A Simple Generic Model of Elastin-Like Polypeptides with Proline Isomerization.

A generic model of elastin-like polypeptides (ELP) is derived that includes proline isomerization (ProI). As a case study, conformational transition of a -[valine-proline-glycine-valine-glycine]- sequence is investigated in aqueous ethanol mixtures. While the non-bonded interactions are based on the Lennard-Jones (LJ) parameters, the effect of ProI is incorporated by tuning the intramolecular 3- and 4-body interactions known from the underlying all-atom simulations into the generic model. One of the key advantages of such a minimalistic model is that it readily decouples the effects of geometry and the monomer-solvent interactions due to the presence of ProI, thus gives a clearer microscopic picture that is otherwise rather nontrivial within the all-atom setups. These results are consistent with the available all-atom and experimental data. The model derived here may pave the way to investigate large scale self-assembly of ELPs or biomimetic polymers in general.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology.

PURPOSE: Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS: To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS: We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION: Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Research on dynamic on-line monitoring method of moisture attribute in three honey-processed Chinese herbal slice based on in-situ general model / 药学学报

Aiming at the hysteresis and destructiveness of off-line static detection of critical quality attribute of the moisture content of the raw material unit of the traditional Chinese medicine manufacturing process, honey-processed Tussilago farfara, honey-processed Astragalus and honey-processed Glycyrrhiza uralensis were used as the research carriers, and the drying method was used to measure the moisture content as a reference value. The moving stage was used to simulate the movement process of samples on the conveyor belt in the actual on-site production process, and near-infrared (NIR) spectra were collected, combined with machine learning, to establish NIR on-site dynamic detection model of moisture content in multi-variety honey-processed Chinese herbal slice. The results show that the second derivative method is used to preprocess the spectrum. The number of decision trees (ntree), the number of random features (max feature), and the minimum number of samples for generating leaf nodes (node size) are selected: 46, 76, and 8, respectively. The quantitative analysis model of moisture content has the best effect. The prediction coefficient of determination (the prediction coefficient of determination, R2pre) and the root mean square error of prediction (root mean square error of prediction, RMSEP) of the model were 0.903 2 and 0.330 2, respectively. The NIR quantitative model for the moisture content of multi-variety honey-processed Chinese herbal slice established in this study has good predictive performance, and can achieve rapid, accurate and non-destructive quantitative analysis of the moisture content of honey-processed Tussilago farfara, honey-processed Astragalus and honey-processed Glycyrrhiza uralensis at the same time, and provides a method for determining the moisture content of honey-processed Chinese herbal slice of the raw material unit of the traditional Chinese medicine manufacturing process.

Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra.

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed "generic" models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Digital twin framework for reconfigurable manufacturing systems (RMSs): design and simulation.

Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system's life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs).

Development of an optimized and generic cost-utility model for analyzing genome-guided treatment data.

Economic evaluation is an integral component of informed public health decision-making in personalized medicine. However, performing economic evaluation assessments often requires specialized knowledge, expertise, and significant resources. To this end, developing generic models can significantly assist towards providing the necessary evidence for the cost-effectiveness of genome-guided therapeutic interventions, compared to the traditional drug treatment modalities. Here, we report a generic cost-utility analysis model, developed in R, which encompasses essential economic evaluation steps. Specifically, critical steps towards a comprehensive deterministic and probabilistic sensitivity analysis were incorporated in our model, while also providing an easy-to-use graphical user interface, which allows even non-experts in the field to produce a fully comprehensive cost-utility analysis report. To further demonstrate the model's reproducibility, two sets of data were assessed, one stemming from in-house clinical data and one based on previously published data. By implementing the generic model presented herein, we show that the model produces results in complete concordance with the traditionally performed cost-utility analysis for both datasets. Overall, this work demonstrates the potential of generic models to provide useful economic evidence for personalized medicine interventions.

The effects of data quantity on performance of temporal response function analyses of natural speech processing.

In recent years, temporal response function (TRF) analyses of neural activity recordings evoked by continuous naturalistic stimuli have become increasingly popular for characterizing response properties within the auditory hierarchy. However, despite this rise in TRF usage, relatively few educational resources for these tools exist. Here we use a dual-talker continuous speech paradigm to demonstrate how a key parameter of experimental design, the quantity of acquired data, influences TRF analyses fit to either individual data (subject-specific analyses), or group data (generic analyses). We show that although model prediction accuracy increases monotonically with data quantity, the amount of data required to achieve significant prediction accuracies can vary substantially based on whether the fitted model contains densely (e.g., acoustic envelope) or sparsely (e.g., lexical surprisal) spaced features, especially when the goal of the analyses is to capture the aspect of neural responses uniquely explained by specific features. Moreover, we demonstrate that generic models can exhibit high performance on small amounts of test data (2-8 min), if they are trained on a sufficiently large data set. As such, they may be particularly useful for clinical and multi-task study designs with limited recording time. Finally, we show that the regularization procedure used in fitting TRF models can interact with the quantity of data used to fit the models, with larger training quantities resulting in systematically larger TRF amplitudes. Together, demonstrations in this work should aid new users of TRF analyses, and in combination with other tools, such as piloting and power analyses, may serve as a detailed reference for choosing acquisition duration in future studies.

Effect of different walking speeds on joint and muscle force estimation using AnyBody and OpenSim.

BACKGROUND: To be able to use muscluloskeletal models in clinical settings, it is important to understand the effect of walking speed on joint and muscle force estimations in different generic musculoskeletal models. RESEARCH QUESTION: The aim of the current study is to compare estimated joint and muscle forces as a function of walking speed between two standard approaches offered in two different modelling environments (AnyBody and OpenSim). METHODS: Experimental data of 10 healthy participants were recorded at three different walking speeds (self-selected, 25 % slower, 25 % faster) using a ten-camera motion capture system together with four force plates embedded into a ten-meter walkway. Joint compression forces and muscle forces were calculated with a generic model in AnyBody and OpenSim. Trend analyses, mean absolute error (MAE) and correlation coefficients were used to compare joint compression forces and muscle forces between the two approaches. A one-way and two-way ANOVA with repeated measures were used to compare MAE and trend analysis changes, respectively (α = 0.05, Bonferroni corrected post-hoc tests). RESULTS: Trend analyses showed the same speed effect for AnyBody and OpenSim. MAEs increased significantly from slow to fast walking for knee joint compression forces, biceps femoris long head, gluteus maximus, gluteus medius and vastus intermedius. Lower correlation coefficients during slower walking were found for quadriceps muscles, gluteus maximus and biceps femoris compared to normal and faster walking. SIGNIFICANCE: Lower correlation coefficients during slower walking are assumed to be due to a higher amount of solutions solving the muscle recruitment in musculoskeletal models. This indicates that decreasing walking speed is more prone to speed dependent differences regarding variability, while the absolute error increased with increasing walking speed. To conclude, different modelling environments can react differently to changes in walking speed, but overall results are promising regarding the generalization across different generic musculoskeletal models.

Using the Generic Model of Psychotherapy to Develop a Culturally-Sensitive Approach to Psychotherapy With Sexual and Gender Minority Patients.

This article discusses how the Generic Model of Psychotherapy (Orlinsky and Howard, 1987) can help to organize the psychotherapy research and the knowledge in the field of psychotherapy for sexual and gender minority patients. The structure that this traditional model provides is a good foundation for research in this field, inasmuch as it stresses macrosocial aspects that determine the provision of psychotherapy and contextualize its outcomes. Each one of the main components offered by the Generic Model of Psychotherapy - Determinants, Processes, and Consequences - are specified for psychotherapy with LGBT patients and are illustrated with a selection of already existing research in the field of sexual minorities; they are also stress areas marked by knowledge gaps that require future developments. In addition, a set of questions are proposed to contribute to new studies, including the clinical implications that can be derived from this model.

Development and evaluation of a high throughput inhalation model for organic chemicals.

Currently it is difficult to prospectively estimate human toxicokinetics (particularly for novel chemicals) in a high-throughput manner. The R software package httk has been developed, in part, to address this deficiency, and the aim of this investigation was to develop a generalized inhalation model for httk. The structure of the inhalation model was developed from two previously published physiologically based models from Jongeneelen and Berge (Ann Occup Hyg 55:841-864, 2011) and Clewell et al. (Toxicol Sci 63:160-172, 2001), while calculated physicochemical data was obtained from EPA's CompTox Chemicals Dashboard. In total, 142 exposure scenarios across 41 volatile organic chemicals were modeled and compared to published data. The slope of the regression line of best fit between log-transformed simulated and observed blood and exhaled breath concentrations was 0.46 with an r2 = 0.45 and a root mean square error (RMSE) of direct comparison between the log-transformed simulated and observed values of 1.11. Approximately 5.1% (n = 108) of the data points analyzed were >2 orders of magnitude different than expected. The volatile organic chemicals examined in this investigation represent small, generally lipophilic molecules. Ultimately this paper details a generalized inhalation component that integrates with the httk physiologically based toxicokinetic model to provide high-throughput estimates of inhalation chemical exposures.

Cross-Validation of Generic Risk Assessment Tools for Animal Disease Incursion Based on a Case Study for African Swine Fever.

In recent years, several generic risk assessment (RA) tools have been developed that can be applied to assess the incursion risk of multiple infectious animal diseases allowing for a rapid response to a variety of newly emerging or re-emerging diseases. Although these tools were originally developed for different purposes, they can be used to answer similar or even identical risk questions. To explore the opportunities for cross-validation, seven generic RA tools were used to assess the incursion risk of African swine fever (ASF) to the Netherlands and Finland for the 2017 situation and for two hypothetical scenarios in which ASF cases were reported in wild boar and/or domestic pigs in Germany. The generic tools ranged from qualitative risk assessment tools to stochastic spatial risk models but were all parameterized using the same global databases for disease occurrence and trade in live animals and animal products. A comparison of absolute results was not possible, because output parameters represented different endpoints, varied from qualitative probability levels to quantitative numbers, and were expressed in different units. Therefore, relative risks across countries and scenarios were calculated for each tool, for the three pathways most in common (trade in live animals, trade in animal products, and wild boar movements) and compared. For the 2017 situation, all tools evaluated the risk to the Netherlands to be higher than Finland for the live animal trade pathway, the risk to Finland the same or higher as the Netherlands for the wild boar pathway, while the tools were inconclusive on the animal products pathway. All tools agreed that the hypothetical presence of ASF in Germany increased the risk to the Netherlands, but not to Finland. The ultimate aim of generic RA tools is to provide risk-based evidence to support risk managers in making informed decisions to mitigate the incursion risk of infectious animal diseases. The case study illustrated that conclusions on the ASF risk were similar across the generic RA tools, despite differences observed in calculated risks. Hence, it was concluded that the cross-validation contributed to the credibility of their results.

The brachial plexus - explaining its morphology and variability by a generic developmental model.

In classic anatomy teaching, the brachial plexus generally features as an enigmatic rote-learned structure, leaving the student with a feeling of complexity. The notion of complexity may increase in dissections, where plexuses significantly differing from the standard plexus model are readily found. This raises questions: what determines the existence and prevalence of variants and to what degree should they be considered anomalous? A model linking brachial plexus morphology and its variability to causative morphological parameters which would also standardize plexus description and teaching would be beneficial. The present study aims to provide such a model by analyzing the context of plexus development and applying this model in the analysis of plexus variability in anatomical specimens. Based on a thorough literature review, a generic developmental model was formulated and different factors of variability defined. In 56 plexuses, the proposed generic principles of plexus variability were found consistent with the variations encountered. Summarized, the modeled generic principles are as follows. Brachial plexus axon bundles grow out into an environment of chemical tracer paths in which constraints and obstacles are present: the geometry of the arm bud, cartilaginous bone precursors and vessels. The overall constancy of these factors generates a gross plexus outline, while the variability in these factors gives rise to typical plexus variations. The usefulness of the model derives from the fact that the variability of the main morphologically determining factors is not random but is the expression of the possibilities of the embryological substrate. Within the model, the major plexus morphological determinant is the segmental position of the subclavian artery, which is determined by the segment level of the intersegmental artery from which it develops. Normally, the subclavian artery develops from intersegmental artery i7. However, the subclavian artery can develop from inferior or superior segmental levels, from intersegmental artery i8 or i6, and possibly also from i9 or i5. Each of these arterial variants creates a typical, morphologically distinct, predictable plexus configuration. Superimposed on these basic plexus configurations, the underlying embryological substrate may develop further variability by integrating remnants of other intersegmental arteries into the arterial network. The resulting plexus configurations are further modified by local factors, e.g. the splitting of outgrowing axon bundles around vessels. A large split in the lateral cord around a large vein or veins crossing from lateral to medial, tangentially cranially over the subclavian artery was found in 54% of the 56 investigated BP and therefore might be added to plexus teaching. The distinct plexus morphologies associated with the subclavian artery segmental levels were further found associated with, among others, typical variations in the pectoral nerves and their ansas; these associations were also modeled. The presented models could allow brachial plexus rote learning to be replaced by a more insightful narrative of formative principles suitable for teaching. Clinically, improved understanding of the relationship between plexus variability and the local anatomical environment should be relevant to brachial plexus surgery and reconstruction.

Risk assessment of exotic disease incursion and spread.

This Technical Report describes the activities developed in the scope of the EU-FORA Fellowship, within the work programme of risk assessment (RA) of exotic disease incursion and spread, developed at Wageningen Bioveterinary Research (WBVR). The programme focused on the work carried out in the Generic risk assessment for introduction of animal diseases (G-RAID) project, which brings together a number of different generic RA tools from multiple European partners. The aim of the fellowship was to gain understanding of veterinary import risk assessment by using different RA tools and to learn how different algorithms can be used to calculate disease incursion risks. G-RAID's tools cover a wide range of RA methodologies; from purely qualitative, to semi-quantitative and fully stochastic quantitative methods, which allowed the fellow to understand a variety of algorithms used to produce the final risk estimate. The fellowship programme provided the fellow with the chance to learn in detail about how generic RAs are performed across Europe, understanding how to deal with the uncertainty and variability involved in RAs and the potential problems of data availability and reliability. The fellow made an inventory of publicly available databases on disease occurrence and international trade that could be used for import RA and assessed their quality and usefulness for the different generic RA tools. The programme also provided the fellow the opportunity to perform several import risk assessments using the RA tools of G-RAID. She completed a RA on African swine fever using the MINTRISK model developed by WBVR. Furthermore, she assessed the risk of foot and mouth disease introduction using the Rapid Risk Assessment Tool (RRAT) model developed by WBVR and the COMPARE model developed by the Animal and Plant Health Agency (APHA). To this end, the fellow completed a short-term visit to APHA, enabling her to have additional training in quantitative RA and to expand her professional network in this area.

Constructing regions of attainable sizes and achieving target size distribution in a batch cooling sonocrystallization process.

The application of ultrasound to a crystallization process has several interesting benefits. The temperature of the crystallizer increases during ultrasonication and this makes it difficult for the temperature controller of the crystallizer to track a set temperature trajectory precisely. It is thus necessary to model this temperature rise and the temperature-trajectory tracking ability of the crystallizer controller to perform model-based dynamic optimization for a given cooling sonocrystallization set-up. In our previous study, we reported a mathematical model based on population balance framework for a batch cooling sonocrystallization of l-asparagine monohydrate (LAM). Here we extend the previous model by including energy balance equations and a Generic Model Control algorithm to simulate the temperature controller of the crystallizer that tracks a cooling profile during crystallization. The improved model yields very good closed-loop prediction and is conveniently used for studies related to particle engineering by optimization. First, the model is used to determine the regions of attainable particle sizes for LAM batch cooling sonocrystallization process by solving appropriate dynamic optimization problems. Then the model is used to determine optimal operating conditions for achieving a target crystal size distribution. The experimental evidence clearly demonstrates the efficiency of the particle engineering approach by optimization.

Training and service provision for people with intellectual disability and mental illness: the views of psychiatrists.

  Objectives: The objectives of this mixed methods study are to 1) investigate the knowledge and skills of mainstream psychiatrists in managing patients with Intellectual Disability (ID) and comorbid mental health disorders, 2) assess their perception of the quality and accessibility of services for this population, and 3) establish the local implementation of the Green Light Toolkit. Method: We surveyed mainstream psychiatrists in the Thames Valley region working in general adult, forensic, and older adult services, to ascertain their opinions about their knowledge and skills in managing patients with ID and comorbid mental disorder, as well as quality and accessibility of services. We compared our findings with previous UK and international research. Results: Respondents mirrored views expressed in earlier studies that inpatient care should be provided in dedicated units for people with ID, rather than general adult inpatient wards. Limited resources, training and competence, and lack of collaborative working were highlighted as key barriers to provision of effective care. Conclusion: Psychiatrists broadly support a specialist service model for people with ID. In the UK, specialist psychiatric services for people with ID have been delivered through inpatient and community services, but there is a current shift towards integrating ID with mainstream service models. Participants expressed concern that mainstream services fail to meet the mental health needs of this patient group, and lead to increased patient vulnerability. The Green Light Toolkit was not well known or used within services. A number of ways of improving collaborative care between services are suggested.

Therapy processes, progress, and outcomes for 2 therapies for gynecological cancer patients.

OBJECTIVE: Although a number of effective psychotherapies have been identified for cancer patients, little is known about therapy processes, as they unfold the course of treatment and the role of therapy processes in treatment outcome. We used growth curve modeling to evaluate the associations between therapy processes and outcomes among gynecological cancer patients participating in 2 types of therapy. METHODS: Two hundred twenty five women newly diagnosed with gynecological cancer were randomly assigned to receive 8 sessions of a coping and communication intervention or a client-centered supportive therapy. Participants completed measures of preintervention and postintervention depression, working alliance after Session 2, and postsession progress and depressive symptoms after each session. Therapists completed measures of perceived patient progress. RESULTS: Both patients and therapists reported a steady increase in session progress and patients reported a steady decrease in depressive symptoms over the course of both the coping and communication intervention and client-centered supportive sessions. Perceived progress in one session predicted progress in the subsequent session. Early working alliance predicted improved session progress and reductions in postsession depressive symptoms over sessions. Working alliance did not predict prepost treatment changes in depression. Patient-rated session progress predicted greater reductions in pretreatment to posttreatment depression, but therapist-rated progress did not. CONCLUSIONS: For 2 types of treatment delivered to women diagnosed with gynecological cancer, patient-rated session progress and depressive symptoms rated over therapy sessions may serve as a yardstick that can be useful to therapists to gauge patient's response to treatment.

Femur, tibia and fibula bone templates to estimate subject-specific knee ligament attachment site locations.

In-vivo estimates of the positions of knee ligament attachment sites are crucial for subject-specific knee modelling. The present study provides template digital models of femur, tibia and fibula that embed the positions of centroids of the origins and insertions of cruciate and collateral ligaments, along with information on their dispersion related to inter-individual variability. By using a shape transformation procedure of choice, these templates can be made to match anatomical information measured on a subject under analysis. Generic bone digital models of the femur, tibia and fibula were first chosen as bone templates. Ligament attachment areas were accurately identified through dissection on the bones of 11 knee specimens, and marked using radio opaque paint. Digital models of these bones embedding the positions of the centroids of the identified ligament attachment areas were thereafter obtained using medical imaging techniques. These centroids were mapped onto the relevant bone template, thus obtaining a cloud of 11 points for each attachment site, and descriptive statistics of the position of these points were thereafter determined. Dispersion of these positions, essentially due to inter-individual variability, was below 6mm for all attachment areas. The accuracy with which subject-specific ligament attachment site positions may be estimated using the bone template models provided in this paper was also assessed using the above-mentioned 11 specimens data set, and a leave-one-out cross validation approach. Average accuracy was found to be 3.3±1.5mm and 5.8±2.9mm for femoral and tibial/fibular attachment sites, respectively.

Development and validation of a generic finite element vehicle buck model for the analysis of driver rib fractures in real life nearside oblique frontal crashes.

OBJECTIVE: Frontal crashes still account for approximately half of all fatalities in passenger cars, despite several decades of crash-related research. For serious injuries in this crash mode, several authors have listed the thorax as the most important. Computer simulation provides an effective tool to study crashes and evaluate injury mechanisms, and using stochastic input data, whole populations of crashes can be studied. The aim of this study was to develop a generic buck model and to validate this model on a population of real-life frontal crashes in terms of the risk of rib fracture. METHOD: The study was conducted in four phases. In the first phase, real-life validation data were derived by analyzing NASS/CDS data to find the relationship between injury risk and crash parameters. In addition, available statistical distributions for the parameters were collected. In the second phase, a generic parameterized finite element (FE) model of a vehicle interior was developed based on laser scans from the A2MAC1 database. In the third phase, model parameters that could not be found in the literature were estimated using reverse engineering based on NCAP tests. Finally, in the fourth phase, the stochastic FE model was used to simulate a population of real-life crashes, and the result was compared to the validation data from phase one. RESULTS: The stochastic FE simulation model overestimates the risk of rib fracture, more for young occupants and less for senior occupants. However, if the effect of underestimation of rib fractures in the NASS/CDS material is accounted for using statistical simulations, the risk of rib fracture based on the stochastic FE model matches the risk based on the NASS/CDS data for senior occupants. CONCLUSION: The current version of the stochastic model can be used to evaluate new safety measures using a population of frontal crashes for senior occupants.

Nonlinear dynamic behaviors and control based on simulation of high-purity heat integrated air separation column.

In this paper, the dynamic behaviors on the basis of simulation for high-purity heat integrated air separation column (HIASC) are studied. A nonlinear generic model control (GMC) scheme is proposed based on the nonlinear behavior analyses of a HIASC process, and an adaptive generic model control (AGMC) scheme is further presented to correct the model parameters online. Related internal model control (IMC) scheme and multi-loop PID (M-PID) scheme are also developed as the comparative base. The comparative researches are carried out among these linear and nonlinear control schemes in detail. The simulation research results show that the proposed AGMC schemes present advantages in both servo control and regulatory control for the high-purity HIASC.