Workflow Model Research Articles

Data for critical infrastructure network modelling of natural hazard impacts: Needs and influence on model characteristics

Natural hazards impact interdependent infrastructure networks that keep modern society functional. While a variety of modelling approaches are available to represent critical infrastructure networks (CINs) on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to assume specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture, thereby eroding the reliability of these models to guide resilience enhancements. Additionally, ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address these challenges, a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations, as well as the six CIN modelling stages, including mapping of infrastructure assets, quantification of dependencies, assessment of natural hazard impacts, response & recovery, quantification of CI services, and adaptation measures. The data requirements of each stage were systematically defined, and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls. The application of the derived workflow funnels into a framework to assess data availability challenges. This is shown through three case studies, taking into account their different modelling purposes: hazard hotspot assessments, hazard risk management, and sectoral adaptation. Based on the three model purpose types provided, a framework is suggested to explore the implications of data scarcity for certain data types, as well as their reasons and consequences for CIN model reliability. Finally, a discussion on overcoming the challenges of data scarcity is presented.

An efficient modeling workflow for high-performance nanowire single-photon avalanche detector

Single-photon detector (SPD), an essential building block of the quantum communication system, plays a fundamental role in developing next-generation quantum technologies. In this work, we propose an efficient modeling workflow of nanowire SPDs utilizing avalanche breakdown at reverse-biased conditions. The proposed workflow is explored to maximize computational efficiency and balance time-consuming drift-diffusion simulation with fast script-based post-processing. Without excessive computational effort, we could predict a suite of key device performance metrics, including breakdown voltage, dark/light avalanche built-up time, photon detection efficiency, dark count rate, and the deterministic part of timing jitter due to device structures. Implementing the proposed workflow onto a single InP nanowire and comparing it to the extensively studied planar devices and superconducting nanowire SPDs, we showed the great potential of nanowire avalanche SPD to outperform their planar counterparts and obtain as superior performance as superconducting nanowires, i.e. achieve a high photon detection efficiency of 70% with a dark count rate less than 20 Hz at non-cryogenic temperature. The proposed workflow is not limited to single-nanowire or nanowire-based device modeling and can be readily extended to more complicated two-/three dimensional structures.

Sediment Thickness Model of Andalusia’s Nearshore and Coastal Inland Topography

This study represents the first attempt to map the sediment thickness spatial distribution along the Andalusian coastal zone by integrating various publicly available datasets. While prior studies have presented bedform- and sediment-type syntheses, none have attempted to quantify sediment thickness at the scale and resolution performed in this study. The study area has been divided into 18 physiographic zones, and we have used BGS Groundhog Desktop v2.6 software for 3D modeling and sediment thickness model calculations. We present here the modeling workflow, model results, and the challenges that we have encountered, including discrepancies in geological maps, difficulty managing data input for grain size/consolidation, and the need for additional geological information. We have compared the modeled sediment fractions of the unconsolidated material with 4194 seabed samples distributed along the study area and found that the differences between the modeled versus the sampled emphasized the importance of incorporating river contributions, particularly from the Guadalquivir River, into the model for more accurate results. The model intermediate and final outputs and the software routines used to query the sediment thickness model are provided as publicly accessible datasets and tools. The modeled sediment thickness could contribute to making quantitative predictions of morphological change at a scale that is relevant to longer-term strategic coastal management in Andalusia. The methodology and tools used for this study are transferable to any study area.

The impact of neutral beam parameters on current drive and neutron yield in DEMO-FNS tokamak

DEMO-FNS (DEMO Fusion Neutron Source) will be a hybrid reactor designed to combine fusion and fission technologies. In a hybrid reactor the power is mainly produced by a fission blanket which is exposed to the neutron flux generated by confined fusion plasma used as a Fusion Neutron Source (FNS). The steady-state operation of the FNS is easier to achieve as compared to pure fusion reactors, allowing the FNS to be more compact. The DEMO-FNS will produce 40 MW of fusion power. The neutral beam injectors (NBI) are designed to provide a steady state plasma heating, rotation, fueling, current drive and neutron generation in DEMO-FNS. Four injector units will deliver 30 MW power in deuterium with energy Eb = 500 keV. The NBI concept and main components are inspired from ITER HNBI design implementing acceleration of negative ions with their neutralization on gas.The high density of injected power in DEMO-FNS leads to a very specific operation scenario with ahigher ratio of high-energy ions in the plasma with respect to conventional tokamaks, where the mainpart of neutron flux will result from fusion between hot-tail ions and relatively coldbackground. The current drive and neutron yield produced by the tangentially injected beam in asteady-state operation are strongly affected by magnetic configuration and plasma kineticprofiles. The fast ions deposition and performance in plasma are limited by the losses associatedwith the neutral beam shape and aiming. The main channels of direct beam losses includeshine-through losses, as well as fast ion orbital losses. The detailed evaluation of NB parametersand geometry effects is a necessary step of scenario optimization.BTR code (Beam Transmission or, in earlier versions, Beam Transmission with Reionization) is intended for injector beamlines study, and BTR detailed beam model can be also applied to perform a detailed analysis of beam losses and performance in plasma. The analysis of beam in plasma shows the ways towards higher values of beam driven current (NBCD) and neutral yield; the beam-in-plasma behaviour can be tuned for a given tokamak configuration. The NB tracing model workflow is implemented in the BTOR suite (Beam in TORoids). The results for DEMO-FNS are compared with the NBI performance in ITER tokamak, as both machines are beam-driven and implement a similar NBI concept.

Correctness Notions for Petri Nets with Identifiers

A model of an information system describes its processes and how resources are involved in these processes to manipulate data objects. This paper presents an extension to the Petri nets formalism suitable for describing information systems in which states refer to object instances of predefined types and resources are identified as instances of special object types. Several correctness criteria for resource- and object-aware information systems models are proposed, supplemented with discussions on their decidability for interesting classes of systems. These new correctness criteria can be seen as generalizations of the classical soundness property of workflow models concerned with process control flow correctness.

Observation of spikelets in steady-state surface nuclear magnetic resonance data

Spikelets in nuclear magnetic resonance (NMR) data occur at predictable frequencies dependent on the repetition time of the excitation sequence. Although spikelets are well documented in other NMR fields, we report their presence in steady-state surface NMR data for the first time. These observations are accompanied by analytical developments to understand and predict their behavior, which follow directly from existing steady-state surface NMR modeling. We show that spikelets represent copies of the surface NMR signal occurring at multiple locations in the frequency domain, including locations that are distinct from the Larmor frequency. These features are shown to be detectable without requiring additional effort in the field and are shown to be readily processed and modeled with only minor modifications to the processing and modeling workflows. Finally, field spikelet data are also inverted to demonstrate that these data can be fit using subsurface models consistent with a reference surface NMR inversion.

Rational Identification of Novel Antibody-Drug Conjugate with High Bystander Killing Effect against Heterogeneous Tumors.

Bystander-killing payloads can significantly overcome the tumor heterogeneity issue and enhance the clinical potential of antibody-drug conjugates (ADC), but the rational design and identification of effective bystander warheads constrain the broader implementation of this strategy. Here, graph attention networks (GAT) are constructed for a rational bystander killing scoring model and ADC construction workflow for the first time. To generate efficient bystander-killing payloads, this model is utilized for score-directed exatecan derivatives design. Among them, Ed9, the most potent payload with satisfactory permeability and bioactivity, is further used to construct ADC. Through linker optimization and conjugation, novel ADCs are constructed that perform excellent anti-tumor efficacy and bystander-killing effect in vivo and in vitro. The optimal conjugate T-VEd9 exhibited therapeutic efficacy superior to DS-8201 against heterogeneous tumors. These results demonstrate that the effective scoring approach can pave the way for the discovery of novel ADC with promising bystander payloads to combat tumor heterogeneity.

A gentle introduction to computer vision-based specimen classification in ecological datasets.

Classifying specimens is a critical component of ecological research, biodiversity monitoring and conservation. However, manual classification can be prohibitively time-consuming and expensive, limiting how much data a project can afford to process. Computer vision, a form of machine learning, can help overcome these problems by rapidly, automatically and accurately classifying images of specimens. Given the diversity of animal species and contexts in which images are captured, there is no universal classifier for all species and use cases. As such, ecologists often need to train their own models. While numerous software programs exist to support this process, ecologists need a fundamental understanding of how computer vision works to select appropriate model workflows based on their specific use case, data types, computing resources and desired performance capabilities. Ecologists may also face characteristic quirks of ecological datasets, such as long-tail distributions, 'unknown' species, similarity between species and polymorphism within species, which impact the efficacy of computer vision. Despite growing interest in computer vision for ecology, there are few resources available to help ecologists face the challenges they are likely to encounter. Here, we present a gentle introduction for species classification using computer vision. In this manuscript and associated GitHub repository, we demonstrate how to prepare training data, basic model training procedures, and methods for model evaluation and selection. Throughout, we explore specific considerations ecologists should make when training classification models, such as data domains, feature extractors and class imbalances. With these basics, ecologists can adjust their workflows to achieve research goals and/or account for uncertainty in downstream analysis. Our goal is to provide guidance for ecologists for getting started in or improving their use of machine learning for visual classification tasks.

Workflow for modeling of generalized mid-spatial frequency errors in optical systems.

We propose a workflow for modeling generalized mid-spatial frequency (MSF) errors in optical imaging systems. This workflow enables the classification of MSF distributions, filtering of bandlimited signatures, propagation of MSF errors to the exit pupil, and performance predictions that differentiate performance impacts due to the MSF distributions. We demonstrate the workflow by modeling the performance impacts of MSF errors for both transmissive and reflective imaging systems with near-diffraction-limited performance.

Fracture-vuggy carbonate reservoir characterization based on multiple geological information fusion

The complexity and strong heterogeneity of carbonate reservoirs with fracture-vuggy structures present significant challenges in reservoir characterization. To address these challenges, we propose a novel multi-element information fusion modeling approach. This approach is designed to integrate multiple methods and incorporate multi-probability fusion at various facies and scales, thereby bridging the gap between geological information and reservoir modeling. Our methodology involves four key steps. First, the statistics between frequency of karst and geological information are acquired, and we quantify the statistics to regression equations. Second, these regression equations are transferred to probability bodies. The probability bodies can be applied in modeling as a soft control. But just one single body can be input in modeling process. Third, multiple probability bodies are fused into a fusion probability body by a probability fusion algorithm, which can keep the potential information of probability bodies. Finally, we apply the probability body in modeling workflow. By this way, the fusion method bridges the gap between geological information and modeling. The model established through our proposed method showed a significant level of consistency with reservoir re-evaluation, achieving an impressive 90% degree of alignment. Furthermore, the history match analysis revealed a high correlation, indicating the model's reliability. The method effectively integrates various scales and types of geological information, offering an accurate approach to complex carbonate reservoir modeling.

Harnessing the Tide of Innovation: The Dual Faces of Generative AI in Applied Sciences; Letter to Editor

Advancements in Artificial Intelligence (AI) and emerging generative capabilities added paradoxical aspects. One aspect is its positive impact and limitless power it brings to users. On the other hand, concerns about the misuse of this powerful tool have consistently increased [1]. AI advancements affect all domains and sectors as they evolve in their applicable nature in the applied sciences. The more powerful AI the more influence it has on the model workflow within the specific domain and its applied field [2]. This dual nature of generative AI ignited a wide discussion on implementation and produced a debate according to the latest employed tools and technologies by scientists and researchers.

PyDarwin: A Machine Learning Enhanced Automated Nonlinear Mixed-Effect Model Selection Toolbox.

pyDarwin is an open-source Python package for nonlinear mixed-effect model selection. pyDarwin combines machine-learning algorithms and NONMEM to perform a global search for the optimal model in a user-defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor-intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data.

SUBJECT-SPECIFIC KNEE JOINT MODELLING BETWEEN YOUNG AND ELDERLY

It is known that the gait dynamics of elderly substantially differs from that of young people. However, it has not been well studied how this age-related gait dynamics affects the knee biomechanics, e.g., cartilage mechanical response. In this study, we investigated how aging affects knee biomechanics in a female population using subject-specific computational models.Two female subjects (ages of 23 and 69) with no musculoskeletal disorders were recruited. Korea National Institute for Bioethics Policy Review Board approved the study. Participants walked at a self-selected speed (SWS), 110% of SWS, and 120% of SWS on 10 m flat ground. Three-dimensional marker trajectories and ground reaction forces (Motion Analysis, USA), and lower limbs’ muscle activities were measured (EMG, Noraxon USA). Knee cartilage and menisci geometries were obtained from subjects’ magnetic resonance images (3T, GE Health Care). An EMG-assisted musculoskeletal finite element modeling workflow was used to estimate knee cartilage tissue mechanics in walking trials. Knee cartilage and menisci were modeled using a transversely isotropic poroviscoelastic material model.Walking speed in SWS, 110%, and 120% of SWS were 1.38 m/s, 1.51 m/s, and 1.65 m/s for the young, and 1.21 m/s, 1.34 m/s and 1.46 m/s for the elderly, respectively. The maximum tensile stress in the elderly tibial cartilage was ~25%, ~33%, and ~32% lower than the young at SWS, 110%, and 120% of SWS, respectively. These preliminary results suggest that the cartilage in the elderly may not have enough stimulation even at 20% increases in walking speed, which may be one reason for tissue degeneration. To enhance these findings, further study with more subjects and different genders will investigate how age-related gait dynamics affects knee biomechanics.Acknowledgments: Australian NHMRC Ideas Grant (APP2001734), KITECH (JE220006)

ВЛИЯНИЕ ТЕМПЕРАТУРЫ МАСЛА ГИДРОТРАНСФОРМАТОРА НА ВЫХОДНУЮ МОЩНОСТЬ ТРАНСПОРТНОГО СРЕДСТВА В РЕЖИМЕ ГИДРОМУФТЫ

The purpose of the study is to determine the power loss of the hydraulic torque converter from hydraulic resistance that occurs in the vehicle drive when the temperature of the working fluid changes, based on the results of testing special oils for automatic transmissions from Mobil ATF Multi-Vehicle and ATF TCL WS. The data obtained make it possible to determine the optimal distance between the impeller blades, as well as to use it in modeling workflows in hydraulic torque converter.

Seismic facies characterization using Rock Physics Templates and brittleness indices: Stybarrow field

This paper proposes a method for identifying the lithological properties of the medium based on the joint analysis of the Lamé parameters, Young's Modulus, and Poisson's Ratio. Additionally, an analysis of brittleness was proposed to identify brittle/ductile intervals and areas of potential reservoirs. The petroelastic properties were analyzed at well and seismic scales using ternary rock physics templates. The ternary templates were built from a self-consistent micromechanical model. In addition, the analysis allows for preserving the conditions of the environment subsurface in the seismic and log information. A workflow for petroelastic lithology interpretation was coupled with a workflow of brittleness modeling. The results correlate well with conventional qualitative methodologies applied in previous studies. A brittleness analysis methodology was developed and tested to identify reservoirs associated with the Lower Cretaceous in the Stybarrow field in Australia; the results highlight the high brittleness zones attenuated by hydrocarbons ( ). The proposed seismic-based methodology is an improvement to conventional analysis trends for identifying lithologies and prospective hydrocarbon zones.

Geochemistry π: Automated Machine Learning Python Framework for Tabular Data

AbstractAlthough machine learning (ML) has brought new insights into geochemistry research, its implementation is laborious and time‐consuming. Here, we announce Geochemistry π, an open‐source automated ML Python framework. Geochemists only need to provide tabulated data and select the desired options to clean data and run ML algorithms. The process operates in a question‐and‐answer format, and thus does not require that users have coding experience. After either automatic or manual parameter tuning, the automated Python framework provides users with performance and prediction results for the trained ML model. Based on the scikit‐learn library, Geochemistry π has established a customized automated process for implementing classification, regression, dimensionality reduction, and clustering algorithms. The Python framework enables extensibility and portability by constructing a hierarchical pipeline architecture that separates data transmission from the algorithm application. The AutoML module is constructed using the Cost‐Frugal Optimization and Blended Search Strategy hyperparameter search methods from the A Fast and Lightweight AutoML Library, and the model parameter optimization process is accelerated by the Ray distributed computing framework. The MLflow library is integrated into ML lifecycle management, which allows users to compare multiple trained models at different scales and manage the data and diagrams generated. In addition, the front‐end and back‐end frameworks are separated to build the web portal, which demonstrates the ML model and data science workflow through a user‐friendly web interface. In summary, Geochemistry π provides a Python framework for users and developers to accelerate their data mining efficiency with both online and offline operation options.

Alternative approach to evaluate the data of ecological monitoring of ecotopes for highways in Ukraine

We propose alternative approach to evaluate data from ecological monitoring of ecotopes along the roadways of Ukraine, taking into account raised accuracy of treating the ecological impact on the roadside environment. Methodology includes sources analysis, development of a workflow model for monitoring and identifying the main components of input data and their optimal features by system analysis. The monitoring of ecotopes of green plantations along highways in the steppe zone of South Ukraine increases the accuracy of evaluating the ecological trace on roadside environment and facilities. Scientific novelty of study combines various data sampling, physical equipment, and road measurements with information services (computing, analyzing and treating input data, forecasting of system development, and monitoring management through feedback control). The system analysis of data deals with the optimal linear constraints of the roadside ecotope capacity, leading to a simple mathematical model of the monitoring. The problem of processing environmental monitoring data with such data is reduced to the linear programming problem. Specifically, this mathematical model allows for the consideration few factors influencing the ecological situation and provides the ability to analyze and forecast the development of ecosystems along roads. Conclusions of study are criteria for an objective assessment of the degree of pollution of the environment and its impact on soil cover, vegetation and road equipment. For effective monitoring it is necessary data-based quality management system on highways. It helps evaluate the predicted degree of environmental pollution by degradation of soils, vegetation, and road facilities.

AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories

AlabOS is a workflow orchestration framework designed to address the increased complexity in autonomous laboratories, featuring a reconfigurable experiment workflow model and a resource reservation mechanism.

First-principles modelling of the thermoelectric properties of n-type CaTiO3, SrTiO3 and BaTiO3

We benchmark a fully ab initio modelling workflow for predicting the electrical and thermal transport properties and thermoelectric figure of merit ZT on three oxide perovskites, viz. orthorhombic CaTiO3, tetragonal SrTiO3 and rhombohedral BaTiO3.

Empirical Modelling Workflow for Resolution Invariant Assessment of Osteophytes.

Traditional quantitative analysis of bone microstructure in micro-computed tomography (microCT) is dependent on animal scale and requires parametric tuning in new implementations. This study aims to develop an automated and resolution-invariant 3D image processing workflow for quantitative assessment of osteophytes. In this workflow, cortical bone was segmented from microCT scans, and a 3D sphere-fitting transform was performed to obtain a thickness map, for which each voxel is assigned a thickness value corresponding to the size of the largest sphere containing the voxel that fits entirely within the cortical bone. From the thickness map, a 1-voxel thick outer surface was extracted to model surface roughness. The thickness values of the outer surface were empirically estimated by a series of known statistical distributions. Resulting parameters describing best-fit distributions, along with other cortical bone metrics, were analysed to determine sensitivity to osteoarthritis and the presence of osteophytes. The workflow was validated using microCT scans and histological gradings of rabbit and rat tibiofemoral joints. Visual inspection shows that samples with osteoarthritis and the presence of osteophytes have more surface voxels assigned small thickness values. The distribution of surface thickness values for each animal is best described by Gamma distributions, whose shape parameter is consistently sensitive to osteoarthritis and the presence of osteophytes. Combining traditional image processing with empirical distribution fitting provides an automated, objective, and resolution-invariant workflow for osteophyte assessment. The proposed method is simple, yet elegant in its implementation, and can be readily used in new implementations.