Simulation Strategy Research Articles

Numerical simulation and disaster prevention strategies for flood intrusion process in subway stations

Subway stations are prone to serious consequences when occurring a flood disaster due to the isolation of subway stations from the ground. To assess the risk of personnel evacuation when floods invade subway stations and explore the safest evacuation time for people. A numerical simulation model for flood intrusion into a subway station was established in this study based on the Realizable k-ε turbulence and Volume of Fluid (VOF) model, which can be applied to simulate the water flow characteristics in subway stations. The subway station and tunnel model were constructed by taking a subway station in Zhengzhou Metro Line 5 as a research object. The impacts of tunnel floodgate opening/closing, different numbers of water inlets, and different inlet water depths on the flood invasion process were investigated. The flood invasion process, invasion time, and spread pattern of subway stations under various operating conditions were analyzed. Moreover, suggestions for human evacuation within the station were proposed. The results indicate that the closed tunnel floodgate causes an increase in water collection in the subway station. While opening the tunnel floodgate can efficiently relieve flood pressure in the subway station, it is critical to avoid floods pouring through the tunnel to the next station. The increase in water depth and water inlet number led to an increase in the water velocity intrusion into the subway station, resulting in a rapid increase in the water depth in the subway station, and pedestrians ought to evacuate the subway station as soon as possible. The research findings are intended to serve as a reference for the safe evacuation of people when floods invade subway stations.

EXPLORING THE LIMITS OF MCTS IN PAC-MAN: MAZE SIZE, SIMULATIONS, AND PERFORMANCE

This paper explores the performance and limitations of Monte Carlo Tree Search (MCTS) when applied to the Pac-Man game, with a particular focus on how maze complexity and the number of simulations affect the agent’s decision-making process. The game serves as a dynamic environment where the MCTS agent must navigate mazes filled with rewards (capsules and food) and avoid adversarial agents (ghosts), creating a challenging testbed for decision-making algorithms. The primary objective of this work is to assess the efficiency of MCTS across mazes of varying sizes and configurations, from small, optimized layouts to large, non-optimized ones. We aim to understand the trade-offs between computational resources (e.g., number of simulations) and the agent's overall performance, particularly in terms of score, win rate, and decision-making time. The experiments were conducted using different numbers of simulations per move, allowing the MCTS agent to build decision trees that guide its actions. This enabled us to observe how performance metrics evolve as the complexity of the environment increases. The findings indicate that MCTS performs effectively in smaller, optimized mazes where paths are clearer, and the decision space is more manageable. However, as maze complexity grows—particularly in non-optimized environments filled with obstacles and unpredictable paths—the agent's performance deteriorates. A key insight is that increasing the number of simulations does improve decision quality to an extent, but only up to a point; beyond that, additional simulations incur a computational overhead that does not yield proportional gains in performance. Moreover, the experiments reveal that optimized maze designs allow the MCTS agent to make more informed and efficient decisions, while non-optimized mazes exacerbate the agent’s struggle with unpredictability and more complex decision spaces. These findings underscore the limitations of MCTS in handling dynamic and irregular environments. Key conclusions include the necessity of enhancing MCTS for complex scenarios by incorporating more advanced heuristic functions to evaluate game states more accurately, along with adaptive simulation strategies to better manage computational resources. Additionally, combining MCTS with reinforcement learning or neural networks could offer more robust solutions for tackling the growing complexity of both in-game and real-world environments. This research highlights several promising avenues for future exploration, particularly in applying MCTS to more intricate AI challenges such as autonomous navigation and real-time decision-making in robotics.

Optimization Analysis of Clothing Fabric Design Based on Autoencoder and Generative Adversarial Neural Network

In order to meet the challenges of increasingly diverse clothing styles and consumer demands in the market, traditional fabric design methods have become difficult to meet the needs of rapid iteration and innovation due to their time-consuming, costly, and subjective preferences of designers. In view of this, we propose an innovative fabric design optimization simulation strategy aimed at breaking through these bottlenecks through technological means. This strategy cleverly integrates advanced technologies of variational autoencoder (VAE) and generative adversarial network (GAN). First, VAE is used to capture and learn the complex distribution characteristics of existing clothing fabric designs, which include key information such as fabric texture, color matching, and structural details. Subsequently, GAN uses the hidden vectors obtained from VAE as input to generate brand new fabric design samples. During the training phase, GAN continuously iterates and optimizes, engaging in intense “adversarial” interactions between its generator and discriminator. The generator is dedicated to creating new samples that are as close to real fabric designs as possible, while the discriminator is responsible for identifying the authenticity of these samples. This process is implemented through backpropagation (BP) loss function, ensuring that the generated fabric design can visually simulate real fabrics. Experimental verification shows that this method can not only effectively generate high-quality and realistic clothing fabric designs, but also greatly shorten the design cycle and reduce costs.

Integrating time series Sentinel-2 images and tide height to mapping tidal flats in the Chinese mainland

As a unique and important ecosystem, tidal flats provide a variety of ecosystem functions and services. Mapping tidal flats is essential for the protection and management of coastal ecosystems. However, large-scale tidal flats mapping still faces challenges due to the tidal variation and spectral similarity between tidal flats and inland wetlands. Previous methods rely on the coastlines or maximum seawater extent to exclude inland areas, which is limited by its inability to effectively differentiate tidal flats from spectrally similar inland wetlands. To address these issues, we proposed a new tidal-flat mapping method by integrating Sentinel-2 time series imagery with tide height (TH) data from ground-based tide stations on Google Earth Engine. We first generated images at the lowest and highest tidal stages, established the statistical relationship between the Normalized Difference Water Index (NDWI) of each pixel and TH, and then concatenated them into a Random Forest classifier for further classification. The statistical relationship between NDWI and TH amplified the difference between tidal flats and inland wetlands, thus significantly reducing the influence of spectral similarity. This method could produce a high-precision tidal flats map with an overall accuracy of 97.30% in the coastal zone of the Chinese mainland. By quantitatively comparing with the previous tidal flat maps, we found that the strategies of tidal-level information simulation and inland area exclusion were the two main reasons producing the differences among the maps. The proposed method does not rely on space constraints to exclude inland wetlands and can capture more estuarine tidal flats, so it can be used as a reliable means to monitor the tidal flats in large-scale areas.

Equivalent spring model and efficient numerical strategy for simulating mechanical performance of seam-clip connections in aluminum alloy high-vertical standing seam metal cladding systems

The equivalent spring model has been validated as effective in simulating the mechanical performance of seam-clip connections in typical steel high-vertical standing seam metal cladding systems (SSMCSs). To enhance its applicability, this study further refines the equivalent spring model by developing thickness-related stiffness calibration models and an efficient numerical strategy for seam-clip connections in the aluminum alloy SSMCSs. Tensile tests are initially conducted to analyze the material properties of specimens with three commonly used thicknesses. Subsequently, the mechanical performance of the aluminum alloy seam-clip connections with different thicknesses is systematically studied, followed by the development of thickness-related stiffness calibration models. The tensile test results reveal notable nonlinearities and variability in the mechanical performance. To simulate this mechanical performance equivalently in the finite element (FE) model using data from tensile tests, an efficient numerical strategy is developed. This strategy involves methods for implementing simplified connections to establish the equivalent spring model and techniques for efficiently creating these simplified connections. It is found that both connectors and spring connections with calibrated parameters from tensile tests can effectively replicate the mechanical performance of seam-clip connections. To further validate the effectiveness of the equivalent spring model and the efficient numerical simulation strategy, a comparison of structural responses of the double-span sheet module (DSSM) obtained from tests and FE analysis is performed. The results reveal that all simulated structural responses on the DSSM with three common sheet widths align well with the corresponding experimental results. This highlights the feasibility and efficacy of the equivalent spring model and efficient numerical strategy in simulating the mechanical performance of seam-clip connections and delving deeper into the structural responses of the aluminum alloy high-vertical SSMCSs.

A control theoretic approach to evaluate and inform ecological momentary interventions.

Ecological momentary interventions (EMI) are digital mobile health interventions administered in an individual's daily life to improve mental health by tailoring intervention components to person and context. Experience sampling via ecological momentary assessments (EMA) furthermore provides dynamic contextual information on an individual's mental health state. We propose a personalized data-driven generic framework to select and evaluate EMI based on EMA. We analyze EMA/EMI time-series from 10 individuals, published in a previous study. The EMA consist of multivariate psychological Likert scales. The EMI are mental health trainings presented on a smartphone. We model EMA as linear dynamical systems (DS) and EMI as perturbations. Using concepts from network control theory, we propose and evaluate three personalized data-driven intervention delivery strategies. Moreover, we study putative change mechanisms in response to interventions. We identify promising intervention delivery strategies that outperform empirical strategies in simulation. We pinpoint interventions with a high positive impact on the network, at low energetic costs. Although mechanisms differ between individuals - demanding personalized solutions - the proposed strategies are generic and applicable to various real-world settings. Combined with knowledge from mental health experts, DS and control algorithms may provide powerful data-driven and personalized intervention delivery and evaluation strategies.

Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins

Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins

Digital Quantum Simulation of a (1+1)D SU(2) Lattice Gauge Theory with Ion Qudits

We present a quantum simulation strategy for a (1+1)-dimensional SU(2) non-Abelian lattice gauge theory with dynamical matter, a hardcore-gluon Hamiltonian Yang-Mills, tailored to a six-level trapped-ion-qudit quantum processor, as recently experimentally realized [Nat. Phys. 18, 1053 (2022)]. We employ a qudit encoding fulfilling gauge invariance, an SU(2) Gauss’s law. We discuss the experimental feasibility of generalized Mølmer-Sørensen gates used to efficiently simulate the dynamics. We illustrate how a shallow circuit with these resources is sufficient to implement scalable digital quantum simulation of the model. We also numerically show that this model, albeit simple, can dynamically manifest physically relevant properties specific to non-Abelian field theories, such as baryon excitations. Published by the American Physical Society 2024

Assessing Irrigation Efficiency Improvements in Paddy Fields Using Granular SWMM Simulations

As water scarcity intensifies due to climate change, improving the efficiency of agricultural water use has become increasingly critical. Current irrigation systems often experience significant water losses, especially in paddy fields in South Korea that largely rely on open-channel water supply networks. However, previous studies have simulated irrigation improvement strategies by aggregating multiple paddy fields into larger unites rather than modeling them individually, which limits the accurate representation of field conditions. To address this limitation, we applied a granular simulation approach, collecting detailed input data through field surveys. Using this granular model, the study evaluated strategies to enhance irrigation efficiency in paddy fields serviced by the Baekma Agricultural Reservoir in South Korea. We assessed four scenarios: the current open-channel system, conversion of open channels to closed conduits, installation of farm ponds, and a combination of closed conduits and farm ponds. These scenarios were simulated using the Strom Water Management Model (SWMM), meticulously configured to represent individual paddy fields and channel networks. The results show that converting open channels to closed conduits increased irrigation efficiency by 5.4% compared to the current open-channel system. Combining closed conduits with farm ponds achieved the highest efficiency, although the independent effect of farm ponds was minimal. These findings suggested that converting open-channels to closed conduits was a highly effective solution for reducing water losses, while farm ponds played a limited role. This study provides valuable insights into the development of precise irrigation strategies, offering a detailed assessment of real-world conditions in paddy fields.

Spatial targeting and integration across vaccination, vitamin A and deworming programs throughout India 2019-21.

Currently, most large-scale public health programs, such as immunization or anti-parasitic deworming, work in relative isolation. Integrating efforts across programs could potentially improve their efficiency, but identifying populations that could benefit from multiple programs has been an operational challenge. We analyzed a nationally representative survey conducted in India between 2019 and 2021 to assess and map coverage of seven vaccines [Bacillus Calmette-Guérin (BCG), hepatitis B, polio, diphtheria-tetanus-pertussis (DTP), haemophilus influenza type b (Hib), rotavirus and measles-containing vaccine (MCV)], plus Vitamin A supplementation and anti-parasitic deworming treatment among 86761 children aged 1-3 years old. National coverage varied widely by program, from 42% (rotavirus) to 95% (BCG). There was high correlation between district-level coverage estimates (r ≥ 0.7) and extensive spatial overlap in low-coverage populations. In simulated implementation strategies, we show that an integrated strategy that targets full immunization coverage for four core vaccines (BCG, polio, DTP, MCV) would achieve similar coverage to an optimal (but unrealistic) implementation strategy and far better coverage than multiple efforts focused on individual vaccines. Targeting the most under-vaccinated districts within states based on spatial clustering or coverage thresholds led to further improvements in full coverage per child targeted. Integration of anti-parasitic deworming or rotavirus vaccination into a core vaccine delivery mission could nearly double their coverage (from ∼45% to ∼85%). Integrated delivery and geographic targeting across core vaccines could accelerate India's progress toward full immunization coverage. An integrated platform could greatly expand coverage of non-core vaccines and other child health interventions.

Fast simulation strategy for capacitively-coupled plasmas based on fluid model

Fluid simulations are widely used in optimizing the reactor geometry and improving the performance of capacitively coupled plasma (CCP) sources in industry, so high computation speed is very important. In this work, a fast method for CCP fluid simulation based on the framework of Multi-physics Analysis of Plasma Sources (MAPS) is developed, which includes a multi-time-step explicit upwind scheme to solve electron fluid equations, a semi-implicit scheme and an iterative method with in-phase initial value to solve Poisson's equation, an explicit upwind scheme with limited artificial diffusion to solve heavy particle fluid equations, and an acceleration method based on fluid equation modification to reduce the periods required to reach equilibrium. In order to prove the validity and efficiency of the newly developed method, benchmarking against COMSOL and comparison with experimental data have been performed in argon discharges on the Gaseous Electronics Conference (GEC) reactor. Besides, the performance of each acceleration method is tested, and the results indicated that the multi-time-step explicit Euler scheme can effectively decline the computational burden in the bulk plasma and reduce the time cost on the electron fluid equations by half. The in-phase initial value method can greatly decrease the iteration times required to solve linear equations and lower the computational time of Poisson's equation by 77 %. The acceleration method based on equation modification can reduce the periods required to reach equilibrium by two-thirds.

Application of Nanomaterials in the Repair and Regeneration of Lymphatic Organs and Corresponding Biophysical Simulation Strategies

Immune system diseases, malignant tumors, and traumatic injuries can directly damage the structure and function of lymphoid organs, while subsequent radiotherapy, chemotherapy, and lymph node dissection further damage the patient's immune system, leading to immune dysfunction, metabolic disorders, and increased susceptibility to infection, which seriously affect the patient's prognosis and quality of life. In this context, nanotechnology plays a key role in lymphoid organ regeneration and immune function recovery, including improving the therapeutic effect through targeted drug delivery systems, using targeted imaging probes to achieve tumor prediction and early detection, combining nanoplatforms with immunotherapy and photodynamic therapy to achieve synergistic therapeutic effects, and using nanomaterials to regulate the tumor microenvironment to enhance the sensitivity of traditional treatments. In addition, biophysical simulation strategies that simulate the microenvironment of lymphoid organs have also attracted widespread attention, aiming to construct a native cell environment to support the regeneration and functional recovery of damaged lymphoid tissues, or to simulate immune cells to regulate lymphocytes and induce specific immune responses. The multifaceted application of nanotechnology provides promising prospects for lymphoid organ regeneration and immune system repair.

Scotland's internal medicine simulation strategy: A 5-year journey

In 2019, the internal medicine (IM) stage 1 curriculum was implemented in the UK. This introduced a revised 3-year training programme for physicians in training. The new IM stage 1 curriculum emphasised simulation-based education, triggering the integration of simulation training on a national scale in Scotland. Bespoke training courses were developed for IM trainees in Scotland, spanning all 3 years of their training programme. Each course was meticulously designed with input from key stakeholders and trainees, aligning learning objectives with curriculum outcomes. The Scottish IM simulation strategy encompasses a 3-day bootcamp in IM year 1, a 1-day skills day in IM year 2, and a 2-day ‘registrar ready’ course in IM year 3. These courses, delivered at central locations, focus on immersive simulation, communication workshops, and simulation-based mastery learning of procedural skills. Evaluation of the programme includes pre- and post-course questionnaires, data tracking over the training programme, interviews with trainees and faculty, and annual review days to gather feedback and make necessary adaptations. The impact of the programme for trainees includes transfer to clinical practice of communication skills, improved non-technical skills, and increased confidence in procedural skills. Alignment with the IM stage 1 curriculum supports trainees’ annual reviews of competence progression. The IM simulation training also offers valuable social benefits and remains crucial, especially with increased pressure in the NHS. It is our belief that ongoing delivery of this training is invaluable to the IM stage 1 programme in Scotland.

Dynamic simulation and control strategy development of molten salt steam generation system for coal-fired power plant flexible retrofit

The coal-fired power plant (CFPP) coupled with the molten salt thermal energy storage system is a potential way to improve its flexibility and peak-shaving ability. The steam generation system (SGS) is a suitable choice to convert the feed water into hot steam by using the heat from the molten salt. In this paper, the schematic of an SGS coupled with the CFPP is presented. A dynamic model of the SGS basing lump parameter method is established and validated to investigate its dynamic response characteristics. Five different disturbance experiments including feed water inlet parameters, molten salt inlet parameters and medium pressure steam valve, are conducted and analyzed. The dynamic response curves of molten salt and steam are obtained. Moreover, the load adjustment process of SGS with three different load changing rates (3 %, 6 %, 10 % Pe/min) under the rated conditions is compared and its influence on the SGS is analyzed. The temperature changing rates in the thick-wall components of SGS are within 2 °C/min which meets the safe operating standard. Based on the above results, the three elements control strategy with excellent robustness for the SGS safety operation is proposed and demonstrated. The results show that the response time for the SGS load regulation process and the water level fluctuation have been significantly improved as the three elements control strategy is imposed. The water level can be stabilized in 200 s with tiny fluctuation when the SGS loads down 10 % thermal load with 10 % Pe/min load changing rate. These results could provide useful references for the design and control strategy making of the CFPP coupled with the molten salt thermal energy storage system.

Intelligence analysis of membrane distillation via machine learning models for pharmaceutical separation

This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes.

Subjective learning gain from a simulation-based health management course: a mixed methods study.

Simulations are increasingly being offered as part of the educational experience of healthcare students. We used a Health Management Scenario Simulation system to create a course. This study aimed to evaluate learning gains before and after the course. Based on the learning strategies of framing, simulation, and debriefing, the Health Management Scenario Simulation course lasted 4 weeks and was conducted online. Learning gain was assessed using a comparative self-assessment questionnaire administered electronically at the beginning and end of the course. We organized focus group interviews and collected quantitative data after students completed the simulations and the questionnaire. These data were subjected to descriptive statistical analysis and thematic grouping using frequency counting. There were 195 health management students enrolled in the course. In total, 265 anonymously completed questionnaires were received, 141 (72.31%) on the pre-simulation and 124 (63.59%) on the post-simulation. All questionnaire item gain values were positive, except the item "I can identify common health risk factors," which showed no change. The skills domain showed the highest learning gain, ranging from 16 to 22%. Six students participated in the focus-group study. The main themes that emerged from students' reflections were learner-centeredness, competencies, and career development. Students acquired health management skills through the simulation, which contributed to the development of basic attitudes and skills in their professional careers. Students' comments highlighted the value of practicing health management skills in a simulated environment.

Hybrid multiscale computational approach for heat transfer in GaN semiconductors

Abstract With ongoing advancements in semiconductor technology, understanding thermal transport in semiconductor devices—where phonons are the primary charge carriers—is crucial. This involves a multiscale process in which phenomena at different scales are governed by distinct control equations, necessitating various numerical methods. In this context, this paper introduces a novel multiscale simulation method called the Second-Order Reconstruction Operator Finite Difference Lattice Boltzmann Method (ROsFDLBM). This method effectively combines the Lattice Boltzmann Method (LBM) with the Finite Difference Method (FDM). ROsFDLBM employs reconstruction operators to accurately map the relationship between distribution functions and macroscopic physical quantities, significantly reducing errors caused by the transmission of interface information. The efficacy of this coupling scheme has been confirmed through an interface-coupled thermal diffusion model. Comparative analyses have revealed that the relative error of ROsFDLBM does not exceed 1.2%, demonstrating its superior accuracy. Further simulations of GaN devices have confirmed that traditional macroscopic methods tend to underestimate the temperature in the heat source area at micro-nano scales, with discrepancies reaching up to 60.9 K. Overall, the ROsFDLBM aligns with current research findings and offers an accurate and efficient simulation strategy for addressing complex heat transfer problems.

Accuracy of four models and update strategies to estimate liver tumor motion from external respiratory motion.

This study investigates different strategies for estimating internal liver tumor motion during radiotherapy based on continuous monitoring of external respiratory motion combined with sparse internal imaging. Fifteen patients underwent three-fraction stereotactic liver radiotherapy. The 3D internal tumor motion (INT) was monitored by electromagnetic transponders while a camera monitored the external marker block motion (EXT). The ability of four external-internal correlation models (ECM) to estimate INT as function of EXT was investigated: a simple linear model (ECM1), an augmented linear model (ECM2), an augmented quadratic model (ECM3), and an extended quadratic model (ECM4). Each ECM was constructed by fitting INT and EXT during the first 60s of each fraction. The fit accuracy was calculated as the root-mean-square error (RMSE) between ECM-estimated and actual tumor motion. Next, the RMSE of the ECM-estimated tumor motion throughout the fractions was calculated for four simulated ECM update strategies: (A) no update, 0.33Hz internal sampling with continuous update of either (B) all ECM parameters based on the last 2 minutes samples or (C) only the baseline term based on the last 5 samples, (D) full ECM update every minute using 20s continuous internal sampling. The augmented quadratic ECM3 had best fit accuracy with mean (± SD)) RMSEs of 0.32 ± 0.11mm (left-right, LR), 0.79 ± 0.30mm (cranio-caudal, CC) and 0.56 ± 0.31mm (anterior-posterior, AP). However, the simpler augmented linear ECM2 combined with frequent baseline updates (update strategy C) gave best motion estimations with mean RMSEs of 0.41 ± 0.14mm (LR), 1.02 ± 0.33mm (CC) and 0.78 ± 0.48mm (AP). This was significantly better than all other ECM-update strategy combinations for CC motion (Wilcoxon signed rank p<0.05). The augmented linear ECM2 combined with frequent baseline updates provided the best compromise between fit accuracy and robustness towards irregular motion. It allows accurate internal motion monitoring by combining external motioning with sparse 0.33Hz kV imaging, which is available at conventional linacs.

Handling delayed or missed direct oral anticoagulant doses: model-informed individual remedial dosing

AbstractNonadherence to direct oral anticoagulant (DOAC) pharmacotherapy may increase the risk of thromboembolism or bleeding, and delayed or missed doses are the most common types of nonadherence. Current recommendations from regulatory agencies or guidelines regarding this issue lack evidence and fail to consider individual differences. This study aimed to develop individual remedial dosing strategies when the dose was delayed or missed for DOACs, including rivaroxaban, apixaban, edoxaban, and dabigatran etexilate. Remedial dosing regimens based on population pharmacokinetic (PK)-pharmacodynamic (PD) modeling and simulation strategies were developed to expeditiously restore drug concentration or PD biomarkers within the therapeutic range. Population PK-PD characteristics of DOACs were retrieved from previously published literature. The effects of factors that influence PK and PD parameters were assessed for their impact on remedial dosing regimens. A web-based dashboard was established with R-shiny to recommend remedial dosing regimens based on patient traits, dosing schedules, and delay duration. Addressing delayed or missed doses relies on the delay time and specific DOACs involved. Additionally, age, body weight, renal function, and polypharmacy may marginally affect remedial strategies. The proposed remedial dosing strategies surpass current recommendations, with less deviation time beyond the therapeutic range. The online dashboard offers quick and convenient solutions for addressing missed or delayed DOACs, enabling individualized remedial dosing strategies based on patient characteristics to mitigate the risks of bleeding and thrombosis.

Two-Step Chirality Transfer to Twisted Assemblies: Synergistic Interplay of Chiral and Aggregation Interactions.

Chirality plays a pivotal role in both the origin of life and the self-assembly of materials. However, the governing principles behind chirality transfer in hierarchical self-assembly across multiple length scales remain elusive. Here, we propose a concise and versatile simulation strategy using the patchy particle chain model to investigate the self-assembly of rods interacting through chiral and aggregation interactions. We reveal that chiral interaction possessing an entropic nature, amplifies the fluctuations and twists in the alignment of rods, while aggregation interaction serves as a foundational platform for aggregation and assembly. When both interactions exhibit moderate absolute and relative values, their synergistic interplay facilitates the chirality transfer from rods to assemblies, resulting in the formation of chiral mesoscale ordered structures. Furthermore, we observe a two-step chirality transfer process by monitoring the formation kinetics of the twisted assemblies. This work not only provides a comprehensive insight into chirality transfer mechanisms, but also introduces a versatile mesoscale simulation framework for exploring the role of chirality in hierarchical self-assembly.