Use Of Computational Models Research Articles

Digital Human Modeling: A Review and Reappraisal of Origins, Present, and Expected Future Methods for Representing Humans Computationally

ABSTRACT The effective use of computational modeling and simulation tools early in the design process is arguably becoming a gold standard for modern product development. Compared to many mechanistic computational design approaches, modeling and simulating humans, due to their inherent complexities of physiological and cognitive attributes, provides one of the most challenging undertakings. With the rapidly expanding use of computer, sensor, and visualization technologies, digital human modeling (DHM) emerged as a computerized design support methodology that enables modeling and simulation of humans within a computer-aided design (CAD) or virtual environment (VE). Implementing DHM with physical or digital mockups brings the advantages of running various “what-if” design scenarios early in design; thus, enhancing concept generation efforts by filtering out infeasible ideas and exploring better design alternatives. A modern product development process with DHM can also help to reduce the overall cost and time required in the long run. Although several DHM software packages are available and many companies have been designing with DHM, the domain has not reached maturity in resolving theoretical research questions and fostering simulation-based ergonomics practice. Besides, the growing body of literature, software platforms, and technology integration makes it challenging for newcomers and specialists from disciplines other than human factors engineering (HFE) to recognize the power of DHM tools. This paper aims to provide a comprehensive review of the DHM domain and summarize the evolution, current status, and future trends of the DHM design support tools. We hope this review will provide a guideline for designers and serve as a roadmap for current and future researchers interested in DHM-related research to find out new venues and opportunities for further international collaborations.

Epidemiological assessment of mutational capabilities in common seasonal viruses.

Background: Influenza vaccine composition is reviewed prior to every flu season since influenza viruses frequently evolve through antigenic variations. Vaccine strains are selected in expectation of the upcoming influenza season to allow sufficient time for production. The aim of the present study is to assess the use of computational models for predicting the evolution of influenza based on the association of genetic mutations and antigenic traits of circulating viruses that may apprise vaccine strain assortment decisions.
 Methodology: This study also focuses on the correlation of viruses with spread rate using statistical methods. For this method, we have worked on four different viruses Influenza, Ebola, Measles and Dengue. The year-wise mutation rate was correlated with the epidemiological data to see the impact of mutations on the disease spread. 
 Results: We highlight the efficiency of this approach by analyzing the mutation rate and correlating it with its spread rate to find out either mutation in viruses causes disease spread or not. Our study identified mutations in viruses get high before the outbreak of disease through which we can assess the upcoming outbreak. We can set a threshold value for nucleotide differences that can predict the next outbreak of viral disease. 
 Conclusion: The concept of correlation between the genomic data and epidemic spread leads to the research analysis that mutations do not follow any pattern. Though most of the mutations are random. Our research concluded that some mutations may suppress the virus outbreak, and some mutate to become more resistant than the existing strain that causes an outbreak.

Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review.

The term "In Silico Trial" indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative.

Simulating Secularities: Challenges and Opportunities in the Computational Science of (Non)Religion

This article provides scholars of nonreligion and secularism with an introduction to some of the major opportunities and challenges associated with the growing application of computational methods to the phenomena they study. It also illustrates these opportunities and challenges by describing several overlapping research projects and some of the models of (non)religion they have produced. Finally, the article addresses some of the significant philosophical issues surrounding the use of computer modeling and simulation, focusing on the ethical and epistemological concerns that these tools often raise. I invite scholars of nonreligion to consider adding these techniques to their methodological toolkits, and to join in on the fascinating and important conversations about simulating secularities that these models engender.

Numerical Optimization of an Open-Ended Coaxial Slot Applicator for the Detection and Microwave Ablation of Tumors.

Simple SummaryIn this work, a parametric analysis of a dual-mode applicator for microwave ablation treatments is proposed to optimize the geometric parameters of the structure. The dual-mode concept means that the applicator comprises an additional sensing mode to determine the dielectric properties of the surrounding tissue before and during the ablation procedure. Based on numerical electromagnetic-thermal coupled simulations the most optimal design in terms of applicator efficiency as well as ablation zone volume and shape is determined that further fulfills the sensitivity requirements of the sensing mode. The multiobjective optimization problem is solved graphically with the so-called Pareto-optimization method. The resulting Pareto-optimal dual-mode applicator designs are characterized by electromagnetic and thermal simulations and discussed.A multiobjective optimization method for a dual-mode microwave applicator is proposed. Dual-modality means that microwaves are used apart from the treatment, and also for the monitoring of the microwave ablation intervention. (1) The use of computational models to develop and improve microwave ablation applicator geometries is essential for further advances in this field. (2) Numerical electromagnetic–thermal coupled simulation models are used to analyze the performance of the dual-mode applicator in liver tissue; the sensitivity evaluation of the dual-mode applicator’s sensing mode constrains the set of optimal solutions. (3) Three Pareto-optimal design parameter sets are derived that are optimal in terms of applicator efficiency as well as volume and sphericity of the ablation zone. The resulting designs of the dual-mode applicator provide a suitable sensitivity to distinguish between healthy and tumorous liver tissue. (4) The optimized designs are presented and numerically characterized. An improvement on the performance of previously proposed dual-mode applicator designs is achieved. The multiphysical simulation model of electromagnetic and thermal properties of the applicator is applicable for future comprehensive design procedures.

Requirements for Computational Approaches to Analyzing Resilience in Human-Machine Teams

Human-machine teams (HMTs) in complex work domains need to be able to adapt to variable and uncertain work demands. Computational modeling and simulation can provide novel approaches to the evaluation of HMTs performing complex joint activities, affording large-scale, quantitative analysis of team characteristics (such as system architecture and governance protocols) and their effects on resilience. Drawing from literature in resilience engineering, human-automation interaction, and cognitive systems engineering, this paper provides a theoretical exploration of the use of computational modeling and simulation to analyze resilience in HMTs. Findings from literature are summarized in a set of requirements that highlight key aspects of resilience in HMTs that need to be accounted for in future modeling and evaluation efforts. These requirements include a need to model HMTs as joint cognitive systems, the need to account for the interdependent nature of activity, the temporal dynamics of work, and the need to support formative exploration and inquiry. We provide a brief overview of existing modeling and simulation approaches to evaluating HMTs and discuss further steps for operationalizing the identified requirements.

On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine

The use of computational models for internal combustion engine development is ubiquitous. Numerical simulations using simpler to complex physical models can predict engine’s performance and emissions, but they require large computational capabilities. By comparison, statistical methodologies are more economical tools in terms of time and resources. This paper investigated the use of an artificial neural network algorithm to simulate the nonlinear combustion process inside the cylinder. Three engine control variables (i.e. spark timing, mixture equivalence ratio, and engine speed) were set as the model inputs. Outputs included peak cylinder pressure and its location, maximum pressure rise rate, indicated mean effective pressure, ignition lag, combustion phasing, burn duration, exhaust temperature, and engine-out emissions (i.e. nitrogen oxides, carbon monoxide, and unburned hydrocarbons). Eighty percent of the experimental data from a heavy-duty natural gas spark ignition engine were utilized to train the model. The perceptions accurately learned the combustion characteristics and predicted engine responses with acceptable errors, evidenced by close-to-unity coefficient of determination and close-to-zero root-mean-square error. Moreover, the regressors captured the effect of key operating variables on the engine response, suggesting the well-trained models successfully identified the complex relationships and can help assist engine analysis. Overall, the neural network algorithm was appropriate for the application investigated in this study.

USE OF VIRTUAL LABORATORIES IN THE PROCESS OF TEACHING CHEMICAL DISCIPLINES

The introduction of information and communication technologies in the educational process of teaching various disciplines is becoming increasingly important. Accordingly, the issue of application of these technologies in the teaching of chemical disciplines is relevant, namely the study of the possibility of using virtual laboratories in Chemistry as an interactive learning tool to improve the learning process and provide opportunities for professional and research skills. The purpose of the article is to study the possibilities of using virtual laboratories in the process of teaching chemical disciplines and to determine the opinion of students on the effectiveness of this type of activity. The following research methods are used in the article: theoretical – analysis of scientific publications on the research problem; systematization and generalization of information; empirical – questionnaires and analysis of its results.
 The article considers the advantages and disadvantages of using virtual Chemistry laboratories in the educational process. The article analyzes the concept of «virtual laboratory» and gives the definition of a virtual laboratory of Chemistry as a software environment that allows to form practical skills by conducting laboratory experiments, simulating the sequence of actions of the researcher in real laboratory (or managing chemical plants by robotic means) with use of computer models or contactless (remotely) with real equipment and reagents. We consider the virtual Chemistry laboratories available on the Internet, which provides free access to the educational resources and explores their didactic and methodological opportunities to increase the level of formation of practical skills and abilities during chemical experiments. It is established that the use of a virtual laboratory in Chemistry contributes to the formation of practical skills in conducting interactive laboratory classes with further consolidation in real conditions; provides an individual pace of mastering the skills of chemical experiment; allows students to explore processes that require special conditions; increases the motivation to master new material by working in a virtual environment that is familiar and interesting for the modern student; provides realization of its independent preparation for performance of experiments in real laboratory conditions. It is substantiated the use of virtual laboratories in Chemistry cannot substitute the traditional performance of laboratory work, but is only a supplementary form.

Development of a resource-saving technology for industrial processing of hematite-containing raw materials based on computer modelling

The paper presents the results of the research aimed at recovery of hematite from current and stockpiled tailings produced at the mineral processing plant of Olkon JSC. The authors investigated material composition of tailing samples. Using methods of computational fluid dynamics and the ANSYS software, the models of spiral separation of the tailings have been developed. The use of computer modelling has shown that spiral separators can be recommended for tailings processing. The modelling results were confirmed with industrial spiral separation tests conducted at the processing plant. The authors developed a gravity separation technology for processing the magnetic separation tailings and stockpiled tailings. The proposed technology allows recovering hematite concentrate with a hematite iron content of 54.5% and recovery of 75.4% from current tailings containing 5.2% of hematite iron. It was also shown that hematite concentrate can be recovered from stockpiled tailings using the separation technology based on the use of spiral separation. The gravity separation technology provides for resources economy at the mineral processing plant. The use of stockpiled tailing as a raw material source can reduce negative environmental impact.

Digital Drug Screening and Computational Modeling Identifies Treatment Opportunities Despite Lack of Directly Actionable Mutations in Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS)

Background: Although virtually all patients (pts) with AML or MDS harbor at least one somatic mutation, a minority of pts possess genetic mutations considered directly targetable by a drug. Moreover, in pts with multiple gene mutations, single-gene/single-drug matching often produces conflicting drug recommendations. This lack of clinically relevant mutation-targeted therapy represents an unmet need in treating AML and MDS, and is a major limitation in personalized medicine. We hypothesized that genomic mutations from pts with AML or MDS could be used to generate pt-specific protein network maps for use in digital drug screening (DDS) even in cases when the gene mutations per se are not directly actionable. This would dramatically increase the percent of pts with actionable findings. Our primary goal was to establish a genomics and computational biology workflow that identified disease-relevant treatment options for every pt.Methods: We prospectively enrolled AML and MDS pts and collected bone marrow (BM), peripheral blood and clinical data relevant to their diagnosis and treatment. BM specimens were examined by conventional cytogenetics (Giemsa), whole exome sequencing (WES, Agilent), and copy number variation (CNV). Directly actionable mutations were defined as IDH1/2, JAK2, NRAS, KRAS, FLT3, TET2 or del(5q), based on currently available drugs that directly act on these mutations. Genomic data from each pt were used as inputs into a computational biology modeling (CBM) software (Cellworks Group), that generated pt-specific protein network maps based on software written from PubMed references and other online databases. DDS was conducted by mathematically quantifying drug effects on a cell growth score (CGS), which is a composite of modeled cell proliferation, viability and apoptosis. 77 drug combinations, comprised of 10 standard of care (SOC) and 16 non-SOC drugs were digitally screened on each pt's case to identify drug or drug combinations with significant reductions in CGS. Evaluation for drug-drug synergy was calculated by coefficient of drug interaction (CDI) and the extent to which each drug reduced the CGS.Results: 80 pts with either AML (n=41, 51%) or MDS (n=31, 39%), or myeloproliferative neoplasms (n=8, 10%) were successfully analyzed by the genomics and computational biology analysis plan. 23/80 (29%) were treatment-naive, while 58/80 (71%) were treatment-refractory. The median age was 65 years (range 36-90). While 28/80 (34%) pts had a directly actionable mutation, 53/80 (66%) patients did not. Disease-relevant biomarkers were identified for all (100%) of the pts using the CBM technology, involving pathways related to kinase activity (BTK, MAPK3, SRC, AURKA/B, AKT, mTOR, etc.) and transcription factors (TP53, MYC_MAX, CTNNB1, NFkB1, RUNX3, STAT3, etc.). DDS identified 77 unique drug combinations with potential therapeutic activity in these 81 patients, where cytarabine + dexamethasone (n=10), cytarabine + ruxolitinib (n=8), cytarabine + everolimus (n=6) were the most commonly identified combinations. A majority of pts (63/80, 79%) were predicted to respond to a combination of SOC + non-SOC, while 18/80 (21%) patient's cases were predicted to respond to a combination of non-SOC + non-SOC. In addition to selecting therapeutic regimens, computational modeling identified 9 pt cases whose simulated drug combinations resulted in synergistic activity, more frequently including cytarabine + ruxolitinib or cytarabine + dexamethasone.Conclusions: In contrast to a widely used practices of next generation sequencing techniques to identify single-gene/single-drug matching, this strategy of using a protein networking mapping and DDS identified therapeutic options for all pts with AML or MDS, even when there was no directly actionable mutation. In clinical situations of no established standard of care, such as refractory AML or MDS, the use of computational modeling and digital drug simulations may increase therapeutic options. CBM is currently being validated in a prospective clinical trial (NCT02435550). These results serve as the basis for a new, non-basketized clinical trial testing the effectiveness of computer-informed treatment based on the pt's malignant mutanome. DisclosuresNorkin:Celgene: Honoraria, Research Funding. Vasista:Cellworks Research India: Employment. Basu:Cellworks: Employment. Usmani:Cellworks: Employment. Roy:Cellworks: Employment. Lala:Cellworks: Employment. Radhakrishnan:Cellworks: Employment. Kumar:Cellworks: Employment. Rajagopalan:Cellworks: Employment. Abbasi:Cellworks Group Inc.: Employment. Vali:Cellworks Group Inc.: Employment. Cogle:Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

A fast and calibrated computer model emulator: an empirical Bayes approach

Mathematical models implemented on a computer have become the driving force behind the acceleration of the cycle of scientific processes. This is because computer models are typically much faster and economical to run than physical experiments. In this work, we develop an empirical Bayes approach to predictions of physical quantities using a computer model, where we assume that the computer model under consideration needs to be calibrated and is computationally expensive. We propose a Gaussian process emulator and a Gaussian process model for the systematic discrepancy between the computer model and the underlying physical process. This allows for closed-form and easy-to-compute predictions given by a conditional distribution induced by the Gaussian processes. We provide a rigorous theoretical justification of the proposed approach by establishing posterior consistency of the estimated physical process. The computational efficiency of the methods is demonstrated in an extensive simulation study and a real data example. The newly established approach makes enhanced use of computer models both from practical and theoretical standpoints.

Mechanical characterization of the human pia-arachnoid complex

Traumatic brain injury (TBI) is a significant problem in global health that affects a wide variety of patients. Mild forms of TBI, commonly referred to as concussion, are a result of rapid accelerations of the head from either direct or indirect impacts. Kinetic energy from the impact is transferred into deformation of the brain, leading to cellular disruption. This transfer of energy is in part mediated by the pia-arachnoid complex (PAC), a layer of anatomical structures that forms the physical connection between the brain and the skull. The importance of properly quantifying the mechanics of the PAC for use in computational models of TBI has been understood for some time, but data from human subjects has been unavailable. In this study, we quantify the normal traction modulus of the PAC in five post-mortem human subjects using hydrostatic fluid pressurization in combination with optical coherence tomography. Testing at multiple locations across each brain reveals that brain-skull stiffness is heterogeneously distributed. The material response to traction loading was linear, with a mean normal traction modulus of 12.6 ± 4.8 kPa. Modulus was 21% greater in superior regions of the brain compared to inferior regions. Comparisons with regional microstructural data suggests a potential relationship between the volume fraction of arachnoid trabeculae and modulus. Comparisons to coincident measurements of microstructural properties showed a positive correlation between arachnoid membrane thickness and normal traction modulus. This study is the first to characterize the mechanics of the human pia-arachnoid complex and quantify material properties in situ. These findings suggest implementing a heterogeneous model of the brain-skull interface in computational models of TBI may lead to more realistic injury prediction.

Statistical Analysis of Patients Waiting Time in Accessing Health Care Services in Ghana: A Case Study of University of Cape Coast Hospital

Delayed access to health care in terms of diagnosis and treatment has unforeseen health and cost implications on the patients and the public health system in general. Thus, the study examines patients’ waiting time in accessing health care services. The study employs a convenience sampling technique in collecting data from a sample of 350 Outpatients using a questionnaire. The main analytic tools used in the study are the paired sample t-test, binary logistic regression and SmartPLS structural equation modelling. Results from the study show that the actual waiting times of patients are generally higher than the expected times. Furthermore, the probability of a patient’s waiting time being rated as unacceptable is affected by the Day of attendance, Arrival time of patients, Large number of patients or long queue, Long registration time, Type of diagnosis, and Type of treatment sought. Finally, the study also establishes that there is no relationship between socio-demographic characteristics and unacceptable waiting time, as well as no relationship between hospital factors and unacceptable waiting time. Additionally, the study shows that unacceptable waiting time does not have any influence on overall patients’ satisfaction. However, a strong positive relationship between process factors and unacceptable waiting time was established. The study recommends that there is the need for health care policy formulators to adopt more efficient strategies, like use of computer modelling to help assign appointment time to patients, so as to significantly reduce the time patients spend waiting at the hospitals.
 Citation: Kunu, E. K. and Agbede, P. S. (2021). Statistical Analysis of Patients Waiting Time in Accessing Health Care Services in Ghana: A Case Study of University of Cape Coast Hospital. International Journal of Technology and Management Research. Vol. 6, Issue 1: 14－27.
 Received: October 23, 2020Accepted: March 30, 2021

Binding affinity and synergy between agonists and peptides of the estrogen receptor‐beta (ERß) needed for arain‐selective SERB Activity

The estrogen receptor (ER) exist predominantly in two isoforms (ERα and ERß). Both are primarily activated by the naturally occurring hormone 17ß-Estradiol (E2) which regulates a variety of physiological processes. One of ER's major role is acting as key neuroprotective modulator for hippocampal plasticity in the brain. The ERß isoform is predominantly found in the brain, which makes it a promising drug target for memory consolidation. Selectivity for ERß over ERα is crucial to the development of drug leads, since ERα–induced cell proliferation results in risk of cancer. We have developed a selective and potent ERß agonist with in vivo efficacy for improving cognitive function within mice. A prior study determined that selectivity for ER is related to the ligand's ability to induce the conformational change necessary for the activation of transcription. Selective estrogen receptor modulators (SERMs) like raloxifene & tamoxifen are used to treat breast cancer by selectively targeting ERα in mammary tissue. This is done through the synergistic binding of coactivator proteins to the ER in the presence of an agonist. Similarly, it is predicted that selective estrogen beta agonists (SERBAs) have the ability to be tissue specific in the presence of specific coactivators. Through the use of computational modeling, a splice variant of SRC1 (steroid receptor coactivator-1), SRC1-4, was identified to be the most favorable brain-relevant peptide for ERß. This study determines the binding affinity of agonist for ERß and ERα in the presence and absence of SRC1-4. In addition we dive into the extent to which binding synergy between agonist and coactivator exist, which could lead to selective activity in the brain. The synergy associated with the conformational change is measured by ΔΔGsynergy based on binding affinity for both agonist and coactivator. The greater the synergy between the agonist and coactivator, the more promising the lead compound will be as a SERBA. Preliminary studies have been done for the binding of SRC1-4 using TR-FRET assays where the concentration of coactivator was varied. Fluorescence polarization (FP) studies were also utilized to determine a Kd. In these studies, FL-SRC1-4 remained constant while ERß-LBD concentrations varied in the presence of 1 μM (saturating) E2. The two methods are compared and contrasted. The same FP assay was completed again in the absence of E2 (no agonoism). This provides a binding affinity for SRC1-4 to the receptor when an agonist is not present. Computational modeling studies were performed to compare ER's affinity for SRC1-4 peptides over other coactivator peptides. 3 unlabeled peptides (SRC1-1, SRC1-3 and PGC1a) were used in a competitive FP binding assay with FL-SRC1-4. The data was consistent with prior data, indicating SRC1-4 has the highest affinity for ERß. The agonistic-synergy effect will be calculated through the measurement of E2 affinity for ERß in the presence and absence of SRC1-4. The comparison of ΔΔG(synergy) for ERß vs. ERα will ultimately provide a measurement of selectivity and synergy that is biologically relevant for SERB activity. Hanson, A. M. et al. A-C estrogens as potent and selective estrogen receptor-beta agonists (SERBAs) to enhance memory consolidation under low-estrogen conditions. J. Med. Chem.61, 4720–4738 (2018).

Assessment of Machine Learning-Based Audiovisual Quality Predictors

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

Computational Simulation Is a Vital Resource for Navigating the COVID-19 Pandemic.

COVID-19 has prompted the extensive use of computational models to understand the trajectory of the pandemic. This article surveys the kinds of dynamic simulation models that have been used as decision support tools and to forecast the potential impacts of nonpharmaceutical interventions (NPIs). We developed the Values in Viral Dispersion model, which emphasizes the role of human factors and social networks in viral spread and presents scenarios to guide policy responses. An agent-based model of COVID-19 was developed with individual agents able to move between 3 states (susceptible, infectious, or recovered), with each agent placed in 1 of 7 social network types and assigned a propensity to comply with NPIs (quarantine, contact tracing, and physical distancing). A series of policy questions were tested to illustrate the impact of social networks and NPI compliance on viral spread among (1) populations, (2) specific at-risk subgroups, and (3) individual trajectories. Simulation outcomes showed large impacts of physical distancing policies on number of infections, with substantial modification by type of social network and level of compliance. In addition, outcomes on metrics that sought to maximize those never infected (or recovered) and minimize infections and deaths showed significantly different epidemic trajectories by social network type and among higher or lower at-risk age cohorts. Although dynamic simulation models have important limitations, which are discussed, these decision support tools should be a key resource for navigating the ongoing impacts of the COVID-19 pandemic and can help local and national decision makers determine where, when, and how to invest resources.

Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Variations in Model Development.

The use of computational modeling to investigate knee joint biomechanics has increased exponentially over the last few decades. Developing computational models is a creative process where decisions have to be made, subject to the modelers' knowledge and previous experiences, resulting in the "art" of modeling. The long-term goal of the KneeHub project is to understand the influence of subjective decisions on the final outcomes and the reproducibility of computational knee joint models. In this paper, we report on the model development phase of this project, investigating model development decisions and deviations from initial modeling plans. Five teams developed computational knee joint models from the same dataset, and we compared each teams' initial uncalibrated models and their model development workflows. Variations in the software tools and modeling approaches were found, resulting in differences such as the representation of the anatomical knee joint structures in the model. The teams consistently defined the boundary conditions and used the same anatomical coordinate system convention. However, deviations in the anatomical landmarks used to define the coordinate systems were present, resulting in a large spread in the kinematic outputs of the uncalibrated models. The reported differences and similarities in model development and simulation presented here illustrate the importance of the "art" of modeling and how subjective decision-making can lead to variation in model outputs. All teams deviated from their initial modeling plans, indicating that model development is a flexible process and difficult to plan in advance, even for experienced teams.

Optimizing the formulation for reduced‐calorie and antioxidant‐rich sapodilla‐based spread using hybrid computational techniques and fuzzy analysis of sensory data

AbstractFormulation of reduced‐calorie, antioxidant‐enriched sapodilla‐based spread was developed using rotatable central composite design of factors/ingredient proportions (mass ratio of sapodilla pulp to grape juice, pectin, and citric acid) for responses (total soluble solids, moisture, phenolics, antioxidant capacity, and firmness). Response surface methodology (RSM) and artificial neural network (ANN) were combined with method of steepest ascent (SA) and genetic algorithms (GA) for optimization, leading to hybrid techniques: RSM‐SA, RSM‐GA, and ANN‐GA. RSM modeling proved statistically more efficient than ANN modeling. Fuzzy logic analysis of RSM‐SA, RSM‐GA, and control (sapodilla spread without grape juice) samples revealed RSM‐SA (sapodilla to grape ratio = 1.14; pectin 0.58 g/100 g; citric acid 0.21 g/100 g with TSS of 29 °Bx, 65% moisture; 11.44 g GAE kg−1 phenolics, 83.2 mg GAE kg−1 antioxidant capacity, and firmness of 47.3 N) had highest sensory acceptability. Its total sugars and antioxidant activity were 0.88 and 1.5 times those of control sample.Practical ApplicationsFood products are conventionally formulated using the cook‐and‐look approach. With the rising consumer awareness regarding healthy foods, it has become essential for food industries to develop food formulations with enhanced nutritional value and quality attributes. The use of computational models helps in studying the effects of ingredients on the characteristics of food products. Further, such models can be used to determine the combination of ingredients that maximize the nutritional value and quality of the food product. Such a systematic approach in formulating food products helps in saving time, resources, and efforts. In this study, a reduced‐calorie, antioxidant‐rich sapodilla‐based spread has been formulated.

3D-modeling of sternal chondrosarcomas from angio-CT-Scan: Clinical application and surgical perspectives

IntroductionMalignant bone tumors of the sternum are rare. Their surgical treatment remains largely based on precise knowledge of preoperative imaging in order to define appropriate margins during tumor resection. We provide a practical 3-dimensional (3D) modeling method to better understand the relationships between these tumors and neighboring organs. MethodsAxial view images from CT-scans were processed by the open source software Horos® after anonymization. A first surface reconstruction was carried out by the software to form a digital mesh. This raw model was processed by Meshmixer® software (Autodesk™) in order to remove artefacts. These models underwent a surface reduction of the mesh using Meshlab® software and were hosted on a secure private platform. ResultsSix angio CT-Scan were performed in three patients with chondrosarcomas of the sternum. The segmentation of organs and vessels has made it possible to create a real 3-D anatomical board. Each of these protected models can be viewed on our server and can be manipulated virtually on 360° using the secure links provided in this article. 3D vision was available using inexpensive cardboard virtual reality glasses. ConclusionThe use of computer modeling could help in the teaching of anatomy, but also in the understanding of the disease by the patient himself. One of the perspectives would be the 3D printing of biocompatible materials in order to carry out tailor-made reconstructions.

Natural oscillatory modes of 3D deformation of the human brain in vivo

Natural modes and frequencies of three-dimensional (3D) deformation of the human brain were identified from in vivo tagged magnetic resonance images (MRI) acquired dynamically during transient mild acceleration of the head. Twenty 3D strain fields, estimated from tagged MRI image volumes in 19 adult subjects, were analyzed using dynamic mode decomposition (DMD). These strain fields represented dynamic, 3D brain deformations during constrained head accelerations, either involving rotation about the vertical axis of the neck or neck extension. DMD results reveal fundamental oscillatory modes of deformation at damped frequencies near 7 Hz (in neck rotation) and 11 Hz (in neck extension). Modes at these frequencies were found consistently among all subjects. These characteristic features of 3D human brain deformation are important for understanding the response of the brain in head impacts and provide valuable quantitative criteria for the evaluation and use of computer models of brain mechanics.