For many kinds of vehicle routing problems (VRPs), a popular heuristic approach involves constructing a Traveling Salesman Problem (TSP) solution, referred to as a long tour, then partitioning segments of the solution into routes for different vehicles with respect to problem constraints. Previously, a Split algorithm with a worst-case runtime of $Θ(n)$ was proposed for the capacitated VRP (CVRP) that finds the most cost-efficient partition of customers, given a long tour. This was an improvement over the previously fastest-known Split algorithm with a worst-case runtime of $Θ(n^2)$ that was based on Bellman's shortest path algorithm. While this linear Split has been an integral part of modern state-of-the-art CVRP approaches, little progress has been made in extending this algorithm to handle additional VRP variants, limiting the general applicability of the algorithm. In this work, we propose an extension of the linear Split that handles two cardinal VRP variants simultaneously: (i) simultaneous pickups and deliveries (VRPSPD) and (ii) time windows (VRPTW). The resulting $Θ(n)$ algorithm is guaranteed to be optimal, assuming travel times between nodes satisfy the triangle inequality. Additionally, we extend the linear Split to handle a capacity penalty for the VRPSPD. For the VRPTW, we extend the linear Split to handle the CVRP capacity penalty in conjunction with the popular time warp penalty function. Computational experiments are performed to empirically validate the speed gains of these linear Splits against their $Θ$($n^2$) counterparts.

Limited work has examined the strategic behaviors of relational networked learning agents under social dilemmas, and has overlooked the intricate social dynamics of complex systems. We address the challenge with Socio-Relational Intrinsic Motivation (SRIM), which endows agents with diverse preferences over sub-graphical social structures in order to study the impact of agents' personal preferences over their sub-graphical relations on their strategic decision-making under sequential social dilemmas. Our results in the Harvest and Cleanup environments demonstrate that preferences over different subgraph structures (degree-, clique-, and critical connection-based) lead to distinct variations in agents' reward gathering and strategic behavior: individual aggressiveness in Harvest and individual contribution effort in Cleanup. Moreover, agents with different subgraphical structural positions consistently exhibit similar strategic behavioral shifts. Our proposed BCI metric captures structural variation within the population, and the relative ordering of BCI across social preferences is consistent in Harvest and Cleanup games for the same topology, suggesting the subgraphical structural impact is robust across environments. These results provide a new lens for examining agents' behavior in social dilemmas and insight for designing effective multi-agent ecosystems composed of heterogeneous social agents.

Protecting public health from infectious diseases requires collective action, as individual behaviors-such as vaccination and mask-wearing-directly influence disease dynamics. During the COVID-19 pandemic, unexpected public responses often undermined the effectiveness of interventions, highlighting the need to understand collective behavioral patterns and motivations to design more effective mitigation strategies. This study presents an agent-based simulation model that captures how individuals adjust self-protective behaviors based on evolving opinions about disease risk and examines how these decisions interact with external factors, such as public health interventions, to shape collective outcomes. To improve the representativeness of the simulated population, multiple datasets were integrated to generate artificial populations. Model behavior was then calibrated against selected observed patterns, acknowledging that this calibration is partial and subject to model assumptions. Simulation experiments were conducted under varying conditions, including optional non-pharmaceutical interventions (NPIs), heterogeneous responses to pro- and anti-intervention messaging, and alternative vaccine eligibility policies. Results suggest that decision-making patterns may vary across demographic groups and that interactions between individual behaviors and external influences can substantially affect disease dynamics within the model. The simulation framework reproduces patterns consistent with observed phenomena, such as reduced mask-wearing following the lifting of mandates for vaccinated populations and higher infection burdens among economically disadvantaged populations. The framework suggests plausible mechanisms through which such patterns may emerge under the model assumptions. The study also suggests subpopulations likely to experience greater pressure under NPI mandates and highlights how vaccine eligibility strategies may enhance disease control when behavioral responses are considered. Overall, this work underscores the importance of integrating behavioral dynamics into epidemic response planning, while recognizing that findings are contingent on model structure and assumptions.

Highway agencies encounter the challenge of limited financial resources while striving to improve the condition of their road networks. As a result, optimization models are increasingly used to schedule pavement maintenance and rehabilitation activities under budget constraints. Previous probabilistic optimization models have primarily focused on incorporating uncertainty in budget constraints, often neglecting other sources of uncertainty. In particular, the failure to account for uncertainties in future pavement condition and maintenance effectiveness within multi-year optimization models may lead to mistimed maintenance interventions, ultimately yielding suboptimal schedules. Hence, this paper introduces a stochastic preventive maintenance optimization model that considers uncertainties in deterioration and improvement of pavement condition, together with budget constraints. The model aims to minimize lifecycle costs while maximizing road network condition. To solve this complex problem, the study employs a Multi-objective Genetic Algorithm (MOGA), known for its robust search capabilities in determining optimal global solutions. To mitigate the computational complexity of the stochastic MOGA model, three approaches are implemented: (1) identifying and incorporating the most used maintenance alternatives, (2) grouping pavement sections by age, and (3) introducing a filtering constraint that imposes a rest period following treatment applications. The results demonstrate that the Pareto optimal solutions are significantly influenced by varying levels of uncertainty in pavement condition deterioration and improvement. The developed stochastic MOGA model provides highway agencies and decision-makers with probabilistic Pareto optimal solutions that account for multiple sources of uncertainty. These solutions can be employed to select maintenance schedules that align with different risk thresholds and certainty levels.

Legal passage retrieval is an important task that assists legal practitioners in the time-intensive process of finding relevant precedents to support legal arguments. This study investigates the task of retrieving legal passages or paragraphs from decisions of the Court of Justice of the European Union (CJEU), whose language is highly structured and formulaic, leading to repetitive patterns. Understanding when lexical or semantic models are more effective at handling the repetitive nature of legal language is key to developing retrieval systems that are more accurate, efficient, and transparent for specific legal domains. To this end, we explore when this routinized legal language is better suited for retrieval using methods that rely on lexical and statistical features, such as BM25, or dense retrieval models trained to capture semantic and contextual information. A qualitative and quantitative analysis with three complementary metrics shows that both lexical and dense models perform well in scenarios with more repetitive usage of language, whereas BM25 performs better than the dense models in more nuanced scenarios where repetition and verbatim quotes are less prevalent and in longer queries. Our experiments also show that BM25 is a strong baseline, surpassing off-the-shelf dense models in 4 out of 7 performance metrics. However, fine-tuning a dense model on domain-specific data led to improved performance, surpassing BM25 in most metrics, and we analyze the effect of the amount of data used in fine-tuning on the model's performance and temporal robustness. The code, dataset and appendix related to this work are available on: https://github.com/larimo/lexsem-legal-ir.

This paper proposes pandemic mitigation vaccination policies for Newfoundland and Labrador (NL) based on two compact mixed integer programming (MIP) models of the distance-based critical node detection problem (DCNDP). Our main focus is on two variants of the DCNDP that seek to minimize the number of connections with lengths of at most one (1-DCNDP) and two (2-DCNDP). A polyhedral study for the 1-DCNDP is conducted, and new aggregated inequalities are provided for the 2-DCNDP. The computational experiments show that the 2-DCNDP with aggregated inequalities outperforms the one with disaggregated inequalities for graphs with a density of at least 0.5%. We also study the strategic vaccine allocation problem as a real-world application of the DCNDP and conduct a set of computational experiments on a simulated contact network of NL. Our computational results demonstrate that the DCNDP-based strategies can have a better performance in comparison with the real-world strategies implemented during COVID-19.

Accurately detecting human trafficking is particularly challenging due to its covert nature, difficulty in distinguishing trafficking from non-trafficking exploitative conditions, and varying operational definitions. Typically, detecting human trafficking requires resource-intensive efforts from resource-constrained anti-trafficking stakeholders. Such measures may need personnel training or machine learning-based identification technologies that suffer from detection errors. Repeated usage of such measures risks biasing detection efforts and reducing detection effectiveness. Such problems raise the question: "How should imperfect detection resources be allocated to most effectively identify human trafficking?" As an answer, we construct a class of resource allocation models that considers various optimal allocation scenarios. These scenarios range from optimal location selection for monitoring to optimal allocation of a finite set of imperfect resources, given error rates. We illustrate the applicability of these models across both human and technology-facilitated detection contexts at the India-Nepal border and in the global seafood industry. Insights from our models help inform operational strategies for allocating limited anti-human trafficking resources in a way that effectively preserves human rights and dignity.

Background Protecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration. Methods We propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis. Results Our results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor. Conclusions Our results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

The development and increasing application area of electric vehicles (EVs) in city logistics has contributed to the necessity of intermediate stops for charging EVs. Due to the limited service range of these vehicles, charging facilities must be located at the same echelon as customers’ and differ from depots or hubs in that they are visited while serving customers. Thereby, these facilities allow vehicles to exchange requests and get recharged. In this study, we present the Intra-route Location Routing for the Pickup and Delivery Problem with Transfers that arises in this novel scenario. There are sets of requests, vehicles, and potential transfer locations (intra-route facilities). Each request consists of a pair of a pickup node and a delivery node. Vehicles start their routes from their respective origins, serve customers (pickup and delivery), and return to the origin nodes. In the generic Pickup and Delivery Problem, a request must be served by a single vehicle that picks and delivers the request. In our study, however, multiple vehicles can service a request collaboratively by transferring it to each other at a transfer facility. In this problem context, we decide vehicle schedules, transfer decisions, and which nodes should serve as intra-route facilities. We propose a mathematical model for this novel NP-Hard problem. Experimental results indicate the computational difficulty of the problem in practice. For several small instances, solving the mathematical model by using a commercial solver does not even find the optimal solutions in six hours, and given the importance of this problem, it must be addressed in future works.

BACKGROUND:There is an urgent need to develop a cytomegalovirus (CMV) vaccine as it remains the leading cause of birth defects in the United States. While several CMV vaccine candidates are currently in late-stage clinical trials, the most effective vaccination program remains an open research question.METHODS:To take into account the critical uncertainties when evaluating the vaccine impact on both vertical (congenital) and horizontal CMV transmissions, we developed a CMV agent-based model representative of the US population and contact network structures.RESULTS:We evaluated 648 vaccination scenarios under various assumptions of vaccination age, vaccine efficacy, protection duration, and vaccination coverage. The optimal age of vaccination under all scenarios is shown to be during early childhood. However, a relatively modest benefit was also seen with vaccination of females of reproduction age (around age of 25) assuming near universal coverage and long vaccine-mediated protection.CONCLUSIONS:This study highlights the important need for a pediatric vaccination program in mitigating CMV in the United States. Our model is poised to investigate further location-based vaccine effectiveness questions in future planning of both clinical trials as well as eventual program implementation.

Maintaining road pavement is a challenging task, due to the vast size of the roads network and the limited maintenance budget available to road agencies. Therefore, most agencies aim at selecting the most deteriorated road segments for maintenance, within their available budget. However, the majority of the current developed asset management tools aim at improving the average performance of the roads network, regardless of the condition of the maintained segments. To overcome this limitation, this study first proposes a novel measure for roads network performance using reliability theory, then it proposes a hybrid optimization approach that is integrated with the proposed performance measure to enable road agencies to focus on the most deteriorated segments in the network while maximizing the network performance. The hybrid optimization approach combines dynamic programming with a traditional genetic algorithm. The proposed algorithm was applied to two major road systems in Jordan and validated by comparing its performance with that of a traditional genetic algorithm. The results show that the proposed algorithm outperforms the traditional genetic algorithm, because of its ability to select the most deteriorated segments in the network, while achieving better network condition at lower cost.

Eastern Equine Encephalitis (EEE) is an arbovirus that, while it has been known to exist since the 1930's, recently had a spike in cases. This increased prevalence is particularly concerning due to the severity of the disease with 1 in 3 symptomatic patients dying. The cause of this peak is currently unknown but could be due to changes in climate, the virus itself, or host behavior. In this paper we propose a novel multi-season deterministic model of EEE spread and its stochastic counterpart. Models were parameterized using a dataset from the Florida Department of Health with sixteen years of sentinel chicken seroconversion rates. The different roles of the enzootic and bridge mosquito vectors were explored. As expected, enzootic mosquitoes like Culiseta melanura were more important for EEE persistence, while bridge vectors were implicated in the disease burden in humans. These models were used to explore hypothetical viral mutations and host behavior changes, including increased infectivity, vertical transmission, and host feeding preferences. Results showed that changes in the enzootic vector transmission increased cases among birds more drastically than equivalent changes in the bridge vector. Additionally, a 5% difference in the bridge vector's bird feeding preference can increase cumulative dead-end host infections more than 20-fold. Taken together, this suggests changes in many parts of the transmission cycle can augment cases in birds, but the bridge vectors feeding preference acts as a valve limiting the enzootic circulation from its impact on dead-end hosts, such as humans. Our what-if scenario analysis reveals and measures possible threats regarding EEE and relevant environmental changes and hypothetically suggests how to prevent potential damage to public health and the equine economy.

Given that node metadata can provide key insights about the relationship between nodes, we investigate if incorporating it as a similarity feature (referred to as metadata similarity) between end nodes of a link can improve the accuracy of weight prediction when using common supervised learning methods. We compare the weight prediction accuracy when metadata similarity is added to a set of baseline topological similarity features to that of using only the topological features. The comparison is performed across four empirical datasets using regression-based and other supervised methods found in the literature. In this preliminary study, we find no significant evidence that metadata similarity improves prediction accuracy in the methods analyzed and within the experimental setup. We encourage further investigation in this research area.

Zika Virus (ZIKV) is a flavivirus that is transmitted predominantly by the Aedes species of mosquito, but also through sexual contact, blood transfusions, and congenitally from mother to child. Although approximately 80% of ZIKV infections are asymptomatic and typical symptoms are mild, multiple studies have demonstrated a causal link between ZIKV and severe diseases such as Microcephaly and Guillain Barré Syndrome. Two goals of this study are to improve ZIKV models by considering the spread dynamics of ZIKV as both a vector-borne and sexually transmitted disease, and also to approximate the degree of under-reporting. In order to accomplish these objectives, we propose a compartmental model that allows for the analysis of spread dynamics as both a vector-borne and sexually transmitted disease, and fit it to the ZIKV incidence reported to the National System of Public Health Surveillance in 27 municipalities of Colombia between January 1 2015 and December 31 2017. We demonstrate that our model can represent the infection patterns over this time period with high confidence. In addition, we argue that the degree of under-reporting is also well estimated. Using the model we assess potential viability of public health scenarios for mitigating disease spread and find that targeting the sexual pathway alone has negligible impact on overall spread, but if the proportion of risky sexual behavior increases then it may become important. Targeting mosquitoes remains the best approach of those considered. These results may be useful for public health organizations and governments to construct and implement suitable health policies and reduce the impact of the Zika outbreaks.

As agents interact and influence one another in a social network, the opinions they hold about some common topic can change over time. These changes may enable us to infer mechanisms of the network that control how interactions lead to opinion change. Inferring such mechanisms from opinion data could enable analysis of social influence in data-sparse scenarios. However, limited work has focused on this problem, despite its clear value. To address this gap, we create opinion data using agent-based simulation and experimental design. By viewing opinion changes as an information-generating process, opinion dynamics can be studied using entropy. This work explores the relationships between aggregated entropy and five simulation design factors. Three entropy measures are calculated on continuous-valued opinions and are analyzed using a main effects model and cluster analysis. Overall, the choices of influence model and error distribution are most important to the entropy measures, activation regime is important to some measures, and population size is unimportant. Also, design variation can be detected using time-series cluster analysis. These findings may support work in inferring properties about real-world social influence networks using opinion data collected from their members.

Co-infection of COVID-19 and other respiratory pathogens, including influenza virus family, has been of importance since the beginning of the recent pandemic. As the upcoming flu season arrives in countries with ongoing COVID-19 epidemic, the need for preventive policy actions becomes more critical. We present a joint compartmental SEIRS-SIRS model for the co-circulation of SARS-CoV-2 and influenza and discuss the characteristics of the model, such as the basic reproduction number (R 0 ) and cases of death and recovery. We implemented the model using 2020 to early 2021 data derived from global healthcare organizations and studied the impact of interventions and policy actions such as vaccination, quarantine, and public education. The VENSIM simulation of the model resulted in R 0 = 7.5, which is higher than what was reported for the COVID-19 pandemic. Vaccination against COVID-19 dramatically slowed its spread and the co-infection of both diseases significantly, while other types of interventions had a limited impact on the co-dynamics of the diseases given our assumptions. These findings can help provide guidance as to which preventive policies would be most effective at the time of concurrent epidemics, and contributes to the literature as a novel model to simulate and analyze the co-circulation of respiratory pathogens in a compartmental setting that can further be used to study the co-infection of COVID-19 or similar respiratory infections with other diseases.

Identifying the primary case, denoted as the index case or commonly "patient zero", of an epidemic is a critical part of epidemiological investigation for understanding outbreak occurrence and how to prevent future outbreaks. In this paper, we devise a Particle Swarm Optimization algorithm to find solutions to the patient zero inverse problem, which aims to estimate the location and time origin of a disease from a single snapshot of the infection status of the post-outbreak population. We consider various compartmental models for disease spread on a regular lattice and also propose a new variant of the patient zero inverse problem where the disease is reintroduced into a population. Experimental results show that the proposed algorithm is effective at identifying the location and time of an epidemic's inception, and shows improvements over other algorithms in the literature. Specifically, the estimates for the location of an outbreak are 50% closer than similar algorithms along with statistically significant improvements in identifying the onset time of an outbreak. Moreover, the proposed algorithm is also able to consistently perform in partially observable environments and scales well when the search space is increased.

Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use wiring rules to synthesize networks closely resembling the topology of a given connectome. Successful models can highlight the principles by which a network is organized (identify structural features that arise from wiring rules versus those that emerge) and potentially uncover the mechanisms by which it grows and develops. Previous research has shown that such models can validate the effectiveness of spatial embedding and other (non-spatial) wiring rules in shaping the network topology of the human connectome. In this research, we propose variants of the action-based model that combine a variety of generative factors capable of explaining the topology of the human connectome. We test the descriptive validity of our models by evaluating their ability to explain between-subject variability. Our analysis provides evidence that geometric constraints are vital for connectivity between brain regions, and an action-based model relying on both topological and geometric properties can account for between-subject variability in structural network properties. Further, we test correlations between parameters of subject-optimized models and various measures of cognitive ability and find that higher cognitive ability is associated with an individual's tendency to form long-range or non-local connections.

With the goal of understanding if the information contained in node metadata can help in the task of link weight prediction, we investigate herein whether incorporating it as a similarity feature (referred to as metadata similarity) between end nodes of a link improves the prediction accuracy of common supervised machine learning methods. In contrast with previous works, instead of normalizing the link weights, we treat them as count variables representing the number of interactions between end nodes, as this is a natural representation for many datasets in the literature. In this preliminary study, we find no significant evidence that metadata similarity improved the prediction accuracy of the four empirical datasets studied. To further explore the role of node metadata in weight prediction, we synthesized weights to analyze the extreme case where the weights depend solely on the metadata of the end nodes, while encoding different relationships between them using logical operators in the generation process. Under these conditions, the random forest method performed significantly better than other methods in 99.07% of cases, though the prediction accuracy was significantly degraded for the methods analyzed in comparison to the experiments with the original weights.