Performance In Real Environment Research Articles

OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler

The material acceleration platform, empowered by robotics and artificial intelligence, is a transformative approach for expediting material discovery processes across diverse domains. However, the development of an operating system for material acceleration platform faces challenges in simultaneously managing diverse experiments from multiple users. Specifically, when it is utilized by multiple users, the overlapping challenges of experimental modules or devices can lead to inefficiencies in both resource utilization and safety hazards. To overcome these challenges, we present an operation control system for material acceleration platform, namely, OCTOPUS, which is an acronym for operation control system for task optimization and job parallelization via a user-optimal scheduler. OCTOPUS streamlines experiment scheduling and optimizes resource utilization through integrating its interface node, master node and module nodes. Leveraging process modularization and a network protocol, OCTOPUS ensures the homogeneity, scalability, safety and versatility of the platform. In addition, OCTOPUS embodies a user-optimal scheduler. Job parallelization and task optimization techniques mitigate delays and safety hazards within realistic operational environments, while the closed-packing schedule algorithm efficiently executes multiple jobs with minimal resource waste. Copilot of OCTOPUS is developed to promote the reusability of OCTOPUS for potential users with their own sets of lab resources, which substantially simplifies the process of code generation and customization through GPT recommendations and client feedback. This work offers a solution to the challenges encountered within the platform accessed by multiple users, and thereby will facilitate its widespread adoption in material development processes.

Negative Effect of the Calendering Process on the Interphase Formation and Electrochemical Behavior of Reduced Graphene Oxide Electrodes.

Many studies on electrode material development for rechargeable batteries have focused on improving the intrinsic physicochemical and electrochemical properties of active materials, but the electrochemical performances of batteries are exhibited by the overall electrode unit consisting of active materials, conductive additives, and a binder. Additionally, the electrodes have undergone an essential calendering process to enhance the physical contact between those components. Therefore, the electrochemical behavior and performance of a cell should be analyzed at the electrode level, as the inherent properties of active materials might be changed in electrode preparation, including the calendaring process and real-operating environments. In this study, we aimed to understand the electrochemical properties of the reduced graphene oxide (RGO)-containing electrodes rather than the RGO-active materials by studying the changes in the RGO electrode before and after the calendering process. Specifically, the study investigates the effect of the calendering process on the electrochemically active interphase formation and electrochemical properties of the RGO electrode. We found that the calendering process deteriorates the electrochemical properties of RGO electrodes by impeding enough electrolyte wetting, limiting the formation of thin and stable solid-electrolyte interphase, and leaving unreacted RGO sheets. Additional experiments with carbon-coated silicon/RGO composite electrodes demonstrate that after the calendering process, the sequential participation of Si/C particles in the electrochemical reaction resulted in much more severe capacity degradation over repeated cycling processes. The studies suggest that fine-controlling the number of RGO sheets and maintaining enough distance between those sheets even after the calendering process are required for the utilization of RGO in rechargeable batteries.

Innovative passive BLE technology as an alternative to UHF RFID: Preliminary lab performance test results for main use cases

In recent years, an innovative passive Bluetooth Low Energy (BLE) technology, namely Wiliot technology, has emerged as an alternative to passive UHF RFID. This paper proposes an experimental campaign aimed at measuring and evaluating the performance of Wiliot technology, when applied to typical use cases of passive UHF RFID technology. In particular, 9 laboratory tests were conducted to assess read distances, temperature tests, inventory accuracy, and tracking. The results provide interesting feedback, especially regarding reading distances, inventory accuracy, and temperature, suggesting that Wiliot technology could be successfully utilized in these use cases. The tracking tests, however, revealed some limitations, primarily due to the requirement for the tag to remain stationary in front of the bridge for at least 10–15 seconds to obtain consistent results. Although the number of tests conducted is limited, they represent a first experience with the targeted technology, and the results can be useful to both researchers and practitioners. The former can find suggestions for further tests to enhance the technology and its potential use in real operating environments, while the latter can derive suggestions for possible practical implementations.

Longevous ionogels with high strength, conductivity, adhesion and thermoplasticity

The longevity of flexible electronics substantially influences their viability for practical use. At present, the service life of pliable devices utilizing conductive gels is frequently compromised due to factors inclusive but not limited to solvent loss. Taking cues from the steadfast structure of human tissues, we hereby delineate a solvent-assisted strategy to fabricate ionogels, predicated on a supramolecular network of hydrogen bonds. Upon subjecting these ionogels to realistic operational environments, it has been empirically ascertained that they maintain a steadfast mechanical and electrical consistency for a minimum duration of 22 months. In terms of dynamic stretchability, they effortlessly endure one million stretch cycles, even when faced with a notch. The induction of a supramolecular structure within the ionogel not only confers exceptional mechanical attributes but also simultaneously enhances conductivity, a feat paralleled by few recent explorations. Furthermore, these ionogels boast of superior anti-freezing and adhesive characteristics, including a toughness extending up to 17.96 MJ/m3 at an ultra-low temperature of −50 °C, and an adhesive capacity permitting movement and bearing of items weighing over 10,000 times its own weight, the lap-shear strength can reach 30.2 kPa. Intriguingly, these gels demonstrate thermoplastic behavior, which facilitates their easy reshaping into diverse forms. Such compelling attributes amplify the potential of supramolecular ionogels, positioning them as formidable contenders for flexible electronics in stringent and challenging scenarios.

(Energy Technology Division Research Award) New Materials vs. Reaction Engineering in Anion Exchange Membrane Fuel Cells

In recent years, the desire to design, create and/or discover advanced functional nanomaterials has changed the way that faculty members and graduate students approach engineering research. Though materials can (and likely will) make a significant impact on most technologies, the truth is that in most disciplines, materials with highly complex structures remain on the fringes of realistic deployment. In some cases, even when so-called “advanced” materials are discovered there are significant difficulties in transitioning them from ex-situ tests to the real reacting environment. Therefore, we must be careful to understand that material chemistry and structure are only two variables in an extremely complex system and often times there are other fundamental (e.g. electronic mobility, thermodynamic barriers, diffusivity) and engineering (porosity, concentration, temperature) properties that drive behavior in such environments. Additionally, there are systems where materials are being sought without a clear understanding of what is truly limiting performance and durability or the properties that are required of a real engineered system to operate effectively.Anion exchange membrane fuel cells (AEMFCs) are an example system where the literature is littered with exotic materials that claim to improve activity and stability (presumed as performance and durability in real devices). However, it will be shown that for several years, such new materials had limited success (if any) in advancing performance and durability. This talk will summarize ~five years of work in AEMFCs where the performance and durability of AEMFCs were improved significantly without any new materials – just using traditional chemical engineering principles for reactor engineering. Only later – once device operation was well understood and controlled – were new materials helpful. On the operational side, the main discussion point will be the transport of water. On the materials side, new membranes and catalysts will be discussed. The talk will end with perspectives on transitioning AEMFCs to more realistic operating environments, including the mitigation of effects from CO2.

Development of A Man-Machine Interface for Managing Photovoltaic Energy: Applied Study

Objective: Develop and evaluate a human-machine interface (HMI) that integrates advanced monitoring, forecasting, and management functionalities for photovoltaic solar energy systems, aiming to optimize energy production and operational efficiency. Theoretical Framework: This study is based on concepts of modeling and simulation, solar energy management, and problem-solving methodologies such as Soft System Methodology (SSM). Method: An applied approach was adopted using modeling, simulation, and statistical analysis techniques. The research included a bibliographic review in scientific databases, a case study, and SSM to organize and solve complex problems. 121 digital solar energy platforms in Brazil were analyzed to define the interface requirements. The interface was developed with React JS, Axios, Bootstrap v5, Apache Echarts, HTML, CSS, JavaScript, and Python libraries for forecasting models. Results and Discussion: The interface, named "Solar Smart Manager," enables efficient monitoring and management of energy production using critical data such as temperature, time of day, and solar irradiation. Tests in a real operational environment demonstrated improvements in energy management, incident response, and preventive maintenance. The functionality of validating solar radiation incidence data represents a significant contribution to the energy sector, promoting sustainability and innovation. Research Implications: The practical and theoretical implications of this research provide insights into the efficient and optimized management of photovoltaic solar energy systems, contributing to a better understanding and optimization of available solar resources. Originality/Value: This study contributes to the literature by developing an innovative interface that improves operational efficiency and solar energy management. The relevance and value of this research are evidenced by its positive impact on the energy sector, promoting sustainability and innovation.

Drag-Mitigating Dynamic Flight Path Design and Sensitivity Analysis for an Ultra-Long Tether Underwater Kite

Abstract This paper presents a computational study of an underwater kite operating in environments requiring tethers exceeding a kilometer in length, referred to herein as ultralong tether (ULT) applications. Leveraging a detailed dynamic model of the kite and tether, we study the relationship between path shape and tether drag at varying tether lengths in order to develop meaningful insights as to the operation of systems that require ultralong tethers in order to reach viable flow resources. An initial study of a ULT kite application operating in a uniform flow field is presented, demonstrating that with an appropriately designed flight path, the kite is able to suppress the motion of the majority of the tether in order to achieve an order of magnitude greater power output than can be achieved with a straight tether, which is the typical assumption used in a performance estimation. Tether drag mitigation is characterized through the use of a novel metric termed effective tether length, which characterizes the total length of tether engaged in drag production. It is shown that a high-performance path shape can reduce the effect of tether drag by over 50%. This performance is shown to be comparable to the multi-airborne wind energy system (MAWES) proposed by Leuthold et al. (2017, “Induction in Optimal Control of Multiple-Kite Airborne Wind Energy Systems,” IFAC-PapersOnLine, 50(1), pp. 153–158; 2018, “Operational Regions of a Multi-Kite Awe System,” 2018 European Control Conference (ECC), IEEE, Limassol, Cyprus, June 13–15, pp. 52–57), which suppresses tether motion through mechanical design rather than merely though careful path selection and control. This initial study in a uniform flow field is followed by two sensitivity studies: one that assesses performance in realistic environments where the flow magnitude is a function of altitude above the seabed and a second that assesses the impact of tether drag reduction techniques. It will be shown that by careful selection of path shape, site, and tether design, a single kite in a ULT marine application can achieve performance rivaling that of the MAWES without the extra required mechanical complexity.

OMS-SLAM: dynamic scene visual SLAM based on object detection with multiple geometric feature constraints and statistical threshold segmentation

Abstract SLAM technology is crucial to robot navigation. Despite the good performance of traditional SLAM algorithms in static environments, dynamic objects typically exist in realistic operating environments. These objects can lead to misassociated features, which in turn considerably impact the system’s localization accuracy and robustness. To better address this challenge, we have proposed the OMS-SLAM. In OMS-SLAM, we adopted the YOLOv8 target detection network to extract object information from environment and designed a dynamic probability propagation model that is coupled with target detection and multiple geometric constrains to determine the dynamic objects in the environment. For the identified dynamic objects, we have designed a foreground image segmentation algorithm based on depth image histogram statistics to extract the object contours and eliminate the feature points within these contours. We then use the GMS (Grid-based Motion Statistics) matching pair as the filtering strategy to enhance the quality of the feature points and use the enhanced feature points for tracking. This combined method can accurately identify dynamic objects and extract related feature points, significantly reducing its interference and consequently enhancing the system's robustness and localization accuracy. We also built static dense point cloud maps to support advanced tasks of robots. Finally, through testing on the high-speed dataset of TUM RGB-D, it was found that the root mean square error of the Absolute Trajectory Error (ATE) in this study decreased by an average of 97.10%, compared to ORB-SLAM2. Moreover, tests in real-world scenarios also confirmed the effectiveness of the OMS-SLAM algorithm in dynamic environments.

A computational search for the optimal microelectronic heat sink using ANSYS Icepak

The efficient thermal management of microelectronic devices is crucial for their reliability and performance. Heat sinks, particularly those with inline and staggered fin arrangements, are vital in achieving this goal. This study leverages the advancements in computational fluid dynamics (CFD) software to explore the optimal heat sink design for microelectronic devices. While prior research has extensively investigated heat sink design, this work offers novelty by employing ANSYS Icepak software for simulations. This approach enables a comprehensive thermal performance and pressure drop analysis for two distinct fin cross-sections. The study investigates heat sink assemblies housed within a confined space, simulating a realistic operating environment for microelectronic devices. The heat sink is designed to cool a single heat source (chip) mounted on a substrate board. In this paper, two types of fin sections, circular and conical, are studied for two types of fin configurations, inline (IA) and staggered (SA), which are analysed with 36 fins in each case. Each fin is constructed from aluminium and has specified dimensions. Air serves as the cooling fluid within the cabinet, dissipating heat generated by the chip. When comparing the performance of circular pin and cone fins at the same power and mass flow rate, the results indicated that the maximum temperature for circular pin fins is 0.46% of the maximum temperature for cone fins, and they have a higher pressure drop, especially for staggered arrangements. Moreover, the maximum temperature for staggered arrangements is lower than that for inline configurations by a factor of up to 1.17% and 2.035% for circular fins and cone pin fins, respectively. This article focuses on a deep understanding of the design of microelectronic cooling systems. It aims to continuously improve pressure and thermal efficiency by maintaining the minimum limits of pressure drop within the confined space and obtaining the optimal configuration for efficient heat dissipation.

Using Wargaming to Model Cyber Defense Decision-Making: Observation-Based Research in Locked Shields

Defensive Cyber Operations (DCO) in complex environments, such as cyber wargames, require in-depth cybersecurity knowledge and the ability to make quick decisions. In a typical DCO, execution rarely follows a pre-planned path because of extensive adversary influence, challenging an already complex decision-making environment. Decision-making models have been extensively studied from perspectives of military operations and business management, but they are not sufficiently researched in the context of cyber. This paper responds to this need by examining the decision-making models of DCO leaders in a live-fire wargame environment. This study was conducted by observing leaders of cyber operations during the world's largest live-fire cyber exercise, NATO Locked Shield 2023. In this exercise, the blue teams plan and execute their defensive cyber operation in a realistic operational environment, while the red team conducts attacks against the defended environment. The large-scale, wargaming-style environment of Locked Shield is one of the best environments for modelling DCO decision-making models; in this exercise, the DCO is broad and multi-faceted, a perspective which cannot be achieved in a typical capture-the-flag competition or a single security incident. DCO leaders must be able to manage two distinct decision-making processes with different sets of required skills to be successful in the mission. While the primary process relates to the execution and evolution of the pre-designed plan with traditional operational leadership skills, the secondary process deals with unplanned and deliberately caused cyber-related events that require a deep understanding of cybersecurity. In this respect, the main contribution of this research is the constructed decision-making model of the DCO leader. This model is based on observations collected and presented in the context of multiple well-known decision-making frameworks. This model can be further used to train future DCO leaders and assess artificial intelligence's usability to support and automate decision-making in such operations.

Learning Newsvendor Problems With Intertemporal Dependence and Moderate Non-stationarities

This work provides performance guarantees for solving data-driven contextual newsvendor problems when the contextual data contains intertemporal dependence and non-stationarities. While machine learning tools have observed increasing use in data-driven inventory management problems, most of the existing work assumes that the contextual data are independent and identically distributed (often referred to as i.i.d.). However, such assumptions are often violated in real operational environments where the contextual data are sequentially generated with intertemporal correlations and possible non-stationarities. By accommodating these naturally arising operational environments, our work adopts comparatively more realistic assumptions and develops out-of-sample performance bounds for learning data-driven contextual newsvendor problems.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

Covalent Organic Frameworks: Cutting-Edge Materials for Carbon Dioxide Capture and Water Harvesting from Air.

The implacable rise of carbon dioxide (CO2 ) concentration in the atmosphere and acute water stress are one of the central challenges of our time. Present-day chemistry is strongly inclined towards more sustainable solutions. Covalent organic frameworks (COFs), attributable to their structural designability with atomic precision, functionalizable chemical environment and robust extended architectures, have demonstrated promising performances in CO2 trapping and water harvesting from air. In this Review, we discuss the major developments in this field as well as sketch out the opportunities and shortcomings that remain over large-scale COF synthesis, device engineering, and long-term performance in real environments.

Virtual Environment Model Generation for CPS Goal Verification using Imitation Learning

Cyber-Physical Systems (CPS) continuously interact with their physical environments through embedded software controllers that observe the environments and determine actions. Field Operational Tests (FOT) are essential to verify to what extent the CPS under analysis can achieve certain CPS goals, such as satisfying the safety and performance requirements, while interacting with the real operational environment. However, performing many FOTs to obtain statistically significant verification results is challenging due to its high cost and risk in practice. Simulation-based verification can be an alternative to address the challenge, but it still requires an accurate virtual environment model that can replace the real environment interacting with the CPS in a closed loop. In this article, we propose ENVI (ENVironment Imitation), a novel approach to automatically generate an accurate virtual environment model, enabling efficient and accurate simulation-based CPS goal verification in practice.To do this, we first formally define the problem of the virtual environment model generation and solve it by leveraging Imitation Learning (IL), which has been actively studied in machine learning to learn complex behaviors from expert demonstrations. The key idea behind the model generation is to leverage IL for training a model that imitates the interactions between the CPS controller and its real environment as recorded in (possibly very small) FOT logs. We then statistically verify the goal achievement of the CPS by simulating it with the generated model. We empirically evaluate ENVI by applying it to the verification of two popular autonomous driving assistant systems. The results show that ENVI can reduce the cost of CPS goal verification while maintaining its accuracy by generating accurate environment models from only a few FOT logs. The use of IL in virtual environment model generation opens new research directions, further discussed at the end of the article.

A Model Proposal of Cybersecurity for the IIoT: Enhancing IIoT Cybersecurity through Machine Learning and Deep Learning Techniques

The Industrial Internet of Things (IIoT) acceleration has caused automation and data exchange enhancements within industrial surroundings. Yet, this evolution has presented security issues due to the raised exposure of critical infrastructure to cyber threats. This study focuses on designing a thorough model for identifying and mitigating vulnerabilities within IIoT networks utilizing Machine Learning (ML) and Deep Learning (DL) techniques. A replicated IIoT network infrastructure was set to communicate and exchange data for simulation. Use of Python script to execute network scanning and data collection, distinct possible vulnerabilities. Then, ML-DL analysis is handled by employing techniques of gradient boosting, logistic regression, decision trees, random forest, multilayer perceptron, and convolutional neural network. Throughout, gradient boosting has proven to higher performance accuracy rate in recognizing the most impactful vulnerabilities. As well as a model integral part of a Cost-Benefit Analysis (CBA) provides security recommendations to mitigate identified vulnerabilities. According to the CBA model vulnerabilities are prioritized based on the severity, related costs, and potential benefits of mitigation. The proposed Cybersecurity model in addition to high accuracy in vulnerability detection also provides a standardized approach for categorizing Cybersecurity countermeasures according to costeffectiveness. This study emphasizes the need for a consolidated Cybersecurity model for the IIoT and shows the capability of ML techniques to advance Cybersecurity posture. Future work considered testing the model in a real operation environment of IIoT, refining the model, and enrichment with more knowledge base actionable mitigations.

The rapid chloride migration test as a method to determine the chloride penetration resistance of concrete in marine environment

This paper analyses the use of the rapid chloride migration test (RCMT) in assessing the long-term chloride penetration resistance of concrete in real marine environment. Based on extensive experimental data, RCMT results were related to the diffusion and penetration properties of a wide range of normal weight and lightweight aggregate concretes, 5-year exposed under real submerged (XS2) and wetting and drying (XS3) conditions. Most usual structural concrete with different water/binder, types of aggregates and binders were covered. Concretes were ranked in terms of their chloride durability performance in real environment and a new classification was proposed to evaluate the durability of concrete to chloride attack. Classes were established based on service life prediction analysis. Overall, it is shown that the RCMT classification can distinguish the resistance to the chloride penetration of different concretes under distinct exposure environments, allowing for an expeditious method to evaluate the concrete quality.

Intrusion Detection of Industrial Internet-of-Things Based on Reconstructed Graph Neural Networks

Industrial Internet-of-Things (IIoT) are highly vulnerable to cyber-attacks due to their open deployment in unattended environments. Intrusion detection is an efficient solution to improve security. However, because the labeled samples are difficult to obtain, and the sample categories are imbalanced in real applications, it is difficult to obtain a reliable model. In this paper, a general framework for intrusion detection is proposed based on graph neural network technologies. In detail, a network embedding feature representation is proposed to deal with the high dimensional, redundant but categories imbalanced and rare labeled data in IIoT. To avoid the influence caused by the inaccurate network structure, a network constructor with refinement regularization is designed to amend it. At last, the network embedding representation weights and network constructor are trained together. The high accuracy and robust properties of the proposed method were verified by conducting intrusion detection tasks based on public datasets. Compared with several state-of-art algorithms, the proposed framework outperforms these methods in many evaluation metrics. In addition, a hard-in-the-loop platform is designed to test the performance in real environments. The results show that the method can not only identify different attacks but also distinguish between cyber-attacks and physical failures.

Using embedded prosthesis sensors for clinical gait analyses in people with lower limb amputation: A feasibility study

BackgroundBiomechanical gait analyses are typically performed in laboratory settings, and are associated with limitations due to space, marker placement, and tasks that are not representative of the real-world usage of lower limb prostheses. Therefore, the purpose of this study was to investigate the possibility of accurately measuring gait parameters using embedded sensors in a microprocessor-controlled knee joint. MethodsTen participants were recruited for this study and equipped with a Genium X3 prosthetic knee joint. They performed level walking, stair/ramp descent, and ascent. During these tasks, kinematics and kinetics (sagittal knee and thigh segment angle, and knee moment) were recorded using an optical motion capture system and force plates (gold standard), as well as the prosthesis-embedded sensors. Root mean square errors, relative errors, correlation coefficients, and discrete outcome variables of clinical relevance were calculated and compared between the gold standard and the embedded sensors. FindingsThe average root mean square errors were found to be 0.6°, 5.3°, and 0.08 Nm/kg, for the knee angle, thigh angle, and knee moment, respectively. The average relative errors were 0.75% for the knee angle, 11.67% for the thigh angle, and 9.66%, for the knee moment. The discrete outcome variables showed small but significant differences between the two measurement systems for a number of tasks (higher differences only at the thigh). InterpretationThe findings highlight the potential of prosthesis-embedded sensors to accurately measure gait parameters across a wide range of tasks. This paves the way for assessing prosthesis performance in realistic environments outside the lab.

A hearing aid “test drive”: Using virtual acoustics to accurately demonstrate hearing aid performance in realistic environments

A common challenge for audiologists when fitting hearing aids is that quiet clinics are unlike the noisy, reverberant places where patients report difficulty. This prevents direct user feedback about hearing aid performance. Virtual acoustics can allow a patient to experience a hearing aid in such environments, without leaving the clinic. A virtual reality (VR) audio-visual demonstration has been created which “test drives” hearing aid features in real-world scenarios, dynamically rendered over custom wired hearing aids and headphones. The scenes were created from 360° photographs and acoustic measurements made in real rooms. A room acoustic model was used for the simulation, tuned to match the measured data. Audio was rendered using principal component-base amplitude panning (PCBAP), which can provide dynamic VR audio with broadband magnitude and phase accuracy. This talk will provide comparisons between PCBAP and higher-order Ambisonics (HOA) for rendering hearing aid beamformers in anechoic and reverberant environments. Results show that PCBAP can render a beamformer’s directivity broadband with 2 to 3 dB error (95%) using only 36 filters in both anechoic and reverberant conditions. Even seventh-order HOA (64 filters) does not achieve similar performance. The talk will also include videos of the demonstration, highlighting viability for clinical applications.

Markov-modulated model for landing flow dynamics: An ordinal analysis validation.

Air transportation is a complex system characterized by a plethora of interactions at multiple temporal and spatial scales; as a consequence, even simple dynamics like sequencing aircraft for landing can lead to the appearance of emergent behaviors, which are both difficult to control and detrimental to operational efficiency. We propose a model, based on a modulated Markov jitter, to represent ordinal pattern properties of real landing operations in European airports. The parameters of the model are tuned by minimizing the distance between the probability distributions of ordinal patterns generated by the real and synthetic sequences, as estimated by the Permutation Jensen-Shannon Distance. We show that the correlation between consecutive hours in the landing flow changes between airports and that it can be interpreted as a metric of efficiency. We further compare the dynamics pre and post COVID-19, showing how this has changed beyond what can be attributed to a simple reduction of traffic. We finally draw some operational conclusions and discuss the applicability of these findings in a real operational environment.