel machine scheduling problem

With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale scheduling problems. A data mining method was proposed for industrial big data to solve the problem of large-scale parallel machines scheduling. This methodology can obtain an effective initial solution for single-operation parallel machine scheduling problem by exploring the effective information in the historical scheduling data. Based on historical customer orders, the offline learning was used to continuously generate simulated data for learning, which makes up for the shortcomings of insufficient data. A TLBO framework (teaching-learning-based optimization) hybrid K-means algorithm was redesigned to enhance the accuracy of offline learning and the efficiency of data searching. In the online operation part, according to the optimal solutions for high-similarity manufacturing orders are the approximate solutions, the new customer order will be quickly matched with the most similar manufacturing order through similarity calculation, and then and then local search is performed. Finally, the globally optimal solution is obtained after screening. Experimental results show that the hybrid teaching–learning methodology can solve the large-scale parallel machines scheduling problem with a better learning performance and computational efficiency.

Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package

We propose a parallel (distributed) version of the spectral proper orthogonal decomposition (SPOD) technique. The parallel SPOD algorithm distributes the spatial dimension of the dataset preserving time. This approach is adopted to preserve the non-distributed fast Fourier transform of the data in time, thereby avoiding the associated bottlenecks. The parallel SPOD algorithm is implemented in the PySPOD library and makes use of the standard message passing interface (MPI) library, implemented in Python via mpi4py. An extensive performance evaluation of the parallel package is provided, including strong and weak scalability analyses. The open-source library allows the analysis of large datasets of interest across the scientific community. Here, we present applications in fluid dynamics and geophysics, that are extremely difficult (if not impossible) to achieve without a parallel algorithm. This work opens the path toward modal analyses of big quasi-stationary data, helping to uncover new unexplored spatio-temporal patterns. Program summaryProgram Title: PySPODCPC Library link to program files:https://doi.org/10.17632/jf5bf26jcj.1Developer's repository link:https://github.com/MathEXLab/PySPODLicensing provisions: MIT LicenseProgramming language: PythonNature of problem: Large spatio-temporal datasets may contain coherent patterns that can be leveraged to better understand, model, and possibly predict the behavior of complex dynamical systems. To this end, modal decomposition methods, such as the proper orthogonal decomposition (POD) and its spectral counterpart (SPOD), constitute powerful tools. The SPOD algorithm allows the systematic identification of space-time coherent patterns. This can be used to understand better the physics of the process of interest, and provide a path for mathematical modeling, including reduced order modeling. The SPOD algorithm has been successfully applied to fluid dynamics, geophysics and other domains. However, the existing open-source implementations are serial, and they prevent running on the increasingly large datasets that are becoming available, especially in computational physics. The inability to analyze via SPOD large dataset in turn prevents unlocking novel mechanisms and dynamical behaviors in complex systems.Solution method: We provide an open-source parallel (MPI distributed) code, namely PySPOD, that is able to run on large datasets (the ones considered in the present paper reach about 200 Terabytes). The code is built on the previous serial open-source code PySPOD that was published in https://joss.theoj.org/papers/10.21105/joss.02862.pdf. The new parallel implementation is able to scale on several nodes (we show both weak and strong scalability) and solve some of the bottlenecks that are commonly found at the I/O stage. The current parallel code allows running on datasets that was not easy or possible to analyze with serial SPOD algorithms, hence providing a path towards unlocking novel findings in computational physics.Additional comments including restrictions and unusual features: The code comes with a set of built-in postprocessing tools, for visualizing the results. It also comes with extensive continuous integration, documentation, and tutorials, as well as a dedicated website in addition to the associated GiHub repository. Within the package we also provide a parallel implementation of the proper orthogonal decomposition (POD), that leverages the I/O parallel capabilities of the SPOD algorithm.

The climate crisis, policy distraction and support for fuel taxation

AbstractThe climate crisis looms but support for fuel taxation is low. How to boost support? The obvious way is to make the connection to the climate crisis explicit. Many observers fear, however, that policy myopia renders this strategy ineffective: As the consequences of the climate crisis are long‐term and insecure, people are loath to pay for costly countermeasures in the short term. We look at policy distraction as a second potential drag. We argue that climate crisis‐induced support for fuel taxation can also be undermined by other salient events which divert attention. To test our argument, we conduct a large‐scale survey experiment with more than 21,000 respondents in 17 European countries. Our results show that a simple climate crisis prime raises support for fuel taxation by 12 percentage points. The effect decreases but remains substantial when stressing the long time horizon of the climate crisis. It almost disappears when other current crises (COVID‐19 and Russian military aggression) are mentioned. Thus, distraction by concurrent events is a serious impediment to mobilising support for fuel taxation.