Simple Scenario Research Articles

The future of screening and early detection in the Netherlands: a foresight study to support new screening policy strategies

Abstract The Dutch government provides several population screening programmes intended for early detection of diseases so that prompt treatment can be provided. These include population screenings for various forms of cancer and screenings for specific diseases in unborn and newborn children. The future provision of these screenings is influenced by various developments, including the ageing population, economic fluctuations and technological advancements. To understand these developments and their potential impacts on the future of screening and early detection, RIVM has conducted a foresight study based upon a systematic six-step approach. Important steps in this process are building a conceptual framework, identifying important developments via horizon scanning, selecting scenario logics and making scenarios in order to select important challenges and opportunities for the future. The final output of the foresight study consists of a broad horizon scan, two simple future scenarios and four societal perspectives on screening and early detection. In this foresight study, there was special attention for the role of different societal perspectives and the variation in desirable futures that these perspectives entail. These perspectives were presented to the Ministry and other important stakeholders in the future strategy planning, by playing a serious role game. The results of the foresight study and the experience of looking to the future through various perspectives forms the input for the Dutch Ministry of Health, Welfare and Sport in formulating a strategy for the future of screening and early detection and making informed decisions about potential actions. The presentation will focus on how the foresight process was done and how this has informed the Ministry of Health in order for them to follow the next steps in setting future goals and ambitions and planning for a future policy strategy.

Using Mathematics to Study How People Influence Each Other’s Opinions

People sometimes change their opinions when they discuss things with each other. Researchers can use mathematics to study opinion changes in simplifications of real-life situations. These simplified scenarios, which are examples of mathematical models, help researchers explore how people influence each other through their social interactions. In today’s digital world, these models can help us learn how to promote the spread of accurate information and reduce the spread of inaccurate information. In this article, we discuss a simple mathematical model of opinion changes that arise from social interactions. We briefly describe what opinion models can tell us and how researchers try to make them more realistic.

TCP Congestion Management Using Deep Reinforcement Trained Agent for RED

ABSTRACTIncreasing data transmission volumes are causing more frequent and more severe network congestion. In order to handle spikes in network traffic, a substantially bigger buffer has been included into the system. Bufferbloat, which happens when a bigger buffer is implemented, exacerbates network congestion. Using the transfer control protocol (TCP) congestion management strategy with active queue management (AQM) can fix this issue. As congestion increases, it becomes increasingly difficult to forecast and fine‐tune dynamic AQM/TCP systems in order to achieve acceptable performance. To shed new light on the AQM system, we plan to use deep reinforcement learning (DRL) techniques. It is possible that AQM can learn about the appropriate drop policy the same way people do when using a model‐free technique like DRL‐AQM. After training in a simple network scenario, DRL‐AQM is able to recognize complex patterns in the data traffic model and apply them to improve performance in a wide variety of scenarios. Offline training precedes deployment in our approach. In many cases, the model does not require any further parameter tweaks after training. Even in the most complicated networks, AQM algorithms have proven to be effective, regardless of the network's complexity. Minimizing buffer capacity use is an important goal of DRL‐AQM. It automatically and continually adjusts to changes in network connectivity.

Interplay between vector-like lepton and seesaw mechanism: oblique corrections

The non-vanishing neutrino mass strongly hints the existence of right-handed neutrinos (RHNs), singlets of the standard model (SM). However, they are highly decoupled from the SM and difficult to probe. In this work, we consider the Majorana RHNs from the type-I seesaw mechanism may well mix with the heavy neutral lepton dwelling in certain vector-like lepton (VLL), thus acquiring a sizable electroweak charge. Such a simple scenario yields many interesting consequences, and the imprint on oblique corrections, well expected from the mass splitting between components of VLL by virtue of VLL-RHN mixing, is our focus here. We analytically calculate the Peskin-Takeuchi parameters S, T and U with full details, carefully treating the Majorana loop to obtain the self consistent expressions free of divergence. Then, we constrain on the VLL-RHN system which only gives a sizable T parameter using the PDG-2021 data and CDF-II data, separately, by imposing T ≲ O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(0.1). It is found that for the RHN and VLL below the TeV scale, with a properly large mixing, stands in the frontier of the electroweak precision test such as W-boson mass.

Optimisation of Active Magnetic Elements in Beam-like Structures—Numerical Modelling Studies

This paper explores integrating advanced materials, including magnetic shape memory alloys, magnetorheological fluids, and classical shape memory alloys, within structural elements to achieve exceptional physical properties. When these materials are integrated within structures—whether as wires, actuators, or dampers—they provide the structures with unique static, dynamic, and damping characteristics not commonly found in nature. This study aimed to evaluate the efficacy of these active materials in enhancing the performance of beam-like structures. This investigation was conducted through a comprehensive numerical analysis, focusing on a composite beam. The study examined the impact of different active elements, their position within the structure, and their influence on key dynamic properties. Additionally, a simplified damage scenario was considered, wherein the adverse effects of structural damage were mitigated through the strategic application of these materials. Numerical simulations were carried out using the finite element method, with custom computational codes developed in MATLAB. The findings of these simulations are presented and discussed in this paper.

Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization

This work aims to solve the problem of low accuracy in recognizing the trajectory of badminton movement. This work focuses on the visual system in badminton robots and conducts side detection and tracking of flying badminton in two-dimensional image plane video streams. Then, the cropped video images are input into a convolutional neural network frame by frame. By adding an attention mechanism, it helps identify the badminton movement trajectory. Finally, to address the detection challenge of flying badminton as a small target in video streams, the deep learning one-stage detection network, Tiny YOLOv2, is improved from both the loss function and network structure perspectives. Moreover, it is combined with the Unscented Kalman Filter algorithm to predict the trajectory of badminton movement. Simulation results show that the improved algorithm performs excellently in tracking and predicting badminton trajectories compared with the existing algorithms. The average accuracy of the proposed method for tracking badminton trajectories is 91.40 %, and the recall rate is 84.60 %. The average precision, recall, and frame rate of the measured trajectories in four simple and complex scenarios of badminton flight video streams are 96.7 %, 95.7 %, and 29.2 frames/second, respectively. They are all superior to other classic algorithms. It is evident that the proposed method can provide powerful support for badminton trajectory recognition and help improve the accuracy of badminton movement recognition.

Rethinking prediction-based video anomaly detection from local–global normality perspective

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Prediction-based VAD methods have gained a lot of attention in recent years due to their simplicity and effectiveness, which learn normality through a frame prediction tasks. However, we find that they often consider only local or global normality in the temporal dimension thus are difficult to generalize for various scenarios. Specifically, some of them focus on learning local spatiotemporal representations from consecutive frames to enhance the representation for normal events while the other methods are devoted to memorizing prototypical normal patterns of whole training videos to weaken the generalization for anomalies. In this paper, we rethink these two types of methods and find that the former methods are difficult to adapt to simple scenarios due to powerful representation allows these methods to represent some anomalies and causes miss detection while the latter struggle with complex scenarios due to the limitations for diverse normal patterns and causes false alarm. To verify our discoveries, we design a two-branch model, Local–Global Normality Network (LGN-Net), to simultaneously learn local and global normality. We verified the effect of local and global normality on the VAD model by ablation study and find that the model can be more adaptive to different scenarios when accounting for both normality. The code is available online: https://github.com/Myzhao1999/LGN-Net.

A model of dyadic merging interactions explains human drivers' behavior from control inputs to decisions.

Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.

UAV Autonomous Navigation Based on Deep Reinforcement Learning in Highly Dynamic and High-Density Environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) based on deep reinforcement learning (DRL) has made great progress. However, most studies assume relatively simple task scenarios and do not consider the impact of complex task scenarios on UAV flight performance. This paper proposes a DRL-based autonomous navigation algorithm for UAVs, which enables autonomous path planning for UAVs in high-density and highly dynamic environments. This algorithm proposes a state space representation method that contains position information and angle information by analyzing the impact of UAV position changes and angle changes on navigation performance in complex environments. In addition, a dynamic reward function is constructed based on a non-sparse reward function to balance the agent’s conservative behavior and exploratory behavior during the model training process. The results of multiple comparative experiments show that the proposed algorithm not only has the best autonomous navigation performance but also has the optimal flight efficiency in complex environments.

An e-learning platform for clinical reasoning in cardiovascular diseases: a study reporting on learner and tutor satisfaction

BackgroundMedical students gain essential skills through hospital training and internships, which complement their theoretical education. However, virtual patient platforms have been shown to effectively promote clinical reasoning and enhance learning outcomes. This study evaluates a web-based platform designed for learning clinical reasoning in cardiovascular diseases, detailing its functionalities and user satisfaction.MethodsThe Virtual Patient platform presents medical students with clinically valid scenarios, encompassing stages such as patient description, anamnesis, objective examination, presumptive diagnosis, health investigations, treatment planning, complications, differential and final diagnoses, and prognosis. Scenarios are generated either automatically or manually by professors, based on labeled and annotated clinical data. The Virtual Patient contains two types of medical cases: simple scenarios describing patients with one pathology, and complex scenarios describing patients with several related pathologies. The platform was evaluated by a total of 210 users: 178 medical students, 7 professors, and 25 engineering students, using questionnaires adjusted for each evaluation round to assess satisfaction and gather feedback. The evaluation by medical students was performed in four rounds, each round corresponding to successive enhancements of the platform functionalities and addition of new cases, with a total number of 1,098 evaluation sessions.ResultsThe platform was evaluated at different implementation stages, involving simple and complex scenarios for various heart diseases. The majority of students found the platform very useful (82.58%), with significant appreciation for its features and functionalities, for example the dialogue module supporting natural language interactions in Romanian and English or the feed-back obtained during interaction. Professors highly valued the platform’s flexibility in scenario generation, real-time feedback provision, and data management capabilities. They appreciated the possibility to provide feedback and score student performance in real-time or after the session, though some professors suggested improving the explainability of the scores.ConclusionsThe Virtual Patient platform enables medical students to virtually replicate hospital interactions, diagnose patients, and plan treatments in clinically valid scenarios for cardiovascular diseases. User evaluations demonstrated high satisfaction and appreciation for the platform’s features. Future work will focus on expanding medical cases, enhancing the dialogue module, improving scenario generation for complex cases, and extending the synthetic data generation component to produce additional types of medical investigations.

International cooperation was key to stabilize wheat prices after the Russian Invasion of Ukraine

The Russian invasion of Ukraine in 2022 triggered a global wheat price spike and food insecurities in import-dependent countries. We combine an analysis of the global wheat supply network with an agricultural commodity price model to investigate national impaired supplies and the global annual wheat price hike, respectively, for the trade year 2022. Using a scenario analysis, we show that international cooperation manifested in the Black Sea Grain and Solidarity Lanes initiatives and the removal of export restrictions may have mitigated the 2022 price hike by 13 percentage points. In a worst case scenario – characterized by multi-breadbasket harvest failures, escalating export restrictions, and blocked Ukrainian exports – wheat price increases by 90% compared to the 2000–2020 average. Coping strategies – such as food-secure countries dispersing stocks, reducing wheat as feed, or boosting wheat production – are effective at mitigating the price spike in simplified scenarios. Our findings underscore the imperative of coordinated policy responses to avoid global food supply disruptions.

Element abundances of galactic RGB stars in the APO-K2 catalogue

Aims. We conducted an investigation on the chemical abundances of 4316 stars in the red giant branch (RGB) phase from the recently released APO-K2 catalogue. Our aim was to characterize the abundance trends of the single elements with [α/Fe], mainly focusing on C, N, and O, which are the most relevant for the estimation of stellar ages. Methods. The chemical analysis of the RGB sample involved cross-matching data from the APO-K2 catalogue with individual element abundances from APOGEE DR17. Results. The analysis detected a statistically significant difference in the [(C+N+O)/Fe]–[α/Fe] trend with respect to the simple α-enhancement scenario. This difference remained robust across different choices for the reference solar mixture and potential zero-point calibrations of C and N abundances. The primary discrepancy was a steeper increase in [O/Fe] with [α/Fe], reaching a 0.1 dex difference at [α/Fe] = 0.3. Notably, the impact on the evolutionary timescale of such oxygen over-abundance with respect to the commonly adopted uniform α-enhancement is rather limited. We verified that stellar models computed using an ad hoc O-rich mixture sped up the evolution by only 1% at [α/Fe] = 0.3, due to the counterbalancing effects of O enrichment on both the evolutionary timescale and the Z-to-[Fe/H] relationship.

Communication is computation: A privacy-protecting routing protocol for Physical Internet

Logistics research has been emphasising the importance of interconnectivity for better resource utilisation and real-time response against disruptions. However, lack of trust has become a significant obstacle in the business world, blocking the way to building an open information system and, thus, more effective route-finding protocols. This paper proposes a decentralised routing protocol for multimodal transportation in a new logistic paradigm named Physical Internet (PI), performing under a privacy-protecting information-sharing policy. We seek the possibility of building a system to connect entities in a trustless environment that strictly limits data usage to the embedded services at a structural level. In the proposed protocol, the only service is to find the shortest path while minimising unnecessary data-sharing, with the potential to plug in more services. If no party initiates the routing, the entities remain in the equivalent state of disconnected. This routing protocol avoids sharing over 90% of the information in a simple scenario. In addition, a numerical experiment is performed to justify the quality of routes planned by the protocol.

Hierarchical Heuristic Species Delimitation under the Multispecies Coalescent Model with Migration.

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data from closely related species to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively heuristic criteria based on population parameters (such as popula- tion split times, population sizes, and migration rates) estimated from genomic data may be used to delimit species. Here we develop hierarchical merge and split algorithms for heuristic species delimitation based on the genealogical divergence index (𝑔𝑑𝑖) and implement them in a python pipeline called hhsd. We characterize the behavior of the 𝑔𝑑𝑖 under a few simple scenarios of gene flow. We apply the new approaches to a dataset simulated under a model of isolation by distance as well as three empirical datasets. Our tests suggest that the new approaches produced sensible results and were less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the hierarchical algorithm, as well as the challenges of species delimitation based on heuristic criteria.

Nonreciprocity in transmission mode with planar structures for arbitrarily polarized light [Invited

Approaching thermodynamic limits in light harvesting requires enabling nonreciprocal thermal emission. The majority of previously reported nonreciprocal thermal emitters operate in reflection mode, following original proposals by M. Green [Nano Lett. 12, 5985 (2012)10.1021/nl3034784] and others. In these proposals, cascaded nonreciprocal junctions that re-direct each junction’s emission towards a subsequent one are employed for efficient light-harvesting. Recently, simplified concepts have been proposed in solar photovoltaics and thermophotovoltaics, respectively, that leverage the concept of tandem junctions to approach thermodynamic limits. In these simplified scenarios, polarization-independent nonreciprocal response in transmission mode is required. We propose a pattern-free heterostructure that enables such functionality, using a magneto-optical material embedded between two dissimilar dielectric layers.

Comparing ChatGPT and a Single Anesthesiologist's Responses to Common Patient Questions: An Exploratory Cross-Sectional Survey of a Panel of Anesthesiologists.

Increased patient access to electronic medical records and resources has resulted in higher volumes of health-related questions posed to clinical staff, while physicians' rising clinical workloads have resulted in less time for comprehensive, thoughtful responses to patient questions. Artificial intelligence chatbots powered by large language models (LLMs) such as ChatGPT could help anesthesiologists efficiently respond to electronic patient inquiries, but their ability to do so is unclear. A cross-sectional exploratory survey-based study comprised of 100 anesthesia-related patient question/response sets based on two fictitious simple clinical scenarios was performed. Each question was answered by an independent board-certified anesthesiologist and ChatGPT (GPT-3.5 model, August 3, 2023 version). The responses were randomized and evaluated via survey by three blinded board-certified anesthesiologists for various quality and empathy measures. On a 5-point Likert scale, ChatGPT received similar overall quality ratings (4.2 vs. 4.1, p = .81) and significantly higher overall empathy ratings (3.7 vs. 3.4, p < .01) compared to the anesthesiologist. ChatGPT underperformed the anesthesiologist regarding rate of responses in agreement with scientific consensus (96.6% vs. 99.3%, p = .02) and possibility of harm (4.7% vs. 1.7%, p = .04), but performed similarly in other measures (percentage of responses with inappropriate/incorrect information (5.7% vs. 2.7%, p = .07) and missing information (10.0% vs. 7.0%, p = .19)). In conclusion,LLMs show great potential in healthcare, but additional improvement is needed to decrease the risk of patient harm and reduce the need for close physician oversight. Further research with more complex clinical scenarios, clinicians, and live patients is necessary to validate their role in healthcare.

Correction factors for induced radioactivity: comparing simplified continuous and periodic irradiation scenarios in medical cyclotron decommissioning.

The use of a Monte Carlo code in the assessment of residual radioactivity for decommissioning of a medical cyclotron facility requires a reasonable approximation to the facility's history of operations. A periodic irradiation scenario simulating the cyclotron's daily operation for radioisotope production is generally considered reasonable, but its implementation in the code's input file can be cumbersome because more than thousands of irradiation cycles must be modeled. In practice, two simplified scenarios with continuous irradiation are commonly used instead: (i) omitting the downtime between two irradiation periods and (ii) extending the irradiation duration across the entire operational lifespan of the facility, albeit with a reduced beam current to maintain workload consistency. A systematic comparison of residual radionuclide productions across various half-lives under these three scenarios was performed. This technical note presents the resulting correction factors for the two simplified continuous irradiation models, enhancing their applicability in estimating radioactive inventories under a range of circumstances.

Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

This paper introduces a novel approach to fault detection in the servo motor bearings of industrial robots within the context of Industry 4.0 prognostics and health management. The proposed solution leverages the innovative feature aggregation network for robotic fault detection in the application of smart factory. Overcoming challenges associated with traditional techniques that include handcrafted features, transfer learning, and deep learning models, the proposed approach offers a hierarchical information aggregation mechanism. The model is customized through hyperparameter tuning, resulting in a streamlined architecture with significantly fewer parameters. This parameter efficiency is notably distinct when compared to off-the-shelf transfer learning models that commonly feature extensive parameter counts in the range of hundreds of thousands or millions. The proposed model subjected to rigorous validation across diverse experimental scenarios that affirm its adaptability and robust performance. The model showcases accuracy in fault detection under both simple and welding motion scenarios, while its generalization capabilities are demonstrated as it successfully predicts health states in welding motion, showcasing versatility and reliability across various operational scenarios.

Inter-Satellite Link Prediction with Supervised Learning Based on Kepler and SGP4 Orbits

Distributed Space Systems (DSS) are gaining prominence in the space industry due to their ability to increase mission performance by allowing cooperation and resource sharing between multiple satellites. In DSS where communication between heterogeneous satellites is necessary, achieving autonomous cooperation while minimizing energy consumption is a critical requirement, particularly in sparse constellations with nano-satellites. In order to minimize the functioning time and energy consumed by the Inter-Satellite Links established for satellite-to-satellite communication, their temporal encounters must be anticipated. This work proposes an autonomous solution based on Supervised Learning that allows heterogeneous satellites in circular polar Low-Earth Orbits to predict their close-approach encounters given the Orbital Elements. The model performance is evaluated and compared in two different scenarios: (1) a simplified scenario assuming Kepler orbits and (2) a realistic scenario assuming Simplified General Perturbations 4 orbital model. The obtained results demonstrate a Balanced Accuracy exceeding 95% when compared to realistic data from an available database. This work represents a promising initial stage in developing an alternative approach within the field of DSS.

Magnetic surface geometry inversion of Kimberlites in Botswana

AbstractSurface geometry inversion of geophysical data has recently been introduced as an effective approach for generating surface‐based geological models. The models obtained through surface geometry inversion clearly delineate the contacts between distinct rock units, making them easily interpretable by geologists. Surface geometry inversion has shown promising preliminary results in other works, but the practical application of surface geometry inversion on real geophysical data has not been thoroughly investigated. To move towards a better understanding of the practicalities involved, we applied surface geometry inversion to a real magnetic dataset acquired over two kimberlite pipes located in north‐central Botswana. The objective was to assess the effectiveness and limitations of the surface geometry inversion approach in accurately characterizing the subsurface geometry and identifying the boundaries of the kimberlite pipes. We first perform an anomaly separation approach to isolate the magnetic anomalies associated with the kimberlite pipes. A surface geometry inversion algorithm was applied to the original and separated datasets using various initial models and other control parameters. Several tests were performed to investigate the effects that data processing, initial models, and other parameter choices have on the surface geometry inversion results. We successfully recover the geometry, extension and dip of the two kimberlite pipes. We discuss the results of our various tests and provide advice for practitioners interested in applying surface geometry inversion methods to their data. Our work indicates that surface geometry inversion can be used as a complementary approach to voxel inversion, and we propose an iterative surface geometry inversion algorithm as a possible alternative approach to voxel inversion for simple geological scenarios. This work provides valuable insights into the appropriate application of surface geometry inversion on real geophysical datasets.