Workflow Model Research Articles

PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs.

Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.

Type Label Framework for Bonded Force Fields in LAMMPS.

New functionality is added to the LAMMPS molecular simulation package, which increases the versatility with which LAMMPS can interface with supporting software and manipulate information associated with bonded force fields. We introduce the "type label" framework that allows atom types and their higher-order interactions (bonds, angles, dihedrals, and impropers) to be represented in terms of the standard atom type strings of a bonded force field. Type labels increase the human readability of input files, enable bonded force fields to be supported by the OpenKIM repository, simplify the creation of reaction templates for the REACTER protocol, and increase compatibility with external visualization tools, such as VMD and OVITO. An introductory primer on the forms and use of bonded force fields is provided to motivate this new functionality and serve as an entry point for LAMMPS and OpenKIM users unfamiliar with bonded force fields. The type label framework has the potential to streamline modeling workflows that use LAMMPS by increasing the portability of software, files, and scripts for preprocessing, running, and postprocessing a molecular simulation.

Statistical shape analysis and computational modeling reveal novel relationships between tibiofemoral bony geometry and knee mechanics in young, female athletes

Young female athletes participating in sports requiring rapid changes of direction are at heightened risk of suffering traumatic knee injury, especially noncontact rupture of the anterior cruciate ligament (ACL). Clinical studies have revealed that geometric features of the tibiofemoral joint are associated with increased risk of suffering noncontact ACL injury. However, the relationship between three-dimensional (3D) tibiofemoral geometry and knee mechanics in young female athletes is not well understood. We developed a statistically augmented computational modeling workflow to determine relationships between 3D geometry of the knee and tibiofemoral kinematics and ACL force in response to an applied loading sequence of compression, valgus, and anterior force, which is known to load the ACL. This workflow included 3D characterization of tibiofemoral bony geometry via principal component analysis and multibody dynamics models incorporating subject-specific knee geometries. A combination of geometric features of both the tibia and the femur that spanned all three anatomical planes was related to increased ACL force and to increased kinematic coupling (i.e., anterior, medial, and distal tibial translations and internal tibial rotation) in response to the applied loads. In contrast, a uniplanar measure of tibiofemoral geometry that is associated with ACL injury risk, sagittal plane slope of the lateral tibial plateau subchondral bone, was not related to ACL force. Thus, our workflow may aid in developing mechanics-based ACL injury screening tools for young, active females based on a unique combination of bony geometric features that are related to increased ACL loading.

Custom orthotic design by integrating 3D scanning and subject-specific FE modelling workflow

The finite element (FE) foot model can help estimate pathomechanics and improve the customized foot orthoses design. However, the procedure of developing FE models can be time-consuming and costly. This study aimed to develop a subject-specific scaled foot modelling workflow for the foot orthoses design based on the scanned foot surface data. Six participants (twelve feet) were collected for the foot finite element modelling. The subject-specific surface-based finite element model (SFEM) was established by incorporating the scanned foot surface and scaled foot bone geometries. The geometric deviations between the scaled and the scanned foot surfaces were calculated. The SFEM model was adopted to predict barefoot and foot-orthosis interface pressures. The averaged distances between the scaled and scanned foot surfaces were 0.23 ± 0.09 mm. There was no significant difference for the hallux, medial forefoot, middle forefoot, midfoot, medial hindfoot, and lateral hindfoot, except for the lateral forefoot region (p = 0.045). The SFEM model evaluated slightly higher foot-orthoses interface pressure values than measured, with a maximum deviation of 7.1%. These results indicated that the SFEM technique could predict the barefoot and foot-orthoses interface pressure, which has the potential to expedite the process of orthotic design and optimization.Graphical abstract

A novel method to build knowledge graph models for the configuration and operation design of smart and connected industrial products

Abstract Smart and connected industrial products (SCIPs), characterized by their capabilities of self-monitoring, environment awareness, machine–machine/machine–human communication and collaboration, intelligent decision-making, etc., have become the fundamental elements for cyber-physical systems, digital twin, industrial internet of things, etc. Configuring the components in SCIPs and modeling their interaction and operation mechanisms are important during SCIPs design. However, existing product design methods were originally developed for none smart and connected products. This could limit the accuracy of SCIP modeling during the design stage and consequently, it may cause more reworks during the implementation stage of the designed SCIPs. In this regard, a SCIP configuration and operation design method is established, including (i) meta knowledge graph (KG)-based configuration of the components in the physical system and status monitoring system of a required SCIP, (ii) event-state swimlane flowchart-based analysis of the dynamic interaction, operation, and data monitoring mechanisms among the components, and (iii) event-state KG based modeling of the overall workflow, monitoring data self-updating and intelligent operation mechanisms of the SCIP. Compared with existing SCIP design methods, the work provides a specific method for not only the configuration of the static components in customized SCIPs, but also the dynamic interaction, data acquisition/storing/transmitting, and intelligent function implementation mechanisms of the configured SCIP using a kind of event-state KG. The event-state KG is both human-readable and computer-programmable, and it can self-update according to predefined reasoning algorithms during the operation of the SCIP. The configuration and operation design modeling of a robot-based grinding processing line is used as a case study.

Three-dimensional geological modelling of igneous intrusions in LoopStructural v1.5.10

Abstract. Over the last 2 decades, there have been significant advances in the 3D modelling of geological structures via the incorporation of geological knowledge into the model algorithms. These methods take advantage of different structural data types and do not require manual processing, making them robust and objective. Igneous intrusions have received little attention in 3D modelling workflows, and there is no current method that ensures the reproduction of intrusion shapes comparable to those mapped in the field or in geophysical imagery. Intrusions are usually partly or totally covered, making the generation of realistic 3D models challenging without the modeller's intervention. In this contribution, we present a method to model igneous intrusions in 3D considering geometric constraints consistent with emplacement mechanisms. Contact data and inflation and propagation direction are used to constrain the geometry of the intrusion. Conceptual models of the intrusion contact are fitted to the data, providing a characterisation of the intrusion thickness and width. The method is tested using synthetic and real-world case studies, and the results indicate that the method can reproduce expected geometries without manual processing and with restricted datasets. A comparison with radial basis function (RBF) interpolation shows that our method can better reproduce complex geometries, such as saucer-shaped sill complexes.

Impact of automated dispensing solutions in long-term care facilities and closed-door pharmacies: A mixed methods study of medication management

BackgroundFinancial, operational, and clinical workflow impacts of deploying an automated dispensing cabinet (ADC) in long-term care (LTC) facilities based on actual observations have not been documented in peer-reviewed literature. ObjectivesTo evaluate the impact of a closed-door pharmacy (CDP) implementing an ADC with unique secure, removable, and transportable locked pockets in an unstudied setting (LTC facilities) for management of first and emergency dose medications. Practice DescriptionThis study was conducted in 1 CDP and 2 LTC facilities. Practice InnovationEnhancing emergency medication management and inventory tracking in an unstudied setting through implementation of an ADC system featuring unique electronically encoded medication storage pockets that can be prepared in the CDP, locked and securely transported to the LTC, and when inserted into ADC it informs staff of its presence, position, and contents. Evaluation MethodsMixed methods, pre- and poststudy to assess the impact of replacing manual emergency medication kits with an ADC. Outcomes were evaluated using rapid ethnography with workflow modeling; inventory and delivery reports; a nursing perception survey; and transactional data from the ADC during postimplementation phase. ResultsPharmacy technician preparation time and pharmacist checking time decreased by 59% and 80%, respectively, and standing inventory was reduced by more than $10,000 combined for the CDP and 2 LTCs by replacing emergency medication kits with the ADC. In the LTCs, this change led to a 71% reduction in emergency medication retrieval time, an increase in emergency medication utilization, and a 96% reduction in the cost of unscheduled deliveries. Over 70% of the nurses surveyed favored replacement of the emergency medication kits with the ADC system. ConclusionReplacing manual emergency medication kit with the described ADC system improved workflow efficiency in the CDP and LTC. It also significantly reduced unscheduled (STAT) deliveries and standing inventory and increased the availability of medications commonly used.

Exploring the implementation of a novel community pharmacist-led support service for people living with severe and persistent mental illness

BackgroundCommunity pharmacists are well-placed to promote and provide mental health medication management services. However, literature evaluating the impact of pharmacy services in supporting people living with severe and persistent mental illness (SPMI) is currently limited. An individualised, goal-oriented pharmacist-led support service that focused on improving the physical and mental health of consumers living with SPMI, namely the PharMIbridge intervention, was provided to consumer participants as part of the PharMIbridge Randomised Controlled Trial (RCT). Objective(s)To explore the experiences of the participants who delivered and supported the implementation of the PharMIbridge intervention and propose ideas and supports needed for broader implementation and sustainability of pharmacist-led support services for people living with SPMI. MethodsInterviews and focus group discussions were undertaken with PharMIbridge pharmacists and mentors, respectively. Audio-recordings were de-identified and transcribed verbatim. Data analysis was conducted using an iterative, inductive approach. The key themes identified were furthered divided into subthemes. Subthemes were then mapped to the EPIS (Exploration, Preparation, Implementation and Sustainment) framework. ResultsData were collected from one focus group involving six RCT mentors and 16 semi-structured interviews with community pharmacists. Five overarching themes emerged: “Training needs”, “Pharmacist integration within the healthcare system”, “Environmental factors”, “Attitudes and behaviour” and “Pharmacy operations”. Twelve subthemes were mapped to EPIS phases “Preparation”, “Implementation” and “Sustainment” and EPIS constructs “Outer” context, “Inner” context and “Bridging” factors. ConclusionAdequate remuneration and supports to encourage healthcare professional collaboration are necessary to establish and sustain functioning, integrated pharmacy mental health services. A shift in pharmacy business and workflow models is necessary to support community pharmacies to implement mental health services. In addition, there is a need to promote psychological support services to ensure that pharmacists are well supported while delivering pharmacy mental health services.

Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs

Inverse modeling can estimate uncertain parameters in subsurface reservoirs and provide reliable numerical models for reservoir development and management. The traditional simulation-based inversion method usually requires numerous numerical simulations, which is time-consuming. Recently, deep learning-based surrogate models have been widely studied as an alternative to numerical simulation, which can significantly improve the solving efficiency of inversion. However, for reservoirs with complex fracture distribution, constructing the surrogate model of numerical simulation presents a significant challenge. In this work, we present a deep graph learning-based surrogate model for inverse modeling of fractured reservoirs. Specifically, the proposed surrogate model integrates the graph attention mechanisms to extract features of fracture network in reservoirs. The graph learning can retain the discrete characteristics and structural information of the fracture network. The extracted features are subsequently integrated with a multi-layer recurrent neural network model to predict the production dynamics of wells. A surrogate-based inverse modeling workflow is then developed by combining the surrogate model with the differential evolutionary algorithm. Numerical studies performed on a synthetic naturally fractured reservoir model with multi-scale fractures illustrate the performance of the proposed methods. The results demonstrate that the proposed surrogate model exhibits promising generalization performance of production prediction. Compared with tens of thousands of numerical simulations required by the simulation-based inverse modeling method, the proposed surrogate-based method only requires 1000 to 1500 numerical simulations, and the solution efficiency can be improved by ten times.

Structural insights into the morpholino nucleic acid/RNA duplex using the new XNA builder Ducque in a molecular modeling pipeline.

The field of synthetic nucleic acids with novel backbone structures [xenobiotic nucleic acids (XNAs)] has flourished due to the increased importance of XNA antisense oligonucleotides and aptamers in medicine, as well as the development of XNA processing enzymes and new XNA genetic materials. Molecular modeling on XNA structures can accelerate rational design in the field of XNAs as it contributes in understanding and predicting how changes in the sugar-phosphate backbone impact on the complementation properties of the nucleic acids. To support the development of novel XNA polymers, we present a first-in-class open-source program (Ducque) to build duplexes of nucleic acid analogs with customizable chemistry. A detailed procedure is described to extend the Ducque library with new user-defined XNA fragments using quantum mechanics (QM) and to generate QM-based force field parameters for molecular dynamics simulations within standard packages such as AMBER. The tool was used within a molecular modeling workflow to accurately reproduce a selection of experimental structures for nucleic acid duplexes with ribose-based as well as non-ribose-based nucleosides. Additionally, it was challenged to build duplexes of morpholino nucleic acids bound to complementary RNA sequences.

Using seismic petrophysical modeling and prestack simultaneous inversion to provide insights into the physical properties of uranium-bearing reservoirs: Implications for favorable sites of sandstone-hosted uranium deposits

Seismic prospecting has been accepted as one of the most widely available methods for exploring sandstone-hosted uranium deposits (SUDs). However, conventional seismic interpretation faces a challenge in the identification and characterization of a uranium reservoir’s complexity. How to characterize in detail a uranium reservoir’s physical complexity and effectively improve uranium reservoir prediction accuracy remain unresolved problems. To address this, we develop a novel combination of petrophysical modeling and prestack simultaneous inversion to understand in detail the physical properties of uranium-bearing reservoirs and efficiently predict favorable SUD sites. First, we develop a workflow of rock-physics modeling for SUD logs using the Xu-White method to calculate the modulus of elasticity of the grain matrix; subsequently, we extend the Walton model for the modulus prediction of the dry rocks and the Gassmann equation for one of the saturated rocks after a massive calculation test; and then, we predict the S-wave data used for the following inversion. Second, we execute a prestack simultaneous inversion to obtain the petrophysical parameters (e.g., P-impedance, density [[Formula: see text]], shear modulus [[Formula: see text]], Lamé coefficient [[Formula: see text]], and Young’s modulus) that can provide insights into the physical properties of a uranium metallogenic environment. Accordingly, we discover that sites bearing uranium mineralization strongly correspond to areas with low elastic-parameter values (especially [Formula: see text] and [Formula: see text]), whereas nonuranium anomalies occur in high-value sites. This indicates that weakened elastic characteristics are caused by the enhancement of the total organic content and total clay mineral volumes of the uranium-bearing layers. In summary, the developed combination approach can yield an effective and accurate characterization of the geologic properties of uranium-bearing formations, and it can provide prediction factors (e.g., parameters related to the shear modulus) for uranium mineralization.

In-silico techniques to inform and improve the personalized prescription of shoe insoles.

Background: Corrective shoe insoles are prescribed for a range of foot deformities and are typically designed based on a subjective assessment limiting personalization and potentially leading to sub optimal treatment outcomes. The incorporation of in silico techniques in the design and customization of insoles may improve personalized correction and hence insole efficiency. Methods: We developed an in silico workflow for insole design and customization using a combination of measured motion capture, inverse musculoskeletal modelling as well as forward simulation approaches to predict the kinematic response to specific insole designs. The developed workflow was tested on twenty-seven participants containing a combination of healthy participants (7) and patients with flatfoot deformity (20). Results: Average error between measured and simulated kinematics were 4.7 ± 3.1, 4.5 ± 3.1, 2.3 ± 2.3, and 2.3 ± 2.7° for the chopart obliquity, chopart anterior-posterior axis, tarsometatarsal first ray, and tarsometatarsal fifth ray joints respectively. Discussion: The developed workflow offers distinct advantages to previous modeling workflows such as speed of use, use of more accessible data, use of only open-source software, and is highly automated. It provides a solid basis for future work on improving predictive accuracy by adapting the currently implemented insole model and incorporating additional data such as plantar pressure.

Data sharing in modeling and simulation of biomechanical systems in interdisciplinary environments

AbstractAll digital objects that result from the modeling and simulation field are valid sets of research data. In general, research data are the result of intense intellectual activity that is worth communicating. This communication is an essential research practice that, whether with the aim of understanding, critiquing or further developing results, smoothly leads to collaboration, which not only involves discussions, and sharing institutional resources, but also the sharing of data and information at several stages of the research process. Data sharing is intended to improve and facilitate collaboration but quickly introduces challenges like reproducibility, reusability, interoperability, and standardization. These challenges are deeply rooted in an apparent reproducibility standard, about which there is a debate worth considering before emphasizing how the modeling and simulation workflow commonly occurs. Although that workflow is almost natural for practitioners, the sharing practices still require special attention because the principles (known as FAIR principles) that guide research practices towards data sharing also guide the requirements for machine actionable results. The FAIR principles, however, do not address the actual implementation of the data sharing process. This implementation requires careful consideration of characteristics of the sharing platforms for benefiting the most of the data sharing activity. This article serves as an invitation to integrate data sharing practices into the established routines of researchers and elaborates on the perspectives, and guidelines surrounding data sharing implementation.

From 2D slices to a 3D model: Training students in digital microanatomy analysis techniques through a 3D printed neuron project.

The reconstruction of two-dimensional (2D) slices to three-dimensional (3D) digital anatomical models requires technical skills and software that are becoming increasingly important to the modern anatomist, but these skills are rarely taught in undergraduate science classrooms. Furthermore, learning opportunities that allow students to simultaneously explore anatomy in both 2D and 3D space are increasingly valuable. This report describes a novel learning activity that trains students to digitally trace a serially imaged neuron from a confocal stack and to model that neuron in 3D space for 3D printing. By engaging students in the production of a 3D digital model, this learning activity is designed to provide students a novel way to enhance their understanding of the content, including didactic knowledge of neuron morphology, technical research skills in image analysis, and career exploration of neuroanatomy research. Moreover, students engage with microanatomy in a way that starts in 2D but results in a 3D object they can see, touch, and keep. This discursive article presents the learning activity, including videos, instructional guides, and learning objectives designed to engage students on all six levels of Bloom's Taxonomy. Furthermore, this work is a proof of principle modeling workflow that is approachable, inexpensive, achievable, and adaptable to cell types in other organ systems. This work is designed to motivate the expansion of 3D printing technology into microanatomy and neuroanatomy education.

Thermal structure of the southern Caribbean and northwestern South America: implications for seismogenesis

Abstract. The seismogenesis of rocks is mainly affected by their mineral composition and in situ conditions (temperature and state of stress). Diverse laboratory experiments have explored the frictional behaviour of the rocks and rock-forming minerals most common in the crust and uppermost mantle. However, it is debated how to “upscale” these results to the lithosphere. In particular, most earthquakes in the crust nucleate down to the crustal seismogenic depth (CSD), which is a proxy for the maximum depth of crustal earthquake ruptures in seismic hazard assessments. In this study we propose a workflow to upscale and validate those laboratory experiments to natural geological conditions relevant for crustal and upper-mantle rocks. We used the southern Caribbean and northwestern South America as a case study to explore the three-dimensional spatial variation of the CSD (mapped as D90, the 90 % percentile of hypocentral depths) and the temperatures at which crustal earthquakes likely occur. A 3D steady-state thermal field was computed for the region with a finite-element scheme using the software GOLEM, considering the uppermost 75 km of a previously published 3D data-integrative lithospheric configuration, lithology-constrained thermal parameters, and appropriate upper and lower boundary conditions. The model was validated using additional, independent measurements of downhole temperatures and heat flow. We found that the majority of crustal earthquakes nucleate at temperatures less than 350 ∘C, in agreement with frictional experiments of typical crustal rocks. A few outliers with larger hypocentral temperatures evidence nucleation conditions consistent with the seismogenic window of olivine-rich rocks, and can be due to either uncertainties in the Moho depths and/or in the earthquake hypocentres or the presence of ultramafic rocks within different crustal blocks and allochthonous terranes accreted to this complex margin. Moreover, the spatial distribution of crustal seismicity in the region correlates with the geothermal gradient, with no crustal earthquakes occurring in domains with low thermal gradient. Finally, we find that the largest earthquake recorded in the region (Mw=7.1, Murindó sequence, in 1992) nucleated close to the CSD, highlighting the importance of considering this lower-stability transition for seismogenesis when characterizing the depth of seismogenic sources in hazard assessments. The approach presented in this study goes beyond a statistical approach in that the local heterogeneity of physical properties is considered in our simulations and additionally validated by the observed depth distribution of earthquakes. The coherence of the calculated hypocentral temperatures with those expected from laboratory measurements provides additional support to our modelling workflow. This approach can be applied to other tectonic settings worldwide, and it could be further refined as new, high-quality hypocentral locations and heat flow and temperature observations become available.

Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians.

Near infrared spectroscopy coupled with predictive modeling is a growing field of study for addressing questions in wildlife science aimed at improving management strategies and conservation outcomes for managed and threatened fauna. To date, the majority of spectroscopic studies in wildlife and fisheries applied chemometrics and predictive modeling with a single-algorithm approach. By contrast, multi-model approaches are used routinely for analyzing spectroscopic datasets across many major industries (e.g., medicine, agriculture) to maximize predictive outcomes for real-world applications. In this study, we conducted a benchmark modeling exercise to compare the performance of several machine learning algorithms in a multi-class problem utilizing a multivariate spectroscopic dataset obtained from live animals. Spectra obtained from live individuals representing eleven amphibian species were classified according to taxonomic designation. Seven modeling techniques were applied to generate prediction models, which varied significantly (p < 0.05) with regard to mean classification accuracy (e.g., support vector machine: 95.8 ± 0.8% vs. K-nearest neighbors: 89.3 ± 1.0%). Through the use of a multi-algorithm approach, candidate algorithms can be identified and applied to more effectively model complex spectroscopic data collected for wildlife sciences. Other key considerations in the predictive modeling workflow that serve to optimize spectroscopic model performance (e.g., variable selection and cross-validation procedures) are also discussed.

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.