Standard PC Research Articles

A universal free energy relationship for both hard and soft radical addition in water

AbstractPrioritization of (eco)toxicological risks and technological endpoints among 100,000+ potential substances, conditions and mechanisms requires computationally ‘inexpensive’ and accurate tools to ‘screen’ reactivity and identify reaction products. Such prediction tools are very scarce. Left unresolved, charge‐transfer and hydration complicate predictions. Based on experimental reaction of radicals (3O2, CH3•, CO3•− etc.) with organic substrates, we hypothesized that a universal linear free energy relationship (LFER) can rationalize these reactions to accurately predict addition rates.We calculated free energies of forming charge‐separated intermediates from explicit descriptions of addition reaction products. We combined these energies, via a thermodynamic cycle, with electron transfer energies to calculate product formation energies. All energies include consideration of hydration effects by water. We ascribe feasibilities of ‘hard’ (ionic) and ‘soft’ (covalent) mechanisms to the relevance of the charge‐separated intermediate.Analysis shows that activation energies effectively relate to a combination of product formation energies and charge‐separation energies. The relative importance of these is determined by a mixing parameter, which is mostly constant for a given radical (substrate). Via the Eyring equation, our universal LFER explains up to 94% of the variance in data for rate constants. Prediction error is a factor 5 (2 SD), only slightly larger than variation in experimentation, particularly sensitive to varying reaction conditions.Minimal parametrization ensures that our new framework for calculating reactivity of radical addition is accurate, robust and with satisfactory rational. Calculation time for 100 reactions is <1 h on a standard desktop PC. In anticipation of underpinning with an even wider range of reagents, this ‘inexpensive’ calculus can more easily assess a greater domain of structures and extrapolate to new structures. This helps to better assess and select favourable non‐toxic, environmentally friendly and technologically superior chemical (sub)structures.

Visualization and Interaction Technologies in Serious and Exergames for Cognitive Assessment and Training: A Survey on Available Solutions and Their Validation

Exergames and serious games, based on standard personal computers, mobile devices and gaming consoles or on novel immersive Virtual and Augmented Reality techniques, have become popular in the last few years and are now applied in various research fields, among which cognitive assessment and training of heterogeneous target populations. Moreover, the adoption of Web based solutions together with the integration of Artificial Intelligence and Machine Learning algorithms could bring countless advantages, both for the patients and the clinical personnel, as allowing the early detection of some pathological conditions, improving the efficacy and adherence to rehabilitation processes, through the personalisation of training sessions, and optimizing the allocation of resources by the healthcare system. The current work proposes a systematic survey of existing solutions in the field of cognitive assessment and training. We evaluate the visualization and interaction technologies commonly adopted and the measures taken to fulfil the need of the pathological target populations. Moreover, we analyze how implemented solutions are validated, i.e. the chosen experimental designs, data collection and analysis. Finally, we consider the availability of the applications and raw data to the large community of researchers and medical professionals and the actual application of proposed solutions in the standard clinical practice. Despite the potential of these technologies, research is still at an early stage. Although the recent release of accessible immersive virtual reality headsets and the increasing interest on vision-based techniques for tracking body and hands movements, many studies still rely on non-immersive virtual reality (67.2%), mainly mobile and personal computers, and standard gaming tools for interactions (41.5%). Finally, we highlight that although the interest of research community in this field is increasingly higher, the sharing of dataset (10.6%) and implemented applications (3.8%) should be promoted and the number of healthcare structures which have successfully introduced the new technological approaches in the treatment of their host patients is limited (10.2%).

Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images.

The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is- rather than improving the segmentation quality compared to the baseline method- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitatively on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).

Immune Repertoire Analysis on High-Performance Computing Using VDJServer V1: A Method by the AIRR Community.

AIRR-seq data sets are usually large and require specialized analysis methods and software tools. A typical Illumina MiSeq sequencing run generates 20-30 million 2×300bp paired-end sequence reads, which roughly corresponds to 15GB of sequence data to be processed. Other platforms like NextSeq, which is useful in projects where the full V gene is not needed, create about 400million 2×150bp paired-end reads. Because of the size of the data sets, the analysis can be computationally expensive, particularly the early analysis steps like preprocessing and gene annotation that process the majority of the sequence data. A standard desktop PC may take 3-5days of constant processing for a single MiSeq run, so dedicated high-performance computational resources may be required.VDJServer provides free access to high-performance computing (HPC) at the Texas Advanced Computing Center (TACC) through a graphical user interface (Christley et al. Front Immunol 9:976, 2018). VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provides access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene assignment, repertoire characterization, and repertoire comparison. Furthermore, VDJServer has parallelized execution for tools such as IgBLAST, so more compute resources are utilized as the size of the input data grows. Analysis that takes days on a desktop PC might take only a few hours on VDJServer. VDJServer is a free, publicly available, and open-source licensed resource. Here, we describe the workflow for performing immune repertoire analysis on VDJServer's high-performance computing.

POSC302 Discrete Event Simulation in Severe COPD: A Feasibility Study Using DICE Methodology

To assesses the feasibility of probabilistic, discrete event simulation (DES) in moderate to severe chronic obstructive pulmonary disease (COPD). To validate the model results against outcomes reported from a German COPD disease management program (DMP). Probabilistic DES was implemented using the Discretely Integrated Condition Event (DICE) framework by Caro et. al. Distributions defining model mortality and exacerbation rates were based on an epidemiological study by Rothnie et al. (99,574 patients, mean follow-up of 4.9 years) from the UK and implemented in the model through 18 patient profiles to support sub-group analyses. Exacerbations and pharmaceutical therapy with stepwise escalation/de-escalation were considered according to German treatment guidelines. Severe exacerbation costs were based on the German full inpatient statistics calculating weighted mean costs for Global Initiative for Chronic Obstructive Lung Disease (GOLD) groups 2-4 according to ICD-10 J44. Moderate exacerbation costs were based on official German reimbursement catalogues to account for outpatient visits and acute drug treatment. A total of 9,000 patients (500 per profile) were simulated to achieve stable Results: Modelled survival and exacerbation outcomes matched the Rothnie data without any major differences. Total model costs vs. reported DMP costs were: 3,145€ vs. 3,603€ (GOLD 2); 4,415€ vs. 4,579€ (GOLD 3); 5,662€ vs. 6,233€ (GOLD 4). Reported DMP cost were covered by confidence intervals of model Results: Total model runtime was 515 seconds. Generally, probabilistic DES for COPD is feasible with the DICE framework. The most recent DICE engine improved model runtime significantly and enables sensitivity analyses on standard PC hardware. Although modelled mean costs differed slightly from reported DMP costs, it is important to note that this model included COPD-related healthcare costs only while the DMP reported total healthcare costs.

CitiusSynapse: A Deep Learning Framework for Embedded Systems

As embedded systems, such as smartphones with limited resources, have become increasingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that of standard PCs. The proposed framework supports an OpenCL-based accelerator engine for accelerator deep learning operations in various embedded systems. Moreover, the parallel processing performance of OpenCL is maximized through an OpenCL kernel that is optimized for embedded GPUs, and the structural characteristics of embedded systems, such as unified memory. Furthermore, an on-device optimizer for optimizing the performance in on-device environments, and model converters for compatibility with conventional frameworks, are provided. The results of a performance evaluation show that the proposed on-device framework outperformed conventional methods.

Optimizing the allocation of technological resources of an air transport system in the presence of a schedule and fuzzy input data

The problem of optimal allocation of technological resources (operators) of a technical or organizational-technical system, designed to serve certain objects (operands) according to a given schedule, is considered. We take into account the necessity of incorporating, together with the main, preparatory and final operations, the possibility to select one or several operators for operand service into the service process, as well as the dependence of the operations duration on the factors characterized by uncertainty. Due to the supposed absence of statistics, expert-assigned indefinite values in the form of triangular fuzzy numbers are used. The optimization problem is formulated as a mathematical programming problem with a fuzzy criterion and clear-cut constraints, consisting in finding such a distribution of a given number of operators to serve each operand from a given set which minimizes the target function that takes into account deviations from the schedule (delay) with the service termination. Typical examples of systems for which the problem is relevant are the production complexes of air transport enterprises operating in conditions of uncertainty when it is necessary to ensure the regularity and safety of air transportation. A model example of solving the problem of allocating mobile refueling facilities at a hub airport, taking into account the peculiarities of its schedule, is presented. It is shown that the capabilities of standard personal computer software are sufficient for the solution.

A Simple and Efficient Technique to Generate Bounded Solutions for the Multidimensional Knapsack Problem: a Guide for OR Practitioners

The 0-1 Multidimensional Knapsack Problem (MKP) is a NP-Hard problem that has important applications in business and industry. Approximate solution approaches for the MKP in the literature typically provide no guarantee on how close generated solutions are to the optimum. This article demonstrates how general-purpose integer programming software (Gurobi) is iteratively used to generate solutions for the 270 MKP test problems in Beasley’s OR-Library such that, on average, the solutions are guaranteed to be within 0.094% of the optimums and execute in 88 seconds on a standard PC. This methodology, called the simple sequential increasing tolerance (SSIT) matheuristic, uses a sequence of increasing tolerances in Gurobi to generate a solution that is guaranteed to be close to the optimum in a short time. This solution strategy generates bounded solutions in a timely manner without requiring the coding of a problem-specific algorithm. The SSIT results (although guaranteed within 0.094% of the optimums) when compared to known optimums deviated only 0.006% from the optimums—far better than any published results for these 270 MKP test instances.

Design‐oriented elasto‐plastic analysis of reinforced concrete structures with in‐plane forces applying convex optimization

AbstractAssessment of the cracked behavior at service load and verification of sufficient deformation capacity in the ultimate state are often required when designing reinforced concrete structures. Most existing methods dedicated to nonlinear analysis of reinforced concrete, however, are not well‐suited in practice for use in design processes involving large‐scale structural problems due to an enormous modeling effort and lack of numerical stability. This paper presents a new finite element framework for efficient elasto‐plastic analysis of two‐dimensional reinforced concrete structures subjected to in‐plane forces. The basic concept is to adopt a stress‐based finite element formulation and cast the problem as a convex optimization problem where energy principles are invoked through a formalistic application of nonlinear‐elastic material models. The method accounts for reinforcement yielding and concrete crushing, including the strength reduction due to cracking, and can be used to imitate the elasto‐plastic response of fully‐cracked structures subjected to monotonic loading. The efficiency of the method is demonstrated, inter alia, by an analysis of a complex structure, where the discretized problem has more than 1 million variables and is solved within a few minutes on a standard personal computer.

Calibration of NMR Receiver using Spectrometer Characteristics

Abstract This article describes the measurement of the relation between input and output signals using two techniques: with a signal generator and with the thermal noise of a known resistance. Each of the techniques has its advantages and disadvantages. Both methods are tested and the results are compared. The input signal of the receiver is known in volts, while the output signal is in ADC (analogue-to-digital converter) units. It is the main difference versus the gain. Knowledge of the relation enables recalculation of the output signal into the input of the receiver or vice versa. It is important in some experiments. The method with the harmonic signal requires a suitable NMR spectroscopic console, generator of the harmonic signal and an attenuator, the method with the noise requires only the NMR console. It indicates that both methods are simple and cheap. The measured data are processed on a standard PC using common programs.

TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning

SummaryRecent advances in machine learning have greatly enhanced automatic methods to extract information from fluorescence microscopy data. However, current machine-learning-based models can require hundreds to thousands of images to train, and the most readily accessible models classify images without describing which parts of an image contributed to classification. Here, we introduce TDAExplore, a machine learning image analysis pipeline based on topological data analysis. It can classify different types of cellular perturbations after training with only 20–30 high-resolution images and performs robustly on images from multiple subjects and microscopy modes. Using only images and whole-image labels for training, TDAExplore provides quantitative, spatial information, characterizing which image regions contribute to classification. Computational requirements to train TDAExplore models are modest and a standard PC can perform training with minimal user input. TDAExplore is therefore an accessible, powerful option for obtaining quantitative information about imaging data in a wide variety of applications.

Optimization-based online estimation of vehicle mass and road grade: Theoretical analysis and experimental validation

The gross vehicle mass (GVM) and the road grade are two factors that both have a substantial influence on the performance of a vehicle’s powertrain. In this paper, we propose a novel model-based estimation method for the GVM and the road grade that exploits entire sequences of powertrain measurements at once and is formulated as a nonlinear program (NLP). The estimator is based on a simple model for the vehicle’s longitudinal dynamics with only few intuitive vehicle parameters. By assuming the GVM to remain constant during certain sections of the trip and by describing the road grade profile in the distance domain, we achieve a separation of scales, which enhances disturbance rejection and significantly lowers the number of optimization variables. The resulting estimator is thoroughly analyzed both analytically and numerically. We show that a closed-form solution exists for the grade profile as a function of the GVM. Furthermore, if the GVM can be assumed to be constant during the journey considered, the estimation problem can be translated to a scalar NLP for finding the GVM. Although a rigorous proof is missing, our experiments show that in practice, the objective function is quasi-convex on a reasonable interval of GVM values and that thus a unique solution exists. Furthermore, robustness and sensitivity studies are conducted, where various perturbations are considered in a controlled environment, including uncorrelated and correlated noise, sensor offset, and model mismatches. Compared to two well-known recursive filters described in literature, our estimator shows significantly higher robustness with respect to all perturbations. Finally, we validate the estimator and the two recursive filters on real data from an electric city bus. The proposed estimator outperforms the recursive algorithms and achieves an average relative GVM estimation error of 3.4%. On a standard personal computer, the NLP for a driving phase of around one hour is solved in roughly 7.5 s, while the scalar NLP representing a driving phase of around 75 s is solved in roughly 12 ms. Both results indicate the real-time applicability of our algorithm.

A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods.

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.

Modeling glacial and fluvial landform evolution at large scales using a stream-power approach

Abstract. Modeling glacial landform evolution is more challenging than modeling fluvial landform evolution. While several numerical models of large-scale fluvial erosion are available, there are only a few models of glacial erosion, and their application over long time spans requires a high numerical effort. In this paper, a simple formulation of glacial erosion which is similar to the fluvial stream-power model is presented. The model reproduces the occurrence of overdeepenings, hanging valleys, and steps at confluences at least qualitatively. Beyond this, it allows for a seamless coupling to fluvial erosion and sediment transport. The recently published direct numerical scheme for fluvial erosion and sediment transport can be applied to the entire domain, where the numerical effort is only moderately higher than for a purely fluvial system. Simulations over several million years on lattices of several million nodes can be performed on standard PCs. An open-source implementation is freely available as a part of the landform evolution model OpenLEM.

Modular Data Acquisition System for Recording Activity and Electrical Stimulation of Brain Tissue Using Dedicated Electronics.

In this paper, we present a modular Data Acquisition (DAQ) system for simultaneous electrical stimulation and recording of brain activity. The DAQ system is designed to work with custom-designed Application Specific Integrated Circuit (ASIC) called Neurostim-3 and a variety of commercially available Multi-Electrode Arrays (MEAs). The system can control simultaneously up to 512 independent bidirectional i.e., input-output channels. We present in-depth insight into both hardware and software architectures and discuss relationships between cooperating parts of that system. The particular focus of this study was the exploration of efficient software design so that it could perform all its tasks in real-time using a standard Personal Computer (PC) without the need for data precomputation even for the most demanding experiment scenarios. Not only do we show bare performance metrics, but we also used this software to characterise signal processing capabilities of Neurostim-3 (e.g., gain linearity, transmission band) so that to obtain information on how well it can handle neural signals in real-world applications. The results indicate that each Neurostim-3 channel exhibits signal gain linearity in a wide range of input signal amplitudes. Moreover, their high-pass cut-off frequency gets close to making it suitable for recording both Local Field Potential (LFP) and spiking brain activity signals. Additionally, the current stimulation circuitry was checked in terms of the ability to reproduce complex patterns. Finally, we present data acquired using our system from the experiments on a living rat’s brain, which proved we obtained physiological data from non-stimulated and stimulated tissue. The presented results lead us to conclude that our hardware and software can work efficiently and effectively in tandem giving valuable insights into how information is being processed by the brain.

When to use Integer Programming Software to solve large multi-demand multidimensional knapsack problems: a guide for operations research practitioners

An important generalization of the classic 0-1 knapsack problem is the multi-demand multidimensional knapsack problem (MDMKP). In addition to being theoretically difficult to solve (it is NP-hard), it can be in practice difficult to solve because of its conflicting knapsack and demand constraints. Since there are significant large-scale applications of the MDMKP, approximate solution approaches are commonly used to solve these problems. However, using 1620 MDMKPs discussed in the literature, this article demonstrates which types of large MDMKPs can be solved efficiently by operations research practitioners using general purpose integer programming software on a standard personal computer within 0.1% of optimum. Statistical analyses are used to determine which problem parameters significantly impact solution time. Finally, based on these 1620 MDMKP instances, a classification tree is generated. This tree can be used to guide practitioners in solving MDMKPs that arise in business and industry.

Automated medical avatar animation for warfighter mission simulation.

Virtual representations of human internal anatomy are important for military applications such as protective equipment design, injury severity prediction, thermal analysis, and physiological simulations. High-fidelity volumetric models based on imaging data are typically in static postures and difficult to use in simulations of realistic mission scenarios. This study aimed to investigate a hybrid approach to reposition medical avatars that preserves internal anatomy but allows rapid repositioning of full three-dimensional (3D) meshes. A software framework that accepts a medical avatar in a 3D tetrahedral mesh format representing 72 organs and tissues with an articulated skeleton was developed. The skeleton is automatically resized and associated to the avatar using rigging and skinning algorithms inspired by computer animation techniques. Military relevant motions were used for animations. A motion retargeting algorithm was implemented to apply animation to avatars of various sizes, and a motion blending algorithm was implemented to smoothly transition between movements. These algorithms were incorporated into a path generation tool that accepts initial, intermediate, and final coordinates of a multisegment action along with the specific motion for each segment to synthesize a realistic compound set of movements comprising the animation. The developed pipeline for dynamic repositioning of medical avatars was demonstrated. Various complex motions were automatically animated. Retargeting was demonstrated on models of varying sizes. Movements along a path were animated to demonstrate smooth motion transitions. Animation of the full 3D avatar mesh ran in real time on a standard desktop personal computer. The repositioning algorithm successfully preserved the shape and volume of rigid structures such as bone. The developed software leverages techniques from various disciplines to create a hybrid approach enabling real-time 3D mesh repositioning appropriate for use in simulated military missions using avatars containing a complete anatomy representation. The workflow is largely automated, enabling rapid evaluation of new mission scenarios.

Smartphone-Based VO2max Measurement With Heart Snapshot in Clinical and Real-world Settings With a Diverse Population: Validation Study.

BackgroundMaximal oxygen consumption (VO2max) is one of the most predictive biometrics for cardiovascular health and overall mortality. However, VO2max is rarely measured in large-scale research studies or routine clinical care because of the high cost, participant burden, and requirement for specialized equipment and staff.ObjectiveTo overcome the limitations of clinical VO2max measurement, we aim to develop a digital VO2max estimation protocol that can be self-administered remotely using only the sensors within a smartphone. We also aim to validate this measure within a broadly representative population across a spectrum of smartphone devices.MethodsTwo smartphone-based VO2max estimation protocols were developed: a 12-minute run test (12-MRT) based on distance measured by GPS and a 3-minute step test (3-MST) based on heart rate recovery measured by a camera. In a 101-person cohort, balanced across age deciles and sex, participants completed a gold standard treadmill-based VO2max measurement, two silver standard clinical protocols, and the smartphone-based 12-MRT and 3-MST protocols in the clinic and at home. In a separate 120-participant cohort, the video-based heart rate measurement underlying the 3-MST was measured for accuracy in individuals across the spectrum skin tones while using 8 different smartphones ranging in cost from US $99 to US $999.ResultsWhen compared with gold standard VO2max testing, Lin concordance was pc=0.66 for 12-MRT and pc=0.61 for 3-MST. However, in remote settings, the 12-MRT was significantly less concordant with the gold standard (pc=0.25) compared with the 3-MST (pc=0.61), although both had high test-retest reliability (12-MRT intraclass correlation coefficient=0.88; 3-MST intraclass correlation coefficient=0.86). On the basis of the finding that 3-MST concordance was generalizable to remote settings whereas 12-MRT was not, the video-based heart rate measure within the 3-MST was selected for further investigation. Heart rate measurements in any of the combinations of the six Fitzpatrick skin tones and 8 smartphones resulted in a concordance of pc≥0.81. Performance did not correlate with device cost, with all phones selling under US $200 performing better than pc>0.92.ConclusionsThese findings demonstrate the importance of validating mobile health measures in the real world across a diverse cohort and spectrum of hardware. The 3-MST protocol, termed as heart snapshot, measured VO2max with similar accuracy to supervised in-clinic tests such as the Tecumseh (pc=0.94) protocol, while also generalizing to remote and unsupervised measurements. Heart snapshot measurements demonstrated fidelity across demographic variation in age and sex, across diverse skin pigmentation, and between various iOS and Android phone configurations. This software is freely available for all validation data and analysis code.

In COM we trust: Feasibility of USB-based event marking

Modern experimental research often relies on the synchronization of different events prior to data analysis. One way of achieving synchronization involves marking distinct events with electrical pulses (event markers or “TTL pulses”), which are continuously recorded with research hardware, and can later be temporally aligned. Traditionally, this event marking was often performed using the parallel port in standard personal computers. However, the parallel port is disappearing from the landscape of computer hardware, being replaced by a serial (COM) port, namely the USB port. To find an adequate replacement for the parallel port, we evaluated four microcontroller units (MCUs) and the LabJack U3, an often-used USB data acquisition device, in terms of their latency and jitter for sending event markers in a simulated experiment on both Windows and Linux. Our results show that all four MCUs were comparable to the parallel port in terms of both latency and jitter, and consistently achieved latencies under 1 ms. With some caveats, the LabJack U3 can also achieve comparable latencies. In addition to the collected data, we share extensive documentation on how to build and use MCUs for event marking, including code examples. MCUs are a cost-effective, flexible, and performant replacement for the disappearing parallel port, enabling event marking and synchronization of data streams.

Introducing regularization into the virtual fields method (VFM) to identify nonhomogeneous elastic property distributions

The identification of nonhomogeneous elastic property distributions has been traditionally achieved with well acknowledged optimization based inverse approaches, but when full-field displacement measurements are available, the virtual fields method (VFM) can be computationally more efficient by converting the large-scale optimization problem into multiple small-scale optimization problems. A possible downside of the VFM so far was not to take into account prior knowledge, which is often available and needed when there is a very large number of unknowns and the inverse problem is ill-posed. In this work, different approaches are proposed for introducing regularization into the VFM, aiming to penalize the local variations of identified stiffness properties in order to reduce the effects of uncertainty in the inverse problem resolution. The feasibility and accuracy of the regularized VFM are tested through several numerical and experimental datasets. It is shown that the main advantage of the novel VFM approaches is the low computational cost, as large-scale inverse problems with 10,000 unknown parameters can be solved within several seconds using a standard personal computer. Although the regularized VFM can successfully detect a stiff inclusion in a soft solid with high accuracy, regularization also introduces unexpected spurious effects in the results, blurring the interface between soft and stiff regions. We also observed that the regularization did not improve the smoothness significantly due to local effects of the small-scale optimization problem introduced in the proposed VFM method. Therefore, traditional regularization, which penalizes local variations of identified stiffness properties, can be combined with the VFM to solve inverse problems with a high computational efficiency, but supplemental regularization conditions will need to be adapted in the future to better delineate soft-stiff interfaces with this methodology.