Modeling Of Computer Systems Research Articles

Generating a system of group constants for neutron-physical calculations of fast reactors based on ROSFOND-2020.2 library files

The subject of constant support development is currently becoming increasingly important in connection with the national strategy of transition to a closed nuclear fuel cycle. At the same time, the importance of the tasks of refining calculation models and minimizing methodological, statistical, and constant errors is increasing. In this connection, the idea of creating a universal system of constants describing with equal accuracy both uranium loading and loading with mixed oxide uranium-plutonium fuel and the possibility to perform on its basis both precision Monte Carlo calculations (data format – ACE) and multigroup calculations (formats – ABBN and ANISN) was laid in the basis of this work. In this paper we analyze the freely available libraries of evaluated nuclear data in order to justify the choice of source files for the formation of a new system of group constants for neutron-physical calculations of fast reactor cores, describe the process of formation of a new library of reactor constants, and verify the obtained system on computational test models of BN-800 reactor and critical systems. The process of formation of the new system of group constants included selection of initial neutron data files, updating of data tables of basic neutron cross-sections, self-shielding factors and Doppler coefficients, as well as data on fission spectra for the main fuel nuclides. The methodological component of the error for test models of the BN-800 core was evaluated. Using the ABBN-RF22 system of group constants it was possible to estimate the correction in the 299 groups calculation, which amounted to 0.3%. Previously, when using the ABBN-93 library, there was no such possibility due to the lack of continuity between the files of evaluated neutron data and the group constants used.

An Operational Quality Model of Embedded Software Aligned with ISO 25000

Embedded systems omnipresent in everyday life and industry are mainly composed of hardware and software that must comply with a number of standards and regulations. However, there is no consensus on the quality characteristics and sub-characteristics of embedded software. This paper presents the steps for modeling an operational quality model for embedded software aligned with the ISO 25000 series of quality models for traditional computer systems. From a literature review composed of 40 studies on quality modeling for embedded systems and software, 85 of the most frequent quality characteristics and sub-characteristics were first identified, including a subset of 16 referenced or cited in at least 25% of the literature. Next, the design of a quality model for embedded software aligned with the ISO 25000 series was proposed with 13 characteristics and 27 sub-characteristics. The operational aspect of this quality model for embedded software is addressed next through a set of measures and measurement functions from ISO 25000 to aggregate the results of the quantification of the characteristics and sub-characteristics. A survey involving 25 embedded software specialists is presented next to gauge, using Fleiss’ Kappa criteria, their agreement with the proposed quality model. Furthermore, the computed importance weights derived from the survey participants’ individual opinions were compared with those derived from an analysis of 40 embedded software studies, bolstering the credibility of the model. The results of this study suggest that the proposed quality model can serve as a framework for evaluating and understanding the quality characteristics across diverse expertise levels. Furthermore, the convergence between the survey and the literature strengthens the model's credibility by anchoring it in both established literature and practitioners’ agreements.

Subgrid scale modeling of droplet bag breakup in VOF simulations

The mesh-dependency of the breakup of liquid films, including their breakup length scales and resulting drop size distributions, has long been an obstacle inhibiting the computational modeling of large-scale spray systems. With the aim of overcoming this barrier, this work presents a framework for the prediction and modeling of subgrid-thickness liquid film formation and breakup within two-phase simulations using the volume of fluid method. A two-plane interface reconstruction is used to capture the development of liquid films as their thickness decreases below the mesh size. The breakup of the film is predicted with a semi-analytical model that incorporates the film geometry captured through the two-plane reconstruction. The framework is validated against experiments of the bag breakup of a liquid drop at We=13.8 through the comparison of the resulting drop size and velocity distributions. The generated distributions show good agreement with experimental results for drop resolutions as low as 25.6 cells across the initial diameter. The presented framework enables these drop breakup simulations to be performed at a computational cost three orders of magnitude lower than the cost of simulations utilizing adaptive mesh refinement.

Annotation Vocabulary (Might Be) All You Need.

Protein Language Models (pLMs) have revolutionized the computational modeling of protein systems, building numerical embeddings that are centered around structural features. To enhance the breadth of biochemically relevant properties available in protein embeddings, we engineered the Annotation Vocabulary, a transformer readable language of protein properties defined by structured ontologies. We trained Annotation Transformers (AT) from the ground up to recover masked protein property inputs without reference to amino acid sequences, building a new numerical feature space on protein descriptions alone. We leverage AT representations in various model architectures, for both protein representation and generation. To showcase the merit of Annotation Vocabulary integration, we performed 515 diverse downstream experiments. Using a novel loss function and only $3 in commercial compute, our premier representation model CAMP produces state-of-the-art embeddings for five out of 15 common datasets with competitive performance on the rest; highlighting the computational efficiency of latent space curation with Annotation Vocabulary. To standardize the comparison of de novo generated protein sequences, we suggest a new sequence alignment-based score that is more flexible and biologically relevant than traditional language modeling metrics. Our generative model, GSM, produces high alignment scores from annotation-only prompts with a BERT-like generation scheme. Of particular note, many GSM hallucinations return statistically significant BLAST hits, where enrichment analysis shows properties matching the annotation prompt - even when the ground truth has low sequence identity to the entire training set. Overall, the Annotation Vocabulary toolbox presents a promising pathway to replace traditional tokens with members of ontologies and knowledge graphs, enhancing transformer models in specific domains. The concise, accurate, and efficient descriptions of proteins by the Annotation Vocabulary offers a novel way to build numerical representations of proteins for protein annotation and design.

ArborSim: Articulated, branching, OpenSim routing for constructing models of multi-jointed appendages with complex muscle-tendon architecture.

Computational models of musculoskeletal systems are essential tools for understanding how muscles, tendons, bones, and actuation signals generate motion. In particular, the OpenSim family of models has facilitated a wide range of studies on diverse human motions, clinical studies of gait, and even non-human locomotion. However, biological structures with many joints, such as fingers, necks, tails, and spines, have been a longstanding challenge to the OpenSim modeling community, especially because these structures comprise numerous bones and are frequently actuated by extrinsic muscles that span multiple joints-often more than three-and act through a complex network of branching tendons. Existing model building software, typically optimized for limb structures, makes it difficult to build OpenSim models that accurately reflect these intricacies. Here, we introduce ArborSim, customized software that efficiently creates musculoskeletal models of highly jointed structures and can build branched muscle-tendon architectures. We used ArborSim to construct toy models of articulated structures to determine which morphological features make a structure most sensitive to branching. By comparing the joint kinematics of models constructed with branched and parallel muscle-tendon units, we found that among various parameters-the number of tendon branches, the number of joints between branches, and the ratio of muscle fiber length to muscle tendon unit length-the number of tendon branches and the number of joints between branches are most sensitive to branching modeling method. Notably, the differences between these models showed no predictable pattern with increased complexity. As the proportion of muscle increased, the kinematic differences between branched and parallel models units also increased. Our findings suggest that stress and strain interactions between distal tendon branches and proximal tendon and muscle greatly affect the overall kinematics of a musculoskeletal system. By incorporating complex muscle-tendon branching into OpenSim models using ArborSim, we can gain deeper insight into the interactions between the axial and appendicular skeleton, model the evolution and function of diverse animal tails, and understand the mechanics of more complex motions and tasks.

Bridging Continuous and Lattice-Based Models of Two-Dimensional Diffusion: A Systematic Approach for Estimating Transition Probabilities, Grid Size and Diffusivity

Lattice-based models have been broadly applied in mathematical and computational modeling of biological and biomedical systems for which spatial effects are important. These discrete models commonly include diffusion of mobile constituents as a key underlying mechanism. While the direct simulation of diffusion in continuous (off-lattice) domains is possible, it is computationally intensive, particularly when multiple coupled mechanisms are involved. This study presents a systematic approach for connecting continuous models of two-dimensional diffusion with internal obstacles to discrete, lattice-based (surrogate) models of diffusion. Results from continuous model simulations on a representative domain, and over many realizations, are used to develop accurate lattice-based surrogate models by exploiting internal symmetries. Probabilities determined for the lattice-based surrogate models are also connected to theoretical diffusivities for 2D random walks on a square lattice, necessitating the calibration of a spatial grid size. This approach can facilitate the inclusion of more accurate diffusive transport models of complex media within the general framework of lattice-based models that incorporate multiple coupled mechanisms.

Dairy factory milk product processing and sustainable of the shelf-life extension with artificial intelligence: a model study

This study models milk product processing and sustainable of the shelf-life extension in a dairy factory using artificial intelligence. The Cappadocia dairy factory was used to study chemical processes and computational system modeling and simulation. Levenberg–Marquardt algorithm was used to create an artificial neural network model from real-time data. An AI-based method utilizing a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was employed to precisely analyze productivity data in dairy factories. There are 9 product types and production quantities used as input parameters, and 90 datasets of actual dairy products used as output values. The model was trained using the Levenberg–Marquardt algorithm on 62 datasets for training, 14 for validation, and 14 for testing. The accuracy of the model is affected by the optimal data segmentation. The model showed how AI algorithms can improve processes and industrial production by increasing dairy production efficiency from 20 to 40%. Model efficiency values were compared to observed values to determine prediction accuracy. Model mean squared error was 4.02E-06, and coefficient of determination was 0.99984. Model efficiency predictions and observed values differed by −0.04% on average. This study investigated using artificial intelligence to optimize salvage processes and systems to increase energy efficiency and reduce environmental impact. The results show that a neural network model trained with real data can predict dairy plant productivity.

Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study

Objective. Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study. Approach. Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate and the mean arterial pressure for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control. Main results. Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems. Significance. We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

A unified model and method for forecasting energy consumption in distributed computing systems based on stationary and mobile devices

The subject of research in this article is the forecasting of energy consumption when computing distributed tasks on computer networks built on the basis of server solutions and distributed systems based on personal smartphones. The goal of this study was to create a universal computing energy cost prediction model that can be applied to both traditional and mobile cloud systems. Tasks: conduct an analysis of energy-saving approaches and technologies used to calculate data; consider computer system models and actions with them, namely: model of distributed job, model of distributed computing system, model of distribution strategy; develop a common and uniform dynamic method of forecasting spent energy with a focus on heterogeneous systems; conduct a study of the proposed approach on stationary and mobile devices. The obtained results include. The results of the experimental measurement of the energy consumption of mobile digital systems and stationary ones are presented. The energy efficiency of computing on GPUs of a stationary device based on CUDA technology and GPUs on mobile devices based on Apple Metal technology was determined. Computation during the calculation of 600 frames on a distributed system from mobile devices with failure settings showed a consumption of 15320 joules of energy. Simulation of computing on a distributed system with stationary devices showed a consumption of 52806 joules of energy. This gives us 3,45 times the consumption benefit from computing on mobile devices. Forecasted consumption is also very accurate. Conclusions. The energy consumption assessment model proved to be quite effective. The results of the experiments show that the energy consumption estimation model takes into account the features of the hardware platform where data processing is performed. Computation of data on the GPU of stationary devices loses energy efficiency to a similar implementation on the GPU of Apple Metal from mobile devices. Therefore, the presented results demonstrate the rationality of using mobile graphics processors for energy-efficient information processing.

Automatic Context-Aware Inference of Engagement in HMI: A Survey

Engagement is the process by which participants establish, maintain, and end their perceived connection. Automatic engagement inference is one of the tasks required to develop successful human-centered HMI applications. Engagement is a multi-faceted multimodal construct requiring high accuracy in interpretating contextual, verbal and non-verbal cues, making the development of an intelligent automated engagement inference system challenging. Existing surveys concentrate on specific application settings, and a comprehensive survey covering the different engagement facets, definition and inference across various contexts is lacking. Moreover, despite the importance of context-aware modeling, the literature lacks a systematic context-aware overview on the topic. This paper presents a comprehensive survey on previous work in engagement for HMI, entailing interdisciplinary definition, engagement components, publicly available datasets, ground truth assessment, and commonly used features and methods, serving as a guide for the development of future HMI interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement is studied sets apart this survey from existing ones. Our findings suggest four important directions for future research: (1) context-aware computational modeling, (2) temporal dynamics, (3) personalised computing, and (4) bias and fairness of engagement inference systems.

Spatiotemporal Dynamics of Successive Activations across the Human Brain during Simple Arithmetic Processing.

Previous neuroimaging studies have offered unique insights about the spatial organization of activations and deactivations across the brain; however, these were not powered to explore the exact timing of events at the subsecond scale combined with a precise anatomical source of information at the level of individual brains. As a result, we know little about the order of engagement across different brain regions during a given cognitive task. Using experimental arithmetic tasks as a prototype for human-unique symbolic processing, we recorded directly across 10,076 brain sites in 85 human subjects (52% female) using the intracranial electroencephalography. Our data revealed a remarkably distributed change of activity in almost half of the sampled sites. In each activated brain region, we found juxtaposed neuronal populations preferentially responsive to either the target or control conditions, arranged in an anatomically orderly manner. Notably, an orderly successive activation of a set of brain regions-anatomically consistent across subjects-was observed in individual brains. The temporal order of activations across these sites was replicable across subjects and trials. Moreover, the degree of functional connectivity between the sites decreased as a function of temporal distance between regions, suggesting that the information is partially leaked or transformed along the processing chain. Our study complements prior imaging studies by providing hitherto unknown information about the timing of events in the brain during arithmetic processing. Such findings can be a basis for developing mechanistic computational models of human-specific cognitive symbolic systems.

Mechanism of Plasmon-Induced Catalysis of Thiolates and the Impact of Reaction Conditions.

The conversion of the thiols 4-aminothiophenol (ATP) and 4-nitrothiophenol (NTP) can be considered as one of the standard reactions of plasmon-induced catalysis and thus has already been the subject of numerous studies. Currently, two reaction pathways are discussed: one describes a dimerization of the starting material yielding 4,4'-dimercaptoazobenzene (DMAB), while in the second pathway, it is proposed that NTP is reduced to ATP in HCl solution. In this combined experimental and theoretical study, we disentangled the involved plasmon-mediated reaction mechanisms by carefully controlling the reaction conditions in acidic solutions and vapor. Motivated by the different surface-enhanced Raman scattering (SERS) spectra of NTP/ATP samples and band shifts in acidic solution, which are generally attributed to water, additional experiments under pure gaseous conditions were performed. Under such acidic vapor conditions, the Raman data strongly suggest the formation of a hitherto not experimentally identified stable compound. Computational modeling of the plasmonic hybrid systems, i.e., regarding the wavelength-dependent character of the involved electronic transitions of the detected key intermediates in both reaction pathways, confirmed the experimental finding of the new compound, namely, 4-nitrosothiophenol (TP*). Tracking the reaction dynamics via time-dependent SERS measurements allowed us to establish the link between the dimer- and monomer-based pathways and to suggest possible reaction routes under different environmental conditions. Thereby, insight at the molecular level was provided with respect to the thermodynamics of the underlying reaction mechanism, complementing the spectroscopic results.

BioModME for building and simulating dynamic computational models of complex biological systems.

Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed "BioModME," a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization. All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

Generative agent‐based modeling: an introduction and tutorial

AbstractWe discuss the emerging new opportunity for building feedback‐rich computational models of social systems using generative artificial intelligence. Referred to as generative agent‐based models (GABMs), such individual‐level models utilize large language models to represent human decision‐making in social settings. We provide a GABM case in which human behavior can be incorporated into simulation models by coupling a mechanistic model of human interactions with a pre‐trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful dynamic models of various social systems that include realistic human reasoning and decision‐making. © 2024 System Dynamics Society.

(Invited) Challenges and Opportunities for the Computational Analysis of Electrochemical Energy Systems

Electrochemical systems are challenging to analyze and design due to their multi-dimensional, time-dependent, and multi-physics nature. For this reason, many computational electrochemical system models have been developed in the past three decades, some of which can now be found in commercial software packages, e.g., COMSOL or ANSYS Fluent. These models however: i) are based on simplifying assumptions, e.g., reduced dimensionality, infinite dilution, Tafel kinetics; ii) contain uncertain input parameters, e.g., effective transport properties and kinetic parameters; and, iii) are only validated with a limited number of experiments, e.g., polarization curves. These limitations, understandable at the time due to limited computational resources, characterization techniques, and material understanding and stability, are difficult to justify today that parallel programming and computer clusters enable scientists to perform large multi-dimensional simulations; electrochemical testing has expanded to include segmented cell testing, impedance spectroscopy, water flux estimation, and in-operando visualization; and, characterization tools and techniques have been developed to, for example, easily measure adsorption isotherms, effective proton conductivity and, within a given resolution, visualize the three-dimensional microstructure of the electrodes.Scientists working on computational analysis of electrochemical systems must acknowledge that further model development is still needed and must take advantage of new resources to improve the robustness and accuracy of cell-level models. The path is long and crooked, but it is very likely that the opportunity for a truly useful computational model, one that can be used for design, is beyond simplistic implementations and polarization curves. The effort is great, but it can be minimized by concurrent software development, as well as, by careful experimental work dedicated to parameter estimation and model validation.This presentation aims at outlining the limitations of state-of-the-art models, the challenges and opportunities of concurrent development and maintenance of a multi-scale, transient electrochemical energy system software, such as the open-source fuel cell simulation toolbox (OpenFCST) [1], and the new opportunities provided by combining advanced characterization tools with computational analysis. As an example, the development and validation of the transient, two-phase fuel cell and electrolyzer cell-level models in OpenFCST will be discussed [2]. The use of a hydrogen-pump model to study the effect of an active catalyst in CL effective proton conductivity measurements will also be discussed as an example of concurrent experimental/model design [3].

Stochastic Model of Computer Systems Functioning with Accumulator Storage for Serving Groups

To assess the potential capabilities of computer systems, feasibility indicators for solving problems are used. These indicators characterize the quality of the systems taking into account reliability and parameters of incoming tasks. One of the modes of computer systems functioning is the task flow servicing mode. Due to the peculiarities of this mode, when analyzing the functioning of computer systems, they are considered as a stochastic object, which in turn is well studied by stochastic methods. The paper proposes a mathematical model for calculating feasibility indicators for solving tasks in servicing mode of task flow on computer systems with accumulator storage. The model is constructed within the framework of queuing theory. Analytical solutions for assessing the fullness of accumulator storage are obtained.

A neural network finite element method for contact mechanics

Computational biomechanical models of organ systems utilizing the finite element (FE) method have been extensively applied to the study of normal and pathophysiological function. While a robust method and providing for many unique pathophysiological insights, traditional FE methods remain prohibitively slow for real-time many-query clinical applications. To meet these demanding computational requirements, we have developed a general neural network finite element (NNFE) approach for hyperelastic soft tissue organ simulations that can produce equivalent accuracy within clinically relevant time frames. However, many organ systems involve contact between structural elements (e.g. heart valve leaflets), which has not been previously addressed in the NNFE and related approaches. In the present work, we developed a NNFE-based method for contact between elastic deformable bodies. We exploited the fact that stable equilibrium solutions in nonlinear hyperelasticity minimize the potential energy to train the neural network, with contact incorporated through an potential energy penalty. This penalty was discretized and computationally implemented in JAX (Google, Inc.) in a way that could be statically allocated and jit compiled for rapid computation on modern GPU hardware. Following our previous NNFE formulation, we represented the problem domain geometry and enforced the necessary boundary conditions using a Non-Uniform Rational B-Splines (NURBS) based geometry scheme. NURBS were chosen for their ability to smoothly represent body and surface geometries. To demonstrate the method, we developed and verified two basic contact problems using a hyperelastic material model: indentation and flap. Both problems were simulated with a fully connected neural network with a parallel linear layer. Training continued until the gradient of the energy with respect to network parameters dropped below 10−2, taking less than six hours in each case studied. Once trained, the NNFE approach was capable of accurately predicting the equilibrium configurations of representative contact problems. Moreover, the NNFE contact approach required nearly six order of magnitude less time to evaluate than an equivalent FEniCSx implementation that utilized a similar mesh of cubic order Lagrange elements (0.01 s compared to 1715 seconds, respectively). Moreover, the solutions were trained over full ranges of loading and were thus able to faithfully represent families of solutions without requiring retraining. While addressing basic problems in contact, this study serves as crucial stepping stone to generate NNFE organ simulations for rapid prediction of patient specific solutions that involve contact.

A simulation study of magnetic nanoparticle clustering in a fluid flow

The assembly of magnetic nanoparticles has been promisingly used in biomedical applications. Several computational studies have addressed the study of cluster formation in the colloidal regime. However, these studies did not mention the formation of clusters in nonequilibrium conditions, such as fluid flow. To fill this gap, we develop computational models of magnetohydrodynamic systems to study their behavior within a flow in terms of cluster shape formation. All simulations were performed by Verlet integration through LAMMPS, a molecular dynamics framework. Through simulations, we discover that the fluid flow promotes formation of non-linear clusters, and linear clusters tend to orient toward the direction of the flow. These phenomena are investigated by using the equilibrium of magnetic torque and fluid torque. The analysis shows that the formation of non-linear clusters are mainly assisted by a bias-orientation of the unstable linear clusters, while the bias-orientation is originated from the torque cancellation in the direction of the flow.

Joint Task Offloading and Resource Allocation for Fog-Based Intelligent Transportation Systems: A UAV-Enabled Multi-Hop Collaboration Paradigm

Unmanned aerial vehicles (UAVs) have been widely used in Intelligent Transportation Systems (ITS) due to their rapid deployment and high mobility, which are considered as a promising solution to expand the scope of communication, especially in inaccessible areas. However, there is a lack of a universal and extensible multi-hop collaboration model in the existing research on UAV-involved ITS. In this paper, we innovatively introduce a novel UAV-enabled multi-hop collaborative fog computing (FC) system model, in which several moving UAVs with unpredictable locations provide effective and efficient communication and computation services for ground user equipments (UEs). With this model, we mathematically formulate a joint user association, UAV association, task offloading, transmission power, computation resource allocation, and UAV location optimization problem, which is a mixed integer nonlinear programming (MINLP) problem and challenging to deal with. To solve the non-convex problem, we propose a novel multi-hop collaborative algorithm to derive the optimal task offloading and resource allocation decisions for each UAV. Simulation results demonstrate the superiority of the UAV-enabled multi-hop collaborative FC system and validate the effectiveness of the proposed scheme.

Calibration methods to fit parameters within complex biological models.

Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.