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The prevailing approaches to modelling interactive uncertainty with epistemic models in economics are state-based and type-based. We explicitly formulate two general procedures that transform state models into type models and vice versa. Both transformation procedures preserve the belief hierarchies as well as the common prior assumption. By means of counterexamples it is shown that the two procedures are not inverse to each other. However, if attention is restricted to maximally reduced epistemic models, then isomorphisms can be constructed and an inverse relationship emerges.

Matchings in a market may have varying degrees of compromise from efficiency, fairness, and or stability. A distance function allows to quantify such concepts or the (dis)similarity between any two matchings. There are a few attempts to propose such functions; however, these are tailored for specific applications and ignore the individual preferences completely. In this paper, we construct a normative framework to quantify the difference between outcomes of market mechanisms in matching markets, while endogenizing the preferences of the individuals into the distance concept. Several conditions are introduced to capture natural and appealing behavior of such functions. We find a class of distance functions called scaled Borda distances, which is the only class of distance functions that satisfies these conditions simultaneously.

Objectives: To investigate the association of polygenic risk score (PRS) and bladder cancer (BC) risk and whether this PRS can be offset by a healthy lifestyle. Methods: Individuals with BC (n = 563) and non-BC controls (n = 483 957) were identified in the UK Biobank, and adjusted Cox regression models were used. A PRS was constructed based on 34 genetic variants associated with BC development, while a healthy lifestyle score (HLS) was constructed based on three lifestyle factors (i.e., smoking, physical activity, and diet). Results: Overall, a negative interaction was observed between the PRS and the HLS (P = 0.02). A 7% higher and 28% lower BC risk per 1-standard deviation (SD) increment in PRS and HLS were observed, respectively. A simultaneous increment of 1 SD in both HLS and PRS was associated with a 6% lower BC risk. In addition, individuals with a high genetic risk and an unfavourable lifestyle showed an increased BC risk compared to individuals with low genetic risk and a favourable lifestyle (hazard ratio 1.55, 95% confidence interval 1.16–1.91; P for trend

Unit square visibility graphs (USV) are described by axis-parallel visibility between unit squares placed in the plane. If the squares are required to be placed on integer grid coordinates, then USV become unit square grid visibility graphs (USGV), an alternative characterisation of the well-known rectilinear graphs. We extend known combinatorial results for USGV and we show that, in the weak case (i.e., visibilities do not necessarily translate into edges of the represented combinatorial graph), the area minimisation variant of their recognition problem is $${{\,\mathrm{{\textsf{N}}{\textsf{P}}}\,}}$$ N P -hard. We also provide combinatorial insights with respect to USV, and as our main result, we prove their recognition problem to be $${{\,\mathrm{{\textsf{N}}{\textsf{P}}}\,}}$$ N P -hard, which settles an open question.

Developing a biobased chemical industry that copes with the current environmental challenges relies on understanding the influence of the different stages of the value chain on economic and environmental performance. This paper investigates the use of a life cycle optimization framework to assess trade-offs between environmental and economic criteria for designing supply chains for biobased polyethylene terephthalate as a case study. A multi-objective optimization model was formulated to account for the environmental impacts (through life cycle assessment) and total costs (through life cycle costing) of the case study. High environmental priority (w = 0.9) in the design of the supply chain resulted in environmental gains (in 8 out of 9 midpoint indicators) with low increments (

We present a new characterization of the class of weight-based scoring indices for ranking problems with top-truncated preferences. The main novel axiom is Splitting Invariance: if an alternative is split up into a number of distinct yet unranked alternatives, then the total score of these alternatives should increase by the score of the original alternative, and the scores of the other alternatives should not change. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

We present an $O(n^2)$-time algorithm to test whether an $n$-vertex directed partial $2$-tree is upward planar. This result improves upon the previously best known algorithm, which runs in $O(n^4)$ time., Appears in the Proceedings of the 30th International Symposium on Graph Drawing and Network Visualization (GD 2022)

The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive Vector AutoRegressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is "simplest" in a certain sense. A user-specified strongly convex penalty is used to measure model simplicity, and that same penalty is then used to define an estimator that can be efficiently computed. We establish consistency of our estimators in a double-asymptotic regime. Our non-asymptotic error bound analysis accommodates both model specification and parameter estimation steps, a feature that is crucial for studying large-scale VARMA algorithms. Our analysis also provides new results on penalized estimation of infinite-order VAR, and elastic net regression under a singular covariance structure of regressors, which may be of independent interest. We illustrate the advantage of our method over VAR alternatives on three real data examples.

Background Discrete choice experiments (DCEs) are frequently used to study preferences and quantify tradeoffs in decision making. It is important to understand how stable their results are. Objective To investigate to what extent an extreme change in context, the COVID-19 pandemic, affected preferences for vaccine priority setting, as observed in an earlier DCE. Methods We replicated a DCE in which participants had to prioritize vaccination programs for public funding. The initial DCE was executed in Flanders (Belgium) right before the onset of the SARS-CoV-2 pandemic (December 2019, N = 1,636). The replicated DCE was executed 6 months later when the population was in lockdown (April 2020, N = 1,127). A total of 612 respondents participated in both waves of the DCE. We used panel mixed logit models to quantify attribute and level importance and compared utility estimates for consistency. Results The number of vaccine-preventable deaths became less important during the pandemic than before, whereas the influential attributes, the vaccine’s contribution to disease eradication and certainty about vaccine effectiveness became even more important. Respondents attached equal importance to the number of patients with transient or permanent morbidity, to the disease’s economic impact as well as to its equity profile. Conclusion Different preferences for vaccine priority setting were observed during the first COVID-19 lockdown as compared with before, although these differences were, given the extreme nature of the changing circumstances, relatively small. Highlights We replicated a discrete choice experiment (DCE) about vaccine priority setting during the first COVID-19 lockdown and compared results with those from the original setting. The major attributes, contribution to disease eradication, and scientific certainty about vaccine effectiveness became even more important than they already were, whereas avoidable mortality became less important. Respondents attached equal importance to the number of patients with transient or permanent morbidity, to the disease’s economic impact as well as to its equity profile. An extreme change in directly related context to the choice assignment led to changes in stated preferences, although these changes were relatively small, given the extreme change in context. Priorities in the second DCE were even less aligned with cost-effectiveness analysis than those observed initially.

This paper studies Davis–Maschler reduced games of two-bound core games and shows that all these reduced games with respect to core elements are two-bound core games with the same pair of bounds. Based on associated reduced game properties, we axiomatically characterize the core, the nucleolus, and the egalitarian core for two-bound core games. Moreover, we show that the egalitarian core for two-bound core games is single-valued and we provide an explicit expression.

Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies. However, as the number of series and high-frequency observations per low-frequency period grow, MF-VARs suffer from the "curse of dimensionality". We curb this curse through a regularizer that permits hierarchical sparsity patterns by prioritizing the inclusion of coefficients according to the recency of the information they contain. Additionally, we investigate the presence of nowcasting relations by sparsely estimating the MF-VAR error covariance matrix. We study predictive Granger causality relations in a MF-VAR for the U.S. economy and construct a coincident indicator of GDP growth. Supplementary Materials for this article are available online., Forthcoming in Journal of Computational and Graphical Statistics

The Kemeny rule is one of the well studied decision rules. In this paper we show that the Kemeny rule is the only rule which is unbiased, monotone, strongly tie-breaking, strongly gradual, and weighed tournamental. We show that these conditions are logically independent.

The complexity of computing the flip distance between two triangulations of a simple convex polygon is unknown. Here we approach the problem from a parameterized complexity perspective and improve upon the 2k kernel of Lucas [12]. Specifically, we describe a kernel of size 4k3 and then show how it can be improved to (1 + c)k for every constant c > 0. By ensuring that the kernel consists of a single instance our result yields a kernel of the same magnitude (up to additive terms) for the almost equivalent rotation distance problem on rooted, ordered binary trees. The earlier work of Lucas left the kernel as a disjoint set of instances, potentially allowing very minor differences in the definition of the size of instances to accumulate, causing a constant-factor distortion in the kernel size when switching between flip distance and rotation distance formulations. Our approach avoids this sensitivity. We have also undertaken experiments to understand how much reduction is achieved by our kernel in practice.(c) 2023 The Author(s).

In platelets, elevated cytosolic Ca 2+ is a crucial second messenger, involved in most functional responses, including shape change, secretion, aggregation and procoagulant activity. The platelet Ca 2+ response consists of Ca 2+ mobilization from endoplasmic reticulum stores, complemented with store-operated or receptor-operated Ca 2+ entry pathways. Several channels can contribute to the Ca 2+ entry, but their relative contribution is unclear upon stimulation of ITAM-linked receptors such as glycoprotein VI (GPVI) and G-protein coupled receptors such as the protease-activated receptors (PAR) for thrombin. We employed a 96-well plate high-throughput assay with Fura-2-loaded human platelets to perform parallel [Ca 2+] i measurements in the presence of EGTA or CaCl 2. Per agonist condition, this resulted in sets of EGTA, CaCl 2 and Ca 2+ entry ratio curves, defined by six parameters, reflecting different Ca 2+ ion fluxes. We report that threshold stimulation of GPVI or PAR, with a variable contribution of secondary mediators, induces a maximal Ca 2+ entry ratio of 3-7. Strikingly, in combination with Ca 2+-ATPase inhibition by thapsigargin, the maximal Ca 2+ entry ratio increased to 400 (GPVI) or 40 (PAR), pointing to a strong receptor-dependent enhancement of store-operated Ca 2+ entry. By pharmacological blockage of specific Ca 2+ channels in platelets, we found that, regardless of GPVI or PAR stimulation, the Ca 2+ entry ratio was strongest affected by inhibition of ORAI1 (2-APB, Synta66) > Na +/Ca 2+ exchange (NCE) > P2× 1 (only initial). In contrast, inhibition of TRPC6, Piezo1/2 or STIM1 was without effect. Together, these data reveal ORAI1 and NCE as dominating Ca 2+ carriers regulating GPVI- and PAR-induced Ca 2+ entry in human platelets.

ObjectivesThe COVID-19 pandemic forms an unprecedented public health, economic, and social crisis. Uptake of vaccination is critical for controlling the pandemic. Nevertheless, vaccination hesitancy is considerable, requiring policies to promote uptake. We investigate Dutch citizens’ preferences for policies that aim to promote vaccination through facilitating choice of vaccination, profiling it as the norm, making vaccination more attractive through rewards, or punishing people who reject vaccination.MethodsWe conducted a discrete choice experiment in which 747 respondents were asked to choose between policies to promote vaccination uptake and their impacts on the number of deaths, people with permanent health problems, households with income loss, and a tax increase.ResultsRespondents generally had a negative preference for policies that promote vaccination. They particularly disliked policies that punish those who reject the vaccine and were more favorable toward policies that reward vaccination, such as awarding additional rights to vaccinated individuals through vaccination passports. Respondents who reject vaccination were in general much more negative about the policy options than respondents who consider accepting the vaccine. Nevertheless, vaccination passports are supported by both respondents who accept the vaccine, those who reject vaccination, and those who are unsure about vaccination.ConclusionsThis study provides concrete directions for governments attempting to increase the vaccination uptake in ways that are supported by the public. Our results could encourage policy makers to focus on policy options that make vaccination easier and reward people who take the vaccine, as especially the implementation of vaccination passports was supported.

A rectangular dual of a graph G is a contact representation of G by axis-aligned rectangles such that (i) no four rectangles share a point and (ii) the union of all rectangles is a rectangle. The partial representation extension problem for rectangular duals asks whether a given partial rectangular dual can be extended to a rectangular dual, that is, whether there exists a rectangular dual where some vertices are represented by prescribed rectangles. The simultaneous representation problem for rectangular duals asks whether two (or more) given graphs that share a subgraph admit rectangular duals that coincide on the shared subgraph. Combinatorially, a rectangular dual can be described by a regular edge labeling (REL), which determines the orientations of the rectangle contacts.We describe linear-time algorithms for the partial representation extension problem and the simultaneous representation problem for rectangular duals when each input graph is given together with a REL. Both algorithms are based on formulations as linear programs, yet they have geometric interpretations and can be seen as extensions of the classic algorithm by Kant and He that computes a rectangular dual for a given graph. (C) 2022 Elsevier B.V. All rights reserved.

The COVID-19 pandemic has drastically impacted people's travel behaviour and introduced uncertainty in the demand for public transport. To investigate user preferences for travel by London Underground during the pandemic, we conducted a stated choice experiment among its pre-pandemic users (N = 961). We analysed the collected data using multinomial and latent class logit models. Our discrete choice analysis provides two sets of results. First, we derive the crowding multiplier estimate of travel time valuation (i.e., the ratio of the value of travel time in uncrowded and crowded situations) for London underground users. The results indicate that travel time valuation of Underground users increases by 73% when it operates at technical capacity. Second, we estimate the sensitivity of the preference for the London Underground relative to the epidemic situation (confirmed new COVID-19 cases) and interventions (vaccination rates and mandatory face masks). The sensitivity analysis suggests that making face masks mandatory is a main driver for recovering the demand for the London underground. The latent class model reveals substantial preference heterogeneity. For instance, while the average effect of mandatory face masks is positive, the preferences of 30% of pre-pandemic users for travel by the Underground are negatively affected. The positive effect of mandatory face masks on the likelihood of taking the Underground is less pronounced among males with age below 40 years, and a monthly income below 10,000 GBP. The estimated preference sensitivities and crowding multipliers are relevant for supply–demand management in transit systems and the calibration of advanced epidemiological models.

Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.

This paper studies the routing of multiple commodities (shipments) through a network with the aim to minimize the total cost. To transport these commodities from their origin to their destination hub, a combination of different services can be used, including scheduled trucks (following a dedicated trajectory, similar to bus routes) and express delivery. Each commodity starts its itinerary at its origin hub and needs to arrive at its destination hub before its deadline. The following cost factors are considered in the model: a fixed cost as well as a distance-based travel cost for the scheduled truck services, a cost for express delivery between each pair of hubs based on the size of the commodity, and the inventory holding cost at each hub. We first define the problem as a mixed-integer linear program (MILP). To solve this MILP, we apply a branch-and-price algorithm that relies on column generation. In a second phase, we extend our model formulation to also deal with demand uncertainty (i.e., the size of each shipment varies) and present a two-stage, scenario-based stochastic model which we also solve using the branch-and-price algorithm. To generate the scenarios for the stochastic model, we apply Sample Average Approximation (SAA). Extensive computational experiments, including a sensitivity analysis are presented.

This paper presents an improved exact algorithm for solving the order picking problem, a special case of the planar Travelling Salesperson Problem. The algorithm heavily relies on graph reduction techniques: it removes unnecessary vertices and edges from the planar graph that are not necessary in the optimal solution. As a result, we achieve a significant increase in calculation speed and reduction in the running time. The order pickers routing problem entails collecting items from storage in response to customer requests. We use the Traveling Salesperson Problem (TSP) to optimize the routes taken by order pickers. In the literature, exact algorithms — typically based on dynamic programming — only exist for small warehouses with a small number of blocks (two), while for larger warehouse layouts mainly heuristic and metaheuristic methods are provided. The presented graph reduction method allows us to adequately solve larger — more realistic — instances in a short amount of time. Our algorithm is tested on different problem instances from the literature and its performance is compared with the current state-of-the-art. We conclude that our algorithm outperforms existing algorithms in terms of simplicity, size and calculation time.

This paper introduces the new class of one-bound core games, where the core can be described by either a lower bound or an upper bound on the payoffs of the players, named lower bound core games and upper bound core games, respectively. We study the relation of the class of one-bound core games with several other classes of games and characterize the new class by the structure of the core and in terms of Davis-Maschler reduced games. We also provide explicit expressions and axiomatic characterizations of the nucleolus for one-bound core games, and show that the nucleolus coincides with the Shapley value when these games are convex.

Background: The optimal way to determine repolarization time (RT) from the intracardiac unipolar electrogram (UEG) has been a topic of debate for decades. RT is typically determined by either the Wyatt method or the "alternative method," which both consider UEG T-wave slope, but differently.Objective: To determine the optimal method to measure RT on the UEG.Methods: Seven pig hearts surrounded by an epicardial sock with 100 electrodes were Langendorff-perfused with selective cannulation of the left anterior descending (LAD) coronary artery and submersed in a torso-shaped tank containing 256 electrodes on the torso surface. Repolarization was prolonged in the non-LAD-regions by infusing dofetilide and shortened in the LAD-region using pinacidil. RT was determined by the Wyatt (t(Wyatt)) and alternative (t(Alt)) methods, in both invasive (recorded with epicardial electrodes) and in non-invasive UEGs (reconstructed with electrocardiographic imaging). t(Wyatt) and t(Alt) were compared to local effective refractory period (ERP).Results: With contact mapping, mean absolute error (MAE) of t(Wyatt) and t(Alt) vs. ERP were 21 ms and 71 ms, respectively. Positive T-waves typically had an earlier ERP than negative T-waves, in line with theory. t(Wyatt) -but not t(Alt)-shortened by local infusion of pinacidil. Similar results were found for the non-invasive UEGs (MAE of t(Wyatt) and t(Alt) vs. ERP were 30 ms and 92 ms, respectively).Conclusion: The Wyatt method is the most accurate to determine RT from (non) invasive UEGs, based on novel and historical analyses. Using it to determine RT could unify and facilitate repolarization assessment and amplify its role in cardiac electrophysiology. The authors would like to thank Ruben Coronel, MD, PhD, Amsterdam UMC and IHU Liryc, Bordeaux, Bastiaan J. Boukens, PhD, CARIM, Maastricht University and Amsterdam UMC, and André G. Kléber, MD, PhD, BIDMC, Harvard Medical School, Boston, for their useful discussions on T-wave genesis and RT determination in the UEG, and Roel L.H.M.G. Spätjens, BSc, Maastricht University Medical Center+, for his suggestions on the figures. Moreover, the authors wish to thank Michele Orini, PhD, UC London and Barts Heart Centre, London, for providing data from a previous study (Orini et al., 2019) for additional analyses

We study situations where agents can form or sever links in a network: what agents can do exactly is de-scribed by effectivity functions. A power index assigns to such an effectivity function a number for each agent, measuring the opportunities of that agent. We characterize a class of power indices by four ax-ioms: the Transfer Property, the Dummy Property, Symmetry, and Network Neutrality. As a corollary, we obtain power indices for the case where effectivity functions are induced by preferences of agents about the other agents. Applications include one-to-one, one-to-many, and many-to-many matching markets, as well as roommate problems.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

BACKGROUND AND AIMS: This study aims to identify sex-specific transcriptional differences and signaling pathways in circulating monocytes contributing to cardiovascular disease.METHODS AND RESULTS: We generated sex-biased gene expression signatures by comparing male versus female monocytes of coronary artery disease (CAD) patients (n = 450) from the Center for Translational Molecular Medicine-Circulating Cells Cohort. Gene set enrichment analysis demonstrated that monocytes from female CAD patients carry stronger chemotaxis and migratory signature than those from males. We then inferred cytokine signaling activities based on CytoSig database of 51 cytokine and growth factor regulation profiles. Monocytes from females feature a higher activation level of EGF, IFN1, VEGF, GM-CSF, and CD40L pathways, whereas IL-4, INS, and HMGB1 signaling was seen to be more activated in males. These sex differences were not observed in healthy subjects, as shown for an independent monocyte cohort of healthy subjects (GSE56034, n = 485). More pronounced GM-CSF signaling in monocytes of female CAD patients was confirmed by the significant enrichment of GM-CSF-activated monocyte signature in females. As we show these effects were not due to increased plasma levels of the corresponding ligands, sex-intrinsic differences in monocyte signaling regulation are suggested. Consistently, regulatory network analysis revealed jun-B as a shared transcription factor activated in all female-specific pathways except IFN1 but suppressed in male-activated IL-4.CONCLUSIONS: We observed overt CAD-specific sex differences in monocyte transcriptional profiles and cytokine- or growth factor-induced responses, which provide insights into underlying mechanisms of sex differences in CVD.

Algorithmic decision-making (ADM) systems are heavily used by businesses, governments, and the nonprofit sector. Their adoption by the public is important for their optimal performance, but the impact of user perceptions on ADM system adoption is not well-understood. We develop a theoretical model that examines the effect of a user’s transparency concern on perceived fairness, accountability, and privacy; it then captures the effect of these on trust in, usefulness of, and finally, intention to adopt an ADM system. We use results from a large-scale survey among 2612 Dutch citizens to test our research model. Our results shed light on the role of transparency concern on trust and perceived usefulness through its impact on perceived fairness, accountability, and privacy. The survey data of a large representative group enables us to capture public views on ADM systems. Finally, the study provides a comparative view of ADM system perceptions for different application scenarios. Our insights on differences between scenarios can help organizations prioritize relevant measures to improve the adoption of their ADM systems.

We develop a Feasible Generalized Least Squares estimator of the date of a structural break in level and/or trend. The estimator is based on a consistent estimate of a T-dimensional inverse autocovariance matrix. A cubic polynomial transformation of break date estimates can be approximated by a nonstandard yet nuisance parameter free distribution asymptotically. The new limiting distribution captures the asymmetry and bimodality in finite samples and is applicable for inference with a single, known, set of critical values. We consider the confidence intervals/sets for break dates based on both Wald-type tests and by inverting multiple likelihood ratio (LR) tests. A simulation study shows that the proposed estimator increases the empirical concentration probability in a small neighborhood of the true break date and potentially reduces the mean squared errors. The LR-based confidence intervals/sets have good coverage while maintaining informative length even with highly persistent errors and small break sizes.

A morph is a continuous transformation between two representations of a graph. We consider the problem of morphing between contact representations of a plane graph. In an $${\mathcal {F}}$$ F -contact representation of a plane graph G, vertices are realized by internally disjoint elements from a family $${\mathcal {F}}$$ F of connected geometric objects. Two such elements touch if and only if their corresponding vertices are adjacent. These touchings also induce the same embedding as in G. In a morph between two $${\mathcal {F}}$$ F -contact representations we insist that at each time step (continuously throughout the morph) we have an $${\mathcal {F}}$$ F -contact representation. We focus on the case when $$\mathcal {F}$$ F is the family of triangles in $$\mathbb {R}^2$$ R 2 that are the lower-right half of axis-parallel rectangles. Such RT-representations exist for every plane graph and right triangles are one of the simplest families of shapes supporting this property. Moreover, they naturally correspond to 3-orientations. Thus, they provide a natural case to study regarding morphs of contact representations of plane graphs. We characterize the pairs of RT-representations admitting a morph between each other via the respective 3-orientations. Our characterization leads to a polynomial-time algorithm to decide whether there is a morph between two RT-representations of an n-vertex plane triangulation, and, if so, computes a morph with $${\mathcal {O}}(n^2)$$ O ( n 2 ) steps. Each of these steps is a linear morph moving the endpoints of each triangle at constant speed along straight-line trajectories. Our characterization also implies that for 4-connected plane triangulations there is a morph between every pair of RT-representations where the “top-most” triangle in both representations corresponds to the same vertex.

Receptor diffusion plays an essential role in cellular signalling via the plasma membrane microenvironment and receptor interactions, but the regulation is not well understood. To aid in understanding of the key determinants of receptor diffusion and signalling, we developed agent-based models (ABMs) to explore the extent of dimerisation of the platelet- and megakaryocyte-specific receptor for collagen glycoprotein VI (GPVI). This approach assessed the importance of glycolipid enriched raft-like domains within the plasma membrane that lower receptor diffusivity. Our model simulations demonstrated that GPVI dimers preferentially concentrate in confined domains and, if diffusivity within domains is decreased relative to outside of domains, dimerisation rates are increased. While an increased amount of confined domains resulted in further dimerisation, merging of domains, which may occur upon membrane rearrangements, was without effect. Modelling of the proportion of the cell membrane which constitutes lipid rafts indicated that dimerisation levels could not be explained by these alone. Crowding of receptors by other membrane proteins was also an important determinant of GPVI dimerisation. Together, these results demonstrate the value of ABM approaches in exploring the interactions on a cell surface, guiding the experimentation for new therapeutic avenues.

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug’s efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 $$-$$ - 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 $$-$$ - 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable, and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe, and find strong performance gains from using our framework against several industry benchmarks, across all geographical regions, loss functions, and both pre- and post-Covid periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs.

This paper proposes concepts and methods to investigate whether the bubble patterns observed in individual time series are common among them. Having established the conditions under which common bubbles are present within the class of mixed causal–noncausal vector autoregressive models, we suggest statistical tools to detect the common locally explosive dynamics in a Student t-distribution maximum likelihood framework. The performances of both likelihood ratio tests and information criteria were investigated in a Monte Carlo study. Finally, we evaluated the practical value of our approach via an empirical application on three commodity prices.

In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement, and (iii) renewable resource allocation maximizing the total output of a research unit. Suggested resource allocation guarantees the optimal output under strong economic assumptions: the agents are rational, collaborative and have no incentives to behave selfishly. In reality, however, agents often play strategically maximizing their own utilities, e.g., maximizing the resources assigned to them. This strategic behavior is typically mitigated by implementation of performance-driven or uniform resource allocation schemes. Next to the framework presentation, we address the cost of such mitigation.

We consider a model where the same group of players is involved in more than one cooperative (transferable utility) game. A rule determines the payoffs per game, and for each player a utility function evaluates the resulting vector of payoffs. We assume that each player, independently, can make transfers of worth between different games, thereby affecting its payoff vector and, thus, utility. Two transfer systems are considered, resulting in two distinct noncooperative games, and the focus of the paper is on establishing existence and a characterization of Nash equilibria in these games. (C) 2022 The Author(s). Published by Elsevier Inc.

We call a decision maker risk averse for losses if that decision maker is risk averse with respect to lotteries having alternatives below a given reference alternative in their support. A two-person bargaining solution is called invariant under risk aversion for losses if the assigned outcome does not change after correcting for risk aversion for losses with this outcome as pair of reference levels, provided that the disagreement point only changes proportionally. We present an axiomatic characterization of the Nash bargaining solution based on this condition, and we also provide a decision-theoretic characterization of the concept of risk aversion for losses.

Horizontal cooperation in logistics has gathered momentum in the last decade as a way to reach economic as well as environmental benefits. In the literature, these benefits are most often assessed by aggregating all demand and then optimizing the supply chain at the level of the coalition. However, such an approach ignores the individual preferences of the participating companies and forces them to agree on a unique coalition objective. Companies with different (potentially conflicting) preferences could improve their individual outcome by diverging from this joint solution. In order to prevent such individualistic behavior, we propose an optimization framework that explicitly accounts for the individual partners’ interests. In the models presented in this paper, all partners are allowed to specify their preferences regarding the decrease in logistical costs versus reduced CO $$_{2}$$ emissions. Consequently, all stakeholders are more likely to accept the solution, and the long-term viability of the collaboration is improved. The contribution of our work is threefold. First, we formulate a multi-partner multi-objective location-inventory model. Second, we distinguish two approaches to solve such a multi-partner multi-objective optimization problem, each focusing primarily on a single dimension. The result is a set of Pareto-optimal solutions that support the decision and negotiation process. Third, we propose and compare three different solution techniques to construct a unique solution which is fair and efficient for the coalition. Our numerical experiments not only confirm the potential of collaboration but—more importantly—also reveal valuable managerial insights on the effect of dissimilarities between partners with respect to size, geographical overlap and operational preferences.

The linear regression model remains an important workhorse for data scientists. However, many data sets contain many more predictors than observations. Besides, outliers, or anomalies, frequently occur. This paper proposes an algorithm for regression analysis that addresses these features typical for big data sets, which we call “sparse shooting S”. The resulting regression coefficients are sparse, meaning that many of them are set to zero, hereby selecting the most relevant predictors. A distinct feature of the method is its robustness with respect to outliers in the cells of the data matrix. The excellent performance of this robust variable selection and prediction method is shown in a simulation study. A real data application on car fuel consumption demonstrates its usefulness.

In this study, we provide a characterization of the class of proportional bargaining solutions introduced by Kalai. Our result differs from earlier axiomatizations in that we use a property that we label individual disagreement point concavity. This property is a weakening of disagreement point concavity used by Chun and Thomson. An application illustrates the potential usefulness of our new property in a strategic setting.

We develop a dual-control method for approximating investment strategies in multidimensional financial markets with convex trading constraints. The method relies on a projection of the optimal solution to an (unconstrained) auxiliary problem to obtain a feasible and near-optimal solution to the original problem. We obtain lower and upper bounds on the optimal value function using convex duality methods. The gap between the bounds indicates the precision of the near-optimal solution. We illustrate the effectiveness of our method in a market with different trading constraints such as borrowing, short-sale constraints and non-traded assets. We also show that our method works well for state-dependent utility functions.

Inspired by recent terrorist attacks on cereal production fields in Iraq, we introduce and study two types of Stackelberg games. In these games, the leader wants to maximize its production (e.g., cereal), while the follower tries to destroy this production as much as possible. In the first model, the leader can protect its production by spreading his production resources over multiple regions. In the second model, the leader can also decide to allocate some extra protection resources to the regions. For both games, we are interested in a follower's and leader's optimal strategy. We characterise optimal strategies for the follower and present two linear time algorithms (one for each game) that find an optimal strategy for the leader. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Phylogenetic trees are leaf-labelled trees used to model the evolution of species. Here we explore the practical impact of kernelization (i.e. data reduction) on the NP-hard problem of computing the TBR distance between two unrooted binary phylogenetic trees. This problem is better-known in the literature as the maximum agreement forest problem, where the goal is to partition the two trees into a minimum number of common, non-overlapping subtrees. We have implemented two well-known reduction rules, the subtree and chain reduction, and five more recent, theoretically stronger reduction rules, and compare the reduction achieved with and without the stronger rules. We find that the new rules yield smaller reduced instances and thus have clear practical added value. In many cases they also cause the TBR distance to decrease in a controlled fashion. Next, we compare the achieved reduction to the known worst-case theoretical bounds of 15k-9 and 11k-9 respectively, on the number of leaves of the two reduced trees, where k is the TBR distance, observing in both cases a far larger reduction in practice. As a by-product of our experimental framework we obtain a number of new insights into the actual computation of TBR distance. We find, for example, that very strong lower bounds on TBR distance can be obtained efficiently by randomly sampling certain carefully constructed partitions of the leaf labels, and identify instances which seem particularly challenging to solve exactly. The reduction rules have been implemented within our new solver Tubro which combines kernelization with an Integer Linear Programming (ILP) approach. Tubro also incorporates a number of additional features, such as a cluster reduction and a practical upper-bounding heuristic, and it can leverage combinatorial insights emerging from the proofs of correctness of the reduction rules to simplify the ILP., Comment: Updated version. New figures and analysis. Accepted for journal publication

We critically assess the representative consumer with quadratic aggregate utility function which forms the foundation of a well-known class of linear oligopoly demand structures. It is argued that this approach is problematic and redundant. Regarding the latter, we show how the same demand system can be derived directly from a population of heterogeneous buyers for any number of products. Welfare analyses based on aggregate demand is shown to be sensitive to the underlying microfoundation.

Recent studies have concentrated on environmental and economic impacts of ships. In this regard, fuel and C O 2 emission is considered as one of the important factors for such impacts. In particular, the sailing speed of the vessels affects the fuel consumption and therefore the emission directly. In this study, we consider a speed optimization problem in inland waterway, which is characterized by stochastic waiting times at the lock caused by uncertainty in lock processing time estimations of other vessels. The objective is to minimize fuel consumption of an approaching ship, such that it traverses the river segment in a set deadline. We introduce a mathematical model for this problem and evaluate the effectiveness and attractiveness of two solution approaches: an optimal solution and a simple heuristic. This creates intuitive guidelines for skippers based on information provision to select an appropriate speed decision approach to minimize the total expected fuel consumption and C O 2 emission of inland waterway transportation.

In this paper we develop the use of monads, a concept popularised in the functional language Haskell, to provide a general framework for studying control systems with uncertainty. We first develop the theory of monads, notably dependent products and conditioning. We then provide three main applications, namely computing the input-output behaviour, designing observers, and studying compositionality for uncertain systems. Copyright (C) 2022 The Authors.

The human brain effortlessly solves the complex computational task of sound localization using a mixture of spatial cues. How the brain performs this task in naturalistic listening environments (e.g. with reverberation) is not well understood. In the present paper, we build on the success of deep neural net-works at solving complex and high-dimensional problems [1] to develop goal-driven, neurobiological-inspired convolutional neural network (CNN) models of human spatial hearing. After training, we visual-ize and quantify feature representations in intermediate layers to gain insights into the representational mechanisms underlying sound location encoding in CNNs. Our results show that neurobiological-inspired CNN models trained on real-life sounds spatialized with human binaural hearing characteristics can accu-rately predict sound location in the horizontal plane. CNN localization acuity across the azimuth resem-bles human sound localization acuity, but CNN models outperform human sound localization in the back. Training models with different objective functions -that is, minimizing either Euclidean or angular dis-tance -modulates localization acuity in particular ways. Moreover, different implementations of binaural integration result in unique patterns of localization errors that resemble behavioral observations in humans. Finally, feature representations reveal a gradient of spatial selectivity across network layers, starting with broad spatial representations in early layers and progressing to sparse, highly selective spa-tial representations in deeper layers. In sum, our results show that neurobiological-inspired CNNs are a valid approach to modeling human spatial hearing. This work paves the way for future studies combining neural network models with empirical measurements of neural activity to unravel the complex compu-tational mechanisms underlying neural sound location encoding in the human auditory pathway. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

In a planar L-drawing of a directed graph (digraph) each edge e is represented as a polyline composed of a vertical segment starting at the tail of e and a horizontal segment ending at the head of e. Distinct edges may overlap, but not cross. Our main focus is on bimodal graphs, i.e., digraphs admitting a planar embedding in which the incoming and outgoing edges around each vertex are contiguous. We show that every plane bimodal graph without 2-cycles admits a planar L-drawing. This includes the class of upward-plane graphs. Finally, outerplanar digraphs admit a planar L-drawing - although they do not always have a bimodal embedding - but not necessarily with an outerplanar embedding., Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020)

The Shapley value is the solution concept in cooperative game theory that is most used in both theoretical and practical settings. Unfortunately, in general, computing the Shapley value is computationally intractable. This paper focuses on computing the Shapley value of (weighted) connectivity games. For these connectivity games, which are defined on an underlying (weighted) graph, computing the Shapley value is $$\#\textsf {P}$$ # P -hard, and thus (likely) intractable even for graphs with a moderate number of vertices. We present an algorithm that can efficiently compute the Shapley value if the underlying graph has bounded treewidth. Next, we apply our algorithm to several real-world (covert) networks. We show that our algorithm can quickly compute exact Shapley values for these networks, whereas in prior work these values could only be approximated using a heuristic method. Finally, it is demonstrated that our algorithm can also efficiently compute the Shapley value time for several larger (artificial) benchmark graphs from the PACE 2018 challenge.

In this paper, we investigate market- and time-consistent valuation of life-insurance liabilities, which are long-dated by nature. To obtain a market- and time-consistent value, the "two-step market evaluation" introduced by Pelsser and Stadje (Math Finance 24:25-65, 2014) is used to evaluate a hybrid payoff with underlying hedgeable financial and (partially) unhedgeable actuarial risks. The resulting time-consistent and market-consistent (TCMC) price captures the dynamics of the risk drivers over the lifetime of the contract. We show that the EIOPA standard-formula for the risk-margin is not time-consistent, and we construct a time-consistent version of the risk-margin that captures the extra uncertainties from the process dynamics. EIOPA's standard-formula for the Risk-Margin is compared to the TCMC price for a simple unit-linked contract and we show that the effects of time-inconsistency are increasing with maturity and are significant for long-dated contracts.

We consider the estate division or bankruptcy problem and assume that players sequentially put claims on the estate. Each part of the estate is then divided proportionally with respect to the number of claims on it. We focus on myopic play: players first claim the hitherto least claimed parts, and on subgame perfect equilibria. Our main result is that myopic strategies constitute a subgame perfect equilibrium if punishments for deviators are included.