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This article presents a modeling approach to tackle the multidimensional military manpower planning problem. Requirements relevant to military manpower planning include the need to maintain distributions of manpower characteristics (such as rank and age) with respect to career advancement within acceptable proximity to desired norms. Moreover, they include ensuring that the personnel allocated to the various roles of the defence force will be adequate throughout the planning period. Addressing these tasks simultaneously represents a significant challenge for human resources managers. This paper proposes a model that assigns personnel to career paths (CPs) pre-defined as feasible prior to optimization. Adequate solutions through this model are generated by mixed integer goal programming. This solving approach is applied to a case study of the Belgian Defence’s manpower and results in valuable insights for military human resources managers. For the illustration of our approach, we focus on a military organization, but it could be used for any hierarchical organization, such as a police force or a university.

Background/Introduction:The use of computer modelling and simulation is allowing researchers to test operational assumptions in a virtual but controlled experimental environment.Objectives:Developing a valid computer simulation model allowing (1) to model complex medical response systems with several types of victims, (2) to test different aspects of the medical response.Method/Description:The SIMEDIS (Simulation for the Assessment and Optimization of Medical Disaster Management) computer simulator consists of 3 interactive components: the victim creation model, the victim monitoring model, and the medical response model.The objectives of the study were to create a disaster medical response simulation model in the case of an aircraft crash and in a CBRN incident simulation, to test and to optimize existing and future medical disaster response plans, to develop a victim model, to develop a victim creation model and a victim monitoring model, and to produce a pre-hospital medical response model.Results/Outcomes:The case studies showed that the SIMEDIS simulator is offering a valuable tool for testing the impact of several interventional factors on the disaster medical response in specific scenarios including more complex situations such CBRN-incidents.Conclusion:This study reflects the potential of SIMEDIS to model complex systems, to test different aspects of the disaster medical response and to potentially inform changes in practices. This might be of potential interest for disaster response planners allowing them to make the best choices in composing their medical teams and adapting the medical response system.

Social media platforms can play a pivotal role in shaping public opinion during times of crisis and controversy. The COVID-19 pandemic resulted in a large amount of dubious information being shared online. In Belgium, a crisis emerged during the pandemic when a soldier (Jürgen Conings) went missing with stolen weaponry after threatening politicians and virologists. This case created further division and polarization in online discussions. In this paper, we develop a methodology to study the potential of coordinated spread of incorrect information online. We combine network science and content analysis to infer and study the social network of users discussing the case, the news websites shared by those users, and their narratives. Additionally, we examined indications of bots or coordinated behavior among the users. Our findings reveal the presence of distinct communities within the discourse. Major news outlets, conspiracy theory websites, and anti-vax platforms were identified as the primary sources of (dis)information sharing. We also detected potential coordinated behavior and bot activity, indicating possible attempts to manipulate the discourse. We used the rapid semantic similarity network for the analysis of text, but our approach can be extended to the analysis of images, videos, and other types of content. These results provide insights into the role of social media in shaping public opinion during times of crisis and underscore the need for improved strategies to detect and mitigate disinformation campaigns and online discourse manipulation. Our research can aid intelligence community members in identifying and disrupting networks that spread extremist ideologies and false information, thereby promoting a more informed and resilient society.

Recently proposed computational techniques allow the application of various maximum entropy network models at a larger scale. We focus on disinformation campaigns and apply different maximum entropy network models on the collection of datasets from the Twitter information operations report. For each dataset, we obtain additional Twitter data required to build an interaction network. We consider different interaction networks which we compare to an appropriate null model. The null model is used to identify statistically significant interactions. We validate our method and evaluate to what extent it is suited to identify communities of members of a disinformation campaign in a non-supervised way. We find that this method is suitable for larger social networks and allows to identify statistically significant interactions between users. Extracting the statistically significant interaction leads to the prevalence of users involved in a disinformation campaign being higher. We found that the use of different network models can provide different perceptions of the data and can lead to the identification of different meaningful patterns. We also test the robustness of the methods to illustrate the impact of missing data. Here we observe that sampling the correct data is of great importance to reconstruct an entire disinformation operation.

Background Prostate cancer (PCa) is a heterogeneous disease with a variable natural history, genetics, and treatment outcome. The Hedgehog (Hh) signaling pathway is increasingly recognized as being potentially important for the development and progression of PCa. In this retrospective study, we compared the activation status of the Hh signaling pathway between benign and tumor tissue, and evaluated the clinical significance of Hh signaling in PCa. Methods In this tissue microarray (TMA) study, the protein expression of several Hh signaling components and Hh target proteins, along with microvessel density, were compared between benign ( n  = 64) and malignant ( n  = 170) prostate tissue, and correlated with PCa clinicopathological characteristics and biochemical recurrence (BCR). Results The Hh signaling pathway appeared to be more active in PCa than in benign prostate tissue, as demonstrated by lower expression of the negative regulators PTCH1 and GLI3 in the tumor tissue compared to benign. In addition, high epithelial GLI2 expression correlated with higher pathological Gleason score. Overall, higher epithelial GLI3 expression in the tumor was shown to be an independent marker of a favorable prognosis. Conclusion Hh signaling activation might reflect aggressive tumoral behavior, since high epithelial GLI2 expression positively correlates with a higher pathological Gleason score. Moreover, higher epithelial GLI3 expression is an independent marker of a more favorable prognosis.

IntroductionChemical mass casualty incidents (MCIs) pose a substantial threat to public health and safety, with the capacity to overwhelm healthcare infrastructure and create societal disorder. Computer simulation systems are becoming an established mechanism to validate these plans due to their versatility, cost-effectiveness and lower susceptibility to ethical problems.MethodsWe created a computer simulation model of an urban subway sarin attack analogous to the 1995 Tokyo sarin incident. We created and combined evacuation, dispersion and victim models with the SIMEDIS computer simulator. We analyzed the effect of several possible approaches such as evacuation policy (‘Scoop and Run’ vs. ‘Stay and Play’), three strategies (on-site decontamination and stabilization, off-site decontamination and stabilization, and on-site stabilization with off-site decontamination), preliminary triage, victim distribution methods, transport supervision skill level, and the effect of search and rescue capacity.ResultsOnly evacuation policy, strategy and preliminary triage show significant effects on mortality. The total average mortality ranges from 14.7 deaths in the combination of off-site decontamination and Scoop and Run policy with pretriage, to 24 in the combination of onsite decontamination with the Stay and Play and no pretriage.ConclusionOur findings suggest that in a simulated urban chemical MCI, a Stay and Play approach with on-site decontamination will lead to worse outcomes than a Scoop and Run approach with hospital-based decontamination. Quick transport of victims in combination with on-site antidote administration has the potential to save the most lives, due to faster hospital arrival for definitive care.

It is recognized that the study of the disaster medical response (DMR) is a relatively new field. To date, there is no evidence-based literature that clearly defines the best medical response principles, concepts, structures and processes in a disaster setting. Much of what is known about the DMR results from descriptive studies and expert opinion. No experimental studies regarding the effects of DMR interventions on the health outcomes of disaster survivors have been carried out. Traditional analytic methods cannot fully capture the flow of disaster victims through a complex disaster medical response system (DMRS). Computer modelling and simulation enable to study and test operational assumptions in a virtual but controlled experimental environment. The SIMEDIS ( Si mulation for the assessment and optimization of medical disaster management) simulation model consists of 3 interacting components: the victim creation model, the victim monitoring model where the health state of each victim is monitored and adapted to the evolving clinical conditions of the victims, and the medical response model, where the victims interact with the environment and the resources at the disposal of the healthcare responders. Since the main aim of the DMR is to minimize as much as possible the mortality and morbidity of the survivors, we designed a victim-centred model in which the casualties pass through the different components and processes of a DMRS. The specificity of the SIMEDIS simulation model is the fact that the victim entities evolve in parallel through both the victim monitoring model and the medical response model. The interaction between both models is ensured through a time or medical intervention trigger. At each service point, a triage is performed together with a decision on the disposition of the victims regarding treatment and/or evacuation based on a priority code assigned to the victim and on the availability of resources at the service point. The aim of the case study is to implement the SIMEDIS model to the DMRS of an international airport and to test the medical response plan to an airplane crash simulation at the airport. In order to identify good response options, the model then was used to study the effect of a number of interventional factors on the performance of the DMRS. Our study reflects the potential of SIMEDIS to model complex systems, to test different aspects of DMR, and to be used as a tool in experimental research that might make a substantial contribution to provide the evidence base for the effectiveness and efficiency of disaster medical management.

Modelling and simulating mine counter measure search missions performed by autonomous vehicles equipped with a sensor capable of detecting mines at sea is a challenging endeavour. To address this, we formulated and implemented the problem as a stochastic optimal control model. Our implementation computes an optimal path within a user chosen quadrilateral domain such that the mission duration is minimized for a given residual risk of undetected sea mines. First, we compare the stochastic optimal control implementation against the traditionally used boustrophedon implementation. We show that the mission duration in case of the stochastic optimal control implementation is shorter. Then, by building on our previous work, we introduce a novel mathematical approach that enables multiple autonomous survey vehicles to investigate the domain concurrently. We present results for up to six vehicles, including computed trajectories and an analysis of how mission duration varies with the number of vehicles. Our findings show that mission time decreases non-linearly, , i.e., we observe diminishing returns as more vehicles are added.

Modelling and simulating mine countermeasures search missions performed by autonomous vehicles equipped with a sensor capable of detecting mines at sea is a challenging endeavour. The output of our stochastic optimal control implementation consists of an optimal trajectory in a square domain for the autonomous vehicle such that the total mission time is minimized for a given residual risk of not detecting sea mines. We model this risk as an expected value integral. We found that upon completion of the simulation, the user requested residual risk is usually not satisfied. We solved this by implementing a relaxation strategy which consists of incrementally increasing the square search domain. We then combined this strategy with different quasi-Monte Carlo schemes used for solving the integral. We found that using a Rank-1 Lattice scheme yields a speedup up to a factor two with respect to the Monte Carlo scheme. We also present an implementation which allows us to compute a trajectory in a convex quadrilateral domain, as opposed to a square domain, and combine it with our relaxation strategy.

Modelling and simulating mine countermeasures (MCM) search missions performed by autonomous vehicles equipped with a sensor capable of detecting mines at sea is a challenging endeavour. In this work, we present a novel way to model and account for sand ripples present on the bottom of the ocean while calculating trajectories for the autonomous vehicles by means of a stochastic optimal control framework. It is known from the scientific literature that these ripples impact the sea mine detection capabilities of the autonomous vehicles.